missing_percent_for_each_df.column

import matplotlib.pyplot as plt
import seaborn as sns

nan_count=((df.isna().sum()/df.shape[0])*100).sort_values(ascending=False)
### only focus on the variable with missing value
nan_count=nan_count[nan_count>0]
           
plt.figure(figsize=(15,10))
plt.xlabel('Features', fontsize=15)
plt.ylabel('Percent of missing values', fontsize=15)
plt.title('Percent missing data by feature', fontsize=15)
plt.xticks(rotation=90)
sns.barplot(nan_count.index, nan_count.values)


#### Drop the 5 most missing variables


full_train.drop(nan_count.index[:5],axis = 1, inplace=True)