# 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 {
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>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 univariée
## Variable discrète
```python
df["sex"].value_counts(normalize=False, sort=True, ascending=False)
```
sex
Male 157
Female 87
Name: count, dtype: int64
Por exprimer en pourcentage -> normalize = True
```python
df["sex"].value_counts(normalize=True, sort=True, ascending=False)
```
sex
Male 0.643443
Female 0.356557
Name: proportion, dtype: float64
```python
# Avec Pandas
df["sex"].value_counts().plot(kind="bar")
plt.show()
```
```python
# Avec Seaborn
sns.countplot(data=df, x="sex", hue="sex")
```
<Axes: xlabel='sex', ylabel='count'>
## Variable continue
### Statistiques
```python
df["tip"].describe()
```
count 244.000000
mean 2.998279
std 1.383638
min 1.000000
25% 2.000000
50% 2.900000
75% 3.562500
max 10.000000
Name: tip, dtype: float64
### Histogramme
```python
# Avec Pandas
df["tip"].plot(kind="hist", bins=10)
plt.show()
```
```python
# Avec Seaborn
sns.displot(data=df, x="tip", bins=10)
```
<seaborn.axisgrid.FacetGrid at 0x1e3a7f8bad0>
### Boxplot
```python
# Avec Pandas
df["tip"].plot(kind="box")
plt.show()
```
```python
# Avec Seaborn
fig, ax = plt.subplots(figsize=(10,2))
# sns.catplot(data=df, y="tip", kin="box")
sns.boxplot(x=df["tip"])
plt.show()
```
