How To Map DataFrame Index to Dictionary in Pandas

John D K -

In this post, we'll explore how to map DataFrame Index values using a dictionary in Pandas.

Setup

Consider a DataFrame with following data:

import pandas as pd

data = {'Value': [10, 15, 20, 25]}
df = pd.DataFrame(data, index=[1,2,3,4])

result:

Value
1 10
2 15
3 20
4 25

This will create a DataFrame with an index labeled 1, 2, 3 and 4.

1: Map index with df.index.map

To map DataFrame index with Python dictionary we can use method: df.index.map:

index_mapping = {1: 'Red', 2: 'Blue', 3: 'Green', 4: 'White' }
df.index = df.index.map(index_mapping)

The new index is based on the mapping of the provided values in the dictionary:

Value
Red 10
Blue 15
Green 20
White 25

2: Map with a function

To map Pandas index with a function we have two options:

  • lambda
  • predefined functions

lambda

Let's remind us that - lambda function is a small anonymous function.

df.index.map(lambda x: x + 1)

the result is new index with changed values:

Index([2, 3, 4, 5], dtype='int64')

Another lambda example to map index:

df.index.map(lambda x: x.upper())

predefined functions

The example below will map all values and format the them:

df.index.map('Index {}'.format)

the result is new index with changed values:

Index(['Index 1', 'Index 2', 'Index 3', 'Index 4'], dtype='object')

3: Missing values in the dict

There is a parameter na_action which controls behavior of missing values. If the index contains missing values they could be exclude from mapping with function:

import pandas as pd

data = {'Value': [10, 15, 20, 25]}
df = pd.DataFrame(data, index=[1,2,3, None])

df.index.map('Index {}'.format, na_action='ignore')

Will result into:

Index(['Index 1.0', 'Index 2.0', 'Index 3.0', nan], dtype='object')

4: Map with values without mapping

If a value is not found in the index we will end with index full of NaN values:

index_mapping = {1: 'Red', 2: 'Blue'}
df.index = df.index.map(index_mapping)

result:

Value
Red 10
Blue 15
NaN 20
NaN 25

To avoid that we can use method replace:

index_mapping = {1: 'Red', 2: 'Blue'}
df.index = pd.Series(df.index).replace(index_mapping)

Conclusion

Mapping a DataFrame index using a dictionary might help to control index values. Some use cases are:

  • data cleaning
  • memory efficiency
  • anonymization

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