Commit 7e1c7c2c authored by Céline Meillier's avatar Céline Meillier

modification du cours 2-Pretraitements

parent b422ba54
......@@ -509,7 +509,7 @@
"* traiter les les valeurs aberrantes, manquantes, mettre à l'échelle toutes les données, etc, \n",
"* transformation des données : \n",
" * cas de l'analyse de données qualitatives : binarisation des données/classification \n",
" * cas de l'analyse de données textuelle (tokenisation, suppression des mots sans information, lemmatisation, etc)\n",
" * cas de l'analyse de données textuelles (tokenisation, suppression des mots sans information, lemmatisation, etc)\n",
"\n",
"C'est un travail qui peut se révéler long et fastidieux mais qui est nécessaire pour que les analyses soient correctes par la suite. Ce n'est pas forcément facile à automatiser, c'est pourquoi le traiteur de données doit **visualiser** et vérifier les données avant de les traiter."
]
......@@ -611,7 +611,7 @@
}
},
"source": [
"<span style=\"color: #27AE60\"> **Exemple :** </span> Suppression des individus dont au moins une valeur est manquante. Le budget et le score du film Forrest Gump sont manquantes dans DATA. "
"<span style=\"color: #27AE60\"> **Exemple :** </span> Suppression des individus dont au moins une valeur est manquante. Par exemple, le budget et le score du film Forrest Gump sont manquantes dans DATA. "
]
},
{
......@@ -640,6 +640,605 @@
"print(len(df), df['duration'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<span style=\"color: #27AE60\"> **Exemple :** </span> Remplacement des données manquantes par la valeur médiane d'une variable (par exemple le budget)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20000000.0 34606958.79757576\n",
"20000000.0 34606958.79757576\n"
]
},
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" <th></th>\n",
" <th>director_name</th>\n",
" <th>num_critic_for_reviews</th>\n",
" <th>duration</th>\n",
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" <th>budget</th>\n",
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" <th>imdb_score</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>The Shawshank Redemption</th>\n",
" <td>Frank Darabont</td>\n",
" <td>199</td>\n",
" <td>142</td>\n",
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" <tr>\n",
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" <td>Christopher Nolan</td>\n",
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" <td>Christopher Nolan</td>\n",
" <td>642</td>\n",
" <td>148</td>\n",
" <td>Leonardo DiCaprio</td>\n",
" <td>Tom Hardy</td>\n",
" <td>1468200</td>\n",
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" <td>David Fincher</td>\n",
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" <td>Meat Loaf</td>\n",
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" <tr>\n",
" <th>Pulp Fiction</th>\n",
" <td>Quentin Tarantino</td>\n",
" <td>215</td>\n",
" <td>178</td>\n",
" <td>Bruce Willis</td>\n",
" <td>Eric Stoltz</td>\n",
" <td>1324680</td>\n",
" <td>1</td>\n",
" <td>2195</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>8000000</td>\n",
" <td>1994</td>\n",
" <td>8.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Forrest Gump</th>\n",
" <td>Robert Zemeckis</td>\n",
" <td>149</td>\n",
" <td>142</td>\n",
" <td>Tom Hanks</td>\n",
" <td>Siobhan Fallon Hogan</td>\n",
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" <td>55000000</td>\n",
" <td>1994</td>\n",
" <td>8.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>The Lord of the Rings: The Fellowship of the Ring</th>\n",
" <td>Peter Jackson</td>\n",
