columns and index attributes in Pandas - Mini Project: Build & Apply

Exploring columns and index attributes in pandas DataFrame

📖 Scenario: You are working with sales data for a small store. You want to organize the data in a table format using pandas to easily see the products sold, their prices, and quantities.

🎯 Goal: Create a pandas DataFrame with sales data, then explore its columns and index attributes to understand the structure of the data.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with three columns: 'Product', 'Price', and 'Quantity'.

Set the index of the DataFrame to be the product names.

Create a variable cols that stores the column names of sales_data.

Create a variable idx that stores the index labels of sales_data.

💡 Why This Matters

🌍 Real World

Organizing and exploring tabular data like sales records, inventory, or customer lists is common in many jobs.

💼 Career

Knowing how to use pandas DataFrames and their attributes helps in data analysis, reporting, and preparing data for machine learning.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact entries: products 'Apple', 'Banana', 'Cherry'; prices 0.5, 0.3, 0.2; quantities 10, 20, 15.

Pandas

# Import pandas and create the DataFrame sales_data with the given data
# Your code here

Need a hint?

Use pd.DataFrame and pass a dictionary with keys as column names and values as lists of data.

2

Set the index to product names

Set the index of sales_data to the 'Product' column using the set_index() method and update sales_data with this change.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
# Set the index of sales_data to the 'Product' column
# Your code here

Need a hint?

Use sales_data.set_index('Product') and assign it back to sales_data.

3

Get the columns attribute

Create a variable called cols and assign it the columns attribute of sales_data.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
sales_data = sales_data.set_index('Product')
# Assign the columns attribute of sales_data to cols
# Your code here

Need a hint?

Use sales_data.columns to get the column names.

4

Get the index attribute

Create a variable called idx and assign it the index attribute of sales_data.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
sales_data = sales_data.set_index('Product')
cols = sales_data.columns
# Assign the index attribute of sales_data to idx
# Your code here

Need a hint?

Use sales_data.index to get the index labels.