Appending a Dictionary with row number and column name-CodePudding

I have this code that allows me to find all blanks in certain columns of a dataframe. Here is what I have:

req_cols = ['First Name*','Last Name*','Country*','Company*','Email Address*']
bad_nan=df[df[req_cols].isna().any(1)]

I am trying to add the missing cells or NAN values to an existing dictionary called "errors"

if not bad_nan.empty:
    errors.append({
        "row": [0],
        "column": [1],
        "message": "This is a required field"
})

this is what the dictionary looks like:

{'row': [0], 'column': [1], 'message': 'This is a required field'}

but I would like it to look like

{'row': 2, 'column': First Name*, 'message': 'This is a required field'}

I would like this to display all the cells that have a NaN value not just one

CodePudding user response：

It seems to me that you forgot something in your if statement.

if not bad_nan.empty:
    errors.append({
        "row": bad_nan[0],
        "column": bad_nan[1],
        "message": "This is a required field"
})

Could that be the problem?

CodePudding user response：

I would loop through every cell in the dataframe bad_nan to append the row index and column name to the errors list. Please check if np.nan is still the same in the filtered bad_nan

errors = []
req_cols = ['First Name*','Last Name*','Country*','Company*','Email Address*']
#req_cols = df.columns

bad_nan = df.loc[df[req_cols].isna().any(1)]
for col in bad_nan.columns:
    bad_nan[col] = bad_nan[col].astype('str')
    for i in range(bad_nan.shape[0]):
        #print(bad_nan.loc[i, col])
        if bad_nan.loc[i, col] == 'nan':    #use print statement to check if string is 'nan'
            errors.append({ "row": i,
                            "column": col,
                            "message": "This is a required field" })
print(errors)