" <td>297</td>\n",
" <td>171</td>\n",
" <td>Christopher Lee</td>\n",
" <td>Orlando Bloom</td>\n",
" <td>1238746</td>\n",
" <td>2</td>\n",
" <td>5060</td>\n",
" <td>English</td>\n",
" <td>New Zealand</td>\n",
" <td>PG-13</td>\n",
" <td>93000000</td>\n",
" <td>2001</td>\n",
" <td>8.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>The Matrix</th>\n",
" <td>Lana Wachowski</td>\n",
" <td>313</td>\n",
" <td>136</td>\n",
" <td>Keanu Reeves</td>\n",
" <td>Marcus Chong</td>\n",
" <td>1217752</td>\n",
" <td>3</td>\n",
" <td>3646</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>63000000</td>\n",
" <td>1999</td>\n",
" <td>8.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" director_name \\\n",
"movie_title \n",
"The Shawshank Redemption Frank Darabont \n",
"The Dark Knight Christopher Nolan \n",
"Inception Christopher Nolan \n",
"Fight Club David Fincher \n",
"Pulp Fiction Quentin Tarantino \n",
"Forrest Gump Robert Zemeckis \n",
"The Lord of the Rings: The Fellowship of the Ring Peter Jackson \n",
"The Matrix Lana Wachowski \n",
"\n",
" num_critic_for_reviews \\\n",
"movie_title \n",
"The Shawshank Redemption 199 \n",
"The Dark Knight 645 \n",
"Inception 642 \n",
"Fight Club 315 \n",
"Pulp Fiction 215 \n",
"Forrest Gump 149 \n",
"The Lord of the Rings: The Fellowship of the Ring 297 \n",
"The Matrix 313 \n",
"\n",
" duration \\\n",
"movie_title \n",
"The Shawshank Redemption 142 \n",
"The Dark Knight 152 \n",
"Inception 148 \n",
"Fight Club 151 \n",
"Pulp Fiction 178 \n",
"Forrest Gump 142 \n",
"The Lord of the Rings: The Fellowship of the Ring 171 \n",
"The Matrix 136 \n",
"\n",
" actor_1_name \\\n",
"movie_title \n",
"The Shawshank Redemption Morgan Freeman \n",
"The Dark Knight Christian Bale \n",
"Inception Leonardo DiCaprio \n",
"Fight Club Brad Pitt \n",
"Pulp Fiction Bruce Willis \n",
"Forrest Gump Tom Hanks \n",
"The Lord of the Rings: The Fellowship of the Ring Christopher Lee \n",
"The Matrix Keanu Reeves \n",
"\n",
" actor_2_name \\\n",
"movie_title \n",
"The Shawshank Redemption Jeffrey DeMunn \n",
"The Dark Knight Heath Ledger \n",
"Inception Tom Hardy \n",
"Fight Club Meat Loaf \n",
"Pulp Fiction Eric Stoltz \n",
"Forrest Gump Siobhan Fallon Hogan \n",
"The Lord of the Rings: The Fellowship of the Ring Orlando Bloom \n",
"The Matrix Marcus Chong \n",
"\n",
" num_voted_users \\\n",
"movie_title \n",
"The Shawshank Redemption 1689764 \n",
"The Dark Knight 1676169 \n",
"Inception 1468200 \n",
"Fight Club 1347461 \n",
"Pulp Fiction 1324680 \n",
"Forrest Gump 1251222 \n",
"The Lord of the Rings: The Fellowship of the Ring 1238746 \n",
"The Matrix 1217752 \n",
"\n",
" facenumber_in_poster \\\n",
"movie_title \n",
"The Shawshank Redemption 0 \n",
"The Dark Knight 0 \n",
"Inception 0 \n",
"Fight Club 2 \n",
"Pulp Fiction 1 \n",
"Forrest Gump 0 \n",
"The Lord of the Rings: The Fellowship of the Ring 2 \n",
"The Matrix 3 \n",
"\n",
" num_user_for_reviews \\\n",
"movie_title \n",
"The Shawshank Redemption 4144 \n",
"The Dark Knight 4667 \n",
"Inception 2803 \n",
"Fight Club 2968 \n",
"Pulp Fiction 2195 \n",
"Forrest Gump 1398 \n",
"The Lord of the Rings: The Fellowship of the Ring 5060 \n",
"The Matrix 3646 \n",
"\n",
" language country \\\n",
"movie_title \n",
"The Shawshank Redemption English USA \n",
"The Dark Knight English USA \n",
"Inception English USA \n",
"Fight Club English USA \n",
"Pulp Fiction English USA \n",
"Forrest Gump English USA \n",
"The Lord of the Rings: The Fellowship of the Ring English New Zealand \n",
"The Matrix English USA \n",
