{ "cells": [ { "cell_type": "markdown", "id": "9d9c766f", "metadata": {}, "source": [ "

Dataset 1: Conditions_Contributing_to_COVID-19_Deaths__by_State_and_Age__Provisional_2020-2023.csv

" ] }, { "cell_type": "code", "execution_count": 1, "id": "032d5c8f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Data As Of Start Date End Date Group Year Month State \\\n", "0 09/24/2023 01/01/2020 09/23/2023 By Total NaN NaN United States \n", "1 09/24/2023 01/01/2020 09/23/2023 By Total NaN NaN United States \n", "2 09/24/2023 01/01/2020 09/23/2023 By Total NaN NaN United States \n", "3 09/24/2023 01/01/2020 09/23/2023 By Total NaN NaN United States \n", "4 09/24/2023 01/01/2020 09/23/2023 By Total NaN NaN United States \n", "\n", " Condition Group Condition ICD10_codes Age Group \\\n", "0 Respiratory diseases Influenza and pneumonia J09-J18 0-24 \n", "1 Respiratory diseases Influenza and pneumonia J09-J18 25-34 \n", "2 Respiratory diseases Influenza and pneumonia J09-J18 35-44 \n", "3 Respiratory diseases Influenza and pneumonia J09-J18 45-54 \n", "4 Respiratory diseases Influenza and pneumonia J09-J18 55-64 \n", "\n", " COVID-19 Deaths Number of Mentions Flag \n", "0 1569.0 1647.0 NaN \n", "1 5804.0 6029.0 NaN \n", "2 15080.0 15699.0 NaN \n", "3 37414.0 38878.0 NaN \n", "4 82668.0 85708.0 NaN \n" ] } ], "source": [ "import pandas as pd\n", "file_path_1 = 'Conditions_Contributing_to_COVID-19_Deaths__by_State_and_Age__Provisional_2020-2023.csv'\n", "data_1 = pd.read_csv(file_path_1)\n", "print(data_1.head())" ] }, { "cell_type": "code", "execution_count": 2, "id": "a90d5564", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((621000, 14),\n", " Data As Of Start Date End Date Group Year \\\n", " count 621000 621000 621000 621000 608580.000000 \n", " unique 1 45 45 3 NaN \n", " top 09/24/2023 01/01/2020 09/23/2023 By Month NaN \n", " freq 621000 37260 37260 558900 NaN \n", " mean NaN NaN NaN NaN 2021.408163 \n", " std NaN NaN NaN NaN 1.086436 \n", " min NaN NaN NaN NaN 2020.000000 \n", " 25% NaN NaN NaN NaN 2020.000000 \n", " 50% NaN NaN NaN NaN 2021.000000 \n", " 75% NaN NaN NaN NaN 2022.000000 \n", " max NaN NaN NaN NaN 2023.000000 \n", " \n", " Month State Condition Group \\\n", " count 558900.000000 621000 621000 \n", " unique NaN 54 12 \n", " top NaN United States Circulatory diseases \n", " freq NaN 11500 189000 \n", " mean 6.200000 NaN NaN \n", " std 3.350625 NaN NaN \n", " min 1.000000 NaN NaN \n", " 25% 3.000000 NaN NaN \n", " 50% 6.000000 NaN NaN \n", " 75% 9.000000 NaN NaN \n", " max 12.000000 NaN NaN \n", " \n", " Condition ICD10_codes Age Group COVID-19 Deaths \\\n", " count 621000 621000 621000 4.375510e+05 \n", " unique 23 23 10 NaN \n", " top Influenza and pneumonia J09-J18 0-24 NaN \n", " freq 27000 27000 62100 NaN \n", " mean NaN NaN NaN 1.201179e+02 \n", " std NaN NaN NaN 2.980201e+03 \n", " min NaN NaN NaN 0.000000e+00 \n", " 25% NaN NaN NaN 0.000000e+00 \n", " 50% NaN NaN NaN 0.000000e+00 \n", " 75% NaN NaN NaN 1.800000e+01 \n", " max NaN NaN NaN 1.146242e+06 \n", " \n", " Number of Mentions Flag \n", " count 4.434230e+05 183449 \n", " unique NaN 1 \n", " top NaN One or more data cells have counts between 1-9... \n", " freq NaN 183449 \n", " mean 1.293348e+02 NaN \n", " std 3.203936e+03 NaN \n", " min 0.000000e+00 NaN \n", " 25% 0.000000e+00 NaN \n", " 50% 0.000000e+00 NaN \n", " 75% 1.900000e+01 NaN \n", " max 1.146242e+06 NaN ,\n", " Data As Of Start Date End Date Group Year Month \\\n", " 620995 09/24/2023 05/01/2023 05/31/2023 By Month 