Extracting date components (year, month, day) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Extract year from a pandas datetime column

Given the DataFrame df with a datetime column date, what is the output of df['date'].dt.year?

Data Analysis Python

import pandas as pd

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

A [1, 12, 6]

B [15, 31, 1]

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

D [2023, 2022, 2024]

💡 Hint

Use the dt accessor to get date parts from pandas datetime columns.

The dt.year extracts the year component from each datetime in the column, returning a Series of years.

❓ data_output

intermediate

Extract month and day from datetime

What is the output of extracting month and day from the datetime column df['date'] using df['date'].dt.month and df['date'].dt.day?

Data Analysis Python

import pandas as pd

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

A [1, 12, 6] [15, 31, 1]

B [15, 31, 1] [1, 12, 6]

C [2023, 2022, 2024] [15, 31, 1]

D [1, 12, 6] [2023, 2022, 2024]

💡 Hint

Month and day are separate attributes accessed via dt.month and dt.day.

The dt.month extracts the month number, and dt.day extracts the day number from each datetime.

🔧 Debug

advanced

Identify the error when extracting date components

What error will this code raise?

import pandas as pd

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

A AttributeError: Can only use .dt accessor with datetimelike values

B KeyError: 'year'

C TypeError: 'list' object is not callable

D No error, prints [2023, 2022, 2024]

💡 Hint

Check the data type of the 'date' column before using .dt.

The 'date' column is string type, not datetime. The .dt accessor only works on datetime-like data, so it raises AttributeError.

🚀 Application

advanced

Create new columns for year, month, and day

Given a DataFrame df with a datetime column date, which code correctly adds three new columns year, month, and day with the respective date parts?

A df[['year', 'month', 'day']] = df['date'].dt.year, df['date'].dt.month, df['date'].dt.day

B df['year'], df['month'], df['day'] = df['date'].dt.year, df['date'].dt.month, df['date'].dt.day

C

df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day

D

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

💡 Hint

Use the dt accessor and assign each part to a separate column.

Option C assigns each date component separately using the dt accessor, which is the correct way.

🧠 Conceptual

expert

Understanding datetime component extraction behavior

If a pandas Series contains some missing datetime values (NaT), what will be the output of extracting the year component using series.dt.year?

A A Series of strings with years and 'NaT' for missing values

B A float Series with years and NaN for missing values

C An integer Series with years and 0 for missing values

D Raises an error because of missing values

💡 Hint

Consider how pandas handles missing datetime values in numeric extraction.

When extracting year from a datetime Series with missing values, pandas returns a float Series where missing values become NaN because integers cannot represent missing data.