I have a set of coordinates like this: I would like to convert them to 0-1 matrix that would represent a tiles of 100×100 size where 1 is when the point (array row) is within a particular tile. How can I quickly do this? Answer I’m confused about the question — do you want a grid that is 0 everywhere
Tag: numpy
How to create a frequency table of each subject from a given timetable using pandas?
This is a time table, columns=hour, rows=weekday, data=subject [weekday x hour] How do you generate a pandas.Dataframe where, rows=weekday, columns=subject, data = subject frequency in the corresponding weekday? Required table: [weekday x subject] Answer Use melt to flatten your dataframe then pivot_table to reshape your dataframe: Output:
How would I perform an operation on each element in an array and add the new value to a new array in Python?
I have an array of ten random values generated using Numpy. I want to perform an operation on each element in the array by looping over all ten elements and then add the result of each to a new array. The first part of looping over the array but I am stuck on how to add the result to a
Python – How to pass elements stored in an array into a function?
All, I’m new to python, so hopefully this is not a dumb question, but I have not been able to find out directions/information of how to do this task. I am trying to create a program that determines if a given pixel is within a certain region. The method I found that recommended how to test this involves calculating polygonal
How to Eliminate FOR Loop with the extraction of each value from
Unable to Extract correct values from Pandas Series to use in np.select Function as default value. # With For Loop: Getting Perfect values in column “C” [“No”,”No”,4,4,4,4,4,4] ”’ ”’ Answer You code is equivalent to: output: NB. If you want to keep the object type, use: df[‘C’] = df[‘B’].where(m1&m2).ffill().convert_dtypes().astype(object).fillna(df[‘C’])
Pandas Average of row ignoring 0
I have a DataFrame that looks like this: I need to find the mean of each row, ignoring instances of 0. My initial plan was to replace 0 with NaN and then get the mean excluding NaN. I tried to replace 0 with NaN, however this didn’t work, and the DataFrame still contained 0. I tried: The second issue is
Why is this array changing when I’m not operating on it?
I have two arrays: And I’m running the foll)owing code: I get the following result: Why are the last elements changed? I don’t see why X is changed by indexing some elements. edit: added np.array Answer Output:
Consecutively split an array by the next max value
Suppose I have an array (the elements can be floats also): The goal is, starting from the beginning of the array, to find the max value (the last one if there are several equal ones) and cut the anterior part of the array. Then consecutively repeat this procedure till the end of the array. So, the expected result would be:
Time & memory complexity management with multi-dimensional matrices using parallelisation and numpy
I have a time series of very large matrices. I am trying to speed up the process and was wondering the most optimal way to do this. The two things that came to mind are to parallelize the process using numba or to apply a function to the matrices such as with np.apply_along_axis. Speed and memory complexity are very important.
Pandas: Calculate neighbouring differences from a column in dataframe
How can I calculate the differences from neighboured numbers in a dataframe column named ‘y’ by only using Pandas commands? Here is an example where I convert the column ‘y’first to numpy and then use np.diff. Answer You could use diff to find the differences and shift to get the differences align (like in your output): Output: