is there a builtin function of Python that does on python.array what argsort() does on a numpy.array? Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), … Read more
Now there are a lot of similar questions but most of them answer how to delete the duplicate columns. However, I want to know how can I make a list of tuples where each tuple contains the column names of duplicate columns. I am assuming that each column has a unique name. Just to further illustrate my question:
Suppose I have a numpy array x = [5, 2, 3, 1, 4, 5], y = ['f', 'o', 'o', 'b', 'a', 'r']. I want to select the elements in y corresponding to elements in x that are greater than 1 and less than 5.
I’ve been going crazy trying to figure out what stupid thing I’m doing wrong here.
def gradient(X_norm,y,theta,alpha,m,n,num_it): temp=np.array(np.zeros_like(theta,float)) for i in range(0,num_it): h=np.dot(X_norm,theta) #temp[j]=theta[j]-(alpha/m)*( np.sum( (h-y)*X_norm[:,j][np.newaxis,:] ) ) temp[0]=theta[0]-(alpha/m)*(np.sum(h-y)) temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1])) theta=temp return theta X_norm,mean,std=featureScale(X) #length of X (number of rows) m=len(X) X_norm=np.array([np.ones(m),X_norm]) n,m=np.shape(X_norm) num_it=1500 alpha=0.01 theta=np.zeros(n,float)[:,np.newaxis] X_norm=X_norm.transpose() theta=gradient(X_norm,y,theta,alpha,m,n,num_it) print theta My theta from the above code is 100.2 100.2, but it should be 100.2 61.09 in matlab which is correct. … Read more
I have a very similar question to this question, but still one step behind. I have only one version of Python 3 installed on my Windows 7 (sorry) 64-bit system.
I have a pandas data frame with two columns one is temperature the other is time.
At the command prompt, navigate to the directory containing get-pip.py and run
The matplotlib.pyplot.contour() function takes 3 input arrays X, Y and Z.
The arrays X and Y specify the x- and y-coordinates of points, while Z specifies the corresponding value of the function of interest evaluated at the points.