I want to replace the N first identic consecutive numbers from an array with 0. OUT -> np.array([0, 0, 0, 0 2, 3, 1, 2, 3, 2, 2, 2, 3, 3, 3, 1, 1, 2, 2]) Loop works, but what would be a faster-vectorized implementation? Answer You can use argmax on a boolean array to get the index of the
Tag: numpy
Creating 3d Tensor Array from 2d Array (Python)
I have two numpy arrays (4×4 each). I would like to concatenate them to a tensor of (4x4x2) in which the first ‘sheet’ is the first array, second ‘sheet’ is the second array, etc. However, when I try np.stack the output of d[1] is not showing the correct values of the first matrix. Answer If you do np.dstack((x, y)), which
pd.dataframe saving only one line
Hi i’m wondering what should i do to save all those values in a dataframe… already tried to save each one separately but it wont came up.. i needed to save all printed values in a dataframe, thats for 304 images (304 lines) buts it only saves the last line can someone help me? Answer You are overwriting and saving
Begginer/ numpy where and copy
I am trying to copy values from one Field2 into Field1 if Field1 is null or NaN. I have tried below where statement as per documentation, but it cuts outliners instead of copyting the value. dataframe=np.where(dataframe[‘field1’].isnull(),np.copy(dataframe[‘field2’]),1) I have interpreted it as if statement, but apparently its wrong interpretation, as results are not correct. Has anyone of you had similar issues?
How to generate random values for a predefined function?
I have a predefined function, for example this: How can I generate random values against it so I can plot the results of the function using matplotlib? Answer If you want to plot, don’t use random x values but rather a range. Also you should use numpy.exp that can take a vector as input and your y in the lambda
How do you slice a cross section in pandas or numpy?
I have the following data frame which can be copy/pasted and made to a data frame with: df = pd.read_clipboard() I would like to take a cross section from it, I want something like say: [1, 4, 9, 1, 10, 6, 4, 0, 4, 6, 10, 1, 9, 4, 1]) which is index df.loc[1, 0], df.loc[2, 1], df.loc[3, 2], df.loc[4,
How to length the last dimension of a numpy array and fill it up with another array?
I have a numpy array of shape (5, 4, 3) and another numpy array of shape (4,) and what I want to do is expand the last dimension of the first array (5, 4, 3) -> (5, 4, 4) and then broadcast the other array with shape (4,) such that it fills up the new array cells respectively. Example: becomes
Alternative to irregular nested np.where clauses
I’m struggling to simplify my irregular nested np.where clauses. Is there a way to make the code more readable? Answer Using np.select as suggested by @sammywemmy:
How to resample a dataframe an include start and end times?
So I am working with tick data and I am attempting to resample the dataframe to minute bars, but when resample is called the time series begins and ends the first instance that a tick exists. How would I resample this data such that the first and last times can be specified to a certain start and end time? Edit
Converting a numpy array of zeros and ones into an array of binary numbers
I am looking for an efficient way of turning into or into The only thing I found so far is np.packbits but this only works with 8bit numbers. However my arrays have shape of around (20e6,20) I could of course do it by hand: But I assume that there is a faster way. Especially if the conversion directly to a