Date and time feature extraction in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Extracting the hour from a datetime column

What is the output of this code that extracts the hour from a datetime column in a pandas DataFrame?

ML Python

import pandas as pd

df = pd.DataFrame({'timestamp': pd.to_datetime(['2024-06-01 08:15:27', '2024-06-01 22:45:00'])})
df['hour'] = df['timestamp'].dt.hour
print(df['hour'].tolist())

A [8, 22]

B [15, 45]

C [2024, 2024]

D [1, 1]

💡 Hint

The .dt accessor helps extract parts of datetime like hour, minute, or day.

The .dt.hour extracts the hour part from each datetime entry. The first timestamp has hour 8, the second 22.

🧠 Conceptual

intermediate

Why extract date and time features?

Why do we extract features like day of week or month from datetime data in machine learning?

A To reduce the size of the dataset by removing datetime columns.

B To convert datetime into text for natural language processing.

C To help the model learn patterns related to time, like weekends or seasons.

D To encrypt the datetime data for security.

💡 Hint

Think about how time affects behavior or events in real life.

Extracting features like day of week or month helps models find patterns related to time, such as weekend effects or seasonal trends.

❓ Metrics

advanced

Evaluating model with time-based features

You added 'day_of_week' and 'hour' features extracted from timestamps to your model. Which metric best shows if these features improved the model's ability to predict hourly sales?

A Mean Absolute Error (MAE) on a test set split randomly

B Accuracy score on a classification task

C Confusion matrix on training data

D Mean Squared Error (MSE) on a test set split by time (e.g., last month)

💡 Hint

Think about how to fairly test time-related predictions.

Using MSE on a test set split by time ensures the model generalizes to future unseen time periods, which is important for time-based features.

🔧 Debug

advanced

Fixing incorrect extraction of month from datetime

What error does this code raise when trying to extract the month from a datetime column?

ML Python

import pandas as pd

df = pd.DataFrame({'date': ['2024-06-01', '2024-07-15']})
df['month'] = df['date'].dt.month
print(df['month'])

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

B KeyError: 'month' not found

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

D No error, prints [6, 7]

💡 Hint

Check the data type of the 'date' column before using .dt accessor.

The 'date' column is string type, not datetime. The .dt accessor only works on datetime types, so it raises AttributeError.

❓ Model Choice

expert

Choosing model type for datetime feature importance

You have extracted multiple datetime features (hour, day_of_week, month) for a sales forecasting task. Which model type is best to capture complex interactions between these features and other variables?

A Linear Regression without interaction terms

B Decision Tree or Random Forest models

C K-Nearest Neighbors (KNN) without feature scaling

D Naive Bayes classifier

💡 Hint

Consider models that handle non-linear relationships and feature interactions well.

Decision Trees and Random Forests can capture complex, non-linear interactions between datetime features and other variables without needing manual feature engineering.

Practice

1. Which of the following is a common feature extracted from a date to help machine learning models?

easy

A. Font size

B. Color

C. Month

D. Temperature

Solution

1. Step 1: Understand date features

   Date features include parts of a date like year, month, day, hour, and weekday.

2. Step 2: Identify relevant feature

   Among the options, only 'Month' is a part of a date and useful for models.

3. Final Answer:

   Month -> Option C

4. Quick Check:

   Date feature = Month [OK]

Hint: Pick the option that relates directly to date parts [OK]

Common Mistakes:

• Choosing unrelated features like color or font size
• Confusing date features with unrelated data

2. Which Python code correctly extracts the weekday from a pandas datetime column named 'date'?

easy

A. df['weekday'] = df['date'].dt.weekday

B. df['weekday'] = df['date'].weekday()

C. df['weekday'] = df['date'].weekday

D. df['weekday'] = df['date'].dt.weekday()

Solution

1. Step 1: Recall pandas datetime accessor

   To extract weekday, use the .dt accessor followed by .weekday without parentheses.

2. Step 2: Check each option

   df['weekday'] = df['date'].dt.weekday uses .dt.weekday correctly. df['weekday'] = df['date'].weekday() calls weekday() directly on the series, which is invalid. df['weekday'] = df['date'].weekday misses .dt. df['weekday'] = df['date'].dt.weekday() incorrectly uses parentheses after .weekday.

