Resampling time series in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of resampling with mean aggregation

What is the output of this code snippet that resamples a time series to daily frequency and calculates the mean?

Data Analysis Python

import pandas as pd
import numpy as np

dates = pd.date_range('2024-01-01 12:00', periods=4, freq='6H')
data = pd.Series([10, 20, 30, 40], index=dates)
result = data.resample('D').mean()
print(result)

A

2024-01-01    15.0
2024-01-02    35.0
dtype: float64

B

2024-01-01    10.0
2024-01-02    20.0
dtype: float64

C

2024-01-01    25.0
2024-01-02    35.0
dtype: float64

D

2024-01-01    20.0
2024-01-02    40.0
dtype: float64

💡 Hint

Think about how resample groups data by day and then averages values within each day.

The data has 4 values at 6-hour intervals starting at noon on Jan 1. Resampling by day groups values on Jan 1 (12:00 and 18:00) and Jan 2 (00:00 and 06:00). The mean for Jan 1 is (10+20)/2=15, for Jan 2 is (30+40)/2=35.

❓ data_output

intermediate

Number of rows after upsampling with forward fill

Given a time series with hourly data for 3 hours, what is the number of rows after upsampling to 30-minute frequency with forward fill?

Data Analysis Python

import pandas as pd

dates = pd.date_range('2024-01-01 00:00', periods=3, freq='H')
data = pd.Series([1, 2, 3], index=dates)
upsampled = data.resample('30T').ffill()
print(len(upsampled))

A 7

B 5

C 4

D 6

💡 Hint

Count how many 30-minute intervals fit between the first and last timestamp inclusive.

Original data has 3 points at 00:00, 01:00, 02:00. Upsampling to 30 minutes creates timestamps at 00:00, 00:30, 01:00, 01:30, 02:00. That's 5 rows. Forward fill fills missing values.

🔧 Debug

advanced

Identify the error in resampling code

What error does this code raise when trying to resample a DataFrame without a datetime index?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({'value': [1, 2, 3]})
result = data.resample('D').sum()

A TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'

B KeyError: 'D'

C AttributeError: 'DataFrame' object has no attribute 'resample'

D ValueError: Invalid frequency string

💡 Hint

Check the type of index required for resample to work.

Resample requires the DataFrame to have a datetime-like index. Here, the index is default RangeIndex, so it raises a TypeError indicating the index type is invalid.

🚀 Application

advanced

Choosing correct resampling method for downsampling

You have minute-level temperature data and want to get the maximum temperature per hour. Which resampling method and aggregation should you use?

A resample('D').sum()

B resample('T').mean()

C resample('H').max()

D resample('H').min()

💡 Hint

Think about grouping data into hours and picking the highest temperature.

To get max temperature per hour, resample with hourly frequency ('H') and aggregate with max(). Other options either use wrong frequency or wrong aggregation.

🧠 Conceptual

expert

Effect of resampling with different label and closed parameters

When resampling time series data with 'label="right"' and 'closed="right"', how are the bins labeled and which side is included in the interval?

A Bins are labeled by the left edge and intervals include the left edge but exclude the right edge

B Bins are labeled by the left edge and intervals include both edges

C Bins are labeled by the right edge and intervals include the left edge but exclude the right edge

D Bins are labeled by the right edge and intervals include the right edge but exclude the left edge

💡 Hint

Think about how intervals are defined when closed='right' and how labels align.

With label='right' and closed='right', each bin is labeled by its right endpoint, and intervals include the right edge but exclude the left edge. This affects which data points fall into each bin.

Practice

1. What does resampling a time series data mean?

easy

A. Removing missing values

B. Sorting the data by values

C. Changing the time frequency of the data

D. Converting data to strings

Solution

1. Step 1: Understand the meaning of resampling

   Resampling means changing how often data points appear in time, like from daily to monthly.

2. Step 2: Compare options to definition

   Only Changing the time frequency of the data correctly describes changing the time frequency of data.

