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## Working same as np.dot()
np.matmul([[2,3],[3,4]],[[1,2],[5,6]])

#[Output]:
#array([[17, 22],
#       [23, 30]])


## Here matmul will automatically brodcast if dimensiona are not same
np.matmul([[1, 0], [0, 1]],[1,2])

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##importing libraries
import numpy.linalg as lnp
import numpy as np

## dot product for simple numbers.
np.dot(3,4)

#[Output]:
#12

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## Creating a new array.
e = np.array([21,22,23])

## Creating a new array by using copy function.
f = e.copy()

## Checking the ids of both arrays
#3 In this case also both will have different idsIn [13]:

print(id(e))

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## Creating a new array
c = np.array([11,12,13])

## Creating another array using view function
d = c.view()

## Checking the value of "d"
print(d)

#[Output]:

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## Importing numpy library for creating numpy
import numpy as np

## Creating a 1-D array
a = np.array([0,2,1])
print(a)

#[Output]:
#array([0, 2, 1])

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## Searching

import numpy as np

## 1. argmax()
a = np.arange(6).reshape(2,3)
print(a)

#[Output]:
#[[0 1 2]

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import numpy as np

## 1. sort()
## Sorting along flattened array
a = np.array([[5,4],[3,1]])
np.sort(a)

#[Output]:
#array([[4, 5],
#       [1, 3]])

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import numpy as np

## 1. argmax()
a = np.arange(6).reshape(2,3)
print(a)

#[Output]:
#[[0 1 2]
# [3 4 5]]

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y= a.mean(axis=1)

print(y)

#[Output]:
#[0.5 2.5 4.5]

y.reshape(3,1)

#[Output]:

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import numpy as np

a = np.arange(6).reshape(3,2)

print(a)

#[Output]:
#array([[0, 1],
#       [2, 3],
#       [4, 5]])