to_datetime() for parsing dates in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Parsing mixed date formats with to_datetime()

What is the output of this code snippet when parsing mixed date formats using pandas to_datetime()?

Pandas

import pandas as pd

dates = ['2023-01-01', '01/02/2023', 'March 3, 2023', '2023.04.04']
df = pd.DataFrame({'date_str': dates})
df['parsed'] = pd.to_datetime(df['date_str'])
print(df['parsed'].dt.month.tolist())

A [1, 2, 3, 3]

B [1, 1, 3, 4]

C [1, 2, 3, 4]

D [1, 1, 2, 4]

💡 Hint

Remember that to_datetime() can parse many common date formats automatically.

The dates are parsed correctly into datetime objects. '01/02/2023' is interpreted as January 2, 2023 by default (month/day/year). The months extracted are 1, 2, 3, and 4 respectively.

❓ data_output

intermediate

Handling errors with to_datetime()

Given this code, what will be the content of the 'parsed' column after running to_datetime() with errors='coerce'?

Pandas

import pandas as pd

dates = ['2023-01-01', 'not a date', '2023-03-15']
df = pd.DataFrame({'date_str': dates})
df['parsed'] = pd.to_datetime(df['date_str'], errors='coerce')
print(df['parsed'].tolist())

A [Timestamp('2023-01-01 00:00:00'), NaT, Timestamp('2023-03-15 00:00:00')]

B [Timestamp('2023-01-01 00:00:00'), 'not a date', Timestamp('2023-03-15 00:00:00')]

C [Timestamp('2023-01-01 00:00:00'), Timestamp('not a date'), Timestamp('2023-03-15 00:00:00')]

D [Timestamp('2023-01-01 00:00:00'), None, Timestamp('2023-03-15 00:00:00')]

💡 Hint

errors='coerce' replaces invalid parsing with NaT (Not a Time).

The invalid string 'not a date' cannot be parsed, so it becomes NaT. Valid dates are converted to Timestamps.

❓ visualization

advanced

Visualizing parsed dates by year

After parsing these date strings with to_datetime(), which bar chart correctly shows the count of dates per year?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

dates = ['2022-12-31', '2023-01-01', '2023-06-15', '2024-01-01']
df = pd.DataFrame({'date_str': dates})
df['parsed'] = pd.to_datetime(df['date_str'])
year_counts = df['parsed'].dt.year.value_counts().sort_index()
year_counts.plot(kind='bar')
plt.show()

A Bar chart with bars: 2022=1, 2023=2, 2024=1

B Bar chart with bars: 2022=2, 2023=1, 2024=1

C Bar chart with bars: 2022=1, 2023=1, 2024=2

D Bar chart with bars: 2022=0, 2023=3, 2024=1

💡 Hint

Count how many dates fall in each year after parsing.

There is one date in 2022, two in 2023, and one in 2024. The bar chart reflects these counts.

🧠 Conceptual

advanced

Understanding dayfirst parameter in to_datetime()

What is the output of this code when parsing '01/02/2023' with dayfirst=True?

Pandas

import pandas as pd

date_str = '01/02/2023'
parsed_date = pd.to_datetime(date_str, dayfirst=True)
print(parsed_date.strftime('%Y-%m-%d'))

A "2023-02-12"

B "2023-01-02"

C "2023-12-02"

D "2023-02-01"

💡 Hint

dayfirst=True means the first number is the day, not the month.

With dayfirst=True, '01/02/2023' is interpreted as 1st February 2023.

🔧 Debug

expert

Why does this to_datetime() call raise an error?

Consider this code snippet. Why does it raise a ValueError?

Pandas

import pandas as pd

dates = ['2023-01-01', '2023-02-30']
df = pd.DataFrame({'date_str': dates})
df['parsed'] = pd.to_datetime(df['date_str'], format='%Y-%m-%d')

A Because to_datetime() cannot parse strings inside a DataFrame column.

B Because '2023-02-30' is an invalid date and format='%Y-%m-%d' enforces strict parsing.

C Because the format string is incorrect for the given dates.

D Because the dates list contains strings instead of datetime objects.

💡 Hint

Check if all dates are valid calendar dates.

February 30th does not exist. Using format parameter enforces exact matching and raises ValueError on invalid dates.