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Learning Pandas

You're reading from   Learning Pandas Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781783985128
Length 504 pages
Edition 1st 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 (14) Chapters Close

Preface 1. A Tour of pandas 2. Installing pandas FREE CHAPTER 3. NumPy for pandas 4. The pandas Series Object 5. The pandas DataFrame Object 6. Accessing Data 7. Tidying Up Your Data 8. Combining and Reshaping Data 9. Grouping and Aggregating Data 10. Time-series Data 11. Visualization 12. Applications to Finance Index

Example data


Where possible, the examples in this chapter will utilize several datasets provided with the code in the download for the text. These datasets make the examples a little less academic in nature. These datasets will be read from files using the pd.read_csv() function that will load the sample data from the file into a DataFrame object.

Note

pd.read_csv() will be more extensively examined in Chapter 6, Accessing Data. For now, its function is simply to load some example DataFrame objects.

The remainder of the samples will still utilize the DataFrame objects created on demand if they demonstrate the concept being examined in a better way. Let's see the brief descriptions of these datasets.

S&P 500

The first dataset we will use is a snapshot of the S&P 500 from Yahoo! Finance. The following code shows the first three lines of the file:

In [13]:
   # show the first three lines of the file
   !head -n 3 data/sp500.csv # on Mac or Linux
   # !type data\sp500.csv # on Windows, but...
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