checkpoint-two.ipynb

{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {
                "azdata_cell_guid": "151601c9-ca7a-412e-af80-c62a87e0837e"
            },
            "source": [
                "# Checkpoint Two: Exploratory Data Analysis\n",
                "\n",
                "Now that your chosen dataset is approved, it is time to start working on your analysis. Use this notebook to perform your EDA and make notes where directed to as you work.\n",
                "\n",
                "## Getting Started\n",
                "\n",
                "Since we have not provided your dataset for you, you will need to load the necessary files in this repository. Make sure to include a link back to the original dataset here as well.\n",
                "\n",
                "My dataset: https://catalog.data.gov/dataset/alzheimers-disease-and-healthy-aging-data\n",
                "\n",
                "Your first task in EDA is to import necessary libraries and create a dataframe(s). Make note in the form of code comments of what your thought process is as you work on this setup task."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "azdata_cell_guid": "7b503e37-6df5-4433-acfb-678b1346db14"
            },
            "outputs": [],
            "source": [
                "# Pre-filtered the overall excel file down to the two questions I want to analyze from the survey data.\n",
                "# So the file I'm importing is already a filtered dataset from the main dataset.\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "import seaborn as sns\n",
                "import numpy as np\n",
                "\n",
                "df = pd.read_csv(\"./filtered-dataset.csv\")\n",
                "\n",
                "# Looks like 0 duplicated rows\n",
                "df.duplicated().sum()\n",
                "\n",
                "df.columns\n",
                "\n",
                "# Columns\n",
                "list = ['RowId', 'YearStart', 'YearEnd', 'LocationAbbr', 'LocationDesc',\n",
                "       'Datasource', 'Class', 'Topic', 'Question', 'Data_Value_Unit',\n",
                "       'DataValueTypeID', 'Data_Value_Type', 'Data_Value', 'Data_Value_Alt',\n",
                "       'Data_Value_Footnote_Symbol', 'Data_Value_Footnote',\n",
                "       'Low_Confidence_Limit', 'High_Confidence_Limit',\n",
                "       'StratificationCategory1', 'Stratification1', 'StratificationCategory2',\n",
                "       'Stratification2', 'Geolocation', 'ClassID', 'TopicID', 'QuestionID',\n",
                "       'LocationID', 'StratificationCategoryID1', 'StratificationID1',\n",
                "       'StratificationCategoryID2', 'StratificationID2']\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "column_dict = {\n",
                "\t\"id\": 'RowId',\n",
                "\t\"y_start\": \"YearStart\",\n",
                "\t\"y_end\": \"YearEnd\",\n",
                "\t\"location_abbrev\": \"LocationAbbr\",\n",
                "\t\"location_desc\": \"LocationDesc\",\n",
                "\t\"class\": \"Class\",\n",
                "\t\"topic\": \"Topic\",\n",
                "\t\"question\": \"Question\",\n",
                "\t\"q_id\": \"QuestionID\",\n",
                "\t\"dv_type\": \"Data_Value_Type\",\n",
                "\t\"data_value\": \"Data_Value\",\n",
                "\t\"df_footnote\": \"Data_Value_Footnote\",\n",
                "\t\"low_conf_limit\": \"Low_Confidence_Limit\",\n",
                "\t\"high_conf_limit\": \"High_Confidence_Limit\",\n",
                "\t\"strat_cat_1\": \"StratificationCategory1\",\n",
                "\t\"strat_1_value\": \"Stratification1\",\n",
                "\t\"strat_cat_2\": \"StratificationCategory2\",\n",
                "\t\"strat_2_value\": \"Stratification2\",\n",
                "\t\"geolocation\": \"Geolocation\",\n",
                "\t\"location_id\": \"LocationID\"\n",
                "\t}"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {
                "azdata_cell_guid": "129ce836-524b-4ea8-b394-a959f9308d6a"
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            "source": [
                "## Get to Know the Numbers\n",
                "\n",
                "Now that you have everything setup, put any code that you use to get to know the dataframe and its rows and columns better in the cell below. You can use whatever techniques you like, except for visualizations. You will put those in a separate section.\n",
                "\n",
                "When working on your code, make sure to leave comments so that your mentors can understand your thought process."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 113,
            "metadata": {
                "azdata_cell_guid": "0ca2d318-f6e8-43ef-a33e-5304d24d928d",
                "tags": []
            },
            "outputs": [],
            "source": [
                "# Is each row of my dataset an individual data point, or is it representing an aggregation (average, sum, etc.) of a dataset.\n",
                "\t## Each row represents an aggregation of survey responses for a given geographic region\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 114,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "['Alabama' 'Alaska' 'Arizona' 'Arkansas' 'California' 'Colorado'\n",
