Why datetime handling matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to datetime format.

Pandas

import pandas as pd

df = pd.DataFrame({'date': ['2023-01-01', '2023-02-01', '2023-03-01']})
df['date'] = pd.[1](df['date'])

Drag options to blanks, or click blank then click option'

Ato_datetime

Bto_string

Cto_numeric

Dto_list

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_string instead of to_datetime causes no date conversion.

Using to_numeric will cause an error for date strings.

We use pd.to_datetime() to convert strings to datetime objects in pandas.

2fill in blank

medium

Complete the code to extract the year from the 'date' column.

Pandas

df['year'] = df['date'].dt.[1]

Drag options to blanks, or click blank then click option'

Ayear

Bmonth

Cday

Dhour

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'month' or 'day' extracts the wrong part of the date.

Trying to call a method instead of accessing the attribute.

The dt.year attribute extracts the year from datetime values.

3fill in blank

hard

Fix the error in the code to filter rows where the date is after January 31, 2023.

Pandas

filtered = df[df['date'] > pd.[1]('2023-01-31')]

Drag options to blanks, or click blank then click option'

Ato_string

Bto_datetime

Cto_numeric

Dto_list

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing datetime column with a string causes wrong filtering.

Using to_string does not convert to datetime.

We must convert the string '2023-01-31' to datetime to compare with the 'date' column.

4fill in blank

hard

Fill both blanks to create a new column 'month_day' with month and day as strings.

Pandas

df['month_day'] = df['date'].dt.[1].astype(str) + '-' + df['date'].dt.[2].astype(str)

Drag options to blanks, or click blank then click option'

Amonth

Byear

Cday

Dhour

Attempts:

3 left

💡 Hint

Common Mistakes

Using year or hour instead of month or day.

Not converting to string before concatenation.

We extract month and day parts and convert them to strings to join with '-'.

5fill in blank

hard

Fill all three blanks to create a dictionary with date as key and year as value for dates after 2023-01-15.

Pandas

result = {row['date']: row['date'].dt.[1] for _, row in df[df['date'] > pd.[2]('2023-01-15')].iterrows() if row['date'].dt.[3] == 2023}

Drag options to blanks, or click blank then click option'

Ayear

Bto_datetime

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

Using month instead of year for filtering.

Not converting the string date to datetime before comparison.

We use dt.year to get the year, convert the string to datetime with pd.to_datetime, and check if the year equals 2023.