finished SYS-102 - working on HCD-201
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,winsdominoes,Fons-iMac-2.local,20.11.2024 18:02,file:///Users/winsdominoes/Library/Application%20Support/LibreOffice/4;
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,winsdominoes,Fons-iMac-2.local,19.11.2024 23:03,file:///Users/winsdominoes/Library/Application%20Support/LibreOffice/4;
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@ -808,7 +808,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"cells": [
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"cell_type": "code",
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"execution_count": 304,
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"execution_count": 545,
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{
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"cell_type": "code",
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"execution_count": 308,
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"execution_count": 546,
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"metadata": {},
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"source": [
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{
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"cell_type": "code",
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"execution_count": 309,
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"execution_count": 547,
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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', 'India', 'Indonesia', 'Iraq', 'Ireland', 'Israel', 'Italy']"
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"['Tajikistan',\n",
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" 'Thailand',\n",
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" 'Togo',\n",
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" 'Trinidad and Tobago',\n",
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" 'Tunisia',\n",
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" 'Turkmenistan',\n",
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" 'Türkiye']"
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]
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},
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"execution_count": 309,
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"execution_count": 547,
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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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"StartsWith = 'T' # 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": 310,
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{
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"cell_type": "code",
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"execution_count": 311,
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"execution_count": 549,
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"metadata": {},
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"outputs": [
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{
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@ -283,7 +289,7 @@
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"max 0.590000 0.948000 0.843000 0.516000 "
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]
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},
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"execution_count": 311,
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"execution_count": 549,
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"metadata": {},
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"output_type": "execute_result"
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{
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"cell_type": "code",
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"execution_count": 312,
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"execution_count": 550,
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"metadata": {},
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"outputs": [
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{
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"[121 rows x 13 columns]"
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]
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},
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"execution_count": 312,
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"execution_count": 550,
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{
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"cell_type": "code",
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"execution_count": 313,
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"metadata": {},
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"outputs": [
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{
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"0 Asia "
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]
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},
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"execution_count": 313,
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"execution_count": 551,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -667,7 +673,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 314,
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"execution_count": 552,
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"metadata": {},
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"outputs": [
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{
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"35 Northern Europe Europe "
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]
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},
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"execution_count": 314,
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"execution_count": 552,
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{
