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How to replace NaN values by Zeroes in a column of a Pandas DataFrame?
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:
", df
print "After replacing NaN with 0:
", df.fillna(0)
print "After replacing NaN with 5:
", df.fillna(5)
print "After replacing NaN with 7:
", 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
Updated on: 30-Aug-2021
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