367 lines
266 KiB
Plaintext
367 lines
266 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Gamma function\n",
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"from scipy.special import gamma\n",
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"\n",
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"# To calculate statistics\n",
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"from scipy.stats import norm\n",
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"from scipy.stats import hmean, trim_mean, iqr, median_abs_deviation, skew, kurtosis\n",
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"from scipy.stats.mstats import gmean, winsorize"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Reading and preprocessing data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"DataWhr2024 = pd.read_csv(\"DataWhr2024.csv\")\n",
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"UnM49 = pd.read_csv(\"UnM49.csv\", sep=';')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"UnM49 = UnM49[['Country or Area', 'Sub-region Name', 'Region Name']]\n",
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"UnM49 = UnM49.rename({'Country or Area':'Country name', 'Sub-region Name':'Subregion', 'Region Name':'Continent'}, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Hong\"), \"Country name\"] = \"Hong Kong\"\n",
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"DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Somaliland\"), \"Country name\"] = \"Somaliland\"\n",
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"DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Taiwan\"), \"Country name\"] = \"Taiwan\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"UnM49.loc[97, \"Country name\"] = \"Bolivia\"\n",
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"UnM49.loc[33, \"Country name\"] = \"Congo (Brazzaville)\"\n",
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"UnM49.loc[34, \"Country name\"] = \"Congo (Kinshasa)\"\n",
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"UnM49.loc[124, \"Country name\"] = \"Hong Kong\"\n",
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"UnM49.loc[125, \"Country name\"] = \"Macao\"\n",
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"UnM49.loc[126, \"Country name\"] = \"North Korea\"\n",
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"UnM49.loc[145, \"Country name\"] = \"Iran\"\n",
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"UnM49.loc[46, \"Country name\"] = \"Ivory Coast\"\n",
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"UnM49.loc[133, \"Country name\"] = \"Laos\"\n",
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"UnM49.loc[129, \"Country name\"] = \"South Korea\"\n",
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"UnM49.loc[173, \"Country name\"] = \"Moldova\"\n",
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"UnM49.loc[217, \"Country name\"] = \"Netherlands\"\n",
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"UnM49.loc[175, \"Country name\"] = \"Russia\"\n",
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"UnM49.loc[164, \"Country name\"] = \"Syria\"\n",
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"UnM49.loc[26, \"Country name\"] = \"Tanzania\"\n",
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"UnM49.loc[116, \"Country name\"] = \"United States\"\n",
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"UnM49.loc[193, \"Country name\"] = \"United Kingdom\"\n",
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"UnM49.loc[111, \"Country name\"] = \"Venezuela\"\n",
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"UnM49.loc[140, \"Country name\"] = \"Vietnam\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"_ = pd.DataFrame(\n",
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" {\n",
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" \"Country name\": [\"Kosovo\", \"Somaliland\", \"Taiwan\"],\n",
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" \"Subregion\": [\"Southern Europe\", \"Sub-Saharan Africa\", \"Eastern Asia\"],\n",
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" \"Continent\": [\"Europe\", \"Africa\", \"Asia\"],\n",
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" }\n",
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")\n",
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"\n",
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"UnM49 = pd.concat([UnM49, _], axis=0)\n",
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"UnM49 = UnM49.reset_index(drop=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data\n",
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"Dat = pd.merge(DataWhr2024, UnM49)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data of 2023\n",
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"Dat2023 = Dat[Dat['year'] == 2023]\n",
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"Dat2023 = Dat2023.reset_index(drop=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Iceland',\n",
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" 'India',\n",
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" 'Indonesia',\n",
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" 'Iran',\n",
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" 'Iraq',\n",
