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.

Practice

1. What does the dt.year attribute do when applied to a pandas datetime column?

easy

A. Extracts the year part from each date in the column

B. Extracts the day of the week as a number

C. Converts the date to a string format

D. Calculates the difference between dates

Solution

1. Step 1: Understand pandas datetime accessor

   The dt accessor allows extraction of date parts from datetime columns.

2. Step 2: Identify what dt.year extracts

   dt.year extracts the year component from each datetime value.

3. Final Answer:

   Extracts the year part from each date in the column -> Option A

4. Quick Check:

   dt.year = year extraction [OK]

Hint: Remember dt.year gives the year number from dates [OK]

Common Mistakes:

• Confusing dt.year with dt.day or dt.month
• Thinking dt.year converts dates to strings
• Assuming dt.year calculates date differences

2. Which of the following is the correct syntax to extract the month from a pandas datetime column named df['date']?

easy

A. df['date'].month

B. df['date'].dt.month

C. df['date'].get_month()

D. df['date'].extract('month')

Solution

1. Step 1: Recall pandas datetime accessor usage

   To extract date parts, pandas requires the dt accessor before the attribute.

2. Step 2: Identify correct syntax for month extraction

   The correct syntax is df['date'].dt.month. Other options are invalid methods or missing dt.

3. Final Answer:

   df['date'].dt.month -> Option B

4. Quick Check:

   Use dt.month to get month [OK]

Hint: Use .dt before date parts like month or day [OK]

Common Mistakes:

• Omitting .dt accessor
• Using non-existent methods like get_month()
• Trying to extract with extract('month')

3. Given the following code, what will be the output?

import pandas as pd
dates = pd.to_datetime(['2023-01-15', '2023-07-04', '2023-12-31'])
df = pd.DataFrame({'date': dates})
df['day_of_week'] = df['date'].dt.dayofweek
print(df['day_of_week'].tolist())

medium

A. [6, 1, 0]

B. [0, 1, 6]

C. [6, 2, 6]

D. [6, 1, 6]

Solution

1. Step 1: Convert dates to day of week numbers

   Using dt.dayofweek, Monday=0 ... Sunday=6. Dates: 2023-01-15 is Sunday (6), 2023-07-04 is Tuesday (1), 2023-12-31 is Sunday (6).

2. Step 2: List the dayofweek values

   The list is [6, 1, 6].

3. Final Answer:

   [6, 1, 6] -> Option D

4. Quick Check:

   Sunday=6, Tuesday=1, Sunday=6 [OK]

Hint: Remember Monday=0, Sunday=6 for dayofweek [OK]

Common Mistakes:

• Confusing dayofweek with weekday names
• Assuming Sunday=0 instead of 6
• Mixing up dayofweek and day_name()

4. What is wrong with this code snippet to extract the hour from a datetime column df['timestamp']?

df['hour'] = df['timestamp'].hour

medium

A. Missing .dt accessor before .hour

B. The column must be converted to string first

C. The .hour attribute does not exist in pandas

D. The code should use .time.hour instead

Solution

1. Step 1: Check pandas datetime attribute access

   Datetime properties require the dt accessor to access parts like hour.

2. Step 2: Identify missing .dt in code

   The code uses df['timestamp'].hour which is invalid; it should be df['timestamp'].dt.hour.

3. Final Answer:

   Missing .dt accessor before .hour -> Option A

4. Quick Check:

   Use .dt.hour to extract hour [OK]

Hint: Always use .dt before date parts like hour [OK]

Common Mistakes:

• Trying to access .hour directly on Series
• Converting datetime to string unnecessarily
• Using .time.hour which is invalid in pandas

5. You have a pandas DataFrame df with a datetime column 'order_date'. You want to create a new column 'is_weekend' that is True if the order was placed on Saturday or Sunday, and False otherwise. Which code snippet correctly achieves this?

hard

A. df['is_weekend'] = df['order_date'].dt.dayofweek == 6 or 5

B. df['is_weekend'] = df['order_date'].dt.weekday in [5,6]

C. df['is_weekend'] = df['order_date'].dt.dayofweek >= 5

D. df['is_weekend'] = df['order_date'].dt.day_name() in ['Saturday', 'Sunday']

Solution

1. Step 1: Understand dayofweek values for weekend

   In pandas, dt.dayofweek returns 0 for Monday through 6 for Sunday. Weekend days are 5 (Saturday) and 6 (Sunday).

2. Step 2: Check which code correctly marks weekend

   df['is_weekend'] = df['order_date'].dt.dayofweek >= 5 uses df['order_date'].dt.dayofweek >= 5, which is True for 5 and 6, correctly marking weekends. Other options use invalid syntax or incorrect logic.

3. Final Answer:

   df['is_weekend'] = df['order_date'].dt.dayofweek >= 5 -> Option C

4. Quick Check:

   Weekend days are dayofweek >= 5 [OK]

Hint: Weekend days have dayofweek 5 or 6, use >= 5 [OK]

Common Mistakes:

• Using 'in' without vectorized methods
• Using 'or' instead of bitwise operators
• Confusing dayofweek numbers or names