3. Final Answer:

   df['weekday'] = df['date'].dt.weekday -> Option A

4. Quick Check:

   Use .dt.weekday without parentheses [OK]

Hint: Use .dt.weekday without parentheses for pandas datetime [OK]

Common Mistakes:

• Calling weekday() as a method on series
• Missing .dt accessor
• Adding parentheses after .weekday

3. Given the code:

import pandas as pd
df = pd.DataFrame({'date': pd.to_datetime(['2024-06-01 14:30', '2024-06-02 09:15'])})
df['hour'] = df['date'].dt.hour
df['is_weekend'] = df['date'].dt.weekday >= 5
print(df[['hour', 'is_weekend']].to_dict())

What is the printed output?

medium

A. {'hour': {0: 14, 1: 9}, 'is_weekend': {0: False, 1: False}}

B. {'hour': {0: 14, 1: 9}, 'is_weekend': {0: True, 1: True}}

C. {'hour': {0: 14, 1: 9}, 'is_weekend': {0: False, 1: True}}

D. SyntaxError

Solution

1. Step 1: Extract hour values

   The first date has hour 14, second has hour 9, so 'hour' column is {0:14, 1:9}.

2. Step 2: Determine weekend flags

   Weekday 5 and 6 are weekend. Dates are 2024-06-01 (Saturday=5) and 2024-06-02 (Sunday=6). Both are weekend, so 'is_weekend' should be True for both.

3. Step 3: Check code logic

   Code uses df['date'].dt.weekday >= 5, which is True for both dates. So 'is_weekend' is {0: True, 1: True}.

4. Final Answer:

   {'hour': {0: 14, 1: 9}, 'is_weekend': {0: True, 1: True}} -> Option B

5. Quick Check:

   Weekend days are 5 or 6, both dates match [OK]

Hint: Check weekday numbers: 5=Saturday, 6=Sunday for weekend [OK]

Common Mistakes:

• Assuming weekend is false for Saturday/Sunday
• Mixing hour extraction with weekend logic
• Misreading weekday numbers

4. The following code aims to add a 'month' feature from a datetime column but throws an error:

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

What is the error and how to fix it?

medium

A. AttributeError because .month must be accessed via .dt; fix: df['date'].dt.month

B. SyntaxError due to missing parentheses; fix: df['date'].month()

C. TypeError because 'date' is not datetime; fix: convert to datetime first

D. No error; code is correct

Solution

1. Step 1: Understand pandas datetime access

   Datetime properties like month must be accessed with .dt when working on a pandas Series.

2. Step 2: Identify error cause

   Using df['date'].month tries to get 'month' attribute of the Series, causing AttributeError.

3. Step 3: Correct code

   Use df['date'].dt.month to extract month correctly.

4. Final Answer:

   AttributeError because .month must be accessed via .dt; fix: df['date'].dt.month -> Option A

5. Quick Check:

   Use .dt.month for pandas datetime columns [OK]

Hint: Always use .dt before datetime properties on pandas Series [OK]

Common Mistakes:

• Missing .dt accessor
• Trying to call .month() as a method
• Not converting column to datetime type

5. You have a dataset with a datetime column 'timestamp'. You want to create a feature that is 1 if the time is during business hours (9am to 5pm) on weekdays, else 0. Which code correctly creates this feature?

hard

A. df['business_hours'] = ((df['timestamp'].dt.hour > 9) & (df['timestamp'].dt.hour <= 17) & (df['timestamp'].dt.weekday <= 5)).astype(int)

B. df['business_hours'] = ((df['timestamp'].dt.hour > 9) & (df['timestamp'].dt.hour < 17) & (df['timestamp'].dt.weekday < 5)).astype(int)

C. df['business_hours'] = ((df['timestamp'].dt.hour >= 9) & (df['timestamp'].dt.hour <= 17) & (df['timestamp'].dt.weekday <= 5)).astype(int)

D. df['business_hours'] = ((df['timestamp'].dt.hour >= 9) & (df['timestamp'].dt.hour < 17) & (df['timestamp'].dt.weekday < 5)).astype(int)

Solution

1. Step 1: Define business hours range

   Business hours are from 9:00 (inclusive) to 17:00 (exclusive), so hour >= 9 and hour < 17.

2. Step 2: Define weekdays

   Weekdays are Monday (0) to Friday (4), so weekday < 5.

3. Step 3: Combine conditions and convert to int

   Use logical AND (&) to combine conditions and convert boolean to int with .astype(int).

4. Final Answer:

   df['business_hours'] = ((df['timestamp'].dt.hour >= 9) & (df['timestamp'].dt.hour < 17) & (df['timestamp'].dt.weekday < 5)).astype(int) -> Option D

5. Quick Check:

   Use inclusive start, exclusive end for hours and weekday < 5 [OK]

Hint: Use >=9 and <17 for hours, weekday <5 for Mon-Fri [OK]

Common Mistakes:

• Using >9 instead of >=9
• Including weekend days by using <=5
• Using <=17 instead of <17