2023.0 5.0 \n", " 620996 09/24/2023 06/01/2023 06/30/2023 By Month 2023.0 6.0 \n", " 620997 09/24/2023 07/01/2023 07/31/2023 By Month 2023.0 7.0 \n", " 620998 09/24/2023 08/01/2023 08/31/2023 By Month 2023.0 8.0 \n", " 620999 09/24/2023 09/01/2023 09/23/2023 By Month 2023.0 9.0 \n", " \n", " State Condition Group Condition ICD10_codes Age Group \\\n", " 620995 Puerto Rico COVID-19 COVID-19 U071 All Ages \n", " 620996 Puerto Rico COVID-19 COVID-19 U071 All Ages \n", " 620997 Puerto Rico COVID-19 COVID-19 U071 All Ages \n", " 620998 Puerto Rico COVID-19 COVID-19 U071 All Ages \n", " 620999 Puerto Rico COVID-19 COVID-19 U071 All Ages \n", " \n", " COVID-19 Deaths Number of Mentions Flag \n", " 620995 67.0 67.0 NaN \n", " 620996 122.0 122.0 NaN \n", " 620997 114.0 114.0 NaN \n", " 620998 78.0 78.0 NaN \n", " 620999 36.0 36.0 NaN ,\n", " Data As Of object\n", " Start Date object\n", " End Date object\n", " Group object\n", " Year float64\n", " Month float64\n", " State object\n", " Condition Group object\n", " Condition object\n", " ICD10_codes object\n", " Age Group object\n", " COVID-19 Deaths float64\n", " Number of Mentions float64\n", " Flag object\n", " dtype: object)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_1_shape = data_1.shape\n", "\n", "# Descriptive statistics for all columns\n", "data_1_describe = data_1.describe(include='all')\n", "\n", "# Display the last few rows of the DataFrame\n", "data_1_tail = data_1.tail()\n", "\n", "# Display the data types of each column\n", "data_1_dtypes = data_1.dtypes\n", "\n", "data_1_shape, data_1_describe, data_1_tail, data_1_dtypes" ] }, { "cell_type": "code", "execution_count": null, "id": "a33437ba", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "id": "6016716c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Data As Of Start Date End Date Group Year Month \\\n", "0 2023-09-24 2020-01-01 2023-09-23 By Total NaN NaN \n", "1 2023-09-24 2020-01-01 2023-09-23 By Total NaN NaN \n", "2 2023-09-24 2020-01-01 2023-09-23 By Total NaN NaN \n", "3 2023-09-24 2020-01-01 2023-09-23 By Total NaN NaN \n", "4 2023-09-24 2020-01-01 2023-09-23 By Total NaN NaN \n", "... ... ... ... ... ... ... \n", "620995 2023-09-24 2023-05-01 2023-05-31 By Month 2023.0 5.0 \n", "620996 2023-09-24 2023-06-01 2023-06-30 By Month 2023.0 6.0 \n", "620997 2023-09-24 2023-07-01 2023-07-31 By Month 2023.0 7.0 \n", "620998 2023-09-24 2023-08-01 2023-08-31 By Month 2023.0 8.0 \n", "620999 2023-09-24 2023-09-01 2023-09-23 By Month 2023.0 9.0 \n", "\n", " State Condition Group Condition \\\n", "0 United States Respiratory diseases Influenza and pneumonia \n", "1 United States Respiratory diseases Influenza and pneumonia \n", "2 United States Respiratory diseases Influenza and pneumonia \n", "3 United States Respiratory diseases Influenza and pneumonia \n", "4 United States Respiratory diseases Influenza and pneumonia \n", "... ... ... ... \n", "620995 Puerto Rico COVID-19 COVID-19 \n", "620996 Puerto Rico COVID-19 COVID-19 \n", "620997 Puerto Rico COVID-19 COVID-19 \n", "620998 Puerto Rico COVID-19 COVID-19 \n", "620999 Puerto Rico COVID-19 COVID-19 \n", "\n", " ICD10_codes Age Group COVID-19 Deaths Number of Mentions Flag \n", "0 J09-J18 0-24 1569.0 1647.0 NaN \n", "1 J09-J18 25-34 5804.0 6029.0 NaN \n", "2 J09-J18 35-44 15080.0 15699.0 NaN \n", "3 J09-J18 45-54 37414.0 38878.0 NaN \n", "4 J09-J18 55-64 82668.0 85708.0 NaN \n", "... ... ... ... ... ... \n", "620995 U071 All Ages 67.0 67.0 NaN \n", "620996 U071 All Ages 122.0 122.0 NaN \n", "620997 U071 All Ages 114.0 114.0 NaN \n", "620998 U071 All Ages 78.0 78.0 NaN \n", "620999 U071 All Ages 36.0 36.0 NaN \n", "\n", "[621000 rows x 14 columns]\n" ] } ], "source": [ "data_1 = pd.DataFrame(data_1)\n", "\n", "# Convert dates to datetime\n", "data_1['Data As Of'] = pd.to_datetime(data_1['Data As Of'])\n", "data_1['Start Date'] = pd.to_datetime(data_1['Start Date'])\n", "data_1 ['End Date'] = pd.to_datetime(data_1['End Date'])\n", "\n", "# Display the DataFrame\n", "print(data_1)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "08b0192f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Since the dataframe is named data_1, let's perform the analysis using that correct name\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Frequency distribution of Age Group\n", "age_group_counts_data_1 = data_1['Age Group'].value_counts()\n", "\n", "# Bar chart of Age Group counts in 'data_1'\n", "plt.figure(figsize=(10, 6))\n", "plt.bar(age_group_counts_data_1.index, age_group_counts_data_1.values, color='skyblue')\n", "plt.title('Frequency Distribution of Age Groups in Data 1')\n", "plt.xlabel('Age Group')\n", "plt.ylabel('Frequency')\n", "plt.xticks(rotation=45) # Rotate x-axis labels to show clearly\n", "plt.show()\n", "\n", "# Boxplot of COVID-19 Deaths by Age Group in 'data_1'\n", "plt.figure(figsize=(10, 6))\n", "sns.boxplot(x='Age Group', y='COVID-19 Deaths', data=data_1)\n", "plt.title('COVID-19 Deaths by Age Group in Data 1')\n", "plt.xlabel('Age Group')\n", "plt.ylabel('COVID-19 Deaths')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "f1a3d2b8", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Could not interpret input 'Age Group Numeric'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[5], line 6\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Bar chart for 'Condition Group' with vertical x-axis labels\u001b[39;00m\n\u001b[0;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 6\u001b[0m barplot1 \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition Group\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOVID-19 Deaths\u001b[39m\u001b[38;5;124m'\u001b[39m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAge Group Numeric\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mdata_1)\n\u001b[0;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCOVID-19 Deaths by Condition Group and Age Group\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 8\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition Group\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2755\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m 2752\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlen\u001b[39m:\n\u001b[0;32m 2753\u001b[0m estimator \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 2755\u001b[0m plotter \u001b[38;5;241m=\u001b[39m _BarPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m 2756\u001b[0m estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m 2757\u001b[0m orient, color, palette, saturation,\n\u001b[0;32m 2758\u001b[0m width, errcolor, errwidth, capsize, dodge)\n\u001b[0;32m 2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2761\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1530\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m 1526\u001b[0m estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m 1527\u001b[0m orient, color, palette, saturation, width,\n\u001b[0;32m 1528\u001b[0m errcolor, errwidth, capsize, dodge):\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1530\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient,\n\u001b[0;32m 