Applying Functions

Pandas provides flexible ways to apply functions to your data. The most common methods are apply(), map(), and applymap().

`map()` Method

The map() method is used with Series to substitute each value with another value. It's useful for mapping a set of values to another set.

import pandas as pd
import numpy as np

s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
print("Original Series:")
print(s)

# Map values to a dictionary
print("\nMapped Series:")
print(s.map({'cat': 'kitten', 'dog': 'puppy'}))

You can also use map with a function.

import pandas as pd

s = pd.Series([1, 2, 3, 4])
print("Original Series:")
print(s)

# Map values using a function
print("\nMapped Series with a function:")
print(s.map(lambda x: x * 2))

`apply()` Method

The apply() method works on both Series and DataFrames. When used on a DataFrame, it applies a function along an axis (either rows or columns).

Applying to Columns

Here is an example of applying a function to each column.

import pandas as pd
import numpy as np

df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
print("Original DataFrame:")
print(df)

# Apply numpy's sqrt function to each column
print("\nDataFrame after applying sqrt:")
print(df.apply(np.sqrt))

Applying to Rows

You can also apply a function to each row by specifying axis=1.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
print("Original DataFrame:")
print(df)

# Get the sum of each row
print("\nSum of each row:")
print(df.apply(np.sum, axis=1))

Applying to Series

When used on a Series, apply() operates on each element.

import pandas as pd

s = pd.Series([1, 2, 3, 4])
print("Original Series:")
print(s)

# Apply a function to each element
print("\nApplied Series:")
print(s.apply(lambda x: x**2 + 2))

`applymap()` Method

The applymap() method applies a function to each individual element in a DataFrame. This is useful for element-wise transformations.

import pandas as pd
import numpy as np

df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
print("Original DataFrame:")
print(df)

# Format each value to two decimal places
print("\nFormatted DataFrame:")
print(df.applymap(lambda x: f"{x:.2f}"))

Comparison of Methods

Method	Works on	Applies to	Use case
`map()`	Series only	Each element	Value substitution
`apply()`	Series & DataFrame	Each row/column (DataFrame) or element (Series)	Column-wise or row-wise operations
`applymap()`	DataFrame only	Each element	Element-wise transformations

Applying Functions

map() Method

apply() Method