Feature engineering basics in Pandas - Step-by-Step Execution

Concept Flow - Feature engineering basics

Start with raw data

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Identify useful info

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Create new features

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Add features to data

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Use features for analysis or model

Feature engineering means making new useful columns from raw data to help analysis or models.

Execution Sample

Pandas

import pandas as pd

data = pd.DataFrame({'age': [25, 32, 47], 'income': [50000, 60000, 80000]})
data['age_group'] = pd.cut(data['age'], bins=[0,30,50], labels=['Young','Old'])

This code creates a new column 'age_group' by grouping ages into 'Young' or 'Old'.

Execution Table

Step	Action	DataFrame State	New Feature Created
1	Create DataFrame with 'age' and 'income'	{'age': [25,32,47], 'income': [50000,60000,80000]}	None
2	Apply pd.cut to 'age' with bins [0,30,50]	Same as step 1	Series with ['Young', 'Young', 'Old']
3	Assign new series to 'age_group' column	{'age': [25,32,47], 'income': [50000,60000,80000], 'age_group': ['Young','Young','Old']}	'age_group' added
4	Final DataFrame ready for use	{'age': [25,32,47], 'income': [50000,60000,80000], 'age_group': ['Young','Young','Old']}	'age_group' present

💡 New feature 'age_group' created and added to DataFrame for better analysis.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
data	Empty	{'age': [25,32,47], 'income': [50000,60000,80000]}	{'age': [25,32,47], 'income': [50000,60000,80000], 'age_group': ['Young','Young','Old']}	{'age': [25,32,47], 'income': [50000,60000,80000], 'age_group': ['Young','Young','Old']}
age_group	None	Series(['Young','Young','Old'])	Assigned to data['age_group']	Present in data

Key Moments - 2 Insights

Why do we use pd.cut to create 'age_group' instead of just copying 'age'?

pd.cut groups continuous numbers into categories, making it easier to analyze age ranges. See execution_table step 2 where pd.cut creates categories.

Does adding a new column change the original data or create a copy?

Adding a new column modifies the original DataFrame in place, as shown in execution_table step 3 where 'age_group' is added.

Visual Quiz - 3 Questions

Look at the execution table at step 2, what does pd.cut produce?

A A new DataFrame

B A Series with categorical labels

C A list of numbers

D An error

💡 Hint

Check the 'New Feature Created' column at step 2 in execution_table.

At which step is the new feature 'age_group' added to the DataFrame?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look at the 'Action' and 'DataFrame State' columns in execution_table.

If we changed bins in pd.cut to [0, 20, 40, 60], how would the 'age_group' Series change?

A It would become numeric

B It would stay the same

C It would have three categories instead of two

D It would cause an error

💡 Hint

Think about how pd.cut uses bins to create categories, see variable_tracker for 'age_group'.

Concept Snapshot

Feature engineering means creating new columns from raw data.
Use pandas functions like pd.cut to group or transform data.
Add new features to your DataFrame for better analysis or models.
New features can be categories, numbers, or text.
Always check your new feature values before use.

Full Transcript

Feature engineering basics means making new columns from your data to help analysis or machine learning. For example, using pandas, you can create a new column 'age_group' by grouping ages into categories like 'Young' or 'Old' using pd.cut. The process starts with raw data, then you identify useful info, create new features, add them to your data, and finally use them for analysis or models. In the example, we start with a DataFrame with 'age' and 'income'. We use pd.cut to create a series of age groups, then add this as a new column. This helps simplify age data into categories. Adding a new column changes the original DataFrame. Understanding how new features are created and added is key to feature engineering.