1531\u001b[0m order, hue_order, units)\n\u001b[0;32m 1532\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m 1533\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, errorbar, n_boot, seed)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:541\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m 539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 540\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret input \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 541\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m 543\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[0;32m 544\u001b[0m orient \u001b[38;5;241m=\u001b[39m infer_orient(\n\u001b[0;32m 545\u001b[0m x, y, orient, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequire_numeric\n\u001b[0;32m 546\u001b[0m )\n", "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'Age Group Numeric'" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Bar chart for 'Condition Group' with vertical x-axis labels\n", "plt.figure(figsize=(12, 6))\n", "barplot1 = sns.barplot(x='Condition Group', y='COVID-19 Deaths', hue='Age Group Numeric', data=data_1)\n", "plt.title('COVID-19 Deaths by Condition Group and Age Group')\n", "plt.xlabel('Condition Group')\n", "plt.ylabel('COVID-19 Deaths')\n", "plt.legend(title='Age Group')\n", "barplot1.set_xticklabels(barplot1.get_xticklabels(), rotation=90) # Rotate x-axis labels\n", "plt.show()\n", "\n", "plt.figure(figsize=(12, 6))\n", "barplot2 = sns.barplot(x='State', y='COVID-19 Deaths', hue='Age Group Numeric', data=data_1)\n", "plt.title('COVID-19 Deaths by State and Age Group')\n", "plt.xlabel('State')\n", "plt.ylabel('COVID-19 Deaths')\n", "plt.legend(title='Age Group')\n", "barplot2.set_xticklabels(barplot2.get_xticklabels(), rotation=90) # Rotate x-axis labels\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "67710766", "metadata": {}, "outputs": [], "source": [ "# We will remove 'United States' from the 'State' column and then recreate the bar plot.\n", "\n", "# Check if there are other states in the dataset besides 'United States'\n", "unique_states = data_1['State'].unique()\n", "\n", "# If 'United States' is the only state, the following code will not be able to create a meaningful plot.\n", "# We'll proceed under the assumption that there are other states in the full dataset.\n", "\n", "# Filter out the 'United States' entry from the dataset\n", "data_1_no_us = data_1[data_1['State'] != 'United States']\n", "\n", "# Now let's create the bar plot without 'United States'\n", "plt.figure(figsize=(12, 6))\n", "barplot_no_us = sns.barplot(x='State', y='COVID-19 Deaths', hue='Age Group Numeric', data=data_1_no_us)\n", "plt.title('COVID-19 Deaths by State and Age Group (excluding United States)')\n", "plt.xlabel('State')\n", "plt.ylabel('COVID-19 Deaths')\n", "plt.legend(title='Age Group')\n", "barplot_no_us.set_xticklabels(barplot_no_us.get_xticklabels(), rotation=90) # Rotate x-axis labels\n", "plt.tight_layout() # This will adjust the plot to make sure everything fits without overlapping\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0c3a1783", "metadata": {}, "outputs": [], "source": [ "pip install ydata-profiling" ] }, { "cell_type": "code", "execution_count": null, "id": "b3d0f506", "metadata": {}, "outputs": [], "source": [ "from ydata_profiling import ProfileReport\n", "\n", "ProfileReport(data_1)" ] } ], "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }