to_datetime() for date parsing in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this date parsing code?

Given the following pandas code, what is the output of the print(dates) statement?

Pandas

import pandas as pd

dates = pd.to_datetime(['2023-01-15', '2023/02/20', 'March 3, 2023'])
print(dates)

A [Timestamp('2023-01-15 00:00:00') Timestamp('2023-02-20 00:00:00') Timestamp('2023-03-03 00:00:00')]

B [Timestamp('2023-01-15') Timestamp('2023-02-20') Timestamp('2023-03-03')]

C [datetime.date(2023, 1, 15) datetime.date(2023, 2, 20) datetime.date(2023, 3, 3)]

D Error: Invalid date format

💡 Hint

Look at how pandas converts strings to Timestamps with default time 00:00:00.

pandas.to_datetime converts strings to Timestamp objects with time set to midnight by default. The output shows a DatetimeIndex with Timestamps including time 00:00:00.

❓ data_output

intermediate

How many valid dates are parsed?

Using pd.to_datetime with errors='coerce', how many valid dates remain after parsing?

Pandas

import pandas as pd

dates = pd.to_datetime(['2023-01-01', 'not a date', '2023-03-15'], errors='coerce')
valid_count = dates.notna().sum()
print(valid_count)

A 1

B 3

C 2

D 0

💡 Hint

Check how 'not a date' is handled with errors='coerce'.

The invalid string is converted to NaT (missing date). Only two valid dates remain, so the count is 2.

🔧 Debug

advanced

Why does this code raise an error?

This code raises an error. What is the cause?

Pandas

import pandas as pd

dates = ['2023-01-01', '2023-02-30']
pd.to_datetime(dates, format='%Y-%m-%d')

A No error, returns NaT for invalid date

B ValueError because '2023-02-30' is an invalid date

C SyntaxError due to missing parentheses

D TypeError because format string is incorrect

💡 Hint

Check if February 30 exists in the calendar.

February 30 does not exist, so pandas raises a ValueError when parsing with a strict format.

🚀 Application

advanced

Convert mixed-format dates to datetime with dayfirst

You have dates in mixed formats like '31/12/2023' and '2023-12-31'. Which code correctly parses them assuming day comes first?

A pd.to_datetime(['31/12/2023', '2023-12-31'], format='%Y-%m-%d')

B pd.to_datetime(['31/12/2023', '2023-12-31'], format='%d/%m/%Y')

C pd.to_datetime(['31/12/2023', '2023-12-31'], dayfirst=False)

D pd.to_datetime(['31/12/2023', '2023-12-31'], dayfirst=True)

💡 Hint

Use dayfirst=True to handle day/month order in mixed formats.

dayfirst=True tells pandas to interpret the first number as day when ambiguous. This works for mixed formats without specifying a fixed format.

🧠 Conceptual

expert

What does the utc=True parameter do in pd.to_datetime()?

Select the correct explanation of the effect of utc=True when parsing dates with pd.to_datetime().

A Converts parsed dates to UTC timezone, returning timezone-aware Timestamps

B Raises an error if timezone info is missing in input

C Converts all dates to local system timezone

D Ignores timezone information and returns naive datetime objects

💡 Hint

Think about timezone awareness in pandas datetime objects.

Setting utc=True converts all parsed dates to UTC timezone, making them timezone-aware Timestamps.