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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

Creating array views and copies


In the example about ravel(), views were brought up. Views should not be confused with the construct of database views. Views in the NumPy universe are not read-only and you don't have the possibility to protect the underlying information. It is crucial to know when we are handling a shared array view and when we have a replica of the array data. A slice of an array, for example, will produce a view. This entails that if you assign the slice to a variable and then alter the underlying array, the value of this variable will change. We will create an array from the face picture in the SciPy package, and then create a view and alter it at the final stage:

  1. Get the face image:

            face = scipy.misc.face() 
    
  2. Create a copy of the face array:

            acopy = face.copy() 
    
  3. Create a view of the array:

            aview = face.view() 
    
  4. Set all the values in the view to 0 with a flat iterator:

            aview.flat = 0 
    

The final outcome is that only one of the...

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