dt accessor for datetime properties in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the year from the 'date' column using the dt accessor.

Pandas

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

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Aday

Bmonth

Chour

Dyear

Attempts:

3 left

💡 Hint

Common Mistakes

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

Forgetting to use the dt accessor.

The dt.year property extracts the year part from datetime values.

2fill in blank

medium

Complete the code to extract the month from the 'date' column using the dt accessor.

Pandas

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

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

Amonth

Byear

Cminute

Dday

Attempts:

3 left

💡 Hint

Common Mistakes

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

Forgetting to use the dt accessor.

The dt.month property extracts the month part from datetime values.

3fill in blank

hard

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

Pandas

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

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

Adt.day

Bday

Cday.dt

Ddt.day()

Attempts:

3 left

💡 Hint

Common Mistakes

Using df['date'].day without dt accessor.

Using parentheses like dt.day() which is incorrect.

The dt accessor must be used before the property: df['date'].dt.day.

4fill in blank

hard

Fill both blanks to create a new column 'hour' extracting the hour from 'timestamp' column and filter rows where hour is greater than 12.

Pandas

df['hour'] = df['timestamp'].[1].hour
filtered = df[df['hour'] [2] 12]

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Adt

B>

C<

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'date' instead of 'dt' for accessor.

Using '<' instead of '>' in filter condition.

Use dt.hour to get the hour part. Then filter rows where hour is greater than 12 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as day names in uppercase, values as day numbers, filtering days greater than 15 from 'date' column.

Pandas

result = dict(zip([1], [2]))
result = {k: v for k, v in result.items() if v [3] 15}
result = {k.upper(): v for k, v in result.items()}

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Adf['date'].dt.month_name()

Bdf['date'].dt.day

C>

Ddf['date'].dt.day_name()

Attempts:

3 left

💡 Hint

Common Mistakes

Using month_name() instead of day_name() for keys.

Using '<' instead of '>' in filtering.

Not converting keys to uppercase.

Use dt.day_name() for day names, dt.day for day numbers, and filter with > 15.