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Python Data Analysis - Third Edition

You're reading from  Python Data Analysis - Third Edition

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Pages 478 pages
Edition 3rd Edition
Languages
Authors (2):
Avinash Navlani Avinash Navlani
Profile icon Avinash Navlani
Ivan Idris Ivan Idris
Profile icon Ivan Idris
View More author details
Toc

Table of Contents (20) Chapters close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Creating pivot tables

A pivot table is a summary table. It is the most popular concept in Excel. Most data analysts use it as a handy tool to summarize theire results. pandas offers the pivot_table() function to summarize DataFrames. A DataFrame is summarized using an aggregate function, such as mean, min, max, or sum. You can download the dataset from the following GitHub link: https://github.com/PacktPublishing/Python-Data-Analysis-Third-Edition/tree/master/Python-Data-Analysis-Third-Edition/Ch2:

# Import pandas
import pandas as pd

# Load data using read_csv()
purchase = pd.read_csv("purchase.csv")

# Show initial 10 records
purchase.head(10)

This results in the following output:

In the preceding code block, we have read the purchase.csv file using the read_csv() method.

Now, we will summarize the dataframe using the following code:

# Summarise dataframe using pivot table
pd.pivot_table(purchase,values='Number', index=['Weather',],
columns...
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