Ads_csv.ipynb

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        "# 📊 **Project Overview: Exploratory Data Analysis on Ad Display Dataset**\n",
        "\n",
        "## 📘 **Introduction**\n",
        "This project aims to conduct a comprehensive **Exploratory Data Analysis (EDA)** on an advertisement display dataset to gain meaningful insights into user engagement patterns, identify trends, and understand the factors influencing ad displays.\n",
        "\n",
        "## 🔍 **Objectives**\n",
        "- Analyze the distribution of advertisements based on different time intervals.\n",
        "- Identify patterns and correlations in ad display frequency.\n",
        "- Conduct temporal and seasonal analysis to uncover insights.\n",
        "\n",
        "## 📂 **1. Data Loading and Inspection**\n",
        "- **Data Loading**: Load the dataset using pandas to create a DataFrame for analysis.\n",
        "- **Initial Inspection**: Display the first few rows to understand the structure and content.\n",
        "- **Data Summary**: Use `.info()` to check the number of entries, column names, data types, and missing values.\n",
        "- **Statistical Overview**: Generate descriptive statistics using `.describe()` to get insights on mean, standard deviation, min, and max values.\n",
        "\n",
        "## 🛠️ **2. Data Preprocessing**\n",
        "- **Handling Missing Values**:\n",
        "  - Checked for missing values using `.isnull().sum()`.\n",
        "  - Filled missing numerical data using the mean value of the respective columns.\n",
        "- **Removing Duplicates**:\n",
        "  - Checked and removed any duplicate rows to ensure data integrity.\n",
        "\n",
        "## 📈 **3. Data Visualization**\n",
        "- **Distribution Analysis**:\n",
        "  - Created histograms with KDE (Kernel Density Estimation) to visualize the distribution of each numerical feature.\n",
        "- **Correlation Analysis**:\n",
        "  - Plotted a heatmap to understand the relationship between numerical variables.\n",
        "\n",
        "## 🧹 **4. Handling Categorical Variables**\n",
        "- **Encoding Categorical Variables**: Applied One-Hot Encoding to transform categorical variables into numerical representations for better analysis.\n",
        "\n",
        "## 🚨 **5. Outlier Detection and Handling**\n",
        "- **Outlier Identification**:\n",
        "  - Used the Interquartile Range (IQR) method to detect potential outliers in numerical columns.\n",
        "- **Outlier Treatment**:\n",
        "  - Removed detected outliers to improve data quality and analysis accuracy.\n",
        "\n",
        "## ⚖️ **6. Feature Scaling**\n",
        "- **Standardization**:\n",
        "  - Scaled the features using standard scaling techniques to normalize the data for better model performance.\n",
        "\n",
        "## 📝 **7. Task-Based Analysis**\n",
        "### 📅 **Task 1: Time Interval Analysis**\n",
        "- Extracted 'Hour' from a timestamp column to determine the most frequent time intervals for ad displays.\n",
        "- Visualized the number of ads displayed per hour using bar plots.\n",
        "\n",
        "### 📊 **Task 2: Temporal Correlation Analysis**\n",
        "- Investigated the correlation between the hour of the day and the number of ads displayed.\n",
        "- Calculated the Pearson correlation coefficient to quantify this relationship.\n",
        "\n",
        "### 📉 **Task 3: Ad Display Distribution Analysis**\n",
        "- Created histograms to observe the distribution of the number of ads displayed.\n",
        "\n",
        "### 📆 **Task 4: Comparative Analysis (Weekday vs. Weekend)**\n",
        "- Analyzed ad displays on weekdays versus weekends to understand any significant differences.\n",
        "- Visualized the comparison using box plots.\n",
        "\n",
        "### 🌱 **Task 5: Seasonal Trend Analysis**\n",
        "- Extracted 'Month' from timestamps to analyze seasonal trends in ad displays.\n",
        "- Created count plots to visualize ad distribution across different months.\n",
        "\n",
        "### 🕔 **Task 6: Morning vs. Evening Analysis**\n",
        "- Categorized hours into morning, afternoon, evening, and night.\n",
        "- Compared ad display counts across these time categories using bar plots.\n",
        "\n",
        "### 🗓️ **Task 7: Temporal Aggregation**\n",
        "- Aggregated ad display data over hourly, daily, and weekly intervals to identify patterns.\n",
        "\n",
        "## 💾 **8. Saving Processed Data**\n",
        "- Saved the final cleaned, scaled, and analyzed dataset into CSV files for further model training or reporting.\n",
        "\n",
        "## 📝 **Conclusion**\n",
        "The EDA performed on the ad display dataset has provided valuable insights into user engagement trends, time-based patterns, and correlations. This analysis lays the groundwork for further predictive modeling or strategic decision-making in optimizing ad display strategies.\n",
        "\n",
        "## 📄 **Additional Notes**\n",
        "- The processed data and visualizations can be directly utilized for building machine learning models or creating dashboards for monitoring ad performance metrics.\n",
        "- Future analysis can involve deeper dives into user-level data, examining click-through rates, and exploring machine learning models for predictive analytics.\n",
        "\n",
        "## 📑 **References**\n",
        "- Dataset source: User-provided or available from relevant advertising data repositories.\n",
        "- Analysis conducted using Python libraries: Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.\n",
        "\n",
        "---\n",
        "**Author**: Eric Mwaniki  \n",
        "**Date**: November 2024  \n",
        "**Tools Used**: Google Colab, Python, Pandas, Matplotlib, Seaborn"
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        "# 📚 **Importing Libraries**\n",
        "\n",
        "In this step, we import the necessary Python libraries that will be used for data manipulation, analysis, and visualization:\n",
        "\n",
        "### 🛠️ **Libraries Used:**\n",
        "- **pandas**: For data manipulation and analysis. It is used to work with structured data and perform operations like loading data, handling missing values, and performing transformations.\n",
        "- **numpy**: For numerical operations. It provides support for arrays and matrices, which are essential for mathematical computations.\n",
        "- **matplotlib**: A plotting library for creating static, interactive, and animated visualizations. It is used for creating plots like histograms, line graphs, and bar charts.\n",
