# Librairies
```python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
```
# Load Data
```python
df = sns.load_dataset('diamonds')
df.head()
```
<div>
<style scoped>
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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>carat</th>
<th>cut</th>
<th>color</th>
<th>clarity</th>
<th>depth</th>
<th>table</th>
<th>price</th>
<th>x</th>
<th>y</th>
<th>z</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.23</td>
<td>Ideal</td>
<td>E</td>
<td>SI2</td>
<td>61.5</td>
<td>55.0</td>
<td>326</td>
<td>3.95</td>
<td>3.98</td>
<td>2.43</td>
</tr>
<tr>
<th>1</th>
<td>0.21</td>
<td>Premium</td>
<td>E</td>
<td>SI1</td>
<td>59.8</td>
<td>61.0</td>
<td>326</td>
<td>3.89</td>
<td>3.84</td>
<td>2.31</td>
</tr>
<tr>
<th>2</th>
<td>0.23</td>
<td>Good</td>
<td>E</td>
<td>VS1</td>
<td>56.9</td>
<td>65.0</td>
<td>327</td>
<td>4.05</td>
<td>4.07</td>
<td>2.31</td>
</tr>
<tr>
<th>3</th>
<td>0.29</td>
<td>Premium</td>
<td>I</td>
<td>VS2</td>
<td>62.4</td>
<td>58.0</td>
<td>334</td>
<td>4.20</td>
<td>4.23</td>
<td>2.63</td>
</tr>
<tr>
<th>4</th>
<td>0.31</td>
<td>Good</td>
<td>J</td>
<td>SI2</td>
<td>63.3</td>
<td>58.0</td>
<td>335</td>
<td>4.34</td>
<td>4.35</td>
<td>2.75</td>
</tr>
</tbody>
</table>
</div>
```python
df["carat"].plot(kind='box', vert=False)
```
<Axes: >
```python
def iqr_outlier_detection(df, column, threshold=1.5):
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - threshold * IQR
upper_bound = Q3 + threshold * IQR
outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]
return outliers
```
```python
iqr_outlier_detection(df, 'carat', threshold=1.5)
```
<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>carat</th>
<th>cut</th>
<th>color</th>
<th>clarity</th>
<th>depth</th>
<th>table</th>
<th>price</th>
<th>x</th>
<th>y</th>
<th>z</th>
</tr>
</thead>
<tbody>
<tr>
<th>12246</th>
<td>2.06</td>
<td>Premium</td>
<td>J</td>
<td>I1</td>
<td>61.2</td>
<td>58.0</td>
<td>5203</td>
<td>8.10</td>
<td>8.07</td>
<td>4.95</td>
</tr>
<tr>
<th>13002</th>
<td>2.14</td>
<td>Fair</td>
<td>J</td>
<td>I1</td>
<td>69.4</td>
<td>57.0</td>
<td>5405</td>
<td>7.74</td>
<td>7.70</td>
<td>5.36</td>
</tr>
<tr>
<th>13118</th>
<td>2.15</td>
<td>Fair</td>
<td>J</td>
<td>I1</td>
<td>65.5</td>
<td>57.0</td>
<td>5430</td>
<td>8.01</td>
<td>7.95</td>
<td>5.23</td>
</tr>
<tr>
<th>13757</th>
<td>2.22</td>
<td>Fair</td>
<td>J</td>
<td>I1</td>
<td>66.7</td>
<td>56.0</td>
<td>5607</td>
<td>8.04</td>
<td>8.02</td>
<td>5.36</td>
</tr>
<tr>
<th>13991</th>
<td>2.01</td>
<td>Fair</td>
<td>I</td>
<td>I1</td>
<td>67.4</td>
<td>58.0</td>
<td>5696</td>
<td>7.71</td>
<td>7.64</td>
<td>5.17</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>27741</th>
<td>2.15</td>
<td>Ideal</td>
<td>G</td>
<td>SI2</td>
<td>62.6</td>
<td>54.0</td>
<td>18791</td>
<td>8.29</td>
<td>8.35</td>
<td>5.21</td>
</tr>
<tr>
<th>27742</th>
<td>2.04</td>
<td>Premium</td>
<td>H</td>
<td>SI1</td>
<td>58.1</td>
<td>60.0</td>
<td>18795</td>
<td>8.37</td>
<td>8.28</td>
<td>4.84</td>
</tr>
<tr>
<th>27744</th>
<td>2.29</td>
<td>Premium</td>
<td>I</td>
<td>SI1</td>
<td>61.8</td>
<td>59.0</td>
<td>18797</td>
<td>8.52</td>
<td>8.45</td>
<td>5.24</td>
</tr>
<tr>
<th>27746</th>
<td>2.07</td>
<td>Ideal</td>
<td>G</td>
<td>SI2</td>
<td>62.5</td>
<td>55.0</td>
<td>18804</td>
<td>8.20</td>
<td>8.13</td>
<td>5.11</td>
</tr>
<tr>
<th>27749</th>
<td>2.29</td>
<td>Premium</td>
<td>I</td>
<td>VS2</td>
<td>60.8</td>
<td>60.0</td>
<td>18823</td>
<td>8.50</td>
<td>8.47</td>
<td>5.16</td>
</tr>
</tbody>
</table>
<p>1889 rows × 10 columns</p>
</div>