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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis 2. Up and Running with pandas FREE CHAPTER 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

Reading and writing data in Excel format

Pandas supports reading data in Excel 2003 and newer formats, using the pd.read_excel() function or via the ExcelFile class. Internally, both techniques use either the XLRD or OpenPyXL packages, so you will need to ensure that one of them is installed in your Python environment.

For demonstration, a data/stocks.xlsx file is provided with the sample data. If you open it in Excel, you will see something similar to what is shown in the following screenshot:

The workbook contains two sheets, msft and aapl, which hold the stock data for each respective stock.

The following then reads the data/stocks.xlsx file into a DataFrame:

This has read only content from the first worksheet in the Excel file (the msft worksheet), and has used the contents of the first row as column names. To read the other worksheet, you can pass the name of the worksheet...

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