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" 'Ireland',\n",
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" 'Israel',\n",
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" 'Italy',\n",
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" 'Ivory Coast']"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Countries that starts with the same letter that your name\n",
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"StartsWith = 'I' # The first letter of your name\n",
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"list(Dat[Dat['Country name'].str.startswith(StartsWith)]['Country name'].unique())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data of 2023 from the region selected\n",
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"CountrySelected = 'Thailand' # Change to the country that you selected\n",
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"SubregionSelected = Dat[Dat['Country name']==CountrySelected]['Subregion'].unique()[0]\n",
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"\n",
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"DatSelected = Dat2023[Dat2023['Subregion']==SubregionSelected]\n",
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"DatSelected = DatSelected.reset_index(drop=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"Variable = 'Life Ladder'\n",
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"DatThroughTime = Dat[['year', 'Continent' , Variable]].groupby(['year', 'Continent']).mean().reset_index()\n",
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"\n",
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"plt.figure(figsize=[10, 6])\n",
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"\n",
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"sns.lineplot(\n",
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" data = DatThroughTime,\n",
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" x = \"year\",\n",
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" y = Variable,\n",
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" hue = 'Continent',\n",
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" marker = \"o\"\n",
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")\n",
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"\n",
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"plt.title(\"Average Life Ladder from 2005 to 2023 by each continent.\")\n",
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"plt.xlabel(\"Year\")\n",
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"plt.ylabel(\"Life Ladder\")\n",
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"plt.legend(\n",
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" title = \"Continent\",\n",
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" loc = \"upper right\"\n",
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")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=[10, 6])\n",
|
||
|
|
"\n",
|
||
|
|
"sns.regplot(\n",
|
||
|
|
" x = \"Social support\",\n",
|
||
|
|
" y = \"Life Ladder\",\n",
|
||
|
|
" data = Dat2023,\n",
|
||
|
|
" scatter_kws = {'s': 50, 'color': 'blue'},\n",
|
||
|
|
" line_kws = {'color': 'green'}\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Scatter Plot of Life Ladder compared to Social Support')\n",
|
||
|
|
"plt.xlabel(\"Social Support\")\n",
|
||
|
|
"plt.ylabel(\"Life Ladder\")\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"top_5_happiest = Dat2023.nlargest(5, 'Life Ladder')\n",
|
||
|
|
"top_5_unhappiest = Dat2023.nsmallest(5, 'Life Ladder')\n",
|
||
|
|
"\n",
|
||
|
|
"combined_data = pd.concat(\n",
|
||
|
|
" [\n",
|
||
|
|
" top_5_happiest,\n",
|
||
|
|
" top_5_unhappiest\n",
|
||
|
|
" ]\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize = (10, 6))\n",
|
||
|
|
"plt.bar(combined_data['Country name'], combined_data['Life Ladder'],\n",
|
||
|
|
" color=['green'] * 5 + ['red'] * 5)\n",
|
||
|
|
"plt.xlabel('Country')\n",
|
||
|
|
"plt.ylabel('Life Ladder Score')\n",
|
||
|
|
"plt.title('Top 5 Happiest and Top 5 Unhappiest Countries in 2023')\n",
|
||
|
|
"plt.xticks(rotation=90)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"variable = 'Freedom to make life choices'\n",
|
||
|
|
"\n",
|
||
|
|
"subregion_data = DatSelected[variable].dropna()\n",
|
||
|
|
"\n",
|
||
|
|
"Mu = subregion_data.mean()\n",
|
||
|
|
"Sigma = subregion_data.std(ddof=1)\n",
|
||
|
|
"\n",
|
||
|
|
"N = len(subregion_data)\n",
|
||
|
|
"\n",
|
||
|
|
"ALPHA = 0.05\n",
|
||
|
|
"\n",
|
||
|
|
"LowerLimit = norm.ppf(ALPHA / 2, Mu, (np.sqrt(1 + 1 / N)) * Sigma)\n",
|
||
|
|
"UpperLimit = norm.ppf(1 - ALPHA / 2, Mu, (np.sqrt(1 + 1 / N)) * Sigma)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(10, 6))\n",
|
||
|
|
"\n",
|
||
|
|
"sns.kdeplot(subregion_data, label=f'KDE of {variable}')\n",
|
||
|
|
"\n",
|
||
|
|
"sns.rugplot(subregion_data, color='black')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.hlines(y = -.02, xmin = LowerLimit, xmax = UpperLimit, color = 'green')\n",
|
||
|
|
"plt.plot(Mu, -0.02, 'go')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(f'KDE for {variable}')\n",
|
||
|
|
"plt.xlabel(variable)\n",
|
||
|
|
"plt.ylabel('Density')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.12.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|