Mathew K AnalyticsLesson 19 · Mastering Pandas
Mastering Conditional Selection & Boolean Indexing in Pandas for Data Analysis
Welcome! In this lesson, you are going to master conditional selection and boolean indexing in pandas. We will use the Titanic Dataset to practice real…
- CourseMastering Pandas
- Lesson19 of 44
- Video17 min
- FormatJupyter notebook · 16 code cells
What you'll learn
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Download .ipynbIntermediate Pandas: Conditional Selection and Boolean Indexing#
Welcome! In this lesson, you are going to master conditional selection and boolean indexing in pandas.
We will use the Titanic Dataset to practice real filtering and exploration.
By the end, you will know how to select rows and columns using conditions, combine multiple conditions, and use boolean masks for flexible data selection.
import warnings; warnings.filterwarnings('ignore')
# Data setup (Titanic Dataset)
import pandas as pd
import numpy as np
np.random.seed(42)
url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'
df = pd.read_csv(url)
print(df.shape)
print(df.head(3))
Why Learn Conditional Selection?#
Conditional selection lets you focus on data that meets specific rules.
For example, you might want to find all passengers in First Class or all survivors.
This is the foundation for most data analysis in pandas.
# Select all passengers who survived
survived = df[df['Survived'] == 1]
print(survived.head(3))
# Select all children (under 18 years old)
kids = df[df['Age'] < 18]
print(kids[['Name','Age']].head(3))
# Combine multiple conditions: Female passengers in First Class
first_class_women = df[(df['Sex'] == 'female') & (df['Pclass'] == 1)]
print(first_class_women[['Name','Sex','Pclass']].head(3))
# Use OR logic with | to select male passengers OR children
men_or_kids = df[(df['Sex'] == 'male') | (df['Age'] < 18)]
print(men_or_kids[['Name','Sex','Age']].head(3))
# Select rows using isin for multiple values
third_or_first = df[df['Pclass'].isin([1,3])]
print(third_or_first[['Name','Pclass']].head(3))
# Invert a condition with ~ (tilde)
not_survived = df[~(df['Survived'] == 1)]
print(not_survived[['Name','Survived']].head(3))
Boolean Masks and Saving Them#
You can save a filter as a mask for repeated use. This helps with more complex filtering and for explanations.
Lets create a mask for adults in Second Class.
# Create a boolean mask and reuse it
mask = (df['Age'] >= 18) & (df['Pclass'] == 2)
adults_second = df[mask]
print(adults_second[['Name','Age','Pclass']].head(3))
# Select rows where the Name contains 'Mrs.'
mrs = df[df['Name'].str.contains('Mrs.')]
print(mrs[['Name','Sex']].head(3))
# Use isnull to find missing Ages
missing_age = df[df['Age'].isnull()]
print(missing_age[['Name','Age']].head(3))
# Using query for readable conditions
over40women = df.query('Sex == "female" and Age > 40')
print(over40women[['Name','Age','Sex']].head(3))
# Find all passengers with fares between 50 and 100 (inclusive)
mid_fares = df[(df['Fare'] >= 50) & (df['Fare'] <= 100)]
print(mid_fares[['Name','Fare']].head(3))
# Use .loc to select by condition and pick columns
survived_names = df.loc[df['Survived'] == 1, ['Name','Sex','Pclass']]
print(survived_names.head(3))
# Use .copy to avoid SettingWithCopyWarning
subset = df[df['Pclass'] == 3].copy()
subset['Deck'] = subset['Cabin'].str[0]
print(subset[['Name','Cabin','Deck']].head(3))
# Use input to select a passenger name to look up
search_name = input('Enter a name keyword to search for: ')
found = df[df['Name'].str.contains(search_name, case=False)]
print(found[['Name','Survived','Pclass']].head(5))
# Challenge: Find all passengers NOT in Third Class, under 30, and who survived
challenge = df[(df['Pclass'] != 3) & (df['Age'] < 30) & (df['Survived'] == 1)]
print(challenge[['Name','Age','Pclass','Survived']].head(3))
Practice Time#
Now it is your turn! Try filtering for:
- All siblings traveling together (siblings > 0)
- Any passenger with 'Master.' in their name
- People with fares above 200
Pause here to explore. Which group is the largest?
Recap: Mastering Conditional Selection in Pandas#
You practiced basic and advanced filtering, combined rules, and used masks.
With these skills, you can answer real-world questions and spot trends in any dataset.
Conditional selection is a superpower for analysts and data scientists.
What Will You Explore Next?#
Like this lesson? Give this video a thumbs up and subscribe for more hands-on pandas tutorials!
Comment below: What data question will you try solving with conditional selection?
Happy coding!
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