With a function that creates a ragged array: In [60]: def foo(): …: print(‘one’) …: x = np.array([[1],[1,2]]) …: return x …: In [61]: foo() one /usr/local/bin/ipython3:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, … Read more
Use argsort as follows: arr1inds = arr1.argsort() sorted_arr1 = arr1[arr1inds[::-1]] sorted_arr2 = arr2[arr1inds[::-1]] This example sorts in descending order.
Happy to suggest a (possibly obvious) implementation of this, which could be made in pure Python or C/Cython if you’ve got time and space for new dependencies, and need it to be faster. A sparse matrix in N dimensions can assume most elements are empty, so we use a dictionary keyed on tuples: class NDSparseMatrix: … Read more
This worked for me. pip3 install –user package-name # for Python3 pip install –user package-name # for Python2 The –user flag tells Python to install in the user home directory. By default it will go to system locations. credit
np.random.uniform fits your use case: sampl = np.random.uniform(low=0.5, high=13.3, size=(50,)) Update Oct 2019: While the syntax is still supported, it looks like the API changed with NumPy 1.17 to support greater control over the random number generator. Going forward the API has changed and you should look at https://docs.scipy.org/doc/numpy/reference/random/generated/numpy.random.Generator.uniform.html The enhancement proposal is here: https://numpy.org/neps/nep-0019-rng-policy.html
When a numpy array is no longer referenced, it will be automatically freed by the GC. The C objects are wrapped in Python objects, so for you it should not matter how it’s implemented. Make sure that arrays are not referenced in global variables, since those stick around until overwritten or the program exits. If … Read more
distplot has been removed: removed in a future version of seaborn. Therefore, alternatives are to use histplot and displot. sns.histplot import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np df = pd.DataFrame(np.random.randn(100, 4), columns=list(‘ABCD’)) X = df[‘A’] sns.histplot(X, kde=True, bins=20) plt.show() sns.displot import pandas as pd import matplotlib.pyplot … Read more
Using direct numpy broadcasting, you can do this: dist = np.sqrt(((a[:, None] – b[:, :, None]) ** 2).sum(0)) Alternatively, scipy has a routine that will compute this slightly more efficiently (particularly for large matrices) from scipy.spatial.distance import cdist dist = cdist(a, b) I would avoid solutions that depend on factoring-out matrix products (of the form … Read more
Here’s a bit of code that might help. from __future__ import division import numpy as np def find_transition_times(t, y, threshold): “”” Given the input signal `y` with samples at times `t`, find the times where `y` increases through the value `threshold`. `t` and `y` must be 1-D numpy arrays. Linear interpolation is used to estimate … Read more
UPDATE: this post answered is now superseded by this new one (which take into account the updates of the question) providing an even faster code based on a different approach. Step 1: better algorithm First of all, building a k-d tree runs in O(n log n) time and doing a query runs in O(log n) … Read more