Date feature extraction in Data Analysis Python - Mini Project: Build & Apply

Date Feature Extraction

📖 Scenario: You work in a retail company. You have a list of sales dates. You want to learn more about these dates by extracting useful parts like year, month, and day.

🎯 Goal: Build a small program that extracts the year, month, and day from a list of sales dates.

📋 What You'll Learn

Create a list of date strings in 'YYYY-MM-DD' format

Create a variable to hold the date format string

Use a loop to convert each date string to a date object and extract year, month, and day

Print the extracted features as a list of dictionaries

💡 Why This Matters

🌍 Real World

Extracting date features helps businesses analyze sales trends by month, day, or year.

💼 Career

Data analysts and scientists often need to break down dates to find patterns and make reports.

1

Create a list of sales dates

Create a list called sales_dates with these exact date strings: '2023-01-15', '2023-02-20', '2023-03-10', '2023-04-05', '2023-05-25'.

Data Analysis Python

# Create the list 'sales_dates' with the given date strings
# Your code here

Hint

Use square brackets to create a list. Put each date string inside quotes and separate by commas.

2

Create a date format string

Create a variable called date_format and set it to the string '%Y-%m-%d' to match the sales date format.

Data Analysis Python

sales_dates = ['2023-01-15', '2023-02-20', '2023-03-10', '2023-04-05', '2023-05-25']
# Create the variable 'date_format' with the string '%Y-%m-%d'
# Your code here

Hint

This string tells Python how to read the date parts: year, month, day.

3

Extract year, month, and day from dates

Import datetime from the datetime module. Create an empty list called date_features. Use a for loop with variable date_str to go through sales_dates. Inside the loop, convert date_str to a date object using datetime.strptime(date_str, date_format). Then extract the year, month, and day from the date object and add a dictionary with keys 'year', 'month', and 'day' to date_features.

Data Analysis Python

sales_dates = ['2023-01-15', '2023-02-20', '2023-03-10', '2023-04-05', '2023-05-25']
date_format = '%Y-%m-%d'
# Import datetime and create 'date_features' list
# Use a for loop with 'date_str' to convert and extract year, month, day
# Your code here

Hint

Use datetime.strptime() to convert string to date. Use date_obj.year, date_obj.month, and date_obj.day to get parts.

4

Print the extracted date features

Write a print() statement to display the date_features list.

Data Analysis Python

from datetime import datetime

sales_dates = ['2023-01-15', '2023-02-20', '2023-03-10', '2023-04-05', '2023-05-25']
date_format = '%Y-%m-%d'

date_features = []
for date_str in sales_dates:
    date_obj = datetime.strptime(date_str, date_format)
    date_features.append({'year': date_obj.year, 'month': date_obj.month, 'day': date_obj.day})

# Print the 'date_features' list
# Your code here

Hint

Use print(date_features) to show the list of dictionaries.