Datetime type in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this pandas datetime conversion?

Given the following code, what will be the output of df['date_converted'].dtype?

Pandas

import pandas as pd

df = pd.DataFrame({'date_str': ['2023-01-01', '2023-06-15', '2023-12-31']})
df['date_converted'] = pd.to_datetime(df['date_str'])
print(df['date_converted'].dtype)

A datetime64[ns]

B object

C string

D int64

💡 Hint

Think about what type pandas uses to store datetime values internally.

When pandas converts strings to datetime, it uses the numpy datetime64[ns] type internally for efficient storage and operations.

❓ data_output

intermediate

What is the output of extracting the year from datetime?

What will be the output of df['year'] after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({'dates': pd.to_datetime(['2022-05-20', '2023-07-15', '2024-01-01'])})
df['year'] = df['dates'].dt.year
print(df['year'].tolist())

A ['2022', '2023', '2024']

B [2022, 2023, 2024]

C [5, 7, 1]

D [20, 15, 1]

💡 Hint

The dt.year extracts the year as an integer from datetime values.

The dt.year attribute extracts the year part as integers from datetime objects.

🔧 Debug

advanced

Why does this code raise an error?

This code tries to convert a column to datetime but raises an error. What is the cause?

Pandas

import pandas as pd

df = pd.DataFrame({'dates': ['2023-01-01', 'not_a_date', '2023-03-01']})
df['dates_converted'] = pd.to_datetime(df['dates'])

A ValueError because 'not_a_date' cannot be converted to datetime

B TypeError because the column is not a string type

C KeyError because 'dates_converted' column does not exist yet

D SyntaxError due to missing parentheses

💡 Hint

Check if all strings in the column are valid date formats.

The string 'not_a_date' cannot be parsed as a date, so pd.to_datetime raises a ValueError.

❓ visualization

advanced

Which plot shows the count of dates by month correctly?

Given this DataFrame, which option produces a bar plot showing counts of dates grouped by month?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'dates': pd.to_datetime(['2023-01-15', '2023-01-20', '2023-02-10', '2023-02-15', '2023-03-01'])})

A

df['month'] = df['dates'].dt.month
counts = df.groupby('month').size()
counts.plot(kind='line')
plt.show()

B

df['month'] = df['dates'].month
counts = df['month'].count()
counts.plot(kind='bar')
plt.show()

C

df['month'] = df['dates'].dt.month
counts = df['month'].value_counts()
counts.plot(kind='pie')
plt.show()

D

df['month'] = df['dates'].dt.month
counts = df['month'].value_counts().sort_index()
counts.plot(kind='bar')
plt.show()

💡 Hint

Use dt.month and count values grouped by month, then plot a bar chart.

Option D correctly extracts month, counts occurrences sorted by month, and plots a bar chart.

🧠 Conceptual

expert

What is the effect of timezone conversion on pandas datetime?

If a pandas datetime column is timezone-naive and you convert it to a timezone-aware datetime with dt.tz_localize('UTC'), what happens?

A The datetime values become strings with timezone info appended

B The datetime values are shifted to UTC time adjusting the clock time accordingly

C The datetime values are treated as UTC time without changing the clock time, adding timezone info

D The datetime values raise an error because timezone-naive cannot be localized

💡 Hint

Localizing adds timezone info without changing the actual time values.

Localizing a naive datetime assigns a timezone without changing the clock time, marking them as UTC times.

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