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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 4. Statistics and Linear Algebra

Statistics and linear algebra lay the foundational ground for exploratory data analysis. Both of the main statistical methodologies, descriptive and inferential, are useful in gaining insights and making inferences from raw data. For instance, we can compute that the data for a variable has a certain arithmetic mean and standard deviation. From these numbers, we can then infer a range and the expected value for this variable. Then, we can run statistical tests to check how likely it is that we reached the right conclusion.

Linear algebra concerns itself with systems of linear equations. These are easy to solve with NumPy and SciPy using the linalg package. Linear algebra is useful, for instance, to fit data to a model. We shall introduce other NumPy and SciPy packages in this chapter for random number generation and masked arrays.

In this chapter, we will cover the following topics:

  • Basic descriptive statistics with NumPy
  • Linera algebra with...

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