Extracting year, month, day in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Extract year from a pandas datetime column

What is the output of this code snippet that extracts the year from a datetime column in a pandas DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-06-01'])})
df['year'] = df['date'].dt.year
print(df['year'].tolist())

A [2023, 2022, 2024]

B [15, 31, 1]

C [1, 12, 6]

D [2023-01-15, 2022-12-31, 2024-06-01]

💡 Hint

Use the .dt accessor to get the year from datetime values.

The .dt.year extracts the year part from each datetime value in the column, resulting in a list of years.

❓ Predict Output

intermediate

Extract month from a pandas datetime column

What will be printed by this code that extracts the month from a pandas datetime column?

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-06-01'])})
df['month'] = df['date'].dt.month
print(df['month'].tolist())

A [15, 31, 1]

B [2023-01-15, 2022-12-31, 2024-06-01]

C [2023, 2022, 2024]

D [1, 12, 6]

💡 Hint

The .dt.month extracts the month number from datetime values.

The .dt.month extracts the month part from each datetime value, resulting in a list of month numbers.

❓ Predict Output

advanced

Extract day from a pandas datetime column

What is the output of this code that extracts the day from a datetime column in pandas?

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-06-01'])})
df['day'] = df['date'].dt.day
print(df['day'].tolist())

A [2023-01-15, 2022-12-31, 2024-06-01]

B [15, 31, 1]

C [1, 12, 6]

D [2023, 2022, 2024]

💡 Hint

Use .dt.day to get the day number from datetime values.

The .dt.day extracts the day part from each datetime value, resulting in a list of day numbers.

❓ data_output

advanced

Count unique years in a datetime column

Given a pandas DataFrame with a datetime column, how many unique years are in the 'date' column after extracting the year?

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2022-12-31', '2024-06-01', '2023-07-20'])})
df['year'] = df['date'].dt.year
unique_years = df['year'].nunique()
print(unique_years)

A 4

B 1

C 3

D 2

💡 Hint

Use .nunique() to count unique values in a column.

The years extracted are 2023, 2022, 2024, and 2023 again. Unique years are 2023, 2022, and 2024, so count is 3.

🧠 Conceptual

expert

Understanding datetime extraction behavior with missing values

What happens when you extract year, month, or day from a pandas datetime column that contains missing values (NaT)?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', None, '2024-06-01'])})
df['year'] = df['date'].dt.year
print(df['year'].tolist())

A [2023, <NA>, 2024]

B [2023, None, 2024]

C [2023, NaT, 2024]

D [2023, nan, 2024]

💡 Hint

Missing datetime values become pandas in extracted integer columns.

When extracting year from a datetime column with missing values, pandas returns (pandas' missing integer type) for those rows, not nan or None.

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