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marketresearch.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 160,
"id": "e29fb7dd-9cb7-4c6c-898c-8f409f10a519",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn import linear_model\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.cluster import KMeans\n",
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
"from sklearn.cluster import AgglomerativeClustering"
]
},
{
"cell_type": "code",
"execution_count": 161,
"id": "228ce577-eaa5-4ca8-bd29-90c3a3d705ae",
"metadata": {},
"outputs": [],
"source": [
"foodavai = pd.read_csv(r'C:\\Users\\data\\DisponibiliteAlimentaire_2017.csv')"
]
},
{
"cell_type": "code",
"execution_count": 162,
"id": "3d7c9398-b9f4-43d5-a28e-b3c1fe9a5ca4",
"metadata": {},
"outputs": [],
"source": [
"gdp = pd.read_csv(r'C:\\Users\\data\\pib_hab.csv')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"id": "51ac992e-841b-4e27-9ec2-b2c22bb7b9a2",
"metadata": {},
"outputs": [],
"source": [
"population0018 = pd.read_csv(r'C:\\Users\\data\\Population_2000_2018.csv')"
]
},
{
"cell_type": "code",
"execution_count": 164,
"id": "4ac24e68-bad6-4bba-a150-479641e0c7db",
"metadata": {},
"outputs": [],
"source": [
"stabpolitic = pd.read_csv(r'C:\\Users\\data\\stabilité politique.csv')"
]
},
{
"cell_type": "markdown",
"id": "c6cc8853-3af4-48a8-9569-a676c13fb14c",
"metadata": {},
"source": [
"## Merge all data"
]
},
{
"cell_type": "markdown",
"id": "3fbbad1a-d8dc-4f42-adba-2be102e51574",
"metadata": {},
"source": [
"### 1. Merge foodavai and population0018"
]
},
{
"cell_type": "code",
"execution_count": 165,
"id": "e041c3f0-31ef-4fb2-8179-391776a8e4e1",
"metadata": {},
"outputs": [],
"source": [
"food_population = pd.merge(foodavai, population0018, on=['Year','Zone'], how='outer', indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 166,
"id": "7225a069-1916-4874-9cdd-7e35d8490ed1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_merge\n",
"both 176600\n",
"right_only 4237\n",
"left_only 0\n",
"Name: count, dtype: int64"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"food_population._merge.value_counts()"
]
},
{
"cell_type": "markdown",
"id": "e042558c-efaf-4b44-a219-7e6e1f7f9ede",
"metadata": {},
"source": [
"### 2. Select only product poultry meat when analzying the chance of exporting this product"
]
},
{
"cell_type": "code",
"execution_count": 167,
"id": "d28821cd-c3c6-4faa-a2fc-55acb6c079c7",
"metadata": {},
"outputs": [],
"source": [
"food_population = food_population[(food_population._merge == 'both') & (food_population.Product_x == 'Viande de Volailles')]"
]
},
{
"cell_type": "code",
"execution_count": 168,
"id": "8c3b81fc-1300-43e2-a300-580e94f14acd",
"metadata": {},
"outputs": [],
"source": [
"food_population = food_population.drop(columns='_merge')"
]
},
{
"cell_type": "markdown",
"id": "f3ee27e4-9095-4f2b-be2b-961f0a2a7b91",
"metadata": {},
"source": [
"### 3. Merge with gdp"
]
},
{
"cell_type": "code",
"execution_count": 169,
"id": "5fa42d4c-d999-46db-811d-efe3d8d2ca44",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp = pd.merge(food_population, gdp, on='Zone', how='outer', indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "db7d6aba-c42e-4dc5-999e-3f1d5083983a",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp = food_pop_gdp[food_pop_gdp._merge == 'both']"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "c6f7d510-83c6-4074-bbf6-67d915c4e34a",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp = food_pop_gdp.drop(columns='_merge')"
]
},
{
"cell_type": "markdown",
"id": "936b7519-c7c2-4a4c-a7f6-295993ecdc00",
"metadata": {},
"source": [
"### 4. Ignore all columns include only 1 value"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "3c800ed1-534b-4e21-84a4-d01077e275cf",
"metadata": {},
"outputs": [],
"source": [
"uniquecols = food_pop_gdp.nunique() > 1"
]
},
{
"cell_type": "code",
"execution_count": 173,
"id": "819f3ba4-8a70-413c-a7e4-2a608bb625c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Zone code_x', 'Zone', 'Code Element_x', 'Element_x', 'Unit_x',\n",
" 'Value_x', 'Symbol_x', 'Description of Symbol_x', 'Zone code_y',\n",
" 'Value_y', 'Zone code', 'Value'],\n",
" dtype='object')"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniquecols[uniquecols == True].index"
]
},
{
"cell_type": "code",
"execution_count": 174,
"id": "254aee43-0ff8-4375-9804-9d973ada698f",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp = food_pop_gdp[list(uniquecols[uniquecols == True].index)]"
]
},
{
"cell_type": "code",
"execution_count": 175,
"id": "aa9c6f86-5be1-4408-8473-970d6574264e",
"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>Zone code_x</th>\n",
" <th>Zone</th>\n",
" <th>Code Element_x</th>\n",
" <th>Element_x</th>\n",
" <th>Unit_x</th>\n",
" <th>Value_x</th>\n",
" <th>Symbol_x</th>\n",
" <th>Description of Symbol_x</th>\n",
" <th>Zone code_y</th>\n",
" <th>Value_y</th>\n",
" <th>Zone code</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>5511.0</td>\n",
" <td>Production</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>28.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>2.0</td>\n",
" <td>36296.113</td>\n",
" <td>4.0</td>\n",
" <td>2058.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>5611.0</td>\n",
" <td>Importations - Quantité</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>29.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>2.0</td>\n",
" <td>36296.113</td>\n",
" <td>4.0</td>\n",
" <td>2058.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>5072.0</td>\n",
" <td>Variation de stock</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>0.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>2.0</td>\n",
" <td>36296.113</td>\n",
" <td>4.0</td>\n",
" <td>2058.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>5301.0</td>\n",
" <td>Disponibilité intérieure</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>57.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>2.0</td>\n",
" <td>36296.113</td>\n",
" <td>4.0</td>\n",
" <td>2058.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>5123.0</td>\n",
" <td>Pertes</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>2.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>2.0</td>\n",
" <td>36296.113</td>\n",
" <td>4.0</td>\n",
" <td>2058.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2056</th>\n",
" <td>181.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>5142.0</td>\n",
" <td>Nourriture</td>\n",
" <td>Milliers de tonnes</td>\n",
" <td>67.00</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" <td>181.0</td>\n",
" <td>14236.595</td>\n",
" <td>716.0</td>\n",
" <td>3795.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2057</th>\n",
" <td>181.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>645.0</td>\n",
" <td>Disponibilité alimentaire en quantité (kg/pers...</td>\n",
" <td>kg</td>\n",
" <td>4.68</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" <td>181.0</td>\n",
" <td>14236.595</td>\n",
" <td>716.0</td>\n",
" <td>3795.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>181.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>664.0</td>\n",
" <td>Disponibilité alimentaire (Kcal/personne/jour)</td>\n",
" <td>Kcal/personne/jour</td>\n",
" <td>16.00</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" <td>181.0</td>\n",
" <td>14236.595</td>\n",
" <td>716.0</td>\n",
" <td>3795.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>181.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>674.0</td>\n",
" <td>Disponibilité de protéines en quantité (g/pers...</td>\n",
" <td>g/personne/jour</td>\n",
" <td>1.59</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" <td>181.0</td>\n",
" <td>14236.595</td>\n",
" <td>716.0</td>\n",
" <td>3795.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>181.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>684.0</td>\n",
" <td>Disponibilité de matière grasse en quantité (g...</td>\n",
" <td>g/personne/jour</td>\n",
" <td>0.99</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" <td>181.0</td>\n",
" <td>14236.595</td>\n",
" <td>716.0</td>\n",
" <td>3795.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1963 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone code_x Zone Code Element_x \\\n",
"0 2.0 Afghanistan 5511.0 \n",
"1 2.0 Afghanistan 5611.0 \n",
"2 2.0 Afghanistan 5072.0 \n",
"3 2.0 Afghanistan 5301.0 \n",
"4 2.0 Afghanistan 5123.0 \n",
"... ... ... ... \n",
"2056 181.0 Zimbabwe 5142.0 \n",
"2057 181.0 Zimbabwe 645.0 \n",
"2058 181.0 Zimbabwe 664.0 \n",
"2059 181.0 Zimbabwe 674.0 \n",
"2060 181.0 Zimbabwe 684.0 \n",
"\n",
" Element_x Unit_x \\\n",
"0 Production Milliers de tonnes \n",
"1 Importations - Quantité Milliers de tonnes \n",
"2 Variation de stock Milliers de tonnes \n",
"3 Disponibilité intérieure Milliers de tonnes \n",
"4 Pertes Milliers de tonnes \n",
"... ... ... \n",
"2056 Nourriture Milliers de tonnes \n",
"2057 Disponibilité alimentaire en quantité (kg/pers... kg \n",
"2058 Disponibilité alimentaire (Kcal/personne/jour) Kcal/personne/jour \n",
"2059 Disponibilité de protéines en quantité (g/pers... g/personne/jour \n",
"2060 Disponibilité de matière grasse en quantité (g... g/personne/jour \n",
"\n",
" Value_x Symbol_x Description of Symbol_x Zone code_y Value_y \\\n",
"0 28.00 S Données standardisées 2.0 36296.113 \n",
"1 29.00 S Données standardisées 2.0 36296.113 \n",
"2 0.00 S Données standardisées 2.0 36296.113 \n",
"3 57.00 S Données standardisées 2.0 36296.113 \n",
"4 2.00 S Données standardisées 2.0 36296.113 \n",
"... ... ... ... ... ... \n",
"2056 67.00 S Données standardisées 181.0 14236.595 \n",
"2057 4.68 Fc Donnée calculée 181.0 14236.595 \n",
"2058 16.00 Fc Donnée calculée 181.0 14236.595 \n",
"2059 1.59 Fc Donnée calculée 181.0 14236.595 \n",
"2060 0.99 Fc Donnée calculée 181.0 14236.595 \n",
"\n",
" Zone code Value \n",
"0 4.0 2058.4 \n",
"1 4.0 2058.4 \n",
"2 4.0 2058.4 \n",
"3 4.0 2058.4 \n",
"4 4.0 2058.4 \n",
"... ... ... \n",
"2056 716.0 3795.6 \n",
"2057 716.0 3795.6 \n",
"2058 716.0 3795.6 \n",
"2059 716.0 3795.6 \n",
"2060 716.0 3795.6 \n",
"\n",
"[1963 rows x 12 columns]"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"food_pop_gdp"
]
},
{
"cell_type": "markdown",
"id": "a154d91a-096c-489a-8f13-ec71514ae528",
"metadata": {},
"source": [
"### 5. Rename and remove columns"
]
},
{
"cell_type": "code",
"execution_count": 176,
"id": "765ce546-e1fb-4be2-93c5-d4dd0b32dfac",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp.rename(columns={'Element_x':'Element', 'Value_x':'Value_food', 'Value_y':'Value_population', 'Value':'Value_gdp', 'Symbol_x':'Symbol', 'Description of Symbol_x':'Description of Symbol'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"id": "0d53328f-032d-4d71-9cde-1948db48a413",
"metadata": {},
"outputs": [],
"source": [
"food_pop_gdp = food_pop_gdp[['Zone','Element','Value_food','Value_population','Value_gdp','Symbol','Description of Symbol']]"
]
},
{
"cell_type": "code",
"execution_count": 178,
"id": "b80b3bf1-4b8c-4472-8c1e-6c2107cc5514",
"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>Zone</th>\n",
" <th>Element</th>\n",
" <th>Value_food</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Symbol</th>\n",
" <th>Description of Symbol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Production</td>\n",
" <td>28.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>Importations - Quantité</td>\n",
" <td>29.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>Variation de stock</td>\n",
" <td>0.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>Disponibilité intérieure</td>\n",
" <td>57.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>Pertes</td>\n",
" <td>2.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2056</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Nourriture</td>\n",
" <td>67.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2057</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Disponibilité alimentaire en quantité (kg/pers...</td>\n",
" <td>4.68</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Disponibilité alimentaire (Kcal/personne/jour)</td>\n",
" <td>16.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Disponibilité de protéines en quantité (g/pers...</td>\n",
