Python-Test-/Numpy.txt at main · karan96108/Python-Test- · GitHub

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"""
numeric stands for numeric python it is the fundamental package in scitific computing in python. it is python library that provides  a muti dimenwsionl array oject(n-darray)
varoiuos derive object such as masked array and matrices and sortnment of routine for fast operation on array it incluide mathematical logical sghape manipulation sorting selecting input output disctreate
fourier transformation basic linear algebra with statics basic operation and many more

at the core numpy package it is ndarray object this encapsulate n- dimensional array of homogenous datatype with  many operation in compiled code of performance


data type object that describe layout of single fixed elements of the array
array scaler pyhton obj when the single element of array accesed


import numpy as np

arr = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[7,6,5]]])

print(arr[0,-1,-2])
"""
'''
import numpy as np

a = np.array([[0,1,2,3],[7,9,5,3]],ndmin=5)
print(a.shape)

#print(a[0:2,1:4])
'''
'''
import numpy as np

a = np.array([1.1,2.2,3.4,0])

new_a= a.astype(bool)
print(new_a)

print(new_a.dtype)'''
'''
import numpy as np

a = np.array([1,2,3,4,5])

x = a.copy()
y = a.view()
print(x.base)
print(y.base)'''
'''
import numpy as np


a = np.array([1,2,3,4,5,6,7,8])

#nw_a = a.reshape(3,2,3) #first arg defines in how amy pieces it will break 2nd arg defines rows and 3rd arg defines coloumns
print(a.reshape(-1))'''

'''
import numpy as np

a = np.array([[[1,2,3,4],[5,6,7,8]],[[3,4,5,6],[4,7,5,4]]])

for x in np.nditer(a[:,::2]): #nditer is used to print the iterated value
    print(x)
'''
'''
import numpy as np

a = np.array([[[1,2,3,4],[5,6,7,8]],[[3,4,5,6],[4,7,5,4]]])

for idx,x in np.ndenumerate(a):
    print(x)
  '''

'''
import numpy as np

a = np.array([[1,2,3,4],[4,5,7,3]])
b = np.array([[5,6,7,8],[5,7,3,1]])

arr = np.concatenate((a,b),axis=1)


print(arr)
'''
'''
import numpy as np

arr1 = np.array([1, 2, 3])

arr2 = np.array([4, 5, 6])

arr = np.vstack((arr1, arr2))

print(arr)'''
'''
import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18]])

newarr = np.array_split(arr, 3)

print(newarr)
'''
'''
import numpy as np

# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])

# Performing multiplication using arithmetic operator
mul_ans = a*b
print(mul_ans)
'''