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Piyush
Piyush

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📝 70+ Pandas Practice Questions: The Ultimate Titanic Dataset Challenge

#ai #datascience #beginners #python

Are you looking to level up your Pandas skills? You've come to the right place! 🐼

This blog post contains a comprehensive list of 70+ practice questions designed to take you from a Pandas beginner to a data manipulation pro. We'll be using the famous Titanic Dataset — covering everything from basic data inspection to advanced GroupBy operations and Pivot Tables.

Check out the full solutions! I've written a complete walkthrough with code and outputs for every single question above:

👉 Mastering Data Analysis with Pandas: Surviving the Titanic Dataset

[!NOTE]
Get the Dataset: Download the Titanic dataset used in this post from Kaggle:

📥 Titanic Dataset on Kaggle

Once downloaded, load it in Python like this:

import pandas as pd
df = pd.read_csv("titanic.csv")
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🔍 Phase 1: Understanding the Dataset

Before we can analyze anything, we need to know what we're dealing with. These questions will help you inspect and understand the structure of the dataset.

  • 1. Import the Pandas library using the alias pd.
  • 2. Load the titanic.csv dataset into a DataFrame named df.
  • 3. Display the first 5 records of the dataset.
  • 4. Display the last 5 records of the dataset.
  • 5. Find the number of rows and columns in the dataset.
  • 6. Display all column names.
  • 7. Display the data type of each column.
  • 8. Use df.info() to examine the dataset. Identify the columns containing missing values.
  • 9. Use df.describe() to obtain statistical information about numerical columns.
  • 10. Display statistical information for both numerical and categorical columns.

🎯 Phase 2: Selecting and Filtering Data

We rarely need an entire dataset at once. Learn how to slice and filter to extract exactly what you need.

  • 11. Display only the Age column.
  • 12. Display the Survived, Pclass, Sex, Age, and Fare columns.
  • 13. Use iloc to display the first 5 rows and first 4 columns.
  • 14. Display all passengers whose age is greater than 30.
  • 15. Display all female passengers.
  • 16. Find female passengers older than 30 years.
  • 17. Display passengers belonging to passenger classes 1 and 2 using isin().
  • 18. Find passengers whose ages are between 20 and 30 years.
  • 19. Use query() to find passengers older than 40 years.

🧹 Phase 3: Missing Values and Duplicates

Real-world data is messy. Learn how to detect and handle missing values like a pro.

  • 20. Find the number of missing values in each column.
  • 21. Calculate the total number of missing values in the complete dataset.
  • 22. Calculate the percentage of missing values in each column.
  • 23. Replace missing Age values with the median age.
  • 24. Create a new DataFrame after removing rows containing missing values.
  • 25. Check the dataset for duplicate records.
  • 26. Remove duplicate records and display the new shape of the dataset.

📊 Phase 4: Sorting and Frequency Analysis

Who paid the highest fare? Who were the youngest passengers? Sorting and frequency analysis answers it all.

  • 27. Display all unique values in the Sex column.
  • 28. Count the number of male and female passengers.
  • 29. Calculate the percentage of male and female passengers.
  • 30. Sort passengers according to age in ascending order.
  • 31. Sort passengers according to fare in descending order.
  • 32. Display the five passengers who paid the highest fares.
  • 33. Display the five youngest passengers.

🧮 Phase 5: Aggregate Functions

Averages, totals, standard deviations — this is where you extract high-level insights from raw numbers.

  • 34. Calculate the average age of passengers.
  • 35. Calculate the median age.
  • 36. Find the minimum and maximum passenger age.
  • 37. Calculate the total fare paid by all passengers.
  • 38. Find the average fare.
  • 39. Calculate the standard deviation and variance of passenger ages.
  • 40. Find the mode of the Age column.
  • 41. Using agg(), calculate count, mean, median, min, max, and std of passenger ages.
  • 42. Use agg() to calculate different statistics for both Age and Fare.

👥 Phase 6: GroupBy Operations

GroupBy is one of the most powerful tools in Pandas. Use it to segment and summarize data by categories.

  • 43. Find the average age of male and female passengers using groupby().
  • 44. Calculate the survival rate for male and female passengers.
  • 45. Find the number of passengers in each passenger class.
  • 46. Calculate the average fare for each passenger class.
  • 47. Find the average age for each passenger class.
  • 48. Calculate the survival rate for each passenger class.
  • 49. Find the number of survivors in each passenger class.
  • 50. Group passengers by both Pclass and Sex and calculate their survival rate.
  • 51. For each passenger class, calculate the mean, minimum, and maximum age.
  • 52. For each passenger class, calculate the mean, median, and maximum fare.

🛠️ Phase 7: Creating and Transforming Columns

Go beyond reading data — learn to engineer new features and transform existing ones.

  • 53. Create a new column containing the mean age of each passenger's class using transform().
  • 54. Create an Age_Group column with categories such as Child, Teenager, Young Adult, Adult, and Senior.
  • 55. Count the number of passengers in each age group.
  • 56. Using apply(), classify passengers as Minor or Adult.
  • 57. Using map(), convert Survived = 0 to 'Did Not Survive' and Survived = 1 to 'Survived'.
  • 58. Use replace() to change male and female to Male and Female.

🔗 Phase 8: Correlation and Crosstab

Understand how variables relate to each other. Does class affect survival? Does age affect fare?

  • 59. Generate a correlation matrix for all numerical columns.
  • 60. Examine the relationship between Pclass, Fare, Age, and Survived.
  • 61. Create a crosstab showing Sex versus Survived.
  • 62. Create a percentage crosstab showing survival percentages for male and female passengers.
  • 63. Create a crosstab showing passenger class versus survival status.

🔀 Phase 9: Pivot Tables

Pivot tables are the ultimate tool for multi-dimensional summarization. Master these and you're a Pandas wizard.

  • 64. Create a pivot table showing the mean survival rate by passenger class and sex.
  • 65. Create a pivot table showing the average fare by passenger class and sex.
  • 66. Create a pivot table showing the average age by passenger class and sex.
  • 67. Create a pivot table showing the number of passengers by class and sex.
  • 68. Create a pivot table showing the mean, median, and maximum fare according to passenger class and sex.
  • 69. Add margins=True to a survival pivot table and interpret the All row and column.
  • 70. Compare survival rates of male and female passengers across the three passenger classes.

🏆 The Ultimate Challenge Task

Put everything together. This is where data analysis meets storytelling.

  • Challenge. Based on your Pandas analysis, write three observations about the Titanic dataset. Your observations should include:
    • Survival by sex
    • Survival by passenger class
    • Average fare by passenger class

🚀 Wrapping Up

How many were you able to solve without peeking? 👀 Keep practicing — soon these Pandas operations will feel like second nature!

Happy Coding! 🎈

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