02 Variables Data Types Ipynb Colab

Leo Migdal
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02 variables data types ipynb colab

There was an error while loading. Please reload this page. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Introduction to NumPy | Contents | The Basics of NumPy Arrays > Effective data-driven science and computation requires understanding how data is stored and manipulated.

This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this. Understanding this difference is fundamental to understanding much of the material throughout the rest of the book. Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing. While a statically-typed language like C or Java requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification. For example, in C you might specify a particular operation as follows: While in Python the equivalent operation could be written this way:

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There was an error while loading. Please reload this page. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Introduction to NumPy | Contents | The Basics of NumPy Arrays > Effective data-driven science and computation requires understanding how data is stored an...

This Section Outlines And Contrasts How Arrays Of Data Are

This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this. Understanding this difference is fundamental to understanding much of the material throughout the rest of the book. Users of Python are often drawn-in by its ease of use, one piece of which is dynamic typing. While a statically-typed language like C or Java requires eac...

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