" <td>1.59</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Disponibilité de matière grasse en quantité (g...</td>\n",
" <td>0.99</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1963 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Element \\\n",
"0 Afghanistan Production \n",
"1 Afghanistan Importations - Quantité \n",
"2 Afghanistan Variation de stock \n",
"3 Afghanistan Disponibilité intérieure \n",
"4 Afghanistan Pertes \n",
"... ... ... \n",
"2056 Zimbabwe Nourriture \n",
"2057 Zimbabwe Disponibilité alimentaire en quantité (kg/pers... \n",
"2058 Zimbabwe Disponibilité alimentaire (Kcal/personne/jour) \n",
"2059 Zimbabwe Disponibilité de protéines en quantité (g/pers... \n",
"2060 Zimbabwe Disponibilité de matière grasse en quantité (g... \n",
"\n",
" Value_food Value_population Value_gdp Symbol Description of Symbol \n",
"0 28.00 36296.113 2058.4 S Données standardisées \n",
"1 29.00 36296.113 2058.4 S Données standardisées \n",
"2 0.00 36296.113 2058.4 S Données standardisées \n",
"3 57.00 36296.113 2058.4 S Données standardisées \n",
"4 2.00 36296.113 2058.4 S Données standardisées \n",
"... ... ... ... ... ... \n",
"2056 67.00 14236.595 3795.6 S Données standardisées \n",
"2057 4.68 14236.595 3795.6 Fc Donnée calculée \n",
"2058 16.00 14236.595 3795.6 Fc Donnée calculée \n",
"2059 1.59 14236.595 3795.6 Fc Donnée calculée \n",
"2060 0.99 14236.595 3795.6 Fc Donnée calculée \n",
"\n",
"[1963 rows x 7 columns]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"food_pop_gdp"
]
},
{
"cell_type": "markdown",
"id": "9863c11d-2830-48f1-a1c9-39ca914b7dd3",
"metadata": {},
"source": [
"### 6. Merge with stabpolitic"
]
},
{
"cell_type": "code",
"execution_count": 179,
"id": "1b57480d-90cf-4054-a2e3-0107b60ddd73",
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(food_pop_gdp, stabpolitic, on='Zone', how='outer', indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"id": "d68e6063-e33e-4a57-8467-21cfa36a26f9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_merge\n",
"both 1963\n",
"right_only 33\n",
"left_only 0\n",
"Name: count, dtype: int64"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df._merge.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 181,
"id": "04a86f34-63fd-4bab-b078-b116bcaf8288",
"metadata": {},
"outputs": [],
"source": [
"df = df[df._merge == 'both']"
]
},
{
"cell_type": "code",
"execution_count": 182,
"id": "b19ebacd-e7e9-4dc2-83bd-ad22228b9f60",
"metadata": {},
"outputs": [],
"source": [
"df_uniquecols = df.nunique() > 1"
]
},
{
"cell_type": "code",
"execution_count": 183,
"id": "0c58afd2-f059-439c-a747-7b0e16ece323",
"metadata": {},
"outputs": [],
"source": [
"df = df[list(df_uniquecols[df_uniquecols == True].index)]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"id": "9f0c82d2-cffc-468d-9838-9ac64d6e1ef1",
"metadata": {},
"outputs": [],
"source": [
"df = df[['Zone','Value_food','Value_population','Value_gdp','Value','Element_x','Symbol_x','Description of Symbol_x']]"
]
},
{
"cell_type": "code",
"execution_count": 185,
"id": "a6f7d5c5-93a3-40a0-8af8-b031945b7f60",
"metadata": {},
"outputs": [],
"source": [
"df.rename(columns={'Element_x':'Element', 'Value':'Value_politicstab', 'Symbol_x':'Symbol', 'Description of Symbol_x':'Description of Symbol'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "69620f32-d97a-4016-a558-b31aa3b4eb5f",
"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>Zone</th>\n",
" <th>Value_food</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Element</th>\n",
" <th>Symbol</th>\n",
" <th>Description of Symbol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>28.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>Production</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>29.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>Importations - Quantité</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>0.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>Variation de stock</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>57.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>Disponibilité intérieure</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>2.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>Pertes</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>Zimbabwe</td>\n",
" <td>67.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>Nourriture</td>\n",
" <td>S</td>\n",
" <td>Données standardisées</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>Zimbabwe</td>\n",
" <td>4.68</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>Disponibilité alimentaire en quantité (kg/pers...</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>Zimbabwe</td>\n",
" <td>16.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>Disponibilité alimentaire (Kcal/personne/jour)</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>Zimbabwe</td>\n",
" <td>1.59</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>Disponibilité de protéines en quantité (g/pers...</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>Zimbabwe</td>\n",
" <td>0.99</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>Disponibilité de matière grasse en quantité (g...</td>\n",
" <td>Fc</td>\n",
" <td>Donnée calculée</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1963 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_food Value_population Value_gdp Value_politicstab \\\n",
"0 Afghanistan 28.00 36296.113 2058.4 -2.80 \n",
"1 Afghanistan 29.00 36296.113 2058.4 -2.80 \n",
"2 Afghanistan 0.00 36296.113 2058.4 -2.80 \n",
"3 Afghanistan 57.00 36296.113 2058.4 -2.80 \n",
"4 Afghanistan 2.00 36296.113 2058.4 -2.80 \n",
"... ... ... ... ... ... \n",
"1958 Zimbabwe 67.00 14236.595 3795.6 -0.71 \n",
"1959 Zimbabwe 4.68 14236.595 3795.6 -0.71 \n",
"1960 Zimbabwe 16.00 14236.595 3795.6 -0.71 \n",
"1961 Zimbabwe 1.59 14236.595 3795.6 -0.71 \n",
"1962 Zimbabwe 0.99 14236.595 3795.6 -0.71 \n",
"\n",
" Element Symbol \\\n",
"0 Production S \n",
"1 Importations - Quantité S \n",
"2 Variation de stock S \n",
"3 Disponibilité intérieure S \n",
"4 Pertes S \n",
"... ... ... \n",
"1958 Nourriture S \n",
"1959 Disponibilité alimentaire en quantité (kg/pers... Fc \n",
"1960 Disponibilité alimentaire (Kcal/personne/jour) Fc \n",
"1961 Disponibilité de protéines en quantité (g/pers... Fc \n",
"1962 Disponibilité de matière grasse en quantité (g... Fc \n",
"\n",
" Description of Symbol \n",
"0 Données standardisées \n",
"1 Données standardisées \n",
"2 Données standardisées \n",
"3 Données standardisées \n",
"4 Données standardisées \n",
"... ... \n",
"1958 Données standardisées \n",
"1959 Donnée calculée \n",
"1960 Donnée calculée \n",
"1961 Donnée calculée \n",
"1962 Donnée calculée \n",
"\n",
"[1963 rows x 8 columns]"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "75cadf2b-4423-4dd2-bb79-9aee573a7a5c",
"metadata": {},
"source": [
"### Unpivot column Element_x"
]
},
{
"cell_type": "code",
"execution_count": 187,
"id": "149e430a-74d2-4498-a56b-3d676e79fc10",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Production', 'Importations - Quantité', 'Variation de stock',\n",
" 'Disponibilité intérieure', 'Pertes', 'Résidus', 'Nourriture',\n",
" 'Disponibilité alimentaire en quantité (kg/personne/an)',\n",
" 'Disponibilité alimentaire (Kcal/personne/jour)',\n",
" 'Disponibilité de protéines en quantité (g/personne/jour)',\n",
" 'Disponibilité de matière grasse en quantité (g/personne/jour)',\n",
" 'Exportations - Quantité', 'Alimentation pour touristes',\n",
" 'Traitement', 'Autres utilisations (non alimentaire)',\n",
" 'Aliments pour animaux', 'Semences'], dtype=object)"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Element.unique()"
]
},
{
"cell_type": "code",
"execution_count": 188,
"id": "7e7aaf6d-9f85-4d32-9d9f-240d812ed051",
"metadata": {},
"outputs": [],
"source": [
"df = pd.get_dummies(df, columns=df.columns[5:], prefix='', prefix_sep='', dtype=int)"
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "fc56c36d-3465-43c8-b674-d4a04a082e2c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" 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>Zone</th>\n",
" <th>Value_food</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
" <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
" <th>...</th>\n",
" <th>Pertes</th>\n",
" <th>Production</th>\n",
" <th>Résidus</th>\n",
" <th>Semences</th>\n",
" <th>Traitement</th>\n",
" <th>Variation de stock</th>\n",
" <th>Fc</th>\n",
" <th>S</th>\n",
" <th>Donnée calculée</th>\n",
" <th>Données standardisées</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>28.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>29.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>0.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>57.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>2.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>Zimbabwe</td>\n",
" <td>67.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>Zimbabwe</td>\n",
" <td>4.68</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>Zimbabwe</td>\n",
" <td>16.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>Zimbabwe</td>\n",
" <td>1.59</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>Zimbabwe</td>\n",
" <td>0.99</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1963 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_food Value_population Value_gdp Value_politicstab \\\n",
"0 Afghanistan 28.00 36296.113 2058.4 -2.80 \n",
"1 Afghanistan 29.00 36296.113 2058.4 -2.80 \n",
"2 Afghanistan 0.00 36296.113 2058.4 -2.80 \n",
"3 Afghanistan 57.00 36296.113 2058.4 -2.80 \n",
"4 Afghanistan 2.00 36296.113 2058.4 -2.80 \n",
"... ... ... ... ... ... \n",
"1958 Zimbabwe 67.00 14236.595 3795.6 -0.71 \n",
"1959 Zimbabwe 4.68 14236.595 3795.6 -0.71 \n",
"1960 Zimbabwe 16.00 14236.595 3795.6 -0.71 \n",
"1961 Zimbabwe 1.59 14236.595 3795.6 -0.71 \n",
"1962 Zimbabwe 0.99 14236.595 3795.6 -0.71 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"... ... ... \n",
"1958 0 0 \n",
"1959 0 0 \n",
"1960 0 0 \n",
"1961 0 0 \n",
"1962 0 0 \n",
"\n",
" Autres utilisations (non alimentaire) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1958 0 \n",
"1959 0 \n",
"1960 0 \n",
"1961 0 \n",
"1962 0 \n",
"\n",
" Disponibilité alimentaire (Kcal/personne/jour) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"... ... \n",
"1958 0 \n",
"1959 0 \n",
"1960 1 \n",
"1961 0 \n",
"1962 0 \n",
"\n",
" Disponibilité alimentaire en quantité (kg/personne/an) ... Pertes \\\n",
"0 0 ... 0 \n",
"1 0 ... 0 \n",
"2 0 ... 0 \n",
"3 0 ... 0 \n",
"4 0 ... 1 \n",
"... ... ... ... \n",
"1958 0 ... 0 \n",
"1959 1 ... 0 \n",
"1960 0 ... 0 \n",
"1961 0 ... 0 \n",
"1962 0 ... 0 \n",
"\n",
" Production Résidus Semences Traitement Variation de stock Fc S \\\n",
"0 1 0 0 0 0 0 1 \n",
"1 0 0 0 0 0 0 1 \n",
"2 0 0 0 0 1 0 1 \n",
"3 0 0 0 0 0 0 1 \n",
"4 0 0 0 0 0 0 1 \n",
"... ... ... ... ... ... .. .. \n",
"1958 0 0 0 0 0 0 1 \n",
"1959 0 0 0 0 0 1 0 \n",
"1960 0 0 0 0 0 1 0 \n",
"1961 0 0 0 0 0 1 0 \n",
"1962 0 0 0 0 0 1 0 \n",
"\n",
" Donnée calculée Données standardisées \n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 1 \n",
"... ... ... \n",
"1958 0 1 \n",
"1959 1 0 \n",
"1960 1 0 \n",
"1961 1 0 \n",
"1962 1 0 \n",
"\n",
"[1963 rows x 26 columns]"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "23f7ad19-fabe-4202-8a80-64f650ab868a",
"metadata": {},
"source": [
"### Add values from Value_x to all rows in column Element\n",
"### Remove column Value_food and Value_population"
]
},
{
"cell_type": "code",
"execution_count": 190,
"id": "f6456647-bdd3-48c5-bb7f-3f9d6f9c59a9",
"metadata": {},
"outputs": [],
"source": [
"for column in df.columns[5:]: \n",
" df[column] = df.Value_food * df[column]"
]
},
{
"cell_type": "code",
"execution_count": 191,
"id": "ee0a74cc-66fa-4901-9a21-48af88a16c1e",
"metadata": {},
"outputs": [
{
"data": {
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" .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>Zone</th>\n",
" <th>Value_food</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
" <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
" <th>...</th>\n",
