Date-based indexing and slicing in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of date-based slicing on a DataFrame

What is the output of this code snippet that slices a DataFrame by date?

Data Analysis Python

import pandas as pd

dates = pd.date_range('2023-01-01', periods=5)
data = pd.DataFrame({'value': [10, 20, 30, 40, 50]}, index=dates)
result = data['2023-01-02':'2023-01-04']
print(result)

A

            value
2023-01-02     20
2023-01-03     30
2023-01-04     40

B

            value
2023-01-01     10
2023-01-02     20
2023-01-03     30

C

            value
2023-01-03     30
2023-01-04     40
2023-01-05     50

D

Empty DataFrame
Columns: [value]
Index: []

💡 Hint

Remember that slicing with dates in pandas includes both start and end dates.

When slicing a DataFrame by date strings, pandas includes both the start and end dates in the slice. Here, the slice from '2023-01-02' to '2023-01-04' includes those three dates and their corresponding values.

❓ data_output

intermediate

Number of rows after date-based filtering

How many rows remain after filtering this DataFrame for dates in March 2023?

Data Analysis Python

import pandas as pd
import numpy as np

np.random.seed(0)
dates = pd.date_range('2023-01-01', '2023-06-30')
data = pd.DataFrame({'value': np.random.randint(1, 100, len(dates))}, index=dates)
filtered = data['2023-03']
print(len(filtered))

A 28

B 31

C 29

D 30

💡 Hint

March always has 31 days.

The slice '2023-03' selects all rows with dates in March 2023. March has 31 days, so the filtered DataFrame has 31 rows.

🔧 Debug

advanced

Identify the error in date-based slicing code

What error does this code raise when trying to slice a DataFrame by date?

Data Analysis Python

import pandas as pd

dates = pd.date_range('2023-01-01', periods=3)
data = pd.DataFrame({'value': [1, 2, 3]}, index=dates)
result = data['2023-01-04']
print(result)

A KeyError

B IndexError

C ValueError

D TypeError

💡 Hint

The date '2023-01-04' is not in the DataFrame index.

Trying to access a single date not present in the index raises a KeyError because pandas cannot find that label.

❓ visualization

advanced

Plotting data sliced by date range

Which option shows the correct plot output after slicing the DataFrame for April 2023?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

dates = pd.date_range('2023-01-01', '2023-06-30')
values = range(len(dates))
data = pd.DataFrame({'value': values}, index=dates)
slice_april = data['2023-04']
slice_april.plot()
plt.show()

A A bar plot with x-axis from 2023-01-01 to 2023-01-31

B An empty plot with no data points

C A scatter plot with x-axis from 2023-05-01 to 2023-05-31

D A line plot with x-axis from 2023-04-01 to 2023-04-30 and y-axis from 90 to 119

💡 Hint

The slice '2023-04' selects all dates in April 2023.

The slice selects dates from April 1 to April 30, which correspond to indices 90 to 119 in the original range. The plot is a line plot by default showing these values.

🧠 Conceptual

expert

Understanding partial string indexing behavior

Given a DataFrame indexed by daily dates, what happens when you slice with '2023-02'?

A It raises a KeyError because '2023-02' is not a full date.

B It returns only the row for February 1, 2023.

C It returns all rows with dates in February 2023, including all days from 1 to 28 or 29.

D It returns rows for February 2023 and March 2023.

💡 Hint

Pandas supports partial string indexing for date ranges.

When slicing with a partial date string like '2023-02', pandas returns all rows with dates starting with that string, which means all days in February 2023.