Datetime type in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to datetime type using pandas.

Pandas

import pandas as pd

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

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ATimestamp

Bdatetime

Cto_datetime

Ddate_range

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.datetime which is not a function.

Using pd.Timestamp which creates a single timestamp, not conversion.

Using pd.date_range which creates a range of dates.

Use pd.to_datetime() to convert a column to datetime type in pandas.

2fill in blank

medium

Complete the code to extract the year from the datetime column 'date' in the DataFrame.

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-01-01', '2024-02-01', '2025-03-01'])})
df['year'] = df['date'].dt.[1]
print(df[['date', 'year']])

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Ayear

Bhour

Cday

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

Using .dt.month or .dt.day which extract other parts of the date.

Trying to use a method instead of an attribute.

Use .dt.year to get the year from a datetime column.

3fill in blank

hard

Fix the error in the code to filter rows where the 'date' is after January 1, 2024.

Pandas

import pandas as pd

df = pd.DataFrame({'date': pd.to_datetime(['2023-12-31', '2024-01-02', '2024-06-01'])})
filtered = df[df['date'] [1] pd.Timestamp('2024-01-01')]
print(filtered)

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A<

B<=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using < or <= which select earlier dates.

Using == which selects only that exact date.

Use the greater than operator > to filter dates after January 1, 2024.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each date string to its month number if the day is greater than 15.

Pandas

dates = ['2023-01-10', '2023-02-20', '2023-03-25']
months = {d: pd.to_datetime(d).dt.[1] for d in dates if pd.to_datetime(d).dt.[2] > 15}
print(months)

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Amonth

Bday

Cyear

Dhour

Attempts:

3 left

💡 Hint

Common Mistakes

Using .dt.year instead of .dt.month for the first blank.

Using .dt.month instead of .dt.day for the second blank.

Use .dt.month to get the month and .dt.day to check the day.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps the uppercase month name to the day number for dates in 2023 where the day is less than 20.

Pandas

dates = ['2023-01-10', '2023-02-25', '2023-03-15', '2024-04-10']
result = {pd.to_datetime(d).dt.[1]().upper(): pd.to_datetime(d).dt.[2] for d in dates if pd.to_datetime(d).dt.[3] == 2023 and pd.to_datetime(d).dt.day < 20}
print(result)

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Amonth_name

Bday

Cyear

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

Using .dt.month instead of .dt.month_name for the first blank.

Using .dt.month instead of .dt.day for the second blank.

Using .dt.day instead of .dt.year for the third blank.

Use .dt.month_name() for the month name, .dt.day for the day, and .dt.year to filter the year.