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"cell_type": "code",
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"execution_count": 315,
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{
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"np.float64(0.231)"
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]
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},
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"array([4.6125, 5.42 , 6.3215])"
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]
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},
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"(np.float64(-0.6037), np.float64(-0.6037))"
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"(np.float64(-0.6113), np.float64(-0.6113))"
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]
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},
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"execution_count": 328,
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"(np.float64(3.0748), np.float64(3.0748))"
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]
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"(np.float64(3.1292), np.float64(3.1292))"
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]
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},
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"execution_count": 333,
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"execution_count": 571,
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"(np.float64(0.0748), np.float64(0.0748))"
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]
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},
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"execution_count": 335,
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"execution_count": 574,
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"execution_count": 575,
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"(np.float64(0.1292), np.float64(0.1292))"
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]
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},
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"execution_count": 337,
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"execution_count": 575,
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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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"G2_excess_byhand.round(4), G2_excess.round(4)"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 7th question"
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]
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"execution_count": 576,
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Country name</th>\n",
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" <th>year</th>\n",
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" <th>Life Ladder</th>\n",
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" <th>Log GDP per capita</th>\n",
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" <th>Social support</th>\n",
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" <th>Healthy life expectancy at birth</th>\n",
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" <th>Freedom to make life choices</th>\n",
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" <th>Generosity</th>\n",
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" <th>Perceptions of corruption</th>\n",
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" <th>Positive affect</th>\n",
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" <th>Negative affect</th>\n",
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" <th>Subregion</th>\n",
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" <th>Continent</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>109</th>\n",
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" <td>Thailand</td>\n",
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" <td>2023</td>\n",
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" <td>6.282</td>\n",
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" <td>9.807</td>\n",
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" <td>0.873</td>\n",
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" <td>68.6</td>\n",
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" <td>0.926</td>\n",
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" <td>0.338</td>\n",
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" <td>0.889</td>\n",
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" <td>0.811</td>\n",
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" <td>0.217</td>\n",
|
||||
" <td>South-eastern Asia</td>\n",
|
||||
" <td>Asia</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Country name year Life Ladder Log GDP per capita Social support \\\n",
|
||||
"109 Thailand 2023 6.282 9.807 0.873 \n",
|
||||
"\n",
|
||||
" Healthy life expectancy at birth Freedom to make life choices \\\n",
|
||||
"109 68.6 0.926 \n",
|
||||
"\n",
|
||||
" Generosity Perceptions of corruption Positive affect Negative affect \\\n",
|
||||
"109 0.338 0.889 0.811 0.217 \n",
|
||||
"\n",
|
||||
" Subregion Continent \n",
|
||||
"109 South-eastern Asia Asia "
|
||||
]
|
||||
},
|
||||
"execution_count": 576,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Dat2023.loc[Dat2023[\"Country name\"] == \"Thailand\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 577,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Data of 2023 from the region selected\n",
|
||||
"CountrySelected = 'Thailand' # Change to the country that you selected\n",
|
||||
"SubregionSelected = Dat[Dat['Country name']==CountrySelected]['Subregion'].unique()[0]\n",
|
||||
"\n",
|
||||
"DatSelected = Dat2023[Dat2023['Subregion']==SubregionSelected]\n",
|
||||
"DatSelected = DatSelected.reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 578,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0.8805, 0.926 , 0.9465])"
