Why DataFrame is the core data structure in Data Analysis Python - See It in Action

Why DataFrame is the core data structure

📖 Scenario: Imagine you work in a small store. You have a list of products, their prices, and how many you sold each day. You want to organize this information so you can easily see and analyze it.

🎯 Goal: You will create a simple table using a DataFrame to hold product sales data. Then, you will add a rule to find products with sales above a certain number. Finally, you will list those products and their sales.

📋 What You'll Learn

Create a DataFrame with product names, prices, and sales

Create a variable for the sales threshold

Use a filter to find products with sales above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses use tables like DataFrames to keep track of products, prices, and sales to make smart decisions.

💼 Career

Data scientists and analysts use DataFrames daily to clean, explore, and analyze data quickly and clearly.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values: Product with ['Apple', 'Banana', 'Carrot'], Price with [0.5, 0.3, 0.2], and Sales with [30, 45, 25].

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with Product, Price, and Sales columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set the sales threshold

Create a variable called sales_threshold and set it to 30.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Sales': [30, 45, 25]
})
# Create sales_threshold variable and set it to 30
# Your code here

Need a hint?

Just create a variable and assign the number 30 to it.

3

Filter products with sales above the threshold

Create a new DataFrame called top_sellers by filtering sales_data to include only rows where the Sales column is greater than sales_threshold.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Sales': [30, 45, 25]
})
sales_threshold = 30
# Filter sales_data to create top_sellers with Sales > sales_threshold
# Your code here

Need a hint?

Use sales_data[condition] where condition checks if Sales is greater than sales_threshold.

4

Print the filtered top sellers

Print the top_sellers DataFrame to show products with sales above the threshold.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Sales': [30, 45, 25]
})
sales_threshold = 30
top_sellers = sales_data[sales_data['Sales'] > sales_threshold]
# Print the top_sellers DataFrame
# Your code here

Need a hint?

Use print(top_sellers) to show the filtered table.