                        " 'Connecticut' 'Delaware' 'District of Columbia' 'Florida' 'Georgia'\n",
                        " 'Guam' 'Hawaii' 'Idaho' 'Illinois' 'Indiana' 'Iowa' 'Kansas' 'Kentucky'\n",
                        " 'Louisiana' 'Maine' 'Maryland' 'Massachusetts' 'Michigan' 'Midwest'\n",
                        " 'Minnesota' 'Mississippi' 'Missouri' 'Montana' 'Nebraska' 'Nevada'\n",
                        " 'New Hampshire' 'New Jersey' 'New Mexico' 'New York' 'North Carolina'\n",
                        " 'North Dakota' 'Northeast' 'Ohio' 'Oklahoma' 'Oregon' 'Pennsylvania'\n",
                        " 'Puerto Rico' 'Rhode Island' 'South' 'South Carolina' 'South Dakota'\n",
                        " 'Tennessee' 'Texas' 'United States, DC & Territories' 'Utah' 'Vermont'\n",
                        " 'Virgin Islands' 'Virginia' 'Washington' 'West' 'West Virginia'\n",
                        " 'Wisconsin' 'Wyoming']\n"
                    ]
                }
            ],
            "source": [
                "## Unique locations\n",
                "## Are there regions mixed in with specific states in the data? -- YES\n",
                "\n",
                "unique_vals = df[column_dict['location_desc']].unique()\n",
                "unique_vals.sort()\n",
                "\n",
                "print(unique_vals)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 115,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['50-64 years', '65 years or older', 'Overall'], dtype=object)"
                        ]
                    },
                    "execution_count": 115,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "## Unique Age groups, Race/Ethnicity, Gender reported on for these two questions\n",
                "# filtered_df = df[df['City'] == 'New York']\n",
                "category = \"Age Group\"\n",
                "\n",
                "filtered_to_cat = df[df[column_dict['strat_cat_1']] == category]\n",
                "\n",
                "filtered_to_cat[column_dict['strat_1_value']].unique()\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 116,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['Asian/Pacific Islander', 'Hispanic', 'Native Am/Alaskan Native',\n",
                            "       'Black, non-Hispanic', 'White, non-Hispanic'], dtype=object)"
                        ]
                    },
                    "execution_count": 116,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "## Unique Race/Ethnicity, \n",
                "\n",
                "category = 'Race/Ethnicity'\n",
                "\n",
                "filtered_to_cat = df[df[column_dict['strat_cat_2']] == category]\n",
                "\n",
                "filtered_to_cat[column_dict['strat_2_value']].unique()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 117,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['Female', 'Male'], dtype=object)"
                        ]
                    },
                    "execution_count": 117,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "## Unique Gender reported on for these two questions\n",
                "category = 'Gender'\n",
                "\n",
                "filtered_to_cat = df[df[column_dict['strat_cat_2']] == category]\n",
                "\n",
                "filtered_to_cat[column_dict['strat_2_value']].unique()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 118,
            "metadata": {},
            "outputs": [],
            "source": [
                "## Number of 'No Data Available' rows in the dataset for these questions\n",
                "\n",
                "row_val = \"No Data Available\"\n",
                "\n",
                "count_no_data = df[df[column_dict['df_footnote']] == row_val][column_dict['df_footnote']].count()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 119,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "6095\n",
                        "16287\n",
                        "37.42\n",
                        "10192\n"
                    ]
                }
            ],
            "source": [
                "## Number of rows with no data due to sample size warning\n",
                "row_val = \"Sample size of denominator and/or age group for age-standardization is less than 50 or relative standard error is more than 30%\"\n",
                "\n",
                "count_sample_warning = df[df[column_dict['df_footnote']] == row_val][column_dict['df_footnote']].count()\n",
                "\n",
                "\n",
                "total_no_data = count_no_data + count_sample_warning\n",
                "print(total_no_data)\n",
                "\n",
                "num_total_records = df[column_dict['id']].count()\n",
                "print(num_total_records)\n",
                "\n",
                "percent_missing = round((total_no_data / num_total_records) * 100, 2)\n",
                "print(percent_missing)\n",
                "\n",
                "# Total rows with data\n",
                "print(num_total_records - total_no_data)\n",
                "# when I move on to cleaning data, going to filter these no data rows out of dataset"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 120,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['Percentage'], dtype=object)"
                        ]
                    },
