Exploratory data analysis workflow in Pandas - Mini Project: Build & Apply

Exploratory data analysis workflow

📖 Scenario: You are a data analyst working with sales data from a small store. You want to understand the data better by exploring it step-by-step.

🎯 Goal: Build a simple exploratory data analysis workflow using pandas to load data, set a filter, analyze the filtered data, and print the results.

📋 What You'll Learn

Use pandas to create a DataFrame

Create a filter condition variable

Use the filter to select rows

Calculate the average sales from filtered data

Print the average sales

💡 Why This Matters

🌍 Real World

Exploratory data analysis helps businesses understand their sales data to make better decisions.

💼 Career

Data analysts and scientists use these skills daily to clean, filter, and summarize data before deeper analysis.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
'Sales': [100, 150, 80, 200, 50],
'Region': ['North', 'South', 'East', 'West', 'North'].

Pandas

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

Need a hint?

Use pd.DataFrame with a dictionary of lists for columns.

2

Set a filter for the North region

Create a variable called north_filter that holds a filter condition selecting rows where the 'Region' column equals 'North'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})

# Create north_filter to select rows where Region is 'North'
# Your code here

Need a hint?

Use sales_data['Region'] == 'North' to create the filter.

3

Calculate average sales for North region

Use the north_filter to select rows from sales_data and calculate the average of the 'Sales' column. Store this average in a variable called average_sales_north.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})
north_filter = sales_data['Region'] == 'North'

# Calculate average_sales_north using north_filter
# Your code here

Need a hint?

Use sales_data.loc[north_filter, 'Sales'].mean() to get the average.

4

Print the average sales for North region

Write a print statement to display the text "Average sales in North region:" followed by the value of average_sales_north.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})
north_filter = sales_data['Region'] == 'North'
average_sales_north = sales_data.loc[north_filter, 'Sales'].mean()

# Print the average sales for North region
# Your code here

Need a hint?

Use print("Average sales in North region:", average_sales_north).