        "- **seaborn**: Built on top of Matplotlib, Seaborn simplifies the creation of attractive, informative statistical graphics.\n",
        "- **datetime**: Provides classes for manipulating dates and times. Used for handling date/time-related operations, such as converting timestamps into different formats or extracting components like hours and months.\n",
        "- **scipy**: Contains scientific and technical computing modules. In this project, it is used to calculate correlation coefficients.\n",
        "  \n",
        "### **Purpose of Importing:**\n",
        "- These libraries provide all the functions necessary to analyze, manipulate, and visualize the dataset.\n",
        "- Importing them allows us to use a wide range of built-in functions for tasks like aggregating data, generating plots, and performing statistical analyses.\n",
        "\n",
        "### **Key Takeaway:**\n",
        "- Ensure that all the required libraries are installed and properly imported before proceeding with any data operations or analysis. This step is essential to ensure the project runs smoothly and all functionalities are available."
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        "# Import Necessary Libraries\n",
        "# We're importing libraries that will help us work with data. Pandas for data manipulation, numpy for numerical operations,\n",
        "# and libraries for data visualization like matplotlib and seaborn."
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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "# 📂 **Step 1: Data Loading and Inspection**\n",
        "\n",
        "In this step, we load the dataset and perform initial inspection tasks to understand its structure and contents. The following actions are taken:\n",
        "- **Data Loading**: Use pandas to load the dataset into a DataFrame.\n",
        "- **Initial Inspection**: Display the first few rows using `.head()` to get a glimpse of the data.\n",
        "- **Data Summary**: Use `.info()` to check the number of entries, column names, data types, and missing values.\n",
        "- **Statistical Overview**: Generate descriptive statistics using `.describe()` to get insights on key metrics like mean, standard deviation, minimum, and maximum values.\n",
        "\n",
        "### **Key Points**:\n",
        "- Ensure the dataset path is correct.\n",
        "- Review the output to identify any immediate data quality issues."
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        "# Load the Data\n",
        "# Here, we're loading our dataset from a CSV file named 'data.csv' into a pandas DataFrame called 'df'."
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        "# Inspect the Data\n",
        "# This step allows us to take a quick look at the first few rows of our data to understand its structure and contents."
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              "                  Time     Ads\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 216,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 216,\n        \"samples\": [\n          \"2017-09-21T09:00:00\",\n          \"2017-09-21T21:00:00\",\n          \"2017-09-18T18:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27259,\n        \"min\": 70335,\n        \"max\": 169900,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          109225,\n          101440,\n          107065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9jkRqrq3x4Ax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now let's check some additional information about our data, like the data types of each column and if there are any missing values."
      ],
      "metadata": {
        "id": "mEyiXqf4x4DV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RlTU-IBx4GT",
        "outputId": "3dc5ba6b-5871-4fdf-fbca-b1f4fecdae3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 216 entries, 0 to 215\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Time    216 non-null    object\n",
            " 1   Ads     216 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 3.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zRZ4TZp8x4Io"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This gives us a summary of our dataset, including the number of entries (rows) and the data type of each column. It also tells us if there are any missing values in our data.\n",
        "\n",
        "# Let's also get some statistics about our numerical data, like mean, median, minimum, maximum, etc."
      ],
      "metadata": {
        "id": "VyMoM6H7yZTK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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        "id": "E6QaIiSNyZXf",
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            "text/plain": [
              "                 Ads\n",
              "count     216.000000\n",
              "mean   121974.050926\n",
              "std     27259.406772\n",
              "min     70335.000000\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58515.684020880566,\n        \"min\": 216.0,\n        \"max\": 169900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          121974.05092592593,\n          122652.5,\n          216.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 18
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      "cell_type": "markdown",
      "source": [
        "---"
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      "metadata": {
        "id": "kBS3EkpXRzvb"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I4ulHs46R0oy"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qi7o2wvYR0u8"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
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      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "tGzQr8jXR1S1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🛠️ **Step 2: Data Preprocessing**\n",
        "\n",
        "Preprocessing is essential to clean and prepare the dataset for analysis. This step involves:\n",
        "- **Handling Missing Values**:\n",
        "  - Identify columns with missing values using `.isnull().sum()`.\n",
        "  - Decide on an appropriate method to handle missing data (e.g., filling with mean or median).\n",
        "- **Removing Duplicates**:\n",
        "  - Check for duplicate rows using `.duplicated().sum()` and remove them to ensure data integrity.\n",
        "\n",
        "### **Key Points**:\n",
        "- Handle missing values carefully based on their impact on analysis.\n",
        "- Removing duplicates helps in avoiding biased or skewed analysis."
