apply() function for custom logic in Data Analysis Python - Step-by-Step Execution

Concept Flow - apply() function for custom logic

Start with DataFrame

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Define custom function

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Call apply() on DataFrame or Series

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apply() sends each row/column/value to custom function

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Custom function processes input and returns result

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apply() collects all results into new Series/DataFrame

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Use or store the transformed data

The apply() function takes a custom function and runs it on each row, column, or element, then collects the results.

Execution Sample

Data Analysis Python

import pandas as pd

def add_bonus(salary):
    return salary + 100

salaries = pd.Series([1000, 2000, 3000])
bonuses = salaries.apply(add_bonus)

This code adds 100 bonus to each salary using apply() with a custom function.

Execution Table

Step	Input to add_bonus	Function Output	bonuses Series after step
1	1000	1100	[1100, NaN, NaN]
2	2000	2100	[1100, 2100, NaN]
3	3000	3100	[1100, 2100, 3100]
4	End of apply()	All values processed	[1100, 2100, 3100]

💡 All elements processed, apply() returns new Series with updated values

Variable Tracker

Variable	Start	After 1	After 2	After 3	Final
salary	pd.Series([1000, 2000, 3000])	1000	2000	3000	3000
bonuses	Empty	[1100, NaN, NaN]	[1100, 2100, NaN]	[1100, 2100, 3100]	[1100, 2100, 3100]

Key Moments - 2 Insights

Why does apply() return a new Series instead of modifying the original?

apply() creates a new Series to keep original data unchanged, as shown in execution_table rows 1-4 where bonuses build up separately.

What exactly is passed to the custom function in apply()?

Each element of the Series is passed one by one to the custom function, as seen in execution_table column 'Input to add_bonus'.

Visual Quiz - 3 Questions

Look at the execution table, what is the output of add_bonus when input is 2000?

A 2000

B 2100

C 100

D 3000

💡 Hint

Check the row where Input to add_bonus is 2000 in the execution_table.

At which step does the bonuses Series first have all values filled?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'bonuses Series after step' column in execution_table.

If the custom function returned salary * 2 instead of salary + 100, what would bonuses be after step 3?

A [2000, 4000, 6000]

B [1100, 2100, 3100]

C [100, 200, 300]

D [1000, 2000, 3000]

💡 Hint

Think about doubling each input value from the variable_tracker salaries.

Concept Snapshot

apply() runs a custom function on each element (or row/column) of a DataFrame or Series.
It returns a new Series or DataFrame with the results.
Original data stays unchanged.
Use apply() to add custom logic easily.
Example: series.apply(func) where func processes one value at a time.

Full Transcript

The apply() function in pandas lets you run your own function on each element of a Series or each row/column of a DataFrame. You start with your data, define a function that does something to one value, then call apply() with that function. Internally, apply() sends each value to your function, collects the results, and returns a new Series or DataFrame. This way, you can add custom logic without changing the original data. For example, adding a fixed bonus to salaries by defining add_bonus(salary) and applying it to each salary. The execution table shows each step: input value, function output, and how the new Series builds up. This helps beginners see how apply() works step-by-step.