Applying Functions (apply, map, applymap)

First: prefer vectorized operations

Pandas is fastest when you use vectorized operations.

Vectorized example

import pandas as pd
 
df = pd.DataFrame({"amount": [100, 200, 150]})
df["amount_with_tax"] = df["amount"] * 1.18
print(df)

Vectorized example

import pandas as pd
 
df = pd.DataFrame({"amount": [100, 200, 150]})
df["amount_with_tax"] = df["amount"] * 1.18
print(df)

Use applyapply/mapmap when you really need custom logic.

`map()map()` (Series only)

mapmap transforms values in a single Series.

Example: map categories

map categories

import pandas as pd
 
s = pd.Series(["pune", "delhi", "mumbai"])
state_map = {"pune": "MH", "delhi": "DL", "mumbai": "MH"}
 
print(s.map(state_map))

map categories

import pandas as pd
 
s = pd.Series(["pune", "delhi", "mumbai"])
state_map = {"pune": "MH", "delhi": "DL", "mumbai": "MH"}
 
print(s.map(state_map))

If a value is missing from the map, it becomes NaNNaN.

`apply()apply()` (Series or DataFrame)

Apply on a Series

apply on Series

import pandas as pd
 
s = pd.Series([1, 2, 3, 4])
print(s.apply(lambda x: x * x))

apply on Series

import pandas as pd
 
s = pd.Series([1, 2, 3, 4])
print(s.apply(lambda x: x * x))

Apply on rows (axis=1)

apply on rows

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi"],
    "score": [88, 91],
    "bonus": [5, 2],
})
 
def final_score(row):
    return row["score"] + row["bonus"]
 
df["final"] = df.apply(final_score, axis=1)
print(df)

apply on rows

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi"],
    "score": [88, 91],
    "bonus": [5, 2],
})
 
def final_score(row):
    return row["score"] + row["bonus"]
 
df["final"] = df.apply(final_score, axis=1)
print(df)

Row-wise applyapply is easy to write but can be slower for large data.

`applymap()applymap()` (DataFrame element-wise)

Element-wise transform for the whole DataFrame.

applymap

import pandas as pd
 
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.applymap(lambda x: x * 10))

applymap

import pandas as pd
 
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.applymap(lambda x: x * 10))

Quick guidance

Use vectorized operations if possible
Use mapmap for simple value mapping (Series)
Use applyapply for custom logic on a Series or across rows/columns
Use applymapapplymap for element-wise transforms

🧪 Try It Yourself

Exercise 1 – Create a DataFrame

Exercise 2 – Select a Column

Exercise 3 – Filter Rows

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