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Data Structures

Introduction of python data structures like lists, dictionaries, and dataframes.

1. Lists

Lists are used to store multiple items in a single variable.

Creating a List

fruits = ["apple", "banana", "cherry"]
print(fruits)

Accessing Items

print(fruits[0])  # First item
print(fruits[-1])  # Last item

Modifying Lists

fruits.append("orange")  # Add an item
fruits[1] = "blueberry"  # Change an item
print(fruits)

Iterating Through a List

for fruit in fruits:
    print(fruit)

Common List Methods

  • append(item): Add an item
  • remove(item): Remove an item
  • len(list): Get the number of items
  • sort(): Sort the list

Exercise:

  1. Create a list of your favorite hobbies.
  2. Add a new hobby to the list.
  3. Print each hobby using a loop.

2. Dictionaries

Dictionaries store data in key-value pairs.

Creating a Dictionary

student = {"name": "Alex", "age": 16, "grade": "A"}
print(student)

Accessing Items

print(student["name"])  # Access value by key

Adding/Updating Keys

student["school"] = "High School"  # Add a new key
student["grade"] = "A+"  # Update value
print(student)

Iterating Through a Dictionary

for key, value in student.items():
    print(key, ":", value)

Common Dictionary Methods

  • keys(): Get all keys

  • values(): Get all values

  • items(): Get all key-value pairs

    Exercise:

  1. Create a dictionary with details about your favorite book (title, author, year).
  2. Add a new key for the genre.
  3. Print all the keys and values.

3. DataFrames (Using pandas)

What is a DataFrame? A DataFrame is a 2-dimensional table-like data structure in the pandas library. Think of it as a spreadsheet.

Setting Up pandas Make sure you have pandas installed:

pip install pandas

Creating a DataFrame

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [16, 17, 16],
    "Grade": ["A", "B", "A"]
}

df = pd.DataFrame(data)
print(df)

Accessing Columns

print(df["Name"])  # Access a single column
print(df[["Name", "Age"]])  # Access multiple columns

Filtering Rows

print(df[df["Age"] > 16])  # Students older than 16

Adding a New Column

df["Passed"] = [True, False, True]
print(df)

Iterating Through Rows

for index, row in df.iterrows():
    print(row["Name"], "is", row["Age"], "years old.")

Exercise:

  1. Create a DataFrame with data about your favorite movies (columns: Title, Year, Genre).
  2. Add a new column for Rating.
  3. Filter the movies to show only those released after 2010.

4. Comparing Lists, Dictionaries, and DataFrames

Feature List Dictionary DataFrame
Data Organization Ordered, items by index Key-value pairs Rows and columns
Access Method By index By key By row/column
Ideal Use Case Simple collections Mapping relationships Tabular data

5. Final Project Idea: Student Report System

Build a system that:

  1. Stores student data in a DataFrame.
  2. Allows adding a new student (Name, Age, Grade).
  3. Filters students by a minimum grade.
  4. Prints all student data.

Example Code for the System:

import pandas as pd

# Initial data
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [16, 17, 16],
    "Grade": ["A", "B", "A"]
}
df = pd.DataFrame(data)

# Add a new student
new_student = {"Name": "Daisy", "Age": 17, "Grade": "A+"}
df = df.append(new_student, ignore_index=True)

# Filter by grade
print("Students with grade A or higher:")
print(df[df["Grade"] >= "A"])