Concatenate Numpy arrays without copying
In Numpy, I can concatenate two arrays end-to-end with np.append or np.concatenate:
In Numpy, I can concatenate two arrays end-to-end with np.append or np.concatenate:
I have a large NumPy.array field_array and a smaller array match_array, both consisting of int values. Using the following example, how can I check if any match_array-shaped segment of field_array contains values that exactly correspond to the ones in match_array?
I could really use a tip to help me plotting a decision boundary to separate to classes of data. I created some sample data (from a Gaussian distribution) via Python NumPy. In this case, every data point is a 2D coordinate, i.e., a 1 column vector consisting of 2 rows. E.g.,
Is there a way to store NaN in a Numpy array of integers? I get: a=np.array([1],dtype=long) a[0]=np.nan Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot convert float NaN to integer Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you … Read more
Suppose I have the following NumPy array:
I am trying to write a Pandas dataframe (or can use a numpy array) to a mysql database using MysqlDB . MysqlDB doesn’t seem understand ‘nan’ and my database throws out an error saying nan is not in the field list. I need to find a way to convert the ‘nan’ into a NoneType.
I have a huge dataframe which has values and blanks/NA’s in it. I want to remove the blanks from the dataframe and move the next values up in the column. Consider below sample dataframe.
I need to accomplish the following task:
I have a numpy array that contains some image data. I would like to plot the ‘profile’ of a transect drawn across the image. The simplest case is a profile running parallel to the edge of the image, so if the image array is imdat, then the profile at a selected point (r,c) is simply imdat[r] (horizontal) or imdat[:,c] (vertical).