You can replace NaN with zero using either fillna(0) in Pandas or replace(np.nan,0) in Numpy.
Single Column:
Method 1: df['Column_name'].fillna(0)
Method 2: df['Column_name'].replace(np.nan,0)
Whole dataframe:
Method 1: df.fillna(0)
Method 2: df.replace(np.nan,0)
Example 1: single column
The following Python code first creates a dataframe with NaN in both columns and then replaces NaN in the first column with 0 using fillna(0).
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 300,500],
'Col_2': [np.nan, 30, 100, 88, 354,np.nan]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# replace NaN with 0 using fillna()
df['Col_1']=df['Col_1'].fillna(0)
# print out the updated dataframe
print('Dataframe removed NaN in first column: \n', df)The following shows both versions of the dataframe, before being removed and after being removed. You can see that the NaN have been removed in the first column in the updated version of the dataframe.
Dataframe with NaN:
Col_1 Col_2
0 100.0 NaN
1 NaN 30.0
2 200.0 100.0
3 NaN 88.0
4 300.0 354.0
5 500.0 NaN
Dataframe removed NaN in first column:
Col_1 Col_2
0 100.0 NaN
1 0.0 30.0
2 200.0 100.0
3 0.0 88.0
4 300.0 354.0
5 500.0 NaN
Example 2: single column
The following first creates a dataframe with NaN in both columns and then replaces NaN in the first column with 0 using replace(np.nan,0).
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 300,500],
'Col_2': [np.nan, 30, 100, 88, 354,np.nan]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# replace NaN with 0 using replace()
df['Col_1']=df['Col_1'].replace(np.nan,0)
# print out the updated dataframe
print('Dataframe removed NaN in first column: \n', df)The following is the output showing both before and after versions.
Dataframe with NaN:
Col_1 Col_2
0 100.0 NaN
1 NaN 30.0
2 200.0 100.0
3 NaN 88.0
4 300.0 354.0
5 500.0 NaN
Dataframe removed NaN in first column:
Col_1 Col_2
0 100.0 NaN
1 0.0 30.0
2 200.0 100.0
3 0.0 88.0
4 300.0 354.0
5 500.0 NaN
Example 3: whole dataframe
The following code will replace NaN with 0 for both columns using fillna(0).
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 300,500],
'Col_2': [np.nan, 30, 100, 88, 354,np.nan]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# replace NaN with 0
df=df.fillna(0)
# print out the updated dataframe
print('Dataframe removed NaN: \n', df)The following is the updated version of data frame. As we can see, both columns have no NaN anymore.
Dataframe with NaN:
Col_1 Col_2
0 100.0 NaN
1 NaN 30.0
2 200.0 100.0
3 NaN 88.0
4 300.0 354.0
5 500.0 NaN
Dataframe removed NaN:
Col_1 Col_2
0 100.0 0.0
1 0.0 30.0
2 200.0 100.0
3 0.0 88.0
4 300.0 354.0
5 500.0 0.0
Example 4: whole dataframe
The following code will replace NaN with 0 for both columns using replace(np.nan,0).
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 300,500],
'Col_2': [np.nan, 30, 100, 88, 354,np.nan]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# replace NaN with 0 using replace()
df=df.replace(np.nan,0)
# print out the updated dataframe
print('Dataframe removed NaN: \n', df)The following is the output showing two versions of dataframe, before and after removing NaN.
Dataframe with NaN:
Col_1 Col_2
0 100.0 NaN
1 NaN 30.0
2 200.0 100.0
3 NaN 88.0
4 300.0 354.0
5 500.0 NaN
Dataframe removed NaN:
Col_1 Col_2
0 100.0 0.0
1 0.0 30.0
2 200.0 100.0
3 0.0 88.0
4 300.0 354.0
5 500.0 0.0
Further Reading
The following is a tutorial showing how to drop rows or columns with NaN in Pandas.