## Fix Python – How do I create a numpy array of all True or all False?

In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?

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In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?

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Is there a convenient way to calculate percentiles for a sequence or single-dimensional numpy array?

I am looking for something similar to Excel’s percentile function.

I looked in NumPy’s statistics reference, and couldn’t find this. All I could find is the median (50th percentile), but not something more specific.

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I have two numpy arrays of different shapes, but with the same length (leading dimension). I want to shuffle each of them, such that corresponding elements continue to correspond — i.e. shuffle them in unison with respect to their leading indices.

This code works, and illustrates my goals:

def shuffle_in_unison(a, b):

assert len(a) == len(b)

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Given a 1D array of indices:

a = array([1, 0, 3])

I want to one-hot encode this as a 2D array:

b = array([[0,1,0,0], [1,0,0,0], [0,0,0,1]])

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What is the difference between ndarray and array in NumPy? Where is their implementation in the NumPy source code?

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How do I concatenate two one-dimensional arrays in NumPy? I tried numpy.concatenate:

import numpy as np

a = np.array([1, 2, 3])

b = np.array([4, 5])

np.concatenate(a, b)

But I get an error:

TypeError: only length-1 arrays can be converted to Python scalars

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How do I convert a NumPy array into a Python List?

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How do I convert a PIL Image back and forth to a NumPy array so that I can do faster pixel-wise transformations than PIL’s PixelAccess allows? I can convert it to a NumPy array via:

pic = Image.open(“foo.jpg”)

pix = numpy.array(pic.getdata()).reshape(pic.size[0], pic.size[1], 3)

But how do I load it back into the PIL Image after I’ve modified the….

import numpy as np

y = np.array(((1,2,3),(4,5,6),(7,8,9)))

OUTPUT:

print(y.flatten())

[1 2 3 4 5 6 7 8 9]

print(y.ravel())

[1 2 3 4 5 6 7 8 9]

Both function return the same list.

Then what is the need of two different functions performing same job.

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Can someone explain to me what is the purpose of meshgrid function in Numpy? I know it creates some kind of grid of coordinates for plotting, but I can’t really see the direct benefit of it.

I am studying “Python Machine Learning” from Sebastian Raschka, and he is using it for plotting the decision borders. See input 11 here.

I have also tried thi….