" <th>Pertes</th>\n",
" <th>Production</th>\n",
" <th>Résidus</th>\n",
" <th>Semences</th>\n",
" <th>Traitement</th>\n",
" <th>Variation de stock</th>\n",
" <th>Fc</th>\n",
" <th>S</th>\n",
" <th>Donnée calculée</th>\n",
" <th>Données standardisées</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>28.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
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" <td>28.0</td>\n",
" <td>0.00</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>29.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>29.0</td>\n",
" <td>0.00</td>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>0.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>57.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>57.0</td>\n",
" <td>0.00</td>\n",
" <td>57.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>2.00</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>2.0</td>\n",
" <td>0.00</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1958</th>\n",
" <td>Zimbabwe</td>\n",
" <td>67.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>67.0</td>\n",
" <td>0.00</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1959</th>\n",
" <td>Zimbabwe</td>\n",
" <td>4.68</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.68</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.68</td>\n",
" <td>0.0</td>\n",
" <td>4.68</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>Zimbabwe</td>\n",
" <td>16.00</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16.00</td>\n",
" <td>0.0</td>\n",
" <td>16.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>Zimbabwe</td>\n",
" <td>1.59</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.59</td>\n",
" <td>0.0</td>\n",
" <td>1.59</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962</th>\n",
" <td>Zimbabwe</td>\n",
" <td>0.99</td>\n",
" <td>14236.595</td>\n",
" <td>3795.6</td>\n",
" <td>-0.71</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.99</td>\n",
" <td>0.0</td>\n",
" <td>0.99</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1963 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_food Value_population Value_gdp Value_politicstab \\\n",
"0 Afghanistan 28.00 36296.113 2058.4 -2.80 \n",
"1 Afghanistan 29.00 36296.113 2058.4 -2.80 \n",
"2 Afghanistan 0.00 36296.113 2058.4 -2.80 \n",
"3 Afghanistan 57.00 36296.113 2058.4 -2.80 \n",
"4 Afghanistan 2.00 36296.113 2058.4 -2.80 \n",
"... ... ... ... ... ... \n",
"1958 Zimbabwe 67.00 14236.595 3795.6 -0.71 \n",
"1959 Zimbabwe 4.68 14236.595 3795.6 -0.71 \n",
"1960 Zimbabwe 16.00 14236.595 3795.6 -0.71 \n",
"1961 Zimbabwe 1.59 14236.595 3795.6 -0.71 \n",
"1962 Zimbabwe 0.99 14236.595 3795.6 -0.71 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"1958 0.0 0.0 \n",
"1959 0.0 0.0 \n",
"1960 0.0 0.0 \n",
"1961 0.0 0.0 \n",
"1962 0.0 0.0 \n",
"\n",
" Autres utilisations (non alimentaire) \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"... ... \n",
"1958 0.0 \n",
"1959 0.0 \n",
"1960 0.0 \n",
"1961 0.0 \n",
"1962 0.0 \n",
"\n",
" Disponibilité alimentaire (Kcal/personne/jour) \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"... ... \n",
"1958 0.0 \n",
"1959 0.0 \n",
"1960 16.0 \n",
"1961 0.0 \n",
"1962 0.0 \n",
"\n",
" Disponibilité alimentaire en quantité (kg/personne/an) ... Pertes \\\n",
"0 0.00 ... 0.0 \n",
"1 0.00 ... 0.0 \n",
"2 0.00 ... 0.0 \n",
"3 0.00 ... 0.0 \n",
"4 0.00 ... 2.0 \n",
"... ... ... ... \n",
"1958 0.00 ... 0.0 \n",
"1959 4.68 ... 0.0 \n",
"1960 0.00 ... 0.0 \n",
"1961 0.00 ... 0.0 \n",
"1962 0.00 ... 0.0 \n",
"\n",
" Production Résidus Semences Traitement Variation de stock Fc \\\n",
"0 28.0 0.0 0.0 0.0 0.0 0.00 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.00 \n",
"3 0.0 0.0 0.0 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.00 \n",
"... ... ... ... ... ... ... \n",
"1958 0.0 0.0 0.0 0.0 0.0 0.00 \n",
"1959 0.0 0.0 0.0 0.0 0.0 4.68 \n",
"1960 0.0 0.0 0.0 0.0 0.0 16.00 \n",
"1961 0.0 0.0 0.0 0.0 0.0 1.59 \n",
"1962 0.0 0.0 0.0 0.0 0.0 0.99 \n",
"\n",
" S Donnée calculée Données standardisées \n",
"0 28.0 0.00 28.0 \n",
"1 29.0 0.00 29.0 \n",
"2 0.0 0.00 0.0 \n",
"3 57.0 0.00 57.0 \n",
"4 2.0 0.00 2.0 \n",
"... ... ... ... \n",
"1958 67.0 0.00 67.0 \n",
"1959 0.0 4.68 0.0 \n",
"1960 0.0 16.00 0.0 \n",
"1961 0.0 1.59 0.0 \n",
"1962 0.0 0.99 0.0 \n",
"\n",
"[1963 rows x 26 columns]"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 192,
"id": "5ca88399-22fb-48a9-b504-f2baef121b36",
"metadata": {},
"outputs": [],
"source": [
"df = df.drop(columns=['Value_food'],axis=1)"
]
},
{
"cell_type": "markdown",
"id": "3ae6b0e2-a676-4993-a979-77b0e2410ab8",
"metadata": {},
"source": [
"### Group by Zone to aggregate all columns"
]
},
{
"cell_type": "code",
"execution_count": 193,
"id": "34a49b5e-267c-420f-8b90-a7cdd62d7790",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Value_population': 'mean', 'Value_gdp': 'mean', 'Value_politicstab': 'mean', 'Alimentation pour touristes': 'sum', 'Aliments pour animaux': 'sum', 'Autres utilisations (non alimentaire)': 'sum', 'Disponibilité alimentaire (Kcal/personne/jour)': 'sum', 'Disponibilité alimentaire en quantité (kg/personne/an)': 'sum', 'Disponibilité de matière grasse en quantité (g/personne/jour)': 'sum', 'Disponibilité de protéines en quantité (g/personne/jour)': 'sum', 'Disponibilité intérieure': 'sum', 'Exportations - Quantité': 'sum', 'Importations - Quantité': 'sum', 'Nourriture': 'sum', 'Pertes': 'sum', 'Production': 'sum', 'Résidus': 'sum', 'Semences': 'sum', 'Traitement': 'sum', 'Variation de stock': 'sum', 'Fc': 'sum', 'S': 'sum', 'Donnée calculée': 'sum', 'Données standardisées': 'sum'}\n"
]
}
],
"source": [
"dict = {}\n",
"for column in df.columns[1:]: \n",
" if column.startswith('Value_'+''):\n",
" dict[column] = 'mean'\n",
" else:\n",
" dict[column] = 'sum'\n",
"print(dict)"
]
},
{
"cell_type": "code",
"execution_count": 194,
"id": "98ba1563-1100-4106-9759-32d5055b22c8",
"metadata": {},
"outputs": [],
"source": [
"agg_df = df.groupby('Zone').agg(dict).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 195,
"id": "e224081b-5929-47f1-910a-e14f0eef5d92",
"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>Zone</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
" <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
" <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
" <th>...</th>\n",
" <th>Pertes</th>\n",
" <th>Production</th>\n",
" <th>Résidus</th>\n",
" <th>Semences</th>\n",
" <th>Traitement</th>\n",
" <th>Variation de stock</th>\n",
" <th>Fc</th>\n",
" <th>S</th>\n",
" <th>Donnée calculée</th>\n",
" <th>Données standardisées</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>1.53</td>\n",
" <td>0.33</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.40</td>\n",
" <td>171.0</td>\n",
" <td>7.40</td>\n",
" <td>171.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afrique du Sud</td>\n",
" <td>57009.756</td>\n",
" <td>13860.3</td>\n",
" <td>-0.28</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>143.0</td>\n",
" <td>35.69</td>\n",
" <td>9.25</td>\n",
" <td>...</td>\n",
" <td>83.0</td>\n",
" <td>1667.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>202.05</td>\n",
" <td>6480.0</td>\n",
" <td>202.05</td>\n",
" <td>6480.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Albanie</td>\n",
" <td>2884.169</td>\n",
" <td>12771.0</td>\n",
" <td>0.38</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>85.0</td>\n",
" <td>16.36</td>\n",
" <td>6.45</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>114.07</td>\n",
" <td>149.0</td>\n",
" <td>114.07</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Algérie</td>\n",
" <td>41389.189</td>\n",
" <td>11737.4</td>\n",
" <td>-0.92</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>6.38</td>\n",
" <td>1.50</td>\n",
" <td>...</td>\n",
" <td>13.0</td>\n",
" <td>275.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>31.85</td>\n",
" <td>831.0</td>\n",
" <td>31.85</td>\n",
" <td>831.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allemagne</td>\n",
" <td>82658.409</td>\n",
" <td>53071.5</td>\n",
" <td>0.59</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>71.0</td>\n",
" <td>19.47</td>\n",
" <td>4.16</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1514.0</td>\n",
" <td>-38.0</td>\n",
" <td>0.0</td>\n",
" <td>167.0</td>\n",
" <td>-29.0</td>\n",
" <td>102.59</td>\n",
" <td>6450.0</td>\n",
" <td>102.59</td>\n",
" <td>6450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>Émirats arabes unis</td>\n",
" <td>9487.203</td>\n",
" <td>67183.6</td>\n",
" <td>0.62</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>147.0</td>\n",
" <td>43.47</td>\n",
" <td>9.25</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-26.0</td>\n",
" <td>214.52</td>\n",
" <td>1373.0</td>\n",
" <td>214.52</td>\n",
" <td>1373.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>Équateur</td>\n",
" <td>16785.361</td>\n",
" <td>11617.9</td>\n",
" <td>-0.07</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>83.0</td>\n",
" <td>19.31</td>\n",
" <td>6.35</td>\n",
" <td>...</td>\n",
" <td>17.0</td>\n",
" <td>340.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-1.0</td>\n",
" <td>114.81</td>\n",
" <td>1021.0</td>\n",
" <td>114.81</td>\n",
" <td>1021.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>États-Unis d'Amérique</td>\n",
" <td>325084.756</td>\n",
" <td>59914.8</td>\n",
" <td>0.29</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>89.0</td>\n",
" <td>219.0</td>\n",
" <td>55.68</td>\n",
" <td>14.83</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>21914.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>77.0</td>\n",
" <td>80.0</td>\n",
" <td>309.44</td>\n",
" <td>62341.0</td>\n",
" <td>309.44</td>\n",
" <td>62341.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>Éthiopie</td>\n",
" <td>106399.924</td>\n",
" <td>2021.6</td>\n",
" <td>-1.68</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.13</td>\n",
" <td>0.03</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.20</td>\n",
" <td>44.0</td>\n",
" <td>0.20</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>Îles Salomon</td>\n",
" <td>636.039</td>\n",
" <td>2663.5</td>\n",
" <td>0.20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>4.45</td>\n",
" <td>1.31</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>25.27</td>\n",
" <td>15.0</td>\n",
" <td>25.27</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>164 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_population Value_gdp Value_politicstab \\\n",
"0 Afghanistan 36296.113 2058.4 -2.80 \n",
"1 Afrique du Sud 57009.756 13860.3 -0.28 \n",
"2 Albanie 2884.169 12771.0 0.38 \n",
"3 Algérie 41389.189 11737.4 -0.92 \n",
"4 Allemagne 82658.409 53071.5 0.59 \n",
".. ... ... ... ... \n",
"159 Émirats arabes unis 9487.203 67183.6 0.62 \n",
"160 Équateur 16785.361 11617.9 -0.07 \n",
"161 États-Unis d'Amérique 325084.756 59914.8 0.29 \n",
"162 Éthiopie 106399.924 2021.6 -1.68 \n",
"163 Îles Salomon 636.039 2663.5 0.20 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
".. ... ... \n",
"159 0.0 0.0 \n",
"160 0.0 0.0 \n",
"161 0.0 0.0 \n",
"162 0.0 0.0 \n",
"163 0.0 0.0 \n",
"\n",
" Autres utilisations (non alimentaire) \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
".. ... \n",
"159 0.0 \n",
"160 0.0 \n",
"161 89.0 \n",
"162 0.0 \n",
"163 0.0 \n",
"\n",
" Disponibilité alimentaire (Kcal/personne/jour) \\\n",
"0 5.0 \n",
"1 143.0 \n",
"2 85.0 \n",
"3 22.0 \n",
"4 71.0 \n",
".. ... \n",
"159 147.0 \n",
"160 83.0 \n",
"161 219.0 \n",
"162 0.0 \n",
"163 18.0 \n",
"\n",
" Disponibilité alimentaire en quantité (kg/personne/an) \\\n",
"0 1.53 \n",
"1 35.69 \n",
"2 16.36 \n",
"3 6.38 \n",
"4 19.47 \n",
".. ... \n",
"159 43.47 \n",
"160 19.31 \n",
"161 55.68 \n",
"162 0.13 \n",
"163 4.45 \n",
"\n",
" Disponibilité de matière grasse en quantité (g/personne/jour) ... \\\n",
"0 0.33 ... \n",
"1 9.25 ... \n",
"2 6.45 ... \n",
"3 1.50 ... \n",
"4 4.16 ... \n",
".. ... ... \n",
"159 9.25 ... \n",
"160 6.35 ... \n",
"161 14.83 ... \n",
"162 0.03 ... \n",
"163 1.31 ... \n",
"\n",
" Pertes Production Résidus Semences Traitement Variation de stock \\\n",
"0 2.0 28.0 0.0 0.0 0.0 0.0 \n",
