Resampling time series data in Pandas - Step-by-Step Execution

Concept Flow - Resampling time series data

Start with time series data

↓

Choose resampling frequency

↓

Apply resample() method

↓

Aggregate data (mean, sum, etc.)

↓

Get new resampled time series

↓

End

We start with time series data, pick a new time interval, resample using pandas, aggregate values, and get a new summarized time series.

Execution Sample

Pandas

import pandas as pd

# Create sample data
idx = pd.date_range('2024-01-01', periods=6, freq='H')
data = pd.Series([10, 20, 15, 30, 25, 40], index=idx)

# Resample hourly data to 3-hour intervals
resampled = data.resample('3H').mean()
print(resampled)

This code creates hourly data and resamples it to 3-hour intervals by averaging values.

Execution Table

Step	Action	Input Data	Resample Frequency	Aggregation	Output Data
1	Create hourly time series	[10,20,15,30,25,40]	Hourly (H)	None	2024-01-01 00:00:00 -> 10 2024-01-01 01:00:00 -> 20 2024-01-01 02:00:00 -> 15 2024-01-01 03:00:00 -> 30 2024-01-01 04:00:00 -> 25 2024-01-01 05:00:00 -> 40
2	Choose resample frequency	Hourly data	3 Hours (3H)	None	Preparing to group data into 3-hour bins
3	Group data into 3-hour bins	Hourly data	3H	None	Bin 1: 00:00-02:59 -> [10,20,15] Bin 2: 03:00-05:59 -> [30,25,40]
4	Aggregate each bin by mean	Bins	3H	Mean	Bin 1 mean: (10+20+15)/3 = 15 Bin 2 mean: (30+25+40)/3 = 31.67
5	Create new resampled series	Aggregated means	3H	Mean	2024-01-01 00:00:00 -> 15 2024-01-01 03:00:00 -> 31.67
6	Print resampled data	Resampled series	3H	Mean	Output: 2024-01-01 00:00:00 15.00 2024-01-01 03:00:00 31.67 Freq: 3H, dtype: float64

💡 All original data grouped and aggregated into 3-hour intervals, resampling complete.

Variable Tracker

Variable	Start	After Step 1	After Step 3	After Step 4	Final
data	None	[10,20,15,30,25,40] hourly indexed	[[10,20,15],[30,25,40]] grouped by 3H	[15, 31.67] means of groups	[15, 31.67] resampled series
resampled	None	None	None	None	[15.0, 31.67] with 3H freq

Key Moments - 3 Insights

Why does the resampled series have fewer rows than the original?

Because resampling groups multiple original time points into larger intervals (3 hours here), so fewer aggregated points appear as shown in execution_table rows 3 and 5.

What happens if we use sum instead of mean for aggregation?

The grouped values would be added instead of averaged, changing the output values in step 4 and 5 accordingly.

Why is the new index at 00:00 and 03:00 after resampling?

Because pandas labels each resampled bin by the start time of the interval, as shown in execution_table row 5.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what is the mean of the first 3-hour bin?

A 31.67

B 15

C 20

D 10

💡 Hint

Check the 'Output Data' column at step 4 for the first bin's mean.

At which step does the data get grouped into 3-hour bins?

A Step 4

B Step 2

C Step 3

D Step 5

💡 Hint

Look for the step describing grouping in the 'Action' column.

If we changed the resample frequency to '2H', how would the number of output rows change?

A More rows than with '3H'

B Fewer rows than with '3H'

C Same number of rows

D No rows

💡 Hint

Refer to variable_tracker 'data' row showing grouping by frequency.

Concept Snapshot

Resampling time series data with pandas:
- Use .resample('freq') on a time-indexed series or DataFrame
- 'freq' is new time interval (e.g., '3H' for 3 hours)
- Aggregate grouped data with mean(), sum(), etc.
- Result is a new time series with fewer or more points
- Index labels are interval start times

Full Transcript

This visual execution shows how pandas resamples time series data. We start with hourly data points. We pick a new frequency, here 3 hours. The data is grouped into 3-hour bins. Each bin's values are aggregated by mean. The output is a new series with fewer points, each representing the average over 3 hours. The index labels are the start times of each 3-hour interval. This process helps summarize or change the time scale of data easily.

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