"\n",
" content_rating budget \\\n",
"movie_title \n",
"The Shawshank Redemption R 25000000 \n",
"The Dark Knight PG-13 185000000 \n",
"Inception PG-13 160000000 \n",
"Fight Club R 63000000 \n",
"Pulp Fiction R 8000000 \n",
"Forrest Gump PG-13 55000000 \n",
"The Lord of the Rings: The Fellowship of the Ring PG-13 93000000 \n",
"The Matrix R 63000000 \n",
"\n",
" title_year imdb_score \n",
"movie_title \n",
"The Shawshank Redemption 1994 9.3 \n",
"The Dark Knight 2008 9.0 \n",
"Inception 2010 8.8 \n",
"Fight Club 1999 8.8 \n",
"Pulp Fiction 1994 8.9 \n",
"Forrest Gump 1994 8.8 \n",
"The Lord of the Rings: The Fellowship of the Ring 2001 8.8 \n",
"The Matrix 1999 8.7 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DATA.copy()\n",
"print(df.budget.median(), df.budget.mean())\n",
"df.budget=df[\"budget\"].fillna(df.budget.median())\n",
"print(df.budget.median(), df.budget.mean())\n",
"df.head(n=8)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<span style=\"color: #27AE60\"> **Exemple :** </span> Transformation d'une variable qualitative en variable quantitative. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Approved': 0, 'G': 1, 'PG': 2, 'PG-13': 3, 'R': 4, 'NC-17': 5, 'Not rated': 6, 'Passed': 7, 'M': 8}\n"
]
},
{
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" <tr>\n",
" <th>The Shawshank Redemption</th>\n",
" <td>Frank Darabont</td>\n",
" <td>199.0</td>\n",
" <td>142.0</td>\n",
" <td>Morgan Freeman</td>\n",
" <td>Jeffrey DeMunn</td>\n",
" <td>1689764.0</td>\n",
" <td>0</td>\n",
" <td>4144.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>1994</td>\n",
" <td>9.3</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>The Dark Knight</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>645.0</td>\n",
" <td>152.0</td>\n",
" <td>Christian Bale</td>\n",
" <td>Heath Ledger</td>\n",
" <td>1676169.0</td>\n",
" <td>0</td>\n",
" <td>4667.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>185000000.0</td>\n",
" <td>2008</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>The Dark Knight</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>645.0</td>\n",
" <td>152.0</td>\n",
" <td>Christian Bale</td>\n",
" <td>Heath Ledger</td>\n",
" <td>1676169.0</td>\n",
" <td>0</td>\n",
" <td>4667.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>185000000.0</td>\n",
" <td>2008</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inception</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>642.0</td>\n",
" <td>148.0</td>\n",
" <td>Leonardo DiCaprio</td>\n",
" <td>Tom Hardy</td>\n",
" <td>1468200.0</td>\n",
" <td>0</td>\n",
" <td>2803.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>160000000.0</td>\n",
" <td>2010</td>\n",
" <td>8.8</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fight Club</th>\n",
" <td>David Fincher</td>\n",
" <td>315.0</td>\n",
" <td>151.0</td>\n",
" <td>Brad Pitt</td>\n",
" <td>Meat Loaf</td>\n",
" <td>1347461.0</td>\n",
" <td>2</td>\n",
" <td>2968.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>63000000.0</td>\n",
" <td>1999</td>\n",
" <td>8.8</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" director_name num_critic_for_reviews duration \\\n",
"movie_title \n",
"The Shawshank Redemption Frank Darabont 199.0 142.0 \n",
"The Dark Knight Christopher Nolan 645.0 152.0 \n",
"The Dark Knight Christopher Nolan 645.0 152.0 \n",
"Inception Christopher Nolan 642.0 148.0 \n",
"Fight Club David Fincher 315.0 151.0 \n",
"\n",
" actor_1_name actor_2_name num_voted_users \\\n",
"movie_title \n",
