Python Data Structures: Lists, Tuples, Sets & Dicts

Build the mental model behind Python containers

When cleaning a survey dataset, a set can identify unique response categories, a dictionary can map abbreviations to full labels, a list can hold selected columns and a tuple can store an immutable coordinate pair.

• Lists preserve order and are mutable; tuples are ordered but immutable; sets keep unique values; dictionaries map keys to values.
• Choose a data structure by the operation you need most often, not by habit.
• Dictionary lookups are useful for mappings, while sets are efficient for membership tests and deduplication.
• Mutable containers can change in place, so copying behavior matters when the same object is shared across functions.

Run small examples and inspect every intermediate object

For Python containers, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.

# Python containers · example 1
cities = ['Delhi', 'Mumbai', 'Delhi']
unique_cities = set(cities)
labels = {'M': 'Male', 'F': 'Female'}
columns = ['age', 'income', 'city']
point = (23.1, 72.6)
print(unique_cities, labels['F'])

Python containers checkpoint 1: Run the cell and inspect the printed values before changing anything.

# Python containers · example 2
import pandas as pd

df = pd.read_csv('practice.csv')
print(df.shape)
print(df.dtypes)
print(df.head(3).to_string(index=False))

Python containers checkpoint 2: (30, 5)
Column types are printed, followed by the first three rows.

# Python containers · example 3
missing = df.isna().sum().sort_values(ascending=False)
print(missing[missing.gt(0)])

Python containers checkpoint 3: Only columns with at least one missing value are listed.

# Python containers · example 4
summary = df.select_dtypes('number').agg(['count','mean','median','min','max']).T
print(summary.round(2))

Python containers checkpoint 4: A compact numeric summary appears with one row per numeric column.

# Python containers · example 5
test = df.copy()
if len(test):
    test.iloc[0, 0] = test.iloc[0, 0]
print('rows:', len(test), 'duplicates:', test.duplicated().sum())

Python containers checkpoint 5: The row count and duplicate count make the check visible instead of implicit.

Debug shape, dtype and missing-value problems

Self-check with answers

Turn the lesson into a reusable notebook