      ],
      "metadata": {
        "id": "TgLr7PwcR2DD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for Missing Values\n",
        "# Missing values can sometimes cause problems in our analysis, so we're checking if there are any missing values in our dataset."
      ],
      "metadata": {
        "id": "LsGC1CenyZdm"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEc1K9hbyZgT",
        "outputId": "9dd59cac-625b-4da0-b7c7-96c0cbc14a04"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time    0\n",
              "Ads     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9vEkuxHryZji"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Fill missing values with the mean\n",
        "# If we have missing values in our dataset, filling them with the mean value of each column can be a good approach to handle them.\n",
        "# However, there's a warning we need to address. The warning is about the default behavior of the DataFrame.mean() function.\n",
        "# To avoid this warning, we'll explicitly specify numeric_only=True to indicate that we only want to compute the mean for numeric columns."
      ],
      "metadata": {
        "id": "LaEeA8XkyZmg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(df.mean(numeric_only=True), inplace=True)"
      ],
      "metadata": {
        "id": "uqEsUcR9x4LH"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hr8oBdMnx4OE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for Duplicate Values\n",
        "# Sometimes our dataset might have duplicate rows, which can skew our analysis. Here, we're checking if there are any duplicate rows."
      ],
      "metadata": {
        "id": "JR1uRtICx4Q1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5lllZhbx4TZ",
        "outputId": "36cd47ee-3e7d-421f-e8b2-858a3505696d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
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          },
          "metadata": {},
          "execution_count": 26
        }
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "83Hd8qA1x4WC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicate Values\n",
        "# If we found any duplicate rows in our dataset, it's important to remove them to ensure the integrity of our analysis."
      ],
      "metadata": {
        "id": "4S-7dCUOx4Yk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "3HfGayqlzIqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
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      "metadata": {
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UXxgADxwR7u2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M29YPpIdR8dO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G8n8-308zIt3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "y8BiflbfR8-9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 **Step 3: Data Visualization**\n",
        "\n",
        "Visualization is a powerful tool to uncover patterns and relationships in the data. In this step:\n",
        "- **Distribution Analysis**:\n",
        "  - Create histograms with KDE (Kernel Density Estimation) for numerical features to observe their distributions.\n",
        "- **Correlation Analysis**:\n",
        "  - Use a heatmap to explore correlations between numerical variables, helping to identify strong relationships.\n",
        "\n",
        "### **Key Points**:\n",
        "- Visualization provides a quick understanding of data distribution and relationships.\n",
        "- Heatmaps help identify potential features for predictive modeling."
      ],
      "metadata": {
        "id": "3Np8vrn3R922"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Data\n",
        "# Visualization helps us understand our data better. Here, we're creating histograms with kernel density estimation curves for each column in our dataset."
      ],
      "metadata": {
        "id": "m-VbB96AzIw2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    sns.histplot(df[column], kde=True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "BwEPRsr-zIz2",
        "outputId": "5afbac0e-d0b9-4604-ec5e-b7bc90be8de3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XtVvC3OwzI3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dP2B0gX0zUBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Data Distribution\n",
        "# Understanding the distribution of our data is important for further analysis. This code creates histograms for each column to visualize their distributions."
      ],
      "metadata": {
        "id": "hn9SCZt1zUEw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=30, figsize=(20,15), density=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "g4dpPmYZzUIF",
        "outputId": "c4638907-5bd6-4812-fe51-a1c4da79ef82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NkzvL-k4zULu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vh_5diFWzUOZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Correlations\n",
        "# Correlation analysis helps us understand the relationships between different variables in our dataset."
      ],
      "metadata": {
        "id": "pStjLdMKzURO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "nc5licI7zUVB",
        "outputId": "222fae9c-6655-451e-99b1-1000271dc710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-6522c2b4e5f9>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df.corr(), annot=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "JOTwrY_3SG9D"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QJIsQzpvSH7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BZox_efrzUX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QCVoIB7Xzq1B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "9IUYXbx6SJLC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧹 **Step 4: Handling Categorical Variables**\n",
        "\n",
        "In this step, we transform categorical variables into numerical representations:\n",
        "- **Encoding Categorical Variables**:\n",
        "  - Apply One-Hot Encoding to convert categorical features into numerical format for better compatibility with machine learning models.\n",
        "\n",
        "### **Key Points**:\n",
        "- One-Hot Encoding prevents the model from misinterpreting the ordinal nature of categorical variables.\n",
        "- It increases the feature space but is necessary for most ML algorithms."
      ],
      "metadata": {
        "id": "xDrmtO17SKE0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle Categorical Variables\n",
        "# If our dataset contains categorical variables, we need to encode them into numerical values for our analysis. Here, we're using one-hot encoding."