"1 83.0 1667.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 13.0 0.0 0.0 0.0 4.0 \n",
"3 13.0 275.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 1514.0 -38.0 0.0 167.0 -29.0 \n",
".. ... ... ... ... ... ... \n",
"159 0.0 48.0 0.0 0.0 0.0 -26.0 \n",
"160 17.0 340.0 0.0 0.0 0.0 -1.0 \n",
"161 0.0 21914.0 0.0 0.0 77.0 80.0 \n",
"162 1.0 14.0 0.0 0.0 0.0 0.0 \n",
"163 0.0 0.0 0.0 0.0 0.0 3.0 \n",
"\n",
" Fc S Donnée calculée Données standardisées \n",
"0 7.40 171.0 7.40 171.0 \n",
"1 202.05 6480.0 202.05 6480.0 \n",
"2 114.07 149.0 114.07 149.0 \n",
"3 31.85 831.0 31.85 831.0 \n",
"4 102.59 6450.0 102.59 6450.0 \n",
".. ... ... ... ... \n",
"159 214.52 1373.0 214.52 1373.0 \n",
"160 114.81 1021.0 114.81 1021.0 \n",
"161 309.44 62341.0 309.44 62341.0 \n",
"162 0.20 44.0 0.20 44.0 \n",
"163 25.27 15.0 25.27 15.0 \n",
"\n",
"[164 rows x 25 columns]"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_df"
]
},
{
"cell_type": "markdown",
"id": "fd0581fc-c849-45a8-bf9f-6d99a97d26e5",
"metadata": {},
"source": [
"### Check which columns include only 1 value and filter out them"
]
},
{
"cell_type": "code",
"execution_count": 196,
"id": "60d7bc05-68e3-4f57-baf5-51f02bed2432",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Zone 164\n",
"Value_population 164\n",
"Value_gdp 164\n",
"Value_politicstab 136\n",
"Alimentation pour touristes 7\n",
"Aliments pour animaux 1\n",
"Autres utilisations (non alimentaire) 17\n",
"Disponibilité alimentaire (Kcal/personne/jour) 107\n",
"Disponibilité alimentaire en quantité (kg/personne/an) 162\n",
"Disponibilité de matière grasse en quantité (g/personne/jour) 149\n",
"Disponibilité de protéines en quantité (g/personne/jour) 156\n",
"Disponibilité intérieure 130\n",
"Exportations - Quantité 49\n",
"Importations - Quantité 81\n",
"Nourriture 126\n",
"Pertes 29\n",
"Production 119\n",
"Résidus 15\n",
"Semences 1\n",
"Traitement 25\n",
"Variation de stock 54\n",
"Fc 164\n",
"S 147\n",
"Donnée calculée 164\n",
"Données standardisées 147\n",
"dtype: int64"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_df.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 197,
"id": "061fa728-32dc-4f85-b81e-767ed255e9c5",
"metadata": {},
"outputs": [
{
"data": {
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" .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>Zone</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
" <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
" <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
" <th>...</th>\n",
" <th>Pertes</th>\n",
" <th>Production</th>\n",
" <th>Résidus</th>\n",
" <th>Semences</th>\n",
" <th>Traitement</th>\n",
" <th>Variation de stock</th>\n",
" <th>Fc</th>\n",
" <th>S</th>\n",
" <th>Donnée calculée</th>\n",
" <th>Données standardisées</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>164</td>\n",
" <td>1.640000e+02</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
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" <td>164.000000</td>\n",
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" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>...</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.0</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" <td>164.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>164</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Afghanistan</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>3.554589e+04</td>\n",
" <td>20342.903659</td>\n",
" <td>-0.046646</td>\n",
" <td>0.073171</td>\n",
" <td>0.0</td>\n",
" <td>8.719512</td>\n",
" <td>74.054878</td>\n",
" <td>20.066707</td>\n",
" <td>4.861646</td>\n",
" <td>...</td>\n",
" <td>13.646341</td>\n",
" <td>622.573171</td>\n",
" <td>-2.829268</td>\n",
" <td>0.0</td>\n",
" <td>7.365854</td>\n",
" <td>14.286585</td>\n",
" <td>106.067134</td>\n",
" <td>2007.243902</td>\n",
" <td>106.067134</td>\n",
" <td>2007.243902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>1.133016e+05</td>\n",
" <td>20803.735703</td>\n",
" <td>0.875888</td>\n",
" <td>1.630089</td>\n",
" <td>0.0</td>\n",
" <td>66.382656</td>\n",
" <td>60.955807</td>\n",
" <td>15.899542</td>\n",
" <td>4.227377</td>\n",
" <td>...</td>\n",
" <td>62.940204</td>\n",
" <td>2125.487715</td>\n",
" <td>13.580756</td>\n",
" <td>0.0</td>\n",
" <td>31.242574</td>\n",
" <td>76.319297</td>\n",
" <td>86.362759</td>\n",
" <td>6061.153566</td>\n",
" <td>86.362759</td>\n",
" <td>6061.153566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>5.204500e+01</td>\n",
" <td>912.800000</td>\n",
" <td>-2.800000</td>\n",
" <td>-18.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.130000</td>\n",
" <td>0.030000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-125.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>-119.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>2.874480e+03</td>\n",
" <td>5011.275000</td>\n",
" <td>-0.622500</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>21.500000</td>\n",
" <td>6.282500</td>\n",
" <td>1.355000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>11.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>30.820000</td>\n",
" <td>93.500000</td>\n",
" <td>30.820000</td>\n",
" <td>93.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>9.468717e+03</td>\n",
" <td>13265.700000</td>\n",
" <td>0.015000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>62.500000</td>\n",
" <td>17.800000</td>\n",
" <td>3.690000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>66.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>90.665000</td>\n",
" <td>315.500000</td>\n",
" <td>90.665000</td>\n",
" <td>315.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>3.118956e+04</td>\n",
" <td>28880.475000</td>\n",
" <td>0.650000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>104.250000</td>\n",
" <td>29.485000</td>\n",
" <td>6.475000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>345.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>7.250000</td>\n",
" <td>152.065000</td>\n",
" <td>1369.250000</td>\n",
" <td>152.065000</td>\n",
" <td>1369.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>1.338677e+06</td>\n",
" <td>126144.000000</td>\n",
" <td>1.600000</td>\n",
" <td>5.000000</td>\n",
" <td>0.0</td>\n",
" <td>783.000000</td>\n",
" <td>243.000000</td>\n",
" <td>72.310000</td>\n",
" <td>17.860000</td>\n",
" <td>...</td>\n",
" <td>695.000000</td>\n",
" <td>21914.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>306.000000</td>\n",
" <td>859.000000</td>\n",
" <td>355.470000</td>\n",
" <td>62341.000000</td>\n",
" <td>355.470000</td>\n",
" <td>62341.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>11 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_population Value_gdp Value_politicstab \\\n",
"count 164 1.640000e+02 164.000000 164.000000 \n",
"unique 164 NaN NaN NaN \n",
"top Afghanistan NaN NaN NaN \n",
"freq 1 NaN NaN NaN \n",
"mean NaN 3.554589e+04 20342.903659 -0.046646 \n",
"std NaN 1.133016e+05 20803.735703 0.875888 \n",
"min NaN 5.204500e+01 912.800000 -2.800000 \n",
"25% NaN 2.874480e+03 5011.275000 -0.622500 \n",
"50% NaN 9.468717e+03 13265.700000 0.015000 \n",
"75% NaN 3.118956e+04 28880.475000 0.650000 \n",
"max NaN 1.338677e+06 126144.000000 1.600000 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"count 164.000000 164.0 \n",
"unique NaN NaN \n",
"top NaN NaN \n",
"freq NaN NaN \n",
"mean 0.073171 0.0 \n",
"std 1.630089 0.0 \n",
"min -18.000000 0.0 \n",
"25% 0.000000 0.0 \n",
"50% 0.000000 0.0 \n",
"75% 0.000000 0.0 \n",
"max 5.000000 0.0 \n",
"\n",
" Autres utilisations (non alimentaire) \\\n",
"count 164.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 8.719512 \n",
"std 66.382656 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 783.000000 \n",
"\n",
" Disponibilité alimentaire (Kcal/personne/jour) \\\n",
"count 164.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 74.054878 \n",
"std 60.955807 \n",
"min 0.000000 \n",
"25% 21.500000 \n",
"50% 62.500000 \n",
"75% 104.250000 \n",
"max 243.000000 \n",
"\n",
" Disponibilité alimentaire en quantité (kg/personne/an) \\\n",
"count 164.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 20.066707 \n",
"std 15.899542 \n",
"min 0.130000 \n",
"25% 6.282500 \n",
"50% 17.800000 \n",
"75% 29.485000 \n",
"max 72.310000 \n",
"\n",
" Disponibilité de matière grasse en quantité (g/personne/jour) ... \\\n",
"count 164.000000 ... \n",
"unique NaN ... \n",
"top NaN ... \n",
"freq NaN ... \n",
"mean 4.861646 ... \n",
"std 4.227377 ... \n",
"min 0.030000 ... \n",
"25% 1.355000 ... \n",
"50% 3.690000 ... \n",
"75% 6.475000 ... \n",
"max 17.860000 ... \n",
"\n",
" Pertes Production Résidus Semences Traitement \\\n",
"count 164.000000 164.000000 164.000000 164.0 164.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 13.646341 622.573171 -2.829268 0.0 7.365854 \n",
"std 62.940204 2125.487715 13.580756 0.0 31.242574 \n",
"min 0.000000 0.000000 -125.000000 0.0 0.000000 \n",
"25% 0.000000 11.000000 0.000000 0.0 0.000000 \n",
"50% 0.000000 66.000000 0.000000 0.0 0.000000 \n",
"75% 2.000000 345.250000 0.000000 0.0 0.000000 \n",
"max 695.000000 21914.000000 0.000000 0.0 306.000000 \n",
"\n",
" Variation de stock Fc S Donnée calculée \\\n",
"count 164.000000 164.000000 164.000000 164.000000 \n",
"unique NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN \n",
"mean 14.286585 106.067134 2007.243902 106.067134 \n",
"std 76.319297 86.362759 6061.153566 86.362759 \n",
"min -119.000000 0.200000 0.000000 0.200000 \n",
"25% 0.000000 30.820000 93.500000 30.820000 \n",
"50% 0.000000 90.665000 315.500000 90.665000 \n",
"75% 7.250000 152.065000 1369.250000 152.065000 \n",
"max 859.000000 355.470000 62341.000000 355.470000 \n",
"\n",
" Données standardisées \n",
"count 164.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 2007.243902 \n",
"std 6061.153566 \n",
"min 0.000000 \n",
"25% 93.500000 \n",
"50% 315.500000 \n",
"75% 1369.250000 \n",
"max 62341.000000 \n",
"\n",
"[11 rows x 25 columns]"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_df.describe(include='all')"
]
},
{
"cell_type": "markdown",
"id": "fb96400b-1c3c-4b3d-9b29-53c561bb78e4",
"metadata": {},
"source": [
"### Apply standardize features: StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "b5e67544-c75e-4d56-a46b-fd5643638b4d",
"metadata": {},
"outputs": [],
"source": [
"df = agg_df.set_index('Zone')"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "0db13b8d-16d7-49f9-b689-082e1afc15b9",
"metadata": {},
"outputs": [],
"source": [
"X = df.values"
]
},
{
"cell_type": "code",
"execution_count": 200,
"id": "21e941ea-a352-4fb4-90a8-d687091fc382",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 3.62961130e+04, 2.05840000e+03, -2.80000000e+00, ...,\n",
" 1.71000000e+02, 7.40000000e+00, 1.71000000e+02],\n",
" [ 5.70097560e+04, 1.38603000e+04, -2.80000000e-01, ...,\n",
" 6.48000000e+03, 2.02050000e+02, 6.48000000e+03],\n",
" [ 2.88416900e+03, 1.27710000e+04, 3.80000000e-01, ...,\n",
" 1.49000000e+02, 1.14070000e+02, 1.49000000e+02],\n",
" ...,\n",
" [ 3.25084756e+05, 5.99148000e+04, 2.90000000e-01, ...,\n",
" 6.23410000e+04, 3.09440000e+02, 6.23410000e+04],\n",
" [ 1.06399924e+05, 2.02160000e+03, -1.68000000e+00, ...,\n",
" 4.40000000e+01, 2.00000000e-01, 4.40000000e+01],\n",
" [ 6.36039000e+02, 2.66350000e+03, 2.00000000e-01, ...,\n",
" 1.50000000e+01, 2.52700000e+01, 1.50000000e+01]])"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 201,
"id": "4c06bb9a-c715-4084-aae0-ef153f79e012",
"metadata": {},
"outputs": [],
"source": [
"scale = StandardScaler()\n",
"scaled_X = scale.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "b47a1c1a-e887-4035-85c0-d88f0d816751",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.00664176, -0.88159673, -3.15312822, -0.04502505, 0. ,\n",
" -0.13175457, -1.13633765, -1.16943503, -1.07525907, -1.16744927,\n",
" -0.30465506, -0.22595585, -0.30802752, -0.29503457, -0.18560493,\n",
" -0.28059171, 0.20896729, 0. , -0.23648543, -0.18776827,\n",
" -1.14597232, -0.30388076, -1.14597232, -0.30388076])"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scaled_X[0]"
]
},
{
"cell_type": "markdown",
"id": "f34aa196-5cc8-403e-84fe-e2ca7e9565b9",
"metadata": {},
"source": [
