JIYIK CN >

This tutorial explains how we can use DataFrame.fillna()the method to fill NaN values ​​with specified values.

We will use the following DataFrame in this article.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame(
    {
        "Roll No": [501, 502, np.nan, 504, 505, 506],
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan],
        "Age": [17, 18, np.nan, 16, 18, np.nan],
    }
)

print(student_df)

Output:

   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN

DataFrame.fillna()method

grammar

DataFrame.fillna(
    value=None, method=None, axis=None, inplace=False, limit=None, downcast=None
)

DataFrame.fillna()DataFrameMethods allow us to fill the value in with a specified value or method NaN.

Use DataFrame.fillna()the method to fill the entire DataFrame with the specified value.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame(
    {
        "Roll No": [501, 502, np.nan, 504, 505, 506],
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan],
        "Age": [17, 18, np.nan, 16, 18, np.nan],
    }
)
filled_df = student_df.fillna(0)

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      0.0       Bob           0.0   0.0
3    504.0      Emma          30.0  16.0
4    505.0      Luna           0.0  18.0
5    506.0     Anish           0.0   0.0 

It replaces student_dfall the values ​​in the DataFrame with the value passed as an argument to the method.NaN0DataFrame.fillna()

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame(
    {
        "Roll No": [501, 502, np.nan, 504, 505, 506],
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Income(in $)": [200, 400, np.nan, 30, np.nan, np.nan],
        "Age": [17, 18, np.nan, 16, 18, np.nan],
    }
)
filled_df = student_df.fillna(method="ffill")

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma          30.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2    502.0       Bob         400.0  18.0
3    504.0      Emma          30.0  16.0
4    505.0      Luna          30.0  18.0
5    506.0     Anish          30.0  18.0 

It fills all the values student_df​​in NaNwith the value preceding the value NaNin the same column as the value.NaN

NaNFills the specified column with the specified value

To fill a specific value with specified values, we fillna()pass a dictionary to the method with the column name as the key and NaNthe value of that column as the value.

import numpy as np
import pandas as pd

roll_no = [501, 502, 503, 504, 505]

student_df = pd.DataFrame(
    {
        "Roll No": [501, 502, np.nan, 504, 505, 506],
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Income(in $)": [200, 400, np.nan, 300, np.nan, np.nan],
        "Age": [17, 18, np.nan, 16, 18, np.nan],
    }
)
filled_df = student_df.fillna({"Age": 17, "Income(in $)": 300})

print("DataFrame with NaN values")
print(student_df, "\n")

print("After applying fillna() to the DataFrame:")
print(filled_df, "\n")

Output:

DataFrame with NaN values
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob           NaN   NaN
3    504.0      Emma         300.0  16.0
4    505.0      Luna           NaN  18.0
5    506.0     Anish           NaN   NaN 

After applying fillna() to the DataFrame:
   Roll No      Name  Income(in $)   Age
0    501.0  Jennifer         200.0  17.0
1    502.0    Travis         400.0  18.0
2      NaN       Bob         300.0  17.0
3    504.0      Emma         300.0  16.0
4    505.0      Luna         300.0  18.0
5    506.0     Anish         300.0  17.0 

It fills Ageall the values ​​in the column with 17 and all the values ​​in the column with 300. The values ​​in the column remain unchanged.NaNIncome(in $)NaNRoll NoNaN