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Update tutorial-data-science-explore-notebook.md #1784

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2 changes: 2 additions & 0 deletions docs/data-science/tutorial-data-science-explore-notebook.md
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Expand Up @@ -165,6 +165,7 @@ Show the distribution of exited versus nonexited customers across the categorica

```python
attr_list = ['Geography', 'Gender', 'HasCrCard', 'IsActiveMember', 'NumOfProducts', 'Tenure']
df_clean['Exited'] = df_clean['Exited'].astype(str)
fig, axarr = plt.subplots(2, 3, figsize=(15, 4))
for ind, item in enumerate (attr_list):
sns.countplot(x = item, hue = 'Exited', data = df_clean, ax = axarr[ind%2][ind//2])
Expand Down Expand Up @@ -198,6 +199,7 @@ plt.show()
Perform feature engineering to generate new attributes based on current attributes:

```python
df_clean['Tenure'] = df_clean['Tenure'].astype(int)
df_clean["NewTenure"] = df_clean["Tenure"]/df_clean["Age"]
df_clean["NewCreditsScore"] = pd.qcut(df_clean['CreditScore'], 6, labels = [1, 2, 3, 4, 5, 6])
df_clean["NewAgeScore"] = pd.qcut(df_clean['Age'], 8, labels = [1, 2, 3, 4, 5, 6, 7, 8])
Expand Down