Fillna Pandas Example

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

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

Pandas fillna

Sometimes, CSV files may contain null values. When we export data from CSV to DateFrame, the null values are displayed as NaN in the 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. It can be scalar, dictionary, pandas series or a DataFrame
method- Method to use for filling holes in reindexed series pad/ ffill/ backfill/ bfill,
axis- Axis along which to fill missing values. It can take 0 or 'index', 1 or 'columns'.
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, it can be a dictionary or None.

Pandas DataFrame fillna Examples

These are the different examples of the 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)

P    Q    R    S
0  NaN  6.0  4.0  0.0
1  5.0  2.0  NaN  1.0
2  2.0  NaN  NaN  NaN
3  NaN  NaN  7.0  NaN

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-

P    Q    R    S
0  0.0  6.0  4.0  0.0
1  5.0  2.0  0.0  1.0
2  2.0  0.0  0.0  0.0
3  0.0  0.0  7.0  0.0

Replace NaN values with dynamic values in DataFrame

In the below example, we have replaced all NaN elements in columns '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}))

The above code returns the following output-

P    Q    R    S
0  2.0  6.0  4.0  0.0
1  5.0  2.0  7.0  1.0
2  2.0  3.0  7.0  8.0
3  2.0  3.0  7.0  8.0

Pandas fillna method

With the help of method attributes, 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'))

P    Q    R    S
0  2.0  6.0  4.0  0.0
1  5.0  2.0  7.0  1.0
2  2.0  3.0  7.0  8.0
3  2.0  3.0  7.0  8.0

Apply Pandas fillna on a specific column

If we want to fill NaN values in a specific column, we can do this by applying Pandas fillna to 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)

P    Q    R  S
0  0.0  6.0  4.0  0
1  5.0  2.0  NaN  1
2  2.0  NaN  7.0  2
3  9.0  NaN  7.0  8

Pandas fillna with another column

We can replace NaN values in one column with values in another column. This can be done by simply assigning values to the other columns 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)

P    Q    R  S
0  8  6.0  4.0  0
1  5  2.0  5.0  1
2  2  NaN  7.0  2
3  9  NaN  9.0  8

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