Pandas: Counting empty values in columns

Question

I have a pandas dataframe containing some info about purchases. It includes columns like "purchaseID", "purchaseDate", and "purchaseAmount". I want to know the number of missing values in each column, and different columns contain different types of datatypes like strings, numeric, booleans, etc. I tried something like this:

import json
import pandas

# the variable 'data' is my pandas data frame which was read from a json
with open('purchases.json') as f:
    data = pd.DataFrame(json.loads(line) for line in f)

print(data.isnull().sum())
print(data.isna().sum())

However, both isnull and isna are showing that there are no null values in any columns, which is not the case.

When I tried something like this:

for col in data.columns: 
    print((data[col].values == '').sum())

it works for some columns but not for columns that contain numeric or boolean data. Is there a way for me to find the empty values in all the columns?

Thanks!

Example printout using print of couple lines of data

purchaseID purchaseDate purchaseAmount merchantName
1234       2019-01-01   500.0          Walmart
2345       2019-01-03
           2019-01-02   25.1           BP 

Answer 1

Try using pd.read_json . The problem could be your data frame with one row being the json file.

data = pd.read_json(r'purchases.json')
print(data.isnull().sum())
print(data.isna().sum().sum())