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data_prep.py
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data_prep.py
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{
"cells": [
{
"attachments": {
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"
}
},
"cell_type": "markdown",
"id": "7b4770b5",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "649ebe7e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logo color hex: #1fa7ee\n"
]
}
],
"source": [
"# import librairies needed for project\n",
"import missingno as msno\n",
"import numpy as np\n",
"import pandas as pd\n",
"import mysql.connector as connector\n",
"from mysql.connector import Error\n",
"\n",
"# import user-created functions\n",
"from functions import *\n",
"\n",
"# maximise dataframe display\n",
"pd.set_option('display.max_colwidth', None)\n",
"\n",
"# remove scientific notation format for large numbers in dataframes\n",
"pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
"\n",
"# use company logo colour in graphs\n",
"col = findColor('dwfa_blue.png')\n",
"print(\"Logo color hex:\", col)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4fe82ae6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection to DWFA database successful !\n"
]
}
],
"source": [
"# initiate connection to database\n",
"try:\n",
" connection = connector.connect(\n",
" host=\"127.0.0.1\",\n",
" port=3306,\n",
" user=\"root\",\n",
" password=\"1234\",\n",
" auth_plugin='mysql_native_password',\n",
" database=\"dwfa\",\n",
" autocommit=True)\n",
" if connection.is_connected():\n",
" print(\"Connection to DWFA database successful !\")\n",
" \n",
"except Error as e:\n",
" print(\"Error while connecting to DWFA database\", e)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8e05177e",
"metadata": {},
"outputs": [],
"source": [
"cursor = connection.cursor()"
]
},
{
"cell_type": "markdown",
"id": "815cd5a1",
"metadata": {},
"source": [
"### 1 - table countries"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b3f53f83",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>REGION (DISPLAY)</th>\n",
" <th>COUNTRY (DISPLAY)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>194</td>\n",
" <td>194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>6</td>\n",
" <td>194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Europe</td>\n",
" <td>Albania</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" REGION (DISPLAY) COUNTRY (DISPLAY)\n",
"count 194 194\n",
"unique 6 194\n",
"top Europe Albania\n",
"freq 53 1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import file - source : \n",
"countries = pd.read_csv('countries.csv', encoding='latin_1')\n",
"countries.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "815c1f34",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(countries)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "04707a7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are no null values in the table.\n"
]
}
],
"source": [
"# count number of null values in countries dataframe\n",
"nulls_c = countries.isna().sum().sum()\n",
"if nulls_c == 0:\n",
" print(\"There are no null values in the table.\")\n",
"else:\n",
" print (\"There are \", nulls_c, \"null values in the table.\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d3321cc6",
"metadata": {},
"outputs": [],
"source": [
"# rename columns\n",
"countries.rename(columns={'REGION (DISPLAY)': 'region', 'COUNTRY (DISPLAY)':'country_name'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "19e7f8b4",
"metadata": {},
"outputs": [],
"source": [
"# type casting\n",
"countries['country_name'] = countries['country_name'].astype('string')\n",
"countries['region'] = countries['region'].astype('string')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a369a99e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Il y a 194 pays dans la liste.\n",
"Aucun doublon dans la table.\n"
]
}
],
"source": [
"print (\"Il y a\",countries['country_name'].nunique(), \"pays dans la liste.\")\n",
"if (countries.loc[countries.duplicated() == True].shape[0]) != 0:\n",
" print(\"Doublons dans la table.\")\n",
"else:\n",
" print (\"Aucun doublon dans la table.\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f195f881",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"country_name est une clé candidate.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check if country name is a primary key\n",
"isCandKey(countries, 'country_name')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f93110ed",
"metadata": {},
"outputs": [],
"source": [
"# reorder columns to set primary key as first column\n",
"countries_final = countries[['country_name','region']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "cd8fad0a",
"metadata": {},
"outputs": [],
"source": [
"# export df to csv file\n",
"countries_final.to_csv('C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/DWFA_countries_final.csv', index=False, encoding='latin_1')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "948e99a9",
"metadata": {},
"outputs": [],
"source": [
"# load data into database\n",
"load_countries = \"LOAD DATA INFILE 'C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/DWFA_countries_final.csv' INTO TABLE countries CHARACTER SET latin1 FIELDS TERMINATED BY ','\"\n",
"cursor.execute(load_countries)"
]
},
{
"cell_type": "markdown",
"id": "52e1bf62",
"metadata": {},
"source": [
"### 2 - table political_stability"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f6d9de7b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Political_Stability</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3526.000</td>\n",
" <td>3526.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2009.522</td>\n",
" <td>-0.051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.256</td>\n",
