Program 1 import numpy as np # #print(type(ar1)) ar1=np.array([4,7,2,13,40]) #ar2=np.array([4,47,22,12,20]) ar3=np.where(ar1%2==0,ar1,0) print(ar3) # #print(type(ar1)) # arr3=(ar1>=ar2) # ar1=np.arange(1,11,1) # ar2=np.arange(1,11,1) # arr3=(ar1==ar2) # print(arr3) # if(np.any(arr3)): # print(“Prices are same”) # else: # print(“No...
Program 1 import numpy as np ar=np.arange(1,11,1) # r=int(input(“Enter value of row”)) # c=int(input(“Enter value of column”)) # ar=np.zeros((r,c),dtype=int) #print(ar) # ar=np.ones((3,4),dtype=int) # print(ar) # r=int(input(“Enter value of row”)) # c=int(input(“Enter value of column”))...
Program 1 import numpy as np #ar=np.arange(1.5,10,dtype=”float”) # ar=np.arange(100,1,-2,dtype=”int”) # print(ar) # ar=np.arange(100,1000,5) # print(ar) # ar=np.linspace(1,1000,25,dtype=”int”) # print(ar) # ar=np.linspace(100,1,5,dtype=”int”) # print(ar) # ar=np.logspace(1,10,2) # print(ar)
Program 1 import numpy as np # ar=np.array([1,2,3,4,5,6,]) # print(ar.shape) #ar=np.array([[10,20,30],[40,50,60],[70,80,90]]) #print(ar) #print(ar.ndim) #print(ar.shape) # print(ar.size) # print(ar.dtype) #print(ar.itemsize) #print(ar.nbytes)
Program 1 import numpy as np #arr=np.array([10,20,30]) #print(arr) #arr=np.array(100) #print(arr) # ar=np.arange(0,10) # print(ar) # ar=np.linspace(100,1,5,dtype=”int”) # print(ar) # ar=np.logspace(1,4,5,dtype=”int”) # print(ar) # arr1=np.array(100) # 0 D Array # print(“———–0 D Array———“) # print(arr1)...
NumPy and SciPy are two very basic and important libraries of python. They have a wide range of uses in different sectors. They have applications in fields of Data Science, Machine Learning, Finance, etc....
NumPy and SciPy are the two most important libraries in Python. The operations are relative and hence contrasting. Both libraries have a wide range of functions. The prerequisite of working with both the libraries...
SciPy consists of an optimization module. Optimization is the final step in any technological process. It is one of the most important packages. We optimize the input parameters of a function. The module scipy.optimize()...
The SciPy library consists of a package for statistical functions. The scipy.stats is the SciPy sub-package. It is mainly used for probabilistic distributions and statistical operations. There is a wide range of probability functions....
The SciPy library is generally for mathematical and statistical computations. There are sub-packages in the library that manage more specific functionalities. There is the scipy.interpolation that has specific usage in the field of statistics....
