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Using dictionary to remap values in Pandas DataFrame columns

Last Updated : 11 Jul, 2025

While working with data in Pandas, we often need to modify or transform values in specific columns. One common transformation is remapping values using a dictionary. This technique is useful when we need to replace categorical values with labels, abbreviations or numerical representations. In this article, we'll explore different ways to remap values in a Pandas DataFrame using dictionary mapping.

Creating a sample pandas dataframe

Let's start by creating a sample DataFrame that contains event details.


Output
 Date Event Cost
0 10/2/2011 Music 10000
1 11/2/2011 Poetry 5000
2 12/2/2011 Theatre 15000
3 13/2/2011 Comedy 2000

Using replace() function

The replace() function in Pandas allows us to remap values using a dictionary. It works directly on a DataFrame column and modifies the values based on the provided mapping.


Output
 Date Event Cost
0 10/2/2011 M 10000
1 11/2/2011 P 5000
2 12/2/2011 T 15000
3 13/2/2011 C 2000

Explanation:

  • replace() searches for values in the column and replaces them based on the dictionary.
  • This method works on DataFrame columns and can handle multiple column replacements.

Using map() function 

Another way to remap values in a Pandas column is by using the map() function.


Output
 Date Event Cost
0 10/2/2011 M 10000
1 11/2/2011 P 5000
2 12/2/2011 T 15000
3 13/2/2011 C 2000

Explanation:

  • map() applies the dictionary mapping to each element in the column.
  • Unlike replace(), map() only works on Series (single column).

Differences between replace() and map()

Feature

replace()

map()

Works on entire DataFrame?

Yes

No, only on Series (columns)

Supports multiple column replacements?

Yes

No, works on a single column

Handles missing keys?

Yes

No, returns NaN for missing keys

Handling missing values during mapping

If the dictionary used in map() does not contain a key for some values in the column, those values will be replaced with NaN. To handle this, we can use the fillna() function.


Output
 Date Event Cost
0 10/2/2011 M 10000
1 11/2/2011 P 5000
2 12/2/2011 Unknown 15000
3 13/2/2011 Unknown 2000

Explanation:

  • map() function replaces only the values present in the dictionary.
  • Missing values (i.e., Theatre and Comedy) become NaN.
  • fillna('Unknown') ensures that missing values are replaced with "Unknown" instead of NaN.
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