Ambiguity in Pandas Dataframe / Numpy Array “axis” definition
I’ve been very confused about how python axes are defined, and whether they refer to a DataFrame’s rows or columns. Consider the code below:
arrays
Why does numpy.zeros takes up little space
I am wondering why numpy.zeros takes up such little space?
Generalise slicing operation in a NumPy array
This question is based on this older question:
pd.Timestamp versus np.datetime64: are they interchangeable for selected uses?
This question is motivated by an answer to a question on improving performance when performing comparisons with DatetimeIndex in pandas.
Python: slicing a multi-dimensional array
I’m new to Python and numpy. I’ve figured out how to slice 1 dimensional sequence: arr[start:end], and access an element in the array: el = arr
[col].
initialize a numpy array
Is there way to initialize a numpy array of a shape and add to it? I will explain what I need with a list example. If I want to create a list of objects generated in a loop, I can do:
Multiplying across in a numpy array
I’m trying to multiply each of the terms in a 2D array by the corresponding terms in a 1D array. This is very easy if I want to multiply every column by the 1D array, as shown in the numpy.multiply function. But I want to do the opposite, multiply each term in the row.
In other words I want to multiply:
How to extend an array in-place in Numpy?
Currently, I have some code like this
How to split/partition a dataset into training and test datasets for, e.g., cross validation?
What is a good way to split a NumPy array randomly into training and testing/validation dataset? Something similar to the cvpartition or crossvalind functions in Matlab.
How to get a list of all indices of repeated elements in a numpy array
I’m trying to get the index of all repeated elements in a numpy array, but the solution I found for the moment is REALLY inefficient for a large (>20000 elements) input array (it takes more or less 9 seconds).
The idea is simple: