Assignment pt1_nycflights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Second Assignment: Exercise and a Personal Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this second and final assignment you have the opportunity to choose a dataset and perform data analysis to answer the type of questions that interest you or will utilise a technique that you want to gain experience in. \n",
    "\n",
    "You may wish to select a dataset that we have already looked at (e.g. Titanic, or IMDB), or select another open-source dataset from Kaggle (or other). Once you've selected a dataset to investigate, proceed to complete the template cells below. In the markdown cells, you should provide a brief introduction to the dataset, and write the questions that you will investigate. Then use the code cells to answer the questions you select.\n",
    "\n",
    "### Logistics\n",
    "\n",
    "**Deadline.** Please see the Assessment deadline in specified in Canvas. Late submissions will not be accepted. This notebook is the Second of two Assignments for this course.\n",
    "\n",
    "**The Assignment Task.** This Second Assignment portfolio will account for the remaining 70 points of the 100 (Assignment 1 was worth 30 marks). This Second Assignment features two parts: \n",
    "* Part 1 is out of 20 marks, based on a defined set of problems. This will extend your application of numpy, pandas, and matplotlib on the iris dataset. \n",
    "* Part 2 is out of 50 marks, and requires you to perform exploratory data analysis on a dataset of your choice. You should start by selecting a dataset and acquiring the data. Describe the context of the dataset with commentary and descriptive statistics. Then you should choose three questions to ask of the dataset and then outline how to answer them. Where appropriate, seek to display the results with an appropriate plot.\n",
    "\n",
    "**Submission.** Please email your solutions and your completed Declaration of Authorship (DoA) form to weeklyclasses@conted.ox.ac.uk "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 (Set Exercise) (out of 20 marks)\n",
    "\n",
    "In this first part of Assignment 2, you are asked to continue work on the Iris dataset and further utilise libraries such as NumPy, Pandas and Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Installing the python libraries (put this in a separate cell in jupyter notebook)\n",
    "#pip install numpy \n",
    "#pip install pandas \n",
    "#pip install matplotlib \n",
    "## Let's import the libraries we are going to use later on\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import PathPatch\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "# Let's set the precision for NumPy and Pandas\n",
    "np.set_printoptions(precision=3)\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import iris dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input:\n",
    "# Import iris keeping the text column intact\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "iris = np.genfromtxt(url, delimiter=',', \n",
    "                     names=['sepal length', 'sepal width', 'petal length', 'petal width', 'species'],\n",
    "                     dtype=[np.float32, np.float32, np.float32, np.float32, '|S15']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1A. Produce some descriptive statistics for the iris dataset (5 marks)\n",
    "\n",
    "For example, display the count of records, mean, std, min, max and in addtion to the quartiles for each of the columns.\n",
    "\n",
    "*   **Note:** For this question I have shown two ways of producing the summary statistic chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method one to calculate the summary statistics\n",
    "\n",
    "I decided to try two ways to display the summary statistics. The first method is a simple one and I just used the `describe()` function to create the summary statistic chart. But I wanted to experiment a bit and see if there was another way to create the summary statistics chart (or even choose which summary statistics I want to show). I show and explain my code for the second method below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90</td>\n",
       "      <td>4.40</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count        150.00       150.00        150.00       150.00\n",
       "mean           5.84         3.05          3.76         1.20\n",
       "std            0.83         0.43          1.76         0.76\n",
       "min            4.30         2.00          1.00         0.10\n",
       "25%            5.10         2.80          1.60         0.30\n",
       "50%            5.80         3.00          4.35         1.30\n",
       "75%            6.40         3.30          5.10         1.80\n",
       "max            7.90         4.40          6.90         2.50"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I am converting the iris dataset into a Dataframe and am using the .describe() command to produce the summary statistics\n",
    "pd.DataFrame(iris).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method two to calculate the summary statistics\n",
    "\n",
    "The second method I used to calculate the summary statistics table did not use the `describe()` function. Instead, I decided to create the summary statistics through making a new dataframe. I used the `.agg` (aggregate) function to create the summary statistics. What I liked about this method was that I could choose why type of summary statistics I can include in my table. Exploring this alternative route also taught me that I can create a summary statistic table for a specific formula and have the option to customise the table for my own needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f25</th>\n",
       "      <td>5.10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantile</th>\n",
       "      <td>5.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f75</th>\n",
       "      <td>6.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90</td>\n",
       "      <td>4.40</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sepal_length  sepal_width  petal_length  petal_width\n",
       "count           150.00       150.00        150.00       150.00\n",
       "mean              5.84         3.05          3.76         1.20\n",
       "std               0.83         0.43          1.76         0.76\n",
       "min               4.30         2.00          1.00         0.10\n",
       "f25               5.10         2.80          1.60         0.30\n",
       "quantile          5.80         3.00          4.35         1.30\n",
       "f75               6.40         3.30          5.10         1.80\n",
       "max               7.90         4.40          6.90         2.50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class quantiles: \n",
    "    def f25(self,x): \n",
    "        return x.quantile(0.25) #defining the function for 25th Percentile\n",
    "    \n",
    "    def f75(self,x):\n",
    "        return x.quantile(0.75) #defining the function for 75th Percentile\n",
    "\n",
    "#I am converting the 'iris' dataset (which are arrays) into a pandas dataframe    \n",
    "pd.DataFrame(\n",
    "    #I am specifying the columns that I want to display in my summary statistic table (I did not include 'species' since it is a non-numeric column\n",
    "    iris[['sepal_length','sepal_width','petal_length','petal_width']] \n",
    "            \n",
    "    #I am now using the '.agg' function for aggregation and am specifying the summary statistics that I want\n",
    "    #note: any quantile that in not 50 needs to be specified with a function which I have defined above\n",
    "        ).agg(['count','mean','std','min',quantiles().f25,'quantile',quantiles().f75,'max']\n",
    "    \n",
    "    #I am using the '.rename' function to rename the dataframe index (there was a column created for the summary statistic functions that I used but theses names were not in the index but were in a separate column titled 'index')          \n",
    "                 ).rename(index={0: 'count', 1: 'mean', 2: 'std', 3: 'min', 4: '25%', 5: '50%', 6: '75%',7: 'max'}\n",
    "\n",
    "    #I am specifying the columns that I want to display in the final version of my dataframe (I am re-specifying the column names because the column 'index' was automatically created when I created the summary statistics\n",
    "                         )[['sepal_length','sepal_width','petal_length','petal_width']]\n",
    "\n",
    "### Here is the code in one line without the comments\n",
    "#pd.DataFrame(iris[['sepal_length','sepal_width','petal_length','petal_width']]\n",
    "#            ).agg(['count','mean','std','min',quantiles().f25,'quantile',quantiles().f75,'max']\n",
