dt accessor for datetime properties in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the dt accessor in pandas?

The dt accessor allows you to access datetime properties and methods of pandas Series with datetime values, making it easy to extract parts like year, month, day, and more.

beginner

How do you extract the year from a pandas Series of datetime values?

Use series.dt.year to get the year part of each datetime in the Series.

intermediate

Which dt property would you use to get the day of the week as an integer (Monday=0)?

Use series.dt.dayofweek to get the day of the week as an integer where Monday is 0 and Sunday is 6.

beginner

True or False: You can use dt accessor to convert datetime Series to strings.

True. Use series.dt.strftime() to format datetime as strings with the dt accessor.

beginner

What will series.dt.hour return when applied to a datetime Series?

It returns the hour part (0 to 23) of each datetime value in the Series.

Which pandas dt property gives the month number from a datetime Series?

A series.dt.month

B series.dt.day

C series.dt.year

D series.dt.hour

The dt.month property extracts the month number (1 to 12) from each datetime value.

What does series.dt.dayofyear return?

A Day of the year (1 to 365 or 366)

B Day of the week as an integer

C Day of the month

D Week number of the year

dt.dayofyear returns the day number within the year, starting at 1.

If you want to get the weekday name like 'Monday' from a datetime Series, which dt method do you use?

A series.dt.weekday

B series.dt.day_name()

C series.dt.dayofweek

D series.dt.weekday_name

Use series.dt.day_name() to get the weekday name as a string.

Which of these is NOT a valid dt property in pandas?

A series.dt.minute

B series.dt.second

C series.dt.month_name()

D series.dt.hour_of_day

dt.hour_of_day is not a valid property. Use dt.hour instead.

What type of pandas object must you have to use the dt accessor?

A Series with string values

B DataFrame with any dtype

C Series with datetime64 dtype

D List of datetime objects

The dt accessor works only on Series with datetime64 dtype.

Explain how to extract the year, month, and day from a pandas datetime Series using the dt accessor.

Think about the properties that give parts of a date.

Describe how you would find the weekday name and the hour of the day from a datetime Series in pandas.

One returns a string name, the other returns a number.

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