Selecting multiple slices from a numpy array at once
I’m looking for a way to select multiple slices from a numpy array at once. Say we have a 1D data array and want to extract three portions of it like below:
numpy
Using moviepy, scipy and numpy in amazon lambda
I’d like to generate video using AWS Lambda feature.
Efficiently return the index of the first value satisfying condition in array
I need to find the index of the first value in a 1d NumPy array, or Pandas numeric series, satisfying a condition. The array is large and the index may be near the start or end of the array, or the condition may not be met at all. I can’t tell in advance which is more likely. If the condition is not met, the return value should be -1. I’ve considered a few approaches.
Repeating each element of a numpy array 5 times
import numpy as np data = np.arange(-50,50,10) print data [-50 -40 -30 -20 -10 0 10 20 30 40] I want to repeat each element of data 5 times and make new array as follows: ans = [-50 -50 -50 -50 -50 -40 -40 … 40] How can I do it? What about repeating the … Read more
Matplotlib log scale tick label number formatting
With matplotlib when a log scale is specified for an axis, the default method of labeling that axis is with numbers that are 10 to a power eg. 10^6. Is there an easy way to change all of these labels to be their full numerical representation? eg. 1, 10, 100, etc.
Numpy: find first index of value fast
How can I find the index of the first occurrence of a number in a Numpy array?
Speed is important to me. I am not interested in the following answers because they scan the whole array and don’t stop when they find the first occurrence:
python numpy arange unexpected results
I am using the arange function to define my for loop iterations and getting unexpected results.
Dropping infinite values from dataframes in pandas?
How do I drop nan, inf, and -inf values from a DataFrame without resetting mode.use_inf_as_null?
python how to pad numpy array with zeros
I want to know how I can pad a 2D numpy array with zeros using python 2.6.6 with numpy version 1.5.0. But these are my limitations. Therefore I cannot use np.pad. For example, I want to pad a with zeros such that its shape matches b. The reason why I want to do this is so I can do:
numpy.unique with order preserved
['b','b','b','a','a','c','c'] numpy.unique gives ['a','b','c'] How can I get the original order preserved ['b','a','c'] Great answers. Bonus question. Why do none of these methods work with this dataset? http://www.uploadmb.com/dw.php?id=1364341573 Here’s the question numpy sort wierd behavior Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you … Read more