# Librairies
```python
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
```
# Data
```python
df = sns.load_dataset("tips")
df.head()
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
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}
.dataframe tbody tr th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>total_bill</th>
<th>tip</th>
<th>sex</th>
<th>smoker</th>
<th>day</th>
<th>time</th>
<th>size</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>16.99</td>
<td>1.01</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
</tr>
<tr>
<th>1</th>
<td>10.34</td>
<td>1.66</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>21.01</td>
<td>3.50</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
</tr>
<tr>
<th>3</th>
<td>23.68</td>
<td>3.31</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
</tr>
<tr>
<th>4</th>
<td>24.59</td>
<td>3.61</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
# Analyse multivariée
## Discret / Discret
```python
pd.crosstab(df["sex"], df["smoker"], normalize=False)
```
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<style scoped>
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}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>smoker</th>
<th>Yes</th>
<th>No</th>
</tr>
<tr>
<th>sex</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Male</th>
<td>60</td>
<td>97</td>
</tr>
<tr>
<th>Female</th>
<td>33</td>
<td>54</td>
</tr>
</tbody>
</table>
</div>
Pour les pourcentages --> normalize = True
Quand les données à analysées sont nombreuses, on peut utiliser un heatmap:
```python
sns.heatmap(pd.crosstab(df["size"], df["day"]), annot=True)
```
<Axes: xlabel='day', ylabel='size'>
## Discret / Continue
```python
df.groupby("sex")["tip"].describe()
```
C:\Users\steph\AppData\Local\Temp\ipykernel_17268\199809758.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
df.groupby("sex")["tip"].describe()
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>25%</th>
<th>50%</th>
<th>75%</th>
<th>max</th>
</tr>
<tr>
<th>sex</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Male</th>
<td>157.0</td>
<td>3.089618</td>
<td>1.489102</td>
<td>1.0</td>
<td>2.0</td>
<td>3.00</td>
<td>3.76</td>
<td>10.0</td>
</tr>
<tr>
<th>Female</th>
<td>87.0</td>
<td>2.833448</td>
<td>1.159495</td>
<td>1.0</td>
<td>2.0</td>
<td>2.75</td>
<td>3.50</td>
<td>6.5</td>
</tr>
</tbody>
</table>
</div>
```python
# Graphique des moyennes avec Pandas
df.groupby("sex")["tip"].mean().plot(kind="bar")
plt.show()
```
C:\Users\steph\AppData\Local\Temp\ipykernel_17268\1934845824.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
df.groupby("sex")["tip"].mean().plot(kind="bar")
```python
# Graphique des moyennes avec Seaborn
sns.catplot(data=df, x="sex", y="tip", kind="bar", hue="sex")
```
<seaborn.axisgrid.FacetGrid at 0x20e8c9f5910>
```python
# Histogrammes superposés (uniquement Seaborn)
sns.displot(data=df, x="tip", hue="sex")
```
<seaborn.axisgrid.FacetGrid at 0x20e8ccfb1d0>
```python
# Boîtes à moustache (avec Seaborn)
sns.catplot(data=df, x="tip", y="sex", kind="box", hue="sex")
```
<seaborn.axisgrid.FacetGrid at 0x20e8db379b0>
## Continue / Continue
```python
# La seule chose à faire est un scatter plot
# Avec Matplotlib
plt.scatter(df["tip"], df["total_bill"])
plt.show()
```
```python
# Avec Seaborn
sns.scatterplot(data=df, x="tip", y="total_bill")
plt.show()
```
