Find out the percentage of missing values in each column in the given dataset

Question

import pandas as pd df = pd.read_csv('https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0') percent= 100*(len(df.loc[:,df.isnull().sum(axis=0)>=1 ].index) / len(df.index)) print(round(percent,2)) 

input is https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0

and the output should be

Ord_id                 0.00 Prod_id                0.00 Ship_id                0.00 Cust_id                0.00 Sales                  0.24 Discount               0.65 Order_Quantity         0.65 Profit                 0.65 Shipping_Cost          0.65 Product_Base_Margin    1.30 dtype: float64 

Answer 1

How about this? I think I actually found something similar on here once before, but I'm not seeing it now...

percent_missing = df.isnull().sum() * 100 / len(df) missing_value_df = pd.DataFrame({'column_name': df.columns,                                  'percent_missing': percent_missing}) 

And if you want the missing percentages sorted, follow the above with:

missing_value_df.sort_values('percent_missing', inplace=True) 

As mentioned in the comments, you may also be able to get by with just the first line in my code above, i.e.:

percent_missing = df.isnull().sum() * 100 / len(df)