PR by birajparikh16

build.py

@@ -1,26 +1,104 @@
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+import seaborn as sns
+import matplotlib.pyplot as plt
+# %matplotlib inline
+
+
+# we check if the dataframe has been created successfully or not by using the head function,
+# which shows the first 5 rows of the dataframe.
+df = pd.read_csv("data/conversion_data.csv")
+df.head(100)
+
+# Check the info() of the df
+# This provides a concise summary of your data frame
+df.info()
+
+# Question 1: Function that returns a list of the names of categorical variables
+
+print "list of the names of categorical variables :"
 def get_categorical_variables(df):
-    return []
+    return df[['country','new_user','source','converted']]
+
+get_categorical_variables(df)
+# Output: list of the names of categorical variables :
+#        ['country', 'new_user', 'source', 'converted']
 
+# Question 2: Function that returns the list of the names of numeric variables
 
+print "The list of the names of numeric variables :"
 def get_numerical_variables(df):
-    return []
+    numerical = df._get_numeric_data().columns
+    return list(numerical)
 
+# get_numerical_variables(df)
+# Output: The list of the names of numeric variables :
+#         ['age', 'total_pages_visited']
+
+# Question 3: Function that returns, for numeric variables, mean, median, 25, 50, 75th percentile
 
 def get_numerical_variables_percentile(df):
-    pass
+    final = df[get_numerical_variables(df)]
+
+    return final.describe()
 
+# get_numerical_variables_percentile(df)
+# Output:                 count 	 mean 	   std 	       min 	  25% 	50%   75% 	max
+#  age 	                316200.0   30.569858   8.271802    17.0   24.0 	30.0  36.0 	123.0
+# total_pages_visited 	316200.0   4.872966    3.341104    1.0 	  2.0 	4.0   7.0 	29.0
+
+# Question 4: For categorical variables, get modes
 
 def get_categorical_variables_modes(df):
-    pass
 
+    modes = get_categorical_variables(df).mode()
+    return modes
+
+# get_categorical_variables_modes(df)
+# Output:  	country  new_user  source  converted
+#        0 	  US 	    1 	    Seo 	  0
 
+# Question 5: For each column, list the count of missing values
+
+print "Missing values per column:"
 def get_missing_values_count(df):
-    pass
+    df1 = df.apply(lambda x: sum(x.isnull()), axis=0)
+    return pd.DataFrame(df1)
+
+# get_missing_values_count(df)
+# Output:  	0
+#        country 	            0
+#         age 	                0
+#       new_user 	            0
+#        source 	            0
+#       total_pages_visited 	0
+#         converted 	        0
 
+# Question 6: Plot histograms using different subplots of all the numerical values in a single plot
 
 def plot_histogram_with_numerical_values(df):
-    pass
 
+    num_cols = get_numerical_variables(df)
+    #num_cols = df.select_dtypes(exclude=['object','category']).columns
+
+    plt.figure(figsize=(15,6))
+    plt.subplot(121)
+    plt.title(num_cols[0])
+    sns.distplot(df[num_cols[0]], color='yellow', fit=norm, kde=False)
+    plt.subplot(122)
+    plt.title(num_cols[1])
+    sns.distplot(df[num_cols[1]], color='yellow', fit=norm, kde=False)
+
+# plot_histogram_with_numerical_values(df)
+
+# Question 7: Plot facet box plots to check out the distribution according to the target variable
 
 def plot_facet_box(df):
-    pass
+    plt.figure(figsize=(15,5))
+    plt.subplot(221)
+    sns.boxplot('converted','age',data=df)
+    plt.subplot(222)
+    sns.boxplot('converted','total_pages_visited',data=df)
+
+# plot_facet_box(df)