Fillna Pandas Example

In this article, you will learn about fillna() method of Python Pandas with examples.

Python is one of the fast growing programming language in this world. Instead of development of web applications, it also used in the development of complex numeric, scientific applications and data analytics. The Pandas is most popular Python library used for Data Scientists and Analysts. It provides fast and effective way to manage and explore data, handling missing data, merging or joining of datasets and cleaning up data. It provides lot of methods for performing different tasks. Here, we have mentioned all about the fillna() method.

Pandas fillna

Sometimes, CSV file may contain null values. When we export data from CSV to DateFrame, the null values are displayed as NaN in DataFrame. The fillna() method is used to replace these NaN/ NA values using the specified method.

Syntax of fillna() method

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

value - Value to fill instead of NaN/NA,
method - Method to use for filling holes in reindexed Series pad / ffill,
axis - Axis along which to fill missing values,
inplace - If True, fill in place,
limit - If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill,
downcast - A dict which specifies what dtype to downcast to which one.

Pandas DataFrame fillna Examples

These are the different examples of Python Pandas fillna() method -

Replace NaN values with a static value in DataFrame

Suppose we have the following dataset of four columns in which some values are null -

import numpy as np
import pandas as pd

df = pd.DataFrame([[np.nan, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, np.nan, np.nan],
                   [np.nan, np.nan, 7, np.nan]],
                  columns=list('PQRS'))

print(df)

In the below code, we have replaced the NaN values with a static value 0.0 -

import numpy as np
import pandas as pd

df = pd.DataFrame([[np.nan, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, np.nan, np.nan],
                   [np.nan, np.nan, 7, np.nan]],
                  columns=list('PQRS'))

print(df.fillna(0))

The above code returns the following output -

Replace NaN values with dynamic values in DataFrame

In the below example, we have replaced all NaN elements in column 'P', 'Q', 'R' and 'S', with 2, 3, 7, and 8 respectively.

import numpy as np
import pandas as pd

df = pd.DataFrame([[np.nan, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, np.nan, np.nan],
                   [np.nan, np.nan, 7, np.nan]],
                  columns=list('PQRS'))

print(df.fillna(value = {'P' : 2, 'Q' : 3, 'R' : 7, 'S' : 8}))

Pandas fillna method

With the help of method attribute, we can propagate non-null values forward or backward.

import numpy as np
import pandas as pd

df = pd.DataFrame([[np.nan, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, np.nan, np.nan],
                   [np.nan, np.nan, 7, np.nan]],
                  columns=list('PQRS'))

print(df.fillna( method ='ffill'))

Apply Pandas fillna on a specific column

If we want to fill NaN values of a specific column, we can do this by applying Pandas fillna on that column. In this example, we have replaced NaN values of P column with the given value.

import numpy as np
import pandas as pd

df = pd.DataFrame([[np.nan, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, 7, 2],
                   [9, np.nan, 7, 8]],
                  columns=list('PQRS'))

df['P'].fillna(0, inplace=True)
print(df)

Pandas fillna with another column

We can replace NaN values of one column with values of another column. This can be done by simply assigning values of the other column in the 'value' argument. In the given example, we have replaced the NaN values of 'R' column with 'P' column.

import numpy as np
import pandas as pd

df = pd.DataFrame([[8, 6, 4, 0],
                   [5, 2, np.nan, 1],
                   [2, np.nan, 7, 2],
                   [9, np.nan, np.nan, 8]],
                  columns=list('PQRS'))
df['R'].fillna(value=df['P'], inplace=True)
print(df)

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