Extracting date components (year, month, day) in Data Analysis Python - Mini Project: Build & Apply

Extracting date components (year, month, day)

📖 Scenario: You work in a small company that keeps track of employee joining dates. You want to analyze the year, month, and day separately to find trends.

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

📋 What You'll Learn

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

Create a helper variable to store an empty list for years

Use a for loop to extract year, month, and day from each date string

Print the lists of years, months, and days

💡 Why This Matters

🌍 Real World

Extracting parts of dates helps businesses analyze trends like hiring patterns or sales by month.

💼 Career

Data analysts often need to break down dates to group and summarize data for reports.

1

Create the list of joining dates

Create a list called joining_dates with these exact date strings: '2023-01-15', '2022-12-05', '2021-07-23', '2023-03-30', '2020-11-11'.

Data Analysis Python

# Create the list joining_dates with the exact dates
# Your code here

Hint

Use square brackets [] to create a list and put the date strings inside quotes separated by commas.

2

Create empty lists for year, month, and day

Create three empty lists called years, months, and days to store the extracted parts of the dates.

Data Analysis Python

joining_dates = ['2023-01-15', '2022-12-05', '2021-07-23', '2023-03-30', '2020-11-11']
# Create empty lists years, months, and days
# Your code here

Hint

Use empty square brackets [] to create empty lists.

3

Extract year, month, and day from each date

Use a for loop with variable date to go through joining_dates. Inside the loop, extract the year as the first 4 characters, the month as characters 5 to 7, and the day as characters 8 to 10 from date. Append the year to years, the month to months, and the day to days.

Data Analysis Python

joining_dates = ['2023-01-15', '2022-12-05', '2021-07-23', '2023-03-30', '2020-11-11']
years = []
months = []
days = []
# Use a for loop to extract year, month, and day from each date
# Your code here

Hint

Use string slicing like date[0:4] to get the year part.

4

Print the extracted year, month, and day lists

Print the lists years, months, and days each on a separate line.

Data Analysis Python

joining_dates = ['2023-01-15', '2022-12-05', '2021-07-23', '2023-03-30', '2020-11-11']
years = []
months = []
days = []
for date in joining_dates:
    years.append(date[0:4])
    months.append(date[5:7])
    days.append(date[8:10])
# Print years, months, and days lists
# Your code here

Hint

Use three separate print() statements to show each list.

Practice

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

easy

A. Converts the dates to string format

B. Extracts the month part from each date in the column

C. Extracts the day part from each date in the column

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

Solution

1. Step 1: Understand pandas datetime attributes

   The dt accessor allows extraction of date parts like year, month, day from datetime columns.

2. Step 2: Identify what dt.year extracts

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

3. Final Answer:

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

4. Quick Check:

   dt.year = year extraction [OK]

Hint: Remember: dt.year extracts year, dt.month extracts month [OK]

Common Mistakes:

• Confusing dt.year with dt.month or dt.day
• Thinking dt.year converts to string
• Using dt.year on non-datetime columns

2. Which of the following is the correct syntax to extract the month from a pandas datetime column named date_col?

easy

A. date_col.dt.month

B. date_col.month()

C. date_col['month']

D. month(date_col)

Solution

1. Step 1: Recall pandas datetime accessor usage

   To extract date parts, use the dt accessor followed by the attribute without parentheses.

2. Step 2: Identify correct syntax for month extraction

   The correct syntax is date_col.dt.month. Parentheses are not used because it's an attribute, not a method.

3. Final Answer:

   date_col.dt.month -> Option A

4. Quick Check:

   Use dt.month attribute, no parentheses [OK]

Hint: Use .dt.month without parentheses to get month [OK]

Common Mistakes:

• Adding parentheses like date_col.month()
• Trying to access month as a dictionary key
• Using a function month() without import

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

import pandas as pd

dates = pd.Series(pd.to_datetime(['2023-01-15', '2022-12-31', '2024-07-04']))
months = dates.dt.month
print(months.tolist())

medium

A. [1, 12, 7]

B. [15, 31, 4]

C. [2023, 2022, 2024]

D. ['Jan', 'Dec', 'Jul']

Solution

1. Step 1: Understand the input dates and extraction

   The dates are '2023-01-15', '2022-12-31', and '2024-07-04'. Extracting dt.month gets the month numbers: 1, 12, and 7.