|
||||
]
|
||||
},
|
||||
"execution_count": 578,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"p = [0.25, 0.5, 0.75]\n",
|
||||
"np.quantile(DatSelected[\"Freedom to make life choices\"], p)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 579,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.926\n",
|
||||
"0.8914285714285715\n",
|
||||
"0.8867207399346544\n",
|
||||
"0.8814869334699901\n",
|
||||
"0.8914285714285713\n",
|
||||
"0.916\n",
|
||||
"0.8914285714285715\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ll = DatSelected[\"Freedom to make life choices\"]\n",
|
||||
"\n",
|
||||
"Mean = ll.mean()\n",
|
||||
"\n",
|
||||
"print(ll.median())\n",
|
||||
"print(Mean)\n",
|
||||
"print(gmean(ll))\n",
|
||||
"print(hmean(ll))\n",
|
||||
"print(trim_mean(ll, 0.1))\n",
|
||||
"print(trim_mean(ll, 0.25))\n",
|
||||
"print(winsorize(ll).mean())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 580,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.08674899281894888\n",
|
||||
"0.09369962138460887\n",
|
||||
"0.27\n",
|
||||
"0.06599999999999995\n",
|
||||
"0.03499999999999992\n",
|
||||
"0.06481632653061224\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"S2Biased = np.var(ll)\n",
|
||||
"S2Unbiased = np.var(ll, ddof=1)\n",
|
||||
"\n",
|
||||
"S1 = np.sqrt(S2Biased)\n",
|
||||
"S2 = np.sqrt(S2Unbiased)\n",
|
||||
"R = ll.max() - ll.min()\n",
|
||||
"IQR = iqr(ll)\n",
|
||||
"MAD = median_abs_deviation(ll)\n",
|
||||
"AAD = abs(ll-ll.mean()).mean()\n",
|
||||
"\n",
|
||||
"print(S1)\n",
|
||||
"print(S2)\n",
|
||||
"print(R)\n",
|
||||
"print(IQR)\n",
|
||||
"print(MAD)\n",
|
||||
"print(AAD)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 581,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def an(n):\n",
|
||||
" return np.sqrt((n-1)/2) * gamma((n-1)/2) / gamma(n/2)\n",
|
||||
"\n",
|
||||
"def c4(n):\n",
|
||||
" return 1/an(n)\n",
|
||||
"\n",
|
||||
"# d2 for n in 2,...,25\n",
|
||||
"d2 = {2:1.128, 3:1.693, 4:2.059, 5:2.326, 6:2.534, 7:2.704, 8:2.847, 9:2.970, 10:3.078, 11:3.173, 12:3.258, 13:3.336, 14:3.407, 15:3.472, 16:3.532, 17:3.588, 18:3.640, 19:3.689, 20:3.735, 21:3.778, 22:3.819, 23:3.858, 24:3.895, 25:3.931}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 582,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"7\n",
|
||||
"0.959368788699833\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"N = len(ll)\n",
|
||||
"\n",
|
||||
"print(N)\n",
|
||||
"print(c4(N))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 583,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.09766799012879454\n",
|
||||
"0.048925873210684825\n",
|
||||
"0.051891077647695946\n",
|
||||
"0.08123521836967404\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"sigma_1 = S2 / c4(N)\n",
|
||||
"#sigma_2 = R / d2[N]\n",
|
||||
"sigma_3 = IQR / (2 * norm.ppf(0.75))\n",
|
||||
"sigma_4 = MAD / norm.ppf(0.75)\n",
|
||||
"sigma_5 = AAD * np.sqrt(np.pi/2)\n",
|
||||
"\n",
|
||||
"print(sigma_1)\n",
|
||||
"#print(sigma_2)\n",
|
||||
"print(sigma_3)\n",
|
||||
"print(sigma_4)\n",
|
||||
"print(sigma_5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 584,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m2 = S2Biased\n",
|
||||
"m3 = ((ll-Mean)**3).mean()\n",
|
||||
"\n",
|
||||
"k2 = S2Unbiased\n",
|
||||
"k3 = N**2 / ((N-1)*(N-2))*m3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 585,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"g1_byhand = m3 / m2**(3/2)\n",
|
||||
"g1 = skew(ll)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 586,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(-1.4801), np.float64(-1.4801))"
|
||||
]
|
||||
},
|
||||
"execution_count": 586,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"g1_byhand.round(4), g1.round(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 587,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"G1_byhand = k3 / k2**(3/2)\n",
|
||||
"G1 = skew(ll, bias=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 588,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(-1.9184), np.float64(-1.9184))"
|
||||
]
|
||||
},
|
||||
"execution_count": 588,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"G1_byhand.round(4), G1.round(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m4 = ((ll-Mean)**4).mean()\n",
|
||||
"\n",
|
||||
"k4 = N**2*((N+1)*m4 - 3*(N-1)*m2**2) / ((N-1)*(N-2)*(N-3))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Coefficient of kurtosis based on central moments"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"g2_byhand = m4/m2**2\n",
|
||||
"g2 = kurtosis(ll, fisher=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(3.9011), np.float64(3.9011))"
|
||||
]
|
||||
},
|
||||
"execution_count": 535,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"g2_byhand.round(4), g2.round(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"G2_byhand = k4/k2**2 + 3\n",
|
||||
"G2 = kurtosis(ll, fisher=False, bias=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(6.9626), np.float64(6.9626))"
|
||||
]
|
||||
},
|
||||
"execution_count": 537,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"G2_byhand.round(4), G2.round(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"g2_excess_byhand = g2_byhand - 3\n",
|
||||
"g2_excess = kurtosis(ll)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(0.9011), np.float64(0.9011))"
|
||||
]
|
||||
},
|
||||
"execution_count": 539,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"g2_excess_byhand.round(4), g2_excess.round(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"G2_excess_byhand = G2_byhand - 3\n",
|
||||
"G2_excess = kurtosis(ll, bias=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(3.9626), np.float64(3.9626))"
|
||||
]
|
||||
},
|
||||
"execution_count": 541,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1173,7 +1656,7 @@
|
|||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
|
|||
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Loading…
Reference in New Issue