    "#                 ).rename(index={0: 'count', 1: 'mean', 2: 'std', 3: 'min', 4: '25%', 5: '50%', 6: '75%',7: 'max'})[['sepal_length','sepal_width','petal_length','petal_width']]                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1B. Calculate the maximum petal width for each species of flower (5 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the for loop to find the max `petal_width` values\n",
    "\n",
    "My first way of solving this problem was using a for loop to go through each species. I then created a temporary dataframe in the for loop called `species_df` which stored the data for each species. I then used the `species_df` dataframe to filter out the `petal_width` column and calculated the maximum `petal_width` values and rounded the answer by 2 decimal points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Iris-setosa' 0.6\n",
      "b'Iris-versicolor' 1.8\n",
      "b'Iris-virginica' 2.5\n"
     ]
    }
   ],
   "source": [
    "iris_df = pd.DataFrame(iris) #I am converting the iris array into a pandas dataframe\n",
    "\n",
    "#iris_df['species'].unique() prints out a list of the unique values in the `species` column in the `iris_df` dataframe\n",
    "for x in iris_df['species'].unique():\n",
    "    species_df = iris_df.loc[iris_df['species'] == x] #I am creating a new temporary dataframe which filters the dataframe values to each species\n",
    "    print(x,round(species_df['petal_width'].max(),2) ) #I am filtering the dataset further by outputting the `petal_width` column only. I then calculate the max `petal_width` value for each species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the traditional `groupby()` function to calculate the maximum `petal_width`\n",
    "\n",
    "My second approach to finding the maximum `petal_width` for each species is more traditional and does not require many steps. I would like to highlight that at the end of the code I needed to specify the specific columns that I wanted maximum values from. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "b'Iris-setosa'       0.60\n",
       "b'Iris-versicolor'   1.80\n",
       "b'Iris-virginica'    2.50\n",
       "Name: petal_width, dtype: float32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.groupby('species').max()['petal_width']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1C. Data Visualisation (5 marks)\n",
    "\n",
    "Produce a boxplot for each of the four columns of the iris dataset (sepal_length, sepal_width, petal_length and petal_width) in addition to the volume column (if you completed Assignment 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>b'Iris-setosa'</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.90</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>b'Iris-setosa'</td>\n",
       "      <td>35.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.70</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>b'Iris-setosa'</td>\n",
       "      <td>30.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.60</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>b'Iris-setosa'</td>\n",
       "      <td>33.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>b'Iris-setosa'</td>\n",
       "      <td>36.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.70</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>b'Iris-virginica'</td>\n",
       "      <td>244.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>b'Iris-virginica'</td>\n",
       "      <td>207.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>b'Iris-virginica'</td>\n",
       "      <td>230.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.20</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.30</td>\n",
       "      <td>b'Iris-virginica'</td>\n",
       "      <td>217.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.90</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>b'Iris-virginica'</td>\n",
       "      <td>185.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width            species  \\\n",
       "0            5.10         3.50          1.40         0.20     b'Iris-setosa'   \n",
       "1            4.90         3.00          1.40         0.20     b'Iris-setosa'   \n",
       "2            4.70         3.20          1.30         0.20     b'Iris-setosa'   \n",
       "3            4.60         3.10          1.50         0.20     b'Iris-setosa'   \n",
       "4            5.00         3.60          1.40         0.20     b'Iris-setosa'   \n",
       "..            ...          ...           ...          ...                ...   \n",
       "145          6.70         3.00          5.20         2.30  b'Iris-virginica'   \n",
       "146          6.30         2.50          5.00         1.90  b'Iris-virginica'   \n",
       "147          6.50         3.00          5.20         2.00  b'Iris-virginica'   \n",
       "148          6.20         3.40          5.40         2.30  b'Iris-virginica'   \n",
       "149          5.90         3.00          5.10         1.80  b'Iris-virginica'   \n",
       "\n",
       "     volume  \n",
       "0     38.13  \n",
       "1     35.20  \n",
       "2     30.07  \n",
       "3     33.24  \n",
       "4     36.65  \n",
       "..      ...  \n",
       "145  244.45  \n",
       "146  207.82  \n",
       "147  230.07  \n",
       "148  217.37  \n",
       "149  185.91  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['volume'] = (math.pi * iris_df['sepal_length']**2 * iris_df['petal_length'])/3\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method one of graphing (viewing all graphs as a grid)\n",
    "\n",
    "My first method of creating the boxplots is to create all the graphs and display them side by side. I liked using this method since it allowed me to view the data in one go and made it easier to compare the values. One draw back of this method was that I could not display the volume column since it would make the other box plots non-visible (this is because the y-axis range of values in the volume column are much bigger than the other columns so it distorts the y-axis numerical scale). So one important thing to note when using this method is that if you want to display data side-by-side you need to make sure the columns have a similar numerical range so the data can be visible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(iris_df))\n",
    "\n",
    "bp_dict = iris_df.replace({b'Iris-setosa': 'Setosa',b'Iris-versicolor': 'Versicolor',b'Iris-virginica': 'Virginica'} #I am replacing the names of the flower species in the dataframe so the species names can be displayed nicely in the graphs\n",
    "                         ).boxplot(column=['sepal_length','sepal_width','petal_length','petal_width'], #specifying the columns that I want to graph/visualise\n",
    "                                   by='species', #grouping the boxplot charts by species\n",
    "                                   patch_artist = True, #enabling colors in the boxplots\n",
    "                                   figsize=(15, 15), #creating the size of each chart\n",
    "                                   flierprops={'marker': 'D', 'markersize': 5, 'markerfacecolor': '#4c4c4c'}, #defining and creating the markers for outliers\n",
    "                                   return_type='both', #this returns a tuple of each boxplot chart (it allows me to view and edit each individual graph which I do later)\n",
    "                                   medianprops = dict(color = \"black\", linewidth = 1.5), #I am making the median line black\n",
    "                                   whiskerprops = dict(color = \"black\", linewidth = 1.5), #I am making the whisker lines black\n",
    "                                   capprops = dict(color = \"black\", linewidth = 1.5), #I am making the whisker cap (line at the end of the whisker) to be black\n",
    "                                   boxprops = dict(linestyle='-', linewidth=1.5, color='black') #I am coloring the boxplot rectangular outline to be black\n",
    "                                  )\n",
    "\n",
    "colors = ['#5975a4', '#cc8963', '#5f9e6e'] #I am creating the colors that I will use to fill in the box-plots\n",
    "\n",
    "# I am creating a for loop to edit each boxplot graph (editing things such as the title, x-axis labels, etc.) I can do this since I made my graphs into a `bp_dict` variable\n",
    "for row_key, (ax,row) in bp_dict.items():\n",
    "\n",
    "    #I am using this code chunk to create the titles of each plot. (I am just making sure to remove the underscore in the title)\n",
    "    if row_key == 'sepal_length':\n",
    "        row_key = 'Sepal Length'\n",
    "    elif row_key == 'sepal_width':\n",
    "        row_key = 'Sepal Width'\n",