"### Analyse PCA to gain a lower dimensional space"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "3b917c13-8ca1-45a6-bc57-36007d15eb58",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=24)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=24)</pre></div></div></div></div></div>"
],
"text/plain": [
"PCA(n_components=24)"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca = PCA(n_components = 24)\n",
"pca.fit(scaled_X)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"id": "920026ad-ab12-4c60-a149-d569c7c5939c",
"metadata": {},
"outputs": [],
"source": [
"X_proj = pca.transform(scaled_X)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"id": "30690b20-baf1-42c4-8b6d-4b09d26dc024",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(164, 24)"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_proj.shape"
]
},
{
"cell_type": "code",
"execution_count": 206,
"id": "40551316-a1b0-4a92-8511-f8adc1e8c2f8",
"metadata": {},
"outputs": [],
"source": [
"scree = (pca.explained_variance_ratio_*100).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "ea8301a9-a906-4cd5-aa7d-e015fc6cda61",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3.636e+01, 2.162e+01, 9.570e+00, 7.680e+00, 5.460e+00, 4.840e+00,\n",
" 4.310e+00, 3.630e+00, 2.380e+00, 1.720e+00, 1.420e+00, 4.900e-01,\n",
" 3.100e-01, 1.900e-01, 3.000e-02, 0.000e+00, 0.000e+00, 0.000e+00,\n",
" 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00])"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scree"
]
},
{
"cell_type": "code",
"execution_count": 208,
"id": "cbc48f02-0a87-43d1-863b-57cdbad61228",
"metadata": {},
"outputs": [],
"source": [
"scree_cum = scree.cumsum().round()"
]
},
{
"cell_type": "code",
"execution_count": 209,
"id": "0c48da62-2b35-4d8f-a924-0b84f9d5288c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 36., 58., 68., 75., 81., 86., 90., 93., 96., 98., 99.,\n",
" 99., 100., 100., 100., 100., 100., 100., 100., 100., 100., 100.,\n",
" 100., 100.])"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scree_cum"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "ec12a9c2-e191-4a9d-924b-70c182fd98ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1,\n",
" 2,\n",
" 3,\n",
" 4,\n",
" 5,\n",
" 6,\n",
" 7,\n",
" 8,\n",
" 9,\n",
" 10,\n",
" 11,\n",
" 12,\n",
" 13,\n",
" 14,\n",
" 15,\n",
" 16,\n",
" 17,\n",
" 18,\n",
" 19,\n",
" 20,\n",
" 21,\n",
" 22,\n",
" 23,\n",
" 24]"
]
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_list = range(1,25)\n",
"list(x_list)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "1aac7a4f-0082-4efc-93c0-bbe6845d796d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.bar(x_list,scree)\n",
"plt.plot(x_list,scree_cum,c='red',marker='o')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "7eae730a-4b66-4947-be4e-c8f7f5e94904",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=9)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=9)</pre></div></div></div></div></div>"
],
"text/plain": [
"PCA(n_components=9)"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca = PCA(n_components = 9)\n",
"pca.fit(scaled_X)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"id": "2639018f-d53d-46ab-a934-65bcd3b67a81",
"metadata": {},
"outputs": [],
"source": [
"X_proj = pca.transform(scaled_X)"
]
},
{
"cell_type": "markdown",
"id": "a30963da-23ae-4652-a5b1-cea2847fd2ea",
"metadata": {},
"source": [
"### Cluster dataset by KMean"
]
},
{
"cell_type": "code",
"execution_count": 214,
"id": "09273bbd-b526-4584-bc47-fdf16f663165",
"metadata": {},
"outputs": [],
"source": [
"k_list = range(1, 10)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"id": "84aed05a-4cc8-4255-8ced-e2b9571c7a5d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
}
],
"source": [
"intertia = []\n",
"for i in k_list:\n",
" kmean = KMeans(n_clusters=i) # instantiate a KMeans clustering model with a variable number of clusters denoted by i\n",
" kmean.fit(X_proj) # fitting a KMeans clustering model to a dataset represented by X_proj\n",
" intertia.append(kmean.inertia_) # calculating and storing the inertia value for each iteration of the KMeans clustering algorithm"
]
},
{
"cell_type": "code",
"execution_count": 216,
"id": "9307bad7-1601-4fcd-9f77-f7dcfa05d853",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3458.325754304632,\n",
" 2608.0749444315375,\n",
" 1909.1665724829008,\n",
" 1637.218276364663,\n",
" 1416.6284947582112,\n",
" 1179.984493100464,\n",
" 976.489129754445,\n",
" 814.188467706743,\n",
" 685.5079142382809]"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"intertia"
]
},
{
"cell_type": "code",
"execution_count": 217,
"id": "2836c93f-efa7-4b66-972f-44a8e08dfc09",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 217,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(10, 5))\n",
"sns.lineplot(x=k_list, y=intertia, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 218,
"id": "75c95b51-1794-4ac8-ac05-e797f2ec980d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x550 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from yellowbrick.cluster import KElbowVisualizer\n",
"visualizer = KElbowVisualizer(kmean, k = (1, 10))\n",
"visualizer.fit(X_proj)\n",
"visualizer.show()"
]
},
{
"cell_type": "code",
"execution_count": 219,
"id": "ed550bb8-7ff4-42d1-8f4a-4bb1b73e1e5f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DELL\\PycharmProjects\\Book_revenue\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
],
"text/plain": [
"KMeans(n_clusters=3)"
]
},
"execution_count": 219,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmean = KMeans(n_clusters=3) # instantiate a KMeans clustering model with a variable number of clusters denoted by 3\n",
"kmean.fit(X_proj)"
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "7df0e54c-5bef-41ea-9bc3-dd96c10b0ede",
"metadata": {},
"outputs": [],
"source": [
"klabels = kmean.labels_"
]
},
{
"cell_type": "code",
"execution_count": 221,
"id": "eee59f23-8742-442d-9dc3-63291c411bdc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-3.02936038, 3.07659364, -0.46226791, -2.06578191, 2.4575722 ,\n",
" -1.47146495, 3.68980558, 3.0897495 , 3.90156962, -1.24908747,\n",
" 3.91770563, -0.14484444, -1.48054556, 2.56272868, -2.5673846 ,\n",
" 2.75451652, -0.13146576, 0.16701805, 1.78268232, -1.12410773,\n",
" -2.28669468, 13.46080219, -0.11450568, -2.73469626, 0.42190617,\n",
" -1.5128625 , -0.91975893, -2.52462097, -2.62708021, 3.22229006,\n",
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" -0.76962891, -2.4668857 , 2.95280396, -2.30114596, -1.44958605,\n",
" -2.81875509, 0.62294486, -0.24990568, 1.18665245, -2.52444926,\n",
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" -1.6249364 , -2.65970226, 2.95802609, 4.50478199, 3.97988432,\n",
" 3.68321799, -1.88284617, -1.65182539, -2.53549167, -0.9540615 ,\n",
" 0.03470465, -2.8989357 , -1.84751495, -0.42734736, 0.48621991,\n",
" -0.02155223, -2.34630345, -2.41312222, -2.99539805, 0.17203541,\n",
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" -2.33241112, 0.83537001, 0.30627995, -1.60013568, -1.60765685,\n",
" -0.38497733, -2.46107176, -2.4186749 , -0.60887068, 2.59013499,\n",
" -0.29551438, 20.33948129, -3.02387167, -2.34163379])"
]
},
"execution_count": 221,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_proj[:,0]"
]
},
{
"cell_type": "code",
"execution_count": 222,
"id": "81c2da59-d265-4037-b38f-450dae681521",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" -1.16714922e+00, 3.55018702e-02, -2.84719710e+00, -7.31627407e-01,\n",
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" 6.16959520e-01, 7.89156733e+00, -7.94056025e-01, 1.38792274e+00,\n",
" -8.38905275e-01, 3.37750337e-01, -5.12583466e-01, 1.03969718e+00,\n",
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" -3.58389202e+00, 1.97152540e-01, 8.69789354e-01, 1.61967454e+00,\n",
" 8.36748839e-01, 8.35762317e-02, 6.93314037e-02, 1.18846476e+00,\n",
" 1.01196046e+00, 1.15017449e+00, 1.49679402e+00, -2.65593762e+00,\n",
" -3.82440147e-01, 1.32789253e+01, 1.94626508e+00, 7.75882531e-01])"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_proj[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 223,
"id": "b383dc9e-f57a-4215-b296-00803e0665e4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1892d962280>"
]
},
"execution_count": 223,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(x=X_proj[:,0], y=X_proj[:,1], hue = klabels)\n",
"plt.legend(bbox_to_anchor=(1, 1), loc='upper left')"
]
},
{
"cell_type": "markdown",
"id": "b4871e0c-de98-4a73-9994-f216e3a2195f",
"metadata": {},
"source": [
"### Cluster dataset by AgglomerativeClustering"
]
},
{
"cell_type": "code",
"execution_count": 224,
"id": "2c381c65-7fe6-4a15-ba8e-f21306284bb2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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D4VBMTIwiIyML/Ly0xOOSHJKkmJjL1bB2pYBl28Ff2U5nmvb/3+2YmBhFNWwYsOyCuBizC/Ke+ivbDmSTXZSzj6eFK+H/bsfExKhSwzoBy7YL2WSTTbbd8q8ba9SooRo+fPcojGCMd/5xllSipjfZvmXv3bu3wI/1ujyPHz9ey5Yt09SpU9W5c2dJUvXq1c8KTU5OPmtXbishISEex037IjIy0qvXiIhIz3c7vFD53mbbye7s3Hw/fERERFzwtYvTePsz25v31O5sO5FNdlHMdtq4fHmbbTeyySabbLvkXzeGhxfue25hBHK8nWdsvCtJ05ts37ILusu25OV1nmfPnq3ly5dr2rRpuv322933N2/eXDt27PA4xmDjxo1q3ry5Ny8PAAAAAECRVODyHB8frzlz5mjAgAFq1aqVjh496v6vTZs2qlGjhp599lnt2bNH8+bN09atW3XPPff4c9gBAAAAAAiIAu+2/c033ygnJ0dvvvmm3nzzTY+//fbbb5ozZ46ef/559ejRQ5dddpneeOMN1axZ0/YBBgAAAAAg0ApcnmNjYxUbG3vev1922WVasmSJLQMFAMVRVqpTJ+IOFfjxLpdLGY7DOp4WftYxXP52sWWn7Eg85+1AZNvlYsku36CmwioE51g2AACCyedLVQEACi4r1alP6wxTdkq69YPPkGD9EL+5GLN/HjAvaNl2KOrZoRXLquu+mRRoAECJ49UJwwAAvjkRd8in4gwUNdkp6V7tQQEAQHHBlmcACLC282JVoUlty8e5XC73NQsjgrAbL9lk55e6PVHrYwu/VR8AgIsV5RkAAqxCk9qq0rae5eOcTqeSojNVqWGdgF8vkWyyAQCAJ3bbBgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsFAm2AMAXGxyTqTK7NwupzNNuRERBX5exs4d57ztDZfLJZPr01MBAAAAFALlGfDCqdRU7W7VVCY1RfsL8TqOwbG+Pzm6nHI275SiogoxBAAAAAC8wW7bgBdcu+OUm5oS3IFIO6nMPbuDOwwAAABACcOWZ8BHNafPVoUWLQOWl7FzR+G2WAMAAADwGeUZ8FF4g4aKvrptsAcDAAAAQACw2zYAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABbKBHsAAACAlJ3qVMb2wzqeFi5nRERAs10ulzIcF85O2ZF4ztuByPZW+QY1FVYhypbXAgAgD+UZAIAgy0p16usmTys7xamEIA5HQbN/HjAvaNkFEVqxrLrum0mBBgDYit22AQAIshNxh5Sd4gz2YBQb2SnpOhF3KNiDAQAoZtjyDABAEdJidl9Vu6puQDNdLpccDodiYmIUEYRdxu3KTt2eqPWx9m8VBwBAojwDAFCklG9US1Xa1gtoptPpVFJ0pio1rKOoqMDu6hzMbAAAvMFu2wAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABZ8Ls9ZWVm64447tH79evd9EyZMUP369T3+W7JkiS0DCgAAAABAsJTx5UmZmZkaMWKE9uzZ43F/fHy8RowYoe7du7vvi46OLtwQAgAAAAAQZF5ved67d6969eqlhISEs/4WHx+vRo0aqWrVqu7/IiMjbRlQAAAAAACCxevy/PPPP6tt27b64IMPPO5PS0tTUlKSYmJi7Bo2AAAAAACKBK93277vvvvOeX98fLxCQkL01ltv6fvvv1fFihX1yCOPeOzCbcUYI6fT6e0gSZIyMjI8/l9QLlemx21f8n3NtoO/sl0ul8ftUud4X4rjeFvJ/75kZvo2v1yM2VLJnN52Zp+5TBVk+hWH8Sa7YFi+7cn2djkrLuNNNtnnUxLXLfnHOdDZZ2aSfXFkG2MUEhJSoMf6dMzzuezbt08hISGqU6eOHnjgAW3YsEGjR49WdHS0OnXqVKDXyM7O1q5duwo1HA6Hw7vHJ/+1EnE49is6LSlg2XayO9vkez2Hw6GQqPMfu16cxttK/vfl8OHD+qOQ8+vFkp1fSZredmZnOA57vFZSdOYFHm1vdmGQHRj554/Dhw8rJTiL90X/nvu6nF3s40022edTEtct+cc50NlnIvviyQ4LCyvQ42wrz926dVOHDh1UsWJFSVKDBg3kcDi0bNmyApfn0NBQ1atXz6f8jIwMORwOxcTEeHWcdVricUkOSVJMzOVqWLtSwLLt4K9spzNN+//vdkxMjKIaNgxYdkEEKzv/+1KjRg1dco73pThmSyVzetuZfTwtXHlnioiJiVGlhnUClu0LsgObnX/+qFGjhmqwfPvE2+WsuIw32WSfT0lct+QfZ0klanqT7Vv23r17C/xY28pzSEiIuzjnqVOnjtatW+fVa0RFRRVqOCIjI716jYiI9Hy3wwuV7222nezOzo2IcN+OiIi44GsXp/G2kv99CQ8v3PxyMWXnV5Kmt53ZTi+WKbuzC4PswHCyfNuS7etydrGPN9lkn09JXLfkH+dAZ5+J7Isju6C7bEuFuM7zmWbMmKG+fft63BcXF6c6day3rgAAAAAAUJTZVp47dOigDRs2aOHChUpISNDSpUu1atUq9evXz64IAAAAAACCwrby3KxZM82YMUOffvqp7rjjDi1evFivvfaaWrZsaVcEAAAAAABBUahjnn/77TePf3fs2FEdO3Ys1AABAAAAAFDU2LblGQAAAACA4oryDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYIHyDAAAAACABcozAAAAAAAWKM8AAAAAAFigPAMAAAAAYKFMsAcAALJSnToRd+i8f3e5XMpwHNbxtHA5IyICOGT2ZafsSDzn7UBk+6Kw2eUb1FRYhSg/DBkAAEBwUJ4BBFVWqlOf1hmm7JR0y8cmBGB4ApH984B5Qcv2lq/ZoRXLquu+mRRoAABQbLDbNoCgOhF3qEDFGReX7JT0C+5NAAAAcLFhyzOAIqPtvFhVaFL7rPtdLpccDodiYmIUEYTdl8kueHbq9kStj/VuyzoAAMDFgPIMoMio0KS2qrStd9b9TqdTSdGZqtSwjqKiArsbMNmBzwYAACiK2G0bAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALlGcAAAAAACxQngEAAAAAsEB5BgAAAADAAuUZAAAAAAALZYI9AAAAILiyU53K2H5Yx9PC5YyICGi2y+VShsOe7JQdiee8HYhsbxU0u3yDmgqrEBXAIQMAnA/lGQCAEiwr1amvmzyt7BSnEoI4HHZn/zxgXtCyvWGVHVqxrLrum0mBBoAigN22AQAowU7EHVJ2ijPYg4HzyE5J14m4Q8EeDACA2PIMAAD+T4vZfVXtqroBzXS5XHI4HIqJiVFEEHadLqrZqdsTtT624FvPAQD+R3kGAACSpPKNaqlK23oBzXQ6nUqKzlSlhnUUFRXYXZNLajYAwDfstg0AAAAAgAXKMwAAAAAAFijPAAAAAABY8Lk8Z2Vl6Y477tD69evd9yUmJqpv375q0aKFbrvtNv3www+2DCQAAAAAAMHk0wnDMjMzNWLECO3Zs8d9nzFGgwcP1pVXXqmVK1fqP//5j4YMGaIvv/xSNWvWtG2AAQAACiIr1XnByzy5XC5lOA7reFq4nEE42/aFslN2JJ7zdiCy7Va+QU2uUw2gWPC6PO/du1cjRoyQMcbj/nXr1ikxMVHLly9XVFSU6tatq7Vr12rlypUaOnSobQMMAABgJSvVqU/rDFN2SrrlYxMCMDyFyf55gH8uWRWo8Q6tWFZd982kQAO46Hm92/bPP/+stm3b6oMPPvC4f8uWLWrUqJHH5RZatWqlzZs3F3ogAQAAvHEi7lCBijP8Lzsl/YJ7AADAxcLrLc/33XffOe8/evSoqlWr5nFf5cqV9ccff/g2ZAAAADZoOy9WFZrUPut+l8slh8OhmJgYRQRht+3inp26PVHrY/2z1RwAgsGnY57PJSMjQ2FhYR73hYWFKSsrq8CvYYyR0+n0OT///wvK5cr0uO1Lvq/ZdvBXtsvl8rhd6hzvS3Ecbyv535fMTN/ml4sxWwrcvHau8SqJ89rFml2Q6emvbDuwbime65bwelUV1fTs86+EZGQoMjpTkTE1FBkZaWu+lZKQfa71QUlcvoOZXVyX7wvJP86Bzj4zk+yLI9sYo5CQkAI91rbyHB4erpSUFI/7srKyvPpFMzs7W7t27SrUcDgcDu8en/zXSsTh2K/otKSAZdvJ7myT7/UcDodCoqIDlu2NQGfnf18OHz6sPwo5v14s2fnZ/Z5nOA57vHZSdOZ5H1uS5rWLNdub6Wl3tp0CnZ3/fTt8+LBSArh4BzM7P9YtxS/7QtOgOI93Ucoursv3heQf50Bnn4nsiyf7zI3A52Nbea5evbr27t3rcV9ycvJZu3JfSGhoqOrVq+dTfkZGhnsXJG9+RU1LPC7JIUmKiblcDWtXCli2HfyV7XSmaf//3Y6JiVFUw4YByy6IYGXnf19q1KihS87xvhTHbMl/7/nxtHD3SWtiYmJUqWGdgGUXBNneZRdkevor2w7Bys7/vtWoUUM1Arh8BzNbYt1SnLPPNQ1KwngXpeziunxfSP5xllSipjfZvmWf2WEvxLby3Lx5c82bN08ul8u9tXnjxo1q1apVgV8jJCTE44RjvoiMjPTqNSIi0vPdDi9UvrfZdrI7OzffHgMREREXfO3iNN5W8r8v4eGFm18upuz87H7PncxrxSrbm+lpd7adAp3tDOLyHczs/Fi3FL/sC02D4jzeRSm7uC7fF3Lm5ddK0vQm27fsgu6yLflwtu3zadOmjWrUqKFnn31We/bs0bx587R161bdc889dkUAAAAAABAUtpXn0qVLa86cOTp69Kh69Oihzz77TG+88YZq1jz7BB0AAAAAAFxMCrXb9m+//ebx78suu0xLliwp1AABAAAAAFDU2LblGQAAAACA4sq2E4YFW1aOS87cYzqWflDpOQW/PFaK80S+20k6etL7a4O5XC7lmGyvnwcAAAAAuDgUi/KcdcqlL7bPVHaOS/G/WT8+v33HIyWdvnzFj3tW6vAx3y7qXUqhujLnSkUpOGeWAwAAAAD4T7Eoz6kZR5Sd4/LpuXUqZWhh9x2FHoZcZeukK1kVy3l/nWgAAAAAQNFWLMpzfq3/dqeqX1I7YHl/pifpp70rA5YHAAAAAAi8YleeK0RWVdVyfwv2YAAAAAAAihHOtg0AAAAAgAXKMwAAAAAAFijPAAAAAABYoDwDAAAAAGCh2J0wDAAgZaU6dSLukM/Pd7lcynAc1vG0cDkjIgr8vJQdiee8HYjsPOUb1FRYhSifsgEAAM6H8gwAxUxWqlOf1hmm7JT0Qr9WQiGe+/OAeUHJDq1YVl33zaRAAwAAW7HbNgAUMyfiDtlSnC9W2SnphdrqDgAAcC5seQaAYqztvFhVaFLb6+e5XC45HA7FxMQowoddpwvD1+zU7YlaH1u4rd0AAADnQ3kGgGKsQpPaqtK2ntfPczqdSorOVKWGdRQVFdjdn4OZDQAAcD6U54tczolUmZ3b5XSmKdfGrUMZO3ec83Z+LpdLxuHwOTviygYqU6GCz8MIAAAAAIFCeb6InUpN1e5WTWVSU7TfjzmOwbEX/Luv2aUrVlSzHfEUaAAAAABFHuX5IubaHafc1JRgD4bPclJS5Nodp+ir2wZ7UAAAAGCz7FSnMrb7fulBX+S/TGJmfLKOb9gXsGzp9OUSFRqwOAQY5bmYqDl9tiq0aBnQTF9P6pOxc4fl1mwAAOAfgSo057rue2Gv426F67wXHVmpTn3d5GllpzgLddnDwkh6+SslvfxVQDNDK5bVLdtfCWgmAofyXEyEN2gY8C24pZxOhURFK6phQ07qAwDARSBYhebM6777K5vrvBcdpy+b6Az2YARcdkq6Tu7+Q4oO9pDAHyjPAAAAJURxLzR513n35SoD8J8Ws/uq2lV1A5oZ6EsucrnEkoHyDAAAUAIVp0JDcSnayjeqFfAfNLjsIfyB8gwAAFACUWgAwDulgj0AAAAAAAAUdZRnAAAAAAAsUJ4BAAAAALBAeQYAAAAAwALlGQAAAAAAC5xtGwAAAECxkp3qVMb2wzqeFi5nAK7znLIj0X37xK7flRGZHbDs8g1qKqwCZ68PBMozAAAAgGIjK9Wpr5s8rewUpxKCkL958LuSFLDs0Ipl1XXfTAp0ALDbNgAAAIBi40TcIWWnOIM9GAGTnZKuE3GHgj0YJQJbngEAAAAUSy1m91W1q+oGNNPlcsnhcCgmJkYRftxtO3V7otbHzvPb6+NslGcAAAAAxVL5RrVUpW29gGY6nU4lRWeqUsM6iopiV+rihN22AQAAAACwwJZnAAXir7NW5j87Zf7b+blcLmU4fM/mLJQAAAAoLMozAEuBOmvlzwMufNyOr9mchRIAAACFxW7bACxd7Get5CyUAAAAKCy2PAPwysV01krOQgkAAAC7UJ4BeIWzVgIAAKAkYrdtAAAAAAAsUJ4BAAAAALDAbtsAAAAAYBN/Xd7zTOe63GdhL+9ppaRf/pPyDAAAAAA2CNTlPc905uU+/ZVd0i//yW7bAAAAAGCDi/3ynlZK+uU/2fIMAAAAADa7mC7vaYXLf55GeQYAAAAAm3F5z+KH3bYBAAAAALBAeQYAAAAAwAK7bQMAihRfL/Fxrkt2eMvlcilHmT49FwAAFG+UZwBAkWHXJT7OvGSHN0qVC1f9Xa9KHCsGAADyYbdtAECRURQu8ZF7MlMnd/8R1GEAAABFD1ueAQBFUqAv8cFlOAAAwIVQngEARVIwLvEBAABwPpRn+CznRKrMzu1yOtOU68VJfTJ27jjnbW+4XC6ZXJ+eCgAAAABeozzDJ6dSU7W7VVOZ1BTtL8TrOAbH+v7k6HLK2byTk/oAAAAA8DvKM3zi2h2n3NSU4A5E2kll7tmtcpfWCO5wwO+4dBFQfPm6fFspyPLvcrmU4fA9u3yDmgqrwA+4AFBSUJ5RaDWnz1aFFi0Dlpexc0fhtljjosKli4Diy67l24rV8u9rdmjFsuq6byYFGgBKCMozCi28QUNFX9022IOBYqooXbqoQo0qQR0OoLgpCst3YWSnpOtE3CFObAcAJQTlGcBFg0sXAcVXoJdv6fRu2w6HQzExMYrwYrdt1g2+Yzd9ABczW8vz6tWrNWTIEI/7OnfurJkzZ9oZA6CE4tJFQPEVjOXb6XQqKTpTlRrWURSHZfgdu+kDuNjZWp737t2rDh06aPz48e77wsPD7YwAAADARYjd9AFc7Gwtz/Hx8bryyitVtWpVO18WAAAAxQi76QO4GNlentu3b2/nSwJAicUlugAUV+ymD+BiZFt5NsZo//79+uGHHzR37lzl5OTo1ltv1bBhwxQWFmZXDACUCFyiCwAAoGixrTwfOnRIGRkZCgsL0+uvv66DBw9qwoQJcrlceuGFFwr0GsYYOZ3eHwvjcrnctzMzM316DV+RTTbZZPvD8c37gn5sYO7JTCVvO6DQAJ4cp6ROb7IDny1JGRkZHv8vqPzD7XK5fBpuX7MLi+nN9Ca7+GZL/pvXCrIcBGs+L2y2MUYhISEFeqxt5blWrVpav369KlSooJCQEDVs2FC5ubkaNWqUnn32WZUuXdryNbKzs7Vr1y6vs525x9y3Dx8+rNSkLK9fw1fBzDYOh0f2Hz68d2STXRAZjsMe2SmBiyZbUvXnOyu8buCuMZ0Zn6ykl7+SVHLfc7KLf3Z+jnzr14LIP9wOh0NJ0b4f4uBtdmExvZneZBff7Pzsnte8WQ4CPZ/bkV3QPaVtPea5YsWKHv+uW7euMjMzlZqaqkqVKlk+PzQ0VPXqeX/8y7H0g4r/7fTtGjVqqGalwJ2AIpjZTmea9uuv7EsaNiSbbL84nhbu3nW4Ro0aqkF2QLNjrmuqGtcFMHvDPnd5LqnvOdnFP1s6vYUi7wRSkZGRBX6ex/IZE6NKDesELLuwmN5Mb7KLb7bkv3mtIMtBsObzwmbv3bu3wI+1rTz/73//08iRI/Xdd9+5B3jXrl2qWLFigYqzJIWEhPh0Aof0nL9OpBMeHh7Qk0AEMzs3gmyyA8NJNtlkk13MsvOLjIz0Kjv/cEdERBRquL3NLiymN9Ob7OKbnZ/d85o3y0Gg5/PCZhd0l23JxvLcsmVLhYeH64UXXtDgwYOVmJioKVOmqH///nZFAAAAAADOw9crdVgpyJU8XC6XMhy+Z5dvUFNhATzPii9sK8/R0dFauHChJk6cqLvvvltly5ZVnz59KM8AAAAA4Gd2XanDitWVPHzNDq1YVl33zSzSBdrWY56vuOIKvfPOO3a+JAAAAADAwom4Q0G/UkdhZKek60TcoYBfA94btpZnAAAAAEBwtZjdV9WuCtyJjKXTu23nnbQrwovdtlO3J2p97IW3ZhcVlGcAAAAAKEbKN6oV8C24TqdTSdGZqtSwTtBOGOZvlGcAAAAAQKH4erKygpyMzIrL5VKOfL8Ge0FRngEAAAAAPrPrZGVWJyO7kFLlwlV/16uSH7d6U54BAECJVBK2kgBAIBSFk5XlnszUyd1/qEKNKn7LoDwDAIASp6RsJQGAQAv0ycoCecIxyjMAAChxSspWEvyFPQ2AwAjGycoChfIMAABKtOK8lQSnsacBADtQngEAQIlWnLeS4DT2NABgB8ozAAAASgz2NADgK8ozAAAASgz2NADgq1LBHgAAAAAAAIo6yjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABgwdbynJmZqeeee06tW7fWddddp7ffftvOlwcAAAAAICjK2PliU6ZM0fbt2/Xee+/p0KFDevrpp1WzZk3deuutdsYAAAAAABBQtpVnp9Opjz76SPPnz1fjxo3VuHFj7dmzR++//z7lGQAAAABwUbNtt+24uDidOnVKLVu2dN/XqlUrbdmyRbm5uXbFAAAAAAAQcLaV56NHj+qSSy5RWFiY+74qVaooMzNTKSkpdsUAAAAAABBwIcYYY8cLrVq1SjNmzNC3337rvi8xMVEdO3bUmjVrdOmll17w+b/++quMMQoNDfU6Oyf3lFzZJyVJ4aHRKlPK+9fwVTCzTWamsv84LEkqU/1SlYqIIJtsv8jNzFbG4RRJUsSlFVU6InDzOdlkk0022WSTTTbZZPsrOzs7WyEhIbrqqqssH2tbef7Xv/6lCRMm6Mcff3TfFx8fr9tuu03r169XxYoVL/j8TZs2+VyeAQAAAADwVl55zn/48fnYdsKw6tWr688//9SpU6dUpszplz169KgiIiJUvnx5y+cXZGABAAAAAAgG2455btiwocqUKaPNmze779u4caOaNm2qUqVsvZw0AAAAAAABZVurjYyMVLdu3TR27Fht3bpV//nPf/T222/roYcesisCAAAAAICgsO2YZ0nKyMjQ2LFj9fXXXys6OlqPPvqo+vbta9fLAwAAAAAQFLaWZwAAAAAAiiMORgYAAAAAwALlGQAAAAAAC5RnAAAAAAAsUJ4BAAAAALBAeQYAAAiwtLS0YA8CUKwdPXo02IOAYqhYleeSfuLwvPHPzc0N8pAEVqDHuyjMZ7t37w72IJRIwVzGSuryXVJkZWUpKysrKNmUuMCbMWOGPv74Y+Xk5AR7UILm1KlTwR6EIvF5HiglaVwl6e2339Yrr7yinJycEjfu8K+LvjwvWbJEEydOlCSFhISU2AVk69at2rdvnySpVKnTk9XfX7LPfK+D8d4HcrwXLVqksWPHqn///tqyZYtfsy7kzTff1Isvvqjs7OyA5qanp3v8O5DTuyjMa3FxcUpNTZX017wWqOEIxvJdUqd3MLLnzZunp556Sj169NB7772nPXv2+D0zz7JlyzR06FAdPHgwYJlnCvS89v777+vVV1/VY489pu+++y7g4z5p0iTNnTtX7du3V+nSpQOanSeYy7ckjR07VgsXLgxoZn7vv/++MjMzFRISErDMYLznReE7cjDWqZMmTdKUKVP0v//9T06nM6DTOU8wlzE+v/2bXcYvrxpA5cuX16JFi1StWjX179/fvXII9ILyzTffKDo6Wm3btg1oriT99ttvmj9/vpxOp6KiotSpUydde+21qly5st/ei/nz5ystLU116tRRu3btVL169YC/94Ec72nTpumjjz5Snz59lJiYqP79++urr75SpUqVbMsoiEmTJum9995TSEiItm/frpYtWwYkd9myZdqyZYuaNm2qZs2aqWnTpgGbzvPmzVN6enpQ57WtW7dq4sSJql69ukqXLq2HHnpIl19+uSpUqKDc3Fx3ofWHuLi4gC/fwZzewVy3BGNemz17tpYtW6Ynn3xSVapU0bfffqs9e/Zo+PDhAVm/VK1aVWvXrtWcOXM0ePBg1apVy++Z+QV6Xps9e7aWL1+uPn36KCIiQjNnzlRMTIz69u2rZs2a+S03z8SJE/Xpp5/q008/Vb169fyedy7BXL4lafLkyfr444/1wQcfBCwzv7zP0RtuuEG1a9cOSGaw3vNgf0cOxjp10qRJ+vjjj/XOO+9o9uzZSkxMVKNGjQI63sFcxvj89n92iLnIN9V+8803evLJJ5Wdna1+/frpqaeekqSALSTGGDmdTj399NPKysrS0KFD1bRpU7/nnikrK0shISGaNm2aEhMTtWfPHk2dOtVvXwbWrFmjnJwcLViwQFWqVFGFChX00ksvBfxHi0CM99GjRzVkyBCNGDFCbdq0kSR16dJFL7zwgho3bqyoqCiFhYXZlnc+EydO1CeffKI5c+Zozpw5uv/++9WxY0e/lzdJ2rlzp5xOp95//32dOHFCpUuX1rhx41S1alWVKeO/3+BycnK0adMmnThxIujz2p9//qlSpUpp9OjRyszMVE5OjsaMGaPatWv7fX0T6OV7x44dysjICPj0loK7bvnpp5+UmZmpBQsWqHLlyn7Nzs3NVXp6ugYNGqSHH35YHTt2lCStWLFCU6dO1cKFC9WkSRPbc8+0fft2xcbGKiUlRe3bt9eECRMC+oUnkOuWtLQ0Pf744+rZs6fuuusuSdK//vUvzZ8/X1FRUXrqqaf8WqDXr1+vQYMG6cUXX1TXrl0lnf4O8fvvvyskJETR0dGqUKGC3/LzBGt9Lp0uzitWrNCSJUtUv379cz7Gn/PdxIkTtWrVKi1atEgNGjTwyPJnbrDe82B/Rw7kOlU6PX1XrlyppUuXqn79+urUqZO6d++uQYMG+SXvfIL5GcrndwDmNXORmz59uunTp49ZvXq1adasmZk6dar7b7m5uX7Pz8sYPny4adOmjRk2bJj55Zdf/J5rjDF79+41u3fvNqmpqR73JyQkmLFjx5rGjRubTz/91K/DkJSUZP7zn/+Yu+66y9x2221m/fr1JjMz06+ZgR7vhIQEc9VVV5nVq1cbY4w5fvy4adSokbnvvvtMu3btzPPPP+/3af7SSy+Zq6++2uzatcsYY8zTTz9tBg4caHJycvya63K5PP79559/ml27dpn77rvP3H777WblypVnTQe7PPPMM2bevHnufx85ciTg89qGDRvM+vXrzfHjxz3uX7dunfnHP/5hWrRoYTZs2GCMMbZPi+3bt5stW7aYEydOeNwfyOU7kNP71KlTHv8O5LplzJgx5rfffgto9rZt24wxxrRp08asXLnS42+33HKLmT17tjHGf59jea978uRJM2bMGLNz507ToUMH88gjj5hjx44ZY4z5448//JJtTHDWLUeOHDGtW7c2S5cu9bh/4sSJpnHjxmbgwIHu6eIPBw4cMP379zevvPKKMeb0PB8bG2t69Ohh2rRpYwYOHGi+++47v+UHc31ujDG//PKLqV+/vsc45ubmmoMHD5pt27aZ7Oxsj/vtNn/+fFO/fn2/ztdnCvZ7/vrrrwflO3Kg16m5ublmy5Yt5tZbb3V/TzLGmLfeessMHDjQZGRk+P370rmkpqYGdHoHK7ukfX5fdFued+/erTJlysgYo7p162rhwoVKTU3VsGHD9K9//UvPPfecHn74YY0cOVJS4H5d69u3r1wulypVqqQyZcqob9++uuqqq/yWN3DgQCUlJSkzM1MnT57UqFGj3Lsp5MnbQjl9+nR16tSp0FsoX331VZ04cUJZWVl64oknVKNGDY+/Dxo0SAcOHNCoUaN03XXX+eUXrkCO9/79+xUZGanc3Fx98cUXatCgga6//notWLBAX375pZ588knt379fGzdulMvl0jPPPKM6derYObqSpOTkZA0aNEgvvfSSGjRoIEn65JNP9N5772nJkiWKjo72y3w+ZswYJSUl6eTJk+rWrZtuvvlmj91Ix48fr61bt6pr167q1q2boqOjbcu+//77tXHjRl177bXnPC4uEPPao48+qoSEBOXk5CgnJ0crV67UJZdc4j5GMSMjQ5MnT9ann36q999/X40aNbJtL4ABAwbo0KFD7i3Oy5cvP2sXXruX7xkzZigtLU0nTpxQjx491KBBA4+tYP6c3mPHjtWJEyeUmZmpgQMHqmHDhgoNDXX/3Z/T+95779Xx48f12WefKTw8/KxlyR/Zs2bN0htvvKEPPvhAixYt0qWXXqrBgwcrPDxcpUuXVvfu3dW+fXuNGjWq0Fln2r9/v6KiohQaGuqep+699149+OCDuvrqq9W9e3c1bdpU5cuXV7ly5fTMM8/YvnwFet2ybt06NW7cWOXKldOoUaOUk5OjESNGuHdR/+yzz/Sf//xHERERuvTSSzVo0CCFh4fbtk5NSUlRbm6uKlWqpL179+r+++/XY489ptTUVO3cuVOxsbFKSkrSxo0btX79er388stq0aKFLdl5grk+zy82NlapqanuXbYffvhhnTx5Ujt37tQNN9ygTp06qWfPnn7J/uKLLzRixAitWLFCTZo0UXZ2tl555RUdO3ZMKSkpevjhh9WiRQtdcskltuQF4z0/fPiwQkNDFR4ernLlymnhwoU6ceKEhg4dGrDvyMFYpyYnJ6ts2bL6888/VbNmTXfmDz/8oKFDh2rJkiVq3LixXzvBlClTlJqaKpfLpeHDh591CIw/l7FgZpe0z29JF9eW52nTppk77rjDdOjQwdx2221m+fLlJi0tzcTHxxtjjMnOzjZffPGFadKkScB+XcvNzTXHjh0zvXv3Nj/88IPZsGGDGThwoBk6dKjZuHGjXzKnT59u7r77bnPgwAFz9OhRM3PmTNO2bVszZcoUk5iYeNZjW7RoYfbt2+ceXl/07dvXdOvWzbz22mvm1ltvNd27d3f/LSsry317+PDh5pZbbjF79uwxxti7NS6Q4z116lRzxx13mOuvv94MHjzY/Otf/3L/7fDhwyYlJcX973Xr1pmePXua77//vhBjd2F5v5rlvZ8ZGRnm+uuvN3PmzPFLXv/+/c0999xjPvnkE/P000+bBx54wLz55psmKyvLY3pPmTLF9OjRw3z77bfGGHuWtXvvvdf07NnTfPfdd+aBBx4waWlp7vHOPz/5c15bsGCB6datm/n999/N77//bjZt2uTx97zxzMnJMc8++6xp3769OXTokC3Z48ePN7179zYHDhwwW7duNffcc4+ZP3/+WdnG2Ld8P/roo6ZHjx7mjTfeMH379jW9e/c2Y8eONUePHvV4nD+m98CBA023bt3MwoULzX333WdWrVrl/pu/1y29e/c2vXr1Munp6Wf9Lf9WMDuzX3rpJdOsWTPTrl07s27dOvPbb7+ZXbt2mVOnTrl/vb/nnnvMxIkTPZ6XnJzsc2aevPXaddddZwYOHGi2bNlijDk9v0+ZMsUYc3pd06JFC9O4cWOzfv36QmeeKdDrll9//dVcd911Ztq0aSYzM9OsXr3a9OrVy4wfP958++23ZtOmTeaaa64xn3zyifniiy/MjTfeaH7//Xc7RtUYc3rLX9++fc2NN95oBg8ebL777jvzyy+/mCZNmpju3bubHTt2uB8bHx9vHn/88bO2jBdWMNfnefK2wLpcLnPnnXeakSNHmpdfftkMGzbMbNy40Wzfvt0888wz5pFHHjFr1661PT/Pq6++au68806zZ88eM2zYMHP//febadOmmUGDBpnbb7/dvPfeeyYzM7PQ2cF4z6dNm2Z69uxprrvuOvPMM8+YvXv3moSEhIB+Rw7GOnX69OnmnnvuMddee60ZPXq0e72WP2vo0KHm5MmTPmdYeeihh0zXrl3N7NmzTefOnU3v3r3df8s/3v5YxoKZXdI+v/NcNOV5+fLl5vrrrzdbtmwxv/zyi1m+fLlp2rSpe1favJng1KlT7pXDtGnT/DIs+/fvNzt37vRYwX7++efu3Qb+97//mccee8xvBXro0KHmpZde8rhv1qxZpmHDhmbq1KlnFcmRI0ea/v37n7X7Z0Ft2bLF9OjRw73r6uHDh02XLl1MUlLSOR//4IMPmp49e/qUdSGBGu9//vOf5tprrzUbN240q1atcs9j+eXk5HiseO677z4zbtw4r3Ks5C/s+VdCeV+w586dawYOHGgOHjxoa+7OnTvN3Xff7fF+jh8/3nTu3Nk4nU6PYTDm9Pt8++23u+8rzAq5V69epnv37iYzM9McPHjQNGvWzKxbt87jMfmz/TWvjR8/3vzjH/9w/zshIcH861//Mu+++65xOBweK9+kpCQzdOhQM27cuLN20fNF//79zYIFC9z/HjRokJkwYYJZsGCB2bhxo3uX2jyFXb5/++03c8cdd5i9e/e671uwYIG5+eabzciRI82RI0fOyrNrev/xxx+ma9euZ33ZOd/7aOf0fvXVV027du08fiBIT083x44dMxkZGWfthmZH9qRJk0zr1q3N4cOHzYsvvmieffZZY4znB70xxvTs2dO8+OKL7n+/8cYbZsSIEYWav/Kv1z777DPz9ddfu/+2Zs0a97iNHTvWdOjQwVxzzTW2r1+CsW5JS0tz/+A+bdo0k5OTY7788kszZMgQ06JFC3Pdddd5vNe33HKL+eCDD3wdRQ8ffPCBufbaa826devM559/bt58803TsGFDM3/+fDN//nxzww03nLUL8dChQ82wYcNsyTcmuOtzYzw/x/J+BP7pp5/Mgw8+aG666SaPZf/YsWOmV69e5tVXXy1UZn4///yzxw+bBw4cME899ZR56KGHzOOPP+6x3nz99dfNTTfddNZhOt4Kxnv+7rvvmvbt25uff/7ZfPTRR6ZXr15m0aJFZz3On9+Rg7FOXbx4sbn22mvN2rVrzbJly0zv3r3N3LlzjTF/laRPP/3U9OnTx/0juN27b2/YsMF0797dPd/s27fPdO7c2TgcDpOWlnbW+t3OZSyY2SXt8zu/i+ZSVfHx8brpppvUrFkztWrVSp06ddJVV12lX375RdJfpyMvXbq0OnfurClTpmju3LmaPXu2rcMxefJkPf7443rggQc0YMAAbdq0SZJ0++2368orr5QkXXfddbr//vuVnZ2td9991/2YwsobxwoVKuj48eNyuVzu+6677jrVqlVLv/zyi9asWSNJ7utH9urVS2XKlFFSUpIk7y5xs2/fPoWHhys+Pl5Op1OSlJCQoEOHDmnkyJHq27evPvzwQ6WkpLifM23aNOXk5Oidd94p9DjnMcYEbLwPHTqk+vXrq2XLluratatuvvlmbd26Vb/88ov27t0rSXI6ne7LFklSpUqV9Le//c22cZWkZ599Vs8884wkKTQ01D1eebsNt2vXTrt379ZPP/3k1fhdyL59+xQaGqpdu3Z5XH+0f//+SkpK0ubNm93DkPf3qVOnKiwsTJMmTZIkn3eJGj16tKpXr67FixcrLCxMtWrV0o033ugev/zjn3d9ULvntbz3vlatWvrjjz+UnZ2tAwcOqG/fvpo/f76WLl2qbt26admyZe5r41arVk033XSTEhIS3JcPMz4cDZObm6usrCyVL1/efb3fQ4cO6dtvv9XPP/+sVatWadCgQXr77bfd87RUuOU7Pj5eZcqUOevSSI8++qiqVq2qH3/8UdOnT9fx48fdf7Nreu/evVvh4eHat2+fe5jj4uL0yCOPqFevXurTp4+WLl2qP//80/0cu6Z3YmKirrnmGpUtW1Y7duxwZz/++ON68MEH1atXL7322mse73NhsydPnqwlS5a4d9OuVq2a4uPjJcm9K1nefHPq1Cn3SQhnzJihmTNnql+/fgoPD/d5nPOv1+6880517NhRW7Zs0c8//6xTp06pRo0aGjJkiL7//nutWLFC//73v/XDDz9o8uTJtlyPNxjrltzcXIWGhqpmzZoqV66c4uLiNHPmTN14442aNWuWPvroI61cuVLjxo2TdPrEfJdccomqVKlS6PGVpL1796pt27Zq27atbr/9dvfusq+++qqOHTumr7/+WtWrV1dGRob7OWXLltVll11mS34w1+fn+hzLm6ebNm2qRo0aKSwszL0bc95u7W3bttXu3bt9yswvNzdX8fHxeuihh7R8+XIdOnRIkvS3v/1NHTp0UEJCgk6ePKkyZcq4x/2JJ55QTk6O+7uEL4L1nu/evVv33HOPrr76at1zzz2qW7eutm3bpvXr17u/J+fl+uM7cjDWqdLpw1C6du2qdu3aqU+fPmrevLmOHDmiAwcOuKf5XXfdpYiICE2ePFnS6cs92nV99QMHDqh06dJyOBzuz+34+HglJCRo+PDh6tWrlyZPnuwx3nYtY8HMLmmf32cq8uU5bwV89OhRJScnu++vVKmS6tSpox9//FFZWVkex/qVLl1at9xyi2bNmqUuXbrYNixff/21Pv30U02aNEmLFi1Sjx49zjquOe9LRl6BNsZo9uzZ2rp1q23D0axZM61fv17ff/+9+wu8w+FQ27ZtdeONN+rVV19VQkKCu2RdffXVMsa4Z5qCHhe5adMmjRkzRhUrVtTVV1+tJ554Qm+99ZYGDhyom2++Wd26dVO1atX0ySefaPHixcrMzJQkVaxYUbfffrsSEhJsG+eQkBA1a9ZM69at89t4581r5v/OoB4SEqKcnBzdd999Gj16tJ588kkNGzZMb731lrZs2aKnn35ar732msaNG6d169bp+uuvt2Vc81ZE9evX16pVqzRixAhJnh+20un5oF+/fpowYYJ27typUqVKFeqadnnTOzIyUk2aNNGmTZuUk5MjY4z7+JX8X6JLly7tHtbHH39cJ0+edK/AvRUXF6c9e/bopZdeUtmyZd3jceWVV+rzzz9Xdna2Spcu7b4/r2zYPa/lvf4VV1yhY8eO6YcfftA333yjTp066Z133tFXX32lxx9/XDNnznR/CZKkbt266cSJE5o7d64k3z6QQkJCFBYWprvuukutWrWSdHr+fvbZZ7V06VL985//1KhRo/T1119r48aN7uf5unxv3rxZY8eOVdmyZdW8eXN98cUXHl/imzdvro4dOyolJUX//e9/Jck9fQcNGlSo6b1582aNHz9e5cqVU5s2bfTxxx/r999/1/jx43XZZZfp8ccf15VXXqkvv/xSixcvlsvlkmTP9P7111/13HPPqX379rrzzjs1YsQIbd26VVOnTlWdOnU0cuRI3XLLLfrtt9/0+uuvu9c1hcl2uVwqV66cVq5cqYYNG0qSevbsqcOHD+vLL790Py43N1e5ubkKCQlReHi43nnnHS1cuFArVqxQo0aNfBpfq/XaiBEj9NZbb+mrr77SkSNHNG/ePFWqVEnly5fXmjVrNHz48EIfJxasdUupUqUUFhamTp06qX379rrmmmu0bt06zZs3z51ZuXJlTZ06Ve+++67eeOMNHTx40P1DuK/y3vNjx455XG81MjJSzZs31yWXXKJ33nlHH374oU6ePKlhw4Zp8uTJevHFF7V69WrdeeedhcqXgrs+l87/OSZJ0dHRGjBggBYvXqz/9//+n5xOp3u9dfjwYVWrVs3n3DylSpVS3bp1dcUVV2jVqlX69NNP9fvvv0uSbr31VvXt21djx45VZGSk+zvDyZMnVaVKFZ9/PAnGe26M0alTp5SQkOBeT+bk5OiHH37Q1q1b9fTTT2vAgAF67bXXPHLt/I4cjHWqMUa5ublKTk7WkSNHdOrUKZ06dUpr1qzRN998o169eunxxx93nzNl5syZOnr0qIYOHep+Dwpr06ZNeuGFF1SuXDl169ZN6enpcrlc+uyzz9S5c2cNHz5cXbp00b59+zRz5kylpaW5v8MV9jM0mNkl7fP7nGzbhu1neWdO++OPPzx2W73jjjs8zqLmz+ObV6xYYR566CH3LrR5Z/f78ssvzcGDB927R+Tfxfa///2vGT58uDl8+LDPuWPGjDFr1qzxuG/8+PHm73//u3n44YfNsGHDTP369d27F/fv39+921PesCQkJJinnnrKq9zk5GQzfPhwk56ebtavX2+GDRtmevfube69916P3TLmzJljunfv7nEWv/j4eHPDDTeYX3/91adxNsaYyZMnm+eee848+eST7l3bxo0b5/fx3rVrl2nYsKH55JNPzHfffWcef/xxc+jQIbNt2zazePFic/XVV5uBAweaKVOmmHvvvdc8/fTTHmd3tEufPn3MqFGjzN///nczaNAg9/2nTp1yz+eZmZnmxRdfNF26dHEf9+qrvOmdlpZmNmzYYBISEtx5f/75p2nZsuVZZ4LNO4bo0KFD5pZbbvH5TLFJSUlm+PDh7l3a8paltLQ006NHDzNr1qzzPteOee18y1i7du3MQw89ZJYsWeLxt6eeesrcd999Jjc31z2vbdiwwX0W3cJm5+d0Oj3Wa+PGjTP333+/Mcae5TstLc3MnTvX3HfffWbatGlm27Zt5qOPPjJt27Y127dvN6+88orHMVTGFH56Hzt2zDzxxBMmPT3drFq1yvTt29csW7bM/OMf//A4tveNN94w3bp189hVvbDT+9ixY2bYsGHuddjw4cPNtddea5566imTlpbmftzKlSvN3Xff7bHuLkx2/t3kcnNzzcmTJ83jjz/unmfyHwryyiuvmPr165sWLVqYrVu3+jSeZzrfem3Lli3mww8/NK1btzZvvPGG+/Fn7u5XGMFat+S9n0uWLDGPP/64McaYhQsXmn79+plbb73VtG3b1uzcudNMmTLFdO3a1QwcONDs3LmzMKPq4bvvvjvrrPjx8fFm+PDh5sMPPzQdOnQwU6dONePHj3d/lsTFxdmSHcz1eX7n+xzLmza7du0yw4YNM0888YQZPXq0admypceZcwvrmWeeMV26dDEdOnQw06dPNwcOHPD4+9atW83XX39tfvnlFzNr1ixz7bXX+nyoQjDf8w0bNph3333XGHP6ULtHH33UJCYmmoMHD5qvv/7aNGnSxCxevNin17YSrHWqMX+dwf2OO+4w1157rbn33nvN/v37zcaNG83ChQtNixYtzEcffWSMMWb16tWme/fuZx2K5Kv835H//PNP9/0HDhzw6CUrVqwwd999t8cuxoWd3sHMLomf32cq8lue89x8882aN2+eKleu7N6qU7p06bO28Gzbts1vw3Dy5EnFx8e7f0Hs1auXXnrpJT399NMaOXKkJk+erKysLI9dbDt06KDx48fr0ksv9Tn30KFDGjt2rMfWphdeeEEjRoxQs2bN3GdU7Nixo4wxKlu2rPvXzfxnvEtOTnbvel0QlStXVmpqql555RW1adNGM2bMUP/+/RUVFeXx6+kjjzyihIQEbdiwQdLpX5zr1Kmjrl27un9x8tbDDz+sn376STVr1tSuXbs0ePBgSdKLL76o4cOHq2nTpoqOjvbLeDdo0EDDhg3TO++8o0WLFunKK69UjRo11KRJE3Xv3l333nuvypYtq1GjRumdd97Ryy+/7D4LdmFs3bpVJ06ckCQdOXJEOTk56t69u2bOnKlNmzZpyJAhkjy3QIeFhenOO+9Us2bNCrVLp3R6eqekpGjy5Mlq3bq1ateu7c6LiIhwb7mSTv/qO2LECC1dulS5ubmqUaOG7r33Xh07dsynrd/VqlXTyZMn3btP5p1RPzw8XDfccIO2b9+uo0ePurPz2DGvSedfxjp27Kj169fr8OHDHq9fr149XXLJJQoJCXHPa2XLltWmTZuUkZHh1XtwZnbec/O2SERGRnqs52rWrKnKlStLKvzynZKSomnTpmnAgAG68cYb9csvv+jRRx/Vq6++qpEjR6px48bq27evHA6Hdu3aJXP6PBmFnt6VKlVSWlqapkyZorvuukuVK1fWjBkztGXLFo9rpj/66KM6ePCg1q1b535PCju9K1WqpIyMDI0ePVrS6V/hK1SooLS0NJUtW9a9bN1xxx1KTEx07/JY2Oy8LR0hISHua/p269ZNixYt0tatWz32GLj88stVpUoVrVixQk2bNvVpPM90vvVas2bN1KVLF/Xs2VNxcXHKzMyUMcbWM6IGa92St9x06dLFfZhNv3799OeffyopKUmdOnXSFVdcoVGjRmnJkiWaOXOme88AO7Rv314DBw7UmDFj9Pzzz+v1119X7969VaNGDd1zzz1q06aNqlWrphdeeEHvvvuuJkyYcN5rH3srWO95QT/H8qZNRESELrnkEp06dUohISH64IMPCr3lX/rrMJ9GjRqpb9++Gj16tFauXKnPPvtMTqdT//znP5WTk6MPPvhA48aN03PPPaf//ve/mjdv3llnKi6oYH6Gtm7dWg8//LCk03ulzZ49W//v//0/1apVS506ddLQoUO1du1aZWZm2nKIV37BWqdKUqtWrbRixQp1795dMTEx6tq1q2JiYnTVVVfp3nvv1X333ad169bJGKP27dtr0aJFqlq1qi3jnfcdecKECapYsaKk09P1b3/7m8fnWN54531HtuMzNJjZJfHz+0wXTXmWpOrVq6tMmTLuLxlOp1Pp6enuFcH06dPVq1cvHT9+vFC7r+a3detW94funXfeqSpVqmjWrFlasWKFqlevrjfeeEPffPONbr31VsXFxWnRokWS5LGLaVRUlE/ZeeNVrlw592WZ8o7/lE7PHMOHD9eECRPUvHlzHThwQCEhITp58qTKli0r6a8v4rVr19asWbMKPCx5z8s7TufXX3+VdHoXrOzsbP3vf/9zP+bkyZOqU6eOe4WUN33q1KmjmJgYr8f7l19+0cmTJ/XOO+9o8ODBevPNN5WWlqb4+HhlZma6d9V4+eWXbR/vPL169VLr1q21ZcsW9+7o0umCVK5cOR04cEDZ2dnuS8sURt6uR7GxsVqxYoXS09NVoUIFtWrVSjExMWratKl7N+G8Lx5lypRxH1/bunVrjRkzRjVr1vR5GPLerwEDBujIkSNnHaefkZGh3Nxc9/sbGxurTZs2qV+/fu7pXa9ePV177bVe77Kcl92vXz+lpKS4s0NCQlSmTBn16dNHu3bt0oIFC9z35z2nsPOa1TI2evRo9ejRQ4sXL9aqVau0a9cunTx5UmvXrlVkZKTHazVs2FALFiw4q+x6m503fnmvsW/fPv3888+Kj4+Xy+XSjz/+eNb87OvyPWDAACUkJGj37t2KjY3V3LlztXTpUn311Ve65557JElpaWm69NJLVb58eXfxkwo/vfv376/ff/9d+/bt0yuvvKL27dvr+PHjWrRokXt3MpfLpdq1a7uPiyzs9M4/r504cUJbt25V3bp1NWbMGE2fPl3SXyU3LS1NtWvXtm29di633HKLunbtqvfee0/Jycnu97JXr1767LPPVLduXVty8pxvvRYdHa0qVaro8OHD5/xRujCCuW7JExoaqpSUFG3dulUTJ07UiRMn1LNnTx06dEgTJ07UyZMnFR0d7fHFzw6hoaGKjY3VxIkTlZCQoG3btumhhx7SyJEj3cvSTz/9JGOMwsLCbPvBIhjvuS+fY5IUExOjF198UbNnz9bo0aN1xRVX+Dze+Yt73nJ8+eWXa+XKlerQoYNGjBihjz/+WPfee6/eeust5eTkaMKECXrvvfe0YMECLVy4sNCHRwRzPpf++lyJiIjwuD8sLExHjx5V6dKlbbmcYp6isE5t0qSJ+vbtq0aNGnkcehQZGano6GgdPHhQOTk5ioqKsu0STfk/x44fP+7xvUWSRw9JT0/3GG87P0ODmV2SP7/tvzhqAOR9sSxVqpT7F71Zs2Zp0aJF+vDDD8+6HqovcnNzdfz4ccXGxio2Nla9evVShQoVdOutt2rnzp2Ki4vT5Zdf7r6+8L333qvExEStXbtW/fv3l+T7gfjS6RON1KtXT9nZ2dq3b5+6dOmi6OhovfDCC3r55Zd1zTXXuLcCHTx4UHPmzNGmTZtUrlw5ZWZm6rHHHjtrGM78sn8hec+rW7eunE6nVq5cqebNm6tmzZqqWLGi3n33XW3ZskVXXnmlvvrqKxlj1LhxY0l/TZ9u3bp5Pd4XOgHCU089JafTqeuuu06PPvqoIiIiNHHiRG3ZssW28c5TqVIlPfbYYzpx4oTee+891alTRz179nTPF5UqVdKpU6c8tvz5yhijKlWqqGLFipo2bZpOnTql2NhYjRw50r0iaN26tV5//XU9+eSTGjJkiGbPnq3Q0FBlZ2crNDTUp3HML//0Tk9P14oVK9S4cWOFhYUpJyfHvUdFdna2hg0bpsTERH311VfuEh8aGqrrrrvOL9nVq1fXq6++qtjYWFWpUkUDBgw4a9nyZV4ryDIWFhamiRMnqlKlSlqxYoWmTp2qyy+/XDk5Oe7jm/MX3bwvRnZk531QxMXFacqUKe4PolOnTrmP2cyf7evy7XK59O6772rcuHGKjo5WdHS0duzYoddee03t2rXTZ599pujo6LOu627H9Ha5XJo/f75eeeUVTZ48WWPGjNF3332n9evXq1WrVtq4caNycnJ09dVXe4yvL9P7zOz09HQtW7ZMTZs2VZs2bSRJq1ev1n//+19dffXV+vLLL5WTk6PWrVvbkn0+N954o5YsWaK1a9fq1ltvda9T7PgcO9OF1mvJycnuLYB2bnUO5rolT7ly5XTjjTdq0KBBKlu2rN577z3VqlVLM2bMUFxcXKGO7bUSHh6uLl26qGPHjmd9XkREROjyyy+3/bqzwXjPff0ck/46QZ6vP0Sf6/taXkm67LLLlJOTo1OnTqlbt25asWKFNm3apJ49eyolJUXVqlWz5UeqojCfS3+VhCNHjig5Odn9Y8CRI0dUuXJlvy/fwVqnlipVSldeeaXmzJmjevXqud/L9PR01apVy/at7Rea3sePH9fSpUu1detWXXPNNfrf//6nnJwctWzZ0uM1/PGdKZDZJfrzu3B7fQdH/uOYunXrZsaNG2eaNGlitm3bZltG3vFpnTt3No0bNzbz5s0zxhhz/PhxM2rUKNOkSRMzcOBAj+esXr3adOnSxeMawL7YtGmTuf/++01aWpo5duyYGTBggFm7dq05cOCAefHFF02HDh3MTz/95PGcLVu2mNWrV5tPP/3UPexnnqrdVzt37jSNGzd2H5uXlZVlpk2bZu677z5z//33m1GjRrmPvSxM5q+//moeeOABs2fPHjNu3DgTHx9vMjIyzNChQ82TTz5pfvjhBzNr1izTr18/8/zzzxtj/Dvexhjz559/mpkzZ5pGjRqZu+66y/Tq1cu0adPG1uPi8vTu3dv069fP1K9f38yaNeuc167bsGGDufHGG83DDz9se36evOl95uVCevfuberXr29uvfVW9/S287jIC2VnZ2eb2bNnm4EDB9pyXoOCLGM//vijx3MOHDhgNm7caLZs2eK+1IUv4+/L8r1582bzww8/mP/973/u+duu9/5c7/nGjRvNE088YR544AHzxBNPuKe33Zf4OHPdcurUKbN69WrzwgsvmOHDh5vx48fbsm65UHb+8Z47d67p06ePuf/++82IESP8ln2mSZMmmbZt2/rl0obnEsj1Wn7BXLds2bLFdO/e3eOSbKdOnSr0ZYkKKu849759+5pHHnnEDBw40Fx99dW2Ht97LoF+z4PxOXbm97X58+ebjIwM999jY2PNTz/9ZJ555hlzyy23mLlz55qbb77ZTJo06axLhdkhmPO5MafntV9++cU0btzYdO3a1TzwwAOmXbt2fjk3S37BXqempKSY0aNHmzZt2piHH37YxMbGmnbt2tl2HoHzOXO8c3NzzbJly8ygQYPMwIEDzejRowP2ORaM7JL4+X1Rluc8mzdvNvXr1zdNmjQxO3bs8EtG/g+CGTNmGGNOf/F45plnTMeOHc0rr7xicnNzjdPpNFOmTDH333+/+6RHvjp27Jj5xz/+4V75r1692v2a+/fvd1+H88wv2PnZPbN89913pkmTJmbcuHEeJyPIf43Ewn4IeHMChO7du591vVtj/Pcld9euXebDDz80n3/+ufskIHbJzc01Bw4cMF26dDFHjhwx//nPf0z9+vXNG2+84fEFIM/atWtN586dC3USOit50/vll192v89jxowxd999t3s6++NDP3/2hAkTPKbxsWPH3MW5sAXal2XszExfi2RRXr4nTJjgPoGNMcbjtr+n90svveRxwo/876+/s8ePH+9xEkQ712sXkn+eGjFihNm/f7/fss7Fn+u18wnmuiX/NLb7h6CC2r59u3nttdfM3LlzPYq8PwXiPS8Kn2P5v6/Nnj3bvf56/vnnTfPmzc3tt9/ufs+XL19ubr/99nN+j7BDMOdzY07P32vXrjXz5s0zH3zwwVknSvOXYK9TT548ab766ivz4osvmnnz5hX6RKoFlX+883eA/O9BID7HgpVd0j6/L+ry7HQ6zfjx4018fLztr32+D4KZM2caY07/wjVr1ixz1113mdatW5vevXuba665xrYSP2DAADNq1Khz/i3vC/ZNN93k/oLtz7OM51m3bp1p3bq1+cc//mG+/PJLj7/Z9WX+0UcfNc8++6z73+caL5fLZdq0aXPWMFzMMjIyzKxZs9xn+fznP/95wS8e57rPbvmn9/fff+/xt/w/Zvgze/jw4eaLL77w+Jtd81owl7GivHz78z33JdvfJSf/fB7o8TYmeCUumIK5bimpAvGeB+tz7Hzf1/Ku0rB161bz0EMPnbWVP/+XbH8oqfN5sNepwVJUP0P5/LZfiDE2nVkrSOw+fiM/l8ulBQsWqHv37qpVq5Y+//xzjRw5UoMHD9bQoUOVnZ0tp9OpNWvWqFq1aqpdu7bPZ2nMY/5vv/x169bp3Xff1cCBA93HKph8xzc6HA4tXrxYH330kZYuXaomTZoUenwLIj4+XgsWLNDhw4dljNHIkSNVtmxZ1alTp1Cve6Hxlk6fACHv7MZ5xzY988wz7uMZioOsrCz38VGlS5d2z2/Dhg1Tv379zjoBSCDkTe+DBw+qdOnSGjFihCIiIgp1UhdvswM5r/l7GSupyzfZOFMw1y0lVSDe82B9jp3v+9qQIUM0cOBA90nZpL/WtfnXuf5SUufzkrpOLamfYyUt+6Ivz/52vg+CIUOGqH///n77IDh69KiGDx+uv/3tbxozZsxZK33p9AyzZs0aPfzww7Zc8L2g0tPTlZGRoTlz5qhUqVLasWOH3n77bUVERBT6g+hc432uEyD8+eefWrFiRUDHO1Byc3PdZ2L9/PPP9cwzz6hfv34aPHhwoS9H5Yu86f3mm28qJCTE1uld0OxAzWtSYJaxkrp8k438grluKakC9Z4H43PsfN/Xhg4d6j7JaDCU1Pm8pK5TS+rnWEnKpjwX0Lk+CB555BENHjzYbyvkXbt2qWfPnnrkkUc0YsQIj2E581IDeR8WwZCenl7gswwXxJnjbYzRBx984D5rYLVq1TR69Gj39bSLY4HOWyxDQkL08ccf65VXXtG///1vv5yB11t2T+9gZgdzGSupyzfZOB/e88Dz53serM+xovYD9JlK6nzOeJNdXLIpz14IxgfBmjVrNGTIEPXp00eDBg3SJZdcIsm/u6sXRP4tZP7Y9SlvvHv37q0RI0a4L8OTmZnp/vAL9nvgb/nnt7S0NNuuT+jrsPhzegczO5jLWEldvslGHt7zwAvkex6sz7Gi9gN0SZ3PGe+S9TlWUrIpz14KxgfB+vXrNWTIEN1www3q2LGjunTp4jE8xXVllH+8b775Zt12223uvxXXLc5nCuSxWSVZMJexkrp8AygZgvU5VpR+gAZQfFCefRCMDwJOvlCyxhuBV9JOeAEAxR0/QAOwG+X5IsLJF0rWeCPwStIJLwAAAOAdyvNFjJMvAP5VnE94AQAAAO9Qni8ynHyhZI03Aq+knPACAAAA3qE8AwAAAABgoZT1QwAAAAAAKNkozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABYozwAAAAAAWKA8AwAAAABggfIMAAAAAIAFyjMAAAAAABb+Py2b7iMe69uzAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Z = linkage(X_proj, method='ward')\n",
"fig, ax = plt.subplots(1,1,figsize=(12,8))\n",
"_ = dendrogram(Z, truncate_mode='lastp', ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 225,
"id": "ddfdcbe9-9e06-4129-98da-4d4787f1b606",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AgglomerativeClustering</label><div class=\"sk-toggleable__content\"><pre>AgglomerativeClustering(n_clusters=3)</pre></div></div></div></div></div>"
],
"text/plain": [
"AgglomerativeClustering(n_clusters=3)"
]
},
"execution_count": 225,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cah = AgglomerativeClustering(n_clusters=3, linkage='ward')\n",
"cah.fit(X_proj)"
]
},
{
"cell_type": "code",
"execution_count": 226,
"id": "b267cd7d-4670-414e-8ecc-7bc584e24304",
"metadata": {},
"outputs": [],
"source": [
"cah_label = cah.labels_"
]
},
{
"cell_type": "code",
"execution_count": 227,
"id": "72158a42-eefa-4ab3-a479-00c9766b8bb6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1892d95c700>"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(x=X_proj[:,0], y=X_proj[:,1], hue = cah_label)\n",
"plt.legend(bbox_to_anchor=(1, 1), loc='upper left')"
]
},
{
"cell_type": "markdown",
"id": "f2585cb0-fea8-4271-8fb8-fdd7a74058c7",
"metadata": {},
"source": [
"### Add the clusters to dataframe"
]
},
{
"cell_type": "code",
