You're reading from Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839213106

Length 626 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Authors (2):

Matthew Harrison

Theodore Petrou

View More author details

Table of Contents (17) Chapters

Preface

1. Pandas Foundations

2. Essential DataFrame Operations FREE CHAPTER

3. Creating and Persisting DataFrames

4. Beginning Data Analysis

5. Exploratory Data Analysis

6. Selecting Subsets of Data

7. Filtering Rows

8. Index Alignment

9. Grouping for Aggregation, Filtration, and Transformation

10. Restructuring Data into a Tidy Form

11. Combining Pandas Objects

12. Time Series Analysis

13. Visualization with Matplotlib, Pandas, and Seaborn

14. Debugging and Testing Pandas

15. Other Books You May Enjoy

16. Index

Using Excel files

While CSV files are common, it seems that the world is ruled by Excel. I've been surprised in my consulting work to see how many companies are using Excel as a critical if not the critical tool for making decisions.

In this recipe, we will show how to create and read Excel files. You may need to install xlwt or openpyxl to write XLS or XLSX files, respectively.

How to do it...

Create an Excel file using the .to_excel method. You can write either xls files or xlsx files:
```
>>> beatles.to_excel("beat.xls")
>>> beatles.to_excel("beat.xlsx")
```
Excel file

Read the Excel file with the read_excel function:

>>> beat2 = pd.read_excel("/tmp/beat.xls")
>>> beat2
   Unnamed: 0    first       last  birth
0           0     Paul  McCartney   1942
1           1     John     Lennon   1940
2           2  Richard    Starkey   1940
3           3 ...