Find column name in pandas that matches an array
I have a large dataframe (5000 x 12039) and I want to get the column name that matches a numpy array.
numpy
Replacing Numpy elements if condition is met
I have a large numpy array that I need to manipulate so that each element is changed to either a 1 or 0 if a condition is met (will be used as a pixel mask later). There are about 8 million elements in the array and my current method takes too long for the reduction pipeline:
How to copy a 2D array into a 3rd dimension, N times?
I’d like to copy a numpy 2D array into a third dimension. For example, given the 2D numpy array:
Histogram Matplotlib
So I have a little problem. I have a data set in scipy that is already in the histogram format, so I have the center of the bins and the number of events per bin. How can I now plot is as a histogram. I tried just doing
Consistently create same random numpy array
I am waiting for another developer to finish a piece of code that will return an np array of shape (100,2000) with values of either -1,0, or 1.
How to get value counts for multiple columns at once in Pandas DataFrame?
Given a Pandas DataFrame that has multiple columns with categorical values (0 or 1), is it possible to conveniently get the value_counts for every column at the same time?
Problems with pip install numpy – RuntimeError: Broken toolchain: cannot link a simple C program
I’m trying to install numpy (and scipy and matplotlib) into a virturalenv.
What’s the fastest way in Python to calculate cosine similarity given sparse matrix data?
Given a sparse matrix listing, what’s the best way to calculate the cosine similarity between each of the columns (or rows) in the matrix? I would rather not iterate n-choose-two times.
How does condensed distance matrix work? (pdist)
scipy.spatial.distance.pdist returns a condensed distance matrix. From the documentation:
Coalesce values from 2 columns into a single column in a pandas dataframe
I’m looking for a method that behaves similarly to coalesce in T-SQL. I have 2 columns (column A and B) that are sparsely populated in a pandas dataframe. I’d like to create a new column using the following rules: