I have two different arrays, one with strings and another with ints. I want to concatenate them, into one array where each column has the original datatype. My current solution for doing this (see below) converts the entire array into dtype = string, which seems very memory inefficient.
I’m struggling to select the specific columns per row of a NumPy matrix.
I’ve a csv file without header, with a DateTime index. I want to rename the index and column name, but with df.rename() only the column name is renamed. Bug? I’m on version 0.12.0
Recently I needed to do weighted random selection of elements from a list, both with and without replacement. While there are well known and good algorithms for unweighted selection, and some for weighted selection without replacement (such as modifications of the resevoir algorithm), I couldn’t find any good algorithms for weighted selection with replacement. I also wanted to avoid the resevoir method, as I was selecting a significant fraction of the list, which is small enough to hold in memory.
… the is keyword that can be used for equality in strings.
I have a method that sometimes returns a NoneType value. So how can I question a variable that is a NoneType? I need to use if method, for example
The __debug__ variable is handy in part because it affects every module. If I want to create another variable that works the same way, how would I do it?
I recently learned about strides in the answer to this post, and was wondering how I could use them to compute a moving average filter more efficiently than what I proposed in this post (using convolution filters).
I have the following dataframes:
How can I (easily) take a string such as "sin(x)*x^2" which might be entered by a user at runtime and produce a Python function that could be evaluated for any value of x?