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]

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

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]

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

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()}

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

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.

Practice

1.

What does the dt accessor in pandas allow you to do?

easy

A. Extract parts like year, month, and day from datetime columns

B. Convert strings to numbers

C. Sort data alphabetically

D. Create new columns with random values

Solution

1. Step 1: Understand the purpose of dt accessor

   The dt accessor is used with datetime columns to access their components like year, month, day, etc.

2. Step 2: Compare options with this purpose

   Only Extract parts like year, month, and day from datetime columns describes extracting parts of datetime, which matches the dt accessor's use.

3. Final Answer:

   Extract parts like year, month, and day from datetime columns -> Option A

4. Quick Check:

   dt accessor = extract datetime parts [OK]

Hint: Remember: dt means date-time parts extraction [OK]

Common Mistakes:

• Thinking dt converts strings to numbers
• Confusing dt with sorting functions
• Assuming dt creates random data

2.

Which of the following is the correct syntax to get the month from a pandas datetime column df['date']?

df['date']?.month

easy

A. df['date'].month()

B. df['date'].dt.month

C. df['date'].dt('month')

D. df['date'].month.dt

Solution

1. Step 1: Recall correct dt accessor syntax

   To access datetime parts, use .dt followed by the property without parentheses.

2. Step 2: Check each option

   df['date'].dt.month uses .dt.month correctly. df['date'].month() wrongly uses parentheses. df['date'].dt('month') uses wrong syntax. df['date'].month.dt reverses order.

3. Final Answer:

   df['date'].dt.month -> Option B

4. Quick Check:

   Use .dt.property syntax [OK]

Hint: Use .dt before datetime property without parentheses [OK]

Common Mistakes:

• Calling month as a function with ()
• Using dt as a function
• Reversing dt and property order

3.

Given the following code, what will be the output?

import pandas as pd

dates = pd.Series(pd.to_datetime(['2023-01-15', '2023-07-20', '2023-12-31']))
months = dates.dt.month
print(months.tolist())

medium

A. [15, 20, 31]

B. ['January', 'July', 'December']

C. [1, 7, 12]

D. [2023, 2023, 2023]

Solution

1. Step 1: Understand the code

   The code creates a Series of datetime objects for three dates, then extracts the month number using dt.month.

2. Step 2: Determine the month numbers

   January is month 1, July is 7, December is 12, so the list is [1, 7, 12].

3. Final Answer:

   [1, 7, 12] -> Option C

4. Quick Check:

   dt.month extracts month numbers [OK]

Hint: dt.month returns month numbers as integers [OK]

Common Mistakes:

• Confusing day with month
• Expecting month names instead of numbers
• Printing year instead of month

4.

What is wrong with this code snippet?

import pandas as pd

dates = pd.Series(['2023-01-15', '2023-07-20'])
print(dates.dt.day)

medium

A. The Series is not converted to datetime before using dt accessor

B. The dt accessor does not have a day property

C. The print statement syntax is incorrect

D. The dates list is empty

Solution

1. Step 1: Check the data type of the Series

   The Series contains strings, not datetime objects, so dt accessor cannot be used directly.

2. Step 2: Identify the error cause

   Without converting to datetime using pd.to_datetime(), accessing dt.day raises an error.

3. Final Answer:

   The Series is not converted to datetime before using dt accessor -> Option A

4. Quick Check:

   Convert to datetime before using dt [OK]

Hint: Always convert to datetime before using dt accessor [OK]

Common Mistakes:

• Assuming dt works on string Series
• Thinking dt.day does not exist
• Ignoring data type requirements

5.

You have a DataFrame df with a datetime column 'timestamp'. You want to create a new column 'is_weekend' that is True if the date is Saturday or Sunday, and False otherwise. Which code correctly does this?

df['is_weekend'] = df['timestamp'].dt.weekday > 4

hard

A. df['is_weekend'] = df['timestamp'].dt.weekday in [5, 6]

B. df['is_weekend'] = df['timestamp'].dt.day > 4

C. df['is_weekend'] = df['timestamp'].dt.weekday == 5 or 6

D. df['is_weekend'] = df['timestamp'].dt.weekday > 4

Solution

1. Step 1: Understand weekday numbering

   In pandas, dt.weekday returns 0 for Monday up to 6 for Sunday. Weekend days are 5 (Saturday) and 6 (Sunday).

2. Step 2: Check each option's logic

   df['is_weekend'] = df['timestamp'].dt.weekday > 4 uses > 4, which means days 5 and 6 are True. df['is_weekend'] = df['timestamp'].dt.day > 4 checks day of month, not weekday. df['is_weekend'] = df['timestamp'].dt.weekday == 5 or 6 uses incorrect syntax for or. df['is_weekend'] = df['timestamp'].dt.weekday in [5, 6] uses 'in' which is invalid for Series.

3. Final Answer:

   df['is_weekend'] = df['timestamp'].dt.weekday > 4 -> Option D

4. Quick Check:

   Weekend days have weekday > 4 [OK]

Hint: Weekend days have weekday numbers 5 or 6, so > 4 works [OK]

Common Mistakes:

• Using day instead of weekday
• Wrong boolean logic with or
• Using 'in' with pandas Series