In [22]:
#Looking at all possible missing values
total = train_df.isnull().sum().sort_values(ascending=False) #summing all the null values
percent = (train_df.isnull().sum()/train_df.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(10)
Out[22]:
Total Percent
X385 0 0.0
X132 0 0.0
X123 0 0.0
X124 0 0.0
X125 0 0.0
X126 0 0.0
X127 0 0.0
X128 0 0.0
X129 0 0.0
X130 0 0.0