" <td>0.996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2000.000</td>\n",
" <td>-3.310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2005.000</td>\n",
" <td>-0.710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2010.000</td>\n",
" <td>0.050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2014.000</td>\n",
" <td>0.798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2018.000</td>\n",
" <td>1.970</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Political_Stability\n",
"count 3526.000 3526.000\n",
"mean 2009.522 -0.051\n",
"std 5.256 0.996\n",
"min 2000.000 -3.310\n",
"25% 2005.000 -0.710\n",
"50% 2010.000 0.050\n",
"75% 2014.000 0.798\n",
"max 2018.000 1.970"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import file - source : \n",
"political_stability = pd.read_csv('political_stability.csv', encoding='latin_1')\n",
"political_stability.describe()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "51c60cf6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(political_stability)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "380be499",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are no null values in the table.\n"
]
}
],
"source": [
"# count number of null values in countries dataframe\n",
"nulls_ps = political_stability.isna().sum().sum()\n",
"if nulls_ps == 0:\n",
" print(\"There are no null values in the table.\")\n",
"else:\n",
" print (\"There are \", nulls_ps, \"null values in the table.\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "01076b4e",
"metadata": {},
"outputs": [],
"source": [
"# rename columns\n",
"political_stability.rename(columns={'Country': 'ps_country_name', 'Year':'ps_year', 'Political_Stability':'political_stability'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a8b943c5",
"metadata": {},
"outputs": [],
"source": [
"# type casting\n",
"political_stability['ps_country_name'] = political_stability['ps_country_name'].astype('string')\n",
"political_stability['ps_year'] = political_stability['ps_year'].astype('int')\n",
"political_stability['political_stability'] = political_stability['political_stability'].astype('float')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "5f89f805",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Il y a 200 pays dans la liste.\n",
"Aucun doublon dans la table.\n"
]
}
],
"source": [
"print (\"Il y a\",political_stability['ps_country_name'].nunique(), \"pays dans la liste.\")\n",
"if (political_stability.loc[political_stability.duplicated() == True].shape[0]) != 0:\n",
" print(\"Doublons dans la table.\")\n",
"else:\n",
" print (\"Aucun doublon dans la table.\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9964961d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pays ayant un indice de stabilite politique mais absents du referentiel pays:\n",
"['American Samoa', 'Bermuda', 'China, Hong Kong SAR', 'China, Macao SAR', 'China, mainland', 'China, Taiwan Province of', 'Greenland', 'North Macedonia', 'Palestine', 'Puerto Rico']\n"
]
}
],
"source": [
"print(\"Pays ayant un indice de stabilite politique mais absents du referentiel pays:\")\n",
"countries_out = isInList(political_stability['ps_country_name'].unique().tolist(), countries['country_name'].unique().tolist())\n",
"print(countries_out)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "7f8b67dd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pays du referentiel n'ayant pas d'indice de stabilite politique:\n"
]
},
{
"data": {
"text/plain": [
"['China', 'Monaco', 'Republic of North Macedonia', 'San Marino']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Pays du referentiel n'ayant pas d'indice de stabilite politique:\")\n",
"isInList(countries['country_name'].unique().tolist(), political_stability['ps_country_name'].unique().tolist())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "21d50ca5",
"metadata": {},
"outputs": [],
"source": [
"# replace China, mainland with its correct name in countries list\n",
"political_stability.loc[(political_stability['ps_country_name'] == 'China, mainland'), 'ps_country_name'] = 'China'"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "577f329c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pays du referentiel n'ayant pas d'indice de stabilite politique:\n",
"['American Samoa', 'Bermuda', 'China, Hong Kong SAR', 'China, Macao SAR', 'China, mainland', 'China, Taiwan Province of', 'Greenland', 'North Macedonia', 'Palestine', 'Puerto Rico']\n"
]
}
],
"source": [
"print(\"Pays du referentiel n'ayant pas d'indice de stabilite politique:\")\n",
"isInList(countries['country_name'].unique().tolist(), political_stability['ps_country_name'].unique().tolist())\n",
"print(countries_out)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b49a83f3",
"metadata": {},
"outputs": [],
"source": [
"# remove countries for which political stability index is missing\n",
"political_stability_trim = political_stability.loc[(political_stability['ps_country_name'].isin(countries_out) == False)].copy()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "98879320",
"metadata": {},
"outputs": [],
"source": [
"# reorder columns to set primary key as first column\n",
"political_stability_final = political_stability_trim[['ps_country_name', 'ps_year','political_stability']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "f49fb184",
"metadata": {},
"outputs": [],
"source": [
"# export df to csv file\n",
"political_stability_final.to_csv('C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/DWFA_political_stability_final.csv', index=False, encoding='latin_1')"
]
},
{
"cell_type": "markdown",
"id": "8538ef7b",
"metadata": {},
"source": [
"### 3 - table population"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "62e9f2cc",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>20914.000</td>\n",
