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.

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