not is implemented through the __nonzero__ special method, which is required to return either True or False, so it can’t give the required result. Instead the ~ operator is used, which is implemented through the __not__ special method. For the same reason, & and | are used in place of and and or. PEP 335 …
It’s a bit complicated because the names have different meanings depending on the context. int In Python The int is normally just a Python type, it’s of arbitrary precision, meaning that you can store any conceivable integer inside it (as long as you have enough memory). >>> int(10**50) 100000000000000000000000000000000000000000000000000 However, when you use it as …
There are several major differences. The first is that python integers are flexible-sized (at least in python 3.x). This means they can grow to accommodate any number of any size (within memory constraints, of course). The numpy integers, on the other hand, are fixed-sized. This means there is a maximum value they can hold. This …
In a nutshell, neither is “incorrect”. Pandas uses the unbiased estimator (N-1 in the denominator), whereas Numpy by default does not. To make them behave the same, pass ddof=1 to numpy.std(). For further discussion, see Can someone explain biased/unbiased population/sample standard deviation? Population variance and sample variance. Why divide by n-1?
If you are trying to display OpenCV image using matplotlib, use the code below. import numpy as np import cv2 import matplotlib.pyplot as plt %matplotlib inline # if you are running this code in Jupyter notebook # reads image ‘opencv-logo.png’ as grayscale img = cv2.imread(‘/path_to_image/opencv-logo.png’, 0) plt.imshow(img, cmap=’gray’)
If you are enlarging the image, you should prefer to use INTER_LINEAR or INTER_CUBIC interpolation. If you are shrinking the image, you should prefer to use INTER_AREA interpolation. Cubic interpolation is computationally more complex, and hence slower than linear interpolation. However, the quality of the resulting image will be higher.
For a head-like function you can just slice the array using dataset[:10]. For a tail-like function you can just slice the array using dataset[-10:].
Summary Using a loop-based approach with single pass and copying, accelerated with Numba, offers the best overall trade-off in terms of speed, memory efficiency and flexibility. If the execution of the condition function is sufficiently fast, two-passes (filter2_nb()) may be faster, while they are more memory efficient regardless. Also, for sufficiently large inputs, resizing instead …
Like resizing, removing elements from an NumPy array is a slow operation (especially for large arrays since it requires allocating space and copying all the data from the original array to the new array). It should be avoided if possible. Often you can avoid it by working with a masked array instead. For example, consider …
Short answer: rename the file to numpy-1.9.1%2Bmkl-cp34-none-win32.whl to install it. You can check what tags your pip tool accepts for installation by running: import pip; print(pip.pep425tags.get_supported()) In this case pip is incorrectly detecting your operating system to be 32-bits and the file you’re trying to install was win_amd64 in its filename. If you rename the …