PR by datanomnom

build.py

@@ -1,26 +1,56 @@
-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/conversion_data.csv")
 
-def get_numerical_variables(df):
-    return []
+def get_categorical_variables(df):
+    return df[['country','new_user','source','converted']]
 
+def get_numerical_variables(df):
+    numerical = df._get_numeric_data().columns
+    return list(numerical)
 
 def get_numerical_variables_percentile(df):
-    pass
-
+    final = df[get_numerical_variables(df)]
+    return final.describe()
 
 def get_categorical_variables_modes(df):
-    pass
-
+    modes = get_categorical_variables(df).mode()
+    return modes
 
 def get_missing_values_count(df):
-    pass
+    df1 = df.apply(lambda x: sum(x.isnull()), axis=0)
+    return pd.DataFrame(df1)
 
 
 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='yellow', fit=norm, kde=False)
+    plt.subplot(222)
+    plt.title(num_cols[1])
+    sns.distplot(df[num_cols[1]], color='yellow', fit=norm, kde=False)
+    plt.subplot(223)
+    plt.title(num_cols[2])
+    sns.distplot(df[num_cols[2]], color='yellow', fit=norm, kde=False)
+    plt.subplot(224)
+    plt.title(num_cols[3])
+    sns.distplot(df[num_cols[3]], color='yellow', fit=norm, kde=False)
+    plt.tight_layout()
+    plt.show()
 
 def plot_facet_box(df):
-    pass
+    plt.figure(figsize=(10,10))
+    plt.subplot(221)
+    plt.title('Age')
+    sns.boxplot('converted','age',data=df)
+    plt.subplot(222)
+    plt.title('Total Pages Visited')
+    sns.boxplot('converted','total_pages_visited',data=df)
+    plt.tight_layout()
+    plt.show()