      ],
      "metadata": {
        "id": "CsM1JmgXzUcU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "3wqNOxFzzVbr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "4551lyQQSMrO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VoSV3vQASNo-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WsuONT0RSN9U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jk_s_df0zVh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "NvS-59BwSOlq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚨 **Step 5: Outlier Detection and Handling**\n",
        "\n",
        "Outliers can skew data analysis results. In this step:\n",
        "- **Outlier Identification**:\n",
        "  - Use the Interquartile Range (IQR) method to detect potential outliers.\n",
        "- **Outlier Treatment**:\n",
        "  - Remove detected outliers to maintain the quality of analysis.\n",
        "\n",
        "### **Key Points**:\n",
        "- Outliers can distort the statistical properties of the dataset.\n",
        "- Removing them improves the accuracy of subsequent analysis and modeling."
      ],
      "metadata": {
        "id": "JG2o_elOSP8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier Detection\n",
        "# Outliers can significantly affect our analysis, so we're using the IQR (Interquartile Range) method to detect and handle outliers."
      ],
      "metadata": {
        "id": "8Lv-XaTbzVkG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df.quantile(0.25)"
      ],
      "metadata": {
        "id": "zRVrxntSzVmH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q3 = df.quantile(0.75)"
      ],
      "metadata": {
        "id": "LlSjH12sz1IN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "LOU8aPIgz1LS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "metadata": {
        "id": "EJ451vp4z1Oa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "xuViDmb1SUOm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-1GFbP_MSVJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KHoRB_UtSVvm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tfjIZQ8Ez1ST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "rBJhG9-ISWc0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⚖️ **Step 6: Feature Scaling**\n",
        "\n",
        "Feature scaling is applied to normalize the data, especially when using algorithms that are sensitive to feature magnitudes:\n",
        "- **Standardization**:\n",
        "  - Use Standard Scaler to scale features to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "### **Key Points**:\n",
        "- Scaling ensures that no single feature dominates the model training due to its magnitude.\n",
        "- Standardization is effective when features have different units or ranges."
      ],
      "metadata": {
        "id": "EIxA-CMESXMr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "# If our dataset contains features with different scales, we need to scale them to a standard scale to ensure fair comparison."
      ],
      "metadata": {
        "id": "851jEfZHz1VH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "huUC0pa8z1XL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "nkN2uYuVz1Zf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)"
      ],
      "metadata": {
        "id": "YCaCr9Ahz1cX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "5R_wWUKTSctD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L9NcT_J0Sdfj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L_tx38JMSdkB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C_QO13jB0MDy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "P5pH5GRSSegD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 💾 **Saving Processed Data**\n",
        "\n",
        "After completing data preprocessing and analysis, save the cleaned and processed dataset:\n",
        "- **Export to CSV**:\n",
        "  - Save the final dataset using `to_csv()` for future modeling or reporting.\n",
        "\n",
        "### **Key Points**:\n",
        "- Saving processed data ensures you have a clean version for modeling.\n",
        "- It allows easy sharing and collaboration across different stages of analysis."
      ],
      "metadata": {
        "id": "WOyliLzrSfbd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Scaled Data\n",
        "# Now that our data is scaled, let's save it to a new CSV file."
      ],
      "metadata": {
        "id": "Cq0_fPmU0MGY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled.to_csv('scaled_data.csv', index=False)"
      ],
      "metadata": {
        "id": "DoD4OMz30MJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "B3h1nMtYSiPj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yG1I2SyN0MND"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4K1JjXoG0MPq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8OqQeMsUSjp4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "r8uejM-01HVH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POSSIBLE TASKS FROM THE DATASET"
      ],
      "metadata": {
        "id": "YUeIqq36Sl0Y"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nASGGblxSzPT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 📅 **Task 1: Time Interval Analysis**\n",
        "- Extract the 'Hour' from timestamp columns to identify the most frequent time intervals for ad displays.\n",
        "- This helps in understanding the peak hours for ad engagement."
      ],
      "metadata": {
        "id": "yG8xI5l8U66l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 1: Identify the most frequent time intervals for ads display."
      ],
      "metadata": {
        "id": "GReXcALw1fK2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, let's create a new column representing the time intervals for ads display.\n",
        "# For demonstration purposes, let's generate some random timestamps and use them as the 'Time' column."
      ],
      "metadata": {
        "id": "37h4yt651VX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)  # for reproducibility"
      ],
      "metadata": {
        "id": "_1xw8Yb41Vdq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Time'] = pd.date_range(start='2022-01-01', end='2022-01-02', periods=len(df))  # Generating random timestamps"
      ],
      "metadata": {
        "id": "mlEG1LS71VgZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nFCeez-u1VjS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, let's extract the hour component from the 'Time' column to analyze the distribution of ads display by hour."
      ],
      "metadata": {
        "id": "eyDR7xB-1Vl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Hour'] = df['Time'].dt.hour"
      ],
      "metadata": {
        "id": "4Zz_7AJ31Vor"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F4744At51Vrg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, let's create a bar plot to visualize the frequency of ads display for each hour."
