Why data exploration matters in Pandas - See It in Action

Why Data Exploration Matters

📖 Scenario: You have just received a small dataset about sales in a store. Before making any decisions or building models, you want to understand what the data looks like. This helps you find mistakes, see patterns, and know what questions to ask next.

🎯 Goal: Learn how to load data into a pandas DataFrame, check its basic structure, and get simple statistics to understand the data better.

📋 What You'll Learn

Use pandas to create and explore data

Create a DataFrame with exact sales data

Add a variable to select columns for exploration

Use pandas methods to get summary statistics

Print the summary statistics as output

💡 Why This Matters

🌍 Real World

Data exploration helps you understand new data before making decisions or building models. It reveals patterns, errors, and important features.

💼 Career

Data scientists and analysts always start with data exploration to ensure data quality and to guide further analysis or machine learning.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
'Quantity': [30, 45, 25, 10, 5]

Pandas

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

Need a hint?

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

2

Select columns to explore

Create a variable called columns_to_explore and set it to a list containing the exact strings 'Price' and 'Quantity'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
# Create columns_to_explore list with 'Price' and 'Quantity'
# Your code here

Need a hint?

Just create a list with the two column names as strings.

3

Get summary statistics for selected columns

Use the describe() method on sales_data[columns_to_explore] and save the result in a variable called summary_stats.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
columns_to_explore = ['Price', 'Quantity']
# Use describe() on sales_data[columns_to_explore] and save to summary_stats
# Your code here

Need a hint?

Use sales_data[columns_to_explore].describe() to get statistics like mean, min, max.

4

Print the summary statistics

Print the variable summary_stats to see the summary statistics of the selected columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
columns_to_explore = ['Price', 'Quantity']
summary_stats = sales_data[columns_to_explore].describe()
# Print summary_stats
# Your code here

Need a hint?

Use print(summary_stats) to display the statistics.