to_datetime() conversion in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this to_datetime conversion?

Given the following code, what will be the output of print(df['date'])?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': ['2023-01-15', '2023/02/20', 'March 3, 2023']})
df['date'] = pd.to_datetime(df['date'])
print(df['date'])

A

0   2023-01-15 00:00:00
1   2023-02-20 00:00:00
2   2023-03-03 00:00:00
Name: date, dtype: datetime64[ns]

B

0   2023-01-15
1   2023-02-20
2   2023-03-03
Name: date, dtype: datetime64[ns]

C

0   2023-01-15 00:00
1   2023-02-20 00:00
2   2023-03-03 00:00
Name: date, dtype: datetime64[ns]

D

0   2023-01-15T00:00:00
1   2023-02-20T00:00:00
2   2023-03-03T00:00:00
Name: date, dtype: datetime64[ns]

💡 Hint

The default time added by to_datetime when only date is given is midnight with seconds.

Pandas to_datetime converts strings to Timestamp objects with time set to 00:00:00 by default if no time is specified.

❓ data_output

intermediate

How many rows have valid datetime after conversion?

Consider this DataFrame and conversion. How many rows have valid datetime (not NaT) after conversion?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'date': ['2023-01-01', 'invalid_date', '2023-03-15']})
df['date'] = pd.to_datetime(df['date'], errors='coerce')
valid_count = df['date'].notna().sum()
print(valid_count)

A 1

B 0

C 2

D 3

💡 Hint

Invalid dates become NaT with errors='coerce'.

Only two strings are valid dates; the invalid one becomes NaT and is excluded from count.

🔧 Debug

advanced

What error does this to_datetime code raise?

What error will this code raise when run?

Data Analysis Python

import pandas as pd

dates = ['2023-01-01', '2023-02-30']
df = pd.DataFrame({'date': dates})
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d', errors='raise')

A KeyError: 'date'

B ValueError: day is out of range for month

C TypeError: unsupported operand type(s)

D No error, conversion succeeds

💡 Hint

February 30th is not a valid date.

With errors='raise', invalid dates cause ValueError during conversion.

🚀 Application

advanced

Which option converts mixed date formats correctly?

You have a column with dates in formats like '2023-01-01', '01/02/2023', and 'March 3, 2023'. Which code correctly converts all to datetime?

A pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')

B pd.to_datetime(df['date'], format='%m/%d/%Y')

C pd.to_datetime(df['date'], format='%B %d, %Y')

D pd.to_datetime(df['date'], format='%Y-%m-%d')

💡 Hint

Use infer_datetime_format to handle multiple formats.

Only option A uses infer_datetime_format=True and errors='coerce' to handle mixed formats safely.

🧠 Conceptual

expert

What is the dtype of a Series after to_datetime conversion with utc=True?

If you convert a Series of date strings using pd.to_datetime(series, utc=True), what is the dtype of the resulting Series?

A string

B datetime64[ns]

C object

D datetime64[ns, UTC]

💡 Hint

UTC conversion adds timezone info to dtype.

Using utc=True converts dates to timezone-aware datetime64 with UTC timezone.