I think I would do this with two groupbys. First to calculate the “weighted average”: If you set this as a column, you can groupby over it: Now the sum of this column is the desired: or potentially:
Use numpy.set_printoptions:
You almost did it. You were tricked by the fact that abs(imagem-255) will give a wrong result since your dtype is an unsigned integer. You have to do (255-imagem) in order to keep the integers unsigned: You can also invert the image using the bitwise_not function of OpenCV:
You want to reshape the array. where -1 infers the size of the new dimension from the size of the input array.
The error message indicates that df2 is of type pd.Series. You need to convert df2 .to_frame() as .merge() needs a pd.DataFrame() input (see docs): while you probably also just could: Alternatively, you can use pd.DataFrame.join() which accepts a pd.Series.
There are a couple of ways. If timing the results with the following code: We get: So the multiplication seems to be the fastest.
datetime64[ns] is a general dtype, while <M8[ns] is a specific dtype. General dtypes map to specific dtypes, but may be different from one installation of NumPy to the next. On a machine whose byte order is little endian, there is no difference between np.dtype(‘datetime64[ns]’) and np.dtype(‘<M8[ns]’): However, on a big endian machine, np.dtype(‘datetime64[ns]’) would equal np.dtype(‘>M8[ns]’). So datetime64[ns] maps to either <M8[ns] or >M8[ns] depending on the endian-ness of the … Read more
I loaded a csv file into ‘dataset’ and tried to execute dataset.head(), but it reports an error. How to check the head or tail of a numpy array? without specifying specific lines?
You need to put the arrays you want to concatenate into a sequence (usually a tuple or list) in the argument. Check the documentation for np.concatenate. Note that the first argument is a sequence (e.g. list, tuple) of arrays. It does not take them as separate arguments. As far as I know, this API is … Read more
Yeah, you can install opencv (this is a library used for image processing, and computer vision), and use the cv2.resize function. And for instance use: Here img is thus a numpy array containing the original image, whereas res is a numpy array containing the resized image. An important aspect is the interpolation parameter: there are … Read more