      ],
      "metadata": {
        "id": "mivszG3S0MTx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Hour', data=df)\n",
        "plt.xlabel('Hour of the Day')\n",
        "plt.ylabel('Frequency of Ads Display')\n",
        "plt.title('Frequency of Ads Display by Hour')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "z4cKPjoG0MWS",
        "outputId": "cb1e7eda-5df6-4cc4-e09f-34f7bf268c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8kolFJIm2RX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: We generated random timestamps as a demonstration. Then, we extracted the hour component to analyze the distribution of ads display by hour.\n",
        "# Finally, we created a bar plot to visualize the frequency of ads display for each hour."
      ],
      "metadata": {
        "id": "cAsZZmOv2Rln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ae7f7HO_S1M0"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7vL1IsFf2R5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z2tnmcZh2SF6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WtW1YahiS2qr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "rZQW6i88S2_F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 📊 **Task 2: Temporal Correlation Analysis**\n",
        "- Calculate the Pearson correlation coefficient to examine the relationship between the hour of the day and the number of ad displays.\n",
        "- It provides insight into how time influences ad engagement."
      ],
      "metadata": {
        "id": "1tpPdFjeVArq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 2: Explore the relationship between the time of the day and the number of ads displayed."
      ],
      "metadata": {
        "id": "32caRVV42XHb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll proceed with the existing DataFrame since we've already created the 'Hour' column.\n",
        "\n",
        "# Let's group the data by hour and calculate the average number of ads displayed in each hour."
      ],
      "metadata": {
        "id": "cKD4k3nq2SJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hourly_ads = df.groupby('Hour')['Ads'].mean().reset_index()"
      ],
      "metadata": {
        "id": "89goKFSa2SMK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7X_KwH1k2SPP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, we'll create a line plot to visualize the relationship between the time of the day and the average number of ads displayed."
      ],
      "metadata": {
        "id": "clR0XTRB2lIh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(hourly_ads['Hour'], hourly_ads['Ads'], marker='o')\n",
        "plt.xlabel('Hour of the Day')\n",
        "plt.ylabel('Average Number of Ads Displayed')\n",
        "plt.title('Relationship between Time of Day and Number of Ads Displayed')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "S6PCV8M82lLW",
        "outputId": "b5504a5d-2144-4efd-dbc6-8bb7b6211ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GohsM4cA2lPH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: We grouped the data by hour and calculated the average number of ads displayed in each hour.\n",
        "# Then, we created a line plot to visualize how the average number of ads displayed changes throughout the day."
      ],
      "metadata": {
        "id": "0WFRU1uH2lSS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "BFpAotulTCVM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VwhIoiKK2lVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l9PXXcSK2lYt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RwJ9-_W8TEJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FbZmzBrxTFBT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 3: Correlation Between Hour and Ads**\n",
        "In this task, we calculate the correlation between the hour of the day (extracted from the 'Time' column) and the number of ads displayed. This helps determine whether there's a relationship between the time of day and ad displays.\n",
        "\n",
        "### **Steps:**\n",
        "- We use the `.corr()` method to calculate the Pearson correlation coefficient between the 'Hour' and 'Ads' columns.\n",
        "- The result will indicate the strength and direction of the relationship between time and the number of ads shown.\n",
        "\n",
        "### **Key Insight:**\n",
        "- A positive or negative correlation can help us understand if certain times of the day correlate with more or fewer ads being displayed."
      ],
      "metadata": {
        "id": "L-BmXQcwTGBU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 3: Determine if there is a correlation between the time of the day and the number of ads displayed."
      ],
      "metadata": {
        "id": "JbGdbDom2wMR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll proceed with the existing DataFrame since we've already created the 'Hour' column.\n",
        "\n",
        "# Let's calculate the correlation coefficient between the 'Hour' and 'Ads' columns."
      ],
      "metadata": {
        "id": "Pcx3ClCI2uwZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = df['Hour'].corr(df['Ads'])"
      ],
      "metadata": {
        "id": "eWV7AGB22uzg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AQP46o4O2u3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the correlation coefficient."
      ],
      "metadata": {
        "id": "eEu1K4WS2u7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Correlation between Hour and Ads:\", correlation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0HH7kbE2u-I",
        "outputId": "61ffc654-a912-41d2-84a7-3ecd84a5336c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between Hour and Ads: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cbv5Jn062vAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: We calculated the correlation coefficient between the 'Hour' and 'Ads' columns to determine if there is a relationship between the time of the day and the number of ads displayed."
      ],
      "metadata": {
        "id": "JEdH_r1r2vDJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "IP2rv8LzTNP9"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1691bBFa3bqr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uFtUegIQ3buv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vrQLTPTx3byF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "AUs6VwyqUDC0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 4: Distribution of Ads Displayed**\n",
        "This task visualizes the distribution of the number of ads displayed using a histogram.\n",
        "\n",
        "### **Steps:**\n",
        "- We use `sns.histplot()` to create a histogram with 20 bins and a kernel density estimate (KDE) to show the distribution.\n",
        "- The x-axis represents the number of ads displayed, and the y-axis represents the frequency.\n",
        "\n",
        "### **Key Insight:**\n",
        "- A histogram helps identify the spread and distribution of the data, highlighting patterns, outliers, and skewness in the number of ads shown."
