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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
4 (4)
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (16 chapters)
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13
A. Key Concepts
15
C. Online Resources

Chapter 2. NumPy Arrays

Now that we have worked on a real example utilizing the foundational data analysis libraries from SciPy stack, it's time to learn about NumPy arrays. This chapter acquaints you with the fundamentals of NumPy arrays. At the end of this chapter, you will have a basic understanding of NumPy arrays and related functions.

The topics we will address in this chapter are as follows:

  • The NumPy array object
  • Creating a multidimensional array
  • Selecting NumPy array elements
  • NumPy numerical types
  • One-dimensional slicing and indexing
  • Manipulating array shapes
  • Creating array views and copies
  • Fancy indexing
  • Indexing with a list of locations
  • Indexing NumPy arrays with Booleans
  • Broadcasting NumPy arrays

You may want to open the ch-02.ipynb file in Jupyter Notebook to follow along the examples in this chapter or type them in a new notebook of your own.

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