Resampling time series in Data Analysis Python - Step-by-Step Execution

Concept Flow - Resampling time series

Start with time series data

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Choose new frequency

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Apply resampling method

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Aggregate or interpolate values

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Get resampled time series

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End

Resampling changes the time intervals of data by grouping or interpolating values to a new frequency.

Execution Sample

Data Analysis Python

import pandas as pd

dates = pd.date_range('2024-01-01', periods=6, freq='D')
data = pd.Series([10, 20, 15, 30, 25, 40], index=dates)

resampled = data.resample('2D').mean()
print(resampled)

This code resamples daily data into 2-day intervals by averaging values.

Execution Table

Step	Action	Input Data	Resample Frequency	Aggregation	Output
1	Create daily time series	[10,20,15,30,25,40]	N/A	N/A	2024-01-01 to 2024-01-06 daily data
2	Choose frequency	Daily data	2D	N/A	Prepare to group every 2 days
3	Group data by 2-day bins	Daily data	2D	mean	Groups: [Jan 1-2], [Jan 3-4], [Jan 5-6]
4	Calculate mean per group	Groups	2D	mean	[ (10+20)/2=15, (15+30)/2=22.5, (25+40)/2=32.5 ]
5	Create resampled series	Means	2D	mean	2024-01-01:15, 2024-01-03:22.5, 2024-01-05:32.5
6	Print result	Resampled series	2D	mean	Output displayed
7	End	N/A	N/A	N/A	Resampling complete

💡 All original data grouped and averaged into 2-day intervals, resampling finished.

Variable Tracker

Variable	Start	After Step 1	After Step 3	After Step 4	Final
dates	empty	DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-06'])	same	same	same
data	empty	Series with 6 daily values	same	same	same
resampled	empty	empty	empty	Series with means per 2 days	Series with means per 2 days

Key Moments - 3 Insights

Why does the resampled series have fewer data points than the original?

Because resampling groups the original data into larger time bins (2 days here), so multiple original points combine into one aggregated value, as shown in execution_table step 4.

What happens if the original data has missing dates when resampling?

Resampling still creates bins for the new frequency. Missing dates result in NaN values in those bins unless an aggregation method handles them, as implied in step 3 grouping.

Why do we use mean() in resampling?

Mean averages all values in each time bin to summarize data. Other methods like sum() or max() can be used depending on the goal, as shown in step 4 aggregation.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what is the mean value for the group Jan 3-4?

A 15

B 25

C 22.5

D 32.5

💡 Hint

Check the 'Output' column at step 4 in execution_table for the calculated means.

At which step does the code group the original data into 2-day bins?

A Step 3

B Step 2

C Step 5

D Step 6

💡 Hint

Look at the 'Action' column in execution_table to find when grouping happens.

If we change the resample frequency to '3D', how many output points will the resampled series have?

A 4

B 3

C 2

D 6

💡 Hint

Refer to variable_tracker and execution_table to understand how grouping reduces points based on frequency.

Concept Snapshot

Resampling time series changes data frequency.
Use pandas.Series.resample(new_freq) to group data.
Apply aggregation like mean(), sum(), or interpolate.
New series has data at new intervals.
Useful for summarizing or filling missing data.

Full Transcript

Resampling time series means changing the time intervals of your data. You start with your original data indexed by time. Then you pick a new frequency, like every 2 days instead of daily. The data is grouped into these new time bins. Next, you apply an aggregation method such as mean to combine values in each bin. The result is a new time series with fewer or more points depending on the frequency. This process helps summarize data or fill gaps. The example code shows daily data resampled to 2-day averages. The execution table traces each step from creating data to printing the resampled output. Variables like dates, data, and resampled values change as the code runs. Key moments clarify why resampled data has fewer points and how missing dates affect results. The quiz tests understanding of grouping, aggregation, and frequency effects. The snapshot summarizes the main points for quick review.