Home Â» How to Replace NaN Values with Zero in Pandas

# How to Replace NaN Values with Zero in Pandas

You can use the following methods to replace NaN values with zeros in a pandas DataFrame:

Method 1: Replace NaN Values with Zero in One Column

```df['col1'] = df['col1'].fillna(0)
```

Method 2: Replace NaN Values with Zero in Several Columns

`df[['col1', 'col2']] = df[['col1', 'col2']].fillna(0)`

Method 3: Replace NaN Values with Zero in All Columns

`df = df.fillna(0)`

The following examples show how to use each of these methods with the following pandas DataFrame:

```import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'points': [25, np.nan, 15, 14, 19, 23, 25, 29],
'assists': [5, np.nan, 7, np.nan, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, np.nan, 9, np.nan]})

#view DataFrame
print(df)

points  assists  rebounds
0    25.0      5.0      11.0
1     NaN      NaN       8.0
2    15.0      7.0      10.0
3    14.0      NaN       6.0
4    19.0     12.0       6.0
5    23.0      9.0       NaN
6    25.0      9.0       9.0
7    29.0      4.0       NaN
```

### Method 1: Replace NaN Values with Zero in One Column

The following code shows how to replace NaN values with zero in just the â€˜assistsâ€™ column:

```#replace NaN values with zero in 'assists' column
df['assists'] = df['assists'].fillna(0)

#view updated DataFrame
print(df)

points  assists  rebounds
0    25.0      5.0      11.0
1     NaN      0.0       8.0
2    15.0      7.0      10.0
3    14.0      0.0       6.0
4    19.0     12.0       6.0
5    23.0      9.0       NaN
6    25.0      9.0       9.0
7    29.0      4.0       NaN```

Notice that the NaN values in the â€˜assistsâ€™ column have been replaced with zeros, but the NaN values in every other column still remain.

### Method 2: Replace NaN Values with Zero in Several Columns

The following code shows how to replace NaN values with zero in the â€˜pointsâ€™ and â€˜assistsâ€™ columns:

```#replace NaN values with zero in 'points' and 'assists' column
df[['points', 'assists']] = df[['points', 'assists']].fillna(0)

#view updated DataFrame
print(df)

points  assists  rebounds
0    25.0      5.0      11.0
1     0.0      0.0       8.0
2    15.0      7.0      10.0
3    14.0      0.0       6.0
4    19.0     12.0       6.0
5    23.0      9.0       NaN
6    25.0      9.0       9.0
7    29.0      4.0       NaN```

### Method 3: Replace NaN Values with Zero in All Columns

The following code shows how to replace NaN values with zero in every column of the DataFrame:

```#replace NaN values with zero in all columns
df = df.fillna(0)

#view updated DataFrame
print(df)

points  assists  rebounds
0    25.0      5.0      11.0
1     0.0      0.0       8.0
2    15.0      7.0      10.0
3    14.0      0.0       6.0
4    19.0     12.0       6.0
5    23.0      9.0       0.0
6    25.0      9.0       9.0
7    29.0      4.0       0.0```