Search⌘ K
AI Features
Home
Courses
Advanced pandas—Going Beyond the Basics

Resampling—Aggregation and Other Parameters

Explore advanced resampling techniques in pandas for handling time series data. Learn to use aggregation methods, customize resampling origins, and apply offsets to align data bins precisely. Gain skills to manipulate and analyze time-indexed data with flexibility and control.

We'll cover the following...

Introduction

Now that we've learned the general use of the resample() method for upsampling and downsampling, let's explore its other capabilities, such as aggregation functions and origin definition. We’ll again use the truncated dataset version of Spain’s 2018 hourly energy demand data.

Preview of Spain 2018 Hourly Energy Demand Data

Time

Generation Fossil Gas

Generation Fossil Hard Coal

2018-01-01 00:00:00

3471

996

2018-01-01 01:00:00

3269

959

2018-01-01 02:00:00

3541

1014

2018-01-01 03:00:00

3450

1043

2018-01-01 04:00:00

3318

1063

Groupby aggregation functions

Given that resample() is essentially a time-based groupby operation coupled with an aggregation function, we can expect the common groupby functions to be applicable too. For example, the following code demonstrates some of the common aggregation functions that can be applied to each interval group for a downsampling example:

Python 3.10.4
# Mean

print('--------------- Mean ---------------')

print(df.resample(rule='3H').mean().head(3))



# Sum

print('\n--------------- Sum ---------------')

print(df.resample(rule='3H').sum().head(3))



# Standard deviation (std)

print('\n--------------- STD ---------------')

print(df.resample(rule='3H').std().head(3))



# Standard error of the mean

print('\n--------------- SEM ---------------')

print(df.resample(rule='3H').sem().head(3))



# Maximum

print('\n--------------- Max ---------------')

print(df.resample(rule='3H').max().head(3))



# Minimum

print('\n--------------- Min ---------------')

print(df.resample(rule='3H').min().head(3))



# First

print('\n--------------- First ---------------')

print(df.resample(rule='3H').first().head(3))



# Last

print('\n--------------- Last ---------------')

print(df.resample(rule='3H').last().head(3))



# Open, High, Low, Close

print('\n--------------- OHLC ---------------')

print(df.resample(rule='3H').ohlc().head(3))

While it’s common to apply a single aggregation function like mean() or ...