Extracting year, month, day in Pandas - Mini Project: Build & Apply

Extracting year, month, day from dates using pandas

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

🎯 Goal: Build a pandas DataFrame with joining dates, then extract the year, month, and day into new columns.

📋 What You'll Learn

Create a pandas DataFrame with a column named joining_date containing specific dates.

Create a variable called date_column that holds the string 'joining_date'.

Use pandas datetime accessor to extract year, month, and day from the joining_date column into new columns named year, month, and day.

Print the final DataFrame to show the extracted columns.

💡 Why This Matters

🌍 Real World

Extracting year, month, and day from dates helps businesses analyze trends over time, like employee joining patterns or sales by month.

💼 Career

Data analysts and data scientists often need to break down dates to perform time-based analysis and reporting.

1

Create the DataFrame with joining dates

Import pandas as pd and create a DataFrame called df with a column joining_date containing these exact dates as strings: '2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30'.

Pandas

import pandas as pd
# Create DataFrame df with joining_date column
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with key 'joining_date' and the list of date strings as value.

2

Create a variable for the date column name

Create a variable called date_column and set it to the string 'joining_date'.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
# Create variable date_column with value 'joining_date'
# Your code here

Hint

Just assign the string 'joining_date' to the variable date_column.

3

Extract year, month, and day into new columns

Convert the joining_date column to datetime using pd.to_datetime. Then create three new columns in df: year, month, and day by extracting these parts from the datetime column using the pandas datetime accessor .dt and the variable date_column.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
date_column = 'joining_date'
# Convert joining_date to datetime and extract year, month, day
# Your code here

Hint

Use pd.to_datetime to convert the column, then use .dt.year, .dt.month, and .dt.day to get parts.

4

Print the DataFrame with extracted columns

Print the DataFrame df to display the joining_date, year, month, and day columns.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
date_column = 'joining_date'
df[date_column] = pd.to_datetime(df[date_column])
df['year'] = df[date_column].dt.year
df['month'] = df[date_column].dt.month
df['day'] = df[date_column].dt.day
# Print the DataFrame df
# Your code here

Hint

Use print(df) to show the DataFrame with the new columns.

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 month part from each date in the column

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

D. Converts the column to string format

Solution

1. Step 1: Understand pandas datetime attributes

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

2. Step 2: Identify the function of .dt.year

   The .dt.year extracts the year component from each datetime value in the column.

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: year, month, day are .dt.year, .dt.month, .dt.day [OK]

Common Mistakes:

• Confusing .dt.year with .dt.month or .dt.day
• Trying to use .year directly without .dt
• Not converting column to datetime before extraction

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. date_col.get_month()

Solution

1. Step 1: Recall pandas datetime accessor usage

   To extract parts like month, you must use the .dt accessor on a datetime column.

2. Step 2: Identify correct syntax for month extraction

   The correct syntax is date_col.dt.month. Other options either miss .dt or use invalid methods.

3. Final Answer:

   date_col.dt.month -> Option A

4. Quick Check:

   Use .dt before date parts [OK]

Hint: Always use .dt before year/month/day on datetime columns [OK]

Common Mistakes:

• Using .month directly without .dt
• Trying to access month as a dictionary key
• Using non-existent methods like get_month()

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

import pandas as pd
df = pd.DataFrame({
  'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-07-04'])
})
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
df[['year', 'month', 'day']].values.tolist()

medium

A. [[15, 1, 2023], [31, 12, 2022], [4, 7, 2024]]

B. [['2023', '01', '15'], ['2022', '12', '31'], ['2024', '07', '04']]

C. [[2023, 1, 15], [2022, 12, 31], [2024, 7, 4]]

D. Error: Cannot extract year, month, day without conversion

Solution

1. Step 1: Convert strings to datetime

   The pd.to_datetime converts string dates to datetime objects correctly.

2. Step 2: Extract year, month, day

   Using .dt.year, .dt.month, and .dt.day extracts integer parts of the date.

3. Final Answer:

   [[2023, 1, 15], [2022, 12, 31], [2024, 7, 4]] -> Option C

4. Quick Check:

   Extracted parts are integers, not strings [OK]

Hint: Extracted year/month/day are integers, not strings [OK]

Common Mistakes:

• Expecting strings instead of integers
• Mixing order of day, month, year
• Forgetting to convert to datetime first

4. What is wrong with this code snippet?

df['year'] = df['date'].year

Assuming df['date'] is a datetime column.

medium

A. You need to use df.year() instead

B. The column must be converted to string first

C. The code will work fine as is

D. It should be df['date'].dt.year to access year

Solution

1. Step 1: Understand pandas datetime access

   Datetime columns require the .dt accessor to extract parts like year.

2. Step 2: Identify missing .dt accessor

   The code misses .dt, so df['date'].year will raise an error or not work as expected.

3. Final Answer:

   It should be df['date'].dt.year to access year -> Option D

4. Quick Check:

   Use .dt before year/month/day [OK]

Hint: Always use .dt before year/month/day on datetime columns [OK]

Common Mistakes:

• Omitting .dt accessor
• Trying to call year() as a method
• Converting datetime to string unnecessarily

5. You have a DataFrame df with a column 'timestamp' containing mixed string dates and some missing values (NaN). You want to create three new columns: 'year', 'month', and 'day'. Which approach correctly handles conversion and extraction without errors?

hard

A. df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day

B. df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce'); df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day

C. df['year'] = df['timestamp'].str[:4]; df['month'] = df['timestamp'].str[5:7]; df['day'] = df['timestamp'].str[8:10]

D. df['timestamp'] = df['timestamp'].astype(str); df['year'] = df['timestamp'].dt.year

Solution

1. Step 1: Convert mixed strings and NaN to datetime safely

   Using pd.to_datetime(..., errors='coerce') converts valid strings to datetime and invalid/missing to NaT without error.

2. Step 2: Extract year, month, day using .dt accessor

   After conversion, use .dt.year, .dt.month, and .dt.day to get parts safely, even with NaT values.

3. Final Answer:

   df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce'); df['year'] = df['timestamp'].dt.year; df['month'] = df['timestamp'].dt.month; df['day'] = df['timestamp'].dt.day -> Option B

4. Quick Check:

   Convert with errors='coerce' then extract parts [OK]

Hint: Use pd.to_datetime with errors='coerce' before extracting [OK]

Common Mistakes:

• Not converting strings to datetime first
• Extracting parts from string columns directly
• Ignoring missing values causing errors