Exercise to practice Pandas fundamentals

exercise-pandas-fundamentals.md

Practice: Pandas Fundamentals

Instructions

We'll use the Titanic dataset (built into seaborn) — it has missing values, making it perfect for this exercise.

Iteration 1: Import the dataset

import seaborn as sns
import pandas as pd

titanic = sns.load_dataset('titanic')

Iteration 2: Explore the data

• Show the first 5 rows
• Show the shape of the DataFrame (rows, columns)
• Show the list of column names

Iteration 3: Sort the data

• Sort the DataFrame by the fare column, from highest to lowest
• Show the first 5 rows of the sorted result

Iteration 4: Check missing values

• Check if there are any missing values in the DataFrame (True/False)
• Count the number of missing values in each column

Bonus 1

• Sort the DataFrame by age (descending) and then by fare (ascending) for passengers with the same age.
• Show the top 10 rows, but only the age, fare, and class columns.

Bonus 2

• Find which column has the most missing values.
• Then create a new DataFrame that drops that column entirely, and fills the missing values in age with the average age.
• Verify your result has zero missing values left.

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Solutions

Iteration 1

import seaborn as sns
import pandas as pd

titanic = sns.load_dataset('titanic')

Iteration 2

titanic.head()
titanic.shape
titanic.columns.tolist()

Iteration 3

titanic.sort_values('fare', ascending=False).head()

Iteration 4

titanic.isna().values.any()
titanic.isna().sum()

Bonus 1

titanic.sort_values(['age', 'fare'], ascending=[False, True])[['age', 'fare', 'class']].head(10)

Bonus 2

missing_counts = titanic.isna().sum()
col_with_most_missing = missing_counts.idxmax()

titanic_clean = titanic.drop(columns=[col_with_most_missing])
titanic_clean['age'] = titanic_clean['age'].fillna(titanic_clean['age'].mean())

titanic_clean.isna().sum().sum()  # should be 0