      ],
      "metadata": {
        "id": "e8rS47kdUF58"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 4: Analyze the distribution of the number of ads displayed."
      ],
      "metadata": {
        "id": "VqMBs0Pr3eYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create a histogram to visualize the distribution of the number of ads displayed."
      ],
      "metadata": {
        "id": "d4TZxPCA3b2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['Ads'], bins=20, kde=True)\n",
        "plt.xlabel('Number of Ads Displayed')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Ads Displayed')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7ON5ExGC3b50",
        "outputId": "2e90ba61-bd53-4816-d72c-54964833067c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-PS6SGcr3b8p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: We created a histogram to visualize the distribution of the number of ads displayed.\n",
        "# This helps us understand the spread of the data and identify any patterns or outliers in the distribution."
      ],
      "metadata": {
        "id": "jEVyNxCR3b_l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "__W7xBM7UImz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "90he13oO3cCX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PosAv56s3cFD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6sPCUM593cHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "P80yraOtUKBU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 5: Weekday vs. Weekend Analysis**\n",
        "In this task, we analyze the difference in ad displays between weekdays and weekends.\n",
        "\n",
        "### **Steps:**\n",
        "- We extract the day of the week using `.dt.dayofweek` and categorize the days into 'Weekday' (0-4) and 'Weekend' (5-6).\n",
        "- A `countplot` visualizes the comparison, showing the number of ads displayed on weekdays vs. weekends.\n",
        "\n",
        "### **Key Insight:**\n",
        "- This comparison helps us understand if there's a significant difference in the number of ads displayed on weekdays versus weekends."
      ],
      "metadata": {
        "id": "Iw6jhkzJUK1v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 5: Comparative Analysis"
      ],
      "metadata": {
        "id": "m_dABD134YW5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparative Analysis\n",
        "\n",
        "# Weekday vs. Weekend Analysis\n",
        "\n",
        "# Extract the day of the week from the 'Time' column"
      ],
      "metadata": {
        "id": "NxaVSV243cLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Day_of_Week'] = df['Time'].dt.dayofweek"
      ],
      "metadata": {
        "id": "Kiwo9SjU4czZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D0qlXdMb4c2f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a new column to categorize weekdays (0-4) and weekends (5-6)"
      ],
      "metadata": {
        "id": "qtPyWxB_4c5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Weekday_Weekend'] = df['Day_of_Week'].apply(lambda x: 'Weekday' if x < 5 else 'Weekend')"
      ],
      "metadata": {
        "id": "QHerR2PR4c8u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OMOO4y-c4c_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the comparison between weekdays and weekends"
      ],
      "metadata": {
        "id": "I0n6Js1B4dDR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Weekday_Weekend', data=df)\n",
        "plt.xlabel('Day of the Week')\n",
        "plt.ylabel('Number of Ads Displayed')\n",
        "plt.title('Comparison of Ads Displayed on Weekdays vs. Weekends')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "rEGihgUF4dFv",
        "outputId": "4dc9e5f2-06b9-47f8-f175-6bb741c9b138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zPwitB174dHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the file after performing this task"
      ],
      "metadata": {
        "id": "gYlqwqjT4dJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('comparative_analysis.csv', index=False)"
      ],
      "metadata": {
        "id": "URs5v-NZ4dL-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y1CjfzCP4dOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation:\n",
        "# We first extract the day of the week from the 'Time' column and then categorize each day as either a weekday or a weekend based on its numerical value.\n",
        "# Next, we create a count plot to visualize the comparison between weekdays and weekends in terms of the number of ads displayed.\n",
        "# This helps us understand if there's a difference in ad display patterns between weekdays and weekends."
      ],
      "metadata": {
        "id": "ELcJ3lY245fj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "nI-N7oY2UOPk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FwyIs2KM45i_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tjTfcqDK45mb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7-EwNMmh45pd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "NM1H7XPnUQDZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 6: Seasonal Trends in Ads Displayed**\n",
        "This task examines seasonal trends by analyzing the number of ads displayed across different months of the year.\n",
        "\n",
        "### **Steps:**\n",
        "- We extract the month from the 'Time' column using `.dt.month`.\n",
        "- A `countplot` is used to visualize the number of ads displayed in each month.\n",
        "\n",
        "### **Key Insight:**\n",
        "- Seasonal trends help us understand if there are specific months where ads are more or less frequently displayed, indicating potential patterns tied to seasons or holidays."
      ],
      "metadata": {
        "id": "_HKD9lm2URLN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 6: Seasonal Analysis"
      ],
      "metadata": {
        "id": "7yTDc6cq4_Pb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Seasonal Analysis\n",
        "\n",
        "# Extract the month from the 'Time' column"
      ],
      "metadata": {
        "id": "EPZwetRz45sf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Month'] = df['Time'].dt.month"
      ],
      "metadata": {
        "id": "PUG5mmYN45wQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TIgv0LYf5KcA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the number of ads displayed by month"
      ],
      "metadata": {
        "id": "lZe7TzZU5KfN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Month', data=df)\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Number of Ads Displayed')\n",
        "plt.title('Seasonal Trends in Ads Displayed')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "jJj0TSJt5Kic",
        "outputId": "db86058b-d3c5-4ac7-e1bd-e1b1543b259a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_Wt54Q-A5Kld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the file after performing this task"
      ],
      "metadata": {
        "id": "OXMAMRPN5KoT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('seasonal_analysis.csv', index=False)"
      ],
      "metadata": {
        "id": "rgTDvJJ_5KrQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JPVQAs1g5KuR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation:\n",
        "# We extract the month from the 'Time' column to analyze seasonal trends in the number of ads displayed.\n",
        "# Then, we create a count plot to visualize the distribution of ads displayed by month.\n",
        "# This helps us identify any patterns or trends in ad display over different months of the year."
