Pandas DataFrame & Series map() Method Tutorial

import pandas as pd 

s = pd.Series(['cat', 'dog', 'cow'])
mapping = {'cat': 'feline', 'dog': 'canine'}
print(s.map(mapping))

Expected Output:

0    feline
1    canine
2       NaN
dtype: object

1. Overview and Syntax

The map() method performs element-wise value substitutions across Pandas Series and DataFrames (Pandas 2.1+). It accepts dictionaries, callable Python functions, or matching Pandas Series lookup tables.

Method Signature: Series.map(arg, na_action=None) or DataFrame.map(func, na_action=None)
Parameters:
• arg / func: Dictionary, function, or Series lookup.
• na_action: Set to 'ignore' to skip applying functions to NaN values.
Returns: A Series or DataFrame with substituted values.

2. Dictionary Lookups & Categorical Mapping

Map raw category keys or codes to human-readable text labels using Python dictionaries.

import pandas as pd 

df_students = pd.DataFrame({
    'NAME': ['Ravi', 'Ananya', 'Alex'],
    'GRADE_CODE': [1, 2, 3]
})

grade_map = {1: 'Grade A', 2: 'Grade B', 3: 'Grade C'}
df_students['GRADE_NAME'] = df_students['GRADE_CODE'].map(grade_map)
print(df_students)

3. Formatting Strings & Custom Functions

Format floating-point numbers or apply string manipulations using Python format strings or lambdas.

import pandas as pd 

prices = pd.Series([19.99, 5.5, 100.0])
formatted_prices = prices.map(lambda x: f"${x:.2f}")
print(formatted_prices)

4. Frequently Asked Questions (FAQs)

Q: What happens if a key is missing from a mapping dictionary?
Unmapped keys produce NaN in the output series. To preserve unmapped original values, use replace() instead of map().

Q: What is the difference between map() and applymap()?
In Pandas 2.1+, applymap() is deprecated in favor of DataFrame.map() for element-wise transformations on DataFrames.

5. Common Errors & Troubleshooting

AttributeError: 'Series' object has no attribute 'applymap'
Cause: Attempting to call DataFrame-only methods on Series objects.
Fix: Use Series.map() for Series transformations.

Interactive Companion Jupyter Notebook

Explore interactive code cells, dynamically generate classes, test type vs isinstance comparisons, and run self-validating assertions.

View on GitHub

6. Related Tutorials

Pandas apply() Method - Execute custom function reductions and transformations.
Pandas transform() Method - Perform element-wise and GroupBy transformations.
Pandas DataFrame Methods Directory - Complete index of DataFrame methods.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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