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.