np.negative is silghtly faster than multiplying (as it is a ufunc) N = 5 arr = np.arange(N ** 3).reshape(N, N, N) %timeit arr.ravel()[1::2] *= -1 %timeit np.negative(arr.ravel()[1::2], out = arr.ravel()[1::2]) The slowest run took 8.74 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: … Read more
Change the import Pandas to import pandas as pd
It’s not so clear what you’re asking, but here are some options to get you started: Manual Min-max transform You could min-max transform so they are all in the range of [0,1], and then multiply by 255. Then, all your values are in the range of 0 to 255, proportional to your original array (i.e. … Read more
You could make a list of lists, which would be better than dynamicaly making new list names. Something like this: input_list = [[278, 207], [695, 205], [672, 205], [325, 205], [302, 205],[650, 167], [556, 167], [533, 167]] output_list = [[input_list[0]]] # Create an output list, with the first element of input_list as a list of … Read more
while reading csv, use the parameter ‘na_values’. pd.read_csv(”, na_values=”UN”)
There is nothing unreliable about numpy’s behavior in the examples you show as compared to MATLAB, nor do any of the examples you show have anything to do with floating-point issues (with one exception). For the rounding behavior, MATLAB is the one doing it wrong here. Numpy is following the IEEE standard for rounding. The … Read more
You are a Udacity Machine Learning student? Something like this might be useful for you: import numpy as np import matplotlib.pyplot as plt labels = [‘output’,’varA’,’varB’,’varC’] data = np.array([[4,5,6,2,5], [3,6,4,6,3], [12,3,5,3,2], [4,1,1,44, 7]]) colors = [“r”, “g”, “b”, “k”] # make sure you have enough colors to match # the number of variables for i … Read more
try this: output = {} for key in originalDict: output[key] = originalDict[key].tolist() Where originalDict is the dictionary you showed us, with numpy arrays. There’s probably a neater way of writing it but it should work. EDIT: with regards to your comment, if you just need the inner list, then index the first element of each … Read more
Create both B and C as Numpy arrays: C = np.array([ 0, 0, 2, 1, 1, 8, 3, 6, 6, 7, 11, 8, 10, 23, 22, 12, 15, 34, 16, 9, 11, 6, 0, 0]) B = np.array([500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, … Read more