    "    elif row_key == 'petal_length':\n",
    "        row_key = 'Petal Length'\n",
    "    elif row_key == 'petal_width':\n",
    "        row_key = 'Petal Width'\n",
    "        \n",
    "    ax.set_title(row_key + ' (cm.) by Species') #defining the title of each graph\n",
    "    ax.grid(axis = \"x\") #I am making the grid background of each graph to only include horizontal lines\n",
    "    ax.set_xlabel('Species',fontweight ='bold') #I am making a sub x-axis label that lets the viewer know that all flower names are species\n",
    "    \n",
    "    for i,box in enumerate(row['boxes']): #using this for loop to iterate over each individual box plot in each graph\n",
    "        box.set_facecolor(colors[i]) #I am filling in specific colors for each individual boxplot\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method two of graphing (creating each graph individualy)\n",
    "\n",
    "Here is my second method of graphing the boxplots for each column. I like this second method because I was able to graph the volume column by using a `for()` loop. The `for()` loop allows me to present each graph individualy as opposed to a cluster as shown in method one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in list(iris_df.columns): #I am creating a for loop to go over the list of each column in `iris_df` because this method allows me to create each graph individually instead of as a group\n",
    "\n",
    "    if x != 'species':\n",
    "        bp_dict = iris_df.replace({b'Iris-setosa': 'Setosa',b'Iris-versicolor': 'Versicolor',b'Iris-virginica': 'Virginica'} #I am replacing the names of the flower species in the dataframe so the species names can be displayed nicely in the graphs\n",
    "                                 ).boxplot(column=x, #specifying the columns that I want to graph/visualise. I do not need to specify a list since I already go through each column in the for loop\n",
    "                                           by='species', #grouping the boxplot charts by species\n",
    "                                           patch_artist = True, #enabling colors in the boxplots\n",
    "                                           figsize=(15, 10), #creating the size of each chart\n",
    "                                           flierprops={'marker': 'D', 'markersize': 5, 'markerfacecolor': '#4c4c4c'}, #defining and creating the markers for outliers\n",
    "                                           return_type='both', #this returns a tuple of each boxplot chart (it allows me to view and edit each individual graph which I do later)\n",
    "                                           medianprops = dict(color = \"black\", linewidth = 1.5), #I am making the median line black\n",
    "                                           whiskerprops = dict(color = \"black\", linewidth = 1.5), #I am making the whisker lines black\n",
    "                                           capprops = dict(color = \"black\", linewidth = 1.5), #I am making the whisker cap (line at the end of the whisker) to be black\n",
    "                                           boxprops = dict(linestyle='-', linewidth=1.5, color='black') #I am coloring the boxplot rectangular outline to be black\n",
    "                                          )\n",
    "        #creating individual chart titles\n",
    "        if x == 'sepal_length':\n",
    "            x = 'Sepal Length'\n",
    "        elif x == 'sepal_width':\n",
    "            x = 'Sepal Width'\n",
    "        elif x == 'petal_length':\n",
    "            x = 'Petal Length'\n",
    "        elif x == 'petal_width':\n",
    "            x = 'Petal Width'\n",
    "        plt.ylabel(x + ' (cm.)') # I am creating the y-axis label\n",
    "\n",
    "        if x == 'volume':\n",
    "            plt.ylabel(x + ' (cubic centimeters)') # I am creating the y-axis label which specifies the units for volume\n",
    "\n",
    "        plt.grid(axis = \"x\") #I am making the grid background of each graph to only include horizontal lines \n",
    "        plt.suptitle('') #removing the title created by the group_by command (which was \"boxplot grouped by species\")\n",
    "        plt.title(x + ' by Species') #setting the title for each graph\n",
    "\n",
    "        colors = ['#5975a4', '#cc8963', '#5f9e6e'] #creating the individual colors for each boxplot in each graph/chart\n",
    "        for row_key, (ax,row) in bp_dict.items(): #I am going through the boxplot items in the `bp_dict` variable to fill in the color of each boxplot\n",
    "            ax.set_xlabel('Species',fontweight ='bold') #I am making a sub x-axis label that lets the viewer know that all flower names are species. `.ax` represents the axis label\n",
    "            \n",
    "            for i,box in enumerate(row['boxes']): #using this for loop to iterate over each individual box plot in each graph\n",
    "                box.set_facecolor(colors[i]) #I am filling in specific colors for each individual boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1D. Provide a short intrepretation of the data visualised by your 1C solution. (5 marks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Virginica** flower species is the largest flower species when compared with **Versicolor** and **Setosa**. This is because **Virginica** has the biggest median **sepal length, petal length, petal width, and volume**. \n",
    "\n",
    "The **Setosa** flower does have the largest median **sepal width**, but it has the smallest median **sepal length, petal length, petal width, and volume** which shows that it is a small flower when compared to the virginica and versicolor flower. I would have said that perhaps the setosa flower could be as big as the virginica flower but its measurments were small in all the other variables (sepal length, petal length, petal width, and volume) which led to my conclusion that this is the smallest flower.\n",
    "\n",
    "The **versicolor** flower is in the middle and is neither big or small when compared with Setosa and Virginica. When looking at the median **sepal length, petal length, petal width, and volume** of the **versicolor** flower you will notice that its median value is in the middle of the **Virginica** and **Setosa** flowers. \n",
    "\n",
    "I would also like to point out that the **sepal width** median measurements of all flowers were approximatly the same (the values were not drastically far apart) which tells us that **Setosa** having the biggest sepal width does not make it uniquely big. \n",
    "\n",
    "#### Sepal Length\n",
    "*   I say this because if you look at the **sepal length boxplot** the difference between the median sepal length of the Virginica flower (6.5 cm) and the median Setosa (5 cm) is **2.5 cm.**\n",
    "\n",
    "#### Petal Length\n",
    "*   If you look at the **petal length boxplot** the difference between the median petal length of the Virginica flower (around 5.5 cm) and Setosa (around 1.5 cm) is **4 cm.**\n",
    "\n",
    "#### Petal Width\n",
    "*   In the **petal width boxplot** the difference between the median petal width of the Virginica flower (2 cm) and Setosa (around 0.25 cm) is **1.75 cm.**\n",
    "\n",
    "#### Volume\n",
    "*   In the **volume boxplot** the difference between the median volume of the Virginica flower (around 250 cubic centimeters) and Setosa (around 50 cubic centimeters) is **200 cubic centimeters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Personal Project (out of 50 marks)\n",
    "In this second part of Assignment 2 you have the opportunity to perform some data analysis on a dataset of your choice. Choose a dataset or a scenario that interests you and provide an outline of the scenario, clean the dataset (if needed), perform some statistic analysis, visualise this data analysis and comment on what you see.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Provide a brief introduction to your dataset here] (5 marks)\n",
    "\n",
    "It would be good to briefly introduce your dataset here. You could also illustrate an overview of the column information, and a sample of rows from the database using some methods that we have looked at. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction to dataset\n",
    "\n",
    "For this assignment, I will be analysing New York City (nyc) flight data which I have imported from the `flights` library in the `nycflights13` python data package. Before I start to answer my questions and analyse the data further I have displayed the first five rows of the dataset and have created a summary statistic table to get a better understanding of the data. \n",
    "\n",