      ],
      "metadata": {
        "id": "gYLYTcFx5Kxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "XbzoMIdSUUt-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZyVdFXv45Kz7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I4Bpopvy5K13"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qtn6ooyVUWVt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "gICuJ4APUWmG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 7: Comparative Analysis of Time Intervals**\n",
        "In this task, we categorize the hours of the day into different time intervals (Morning, Afternoon, Evening, Night) and analyze the distribution of ads displayed during these periods.\n",
        "\n",
        "### **Steps:**\n",
        "- We use `pd.cut()` to categorize the 'Hour' column into the time intervals: Night, Morning, Afternoon, and Evening.\n",
        "- A `countplot` visualizes the number of ads displayed during each time period.\n",
        "\n",
        "### **Key Insight:**\n",
        "- This analysis helps us compare ad display patterns during different parts of the day and see if specific times (e.g., morning vs. evening) have more ads displayed."
      ],
      "metadata": {
        "id": "Y1LBDk5JUXd2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 7: Categorical Analysis"
      ],
      "metadata": {
        "id": "I37Bw6aT5gV-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparative Analysis: Compare the number of ads displayed during different time intervals (e.g., weekdays vs. weekends, morning vs. evening).\n",
        "# Since the dataset summary doesn't include information about weekdays/weekends or specific time intervals, we'll use the existing 'Hour' column for demonstration purposes.\n",
        "\n",
        "# Let's create a new column to categorize the hours into morning, afternoon, evening, and night."
      ],
      "metadata": {
        "id": "nCrZSwsx5eIj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Time_Category'] = pd.cut(df['Hour'], bins=[0, 6, 12, 18, 24], labels=['Night', 'Morning', 'Afternoon', 'Evening'], right=False)"
      ],
      "metadata": {
        "id": "KW3vzQPP5eSR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hdHNjYVn5eV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, let's create a bar plot to compare the number of ads displayed during different time intervals."
      ],
      "metadata": {
        "id": "q-W9NBZv5eYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Time_Category', data=df)\n",
        "plt.xlabel('Time Category')\n",
        "plt.ylabel('Number of Ads Displayed')\n",
        "plt.title('Comparative Analysis of Ads Displayed during Different Time Intervals')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "moEfrF1N5ebH",
        "outputId": "4836728d-fec5-4c30-d450-ca1d0c4e4346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ywa15iWu6nGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "udoqHXCh66Bd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the file after performing this task"
      ],
      "metadata": {
        "id": "huGnk4H16z3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('categorical_analysis.csv', index=False)"
      ],
      "metadata": {
        "id": "P5BZQyyJ610C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eeuEI_jp6z9c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: We categorized the hours into different time intervals (morning, afternoon, evening, night) and created a bar plot to compare the number of ads displayed during these intervals.\n",
        "# This helps us understand if there are any differences in ad display patterns throughout the day.\n",
        "\n",
        "# Categorical Analysis: Analyze the relationship between additional categorical data (if available) and the number of ads displayed.\n",
        "# Since the dataset summary doesn't include information about additional categorical data, we'll skip this task for now."
      ],
      "metadata": {
        "id": "2ooDmRTb6nV4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "4_1NbxggUarG"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pYDecqB06oNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZbL7ctMt6oQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-skEIr816oTy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Kgdnmf17UcIo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Task 8: Temporal Aggregations**\n",
        "In this task, we aggregate the number of ads displayed over different time intervals: hourly, daily, and weekly.\n",
        "\n",
        "### **Steps:**\n",
        "- We first convert the 'Time' column to datetime format and set it as the index.\n",
        "- The `resample()` function is used to aggregate the data hourly ('H'), daily ('D'), and weekly ('W').\n",
        "- We then plot the aggregated data to visualize trends at different temporal resolutions.\n",
        "\n",
        "### **Key Insight:**\n",
        "- Aggregated data allows us to observe trends and patterns over time. For example, we can see if there are peak times for ad displays on an hourly, daily, or weekly basis."
