Python | Pandas Series.replace()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.replace() function is used to replace values given in to_replace with value. The values of the Series are replaced with other values dynamically.

Syntax

Series.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='None')

Parameters:

• to_replace : How to find the values that will be replaced.
• value : Value to replace any values matching to_replace with.
• inplace : If True, in place.
• limit : Maximum size gap to forward or backward fill.
• regex : Whether to interpret to_replace and/or value as regular expressions
• method : The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

Returns: Object after replacement.

Example 1: Use Series.replace() function to replace some values from the given Series object.

Output :

Coca Cola 10 Sprite 25 Coke 3 Fanta 11 Dew 24 ThumbsUp 6 dtype: int64

Now we will use Series.replace() function to replace the old values with the new ones.

Output :

Coca Cola 10 Sprite 25 Coke 1000 Fanta 11 Dew 24 ThumbsUp 6 dtype: int64

As we can see in the output, the Series.replace() function has successfully replaced the old value with the new one.  

Example 2 : Use Series.replace() function to replace some values from the given Series object.

Output :

City 1 New York City 2 Chicago City 3 Toronto City 4 Lisbon City 5 Rio dtype: object

Now we will use Series.replace() function to replace the old values with the new ones using a list.

Output :

City 1 London City 2 Chicago City 3 Toronto City 4 Lisbon City 5 Brisbane dtype: object

As we can see in the output, the Series.replace() function has successfully replaced the old value with the new one using the list.