dt accessor for datetime properties in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of extracting month using dt accessor

What is the output of this code snippet?

Pandas

import pandas as pd
s = pd.Series(pd.to_datetime(['2023-01-15', '2023-06-20', '2023-12-31']))
result = s.dt.month
print(result.tolist())

A [15, 20, 31]

B [1, 6, 12]

C [2023, 2023, 2023]

D [0, 5, 11]

💡 Hint

The dt accessor extracts datetime components like year, month, day.

The dt.month extracts the month number from each datetime in the Series. The months are January (1), June (6), and December (12).

❓ data_output

intermediate

Number of weekend days in a datetime Series

Given a pandas Series of dates, which option correctly counts how many dates fall on a weekend (Saturday or Sunday)?

Pandas

import pandas as pd
s = pd.Series(pd.to_datetime(['2023-04-01', '2023-04-02', '2023-04-03', '2023-04-04']))
weekends = s.dt.dayofweek >= 5
count = weekends.sum()
print(count)

A 2

B 1

C 0

D 3

💡 Hint

Dayofweek returns 0 for Monday and 6 for Sunday.

Saturday is dayofweek 5 and Sunday is 6, so dates with dayofweek >= 5 are weekends. Here, 2023-04-01 is Saturday and 2023-04-02 is Sunday, so count is 2.

🔧 Debug

advanced

Identify the error in using dt accessor

What error does this code raise?

Pandas

import pandas as pd
s = pd.Series(['2023-01-01', '2023-02-01', '2023-03-01'])
result = s.dt.day
print(result)

A No error, prints day numbers

B TypeError: unsupported operand type(s) for -: 'str' and 'int'

C AttributeError: Can only use .dt accessor with datetimelike values

D KeyError: 'day'

💡 Hint

The dt accessor requires datetime type, not strings.

The Series contains strings, not datetime objects. The dt accessor only works on datetime-like data, so it raises AttributeError.

🚀 Application

advanced

Filter rows by hour using dt accessor

You have a DataFrame with a datetime column 'timestamp'. Which code filters rows where the hour is between 9 AM and 5 PM (inclusive)?

Pandas

import pandas as pd
df = pd.DataFrame({'timestamp': pd.to_datetime(['2023-01-01 08:00', '2023-01-01 09:00', '2023-01-01 17:00', '2023-01-01 18:00'])})

A df[(df['timestamp'].dt.hour >= 9) & (df['timestamp'].dt.hour <= 17)]

B df[(df['timestamp'].dt.hour > 9) & (df['timestamp'].dt.hour < 17)]

C df[(df['timestamp'].dt.hour >= 8) & (df['timestamp'].dt.hour <= 18)]

D df[(df['timestamp'].dt.hour == 9) | (df['timestamp'].dt.hour == 17)]

💡 Hint

Use inclusive comparison operators to include 9 AM and 5 PM.

Option A correctly filters rows where hour is between 9 and 17 inclusive, matching 9 AM to 5 PM.

🧠 Conceptual

expert

Understanding dt accessor behavior with missing values

Given a Series with some missing datetime values, what is the output of accessing dt.day on it?

Pandas

import pandas as pd
s = pd.Series([pd.Timestamp('2023-01-01'), pd.NaT, pd.Timestamp('2023-01-03')])
result = s.dt.day
print(result.tolist())

A [1, <NA>, 3]

B [1, null, 3]

C [1, NaT, 3]

D [1, nan, 3]

💡 Hint

Missing datetime values become pandas NA in dt accessor outputs.

When dt accessor is used on a Series with pd.NaT, the missing values appear as in the output Series, but .tolist() converts it to nan, so print(result.tolist()) shows [1, nan, 3].

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