Date and time feature extraction in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the year from a datetime column in pandas.

ML Python

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

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

Aday

Byear

Chour

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

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

Forgetting to use the .dt accessor before the attribute.

The dt.year attribute extracts the year part from a datetime column in pandas.

2fill in blank

medium

Complete the code to extract the weekday name from a datetime column in pandas.

ML Python

df['weekday'] = df['date_column'].dt.[1]()

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

Aday_name

Bweekday_name

Cday

Dweekday

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'weekday' which returns an integer instead of the name.

Using 'weekday_name' which is deprecated.

The dt.day_name() method returns the weekday name (like Monday, Tuesday) from a datetime column.

3fill in blank

hard

Fix the error in the code to extract the hour from a datetime column.

ML Python

df['hour'] = df['date_column'].[1].hour

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

Atime

Bdatetime

Cdt

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to access .hour directly without .dt.

Using 'datetime' or 'time' which are not pandas accessors.

The .dt accessor is needed to access datetime properties like hour in pandas Series.

4fill in blank

hard

Fill both blanks to create a new column with the month and day extracted from a datetime column.

ML Python

df['month_day'] = df['date_column'].dt.[1].astype(str) + '-' + df['date_column'].dt.[2].astype(str)

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

Amonth

Bday

Chour

Dyear

Attempts:

3 left

💡 Hint

Common Mistakes

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

Not converting numbers to strings before concatenation.

Extracting month and day and converting them to strings allows combining them into a 'month-day' format.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each date to its weekday number if the hour is greater than 12.

ML Python

result = {date: date.[1] for date in df['date_column'] if date.[2] [3] 12}

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

Aweekday

Bhour

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the comparison.

Using 'day' instead of 'weekday' for the first blank.

Using 'date' or 'time' instead of 'dt' accessor (not shown here but common).

The dictionary comprehension extracts the weekday number for dates where the hour is greater than 12.