Resampling time series data in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is resampling in time series data?

Resampling means changing the frequency of your time series data. You can make data points more spread out (downsampling) or more frequent (upsampling).

beginner

What does df.resample('M').mean() do in pandas?

It groups the data by each month ('M') and calculates the average value for each month.

beginner

What is the difference between downsampling and upsampling?

Downsampling reduces data frequency (e.g., daily to monthly). Upsampling increases frequency (e.g., monthly to daily) and may need filling missing values.

intermediate

Why do we need to use a method like ffill() or bfill() after upsampling?

Because upsampling creates new time points with missing values. Forward fill (ffill()) or backward fill (bfill()) fills those gaps with nearby data.

beginner

What does the string 'W' mean in df.resample('W')?

'W' means resampling the data by week. It groups data into weekly bins.

Which pandas function is used to change the frequency of time series data?

A resample()

B groupby()

C pivot()

D merge()

The resample() function changes the frequency of time series data.

What happens when you downsample time series data?

A You delete the data

B You increase the number of data points

C You keep the same number of data points

D You reduce the number of data points

Downsampling reduces the number of data points by grouping them into larger time bins.

After upsampling, which method can fill missing values by copying the last known value forward?

A mean()

B ffill()

C dropna()

D sum()

ffill() fills missing values by carrying forward the last valid observation.

What does the frequency string 'D' stand for in pandas resampling?

A Daily

B Decade

C Data

D Downsample

'D' means daily frequency in pandas resampling.

Which aggregation function would you use to get the total sum after resampling?

A mean()

B count()

C sum()

D max()

sum() adds all values in each resampled group.

Explain how to resample a daily time series to monthly frequency and calculate the average for each month using pandas.

Think about grouping data by month and then averaging.

Describe the difference between downsampling and upsampling in time series data and why filling missing values is important after upsampling.

Consider what happens to data points when changing frequency.

Practice

1. What does resampling a time series in pandas usually do?

easy

A. Sort the data by values in ascending order

B. Remove all missing values from the dataset

C. Change the time frequency of the data to summarize or expand it

D. Convert the data into a different file format

Solution

1. Step 1: Understand the purpose of resampling

   Resampling changes how often data points appear in time, either by grouping or expanding.

2. Step 2: Identify what resampling affects

   It changes the time frequency, not sorting or file formats.

3. Final Answer:

   Change the time frequency of the data to summarize or expand it -> Option C

4. Quick Check:

   Resampling = Change frequency [OK]

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

Common Mistakes:

• Confusing resampling with sorting data
• Thinking resampling removes missing values automatically
• Believing resampling changes file formats

2. Which of the following is the correct syntax to downsample a pandas DataFrame df with a datetime index to monthly frequency and calculate the mean?

easy

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

B. df.resample('M').sum()

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

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

Solution

1. Step 1: Identify the correct resample frequency for monthly data

   'M' stands for month-end frequency in pandas resampling.

2. Step 2: Choose the correct aggregation function

   To calculate the mean after downsampling, use .mean().

3. Final Answer:

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

4. Quick Check:

   Monthly mean = df.resample('M').mean() [OK]

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

Common Mistakes:

• Using 'D' instead of 'M' for monthly frequency
• Using sum() when mean() is asked
• Calling fillna() without upsampling

3. Given the following code, what will be the output of print(resampled)?

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()

medium

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

B. value
2023-01-01 10
2023-01-02 20
2023-01-03 30
2023-01-04 40

C. value
2023-01-01 15
2023-01-03 35

D. {'2023-01-01': 30, '2023-01-03': 70}

Solution

1. Step 1: Understand resampling with '2D' and sum aggregation

   Resampling with '2D' groups data every 2 days and sums values in each group.

2. Step 2: Calculate sums for each 2-day period

   First period: 2023-01-01 + 2023-01-02 = 10 + 20 = 30; Second period: 2023-01-03 + 2023-01-04 = 30 + 40 = 70.

3. Final Answer:

   value 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 after resampling [OK]

Common Mistakes:

• Confusing sum with mean aggregation
• Expecting original daily data instead of grouped
• Misreading the index labels after resampling

4. What is wrong with this code snippet for upsampling daily data to hourly data?

df = pd.DataFrame({'value': [1, 2]}, index=pd.date_range('2023-01-01', periods=2, freq='D'))
upsampled = df.resample('H').mean()

medium

A. The frequency string 'H' is invalid for resampling

B. Upsampling without filling missing values results in NaNs

C. Using mean() is not allowed after resampling

D. The DataFrame index must be integers, not datetime

Solution

1. Step 1: Identify the effect of upsampling daily to hourly

   Upsampling creates new rows for each hour, but original data has only daily points.

2. Step 2: Recognize missing values after upsampling

   New hourly rows have no data, so mean() returns NaN unless filled.

3. Final Answer:

   Upsampling without filling missing values results in NaNs -> Option B

4. Quick Check:

   Upsampling needs fillna() to avoid NaNs [OK]

Hint: Upsampling creates gaps; fill missing values to avoid NaNs [OK]

Common Mistakes:

• Thinking 'H' frequency is invalid
• Believing mean() cannot be used after resampling
• Ignoring datetime index requirement

5. You have minute-level temperature data in a DataFrame df with a datetime index. You want to create a new DataFrame with hourly average temperatures but only for hours where at least 45 minutes of data exist. Which approach correctly achieves this?

hard

A. df.resample('H').mean().dropna()

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

C. df.resample('H').sum().where(lambda x: x >= 45).dropna()

D. df.resample('H').apply(lambda x: x.mean() if len(x) >= 45 else None).dropna()

Solution

1. Step 1: Understand the requirement for minimum data points per hour

   We want hourly averages only if 45 or more minutes of data exist (i.e., at least 45 rows per hour).

2. Step 2: Use a custom function with resample and apply

   Using apply with a lambda checks the length of each group and calculates mean only if condition met; else returns None.

3. Step 3: Remove hours with insufficient data

   Calling dropna() removes hours where the condition failed (null values).

4. Final Answer:

   df.resample('H').apply(lambda x: x.mean() if len(x) >= 45 else None).dropna() -> Option D

5. Quick Check:

   Custom apply filters groups by size before averaging [OK]

Hint: Use apply with condition on group size, then dropna() [OK]

Common Mistakes:

• Using mean() without checking group size
• Using sum() instead of mean() for temperature
• Dropping NaNs without filtering groups first