Convert Multiple Columns from String to Float and Int

Question

I'm trying to convert multiple columns in my df from string to int and float. I used fillna method to get rid of NaN values however I'm still getting an error. Here's what I did:

df = [['column1', 'column2', 'column3', 'column4']]

df = df[['column1', 'column2', 'column3', 'column4']].fillna(0)
convert_dict = {'column1': int,
                 'column2': float,
                  'column3': int, 
                    'column4': float}
df = df.astype(convert_dict)

The error says ValueError: cannot convert float NaN to integer

Edit: Removed inplace=True

df = [[...]].fillna(0, inplace=True) doesn't make sense. I'm assuming you are missing a df in there and you don't need inplace if you are assigning the result to df. — AChampion
– AChampion, Commented Sep 3, 2020 at 17:20
should you be using df = df[['column1'... instead of df = [['column1'... ? — RichieV
– RichieV, Commented Sep 3, 2020 at 17:32
df = df.astype(convert_dict, errors = 'ignore') should work. — r0ot293
– r0ot293, Commented Sep 3, 2020 at 17:44

jsmart · Accepted Answer · 2020-09-03 17:50:59Z

1

You could use Int64, which supports missing integer values:

import numpy as np
import pandas as pd

df = pd.DataFrame({'A': [1, 2, None, 4], 
                   'B': [1.0, 2.0, 3.0, None]})

convert_dict = {'A': 'Int64', 'B': float}
convert_dict

for field, new_type in convert_dict.items():
    df[field] = df[field].astype(new_type)

print(df)
print(df.dtypes)

      A    B
0     1  1.0
1     2  2.0
2  <NA>  3.0
3     4  NaN

A      Int64
B    float64
dtype: object

answered Sep 3, 2020 at 17:50

jsmart

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