PR by gowthamdongari

build.py

@@ -1,22 +1,51 @@
-def get_categorical_variables(df):
-    return []
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+import seaborn as sns
+import matplotlib.pyplot as plt
 
+df = pd.read_csv('data/employee_retention_data.csv')
 
-def get_numerical_variables(df):
-    return []
+def get_categorical_variables(df):
+    categorical_data = list(df[['dept', 'join_date', 'quit_date']])
+    return categorical_data
 
+def get_numerical_variables(df):
+    numeric = pd.DataFrame._get_numeric_data(df)
+    return list(numeric)
 
 def get_numerical_variables_percentile(df):
-    pass
+    per = df.describe().T
+    return per
 
 
 def get_categorical_variables_modes(df):
-    pass
-
+    return df[get_categorical_variables(df)].mode()
 
 def get_missing_values_count(df):
-    pass
+    return pd.DataFrame(df.isnull().sum())
 
 
 def plot_histogram_with_numerical_values(df):
-    pass
+
+    num_cols = get_numerical_variables(df)
+    plt.figure(figsize=(15,6))
+
+    plt.subplot(221)
+    plt.title(num_cols[0])
+    sns.distplot(df[num_cols[0]], color='Blue', fit=norm, kde=False)
+
+    plt.subplot(222)
+    plt.title(num_cols[1])
+    sns.distplot(df[num_cols[1]], color='Blue', fit=norm, kde=False)
+
+    plt.subplot(223)
+    plt.title(num_cols[2])
+    sns.distplot(df[num_cols[2]], color='Blue', fit=norm, kde=False)
+
+    plt.subplot(224)
+    plt.title(num_cols[3])
+    sns.distplot(df[num_cols[3]], color='Blue', fit=norm, kde=False)
+
+    plt.tight_layout()
+    plt.show()