World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Internship Partner
In Association with
In collaboration with
SIGN IN to your Account
New to InsideAIML? Create an account
Employer? Create an account
World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Internship Partner
In Association with
In collaboration with
Forgot Your Password?
Enter your email below and we will send a message to reset your password
World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Internship Partner
In Association with
In collaboration with
Sign Up and Start Learning!
By providing your contact details, you agree to our Terms of Use & Privacy Policy.
Already have an account? Sign In
World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Internship Partner
In Association with
In collaboration with
Sign Up and Start Learning!
By providing your contact details, you agree to our Terms of Use & Privacy Policy.
Already have an account? Sign In
Download our e-book of Introduction To Python
Related Blog
Matplotlib - Working with ImagesPython - Chunks and ChinksMatplotlib - MultiplotsPython Forensics - Memory and ForensicsPython Blockchain - Scope and ConclusionMatplotlib - Object-oriented InterfaceMicrosoft Cognitive Toolkit (CNTK) - CPU and GPUDiscuss Microsoft Cognitive ToolkitCNTK - Creating First Neural NetworkMatplotlib - Three-dimensional Plotting View More
Top Discussion
How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) Create a function that takes in a list of unsorted prices and a maximum possible price value, and return a sorted list of prices What is caching and why it is used in data science? Does Google Colab permit inconsistency in indentation on the initial lines? How to check If Two String Arrays are Equivalent or not? How to leave/exit/deactivate a Python virtualenvironment How to setting up a SQLite database for data storage? Join Discussion
Top Courses
Master's In Artificial Intelligence Program in Collaboration With IBM
4.5 (1,292 Ratings)
589 Learners
Webinars
How Machines Get Trained with Machine Learning
Oct 4th (8:00 PM) 514 Registered
Python - Data Aggregation
Neha Kumawat
2 years ago
Table of Contents
- Introduction
- Applying Aggregations on DataFrame
1. Apply Aggregation on a Whole Dataframe
2. Apply Aggregation on a Single Column of a Dataframe
3. Apply Aggregation on Multiple Columns of a DataFrame
Introduction
In this article, we will try to learn about how we can perform some data aggregation tasks on our dataset to get some useful insights from it.
Python has several methods are available to perform aggregations on data. It is done using the pandas and numpy libraries. The data must be available or converted to
a dataframe to apply the aggregation functions.
Applying Aggregations on DataFrame
Let us create a DataFrame and apply aggregations on it.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print(df)
r = df.rolling(window=3,min_periods=1)
print(r)
Its output is as follows −
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 0.790670 -0.387854 -0.668132 0.267283
2000-01-03 -0.575523 -0.965025 0.060427 -2.179780
2000-01-04 1.669653 1.211759 -0.254695 1.429166
2000-01-05 0.100568 -0.236184 0.491646 -0.466081
2000-01-06 0.155172 0.992975 -1.205134 0.320958
2000-01-07 0.309468 -0.724053 -1.412446 0.627919
2000-01-08 0.099489 -1.028040 0.163206 -1.274331
2000-01-09 1.639500 -0.068443 0.714008 -0.565969
2000-01-10 0.326761 1.479841 0.664282 -1.361169
Rolling [window=3,min_periods=1,center=false,axis=0]
We can aggregate by passing a function to the entire DataFrame, or select a column via the standard get item method.
1. Apply Aggregation on a Whole Dataframe
We can apply aggregation on the whole on which we want to aggregate the data. It can be done as shown below-
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print(df)
r = df.rolling(window=3,min_periods=1)
print(r.aggregate(np.sum))
Its output is as follows −
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
2. Apply Aggregation on a Single Column of a Dataframe
We can also apply aggregation on a particular column on which we want to aggregate the data. It can be done as shown below-
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print(df)
r = df.rolling(window=3,min_periods=1)
print(r['A'].aggregate(np.sum))
Its output is as follows −
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
2000-01-01 1.088512
2000-01-02 1.879182
2000-01-03 1.303660
2000-01-04 1.884801
2000-01-05 1.194699
2000-01-06 1.925393
2000-01-07 0.565208
2000-01-08 0.564129
2000-01-09 2.048458
2000-01-10 2.065750
Freq: D, Name: A, dtype: float64
3. Apply Aggregation on Multiple Columns of a DataFrame
We can also apply aggregation on multiple columns on which we want to aggregate the data. It can be done as shown below-
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print(df)
r = df.rolling(window=3,min_periods=1)
print(r[['A','B']].aggregate(np.sum))
Its output is as follows −
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B
2000-01-01 1.088512 -0.650942
2000-01-02 1.879182 -1.038796
2000-01-03 1.303660 -2.003821
2000-01-04 1.884801 -0.141119
2000-01-05 1.194699 0.010551
2000-01-06 1.925393 1.968551
2000-01-07 0.565208 0.032738
2000-01-08 0.564129 -0.759118
2000-01-09 2.048458 -1.820537
2000-01-10 2.065750 0.383357
So that's how we can apply some of the basic aggregations on our datasets as we want.
I hope you enjoyed reading this article and finally, you came
to know about Python - Data Aggregation.
For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…