Attributes of a NumPy Array
Machine Learning courses with 100+ Real-time projects Start Now!!
Program 1
# ndim , shape , size , dtype , itemsize , nbyte
# max() , min(),sum(),mean() ,any(),all(),where()
import numpy as np
# any(),all(),where()
ar1=np.array([48,13,27,330,448,50,77,88,399,12]) # old price
ar2=np.array([45,13,27,330,448,50,77,88,399,12]) # new price
ar3=np.where(ar1%2==0,ar1,0)
print("ar1=",ar1)
print("ar3=",ar3)
# #ar2=np.array([45,135,67,30,48,80,67,18,99,72]) #new price
# print(ar1)
# print(ar2)
# ar3=(ar1==ar2)
# if(np.all(ar3)):
# print("Same Price")
# else:
# print(" No same Price")
# ar3=(ar1==ar2)
# print(ar3)
# if(np.any(ar3)):
# print("Some prices are same")
# else:
# print("No Prices are Same")
# print("Maximum : ",ar1.max())
# print("Minimum : ",ar1.min())
# print("Total : ",ar1.sum())
# print("Mean : ",ar1.mean())
# ar0=np.array(500)
# ar1=np.array([5,13,27,30,48,50,77,88,99,12])
# ar2=np.array([[5,13,27,],[45,15,27,]])
# ar3=np.array([[[5,10,20],[30,40,50]],[[5,10,20],[30,40,50]]])
# ar4=np.array([12.4,33.4,55.6,77.8,88.9,89.9])
# print(ar1.nbytes)
# print(ar1.itemsize)
# print(ar1.size)
# print(ar1.dtype)
# print(ar4.dtype)
#size
#print(ar3.size)
# # shape
# print(ar0.shape)
# print(ar0)
# print("---------------------------")
# print(ar1.shape)
# print(ar1)
# print("---------------------------")
# print(ar2.shape)
# print(ar2)
# print("---------------------------")
# print(ar3.shape)
# print(ar3)
#ndim
# print(ar0.ndim) # 0
# print(ar1.ndim) # 1
# print(ar2.ndim) # 2
# print(ar3.ndim) # 3
Did you like this article? If Yes, please give DataFlair 5 Stars on Google