    "*   **My first observation**: When viewing the dataset and creating the summary statistic table I noticed that the summary statistic gave me a good overview of the dataset. For example, from the `year` column I could see that the mean was **2013** so that let me know that this dataset contains flight data from around 2013 (this turned out to be true when looking at the minimum and maximum which were both 2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#installed the `nycflights13` python data package for nyc flight data\n",
    "#pip install nycflights13\n",
    "#pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add setup code here - set the path to the chosen dataset, and import the relevant Python libraries.\n",
    "from nycflights13 import flights #impoting the flights library which contains all the nyc flight data\n",
    "#the other python libraries (ex. pandas, numpy) were imported in part 1 of this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>sched_dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>time_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>517.00</td>\n",
       "      <td>515</td>\n",
       "      <td>2.00</td>\n",
       "      <td>830.00</td>\n",
       "      <td>819</td>\n",
       "      <td>11.00</td>\n",
       "      <td>UA</td>\n",
       "      <td>1545</td>\n",
       "      <td>N14228</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.00</td>\n",
       "      <td>1400</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>533.00</td>\n",
       "      <td>529</td>\n",
       "      <td>4.00</td>\n",
       "      <td>850.00</td>\n",
       "      <td>830</td>\n",
       "      <td>20.00</td>\n",
       "      <td>UA</td>\n",
       "      <td>1714</td>\n",
       "      <td>N24211</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.00</td>\n",
       "      <td>1416</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>542.00</td>\n",
       "      <td>540</td>\n",
       "      <td>2.00</td>\n",
       "      <td>923.00</td>\n",
       "      <td>850</td>\n",
       "      <td>33.00</td>\n",
       "      <td>AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>N619AA</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.00</td>\n",
       "      <td>1089</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>544.00</td>\n",
       "      <td>545</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1,004.00</td>\n",
       "      <td>1022</td>\n",
       "      <td>-18.00</td>\n",
       "      <td>B6</td>\n",
       "      <td>725</td>\n",
       "      <td>N804JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>183.00</td>\n",
       "      <td>1576</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.00</td>\n",
       "      <td>600</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>812.00</td>\n",
       "      <td>837</td>\n",
       "      <td>-25.00</td>\n",
       "      <td>DL</td>\n",
       "      <td>461</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.00</td>\n",
       "      <td>762</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01T11:00:00Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  dep_time  sched_dep_time  dep_delay  arr_time  \\\n",
       "0  2013      1    1    517.00             515       2.00    830.00   \n",
       "1  2013      1    1    533.00             529       4.00    850.00   \n",
       "2  2013      1    1    542.00             540       2.00    923.00   \n",
       "3  2013      1    1    544.00             545      -1.00  1,004.00   \n",
       "4  2013      1    1    554.00             600      -6.00    812.00   \n",
       "\n",
       "   sched_arr_time  arr_delay carrier  flight tailnum origin dest  air_time  \\\n",
       "0             819      11.00      UA    1545  N14228    EWR  IAH    227.00   \n",
       "1             830      20.00      UA    1714  N24211    LGA  IAH    227.00   \n",
       "2             850      33.00      AA    1141  N619AA    JFK  MIA    160.00   \n",
       "3            1022     -18.00      B6     725  N804JB    JFK  BQN    183.00   \n",
       "4             837     -25.00      DL     461  N668DN    LGA  ATL    116.00   \n",
       "\n",
       "   distance  hour  minute             time_hour  \n",
       "0      1400     5      15  2013-01-01T10:00:00Z  \n",
       "1      1416     5      29  2013-01-01T10:00:00Z  \n",
       "2      1089     5      40  2013-01-01T10:00:00Z  \n",
       "3      1576     5      45  2013-01-01T10:00:00Z  \n",
       "4       762     6       0  2013-01-01T11:00:00Z  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(flights)[0:5] #viewing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>sched_dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>flight</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>336,776.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>328,521.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>328,521.00</td>\n",
       "      <td>328,063.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>327,346.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>327,346.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>336,776.00</td>\n",
       "      <td>336,776.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>6.55</td>\n",
       "      <td>15.71</td>\n",
       "      <td>1,349.11</td>\n",
       "      <td>1,344.25</td>\n",
       "      <td>12.64</td>\n",
       "      <td>1,502.05</td>\n",
       "      <td>1,536.38</td>\n",
       "      <td>6.90</td>\n",
       "      <td>1,971.92</td>\n",
       "      <td>150.69</td>\n",
       "      <td>1,039.91</td>\n",
       "      <td>13.18</td>\n",
       "      <td>26.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.00</td>\n",
       "      <td>3.41</td>\n",
       "      <td>8.77</td>\n",
       "      <td>488.28</td>\n",
       "      <td>467.34</td>\n",
       "      <td>40.21</td>\n",
       "      <td>533.26</td>\n",
       "      <td>497.46</td>\n",
       "      <td>44.63</td>\n",
       "      <td>1,632.47</td>\n",
       "      <td>93.69</td>\n",
       "      <td>733.23</td>\n",
       "      <td>4.66</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>-43.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-86.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>907.00</td>\n",
       "      <td>906.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>1,104.00</td>\n",
       "      <td>1,124.00</td>\n",
       "      <td>-17.00</td>\n",
       "      <td>553.00</td>\n",
       "      <td>82.00</td>\n",
       "      <td>502.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1,401.00</td>\n",
       "      <td>1,359.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>1,535.00</td>\n",
       "      <td>1,556.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>1,496.00</td>\n",
       "      <td>129.00</td>\n",
       "      <td>872.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>29.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>1,744.00</td>\n",
       "      <td>1,729.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1,940.00</td>\n",
       "      <td>1,945.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3,465.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>1,389.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>44.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2,013.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>2,400.00</td>\n",
       "      <td>2,359.00</td>\n",
       "      <td>1,301.00</td>\n",
       "      <td>2,400.00</td>\n",
       "      <td>2,359.00</td>\n",
       "      <td>1,272.00</td>\n",
       "      <td>8,500.00</td>\n",
       "      <td>695.00</td>\n",
       "      <td>4,983.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>59.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year      month        day   dep_time  sched_dep_time  dep_delay  \\\n",
       "count 336,776.00 336,776.00 336,776.00 328,521.00      336,776.00 328,521.00   \n",
       "mean    2,013.00       6.55      15.71   1,349.11        1,344.25      12.64   \n",
       "std         0.00       3.41       8.77     488.28          467.34      40.21   \n",
       "min     2,013.00       1.00       1.00       1.00          106.00     -43.00   \n",
       "25%     2,013.00       4.00       8.00     907.00          906.00      -5.00   \n",
       "50%     2,013.00       7.00      16.00   1,401.00        1,359.00      -2.00   \n",
       "75%     2,013.00      10.00      23.00   1,744.00        1,729.00      11.00   \n",
       "max     2,013.00      12.00      31.00   2,400.00        2,359.00   1,301.00   \n",
       "\n",
       "        arr_time  sched_arr_time  arr_delay     flight   air_time   distance  \\\n",
       "count 328,063.00      336,776.00 327,346.00 336,776.00 327,346.00 336,776.00   \n",
       "mean    1,502.05        1,536.38       6.90   1,971.92     150.69   1,039.91   \n",