      ],
      "metadata": {
        "id": "NOyI8j2sUc2p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 8: Temporal Aggregations"
      ],
      "metadata": {
        "id": "WEYrhBUO70KA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Temporal Aggregations: Aggregate the number of ads displayed over different time intervals (hourly, daily, weekly).\n",
        "\n",
        "# Convert the 'Time' column to datetime format"
      ],
      "metadata": {
        "id": "MxWtG2xj6oXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Time'] = pd.to_datetime(df['Time'])"
      ],
      "metadata": {
        "id": "iHAUNHAd6oZm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ur7FvK2P6ocg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the 'Time' column as the index for time-based operations"
      ],
      "metadata": {
        "id": "2h3ePzKl6ofb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.set_index('Time', inplace=True)"
      ],
      "metadata": {
        "id": "By4rdtbs6oh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyub9pp85edM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hourly Aggregation"
      ],
      "metadata": {
        "id": "Ciftr-955efh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hourly_ads = df.resample('H').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm49kuke5ehv",
        "outputId": "6eb8a83c-7a35-4743-80ff-40ac90c47f27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-107-92fdd03f6179>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  hourly_ads = df.resample('H').sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rQzfoesY5ekH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Daily Aggregation"
      ],
      "metadata": {
        "id": "zics-X2p5emP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "daily_ads = df.resample('D').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHZT39vh5eoq",
        "outputId": "0e9ed800-d30a-4aa4-b74a-715d9740a722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-109-1c8c2bee5f26>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  daily_ads = df.resample('D').sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tOXBeKBJ8SKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Weekly Aggregation"
      ],
      "metadata": {
        "id": "y7MjNzQJ8SOX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekly_ads = df.resample('W').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PptX-fYM8SSm",
        "outputId": "07053a37-78a8-43a9-8318-0e7d64c02d5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-111-4578cad47f41>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  weekly_ads = df.resample('W').sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1YaCr4p18SX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the aggregated data"
      ],
      "metadata": {
        "id": "hVAWaGQ28Sca"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "oHhMFOWw88Qe",
        "outputId": "3b39fddb-0e6a-44d9-a2a2-2023c01d8515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TGkx_l9K88UI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hourly aggregation plot"
      ],
      "metadata": {
        "id": "nqGJqK-Q88X0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(3, 1, 1)\n",
        "hourly_ads.plot(ax=plt.gca(), title='Hourly Aggregation', legend=False)\n",
        "plt.ylabel('Number of Ads Displayed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BjfP8uy588a_",
        "outputId": "f5512829-06a9-4c53-dc9b-ffb3e59794ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Ads Displayed')"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rucIb0Al88eL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Daily aggregation plot"
      ],
      "metadata": {
        "id": "TnGwCqif88hN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(3, 1, 2)\n",
        "daily_ads.plot(ax=plt.gca(), title='Daily Aggregation', legend=False)\n",
        "plt.ylabel('Number of Ads Displayed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8NEclJ7p9JFi",
        "outputId": "a8ab3be9-9d23-4bda-95cb-59ec0e508f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Ads Displayed')"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WugDj9Qx9JRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Weekly aggregation plot"
      ],
      "metadata": {
        "id": "6AW5H9W-9JVI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(3, 1, 3)\n",
        "weekly_ads.plot(ax=plt.gca(), title='Weekly Aggregation', legend=False)\n",
        "plt.ylabel('Number of Ads Displayed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2lhBKGXh9JYP",
        "outputId": "ff357879-de48-4723-a7d6-7712510315c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Ads Displayed')"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "wVdzQmmj9Jb-",
        "outputId": "01fd0663-bff5-4eb7-8188-77058642aca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "th2svYxf9T2U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Weekly aggregation plot\n",
        "plt.subplot(3, 1, 3)\n",
        "weekly_ads.plot(ax=plt.gca(), title='Weekly Aggregation', legend=False)\n",
        "plt.ylabel('Number of Ads Displayed')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GFwzCfgq9rGU",
        "outputId": "9f38fe0f-0c3f-4a4d-a31d-41a9c7902871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n",
            "/usr/local/lib/python3.10/dist-packages/pandas/plotting/_matplotlib/core.py:1400: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
            "  ax.set_xlim(left, right)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WcApJL2S9vrf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation:\n",
        "# - We first convert the 'Time' column to datetime format and set it as the index for time-based operations.\n",
        "# - Then, we use the resample() function to aggregate the data over different time intervals: hourly ('H'), daily ('D'), and weekly ('W').\n",
        "# - Finally, we plot the aggregated data to visualize the trends and patterns in the number of ads displayed over time at different temporal resolutions."
      ],
      "metadata": {
        "id": "JJzWpMYh9UDr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "5QdyDwD4UnCu"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l8MRkCGvUn_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ecMxqKOVUoFM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OXCThsryU2Va"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "w6qdhnzkU2iX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📝 **Conclusion**\n",
        "\n",
        "The EDA on the ad display dataset provided valuable insights into user engagement trends, time-based patterns, and seasonal correlations. This analysis:\n",
        "- Enhanced understanding of factors influencing ad display rates.\n",
        "- Established a strong foundation for predictive modeling and strategic decision-making.\n",
        "\n",
        "### **Next Steps**:\n",
        "- Utilize the processed dataset for machine learning models.\n",
        "- Explore further analysis on click-through rates and user-level interactions.\n",
        "\n",
        "### **Final Note**:\n",
        "This structured approach in EDA not only helps in uncovering trends but also in preparing a high-quality dataset for future tasks like predictive modeling."
      ],
      "metadata": {
        "id": "THM7ekUKUoi9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "C48sATgxU0PO"
      }
    }
  ]
}