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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

Time-Series Modelling

A time-series is a measurement of one or more variables over a period of time and at a specific interval. Once a time-series is captured, analysis is often performed to identify patterns in the series, in essence, determining what is happening as time goes by. This ability to analyze time-series data is essential in the modern world, be it in order to analyze financial information or to monitor exercise on a wearable device and match your exercises to goals and diet.

pandas provides extensive abilities for modeling time-series data. In this chapter, we will examine many of these capabilities, including:

  • Creating time series with specific frequencies
  • Representation of dates, time, and intervals
  • Representing a point in time with a Timestamp
  • Using a Timedelta to represent a time interval
  • Indexing using DatetimeIndex
  • Creating time-series with specific frequencies...
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