Pandas: Replacing Non-numeric cells with 0

Question

I have the Pandas Dataframe in this format

0          or LIST requests
1                 us-west-2
2                 1.125e-05
3                         0
4          


        

Answer 1

You can use the to_numeric method, but it's not changing the value in place. You need to set the column to the new values:

training_data['usagequantity'] = (
    pd.to_numeric(training_data['usagequantity'],
                  errors='coerce')
      .fillna(0)
    )

to_numeric sets the non-numeric values to NaNs, and then the chained fillna method replaces the NaNs with zeros.

Answer 2

Following code can work:

df.col =pd.to_numeric(df.col, errors ='coerce').fillna(0).astype('int')

Answer 3

Setup

import pandas as pd
from StringIO import StringIO

text = """0          or LIST requests
1                 us-west-2
2                 1.125e-05
3                         0
4                 3.032e-05
5                         0
6                  7.28e-06
7          or LIST requests
8                   3.1e-07
9                         0
10                        0
11                1.067e-05
12               0.00011983
13                0.1075269
14         or LIST requests
15                us-west-2
16                        0
17                 2.88e-06
18           ap-northeast-2
19                 5.52e-06
20                 6.15e-06
21                 3.84e-06
22         or LIST requests"""

df = pd.read_csv(StringIO(text), sep='\s{2,}', engine='python', index_col=[0], header=None)

Use pd.to_numeric

pd.to_numeric(df.iloc[:, 0], errors='coerce').fillna(0)

Assign this column where ever you'd like.