{ "cells": [ { "cell_type": "markdown", "id": "03338ae6", "metadata": {}, "source": [ "# Statistiques\n", "\n", "### Préparation des données et insertion dans les dataframe.\n", "Ainsi aue le nettoyage des données (suppression des données redondantes et les données non renseignées) " ] }, { "cell_type": "code", "execution_count": 1, "id": "ad4af851", "metadata": {}, "outputs": [], "source": [ "import pandas as pd \n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import matplotlib.patches as mpatches\n", "import re\n", "import plotly.express as px\n", "\n", "data = pd.read_csv(r'./csv/Report_2022.csv')\n", "df_2022 = pd.DataFrame(data)\n", "df_2022 = df_2022.drop_duplicates()\n", "df_2022 = df_2022.dropna()" ] }, { "cell_type": "markdown", "id": "7dbda889", "metadata": {}, "source": [ "## Map mondiales des scores globalaux" ] }, { "cell_type": "code", "execution_count": 2, "id": "f66aef89", "metadata": {}, "outputs": [ { "data": { "text/html": 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"paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Situation de la liberté de la presse dans le monde" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.choropleth(df_2022, locations='ISO_Code', color='Situation', hover_name = df_2022.index, hover_data=[\"Position_2022\", \"Region\"], title='Situation de la liberté de la presse dans le monde')\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "1816753d", "metadata": {}, "source": [ "## Moyennes des position sur l'année 2022 par région" ] }, { "cell_type": "code", "execution_count": 4, "id": "c35f8cde", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Asia Pacific\n", "112.5\n", "Europe\n", "50.255319148936174\n", "Arab States\n", "129.44444444444446\n", "Africa\n", "92.37209302325581\n", "South America\n", "82.875\n", "North America\n", "30.5\n", "Middle East\n", "150.06666666666666\n", "South al America\n", "160.0\n" ] } ], "source": [ "for i in df_2022['Region'].unique():\n", " print(i)\n", " dfi = df_2022[(df_2022.Region == i)]\n", " print(dfi['Position_2022'].mean())" ] }, { "cell_type": "markdown", "id": "89df3e7e", "metadata": {}, "source": [ " ## \"Meilleur\" et \"pire\" pays du classement" ] }, { "cell_type": "code", "execution_count": 5, "id": "e334ce6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country ISO_Code Region Position_2022 Position_2021 Global_Score \n", "121 Norway NOR Europe 1 1 92.65 \\\n", "\n", " Politic_Score Economic_Score Legislative_Score Social_Score \n", "121 94.89 90.38 92.23 93.71 \\\n", "\n", " Security_Score Journalist_Killed Media_Workers_Killed \n", "121 92.03 0 0 \\\n", "\n", " Journalist_Imprisoned Media_Workers_Imprisoned Situation \n", "121 0 0 Good \n", " Country ISO_Code Region Position_2022 Position_2021 \n", "118 North Korea PRK Asia Pacific 180 179 \\\n", "\n", " Global_Score Politic_Score Economic_Score Legislative_Score \n", "118 13.92 22.42 0.0 22.81 \\\n", "\n", " Social_Score Security_Score Journalist_Killed Media_Workers_Killed \n", "118 12.0 12.38 0 0 \\\n", "\n", " Journalist_Imprisoned Media_Workers_Imprisoned Situation \n", "118 0 0 Very Serious \n" ] } ], "source": [ "df_2022 = df_2022.sort_values(by='Position_2022')\n", "print(df_2022.head(1)) # Norvège\n", "print(df_2022.tail(1)) # Corée du nord" ] }, { "cell_type": "markdown", "id": "d3504fdb", "metadata": {}, "source": [ "## Meilleure et pire progression de la liberté de la presse " ] }, { "cell_type": "code", "execution_count": 6, "id": "fccc5e83", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "No axis named 2 for object type Series", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(cls, axis)\u001b[0m\n\u001b[0;32m 513\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_AXIS_TO_AXIS_NUMBER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 514\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"No axis named {axis} for object type {cls.__name__}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 2", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10400\\604722169.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdfprog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_2022\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Position_2021'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdf_2022\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Position_2022'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdfprog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_2022\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Country'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfprog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[0mres_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_op_result_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(cls, axis)\u001b[0m\n\u001b[0;32m 511\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAxis\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mAxisInt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 512\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 513\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_AXIS_TO_AXIS_NUMBER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 514\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"No axis named {axis} for object type {cls.__name__}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mValueError\u001b[0m: No axis named 2 for object type Series" ] } ], "source": [ "dfprog = df_2022['Position_2021'] - df_2022['Position_2022']\n", "dfprog.add(df_2022['Country'],axis=2)\n", "print(dfprog)\n", "print(dfprog.head())\n", "print(dfprog.tail())" ] }, { "cell_type": "markdown", "id": "51d34c61", "metadata": {}, "source": [ "## Pays avec le plus de journalistes tués" ] }, { "cell_type": "code", "execution_count": 7, "id": "d2e0b02b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Journalistes tués en fonction des pays (les 5 pays où le nombre de journalistes tués en 2022 est le plus élevé) :\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jkilledtop5 = df_2022.sort_values(by=['Journalist_Killed'])\n", "jkilledtop5 = jkilledtop5.tail(5)\n", "print(\"Journalistes tués en fonction des pays (les 5 pays où le nombre de journalistes tués en 2022 est le plus élevé) :\")\n", "plt.figure()\n", "plt.plot(jkilledtop5[\"Country\"],jkilledtop5[\"Journalist_Killed\"],'r')\n", "plt.plot(jkilledtop5[\"Country\"],jkilledtop5[\"Journalist_Killed\"],'bx')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "56cf49d6", "metadata": {}, "outputs": [], "source": [ "print(df_2022)" ] }, { "cell_type": "code", "execution_count": null, "id": "8f0e74da", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "34fccbe0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "590c7dfe", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5765b4bd", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 5 }