                    "execution_count": 120,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Value data types in dataset\n",
                "\n",
                "df[column_dict['dv_type']].unique()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 121,
            "metadata": {},
            "outputs": [],
            "source": [
                "## <Important> Need to research what these High and Low confidence values mean\n",
                "\n",
                "# A low confidence limit (often referred to as the lower bound) is the lowest value in this range, \n",
                "##  while a high confidence limit (or upper bound) is the highest value. \n",
                "## Together, these limits form a confidence interval, \n",
                "## which provides an estimate of uncertainty around the parameter."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 122,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "59\n",
                        "59\n"
                    ]
                }
            ],
            "source": [
                "## Unique Location values for rows with data and rows without data\n",
                "\n",
                "row_val_1 = \"No Data Available\"\n",
                "row_val_2 = \"Sample size of denominator and/or age group for age-standardization is less than 50 or relative standard error is more than 30%\"\n",
                "\n",
                "exclude = [row_val_1, row_val_2]\n",
                "\n",
                "\n",
                "location_with_data_filtered_df = df[~df[column_dict['df_footnote']].isin(exclude)]\n",
                "# Locations with data\n",
                "locations_with_data = location_with_data_filtered_df[column_dict['location_desc']].unique()\n",
                "\n",
                "locations_no_data_df = df[df[column_dict['df_footnote']].isin(exclude)]\n",
                "locations_without_data = locations_no_data_df[column_dict['location_desc']].unique()\n",
                "\n",
                "\n",
                "print(len(locations_with_data))\n",
                "print(len(locations_without_data))\n",
                "\n",
                "# All unique locations seem to have rows with data and rows without data\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 123,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "6640 3552\n"
                    ]
                }
            ],
            "source": [
                "## See how filled out dataset is for both questions. Does one have more data than the other? That'd be an important thing to note in final \n",
                "### Tableau report if so. \n",
                "## What are the number of complete responses for each question? Can I compare them, or is one question much more answered than the other?\n",
                "\n",
                "binge_drink_q = [\"Percentage of older adults who reported binge drinking within the past 30 days\", \"Q21\"]\n",
                "expected_caretaker_1 = [\"Percentage of older adults currently not providing care who expect to provide care for someone with health problems in the next two years\", \"Q37\"]\n",
                "\n",
                "row_val_1 = \"No Data Available\"\n",
                "row_val_2 = \"Sample size of denominator and/or age group for age-standardization is less than 50 or relative standard error is more than 30%\"\n",
                "\n",
                "exclude = [row_val_1, row_val_2]\n",
                "\n",
                "\n",
                "binge_q_filtered_df = df[df[column_dict['q_id']] == binge_drink_q[1]]\n",
                "expected_caretaker_q_filtered_df = df[df[column_dict['q_id']] == expected_caretaker_1[1]]\n",
                "\n",
                "\n",
                "binge_q_rows_with_data = binge_q_filtered_df[~binge_q_filtered_df[column_dict['df_footnote']].isin(exclude)]\n",
                "binge_q_count = binge_q_rows_with_data[column_dict['id']].count()\n",
                "\n",
                "expected_caretaker_q_rows_with_data = expected_caretaker_q_filtered_df[~expected_caretaker_q_filtered_df[column_dict['df_footnote']].isin(exclude)]\n",
                "expect_care_q_count = expected_caretaker_q_rows_with_data[column_dict['id']].count()\n",
                "\n",
                "\n",
                "print(binge_q_count, expect_care_q_count)\n",
                "\n",
                "\n",
                "# Binge drink questions has almost twice the number of rows with data as expected caretaker question\n",
                "## I'll need to include a note about this in final Tableau report so I don't misrepresent possible relationships\n",
                "## between the response data of these two questions\n",
                "\n"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {
                "azdata_cell_guid": "d45060f3-97bc-484a-9ef4-66145bbe9427"
            },
            "source": [
                "## Visualize\n",
                "\n",
                "Create any visualizations for your EDA here. Make note in the form of code comments of what your thought process is for your visualizations."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 124,
            "metadata": {
                "azdata_cell_guid": "f4565b03-759a-47a8-b58f-01b32a0b4b67"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0.5, 0, 'Number of rows with data')"
                        ]
                    },
                    "execution_count": 124,
                    "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": [
                "\n",
                "# Horizontal bar chart\n",
                "keys = ['binge drink question', 'expected care question']\n",
                "\n",
                "values = [binge_q_count, expect_care_q_count]\n",
                "\n",
                "\n",
                "\n",
                "plt.barh(keys, values, align='center')\n",
                "plt.title(\"Comparing rows with data between two analysized questions\")\n",
                "plt.ylabel(\"Question type\")\n",
                "plt.xlabel(\"Number of rows with data\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 130,
            "metadata": {
                "azdata_cell_guid": "cd64bab1-db4c-4295-820d-c6464b653a3e"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0.5, 0, 'Count rows')"
                        ]
                    },
                    "execution_count": 130,
                    "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": [
                "# Code Here\n",
                "keys = locations_with_data\n",
                "\n",
                "values = []\n",
                "\n",
                "# in billions\n",
                "for k in keys:\n",
                "\tldf = location_with_data_filtered_df\n",
                "\tsum = int(ldf[ldf[column_dict['location_desc']] == k][column_dict['id']].count())\n",
                "\tvalues.append(sum)\n",
                "\n",
                "\n",
                "threshold = 160\n",
                "keys_over_threshold = []\n",
                "values_over_threshold = []\n",
                "\n",
                "exclude = ['Midwest', 'South', 'Northeast', 'United States, DC & Territories', 'West']\n",
                "\n",
                "\n",
                "for index,v in enumerate(keys):\n",
                "\tif values[index] > threshold and v not in exclude:\n",
                "\t\tkeys_over_threshold.append(v)\n",
                "\t\tvalues_over_threshold.append(values[index])\n",
                "\n",
                "\n",
                "plt.barh(keys_over_threshold, values_over_threshold, align='center')\n",
                "plt.title(\"Total Locations with over 160 total rows of data\")\n",
                "plt.ylabel(\"Location\")\n",
                "plt.xlabel(\"Count rows\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 131,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0.5, 0, 'Count rows')"
                        ]
                    },
                    "execution_count": 131,
                    "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": [
                "# Code Here\n",
                "keys = locations_without_data\n",
                "\n",
                "values = []\n",
                "\n",
                "# in billions\n",
                "for k in keys:\n",
                "\tldf = locations_no_data_df\n",
                "\tsum = int(ldf[ldf[column_dict['location_desc']] == k][column_dict['id']].count())\n",
                "\tvalues.append(sum)\n",
                "\n",
                "\n",
                "threshold = 100\n",
                "keys_over_threshold = []\n",
                "values_over_threshold = []\n",
                "\n",
                "exclude = ['Midwest', 'South', 'Northeast', 'United States, DC & Territories', 'West']\n",
                "\n",
                "\n",
                "for index,v in enumerate(keys):\n",
                "\tif values[index] < threshold and v not in exclude:\n",
                "\t\tkeys_over_threshold.append(v)\n",
                "\t\tvalues_over_threshold.append(values[index])\n",
                "\n",
                "\n",
                "plt.barh(keys_over_threshold, values_over_threshold, align='center')\n",
                "plt.title(\"Total Locations with under 100 total rows missing data\")\n",
                "plt.ylabel(\"Location\")\n",
                "plt.xlabel(\"Count rows\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "azdata_cell_guid": "d4931c0c-2c09-4ee1-95d4-73dd08c2f0a6"
            },
            "source": [
                "## Summarize Your Results\n",
                "\n",
                "With your EDA complete, answer the following questions.\n",
                "\n",
                "1. Was there anything surprising about your dataset? \n",
                "\n",
                "\tDifference in response data collected across two questions\n",
                "\n",
                "2. Do you have any concerns about your dataset? \n",
                "\n",
                "\tDifference in response data collected across two questions\n",
                "\n",
                "3. Is there anything you want to make note of for the next phase of your analysis, which is cleaning data? \n",
                "\n",
                "\tDifference in response data collected across two questions\n",
                "\n",
                "\tFilter out non-response rows in dataset, and make sure to make note of that in final Tableau report"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "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": "3.9.6"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}