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# Python Numpy tutorial

NumPy stands for Numerical Python.
NumPy is used for working for arrays.
Numpy are faster than lists because numpy arrays are stored at one continuous place in a memory.

## Install NumPy

pip install numpy

import numpy

## Create an array

import numpy as np
l=[1, 2, 3]
x=np.array(l)
print(x)

## Array dimensions

1. 0-D array
2. 1-D array
3. 2-D array
import numpy as np
x=np.array(0)
print(type(x))

y=np.array([1, 2, 3])
print(type(y))

z=np.array([[1, 2],[3, 4]])
print(type(z))

ndim

## Array indexing

import numpy as np

x=np.array([1, 2, 3, 4, 5])
print(x[0])

y=np.array([ [1, 2, 3], [4, 5, 6]])
print(y[1, 0 ])

## Slicing in 2-D

y=np.array([ [1, 2, 3], [4, 5, 6]])
print(y[1, 0:2 ])

## Shape of an array

Number of elements in each dimension
array.shape

## Reshape an array

Reshape means changing dimension of array.
import numpy as np
x=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print(x.reshape(2, 5))

## Joining Numpy arrays

Concat more than one arrays together.
concatenate(arr1, arr2)
import numpy as np
x=np.array([1,2,3])
y=np.array([4,5,6])
z=np.concatenate((x,y))
print(z)

## Splitting numpy arrays

Break a single array into multiple arrays.
array_split(arr, number_of_array)
import numpy as np
x = np.array([1, 2, 3, 4, 5, 6])
y = np.array_split(x, 2)
print(y)

## Methods

 dot() Return product of two array import numpy as np x=np.array([ [1,2], [3,4]]) y=np.array([ [1,2], [3,4] ]) print(x.dot(y)) max Return max value from an array import numpy as np x=np.array([ [1,2], [3,4]]) print(x.max()) min Return minimum value fron an array import numpy as np x=np.array([ [1,2], [3,4]]) print(x.min()) prod Return product of elements of given axis. import numpy as np x=np.array([ [1,2], [3,4]]) print(np.prod( x, axis=1 )) sum() Return sum of elements of given array import numpy as np x=np.array([ [1,2], [3,4]]) print(np.sum( x))