Five NumPy Functions a beginner should know about๐Ÿ๐ŸŒŸ

๐Ÿงต๐Ÿ‘‡
1โƒฃ ๐—ป๐—ฝ.๐—ฎ๐—ฟ๐—ฟ๐—ฎ๐˜†

In Numpy, we create arrays using the "array" function.

Numpy gives the power to create multi-dimensional arrays.

๐Ÿ”— Code
2โƒฃ ๐—ป๐—ฝ.๐˜€๐—ต๐—ฎ๐—ฝ๐—ฒ

The shape of an array is the number of elements in each dimension.

NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.

๐Ÿ”— Code
3โƒฃ ๐—ป๐—ฝ.๐—ฟ๐—ฒ๐˜€๐—ต๐—ฎ๐—ฝ๐—ฒ

reshape is used to change the shape of the NumPy array without changing the elements of the array.

reshape is one the best function of NumPy, it helps a lot in calculations.

๐Ÿ”— Code
4โƒฃ ๐—ป๐—ฝ.๐—ฎ๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ

This mathematical function helps the user to evenly round array elements to given number of decimals

numpy.around(arr, decimals = 0, out = None)

decimal : [int, optional] Decimal places we want to round off

out : [optional] Output resulted array

๐Ÿ”— Code
5โƒฃ ๐—ป๐—ฝ.๐˜๐—ฟ๐—ฎ๐—ป๐˜€๐—ฝ๐—ผ๐˜€๐—ฒ

Remember, in your maths class, to find the transpose of a matrix you have to solve long calculations.

In NumPy, it can be done only in a single line using transpose.

It has no effect on a 1-D array!

๐Ÿ”— Code
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Perfect Explanation๐Ÿ‘‡
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A BIG Thread ๐Ÿงต๐Ÿ‘‡
We are living in an era, where object detection is used everywhere.

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๐Ÿ”— Code Image
2โƒฃ ๐—ฝ๐—ผ๐˜„

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2โƒฃ ๐—ž๐—ฒ๐—ฟ๐—ฎ๐˜€

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