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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

The statsmodels modules

statsmodels is an open source Python module that offers functionality for various statistical operations, such as central values (mean, mode, and median), dispersion measures (standard deviation and variance), correlations, and hypothesis tests.

Let's install statsmodels using pip and run the following command:

pip3 install statsmodels

statsmodels provides the statsmodels.tsa submodule for time-series operations. statsmodels.tsa provides useful time-series methods and techniques, such as autoregression, autocorrelation, partial autocorrelation, moving averages, SimpleExpSmoothing, Holt's linear, Holt-Winters, ARMA, ARIMA, vector autoregressive (VAR) models, and lots of helper functions, which we will explore in the upcoming sections.

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