       "std       533.26          497.46      44.63   1,632.47      93.69     733.23   \n",
       "min         1.00            1.00     -86.00       1.00      20.00      17.00   \n",
       "25%     1,104.00        1,124.00     -17.00     553.00      82.00     502.00   \n",
       "50%     1,535.00        1,556.00      -5.00   1,496.00     129.00     872.00   \n",
       "75%     1,940.00        1,945.00      14.00   3,465.00     192.00   1,389.00   \n",
       "max     2,400.00        2,359.00   1,272.00   8,500.00     695.00   4,983.00   \n",
       "\n",
       "            hour     minute  \n",
       "count 336,776.00 336,776.00  \n",
       "mean       13.18      26.23  \n",
       "std         4.66      19.30  \n",
       "min         1.00       0.00  \n",
       "25%         9.00       8.00  \n",
       "50%        13.00      29.00  \n",
       "75%        17.00      44.00  \n",
       "max        23.00      59.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in your data and display some descriptive statistics and commentary to provide a summary of the dataset.\n",
    "pd.DataFrame(flights).describe() #summary statistics of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "## 1. [Choose and write a question here] (15 marks)\n",
    "### (e.g. What percentage of male and female passengers perished?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1) Is there a relationship between the average delay and flights that are cancelled (on that day)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer this question I needed to note down which flights got cancelled. I do this by creating a new column `cancelled` and check if there are `NA` values in the `dep_delay` **and** `arr_delay` columns. If there is then I mark the row with a one which means that the specific flight was delayed. (**Note:** I determine that the flights is cancelled because if was on time then the `dep_delay` or `arr_delay` columns would have a zero value. \n",
    "\n",
    "After I do this I would need to group all my variables by day and would then need to calculate the means of the departure delays and proportion of flights cancelled. I would then graph these variables to see if there is a relationship between the average delay and flights cancelled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_delay1</th>\n",
       "      <th>flights_proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2013</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>11.55</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.86</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.99</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.95</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.73</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                avg_delay1  flights_proportion\n",
       "year month day                                \n",
       "2013 1     1         11.55                0.48\n",
       "           2         13.86                0.85\n",
       "           3         10.99                1.09\n",
       "           4          8.95                0.66\n",
       "           5          5.73                0.42"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights['cancelled'] = ((flights['dep_delay'].isna()) & (flights['arr_delay'].isna())).astype(int)\n",
    "flights_summary = flights.groupby(['year', 'month', 'day']).agg( # I am using the aggregate function to calculate the means and proportions of the grouping that I have made\n",
    "    avg_delay1 = ('dep_delay','mean'), #I am calculating the mean of the `dep_delay` column\n",
    "    flights_proportion = ('cancelled', lambda x: 100 * x.mean()) #the `lambda()` function automatically gets the values in the `cancelled` column and assigned the values to x where the mean and proportion gets calculated\n",
    ")\n",
    "flights_summary = pd.DataFrame(flights_summary)\n",
    "flights_summary[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion and Answer\n",
    "\n",
    "After looking at the grapg I have concluded that flights that have longer delay times have a higher proportion/percentage to be cancelled. This pattern can be seen in the second graph which shows that as a flights average delay time increases, then the proportion/percentage of the flight being cancelled increases.\n",
    "\n",
    "*   **Note:** I filtered the flights proportion in the second graph because I noticed that there were too many outliers in the first graph which prevented me from analysing the data correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the average deplay and flight cancelled proportions (without filtering the flights proportion)\n",
    "plt.scatter(flights_summary['avg_delay1'], flights_summary['flights_proportion'], alpha=0.1)\n",
    "plt.xlabel('avg_delay1')\n",
    "plt.ylabel('flights_proportion')\n",
    "plt.title('Relationship between Cancelled Flights Proportion and Average Flights Delay' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the average deplay and flight cancelled proportions (filtering the flights proportion)\n",
    "plt.scatter(flights_summary[flights_summary['flights_proportion'] < 10]['avg_delay1']\n",
    "            , flights_summary[flights_summary['flights_proportion'] < 10]['flights_proportion'], alpha=0.1)\n",
    "plt.xlabel('avg_delay1')\n",
    "plt.ylabel('flights_proportion')\n",
    "plt.title('Relationship between Cancelled Flights Proportion and Average Flights Delay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. What hour of the day will have the least delays? (15 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer \n",
    "As you can see from the graph below around 5-8 AM is the best time to book a flight since there is a high chance that the flights going around those times will depart early or on time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>sched_dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>time_hour</th>\n",
       "      <th>cancelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>544.00</td>\n",
       "      <td>545</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1,004.00</td>\n",
       "      <td>1022</td>\n",
       "      <td>-18.00</td>\n",
       "      <td>B6</td>\n",
       "      <td>725</td>\n",
       "      <td>N804JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>183.00</td>\n",
       "      <td>1576</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.00</td>\n",
       "      <td>600</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>812.00</td>\n",
       "      <td>837</td>\n",
       "      <td>-25.00</td>\n",
       "      <td>DL</td>\n",
       "      <td>461</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.00</td>\n",
       "      <td>762</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01T11:00:00Z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.00</td>\n",
       "      <td>558</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>740.00</td>\n",
       "      <td>728</td>\n",
       "      <td>12.00</td>\n",
       "      <td>UA</td>\n",
       "      <td>1696</td>\n",
       "      <td>N39463</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>150.00</td>\n",
       "      <td>719</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>2013-01-01T10:00:00Z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>555.00</td>\n",
       "      <td>600</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>913.00</td>\n",
       "      <td>854</td>\n",
       "      <td>19.00</td>\n",
       "      <td>B6</td>\n",
       "      <td>507</td>\n",
       "      <td>N516JB</td>\n",
       "      <td>EWR</td>\n",
       "      <td>FLL</td>\n",
       "      <td>158.00</td>\n",
       "      <td>1065</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01T11:00:00Z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>557.00</td>\n",
       "      <td>600</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>709.00</td>\n",
       "      <td>723</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>EV</td>\n",
       "      <td>5708</td>\n",
       "      <td>N829AS</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>53.00</td>\n",
       "      <td>229</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-01T11:00:00Z</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  dep_time  sched_dep_time  dep_delay  arr_time  \\\n",
       "3  2013      1    1    544.00             545      -1.00  1,004.00   \n",
       "4  2013      1    1    554.00             600      -6.00    812.00   \n",
       "5  2013      1    1    554.00             558      -4.00    740.00   \n",
       "6  2013      1    1    555.00             600      -5.00    913.00   \n",
       "7  2013      1    1    557.00             600      -3.00    709.00   \n",
