Extracting date components (year, month, day) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the year from the 'date' column in the DataFrame.

Data Analysis Python

df['year'] = df['date'].dt.[1]

Drag options to blanks, or click blank then click option'

Aday

Bhour

Cyear

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'month' or 'day' instead of 'year'.

Forgetting to use the dt accessor.

The dt.year attribute extracts the year component from datetime values.

2fill in blank

medium

Complete the code to extract the month from the 'date' column in the DataFrame.

Data Analysis Python

df['month'] = df['date'].dt.[1]

Drag options to blanks, or click blank then click option'

Amonth

Bday

Cyear

Dminute

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'day' or 'year' instead of 'month'.

Forgetting to use the dt accessor.

The dt.month attribute extracts the month component from datetime values.

3fill in blank

hard

Fix the error in the code to extract the day from the 'date' column.

Data Analysis Python

df['day'] = df['date'].[1]

Drag options to blanks, or click blank then click option'

Adt.day()

Bday

Cdate.day

Ddt.day

Attempts:

3 left

💡 Hint

Common Mistakes

Using date.day which is invalid for pandas Series.

Calling dt.day() as a function.

To extract the day, use the dt.day attribute. It must be accessed via dt without parentheses.

4fill in blank

hard

Fill both blanks to create a dictionary with years as keys and months as values from the 'date' column.

Data Analysis Python

date_dict = {df['date'].dt.[1][i]: df['date'].dt.[2][i] for i in range(len(df))}

Drag options to blanks, or click blank then click option'

Ayear

Bday

Cmonth

Dhour

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping year and month in keys and values.

Using day or hour instead of month.

This dictionary comprehension uses dt.year for keys and dt.month for values.

5fill in blank

hard

Fill all three blanks to create a list of tuples (year, month, day) from the 'date' column.

Data Analysis Python

date_tuples = [(df['date'].dt.[1][i], df['date'].dt.[2][i], df['date'].dt.[3][i]) for i in range(len(df))]

Drag options to blanks, or click blank then click option'

Ayear

Bmonth

Cday

Dminute

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of year, month, day.

Using minute instead of day.

This list comprehension extracts year, month, and day components to form tuples.

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