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

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

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)