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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
Author Profile Icon Olivier Verdier
Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
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

14.2.3 loadtxt

Reading to an array from a text file is done with the help of the following syntax:

filename = 'test.txt'
data = loadtxt(filename)

Due to the fact that each row in an array must have the same length, each row in the text file must have the same number of elements. Similar to savetxt, the default values are float and the delimiter is a space. These can be set using the parameters dtype and delimiter. Another useful parameter is comments, which can be used to mark what symbol is used for comments in the data file. An example of using the formatting parameters is as follows:

data = loadtxt('test.txt',delimiter=';')    # data separated by semicolons

# read to integer type, comments in file begin with a hash character
data = loadtxt('test.txt',dtype=int,comments='#')
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