How to replace NaN values by Zeroes in a column of a Pandas DataFrame?

Pandas Server Side Programming Programming

To replace NaN values by zeroes or other values in a column of a Pandas DataFrame, we can use df.fillna() method.

Steps

Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.
Print the input DataFrame, df.
Use df.fillna(0) to replace NaN in DataFrame with value 0.
Similarly use df.fillna(5) and df.fillna(7) to replace NaN in DataFrame with 5 and 7, respectively.
Print the replaced NaN, DataFrame.

Example

import pandas as pd
import numpy as np

df = pd.DataFrame(
   {
      "x": [5, np.nan, 1, np.nan],
      "y": [np.nan, 1, np.nan, 10],
      "z": [np.nan, 1, np.nan, np.nan]
   }
)
print "Input series is:\n", df
print "After replacing NaN with 0:\n", df.fillna(0)
print "After replacing NaN with 5:\n", df.fillna(5)
print "After replacing NaN with 7:\n", df.fillna(7)

Output

Input series is:
    x  y    z
0 5.0  NaN  NaN
1 NaN  1.0  1.0
2 1.0  NaN  NaN
3 NaN  10.0 NaN

After replacing NaN with 0:
    x   y    z
0 5.0   0.0  0.0
1 0.0   1.0  1.0
2 1.0   0.0  0.0
3 0.0  10.0  0.0

After replacing NaN with 5:
   x    y    z
0 5.0  5.0   5.0
1 5.0  1.0   1.0
2 1.0  5.0   5.0
3 5.0  10.0  5.0

After replacing NaN with 7:
    x    y     z
0  5.0  7.0   7.0
1  7.0  1.0   1.0
2  1.0  7.0   7.0
3  7.0  10.0  7.0

Rishikesh Kumar Rishi

Updated on 30-Aug-2021 12:03:02

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