to_datetime() conversion in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

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

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

Ato_numeric

Bto_string

Cto_list

Dto_datetime

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_string() instead of to_datetime()

Trying to convert with to_numeric() which is for numbers

The pd.to_datetime() function converts a column to datetime format.

2fill in blank

medium

Complete the code to convert the 'date' column with a specific format 'YYYY-MM-DD'.

Data Analysis Python

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

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

A'%d/%m/%Y'

B'%Y-%m-%d'

C'%m-%d-%Y'

D'%Y/%m/%d'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong order like day before month

Using slashes instead of 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 'date' column to datetime, allowing errors to become NaT.

Data Analysis Python

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

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

A'fail'

B'ignore'

C'coerce'

D'raise'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' which stops on errors

Using 'ignore' which leaves original data unchanged

The errors='coerce' option converts invalid parsing to NaT (missing datetime).

4fill in blank

hard

Fill both blanks to convert 'date' column to datetime and set dayfirst to True.

Data Analysis Python

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

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

Adayfirst

BTrue

CFalse

Dformat

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'format' instead of 'dayfirst'

Setting dayfirst to False when day comes first

Setting dayfirst=True tells pandas to interpret the day before the month.

5fill in blank

hard

Fill all three blanks to convert 'date' column to datetime with format '%d-%m-%Y', coerce errors, and set utc to True.

Data Analysis Python

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

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

A'%Y/%m/%d'

B'coerce'

CTrue

D'%d-%m-%Y'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong format string

Not setting errors='coerce' causing exceptions

Forgetting to set utc=True when needed

This code converts dates with day-month-year format, replaces errors with NaT, and sets timezone to UTC.

Practice

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

easy

A. Calculates the difference between two dates

B. Converts datetime objects into strings

C. Converts strings or numbers into datetime objects

D. Sorts dates in ascending order

Solution

1. Step 1: Understand the function purpose

   pd.to_datetime() is used to convert text or numbers that represent dates into pandas datetime objects.

2. Step 2: Identify the correct description

   Among the options, only converting strings or numbers into datetime objects matches the function's purpose.

3. Final Answer:

   Converts strings or numbers into datetime objects -> Option C

4. Quick Check:

   to_datetime() = Converts to datetime [OK]

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

Common Mistakes:

• Thinking it converts dates back to strings
• Confusing it with date difference calculation
• Assuming it sorts dates automatically

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

easy

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

B. df.to_datetime('date_str')

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

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

Solution

1. Step 1: Recall correct function usage

   The correct way is to call pd.to_datetime() on the column and assign it back to the DataFrame column.

2. Step 2: Check each option

   df['date_str'] = pd.to_datetime(df['date_str']) correctly uses pd.to_datetime(df['date_str']) and assigns it back. Others misuse syntax or methods.

3. Final Answer:

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

4. Quick Check:

   Assign converted column back = df['date_str'] = pd.to_datetime(df['date_str']) [OK]

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

Common Mistakes:

• Calling to_datetime() directly on DataFrame
• Using inplace=true which is invalid here
• Trying to call to_datetime() as a method on Series

3. What will be the output of this code?

import pandas as pd
dates = ['12/11/2023', '01/02/2024']
df = pd.DataFrame({'date': dates})
df['date'] = pd.to_datetime(df['date'], dayfirst=True)
print(df['date'])

medium

A. [Timestamp('2023-12-11'), Timestamp('2024-02-01')]

B. [Timestamp('2023-12-11'), Timestamp('2024-01-02')]

C. [Timestamp('2023-11-12'), Timestamp('2024-01-02')]

D. [Timestamp('2023-11-12'), Timestamp('2024-02-01')]

Solution

1. Step 1: Understand dayfirst=True effect

   With dayfirst=True, the date strings are parsed as day/month/year.

2. Step 2: Convert each date string

   '12/11/2023' becomes 12 Nov 2023 (2023-11-12), '01/02/2024' becomes 1 Feb 2024 (2024-02-01).

3. Final Answer:

   [Timestamp('2023-11-12'), Timestamp('2024-02-01')] -> Option D

4. Quick Check:

   dayfirst=True swaps day and month [OK]

Hint: dayfirst=True means day/month/year format [OK]

Common Mistakes:

• Ignoring dayfirst and assuming month/day/year
• Mixing up which part is day or month
• Confusing output format with input string

4. Identify the error in this code snippet:

import pandas as pd
dates = ['2023-31-12', '2024-01-15']
df = pd.DataFrame({'date': dates})
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')

medium

A. The date format string does not match the input dates

B. pd.to_datetime() cannot convert strings with dashes

C. DataFrame creation syntax is incorrect

D. Missing dayfirst=True parameter

Solution

1. Step 1: Check input date format

   The dates are '2023-31-12' and '2024-01-15', which look like year-day-month format.

2. Step 2: Compare with format string

   The format string '%Y-%m-%d' expects year-month-day, so it does not match the input dates.

3. Final Answer:

   The date format string does not match the input dates -> Option A

4. Quick Check:

   Format string must match input date pattern [OK]

Hint: Match format string exactly to date string pattern [OK]

Common Mistakes:

• Assuming default format works for all date strings
• Using wrong format codes for day and month
• Ignoring error messages about parsing failure

5. You have a DataFrame with a column 'date_str' containing dates in mixed formats like '2023/12/31', '31-01-2024', and 'Feb 15, 2024'. How can you convert this column to datetime correctly?

hard

A. Manually split and parse each date format before conversion

B. Use pd.to_datetime(df['date_str'], errors='coerce') without format to parse mixed formats

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

D. Use pd.to_datetime(df['date_str'], format='%Y/%m/%d') to parse all dates

Solution

1. Step 1: Understand mixed date formats challenge

   Mixed formats cannot be parsed with a single fixed format string.

2. Step 2: Use flexible parsing with errors='coerce'

   Calling pd.to_datetime() without a format lets pandas try multiple formats. Using errors='coerce' converts unparseable dates to NaT safely.

3. Step 3: Avoid rigid format or manual splitting

   Using a fixed format or manual splitting is inefficient and error-prone.

4. Final Answer:

   Use pd.to_datetime(df['date_str'], errors='coerce') without format to parse mixed formats -> Option B

5. Quick Check:

   Flexible parsing with errors='coerce' handles mixed dates [OK]

Hint: Omit format and use errors='coerce' for mixed date formats [OK]

Common Mistakes:

• Forcing a single format on mixed date strings
• Using astype('datetime64') which fails on strings
• Trying to manually parse each format separately