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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 FREE CHAPTER 2. Up and Running with pandas 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

Introducing the pandas Series and DataFrame

Let's jump into using some pandas with a brief introduction to pandas two main data structures, the Series and the DataFrame. We will examine the following:

  • Importing pandas into your application
  • Creating and manipulating a pandas Series
  • Creating and manipulating a pandas DataFrame
  • Loading data from a file into a DataFrame

Importing pandas

Every notebook we will use starts by importing pandas and several other useful Python libraries first. It will also set up several options to control how pandas renders output in a Jupyter Notebook. This code consists of the following:

The first statement imports NumPy and refers to items in the library as np.. We won't go into much...

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