anova
This commit is contained in:
parent
29ac99df14
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@ -4168,355 +4168,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "58cbe8a5-3899-4aa3-91ab-48bed9124fbd",
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"metadata": {},
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"outputs": [],
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"source": [
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"#creation des base servant au test d'anova\n",
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"\n",
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"time_between_purchase_10 = products_purchased_reduced_spectacle.loc[products_purchased_reduced_spectacle['number_compagny'] == 10]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "f563009c-e9a8-4e09-a345-87c49cbd4485",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 581.065880\n",
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"Name: time_between_purchase, Length: 35222, dtype: float64\n",
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"Name: time_between_purchase, Length: 213499, dtype: float64\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Supposons que vous avez un DataFrame appelé products_purchased_reduced_spectacle\n",
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"# qui contient les données de vos produits achetés avec la colonne 'number_compagny'\n",
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"\n",
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"# Créez une liste des identifiants d'entreprise pour lesquels vous voulez effectuer la sélection\n",
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"entreprises = [11, 12, 13, 14]\n",
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"\n",
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"# Parcourez chaque entreprise et effectuez la sélection\n",
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"for entreprise in entreprises:\n",
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" nom_variable = f\"time_between_purchase_{entreprise}\"\n",
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" globals()[nom_variable] = products_purchased_reduced_spectacle.loc[products_purchased_reduced_spectacle['number_compagny'] == entreprise,\"time_between_purchase\"]\n",
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"\n",
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"# Maintenant, les résultats pour chaque entreprise sont stockés dans des variables distinctes\n",
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"# Vous pouvez y accéder directement\n",
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"print(time_between_purchase_11)\n",
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"print(time_between_purchase_12)\n",
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"# et ainsi de suite pour les autres entreprises\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"id": "58f49748-e55f-4d1b-b58b-102d02a9e0eb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" sum_sq df F PR(>F)\n",
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"number_compagny 4.108441e+09 1.0 23548.336165 0.0\n",
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"Residual 1.334471e+11 764878.0 NaN NaN la p-value associé à la stat de fisher est inferieure à 5% donc il y a un lien entre les entreprise et le temps écoulés entre le premier et le dernier achat\n"
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]
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}
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],
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"source": [
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"#test anova entre les entreprise de spectacle et time_between_purchase\n",
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"import statsmodels.api as sm\n",
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"from statsmodels.formula.api import ols\n",
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"model = ols('time_between_purchase ~ number_compagny', data=products_purchased_reduced_spectacle).fit()\n",
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"anova_table = sm.stats.anova_lm(model, typ=2)\n",
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"anova_table\n",
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"print(anova_table,\"la p-value associé à la stat de fisher est inferieure à 5% donc il y a un lien entre les entreprise et le temps écoulés entre le premier et le dernier achat\" )\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "aa000e3e-a44d-4cb4-b44b-9794f4b711ca",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>customer_id</th>\n",
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" <th>nb_tickets</th>\n",
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" <th>nb_purchases</th>\n",
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" <th>total_amount</th>\n",
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" <th>nb_suppliers</th>\n",
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" <th>vente_internet_max</th>\n",
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" <th>purchase_date_min</th>\n",
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" <th>purchase_date_max</th>\n",
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" <th>time_between_purchase</th>\n",
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" <th>nb_tickets_internet</th>\n",
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" <th>number_compagny</th>\n",
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"</div>"
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],
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"text/plain": [
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" customer_id nb_tickets nb_purchases total_amount nb_suppliers \\\n",
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"0 1 12 11 271.60 2 \n",
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"1 2 2 2 46.00 1 \n",
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"2 3 9 8 261.25 2 \n",
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"3 4 50 48 646.00 1 \n",
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"35220 520151 2 1 126.00 1 \n",
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"35221 520152 2 1 126.00 1 \n",
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"\n",
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" vente_internet_max purchase_date_min purchase_date_max \\\n",
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"0 0 1778.407639 1197.341759 \n",
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"\n",
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" time_between_purchase nb_tickets_internet number_compagny \n",
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"0 581.065880 0.0 11 \n",
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"1 0.000023 0.0 11 \n",
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"... ... ... ... \n",
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"\n",
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"[35222 rows x 11 columns]"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"time_between_purchase_11"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 29,
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"id": "e2c51e28-6197-48f0-ab6d-9fc7b3b0de74",
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"metadata": {},
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"outputs": [
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@ -4528,7 +4180,7 @@
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"Valeur de p : 0.0\n",
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"Nombre de degrés de liberté entre les groupes : 4\n",
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"Nombre de degrés de liberté à l'intérieur des groupes : 764875\n",
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"Il y a des différences significatives entre au moins une paire de catégories.\n"
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"Il y a des différences significatives entre au moins une des entrepries .\n"
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]
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}
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],
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