       "\n",
       "   sched_arr_time  arr_delay carrier  flight tailnum origin dest  air_time  \\\n",
       "3            1022     -18.00      B6     725  N804JB    JFK  BQN    183.00   \n",
       "4             837     -25.00      DL     461  N668DN    LGA  ATL    116.00   \n",
       "5             728      12.00      UA    1696  N39463    EWR  ORD    150.00   \n",
       "6             854      19.00      B6     507  N516JB    EWR  FLL    158.00   \n",
       "7             723     -14.00      EV    5708  N829AS    LGA  IAD     53.00   \n",
       "\n",
       "   distance  hour  minute             time_hour  cancelled  \n",
       "3      1576     5      45  2013-01-01T10:00:00Z          0  \n",
       "4       762     6       0  2013-01-01T11:00:00Z          0  \n",
       "5       719     5      58  2013-01-01T10:00:00Z          0  \n",
       "6      1065     6       0  2013-01-01T11:00:00Z          0  \n",
       "7       229     6       0  2013-01-01T11:00:00Z          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am creating a `not_cancelled` variable to drop the rows that included flights that were cancelled (potentially cancelled)\n",
    "not_cancelled = flights.dropna(subset=['dep_delay', 'arr_delay']) # filtering delayed flights because we want to see non-cancelled flights\n",
    "\n",
    "not_cancelled[not_cancelled['dep_delay'] <= 0][0:5] #I want to filter out the rows that had a negative departure delay since that means that those flights were early or on time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Proportion of on time flights by each hours')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "not_cancelled.loc[:,'flight_early'] = (not_cancelled['dep_delay'] <= 0).astype(int) #I am labelling the early flights with a one in the new 'flight_early' column that I am creating. This way I can calculate and graph the proportions of the early flights\n",
    "\n",
    "not_cancelled_hourly = not_cancelled.groupby('hour').agg( #I am now grouping the not cancelled flights by hour and am aggrgating the proportions of early flights and calcuating their means\n",
    "    ontime_proportion = ('flight_early', lambda x: 100 * x.mean())\n",
    ")\n",
    "\n",
    "sns.scatterplot(data=not_cancelled_hourly, x='hour', y='ontime_proportion').set(title= ('Proportion of on time flights by each hours'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Which Carriers will most likley have a departure delay of 30 min.? (15 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ExpressJet (EV) is the airline that will most likely have a departure delay for 30 minutes or more with a proportion of roughly 26%. Mesa Airlines (YV) comes in second with a proportion of roughly 23%. I reached this conclusion from the scatterplot below. I used the code below to reach this conclusion. I also displayed this data in a boxplot to display the data in another format visualization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data as Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Proportion of on 30 min. delayed flights by airline carrier')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "not_cancelled.loc[:,'flight_30minlate'] = (not_cancelled['dep_delay'] >= 30).astype(int) #creating a proportion columns of 1s and 0s that indicate flights that are 30 min. late\n",
    "not_cancelled_carrier = not_cancelled.groupby('carrier').agg( #grouping the dataframe by carrier\n",
    "    f_30minlate_proportion = ('flight_30minlate', lambda x: 100 * x.mean()) #calculating the proportions of 30 min. or more late flights\n",
    ")\n",
    "sns.scatterplot(data=not_cancelled_carrier, x='carrier', y='f_30minlate_proportion').set(title= ('Proportion of on 30 min. delayed flights by airline carrier'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data as Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Proportion of on 30 min. delayed flights by airline carrier')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uXUREREhta9euhZWVFTp37gwAOH/+PGJiYvDZZ58hMTFRquXFixdo3rw5jhw5ApVKhYyMDOzduxcdOnRAmTJlpPlVrlwZfn5+uV5WuXH37l1cvHgRgYGBalskvb29Ub16da2Pyct6LVKkCF68eIH9+/frVW+HDh3U3iPr1q2LevXqYdeuXVKbLusmK++u/7S0NCQmJqJ8+fIoUqQIzp49m2N9SqUSffr0yc1T0jBo0CC1+02aNFFbtps3b4adnR1atmwpvaYeP34MLy8vWFtbqz3v9z169AhHjhxB37591V5rANR2BeZ2OWT1maDr8ti8eTMqV64MDw8PteeUefjF+8/J29sbVapUyXG+hsDdTDlYuHAhKlasCDMzMzg6OqJSpUoaZ/yYmZlJ+x8z3bp1C87Ozhq7DSpXrixNf1eFChU0xq5YsSJevnwpHfj68uVLVKpUSaNf5cqVoVKpEB8fj6pVq0rtZcuWzcUz1XTr1i2NXVfvP4fMXWAANP7oMt9Is9s//OjRo1w/L11rr1Chgsa6ymr561P7u1xdXaVjenr27IkBAwagRYsWuHr1KqysrKQ3nff3KwPA69evAai/MWXl/Trt7OwAAC4uLlrbdak/r8/9Xbdu3ULJkiU1dgG+v36vX78OIQQmTJiACRMmaJ3Xw4cPs9zVcPv2bUycOBF//PGHRp1JSUnS/1u2bImSJUti7dq1aN68OVQqFdavX4/27dtLf5sxMTEAoHVX2LvzTE1NxatXr7T+rVaqVEntg9rQMl+v2s6AKl++vNYPrrys18GDB2PTpk0ICAhAqVKl0KpVK3Tr1g3+/v461ZvV+9m7x4bpsm6y8urVK0yfPh2hoaFISEhQOw7x3fWflVKlSuXp4FZLS0uN3fNFixZVW7YxMTFISkpCiRIltM4j82BwbTJD0bvvr9rkdjlk9Zmg6/KIiYnBlStXsjw04f3nlNfPoNxgmMlB3bp1c7yWiVKpLJCnNOvy4WhIWW0FElqut1LQGLr2Ll26YPny5Thy5Aj8/PxQrFgxKJVK3Lt3T6NvZpuzs7Pedeal/vxYb5mniI8ePTrLrRpZnbqckZGBli1b4smTJxg3bhw8PDxQuHBhJCQkIDg4WO30c1NTU3z22WdYvnw5Fi1ahOPHj0vXBnq/lh9//BE1a9bUOqa1tbXWIFqQ5WW9lihRAufPn8fevXuxe/du7N69G6GhoQgMDNQ4iD4v9eW0brIybNgwhIaGYsSIEWjQoIF0Ec0ePXrodPmBvL436rLFW6VSoUSJEli7dq3W6VkFgtzI7XLI6nnrujxUKhWqV6+O2bNna53+/peqD/kZxDBjJK6urjhw4IDGQZ3R0dHS9Hdlfjt817Vr11CoUCHpRV+oUCFcvXpVo190dDRMTEw0Xkja6HqV0swasxovc3peOTg4GOR5vc/V1RVRUVFQqVRqQdOQtWcnc5dR5rcjExMTVK9eHX///bdG31OnTqFcuXI5fhuVA1dXV4SHhyMlJUVt68z76zdzl6K5uTlatGiRqzEuXryIa9euYfXq1QgMDJTas9olEhgYiFmzZuHPP//E7t274eDgoBag3N3dAQC2trbZ1pJ5Rom2v1Vtr19Dyny9ajs7UNczBrXJ7v3AwsICbdu2Rdu2baFSqTB48GAsXboUEyZMyPYaOUDW72fvHwCa07rJypYtWxAUFIRZs2ZJba9fvy5QFwN1d3fHgQMH0KhRo1x/qGf+fbx7Bpg2H3o5uLu748KFC2jevHmuPks+hIK3OeEj0bp1a2RkZGDBggVq7XPmzIFCoUBAQIBae2RkpNqm4vj4eGzfvh2tWrWCqakpTE1N0apVK2zfvl3ttLsHDx5g3bp1aNy4MWxtbXOsK/O6Arq82Fu3bo3Tp08jMjJSanvx4gWWLVsGNzc3g+wLNdTz0lb7/fv3sXHjRqktPT0d8+fPh7W1Nby9vfNcO/C/a9+8b+XKlVAoFPjkk0+kti5duuCvv/5SCzRXr17FwYMH0bVrV4PUYyxJSUmIjo7OcRN+69atkZ6ejsWLF0ttGRkZmD9/vlq/EiVKwMfHB0uXLtW6tSqr5Qr871vxu1sYhBBZnjZco0YN1KhRAytWrMDWrVvRo0cPmJn973ucl5cX3N3d8dNPPyElJSXLWkxNTeHn54fff/8dt2/flqZfuXIFe/fuzbJeQ3B2dka1atWwZs0atRoPHz4snZqvj6zeDxITE9Xum5iYSGde6bKF6vfff1c75un06dM4deqUxvteTusmK6amphpbmObPn69xanR+6tatGzIyMjB16lSNaenp6dm+Bzs4OKBp06ZYtWqV2msNUH/df+jl0K1bNyQkJGD58uUa0zLP6Mwv3DJjJG3btoWvry+++eYbxMXFwdPTE/v27cP27dsxYsQI6dtgpmrVqsHPz0/t1GwA+O6776Q+33//Pfbv34/GjRtj8ODBMDMzw9KlS5GamoqZM2fqVFfmQcbffPMNevToAXNzc7Rt21brxZO+/vprrF+/HgEBAQgJCUGxYsWwevVqxMbGYuvWrQbbtWaI5/W+AQMGYOnSpQgODsaZM2fg5uaGLVu24Pjx45g7d67BtoJMmzYNx48fh7+/P8qUKYMnT55g69at+OuvvzBs2DC1b7CDBw/G8uXL8emnn2L06NEwNzfH7Nmz4ejoiK+++sog9RjLtm3b0KdPH4SGhmb7+11t27ZFo0aN8PXXXyMuLg5VqlTBb7/9pjUELVy4EI0bN0b16tXRv39/lCtXDg8ePEBkZCTu3LmDCxcuaB3Dw8MD7u7uGD16NBISEmBra4utW7dmeyxIYGAgRo8eDQAauzFMTEywYsUKBAQEoGrVqujTpw9KlSqFhIQEREREwNbWFn/++SeAt3+Pe/bsQZMmTTB48GApIFetWhVRUVE5LcY8+eGHH9C+fXs0atQIffr0wdOnT7FgwQJUq1ZNawjTReb7QUhICPz8/GBqaooePXrgiy++wJMnT9CsWTOULl0at27dwvz581GzZk3puLPslC9fHo0bN8aXX36J1NRUzJ07F/b29hg7dqxG3+zWTVbatGmDX375BXZ2dqhSpQoiIyNx4MABo19FOze8vb0xcOBATJ8+HefPn0erVq1gbm6OmJgYbN68GT///DO6dOmS5ePnzZuHxo0b45NPPsGAAQNQtmxZxMXFYefOnTh//jyAD78cPv/8c2zatAmDBg1CREQEGjVqhIyMDERHR2PTpk3Yu3dv/v3EzAc5Z0qGMk9l++uvv7LtFxQUJAoXLqx1WnJyshg5cqRwdnYW5ubmokKFCuLHH39UO7VOiLensA0ZMkT8+uuvokKFCkKpVIpatWqpnU6Y6ezZs8LPz09YW1uLQoUKCV9fX3HixIlc1T516lRRqlQpYWJionb67vunZgshxI0bN0SXLl1EkSJFhKWlpahbt67YsWOHWp/M0x83b96s1q7ttMas6PK8cnNqthBCPHjwQPTp00cUL15cWFhYiOrVq2vUkt08AYhJkyZlO8a+fftEmzZtpHVsY2MjGjVqJEJDQzXWsxBCxMfHiy5dughbW1thbW0t2rRpI2JiYnR6PpmvE13q17ZOsjo1W5fnnvma0mVdJiYmis8//1zY2toKOzs78fnnn4tz585pffyNGzdEYGCgcHJyEubm5qJUqVKiTZs2YsuWLRrP5d2/h8uXL4sWLVoIa2trUbx4cdG/f39x4cKFLGu8d++eMDU1FRUrVsyy7nPnzolOnToJe3t7oVQqhaurq+jWrZsIDw9X63f48GHh5eUlLCwsRLly5cSSJUukU65zkpdTs4UQYsOGDcLDw0MolUpRrVo18ccff4jOnTsLDw8Pjcfqsl7T09PFsGHDhIODg1AoFNJz2LJli2jVqpUoUaKEsLCwEGXKlBEDBw4U9+7dy/b5vTv2rFmzhIuLi1AqlaJJkybiwoULWh+jy7p539OnT6W/bWtra+Hn5yeio6M13sOyOjW7atWqWuer66nZ2t7zs3oNLFu2THh5eQkrKythY2MjqlevLsaOHSvu3r2b4/O8dOmS6Nixo/T+W6lSJTFhwoRcL4fsPhNyszyEeHuJi//+97+iatWqQqlUiqJFiwovLy/x3XffiaSkJKmftvcrY1L8/6CUjxQKBYYMGaKxS4qIDOPx48coWbIkJk6cmOXZU3JVs2ZNODg46H0adX77mNcNfTg8ZoaIPnphYWHIyMjA559/nt+l6C0tLQ3p6elqbYcOHcKFCxc0LjkvJx/DuqH8x2NmiOijdfDgQVy+fBnTpk1Dhw4djHs5dSNLSEhAixYt0Lt3bzg7OyM6OhpLliyBk5OTxgXc5OBjWjeU/xhmiOijNWXKFJw4cQKNGjXSOKNKbooWLQovLy+sWLECjx49QuHChfHpp59ixowZBerAV119TOuG8h+PmSEiIiJZ4zEzREREJGsMM0RERCRrH/0xMyqVCnfv3oWNjU2Bu/wyERERaSeEQHJyMpydnXO8SOtHH2bu3r2r12/7EBERUf6Lj49H6dKls+3z0YeZzMvWx8fH6/UbP0RERPThPX/+HC4uLjr9/MxHH2Yydy3Z2toyzBAREcmMLoeI8ABgIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNbP8LoDoY+U1Zo3RxzjzY6DRxyAiKui4ZYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGTNLL8LICIiInnwGrPG6GOc+TEw14/hlhