to_datetime() for date parsing in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to datetime format.

Pandas

df['date'] = pd.[1](df['date'])

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

Adatetime_parse

Bto_date

Cconvert_date

Dto_datetime

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent function like to_date or convert_date.

Trying to convert without using pandas functions.

The pd.to_datetime() function converts strings or other date formats into pandas datetime objects.

2fill in blank

medium

Complete the code to parse dates with a specific format 'YYYY-MM-DD'.

Pandas

df['date'] = pd.to_datetime(df['date'], format=[1])

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

A'%Y-%m-%d'

B'%d/%m/%Y'

C'%m-%d-%Y'

D'%Y/%d/%m'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong order of year, month, day in the format string.

Using slashes instead of dashes when the date string uses dashes.

The format string '%Y-%m-%d' matches dates like '2023-06-15'.

3fill in blank

hard

Fix the error in the code to convert the 'date' column to datetime, allowing errors to be set as NaT.

Pandas

df['date'] = pd.to_datetime(df['date'], errors=[1])

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

A'raise'

B'ignore'

C'coerce'

D'skip'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' which causes the code to stop on errors.

Using 'ignore' which returns the original data without conversion.

The errors='coerce' option converts invalid parsing to NaT (Not a Time) instead of raising an error.

4fill in blank

hard

Fill both blanks to convert the 'date' column to datetime and extract the year into a new column.

Pandas

df['date'] = pd.to_datetime(df['date'], [1])
df['year'] = df['date'].[2]

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

Aerrors='coerce'

Berrors='ignore'

Cdt.year

Ddt.month

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong error handling options.

Trying to access year without using the dt accessor.

Use errors='coerce' to handle bad dates, then dt.year to get the year from datetime.

5fill in blank

hard

Fill all three blanks to convert 'date' column with a format, coerce errors, and extract month name.

Pandas

df['date'] = pd.to_datetime(df['date'], format=[1], errors=[2])
df['month_name'] = df['date'].[3]

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

A'%d-%m-%Y'

B'coerce'

Cdt.month_name()

D'ignore'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong format string for the date.

Not coercing errors causing exceptions.

Trying to get month name without using dt accessor.

Use the format '%d-%m-%Y' for day-month-year, coerce errors to NaT, then get month names with dt.month_name().