3. Final Answer:

   Changing the time frequency of the data -> Option C

4. Quick Check:

   Resampling = Changing frequency [OK]

Hint: Resampling changes how often data points appear [OK]

Common Mistakes:

• Thinking resampling sorts data
• Confusing resampling with cleaning data
• Believing resampling changes data type

2. Which of the following is the correct syntax to resample a pandas DataFrame df to monthly frequency and take the mean?

easy

A. df.resample('month').mean()

B. df.resample('monthly').mean()

C. df.resample('M').average()

D. df.resample('M').mean()

Solution

1. Step 1: Identify the correct frequency string

   Pandas uses 'M' for monthly frequency, not 'monthly' or 'month'.

2. Step 2: Check aggregation function name

   The correct function is mean(), not average().

3. Final Answer:

   df.resample('M').mean() -> Option D

4. Quick Check:

   Use 'M' and mean() for monthly average [OK]

Hint: Use 'M' for month and mean() to aggregate [OK]

Common Mistakes:

• Using 'monthly' or 'month' instead of 'M'
• Using average() instead of mean()
• Missing parentheses after mean

3. Given the following code, what will be the output?

import pandas as pd
idx = pd.date_range('2023-01-01', periods=4, freq='D')
data = [10, 20, 30, 40]
df = pd.DataFrame({'value': data}, index=idx)
resampled = df.resample('2D').sum()
print(resampled)

medium

A. 2023-01-01 30
2023-01-03 70

B. 2023-01-01 10
2023-01-03 30
2023-01-05 40

C. 2023-01-01 30
2023-01-03 40

D. 2023-01-01 10
2023-01-02 20
2023-01-03 30
2023-01-04 40

Solution

1. Step 1: Understand the original data and frequency

   Data has values 10,20,30,40 on consecutive days from 2023-01-01 to 2023-01-04.

2. Step 2: Resample with '2D' and sum

   Resampling groups every 2 days: (Jan 1+2) and (Jan 3+4). Sums are 10+20=30 and 30+40=70.

3. Final Answer:

   2023-01-01 30 2023-01-03 70 -> Option A

4. Quick Check:

   Sum every 2 days = 30, 70 [OK]

Hint: Sum values in 2-day groups starting from first date [OK]

Common Mistakes:

• Summing only one day instead of two
• Confusing sum with mean
• Misreading the date index

4. What is wrong with this code snippet?

df = pd.DataFrame({'value': [1, 2, 3]}, index=pd.date_range('2023-01-01', periods=3))
resampled = df.resample('W').mean

medium

A. Missing parentheses after 'mean' function call

B. Wrong frequency string 'W' for weekly resampling

C. DataFrame index is not a datetime index

D. Aggregation function 'mean' does not exist

Solution

1. Step 1: Check the resample syntax

   The code uses mean without parentheses, so it returns a method, not the result.

2. Step 2: Verify frequency and index

   'W' is correct for weekly, and the index is a datetime index, so no issues there.

3. Final Answer:

   Missing parentheses after 'mean' function call -> Option A

4. Quick Check:

   Call mean() with () to get result [OK]

Hint: Add () after mean to execute function [OK]

Common Mistakes:

• Forgetting parentheses on aggregation functions
• Thinking 'W' is invalid frequency
• Not setting datetime index before resampling

5. You have minute-level temperature data in a DataFrame df indexed by datetime. You want to resample it to hourly data but only keep hours where the average temperature is above 20°C. Which code snippet correctly does this?

hard

A. df.resample('H').mean()[lambda x: x['temperature'] > 20]

B. df.resample('H').mean().query('temperature > 20')

C. df.resample('H').mean()[df.resample('H').mean() > 20]

D. df.resample('H').mean().filter(lambda x: x > 20)

Solution

1. Step 1: Resample to hourly and calculate mean

   Use df.resample('H').mean() to get hourly averages.

2. Step 2: Filter rows where temperature > 20

   Using .query('temperature > 20') filters rows correctly by the 'temperature' column.

3. Final Answer:

   df.resample('H').mean().query('temperature > 20') -> Option B

4. Quick Check:

   Use query() to filter by column condition [OK]

Hint: Use query() after resample mean to filter rows [OK]

Common Mistakes:

• Applying boolean filter on DataFrame without column name
• Using filter() incorrectly for row filtering
• Repeating resample twice causing inefficiency