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpK1fA0z06dPR506dWBjY4MSJUqgQ4cOuHr1qlqf169fY8iQIbC3t4e1tTU6d+6MBw8e5FPFREREVNDka5g5fPgwhgwZgpMnT2L//v1IS0tDq1at8OLFC6nPyJEj8eeff2Lz5s04fPgw7t69i06dOuVj1URERFSQmOXn4Hv27FG7HxYWhhIlSuDMmTNo2rQpkpKSsHLlSqxbtw7NmjUDAISGhqJy5co4efIk6tevnx9lExERUQFSoI6ZSUpKAgAUK1YMAHDmzBmkpaWhRYsWUh8PDw+UKVMGkZGR+VIjERERFSz5umXmXSqVCiNGjECjRo1QrVo1AMD9+/dhYWGBIkWKqPV1dHTE/fv3tc4nNTUVqamp0v3nz58brWYiIiLKfwVmy8yQIUNw6dIlbNiwIU/zmT59Ouzs7KSbi4uLgSokIiKigqhAhJmhQ4dix44diIiIQOnSpaV2JycnvHnzBs+ePVPr/+DBAzg5OWmd1/jx45GUlCTd4uPjjVk6ERER5bN8DTNCCAwdOhTbtm3DwYMHUbZsWbXpXl5eMDc3R3h4uNR29epV3L59Gw0aNNA6T6VSCVtbW7UbERERfbzy9ZiZIUOGYN26ddi+fTtsbGyk42Ds7OxgZWUFOzs79OvXD6NGjUKxYsVga2uLYcOGoUGDBjyTiYiIiADkc5hZvHgxAMDHx0etPTQ0FMHBwQCAOXPmwMTEBJ07d0Zqair8/PywaNGiD1wpERERFVT5GmaEEDn2sbS0xMKFC7Fw4cIPUBERERHJTYE4AJiIiIhIXwwzREREJGsMM0RERCRrBeYKwERkOF5j1hh9jDM/Bhp9DCIiXXDLDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJ2r/uonm8mBgREdHHhVtmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNbM8rsAIvq4eI1ZY/QxzvwYaPQxiEg+uGWGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjadmExHJHE+H/3fh+takd5gJDw9HeHg4Hj58CJVKpTZt1apVeS6MiIiISBd6hZnvvvsOU6ZMQe3atVGyZEkoFApD10VERESkE73CzJIlSxAWFobPP//c0PUQERER5YpeBwC/efMGDRs2NHQtRERERLmmV5j54osvsG7dOkPXQkRERJRreu1mev36NZYtW4YDBw6gRo0aMDc3V5s+e/ZsgxRHRERElBO9wkxUVBRq1qwJALh06ZLaNB4MTERERB+SXmEmIiLC0HUQERER6SXPVwC+c+cO7ty5Y4haiIiIiHJNrzCjUqkwZcoU2NnZwdXVFa6urihSpAimTp2qcQE9IiIiImPSazfTN998g5UrV2LGjBlo1KgRAODYsWOYPHkyXr9+jWnTphm0SCIiIqKs6BVmVq9ejRUrVqBdu3ZSW40aNVCqVCkMHjyYYYaIiIg+GL12Mz158gQeHh4a7R4eHnjy5EmeiyIiIiLSlV5hxtPTEwsWLNBoX7BgATw9PfNcFBEREZGu9NrNNHPmTHz66ac4cOAAGjRoAACIjIxEfHw8du3aZdACiYiIiLKj15YZb29vXLt2DR07dsSzZ8/w7NkzdOrUCVevXkWTJk0MXSMRERFRlvTaMgMAzs7OPNCXiIiI8p3OYSYqKgrVqlWDiYkJoqKisu1bo0aNPBdGREREpAudw0zNmjVx//59lChRAjVr1oRCoYAQQqOfQqFARkaGQYskIiIiyorOYSY2NhYODg7S/4mIiIgKAp3DjKurq/T/W7duoWHDhjAzU394eno6Tpw4odaXyGvMGqOPcebHQKOPQUREBZNeZzP5+vpqvTheUlISfH1981wUERERka70CjNCCCgUCo32xMREFC5cOM9FEREREekqV6dmd+rUCcDbg3yDg4OhVCqlaRkZGYiKikLDhg0NWyERERFRNnIVZuzs7AC83TJjY2MDKysraZqFhQXq16+P/v37G7ZCIiIiomzkKsyEhoZKp2PPnz8f1tbWRimKiIiISFe5PmZGCIG1a9fi3r17xqiHiIiIKFdyHWZMTExQoUIFJCYmGqMeIiIiolzR62ymGTNmYMyYMbh06ZKh6yEiIiLKFb3CTGBgIE6fPg1PT09YWVmhWLFiajddHTlyBG3btoWzszMUCgV+//13tenBwcFQKBRqN39/f31KJiIioo+UXr+aPXfuXIMM/uLFC3h6eqJv377Sad/v8/f3R2hoqHT/3dPBiYiIiPQKM0FBQQYZPCAgAAEBAdn2USqVcHJyMsh4RERE9PHRK8wAby+S9/vvv+PKlSsAgKpVq6Jdu3YwNTU1WHEAcOjQIZQoUQJFixZFs2bN8P3338Pe3j7L/qmpqUhNTZXuP3/+3KD1EBFpw98gI8o/eoWZ69evo3Xr1khISEClSpUAANOnT4eLiwt27twJd3d3gxTn7++PTp06oWzZsrhx4wb+85//ICAgAJGRkVmGpunTp+O7774zyPhERERU8Ol1AHBISAjc3d0RHx+Ps2fP4uzZs7h9+zbKli2LkJAQgxXXo0cPtGvXDtWrV0eHDh2wY8cO/PXXXzh06FCWjxk/fjySkpKkW3x8vMHqISIiooJHry0zhw8fxsmTJ9XOXLK3t8eMGTPQqFEjgxX3vnLlyqF48eK4fv06mjdvrrWPUqnkQcJERET/InptmVEqlUhOTtZoT0lJgYWFRZ6LysqdO3eQmJiIkiVLGm0MIiIikhe9wkybNm0wYMAAnDp1CkIICCFw8uRJDBo0CO3atdN5PikpKTh//jzOnz8PAIiNjcX58+dx+/ZtpKSkYMyYMTh58iTi4uIQHh6O9u3bo3z58vDz89OnbCIiIvoI6RVm5s2bB3d3dzRo0ACWlpawtLREo0aNUL58efz88886z+fvv/9GrVq1UKtWLQDAqFGjUKtWLUycOBGmpqaIiopCu3btULFiRfTr1w9eXl44evQodyMRERGRRK9jZooUKYLt27cjJiYGV65cgUKhQOXKlVG+fPlczcfHx0f6FW5t9u7dq095RERE9C+i93VmAKBChQpSgFEoFAYpiIiIiCg39NrNBAArV65EtWrVpN1M1apVw4oVKwxZGxEREVGO9NoyM3HiRMyePRvDhg1DgwYNAACRkZEYOXIkbt++jSlTphi0SCIiIqKs6BVmFi9ejOXLl6Nnz55SW7t27VCjRg0MGzaMYYaIiIg+GL12M6WlpaF27doa7V5eXkhPT89zUURERES60ivMfP7551i8eLFG+7Jly9CrV688F0VERESkK73PZlq5ciX27duH+vXrAwBOnTqF27dvIzAwEKNGjZL6zZ49O+9VEhEREWVBrzBz6dIlfPLJJwCAGzduAACKFy+O4sWL49KlS1I/nq5NRERExqZXmImIiDB0HURERER60fs6M5nu3LmDO3fuGKIWIiIiolzTK8yoVCpMmTIFdnZ2cHV1haurK4oUKYKpU6dCpVIZukYiIiKiLOm1m+mbb77BypUrMWPGDDRq1AgAcOzYMUyePBmvX7/GtGnTDFokERERUVb0CjOrV6/GihUr0K5dO6mtRo0aKFWqFAYPHswwQ0RERB+MXruZnjx5Ag8PD412Dw8PPHnyJM9FEREREelKrzDj6emJBQsWaLQvWLAAnp6eeS6KiIiISFd67WaaOXMmPv30Uxw4cEDthybj4+Oxa9cugxZIRERElB29tsx4e3vj2rVr6NixI549e4Znz56hU6dOuHr1Kpo0aWLoGomIiIiylOstM2lpafD398eSJUt4oC8R0b+c15g1Rh/jzI+BRh+D5C3XW2bMzc0RFRVljFqIiIiIck2v3Uy9e/fGypUrDV0LERERUa7pdQBweno6Vq1ahQMHDsDLywuFCxdWm85fyiYiIqIPJc+/mn3t2jW1afylbCIiIvqQ+KvZREREJGt5/tXs+Ph4xMfHG6IWIiIiolzTK8ykp6djwoQJsLOzg5ubG9zc3GBnZ4dvv/0WaWlphq6RiIiIKEt67WYaNmwYfvvtN8ycOVPtCsCTJ09GYmIiFi9ebNAiiYiIiLKiV5hZt24dNmzYgICAAKmtRo0acHFxQc+ePRlmiIiI6IPRazeTUqmEm5ubRnvZsmVhYWGR15qIiIiIdKZXmBk6dCimTp2K1NRUqS01NRXTpk3D0KFDDVYcERERUU702s107tw5hIeHo3Tp0vD09AQAXLhwAW/evEHz5s3RqVMnqe9vv/1mmEqJiIiItNArzBQpUgSdO3dWa3NxcTFIQURERES5oVeYCQ0N1anf8ePHkZqaCqVSqc8wRERERDnK80XzshMQEICEhARjDkFERET/ckYNM0IIY86eiIiIyLhhhoiIiMjYGGaIiIhI1hhmiIiISNaMGmYUCoUxZ09ERETEA4CJiIhI3vS6zgwApKen49ChQ7hx4wY+++wz2NjY4O7du7C1tYW1tTUAIDk52WCFEhEREWmjV5i5desW/P39cfv2baSmpqJly5awsbHBf//7X6SmpmLJkiWGrpOIiIhIK712Mw0fPhy1a9fG06dPYWVlJbV37NgR4eHhBiuOiIiIKCd6bZk5evQoTpw4AQsLC7V2Nzc3XvGXiIiIPii9tsyoVCpkZGRotN+5cwc2NjZ5LoqIiIhIV3qFmVatWmHu3LnSfYVCgZSUFEyaNAmtW7c2VG1EREREOdJrN9OsWbPg5+eHKlWq4PXr1/jss88QExOD4sWLY/369YaukYiIqEDxGrPG6GOc+THQ6GN8LPQKM6VLl8aFCxewceNGXLhwASkpKejXrx969eqldkAwERERkbHpFWaOHDmChg0bolevXujVq5fUnp6ejiNHjqBp06YGK5CIiIgoO3odM+Pr64snT55otCclJcHX1zfPRRERERHpSq8wI4TQ+rtLiYmJKFy4cJ6LIiIiItJVrnYzderUCcDbs5eCg4OhVCqlaRkZGYiKikLDhg0NWyERERFRNnIVZuzs7AC83TJjY2OjdrCvhYUF6tevj/79+xu2wo8Ij34nIiIyvFyFmdDQUABvr/Q7evRo7lIiIiKifKfX2UyTJk0ydB1EREREetErzADAli1bsGnTJty+fRtv3rxRm3b27Nk8F0ZERESkC73OZpo3bx769OkDR0dHnDt3DnXr1oW9vT1u3ryJgIAAQ9dIRERElCW9wsyiRYuwbNkyzJ8/HxYWFhg7diz279+PkJAQJCUlGbpGIiIioizpFWZu374tnYJtZWWF5ORkAMDnn3/O32YiIiKiD0qvMOPk5CRdAbhMmTI4efIkACA2NhZCCMNVR0RERJQDvQ4AbtasGf744w/UqlULffr0wciRI7Flyxb8/fff0oX1iAoCXtuHiOjjp1eYWbZsGVQqFQBgyJAhsLe3x4kTJ9CuXTsMHDjQoAUSERERZUevMGNiYgITk//toerRowd69OhhsKKIiIiIdKVzmImKitJ5pjVq1NCrGCIiIqLc0jnM1KxZEwqFIscDfBUKBTIyMvJcGBEREZEudA4zsbGxxqyDiIiISC86n5rt6uqq801XR44cQdu2beHs7AyFQoHff/9dbboQAhMnTkTJkiVhZWWFFi1aICYmRuf5ExER0cdP799miomJQUREBB4+fCid2ZRp4sSJOs3jxYsX8PT0RN++fbWe0j1z5kzMmzcPq1evRtmyZTFhwgT4+fnh8uXLsLS01Ld0IiIi+ojoFWaWL1+OL7/8EsWLF4eTkxMUCoU0TaFQ6BxmAgICsvwtJyEE5s6di2+//Rbt27cHAKxZswaOjo74/fffefYUERERAdAzzHz//feYNm0axo0bZ+h6JLGxsbh//z5atGghtdnZ2aFevXqIjIxkmCEiIiIAeoaZp0+fomvXroauRc39+/cBAI6Ojmrtjo6O0jRtUlNTkZqaKt1//vy5cQokIiKiAkGv32bq2rUr9u3bZ+haDGL69Omws7OTbi4uLvldEhERERmRXltmypcvjwkTJuDkyZOoXr06zM3N1aaHhITkuTAnJycAwIMHD1CyZEmp/cGDB6hZs2aWjxs/fjxGjRol3X/+/DkDDRER0UdM799msra2xuHDh3H48GG1aQqFwiBhpmzZsnByckJ4eLgUXp4/f45Tp07hyy+/zPJxSqUSSqUyz+MTERGRPOgVZgx1Ab2UlBRcv35dbb7nz59HsWLFUKZMGYwYMQLff/89KlSoIJ2a7ezsjA4dOhhkfCIiIpI/va8zYwh///03fH19pfuZu4eCgoIQFhaGsWPH4sWLFxgwYACePXuGxo0bY8+ePbzGDBEREUl0DjOjRo3C1KlTUbhwYbVjUrSZPXu2TvP08fHJ9reeFAoFpkyZgilTpuhaJhEREf3L6Bxmzp07h7S0NOn/WXn3AnpERERExqZzmImIiND6fyIiIqL8pNd1ZoiIiIgKCr0OAH79+jXmz5+f5Q9Nnj171iDFEREREeVErzDTr18/7Nu3D126dEHdunV5nAwRERHlG73CzI4dO7Br1y40atTI0PUQERER5Ypex8yUKlUKNjY2hq6FiIiIKNf0CjOzZs3CuHHjcOvWLUPXQ0RERJQreu1mql27Nl6/fo1y5cqhUKFCGj80+eTJE4MUR0RERJQTvcJMz549kZCQgB9++AGOjo48AJiIiIjyjV5h5sSJE4iMjISnp6eh6yEiIiLKFb3CjIeHB169emXoWsiIvMasMfoYZ34MNPoYRERE79PrAOAZM2bgq6++wqFDh5CYmIjnz5+r3YiIiIg+FL22zPj7+wMAmjdvrtYuhIBCoUBGRkbeKyMiIiLSgV5hhj80SURERAWFXmHG29vb0HUQERER6UWvMHP69GlERkbi/v37AAAnJyc0bNgQderUMWhxRERERDnJVZh5+PAhOnfujOPHj6NMmTJwdHQEADx48AAjR45Eo0aNsHXrVpQoUcIoxRIRERG9L1dnMw0ePBgZGRm4cuUK4uLicOrUKZw6dQpxcXG4cuUKVCoVhgwZYqxaiYiIiDTkasvM3r17ceTIEVSqVEljWqVKlTBv3jz4+PgYqjYiIiKiHOUqzCiVymyvI5OcnAylUpnnooiIiHLCi4FSplztZurevTuCgoKwbds2tVDz/PlzbNu2DX369EHPnj0NXiQRERFRVnK1ZWb27NlQqVTo0aMH0tPTYWFhAQB48+YNzMzM0K9fP/z0009GKZSIiIhIm1zvZlq8eDH++9//4syZM2qnZnt5ecHW1tYoRRIRERFlRa/rzNja2sLX19fQtRARERHlWq5/aPLKlSsIDQ1FdHQ0ACA6Ohpffvkl+vbti4MHDxq8QCIiIqLs5GrLzJ49e9C+fXtYW1vj5cuX2LZtGwIDA+Hp6QmVSoVWrVph3759aNasmbHqJSIiIlKTqy0zU6ZMwZgxY5CYmIjQ0FB89tln6N+/P/bv34/w8HCMGTMGM2bMMFatRERERBpyFWb++ecfBAcHAwC6deuG5ORkdOnSRZreq1cvREVFGbRAIiIiouzk+pgZhULx9oEmJrC0tISdnZ00zcbGBklJSYarjoiIiCgHuQozbm5uiImJke5HRkaiTJky0v3bt2+jZMmShquOiIiIKAe5OgD4yy+/REZGhnS/WrVqatN3797Ng3+JiIjog8pVmBk0aFC203/44Qe1+3fu3IGzszNMTHK9N4uIiIhIJ0ZNGVWqVEFcXJwxhyAiIqJ/OaOGGSGEMWdPREREZNwwQ0RERGRsDDNEREQkawwzREREJGs6h5moqCioVKpczTzzAntERERExqJzmKlVqxYeP34MAChXrhwSExNzfAwPACYiIiJj0znMFClSBLGxsQCAuLg4nbbSXL58Ga6urvpXR0RERJQDnS+a17lzZ3h7e6NkyZJQKBSoXbs2TE1Ntfa9efMmAMDFxcUwVRIRERFlQecws2zZMnTq1AnXr19HSEgI+vfvDxsbG2PWRkRERJSjXP2cgb+/PwDgzJkzGD58OMMMERER5btchZlMoaGhhq6DiIiISC+8zgwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREclagQ8zkydPhkKhULt5eHjkd1lERERUQJjldwG6qFq1Kg4cOCDdNzOTRdlERET0AcgiFZiZmcHJySm/yyAiIqICqMDvZgKAmJgYODs7o1y5cujVqxdu376dZd/U1FQ8f/5c7UZEREQfrwIfZurVq4ewsDDs2bMHixcvRmxsLJo0aYLk5GSt/adPnw47Ozvp5uLi8oErJiIiog+pwIeZgIAAdO3aFTVq1ICfnx927dqFZ8+eYdOmTVr7jx8/HklJSdItPj7+A1dMREREH5Isjpl5V5EiRVCxYkVcv35d63SlUgmlUvmBqyIiIqL8UuC3zLwvJSUFN27cQMmSJfO7FCIiIioACnyYGT16NA4fPoy4uDicOHECHTt2hKmpKXr27JnfpREREVEBUOB3M925cwc9e/ZEYmIiHBwc0LhxY5w8eRIODg75XRoRFTBeY9YYfYwzPwYafQwiyp0CH2Y2bNiQ3yUQERFRAVbgdzMRERERZYdhhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGSNYYaIiIhkjWGGiIiIZI1hhoiIiGRNFmFm4cKFcHNzg6WlJerVq4fTp0/nd0lERERUQBT4MLNx40aMGjUKkyZNwtmzZ+Hp6Qk/Pz88fPgwv0sjIiKiAqDAh5nZs