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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
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Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

14.3 Pickling

The read and write methods you just saw convert data to strings before writing. Complex types (such as objects and classes) cannot be written this way. With Python's module pickle, you can save any object and also multiple objects to a file.

Data can be saved in plain-text (ASCII) format or using a slightly more efficient binary format. There are two main methods: dump, which saves a pickled representation of a Python object to a file, and load, which retrieves a pickled object from the file. The basic usage is like this:

import pickle
with open('file.dat','wb') as myfile:
    a = random.rand(20,20)
    b = 'hello world'
    pickle.dump(a,myfile)    # first call: first object
    pickle.dump(b,myfile)    # second call: second object

import pickle
with open('file.dat','rb') as myfile:
    numbers = pickle.load(myfile) # restores the array
    text = pickle.load(myfile)    # restores the string

Note...

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