Why Pandas performance matters - See It in Action

Why Pandas Performance Matters

📖 Scenario: You work in a small online store team. You have a list of daily sales data. Sometimes the data grows big and slow to analyze. You want to learn how to handle data efficiently with Pandas.

🎯 Goal: You will create a small sales data table, set a filter value, select rows faster using Pandas, and print the filtered data. This shows why Pandas performance matters for quick answers.

📋 What You'll Learn

Create a Pandas DataFrame with exact sales data

Create a variable to hold a sales threshold

Use Pandas filtering to select sales above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

In real business, data grows fast. Efficient filtering helps find important info quickly.

💼 Career

Data analysts and scientists use Pandas to handle big data and get fast results for decisions.

1

Create the sales data table

Import pandas as pd and create a DataFrame called sales_data with these exact entries: columns 'day' and 'sales', and rows: 'Monday', 150, 'Tuesday', 200, 'Wednesday', 50, 'Thursday', 300, 'Friday', 100.

Pandas

import pandas as pd
# Create the DataFrame sales_data with the exact days and sales values
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for columns.

2

Set the sales threshold

Create a variable called threshold and set it to 150.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'sales': [150, 200, 50, 300, 100]
})
# Create the variable threshold and set it to 150
# Your code here

Need a hint?

Just assign 150 to the variable threshold.

3

Filter sales above the threshold

Create a new DataFrame called high_sales by selecting rows from sales_data where the sales column is greater than threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'sales': [150, 200, 50, 300, 100]
})
threshold = 150
# Filter sales_data to get rows where sales > threshold and save to high_sales
# Your code here

Need a hint?

Use boolean indexing with sales_data['sales'] > threshold.

4

Print the filtered sales data

Print the DataFrame high_sales to show the sales above the threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'sales': [150, 200, 50, 300, 100]
})
threshold = 150
high_sales = sales_data[sales_data['sales'] > threshold]
# Print the high_sales DataFrame
# Your code here

Need a hint?

Use print(high_sales) to display the filtered data.