Feature engineering basics in Pandas

Introduction

Feature engineering helps us create new useful data from existing data. This makes it easier for computers to learn and find patterns.

When you want to improve a model's accuracy by adding new information.

When your data is raw and needs cleaning or transforming before analysis.

When you want to combine or split columns to get better insights.

When you want to convert text or dates into numbers for machine learning.

When you want to reduce the number of features but keep important information.

Syntax

Pandas

import pandas as pd

# Create new feature by combining columns
DataFrame['new_feature'] = DataFrame['col1'] + DataFrame['col2']

# Create feature by applying a function
DataFrame['new_feature'] = DataFrame['col'].apply(function)

# Create feature from date column
DataFrame['year'] = DataFrame['date_col'].dt.year

You can create new columns by simple math or functions.

Date columns can be split into year, month, day easily with pandas.

Examples

This example creates a new feature by multiplying age and income.

Pandas

import pandas as pd

data = {'age': [25, 32, 47], 'income': [50000, 60000, 80000]}
df = pd.DataFrame(data)

df['age_income'] = df['age'] * df['income']

This example extracts year and month from a date column.

Pandas

import pandas as pd

data = {'date': pd.to_datetime(['2020-01-01', '2021-06-15'])}
df = pd.DataFrame(data)

df['year'] = df['date'].dt.year

df['month'] = df['date'].dt.month

This example creates a new feature by categorizing income into 'low' or 'high'.

Pandas

import pandas as pd

def categorize_income(x):
    if x < 60000:
        return 'low'
    else:
        return 'high'

data = {'income': [50000, 70000, 40000]}
df = pd.DataFrame(data)

df['income_level'] = df['income'].apply(categorize_income)

Sample Program

This program shows how to create new features by combining columns, extracting date parts, and categorizing numeric data.

Pandas

import pandas as pd

# Sample data
data = {
    'age': [22, 35, 58, 45],
    'salary': [30000, 60000, 80000, 50000],
    'join_date': pd.to_datetime(['2019-01-01', '2018-05-15', '2020-07-30', '2017-12-10'])
}

df = pd.DataFrame(data)

# Create new feature: age times salary
df['age_salary'] = df['age'] * df['salary']

# Extract year from join_date
df['join_year'] = df['join_date'].dt.year

# Categorize salary
 def salary_category(salary):
     return 'High' if salary > 55000 else 'Low'

df['salary_cat'] = df['salary'].apply(salary_category)

print(df)

Important Notes

Feature engineering can greatly improve model results but requires understanding your data.

Always check new features for errors or unexpected values.

Try simple features first before complex transformations.

Summary

Feature engineering means creating new useful columns from existing data.

It helps models learn better by giving clearer information.

Common methods include combining columns, extracting date parts, and categorizing values.

Practice

1. What is the main purpose of feature engineering in pandas?

easy

A. To create new useful columns from existing data

B. To delete unnecessary rows from the dataset

C. To change the data type of columns

D. To visualize data using charts

Solution

1. Step 1: Understand feature engineering definition

   Feature engineering means creating new columns that help models learn better.

2. Step 2: Compare options with definition

   Only To create new useful columns from existing data matches the purpose of creating new useful columns from existing data.

3. Final Answer:

   To create new useful columns from existing data -> Option A

4. Quick Check:

   Feature engineering = create new columns [OK]

Hint: Feature engineering = making new columns from old data [OK]

Common Mistakes:

• Confusing feature engineering with data cleaning
• Thinking it only changes data types
• Mixing it up with data visualization

2. Which of the following is the correct way to create a new column total as sum of columns 2a and 2b in a pandas DataFrame df?

easy

A. df['total'] <- df['2a'] + df['2b']

B. df.total = df.2a + df.2b

C. df['total'] == df['2a'] + df['2b']

D. df['total'] = df['2a'] + df['2b']

Solution

1. Step 1: Recall pandas syntax for new column

   Use df['new_col'] = expression to create new columns.

2. Step 2: Check each option

   df['total'] = df['2a'] + df['2b'] uses correct syntax. df.total = df.2a + df.2b misses brackets, A uses wrong operator, D uses comparison operator.

3. Final Answer:

   df['total'] = df['2a'] + df['2b'] -> Option D

4. Quick Check:

   New column = df['col'] = ... [OK]

Hint: Use df['col'] = ... to add new columns [OK]

Common Mistakes:

• Using == instead of = for assignment
• Using <- which is not Python syntax
• Accessing columns without brackets

3. Given the DataFrame df below, what will be the output of df['age_group'] after this code?

import pandas as pd
df = pd.DataFrame({'age': [15, 22, 37, 45]})
df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60], labels=['child', 'young_adult', 'adult'])

medium

A. [child, young_adult, young_adult, adult]

B. [child, child, young_adult, adult]

C. [child, young_adult, adult, adult]

D. [child, young_adult, adult, young_adult]

Solution

1. Step 1: Understand pd.cut bins and labels

   Bins: 0-18 = child, 18-35 = young_adult, 35-60 = adult.

2. Step 2: Assign each age to bin

   15 -> child, 22 -> young_adult, 37 -> adult, 45 -> adult.

3. Final Answer:

   [child, young_adult, adult, adult] -> Option C

4. Quick Check:

   Age 37 and 45 fall in adult bin [OK]

Hint: Match values to bins to find labels [OK]

Common Mistakes:

• Misunderstanding bin edges (inclusive/exclusive)
• Assigning wrong labels to ages
• Confusing order of labels

4. What is wrong with this code to create a new column year from a datetime column date in DataFrame df?

df['year'] = df['date'].year

medium

A. There is no error; the code is correct

B. You must use dt.year to access year from datetime column

C. You should use df.year = df.date.year instead

D. The column date must be converted to string first

Solution

1. Step 1: Recall how to extract year from datetime column

   Use df['date'].dt.year to get year from pandas datetime column.

2. Step 2: Identify error in code

   Code uses df['date'].year which is invalid; .year is not attribute of Series.

3. Final Answer:

   You must use dt.year to access year from datetime column -> Option B

4. Quick Check:

   Use df['date'].dt.year for datetime year [OK]

Hint: Use .dt accessor for datetime column parts [OK]

Common Mistakes:

• Using .year directly on Series instead of .dt.year
• Converting datetime to string unnecessarily
• Assigning to df.year instead of df['year']

5. You have a DataFrame df with columns height_cm and weight_kg. You want to create a new feature bmi using the formula bmi = weight_kg / (height_m)^2, where height in meters is height_cm divided by 100. Which code correctly creates the bmi column?

hard

A. df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2

B. df['bmi'] = df['weight_kg'] / df['height_cm'] ** 2 / 10000

C. df['bmi'] = df['weight_kg'] * (df['height_cm'] / 100) ** 2

D. df['bmi'] = df['weight_kg'] / (df['height_cm'] * 100) ** 2

Solution

1. Step 1: Convert height from cm to meters

   Height in meters = height_cm / 100.

2. Step 2: Apply BMI formula correctly

   BMI = weight_kg / (height_m)^2 = weight_kg / (height_cm / 100)^2.

3. Step 3: Check each option

   df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2 matches formula exactly. Others have wrong operations or units.

4. Final Answer:

   df['bmi'] = df['weight_kg'] / (df['height_cm'] / 100) ** 2 -> Option A

5. Quick Check:

   BMI formula with height in meters = weight / height² [OK]

Hint: Divide height_cm by 100 before squaring for BMI [OK]

Common Mistakes:

• Forgetting to convert cm to meters
• Multiplying instead of dividing
• Incorrect order of operations