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.