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].