With einsum, you are looking at around 20% improvement over np.any and np. This introduction to pandas is derived from Data Schools pandas Q&A with my own notes and code. Thus, it seems that supplying masks to np.all or np.any gives a bit (about 9%) of performance boost over non-mask based approach. import numpy as np Alter dimensions as needed x,y 3,4 create a default array of specified dimensions a np.arange (xy).reshape (x,y) print a print a.diagonal returns the top-left-to-lower-right diagonal 'i' according to this diagram: 0 1 2 3 4. While the nonzero values can be obtained with a nonzero (a), it is recommended to use x x.astype (bool) or x x 0 instead, which will correctly handle 0-d arrays. sparsebool, default False Whether the dummy-encoded columns should be backed by a SparseArray (True) or a regular NumPy array (False). If columns is None then all the columns with object, string, or category dtype will be converted. In : data = np.random.randint(-10,10,(10000,10)) columnslist-like, default None Column names in the DataFrame to be encoded. This section times the three solutions proposed in this solution and also includes timings for Chaudhary's approach that is also np.all based approach, but does not use mask or boolean array (not at least in the frontend). NumPys sequential functions can act on an arrays entries as if they form a single. It should be of the appropriate shape and dtype. Compare the performance of a simple non-vectorized computation to a. outarray, optional If provided, the result will be inserted into this array. Thus, you would have three approaches as listed next.Īpproach #1: rows_without_zeros = dataĪpproach #2: rows_without_zeros = dataĪpproach #3: rows_without_zeros = data If choices is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to choices.shape 0) is taken as defining the sequence. A scaling factor (e.g., 1.25mean) may also be used. mean), then the threshold value is the median (resp. Features whose absolute importance value is greater or equal are kept while the others are discarded. One can also use np.einsum to replace np.any, which I personally think is crazy, but in a good way, as it gives us a noticeable performance boost as we would confirm later on in this solution. Each array must match the size of x0 or be a scalar, in the latter case a bound will be the same for all variables. The threshold value to use for feature selection. Alternatively, you can detect all non-zeros with data!=0 and then do np.all to get us row mask of rows without any zero.
The matrix L is not always positive definite, so an appropriate identity matrix is. You can detect all zeros with data =0 which will give you a boolean array and then perform np.any along each row on it. Note that the size of the resulting matrix L is (nc+np) × (nc + np).
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