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.

Practice

1. What does the pandas.to_datetime() function do?

easy

A. It sorts dates in ascending order.

B. It converts text or numbers into date objects.

C. It converts numbers into strings.

D. It calculates the difference between two dates.

Solution

1. Step 1: Understand the purpose of to_datetime()

   This function is used to convert strings or numbers that represent dates into actual date objects that pandas can work with easily.

2. Step 2: Compare options with the function's purpose

   Only It converts text or numbers into date objects. correctly describes this conversion. Other options describe different operations unrelated to to_datetime().

3. Final Answer:

   It converts text or numbers into date objects. -> Option B

4. Quick Check:

   to_datetime() converts to dates [OK]

Hint: Remember: to_datetime() turns text into dates [OK]

Common Mistakes:

• Thinking it sorts dates
• Confusing it with string conversion
• Assuming it calculates date differences

2. Which of the following is the correct syntax to convert a column 'date_str' in a DataFrame 'df' to datetime using pandas?

easy

A. df['date_str'].to_datetime()

B. df.to_datetime('date_str')

C. pd.to_datetime(df.date_str, inplace=True)

D. df['date_str'] = pd.to_datetime(df['date_str'])

Solution

1. Step 1: Recall the correct usage of pd.to_datetime()

   The function is called from pandas as pd.to_datetime() and takes the column as input.

2. Step 2: Check each option's syntax

   df['date_str'] = pd.to_datetime(df['date_str']) correctly assigns the converted datetime back to the column. Options B, C, and D use incorrect syntax or methods that don't exist.

3. Final Answer:

   df['date_str'] = pd.to_datetime(df['date_str']) -> Option D

4. Quick Check:

   Use pd.to_datetime() on column and assign [OK]

Hint: Use pd.to_datetime(column) and assign back [OK]

Common Mistakes:

• Calling to_datetime() as a DataFrame method
• Using inplace=True which is invalid here
• Passing column name as string without df[]

3. What will be the output of this code?

import pandas as pd
s = pd.Series(['2023-12-01', '2023-01-15', '2023-07-30'])
dates = pd.to_datetime(s, dayfirst=True)
print(dates.dt.day)

medium

A. [12, 1, 7]

B. [1, 15, 7]

C. [1, 15, 30]

D. [12, 15, 30]

Solution

1. Step 1: Understand the input and parameters

   The series has dates in 'YYYY-MM-DD' format. The parameter dayfirst=True tells pandas to interpret the first number as day, but here the format is year-month-day, so it will parse correctly ignoring dayfirst.

2. Step 2: Extract the day component

   For '2023-12-01', day is 1; for '2023-01-15', day is 15; for '2023-07-30', day is 30.

3. Final Answer:

   [1, 15, 30] -> Option C

4. Quick Check:

   Day extracted matches actual day values [OK]

Hint: dayfirst=True doesn't change ISO format parsing [OK]

Common Mistakes:

• Assuming dayfirst changes ISO format parsing
• Confusing month and day values
• Expecting month numbers instead of days

4. You run this code but get an error:

import pandas as pd
s = pd.Series(['31/12/2023', '01/01/2024'])
dates = pd.to_datetime(s)

What is the likely cause and how to fix it?

medium

A. Error due to wrong date format; fix by adding dayfirst=True.

B. Error due to missing time info; fix by adding format='%d/%m/%Y %H:%M'.

C. Error due to invalid strings; fix by removing invalid dates.

D. Error due to Series type; fix by converting to list first.

Solution

1. Step 1: Identify the date format in the series

   The dates are in 'day/month/year' format, e.g., '31/12/2023'.

2. Step 2: Understand default parsing behavior

   By default, pandas assumes month first (US style). So '31/12/2023' causes an error because month 31 is invalid.

3. Step 3: Fix by specifying dayfirst=True

   Adding dayfirst=True tells pandas to interpret the first number as day, fixing the error.

4. Final Answer:

   Error due to wrong date format; fix by adding dayfirst=True. -> Option A

5. Quick Check:

   Use dayfirst=True for 'DD/MM/YYYY' dates [OK]

Hint: Use dayfirst=True for 'DD/MM/YYYY' formats [OK]

Common Mistakes:

• Ignoring dayfirst parameter
• Trying to remove dates instead of fixing format
• Assuming time info is required

5. You have a DataFrame with a column 'date_str' containing dates in mixed formats: '2023-12-01', '01/12/2023', and '12-01-2023'. How can you convert this column to datetime correctly?

hard

A. Use pd.to_datetime(df['date_str'], dayfirst=True, errors='coerce') to parse and handle errors.

B. Use pd.to_datetime(df['date_str'], format='%Y-%m-%d') to force one format.

C. Convert each row manually with if-else checks for format.

D. Use df['date_str'].astype('datetime64') to convert directly.

Solution

1. Step 1: Recognize mixed date formats problem

   The column has dates in different formats, so forcing one format will cause errors.

2. Step 2: Use dayfirst=True and errors='coerce'

   Setting dayfirst=True helps parse '01/12/2023' as 1 Dec. Using errors='coerce' converts unparseable dates to NaT instead of error.

3. Step 3: Avoid forcing single format or manual parsing

   Use pd.to_datetime(df['date_str'], format='%Y-%m-%d') to force one format. forces one format and will fail. Convert each row manually with if-else checks for format. is complex and inefficient. Use df['date_str'].astype('datetime64') to convert directly. does not parse strings correctly.

4. Final Answer:

   Use pd.to_datetime(df['date_str'], dayfirst=True, errors='coerce') to parse and handle errors. -> Option A

5. Quick Check:

   Use dayfirst and errors='coerce' for mixed formats [OK]

Hint: Use dayfirst=True and errors='coerce' for mixed formats [OK]

Common Mistakes:

• Forcing one format on mixed data
• Trying manual row-wise parsing
• Using astype() which fails on strings