Using dictionary to remap values in Pandas DataFrame columns (original) (raw)

Last Updated : 22 Mar, 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.

Python `

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost': [10000, 5000, 15000, 2000]})

print(df)

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.

Python `

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost': [10000, 5000, 15000, 2000]})

Define a dictionary for remapping values

d = {'Music': 'M', 'Poetry': 'P', 'Theatre': 'T', 'Comedy': 'C'}

Remap the values using replace()

df['Event'] = df['Event'].replace(d)

print(df)

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.

Python `

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost': [10000, 5000, 15000, 2000]})

Define a dictionary for remapping values

d = {'Music': 'M', 'Poetry': 'P', 'Theatre': 'T', 'Comedy': 'C'}

Remap the values using map()

df['Event'] = df['Event'].map(d) print(df)

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.

Python `

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost': [10000, 5000, 15000, 2000]})

Define an incomplete dictionary

d = {'Music': 'M', 'Poetry': 'P'}

Apply map() and handle missing values

df['Event'] = df['Event'].map(d).fillna('Unknown')

print(df)

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.