2. Step 2: Convert the extracted months to list

   The tolist() method converts the pandas Series to a Python list: [1, 12, 7].

3. Final Answer:

   [1, 12, 7] -> Option A

4. Quick Check:

   Months extracted = [1, 12, 7] [OK]

Hint: dt.month returns month numbers as integers [OK]

Common Mistakes:

• Confusing day with month values
• Expecting month names instead of numbers
• Using dt.day instead of dt.month

4. The following code throws an error. What is the main issue?

import pandas as pd

dates = pd.Series(['2023-01-15', '2022-12-31'])
years = dates.dt.year
print(years)

medium

A. print statement syntax is incorrect

B. dt.year is not a valid attribute

C. dates is not converted to datetime type before using dt accessor

D. dates should be a list, not a pandas Series

Solution

1. Step 1: Check the data type of dates

   The dates Series contains strings, not datetime objects.

2. Step 2: Understand dt accessor requirements

   The dt accessor works only on datetime dtype Series. Without conversion, it raises an error.

3. Final Answer:

   dates is not converted to datetime type before using dt accessor -> Option C

4. Quick Check:

   Convert strings to datetime before dt.year [OK]

Hint: Always convert to datetime before using dt attributes [OK]

Common Mistakes:

• Forgetting pd.to_datetime conversion
• Assuming dt.year works on string Series
• Misreading error as syntax problem

5. You have a DataFrame df with a datetime column 'order_date'. You want to add three new columns: 'year', 'month', and 'day' extracted from 'order_date'. Which code correctly does this?

hard

A. df['year'] = df['order_date'].year
df['month'] = df['order_date'].month
df['day'] = df['order_date'].day

B. df['year'] = df['order_date'].dt.year
df['month'] = df['order_date'].dt.month
df['day'] = df['order_date'].dt.day

C. df['year'] = df['order_date'].apply(lambda x: x.year)
df['month'] = df['order_date'].apply(lambda x: x.month)
df['day'] = df['order_date'].apply(lambda x: x.day)

D. df['year'] = df['order_date'].str.year
df['month'] = df['order_date'].str.month
df['day'] = df['order_date'].str.day

Solution

1. Step 1: Confirm datetime column usage

   To extract date parts efficiently, use the dt accessor on the datetime column.

2. Step 2: Check each option's method

   df['year'] = df['order_date'].dt.year df['month'] = df['order_date'].dt.month df['day'] = df['order_date'].dt.day uses dt.year, dt.month, and dt.day correctly. df['year'] = df['order_date'].year df['month'] = df['order_date'].month df['day'] = df['order_date'].day tries to access attributes directly on Series, which fails. df['year'] = df['order_date'].apply(lambda x: x.year) df['month'] = df['order_date'].apply(lambda x: x.month) df['day'] = df['order_date'].apply(lambda x: x.day) works but is slower due to apply. df['year'] = df['order_date'].str.year df['month'] = df['order_date'].str.month df['day'] = df['order_date'].str.day incorrectly uses str accessor.

3. Final Answer:

   df['year'] = df['order_date'].dt.year df['month'] = df['order_date'].dt.month df['day'] = df['order_date'].dt.day -> Option B

4. Quick Check:

   Use .dt accessor for vectorized date extraction [OK]

Hint: Use .dt.year, .dt.month, .dt.day for fast extraction [OK]

Common Mistakes:

• Accessing year/month/day directly on Series
• Using .str accessor on datetime columns
• Using apply unnecessarily slowing code