pythonLearning/01numpy.py at main · hiralmodi14/pythonLearning · GitHub

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import numpy as np
# arr_f32 = np.array([10, 20, 10], dtype=np.int64)
# print(arr_f32)
# name=np.linspace(0,1,5)
# print(name)
# array=np.empty((2,3))
# print(array)
# arr=np.array([1,2,3,4,5])
# name=np.full_like(arr,9)
# print(name)
# arr = np.array([[1,2,3],[4,5,6]], dtype=np.int64)
# arr.itemsize
# print(arr)
# arr=np.array([1.0,3])
# array_f32=(arr.astype('float32'))
# print(array_f32)
# d= np.array([10,20,30,40,50])
# a= np.array([1,2,3])
# d = a[[0,1,2]]     # copy! list index
# e = a[a > 20]
# print(d)
#indexing
# m=np.array([[1,2,3],[4,5,6],[7,8,9]])
# print(m[1,2])
# print(m[0,:])
# print(m[0:2,1:3])
# a = np.array([10, 20, 30, 40, 50])
 # print(a[[0,2,4]])
# print(a[a > 25])
# print(a[(a > 10) & (a < 50)])
# shape:
# arr=np.arange(12)
# # print(arr.reshape(-1,3).ravel())
# print(arr.reshape(3,4).flatten())
# m=np.array([[1,2,3],[4,5,6],[7,8,9]])
# print( np.swapaxes(m, 0, 1))
# v = np.array([1,2,3])
# print(np.expand_dims(v,axis=0))
# print(v[:,np.newaxis])
# m2 = np.ones((1,5,1))
# print(m2.squeeze())