2ejf//+6NOnD6pUqYIlS5agUKFCWLVqVX6XRkRERAVAgQ4zb968wZkzZ9CiRQupzcTEBC1atEBkZGQ+VkZEREQFhVl+F5Cdx48fIyMjA46Ojmrtjo6OiI6O1vqY1NRUpKamSveTkpIAAM+fPwcAZKS+MlK1/5M51vs4Nsfm2BybY3Nsjq3b2Jn/CiFyfpAowBISEgQAceLECbX2MWPGiLp162p9zKRJkwQA3njjjTfeeOPtI7jFx8fnmBcK9JaZ4sWLw9TUFA8ePFBrf/DgAZycnLQ+Zvz48Rg1apR0X6VS4cmTJ7C3t4dCoch1Dc+fP4eLiwvi4+Nha2ub68fnBcfm2BybY3Nsjv1vHVsIgeTkZDg7O+fYt0CHGQsLC3h5eSE8PBwdOnQA8DachIeHY+jQoVofo1QqoVQq1dqKFCmS51psbW0/+IuAY3Nsjs2xOTbH/jePbWdnp1O/Ah1mAGDUqFEICgpC7dq1UbduXcydOxcvXrxAnz598rs0IiIiKgAKfJjp3r07Hj16hIkTJ+L+/fuoWbMm9uzZo3FQMBEREf07FfgwAwBDhw7NcreSsSmVSkyaNElj1xXH5tgcm2NzbI7NsQvG2AohdDnniYiIiKhgKtAXzSMiIiLKCcMMERERyRrDDBEREckawwwRERHJGsPM/0tOTsaIESPg6uoKKysrNGzYEH/99Zc03cfHBwqFQuM2aNCgPI8dGRkJU1NTfPrpp1n2Wb9+PUxNTTFkyJA8j5eb8bdt24b69evDzs4ONjY2qFq1KkaMGJHnMYODg9WWo729Pfz9/REVFaXWb+fOnahXrx6srKxQtGhR6eKJhhrb3Nwcjo6OaNmyJVatWgWVSiX1c3Nzw9y5c/M8XlZjv3tr1qwZihcvjhkzZmh93NSpU+Ho6Ii0tDSDj339+vUcX/+GkN34wcHBBlm32sbUNt9Dhw5BoVDg2bNnau0eHh5QKpW4f/9+nsbM6r1hyJAhUCgUCA4Oltri4+PRt29fODs7w8LCAq6urhg+fDgSExP1rkHXef7zzz/o1q0bHBwcoFQqUbFiRUycOBEvX77Ue2wfHx+t7xFhYWEaFzC9c+cOLCwsUK1aNb3Hy+3YL1++xPjx4+Hu7g5LS0s4ODjA29sb27dv13vctm3bwt/fX+u0o0ePQqFQYN68ebCwsMDZs2fVps+aNQvFixfP9WtuyZIlsLGxQXp6utSWkpICc3Nz+Pj4qPXNfL3fuHEDbm5uUCgUOHnypFqfESNGaDxOF0IItGjRAn5+fhrTFi1aBBsbG5iamiIhIUHr4ytUqKB21X59Mcz8vy+++AL79+/HL7/8gosXL6JVq1Zo0aKF2gro378/7t27p3abOXNmnsdeuXIlhg0bhiNHjuDu3btZ9hk7dizWr1+P169f53lMXcYPDw9H9+7d0blzZ5w+fRpnzpzBtGnT8vSB+i5/f39pOYaHh8PMzAxt2rSRpm/duhWff/45+vTpgwsXLuD48eP47LPPDDp2XFwcdu/eDV9fXwwfPhxt2rRRe3Mwhnefd+Zt69at6N27N0JDQzX6CyEQFhaGwMBAmJubG3zssmXL6vT6N4Ssxi8Ijh07hlevXqFLly5YvXp1nubl4uKCDRs24NWr//0o3+vXr7Fu3TqUKVNGart58yZq166NmJgYrF+/HtevX8eSJUsQHh6OBg0a4MmTJ7keW9d5njx5EvXq1cObN2+wc+dOXLt2DdOmTUNYWBhatmyJN2/e5GkZ6CIsLAzdunXD8+fPcerUKaOPBwCDBg3Cb7/9hvnz5yM6Ohp79uxBly5d8hQe+/Xrh/379+POnTsa00JDQ1G7dm2EhIQgMDAQgYGB0o8hX758Gd9++y0WLlyY5U/0ZMXX1xcpKSn4+++/pbajR4/CyckJp06dUvuciIiIQJkyZeDu7g4AsLS0xLhx4/R5qhoUCgVCQ0Nx6tQpLF26VGqPjY3F2LFjsWjRItjb22v9mzpy5AiuX7+Ofv365b2QvP4Y5Mfg5cuXwtTUVOzYsUOt/ZNPPhHffPONEEIIb29vMXz4cIOPnZycLKytrUV0dLTo3r27mDZtmkafmzdvCisrK/Hs2TNRr149sXbt2g8y/vDhw4WPj4/BxnpXUFCQaN++vVrb0aNHBQDx8OFDkZaWJkqVKiVWrFjxQcYWQojw8HABQCxfvlwIIYSrq6uYM2fOBxlbCCGioqIEAHH06FG19oiICAFAXLlyxShj6/L6N4Tsnnt204wxZuYyffr0qdQWHBwsvv76a7F7925RsWLFPI9ZrVo18euvv0rta9euFTVq1BDt27cXQUFBQggh/P39RenSpcXLly/V5nHv3j1RqFAhMWjQoFyPr8s8VSqVqFKliqhdu7bIyMhQ63f+/HmhUCjEjBkzcj22EFm/V4aGhgo7OzvpvkqlEuXKlRN79uwR48aNE/3799drvNyObWdnJ8LCwvI81rvS0tKEo6OjmDp1qlp75vvr4sWLhRBCPH/+XLi6uopx48aJtLQ0Ubt2bdG1a1e9xy1ZsqSYPn26dH/s2LFiyJAhonLlyiIiIkJqb9q0qfSac3V1FSEhIcLCwkLs3LlT6jN8+HDh7e2tdy1hYWHC2tpa3Lx5U6hUKuHr6ys6duwohBBi1KhRokKFChqPCQoKEvXq1dN7zHdxywyA9PR0ZGRkwNLSUq3dysoKx44dM+rYmzZtgoeHBypVqoTevXtj1apVGj93Hhoaik8//RR2dnbo3bs3Vq5c+UHGd3Jywj///INLly4ZbLyspKSk4Ndff0X58uVhb2+Ps2fPIiEhASYmJqhVqxZKliyJgIAAo9bSrFkzeHp64rfffjPaGNmpXr066tSpg1WrVqm1h4aGomHDhvDw8DDKuPn5+i8okpOTsXnzZvTu3RstW7ZEUlISjh49mqd59u3bV21L26pVq9R+huXJkyfYu3cvBg8eDCsrK7XHOjk5oVevXti4caPG+0F2dJ3n+fPncfnyZYwaNQomJuofA56enmjRogXWr1+fm6ebaxEREXj58iVatGiB3r17Y8OGDXjx4oVRxwTeLoddu3YhOTnZYPM0MzNDYGAgwsLC1NbX5s2bkZGRgZ49ewIAbGxssGrVKsyaNQu9evVCfHw8Fi9erPe4vr6+iIiIkO5HRETAx8cH3t7eUvurV69w6tQp+Pr6Sv3Kli2LQYMGYfz48Wq71vMiKCgIzZs3R9++fbFgwQJcunRJ2lLTr18/xMTE4MiRI1L/lJQUbNmyxTBbZcDdTADevsAaNGiAqVOn4u7du8jIyMCvv/6KyMhI3Lt3T+q3aNEiWFtbq93Wrl2bp7FXrlyJ3r17A3i7CT4pKQmHDx+WpqtUKoSFhUl9evTogWPHjiE2NjZP4+oy/rBhw1CnTh1Ur14dbm5u6NGjB1atWiVtIs2rHTt2SMvRxsYGf/zxBzZu3AgTExPcvHkTADB58mR8++232LFjB4oWLQofHx+9Nr3rysPDA3FxcUabP6D+vDNvP/zwA4C3f/SbN29GSkoKgLcfslu2bEHfvn2NMnbXrl11fv0ba3xj07a8AwIC1Pps2LABFSpUQNWqVWFqaooePXrk+UtD7969cezYMdy6dQu3bt3C8ePHpb81AIiJiYEQApUrV9b6+MqVK+Pp06d49OiRzmPqOs9r165J97Pql9nHWFauXIkePXrA1NQU1apVQ7ly5bB582ajjgkAy5Ytw4kTJ2Bvb486depg5MiROH78eJ7n27dvX9y4cUPt/Ts0NBSdO3dW+7HEZs2aoUuXLti0aRPmzZsHe3t7vcf09fXF8ePHkZ6ejuTkZJw7dw7e3t5o2rQpDh06BODtMZGpqalqYQYAvv32W8TGxub5M+xdy5Ytw6VLlzBixAgsW7YMDg4OAIAqVaqgfv36al/UNm3aBCEEevToYZCxGWb+3y+//AIhBEqVKgWlUol58+ahZ8+eat9aevXqhfPnz6vd2rVrp/eYV69exenTp6XUbmZmhu7du6u9ie7fvx8vXrxA69atAQDFixeXDlbNq5zGL1y4MHbu3Inr16/j22+/hbW1Nb766ivUrVs3TwcIZvL19ZWW4+nTp+Hn54eAgADcunVL+rbwzTffoHPnzvDy8kJoaCgUCoVR3/CEEFAoFEabP6D+vDNvmQeL9uzZExkZGdi0aRMASOGue/fuRhl73rx5AHR7/RtzfGPStrxXrFih1mfVqlVqQaN3797YvHlznr69Ozg44NNPP0VYWJi0dbV48eIa/XLa8mJhYZHrsXXdmpObrT6G9OzZM/z2228ay9yQW52z0rRpU9y8eRPh4eHo0qUL/vnnHzRp0gRTp07N03w9PDzQsGFD6b35+vXrOHr0qMaWh4SEBOzZsweFChXK89Y/Hx8fvHjxAn/99ReOHj2KihUrSgc0Zx43c+jQIZQrV07tWC3g7etz9OjRmDhxosGOjypRogQGDhyIypUraxx437dvX2zZskX6m1q1apX0ZcoQGGb+n7u7Ow4fPoyUlBTEx8fj9OnTSEtLQ7ly5aQ+dnZ2KF++vNotLyti5cqVSE9Ph7OzM8zMzGBmZobFixdj69atSEpKkvo8efIEVlZWUp9du3Zh9erVed48qMv4wNtl88UXX2DFihU4e/YsLl++jI0bN+ZpbOBtWMpcjnXq1MGKFSvw4sULLF++HCVLlgTwNtFnUiqVKFeuHG7fvp3nsbNy5coVox+Q+u7zzrwVK1YMAGBra4suXbpIuydCQ0PRrVs3WFtbG2XszOWsy+vfmOMbk7blXapUKWn65cuXcfLkSYwdO1b6O6hfvz5evnyJDRs25Gnsvn37IiwsDKtXr9bYula+fHkoFApcuXJF62OvXLkCBwcHjTOAsqPLPIsWLYqKFStK97Pql9knt2xtbdXePzI9e/ZM2kKxbt06vH79GvXq1ZOW+bhx43Ds2LE8bRHSZWwAMDc3R5MmTTBu3Djs27cPU6ZMwdSpU/P8od6vXz9s3boVycnJCA0Nhbu7O7y9vdX69O/fH15eXtixYwcWL16stiUnt8qXL4/SpUsjIiICERER0ljOzs5wcXHBiRMnEBERgWbNmml9/KhRo/Dq1SssWrRI7xrel7k+35e5BWbTpk2IiYnB8ePHDbaLCWCY0VC4cGGULFkST58+xd69e9G+fXujjJOeno41a9Zg1qxZat8YL1y4AGdnZ6xfvx6JiYnYvn07NmzYoNbn3LlzePr0Kfbt22fU8bVxc3NDoUKFjLJvW6FQwMTEBK9evYKXlxeUSiWuXr0qTU9LS0NcXBxcXV0NPjYAHDx4EBcvXkTnzp2NMn9d9evXD8eOHcOOHTtw4sQJg/7B5+RDvf4LkpUrV6Jp06a4cOGC2t/CqFGj8rylwN/fH2/evEFaWprGqav29vZo2bIlFi1apHbWEwDcv38fa9euVTuFWxe6zLN79+6oWbMmPDw8MGfOHI0vRRcuXMCBAwekLba5ValSJY3TjwHg7NmzUkBauXIlvvrqK433niZNmuRpq7MuY2tTpUoVpKen5/lM0W7dusHExATr1q3DmjVr0LdvX7UtvStWrMCxY8ewcuVK+Pr64ssvv0Tfvn3z9H7q6+uLQ4cO4dChQ2qnVjdt2hS7d+/G6dOnNXYxZbK2tsaECRMwbdo0gx5DpI2NjQ26du2KVatWITQ0FBUrVkSTJk0MN4BBDiP+COzZs0fs3r1b3Lx5U+zbt094enqKevXqiTdv3ggh3h4l379/f3Hv3j2125MnT/Qab9u2bcLCwkI8e/ZMY9rYsWNF7dq1xZw5c0TJkiWFSqXS6NOtWzfRpUsXvcbWdfxJkyaJMWPGiIiICHHz5k1x9uxZERwcLKysrER0dLTeYwvx9ih2f39/aTlevnxZDB48WCgUCuko/OHDh4tSpUqJvXv3iujoaNGvXz9RokQJvZe5trHv3Lkjzpw5I6ZNmyasra1FmzZtRHp6uhDi7VH/o0ePFufOnVO75WX895935u3Ro0dSH5VKJcqXLy+KFi0qPDw88vRc3x87qzOGcnr9G3v8oKAg4ePjo7Gsb9++bZQxM89mevjwoXBwcJDONnnX5cuXBQBx6dKlPI2ZlJQkkpKSpPvvns107do1Ubx4cdGkSRNx+PBhcfv2bbF7925RrVo1UbNmTZGcnJyrsXOaZ4UKFURiYqIQQojjx4+LQoUKiQ4dOohTp06JW7duiU2bNgkXFxfRsGFD8fr161yPLYQQN27cEJaWlmLYsGHiwoULIjo6WsyaNUuYmZmJ3bt3i3PnzmV5dt6iRYuEk5OTSEtLM8rYQrx9L1+yZIn4+++/RWxsrNi5c6eoVKmSaNasmV5jvq9fv36iaNGiwtTUVCQkJEjtcXFxwsbGRixdulRqe/HihXB3dxdDhw7Ve7xVq1YJKysrYWZmJu7fvy+1r169WtjY2AgA4u7du1L7+2dpvnnzRri7uwtLS8s8nc2UadKkScLT01PrtMwzVosWLar32XJZYZj5fxs3bhTlypUTFhYWwsnJSQwZMkTtg97b21sA0Lj5+fnpNV6bNm1E69attU47deqUACAUCoUYPHhwlvVaWFiofQgaevy5c+eKzp07CxcXF2FhYSEcHR2Fv7+/xqnD+ggKClJbjjY2NqJOnTpiy5YtUp83b96Ir776SpQoUULY2NiIFi1a5PqDJaexzczMhIODg2jRooVYtWqV2mmqrq6uWtf5L7/8YpCx371VqlRJrd8PP/wgAIiZM2fqPZa2sbMKEzm9/o09flbLpV+/fkYZMzPMbNmyRZiYmKh9CLyrcuXKYuTIkQYZM9O7YUYIIWJjY0VQUJBwdHQUCoVCABCdOnUSL168yNW474qLi5PmaW5uLlxcXMSwYcPE48eP1fpFRUWJzp07i2LFiglzc3Ph7u4uvv322zyNLYQQp0+fFi1bthQODg7Czs5O1KtXT2zbtk0IIcTQoUNFlSpVtD7u3r17wsTERGzfvt0oYwvx9m+rQYMGolixYsLS0lKUK1dOhISEaCwbfZ04cUIAUHt/ValUonnz5qJVq1Ya/Y8ePSpMTU3FoUOH9BovNjZWAND44hMXF6f1vUXbJSfWrVsnABg9zAghRKVKlYSpqalawDIEhRD5dAQYERFpmDRpEmbPno39+/ejfv36+V0OkSwwzBARFTChoaFISkpCSEiIwc8oI/oYMcwQERGRrDHyExERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQ0Ufn0KFDUCgUePbsWX6XQkQfAE/NJqKPzps3b/DkyRM4Ojoa/VfQiSj/McwQkSy9efMGFhYWGu1paWkwNzc32vyJqODhbiYi+mBUKhVmzpyJ8uXLQ6lUokyZMpg2bRoAYNy4cahYsSIKFSqEcuXKYcKECUhLS5MeO3nyZNSsWRMrVqxA2bJlYWlpCeDtr60vXrwY7dq1Q+HChTFt2jStu5mOHTuGJk2awMrKCi4uLggJCVH7tWI3NzdMnToVgYGBsLW1xYABAz7MQiGiPGOYIaIPZvz48ZgxYwYmTJiAy5cvY926dXB0dAQA2NjYICwsDJcvX8bPP/+M5cuXY86cOWqPv379OrZu3YrffvsN58+fl9onT56Mjh074uLFi+jbt6/GuDdu3IC/vz86d+6MqKgobNy4EceOHcPQoUPV+v3000/w9PTEuXPnMGHCBMMvACIyCu5mIqIPIjk5GQ4ODliwYAG++OKLHPv/9NNP2LBhA/7++28AbwPLDz/8gISEBDg4OEj9FAoFRowYoRZ8Dh06BF9fXzx9+hRFihTBF198AVNTUyxdulTqc+zYMXh7e+PFixewtLSEm5sbatWqhW3bthnwWRPRh2CW3wUQ0b/DlStXkJqaiubNm2udvnHjRsybNw83btxASkoK0tPTYWtrq9bH1dVVLchkql27drZjX7hwAVFRUVi7dq3UJoSASqVCbGwsKleurNN8iKhgYpghog/Cysoqy2mRkZHo1asXvvvuO/j5+cHOzg4bNmzArFmz1PoVLlxY6+Ozas+UkpKCgQMHIiQkRGNamTJldJ4PERVMDDNE9EFUqFABVlZWCA8P19jNdOLECbi6uuKbb76R2m7dumWwsT/55BNcvnwZ5cuXN9g8iajgYJghog/C0tIS48aNw9ixY2FhYYFGjRrh0aNH+Oeff1ChQgXcvn0bGzZsQJ06dbBz506DHrsybtw41K9fH0OHDsUXX3yBwoUL4/Lly9i/fz8WLFhgsHGIKH/wbCYi+mAmTJiAr776ChMnTkTlypXRvXt3PHz4EO3atcPIkSMxdOhQ1KxZEydOnDDo2UQ1atTA4cOHce3aNTRp0gS1atXCxIkT4ezsbLAxiCj/8GwmIiIikjVumSEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIln7P7dUs1ksIAwwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=not_cancelled_carrier, x='carrier', y='f_30minlate_proportion').set(title= ('Proportion of on 30 min. delayed flights by airline carrier'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "c24ecc2928a1df641fb89905a028e959d05eac4f79c1af53f0bb766b01409011"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}