Why Pandas for data analysis - See It in Action

Why Pandas for Data Analysis

📖 Scenario: Imagine you work in a small store. You have a list of sales data with product names and amounts sold. You want to understand this data quickly and easily.

🎯 Goal: You will create a simple table of sales data using Pandas, set a filter to find products with sales above a certain number, and then display those filtered results.

📋 What You'll Learn

Create a Pandas DataFrame with exact sales data

Create a variable to hold the sales threshold

Use Pandas filtering to select products with sales above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses use Pandas to quickly analyze sales data and make decisions.

💼 Career

Data analysts and scientists use Pandas daily to clean, filter, and explore data efficiently.

1

Create the sales data table

Import the pandas library as pd. Then create a DataFrame called sales_data with two columns: 'Product' and 'Sales'. Use these exact entries: 'Apples' with 50, 'Bananas' with 30, 'Cherries' with 20, 'Dates' with 15, and 'Elderberries' with 5.

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact products and sales
# 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 threshold and set it to 20. This will help us find products with sales greater than this number.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [50, 30, 20, 15, 5]
})
# Create the threshold variable and set it to 20
# Your code here

Need a hint?

Just assign the number 20 to a variable named threshold.

3

Filter products with sales above the threshold

Create a new DataFrame called filtered_sales by selecting rows from sales_data where the 'Sales' value is greater than threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [50, 30, 20, 15, 5]
})
threshold = 20
# Filter sales_data to get products with Sales > threshold
# Your code here

Need a hint?

Use sales_data[sales_data['Sales'] > threshold] to filter the rows.

4

Display the filtered sales data

Print the filtered_sales DataFrame to see the products with sales above the threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [50, 30, 20, 15, 5]
})
threshold = 20
filtered_sales = sales_data[sales_data['Sales'] > threshold]
# Print the filtered_sales DataFrame
# Your code here

Need a hint?

Use print(filtered_sales) to show the filtered table.