Date feature extraction in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Extracting Month and Day from Dates

What is the output of the following code that extracts the month and day from a pandas datetime column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-15', '2023-07-04', '2023-12-31'])})
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
print(df[['month', 'day']].to_dict(orient='list'))

A {"month": [1, 7, 12], "day": [15, 4, 31]}

B {"month": [15, 4, 31], "day": [1, 7, 12]}

C {"month": [2023, 2023, 2023], "day": [15, 4, 31]}

D {"month": [1, 7, 12], "day": [2023, 2023, 2023]}

💡 Hint

Look at how pandas datetime properties .dt.month and .dt.day extract parts of the date.

The .dt accessor allows extraction of datetime components. Here, month and day are extracted correctly from each date.

❓ data_output

intermediate

Counting Weekdays in a Date Range

Given a date range, how many Mondays are there in the DataFrame after extracting the weekday?

Data Analysis Python

import pandas as pd

dates = pd.date_range(start='2023-01-01', end='2023-01-31')
df = pd.DataFrame({'date': dates})
df['weekday'] = df['date'].dt.day_name()
monday_count = df[df['weekday'] == 'Monday'].shape[0]
print(monday_count)

A 3

B 4

C 6

D 5

💡 Hint

Count how many Mondays fall between Jan 1 and Jan 31, 2023.

January 2023 has Mondays on 2nd, 9th, 16th, 23rd, and 30th, totaling 5.

❓ visualization

advanced

Plotting Hourly Distribution of Events

Which option produces the correct bar plot showing counts of events per hour from a datetime column?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

# Sample data
timestamps = pd.to_datetime([
    '2023-06-01 08:15', '2023-06-01 08:45', '2023-06-01 09:00',
    '2023-06-01 10:30', '2023-06-01 10:45', '2023-06-01 10:50'
])
df = pd.DataFrame({'timestamp': timestamps})
df['hour'] = df['timestamp'].dt.hour
counts = df['hour'].value_counts().sort_index()

plt.bar(counts.index, counts.values)
plt.xlabel('Hour of Day')
plt.ylabel('Event Count')
plt.title('Events per Hour')
plt.show()

A A line chart showing event counts over hours 8 to 10.

B A bar chart with bars at hours 8, 9, and 10 with heights 2, 1, and 3 respectively.

C A pie chart showing percentage of events per hour.

D A scatter plot with hours on x-axis and event counts on y-axis.

💡 Hint

Look for a bar chart with counts grouped by hour.

The code groups events by hour and plots a bar chart with counts 2 at 8, 1 at 9, and 3 at 10.

🔧 Debug

advanced

Identifying Error in Date Extraction Code

What error does the following code raise when trying to extract the week number from a datetime column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-01', '2023-06-15'])})
df['week_num'] = df['date'].dt.week
print(df)

A KeyError: 'week_num'

B TypeError: dt.week is not callable

C AttributeError: 'DatetimeProperties' object has no attribute 'week'

D No error, prints week numbers correctly

💡 Hint

Check if .dt.week is a valid attribute in pandas datetime accessor.

The .dt.week attribute was deprecated and removed; use .dt.isocalendar().week instead.

🚀 Application

expert

Calculating Age from Birthdate Column

Given a DataFrame with a 'birthdate' column, which option correctly calculates the age in years as of today?

Data Analysis Python

import pandas as pd
from datetime import datetime

df = pd.DataFrame({'birthdate': pd.to_datetime(['1990-05-15', '2000-12-01', '1985-07-30'])})

# Calculate age here

print(df['age'].tolist())

A df['age'] = (pd.to_datetime('today') - df['birthdate']).dt.days // 365

B df['age'] = datetime.now().year - df['birthdate'].dt.year

C df['age'] = (datetime.now() - df['birthdate']).days // 365

D df['age'] = (pd.Timestamp(datetime.now()) - df['birthdate']).dt.days // 365

💡 Hint

Use pandas datetime subtraction and .dt.days to get days difference.

Option A correctly uses pandas Timestamp subtraction and .dt.days to get days difference, then divides by 365 for age.