"execution_count": 228,
"id": "22e2e58f-0519-4ff7-b50c-a07f3e4f7a30",
"metadata": {},
"outputs": [],
"source": [
"agg_df['kmean'] = klabels"
]
},
{
"cell_type": "code",
"execution_count": 229,
"id": "480bed93-69ae-4998-b713-b8dc363a104a",
"metadata": {},
"outputs": [],
"source": [
"agg_df['linkage'] = cah_label"
]
},
{
"cell_type": "code",
"execution_count": 230,
"id": "7f3abca1-6ebf-4745-9fdb-bf3416ce4de1",
"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>Zone</th>\n",
" <th>Value_population</th>\n",
" <th>Value_gdp</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité alimentaire (Kcal/personne/jour)</th>\n",
" <th>Disponibilité alimentaire en quantité (kg/personne/an)</th>\n",
" <th>Disponibilité de matière grasse en quantité (g/personne/jour)</th>\n",
" <th>...</th>\n",
" <th>Résidus</th>\n",
" <th>Semences</th>\n",
" <th>Traitement</th>\n",
" <th>Variation de stock</th>\n",
" <th>Fc</th>\n",
" <th>S</th>\n",
" <th>Donnée calculée</th>\n",
" <th>Données standardisées</th>\n",
" <th>kmean</th>\n",
" <th>linkage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>36296.113</td>\n",
" <td>2058.4</td>\n",
" <td>-2.80</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>1.53</td>\n",
" <td>0.33</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.40</td>\n",
" <td>171.0</td>\n",
" <td>7.40</td>\n",
" <td>171.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afrique du Sud</td>\n",
" <td>57009.756</td>\n",
" <td>13860.3</td>\n",
" <td>-0.28</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>143.0</td>\n",
" <td>35.69</td>\n",
" <td>9.25</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>202.05</td>\n",
" <td>6480.0</td>\n",
" <td>202.05</td>\n",
" <td>6480.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Albanie</td>\n",
" <td>2884.169</td>\n",
" <td>12771.0</td>\n",
" <td>0.38</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>85.0</td>\n",
" <td>16.36</td>\n",
" <td>6.45</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>114.07</td>\n",
" <td>149.0</td>\n",
" <td>114.07</td>\n",
" <td>149.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Algérie</td>\n",
" <td>41389.189</td>\n",
" <td>11737.4</td>\n",
" <td>-0.92</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>6.38</td>\n",
" <td>1.50</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>31.85</td>\n",
" <td>831.0</td>\n",
" <td>31.85</td>\n",
" <td>831.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allemagne</td>\n",
" <td>82658.409</td>\n",
" <td>53071.5</td>\n",
" <td>0.59</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>71.0</td>\n",
" <td>19.47</td>\n",
" <td>4.16</td>\n",
" <td>...</td>\n",
" <td>-38.0</td>\n",
" <td>0.0</td>\n",
" <td>167.0</td>\n",
" <td>-29.0</td>\n",
" <td>102.59</td>\n",
" <td>6450.0</td>\n",
" <td>102.59</td>\n",
" <td>6450.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>Émirats arabes unis</td>\n",
" <td>9487.203</td>\n",
" <td>67183.6</td>\n",
" <td>0.62</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>147.0</td>\n",
" <td>43.47</td>\n",
" <td>9.25</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-26.0</td>\n",
" <td>214.52</td>\n",
" <td>1373.0</td>\n",
" <td>214.52</td>\n",
" <td>1373.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>Équateur</td>\n",
" <td>16785.361</td>\n",
" <td>11617.9</td>\n",
" <td>-0.07</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>83.0</td>\n",
" <td>19.31</td>\n",
" <td>6.35</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-1.0</td>\n",
" <td>114.81</td>\n",
" <td>1021.0</td>\n",
" <td>114.81</td>\n",
" <td>1021.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>États-Unis d'Amérique</td>\n",
" <td>325084.756</td>\n",
" <td>59914.8</td>\n",
" <td>0.29</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>89.0</td>\n",
" <td>219.0</td>\n",
" <td>55.68</td>\n",
" <td>14.83</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>77.0</td>\n",
" <td>80.0</td>\n",
" <td>309.44</td>\n",
" <td>62341.0</td>\n",
" <td>309.44</td>\n",
" <td>62341.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>Éthiopie</td>\n",
" <td>106399.924</td>\n",
" <td>2021.6</td>\n",
" <td>-1.68</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.13</td>\n",
" <td>0.03</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.20</td>\n",
" <td>44.0</td>\n",
" <td>0.20</td>\n",
" <td>44.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>Îles Salomon</td>\n",
" <td>636.039</td>\n",
" <td>2663.5</td>\n",
" <td>0.20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>4.45</td>\n",
" <td>1.31</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>25.27</td>\n",
" <td>15.0</td>\n",
" <td>25.27</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>164 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" Zone Value_population Value_gdp Value_politicstab \\\n",
"0 Afghanistan 36296.113 2058.4 -2.80 \n",
"1 Afrique du Sud 57009.756 13860.3 -0.28 \n",
"2 Albanie 2884.169 12771.0 0.38 \n",
"3 Algérie 41389.189 11737.4 -0.92 \n",
"4 Allemagne 82658.409 53071.5 0.59 \n",
".. ... ... ... ... \n",
"159 Émirats arabes unis 9487.203 67183.6 0.62 \n",
"160 Équateur 16785.361 11617.9 -0.07 \n",
"161 États-Unis d'Amérique 325084.756 59914.8 0.29 \n",
"162 Éthiopie 106399.924 2021.6 -1.68 \n",
"163 Îles Salomon 636.039 2663.5 0.20 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
".. ... ... \n",
"159 0.0 0.0 \n",
"160 0.0 0.0 \n",
"161 0.0 0.0 \n",
"162 0.0 0.0 \n",
"163 0.0 0.0 \n",
"\n",
" Autres utilisations (non alimentaire) \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
".. ... \n",
"159 0.0 \n",
"160 0.0 \n",
"161 89.0 \n",
"162 0.0 \n",
"163 0.0 \n",
"\n",
" Disponibilité alimentaire (Kcal/personne/jour) \\\n",
"0 5.0 \n",
"1 143.0 \n",
"2 85.0 \n",
"3 22.0 \n",
"4 71.0 \n",
".. ... \n",
"159 147.0 \n",
"160 83.0 \n",
"161 219.0 \n",
"162 0.0 \n",
"163 18.0 \n",
"\n",
" Disponibilité alimentaire en quantité (kg/personne/an) \\\n",
"0 1.53 \n",
"1 35.69 \n",
"2 16.36 \n",
"3 6.38 \n",
"4 19.47 \n",
".. ... \n",
"159 43.47 \n",
"160 19.31 \n",
"161 55.68 \n",
"162 0.13 \n",
"163 4.45 \n",
"\n",
" Disponibilité de matière grasse en quantité (g/personne/jour) ... \\\n",
"0 0.33 ... \n",
"1 9.25 ... \n",
"2 6.45 ... \n",
"3 1.50 ... \n",
"4 4.16 ... \n",
".. ... ... \n",
"159 9.25 ... \n",
"160 6.35 ... \n",
"161 14.83 ... \n",
"162 0.03 ... \n",
"163 1.31 ... \n",
"\n",
" Résidus Semences Traitement Variation de stock Fc S \\\n",
"0 0.0 0.0 0.0 0.0 7.40 171.0 \n",
"1 0.0 0.0 0.0 0.0 202.05 6480.0 \n",
"2 0.0 0.0 0.0 4.0 114.07 149.0 \n",
"3 0.0 0.0 0.0 0.0 31.85 831.0 \n",
"4 -38.0 0.0 167.0 -29.0 102.59 6450.0 \n",
".. ... ... ... ... ... ... \n",
"159 0.0 0.0 0.0 -26.0 214.52 1373.0 \n",
"160 0.0 0.0 0.0 -1.0 114.81 1021.0 \n",
"161 0.0 0.0 77.0 80.0 309.44 62341.0 \n",
"162 0.0 0.0 0.0 0.0 0.20 44.0 \n",
"163 0.0 0.0 0.0 3.0 25.27 15.0 \n",
"\n",
" Donnée calculée Données standardisées kmean linkage \n",
"0 7.40 171.0 0 2 \n",
"1 202.05 6480.0 1 0 \n",
"2 114.07 149.0 0 0 \n",
"3 31.85 831.0 0 2 \n",
"4 102.59 6450.0 1 0 \n",
".. ... ... ... ... \n",
"159 214.52 1373.0 1 0 \n",
"160 114.81 1021.0 0 0 \n",
"161 309.44 62341.0 2 1 \n",
"162 0.20 44.0 0 2 \n",
"163 25.27 15.0 0 2 \n",
"\n",
"[164 rows x 27 columns]"
]
},
"execution_count": 230,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_df"
]
},
{
"cell_type": "code",
"execution_count": 231,
"id": "c38610ad-e573-4be8-a66b-90f3843aef35",
"metadata": {},
"outputs": [],
"source": [
"df_linkage = agg_df.set_index('Zone')"
]
},
{
"cell_type": "code",
"execution_count": 232,
"id": "06fd49ac-10fe-4978-a27a-28303b2182f9",
"metadata": {},
"outputs": [],
"source": [
"df_linkage = df_linkage.groupby('linkage').mean().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 233,
"id": "e2aeab41-47e5-4c15-9552-1a212757b8b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['linkage', 'Value_population', 'Value_gdp', 'Value_politicstab',\n",
" 'Alimentation pour touristes', 'Aliments pour animaux',\n",
" 'Autres utilisations (non alimentaire)',\n",
" 'Disponibilité alimentaire (Kcal/personne/jour)',\n",
" 'Disponibilité alimentaire en quantité (kg/personne/an)',\n",
" 'Disponibilité de matière grasse en quantité (g/personne/jour)',\n",
" 'Disponibilité de protéines en quantité (g/personne/jour)',\n",
" 'Disponibilité intérieure', 'Exportations - Quantité',\n",
" 'Importations - Quantité', 'Nourriture', 'Pertes', 'Production',\n",
" 'Résidus', 'Semences', 'Traitement', 'Variation de stock', 'Fc', 'S',\n",
" 'Donnée calculée', 'Données standardisées', 'kmean'],\n",
" dtype='object')"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_linkage.columns"
]
},
{
"cell_type": "code",
"execution_count": 234,
"id": "ce342d11-ecc4-40b8-9548-b29ac0a0b37c",
"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>linkage</th>\n",
" <th>Value_population</th>\n",
" <th>Value_politicstab</th>\n",
" <th>Value_gdp</th>\n",
" <th>Alimentation pour touristes</th>\n",
" <th>Aliments pour animaux</th>\n",
" <th>Autres utilisations (non alimentaire)</th>\n",
" <th>Disponibilité intérieure</th>\n",
" <th>Exportations - Quantité</th>\n",
" <th>Importations - Quantité</th>\n",
" <th>Production</th>\n",
" <th>kmean</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>25720.330372</td>\n",
" <td>0.264787</td>\n",
" <td>29560.870213</td>\n",
" <td>0.074468</td>\n",
" <td>0.0</td>\n",
" <td>5.797872</td>\n",
" <td>615.361702</td>\n",
" <td>98.287234</td>\n",
" <td>134.521277</td>\n",
" <td>603.212766</td>\n",
" <td>0.723404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>266459.289500</td>\n",
" <td>-0.090000</td>\n",
" <td>37219.700000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>44.500000</td>\n",
" <td>14124.000000</td>\n",
" <td>3957.500000</td>\n",
" <td>63.000000</td>\n",
" <td>18057.500000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>42336.708794</td>\n",
" <td>-0.475882</td>\n",
" <td>7104.044118</td>\n",
" <td>0.073529</td>\n",
" <td>0.0</td>\n",
" <td>11.705882</td>\n",
" <td>155.441176</td>\n",
" <td>1.573529</td>\n",
" <td>20.544118</td>\n",
" <td>136.544118</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" linkage Value_population Value_politicstab Value_gdp \\\n",
"0 0 25720.330372 0.264787 29560.870213 \n",
"1 1 266459.289500 -0.090000 37219.700000 \n",
"2 2 42336.708794 -0.475882 7104.044118 \n",
"\n",
" Alimentation pour touristes Aliments pour animaux \\\n",
"0 0.074468 0.0 \n",
"1 0.000000 0.0 \n",
"2 0.073529 0.0 \n",
"\n",
" Autres utilisations (non alimentaire) Disponibilité intérieure \\\n",
"0 5.797872 615.361702 \n",
"1 44.500000 14124.000000 \n",
"2 11.705882 155.441176 \n",
"\n",
" Exportations - Quantité Importations - Quantité Production kmean \n",
"0 98.287234 134.521277 603.212766 0.723404 \n",
"1 3957.500000 63.000000 18057.500000 2.000000 \n",
"2 1.573529 20.544118 136.544118 0.000000 "
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_linkage[['linkage', 'Value_population', 'Value_politicstab','Value_gdp','Alimentation pour touristes', 'Aliments pour animaux',\n",
" 'Autres utilisations (non alimentaire)','Disponibilité intérieure','Exportations - Quantité','Importations - Quantité','Production','kmean']]"
]
},
{
"cell_type": "markdown",
"id": "9e03e520-1bc5-4e10-a4d6-bf85f4e17b28",
"metadata": {},
"source": [
"## CONCLUSIONS"
]
},
{
"cell_type": "markdown",
"id": "f7e8e820-773c-4876-93a7-9729a9218597",
"metadata": {},
"source": [
"1. Group 0 does not show potential as a market. Their production is lower in comparison with the other two groups low indicators for food usage suggest limited potential.\n",
"2. Group 2 is either not a potential market. Their production and export quantities are lowest indicators with negative politic stability.\n",
"3. Group 1, on the other hand, shows potential as a market. They have high production levels, with 78% of the supply being domestic and the remaining 22% being exported, this indicates that there is a spare room of demand for food imports."
]
}
],
"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": "3.8.0"
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"nbformat": 4,
"nbformat_minor": 5
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