"The Shawshank Redemption Morgan Freeman Jeffrey DeMunn 1689764.0 \n",
"The Dark Knight Christian Bale Heath Ledger 1676169.0 \n",
"The Dark Knight Christian Bale Heath Ledger 1676169.0 \n",
"Inception Leonardo DiCaprio Tom Hardy 1468200.0 \n",
"Fight Club Brad Pitt Meat Loaf 1347461.0 \n",
"\n",
" facenumber_in_poster num_user_for_reviews language \\\n",
"movie_title \n",
"The Shawshank Redemption 0 4144.0 English \n",
"The Dark Knight 0 4667.0 English \n",
"The Dark Knight 0 4667.0 English \n",
"Inception 0 2803.0 English \n",
"Fight Club 2 2968.0 English \n",
"\n",
" country content_rating budget title_year \\\n",
"movie_title \n",
"The Shawshank Redemption USA R NaN 1994 \n",
"The Dark Knight USA PG-13 185000000.0 2008 \n",
"The Dark Knight USA PG-13 185000000.0 2008 \n",
"Inception USA PG-13 160000000.0 2010 \n",
"Fight Club USA R 63000000.0 1999 \n",
"\n",
" imdb_score numeric_content_rating \n",
"movie_title \n",
"The Shawshank Redemption 9.3 4.0 \n",
"The Dark Knight 9.0 3.0 \n",
"The Dark Knight 9.0 3.0 \n",
"Inception 8.8 3.0 \n",
"Fight Club 8.8 4.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liste_rating = ['Approved','G', 'PG', 'PG-13', 'R', 'NC-17', 'Not rated', 'Passed', 'M']\n",
"dico = {}\n",
"i = 0\n",
"for rate in liste_rating:\n",
" df = DATA[DATA.content_rating == rate]\n",
" dico[rate] = i\n",
" i = i+1 \n",
"print(dico)\n",
"DATA_modif = DATA.copy()\n",
"DATA_modif[\"numeric_content_rating\"] = DATA[\"content_rating\"].map(str).map(dico)\n",
"DATA_modif.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
......@@ -656,13 +1255,24 @@
]
},
{
"cell_type": "code",
"execution_count": 5,
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<span style=\"color: #27AE60\"> **Exemple :** </span> Calcul des moyennes des variables quantitaves avant nettoyage des données "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
......@@ -685,9 +1295,20 @@
"print(DATA.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Après netoyage des données"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
......@@ -739,7 +1360,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "subslide"
......@@ -752,7 +1373,7 @@
"Text(0.5, 1.0, 'Variables centrées-réduites')"
]
},
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
......@@ -805,8 +1426,12 @@
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"DATA = pd.read_csv('data/movie_metadata2.csv', delimiter=';', index_col='movie_title')"
......@@ -837,7 +1462,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "subslide"
......@@ -900,7 +1525,7 @@
"duration 2.141788e+00 6.873079e+08 1.061462e+03"
]
},
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
......@@ -912,7 +1537,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
......@@ -922,10 +1547,10 @@
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a16193320>"
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1b872b70>"
]
},
"execution_count": 10,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
......@@ -988,7 +1613,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "subslide"
......@@ -1051,7 +1676,7 @@
"duration 0.173475 0.327186 1.000000"
]
},
"execution_count": 11,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1063,7 +1688,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "fragment"
......@@ -1073,10 +1698,10 @@
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1600ce48>"
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1b7ac8d0>"
]
},
"execution_count": 12,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
......@@ -1221,7 +1846,7 @@
},
{