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)

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

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']])

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

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)

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

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)

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

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)

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

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.

Practice

1. What is the main purpose of the Datetime type in pandas?

easy

A. To store images and multimedia files

B. To store only text data

C. To store dates and times in a format pandas can understand and work with

D. To store numerical data like integers and floats

Solution

1. Step 1: Understand what Datetime type stores

   Datetime type is designed to hold date and time information in a structured way.

2. Step 2: Compare with other data types

   Unlike text or numbers, Datetime allows easy date calculations and filtering.

3. Final Answer:

   To store dates and times in a format pandas can understand and work with -> Option C

4. Quick Check:

   Datetime type = stores dates/times [OK]

Hint: Datetime type is for dates and times only [OK]

Common Mistakes:

• Thinking Datetime stores text or numbers
• Confusing Datetime with string type
• Assuming Datetime stores images

2. Which of the following is the correct way to convert a string column df['date_str'] to datetime type in pandas?

easy

A. df['date_str'].astype('datetime')

B. pd.to_datetime(df['date_str'])

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

D. pd.datetime(df['date_str'])

Solution

1. Step 1: Recall the pandas function for datetime conversion

   The correct function is pd.to_datetime() to convert strings to datetime.

2. Step 2: Check the options for correct syntax

   Only pd.to_datetime(df['date_str']) uses pd.to_datetime() correctly with the column.

3. Final Answer:

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

4. Quick Check:

   Use pd.to_datetime() for conversion [OK]

Hint: Use pd.to_datetime() to convert strings to datetime [OK]

Common Mistakes:

• Using astype('datetime') which is invalid
• Calling to_datetime() as a method on Series
• Using pd.datetime which does not exist

3. What will be the output of this code?

import pandas as pd
s = pd.Series(['2023-01-01', '2023-02-15', 'not a date'])
s_dt = pd.to_datetime(s, errors='coerce')
s_dt.isna().sum()

medium

A. 0

B. Raises an error

C. 2

D. 1

Solution

1. Step 1: Convert strings to datetime with errors='coerce'

   The invalid string 'not a date' becomes NaT (missing datetime).

2. Step 2: Count missing datetime values

   Only one value is invalid, so isna().sum() returns 1.

3. Final Answer:

   1 -> Option D

4. Quick Check:

   Invalid dates become NaT, count is 1 [OK]

Hint: errors='coerce' makes invalid dates NaT [OK]

Common Mistakes:

• Expecting error instead of NaT
• Counting all as valid dates
• Confusing NaT with zero

4. The following code throws an error. What is the mistake?

import pandas as pd
s = pd.Series(['2023-01-01', '2023-02-15'])
s_dt = s.to_datetime()

medium

A. Series object has no method to_datetime()

B. pd.to_datetime() requires a list, not Series

C. Missing import for datetime module

D. Strings are not in correct date format

Solution

1. Step 1: Check method usage on Series

   Series does not have a method called to_datetime().

2. Step 2: Correct method to convert Series to datetime

   Use pd.to_datetime(s) instead of s.to_datetime().

3. Final Answer:

   Series object has no method to_datetime() -> Option A

4. Quick Check:

   Use pd.to_datetime(), not s.to_datetime() [OK]

Hint: Use pd.to_datetime(), not Series.to_datetime() [OK]

Common Mistakes:

• Trying to call to_datetime() on Series
• Assuming pd.to_datetime() needs list input
• Thinking import datetime is required here

5. You have a DataFrame df with a column 'date_str' containing dates as strings in mixed formats like '2023-01-01', '01/02/2023', and 'March 3, 2023'. How do you convert this column to datetime type reliably?

hard

A. Use pd.to_datetime(df['date_str'], errors='coerce')

B. Use pd.to_datetime(df['date_str'], errors='coerce', dayfirst=true)

C. Use df['date_str'].astype('datetime64')

D. Use pd.to_datetime(df['date_str'], errors='ignore')

Solution

1. Step 1: Understand mixed date formats and errors

   Mixed formats can cause parsing errors; using errors='coerce' converts unparseable dates to NaT safely.

2. Step 2: Evaluate options for handling mixed formats

   Use pd.to_datetime(df['date_str'], errors='coerce') uses pd.to_datetime() with errors='coerce', which is reliable for mixed formats without forcing dayfirst.

3. Final Answer:

   Use pd.to_datetime(df['date_str'], errors='coerce') -> Option A

4. Quick Check:

   errors='coerce' handles mixed formats safely [OK]

Hint: Use errors='coerce' to handle mixed date formats safely [OK]

Common Mistakes:

• Using errors='ignore' which keeps strings unconverted
• Using astype('datetime64') which fails on mixed formats
• Setting dayfirst=true incorrectly for all dates