" <td>20914.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2009.047</td>\n",
" <td>22531.641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.479</td>\n",
" <td>100016.851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2000.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2004.000</td>\n",
" <td>348.346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2009.000</td>\n",
" <td>3016.337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2014.000</td>\n",
" <td>11150.426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2018.000</td>\n",
" <td>1459377.612</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Population\n",
"count 20914.000 20914.000\n",
"mean 2009.047 22531.641\n",
"std 5.479 100016.851\n",
"min 2000.000 0.000\n",
"25% 2004.000 348.346\n",
"50% 2009.000 3016.337\n",
"75% 2014.000 11150.426\n",
"max 2018.000 1459377.612"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import file - source : \n",
"pop = pd.read_csv('population.csv', encoding='latin_1')\n",
"pop.describe()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "095c3dcc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(pop)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "9ab9a866",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are no null values in the table.\n"
]
}
],
"source": [
"# count number of null values in countries dataframe\n",
"nulls_pop = pop.isna().sum().sum()\n",
"if nulls_pop == 0:\n",
" print(\"There are no null values in the table.\")\n",
"else:\n",
" print (\"There are \", nulls_pop, \"null values in the table.\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "9d755cd9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Granularity</th>\n",
" <th>Country</th>\n",
" <th>Year</th>\n",
" <th>Female</th>\n",
" <th>Male</th>\n",
" <th>Rural</th>\n",
" <th>Total</th>\n",
" <th>Urban</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>2000</td>\n",
" <td>10090.449</td>\n",
" <td>10689.508</td>\n",
" <td>15657.474</td>\n",
" <td>20779.953</td>\n",
" <td>4436.282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>2001</td>\n",
" <td>10489.238</td>\n",
" <td>11117.754</td>\n",
" <td>16318.324</td>\n",
" <td>21606.988</td>\n",
" <td>4648.139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>2002</td>\n",
" <td>10958.668</td>\n",
" <td>11642.106</td>\n",
" <td>17086.910</td>\n",
" <td>22600.770</td>\n",
" <td>4893.013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>2003</td>\n",
" <td>11466.237</td>\n",
" <td>12214.634</td>\n",
" <td>17909.063</td>\n",
" <td>23680.871</td>\n",
" <td>5155.788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>2004</td>\n",
" <td>11962.963</td>\n",
" <td>12763.726</td>\n",
" <td>18692.107</td>\n",
" <td>24726.684</td>\n",
" <td>5426.872</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Granularity Country Year Female Male Rural Total \\\n",
"0 Afghanistan 2000 10090.449 10689.508 15657.474 20779.953 \n",
"1 Afghanistan 2001 10489.238 11117.754 16318.324 21606.988 \n",
"2 Afghanistan 2002 10958.668 11642.106 17086.910 22600.770 \n",
"3 Afghanistan 2003 11466.237 12214.634 17909.063 23680.871 \n",
"4 Afghanistan 2004 11962.963 12763.726 18692.107 24726.684 \n",
"\n",
"Granularity Urban \n",
"0 4436.282 \n",
"1 4648.139 \n",
"2 4893.013 \n",
"3 5155.788 \n",
"4 5426.872 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# un-pivot data\n",
"pop_trim = pop.pivot(index=['Country','Year'], columns='Granularity', values='Population').reset_index()\n",
"pop_trim.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "c16454b1",
"metadata": {},
"outputs": [],
"source": [
"# change units\n",
"pop_trim['female_population'] = pop_trim['Female'] * 1000\n",
"pop_trim['male_population'] = pop_trim['Male'] * 1000\n",
"pop_trim['rural_population'] = pop_trim['Rural'] * 1000\n",
"pop_trim['urban_population'] = pop_trim['Urban'] * 1000\n",
"pop_trim.rename(columns={'Country': 'p_country_name', 'Year':'p_year'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "fc893867",
"metadata": {},
"outputs": [],
"source": [
"# reorder columns\n",
"population = pop_trim[['p_country_name', 'p_year', 'female_population','male_population','rural_population','urban_population']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "13cd7852",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pays ayant des donnees de population mais absents du referentiel pays:\n",
"['American Samoa', 'Anguilla', 'Aruba', 'Bermuda', 'Bonaire, Sint Eustatius and Saba', 'British Virgin Islands', 'Cayman Islands', 'Channel Islands', 'China, Hong Kong SAR', 'China, Macao SAR', 'China, Taiwan Province of', 'China, mainland', 'Curaçao', 'Falkland Islands (Malvinas)', 'Faroe Islands', 'French Guyana', 'French Polynesia', 'Gibraltar', 'Greenland', 'Guadeloupe', 'Guam', 'Holy See', 'Isle of Man', 'Liechtenstein', 'Martinique', 'Mayotte', 'Montserrat', 'Netherlands Antilles (former)', 'New Caledonia', 'North Macedonia', 'Northern Mariana Islands', 'Palestine', 'Puerto Rico', 'Réunion', 'Saint Barthélemy', 'Saint Helena, Ascension and Tristan da Cunha', 'Saint Pierre and Miquelon', 'Saint-Martin (French part)', 'Serbia and Montenegro', 'Sint Maarten (Dutch part)', 'Sudan (former)', 'Tokelau', 'Turks and Caicos Islands', 'United States Virgin Islands', 'Wallis and Futuna Islands', 'Western Sahara']\n"
]
}
],
"source": [
"print(\"Pays ayant des donnees de population mais absents du referentiel pays:\")\n",
"countries_out_2 = isInList(population['p_country_name'].unique().tolist(), countries['country_name'].unique().tolist())\n",
"print(countries_out_2)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "87b22ed0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pays du referentiel n'ayant pas de donnees de population:\n"
]