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Diffstat (limited to 'doc/source/user/quickstart.rst')
| -rw-r--r-- | doc/source/user/quickstart.rst | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index a9cfeca31..8e0e3b6ba 100644 --- a/doc/source/user/quickstart.rst +++ b/doc/source/user/quickstart.rst @@ -193,7 +193,7 @@ state of the memory. By default, the dtype of the created array is [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) - >>> np.empty((2, 3)) + >>> np.empty((2, 3)) #doctest: +SKIP array([[3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) @@ -868,9 +868,9 @@ copy. >>> def f(x): ... print(id(x)) ... - >>> id(a) # id is a unique identifier of an object + >>> id(a) # id is a unique identifier of an object #doctest: +SKIP 148293216 # may vary - >>> f(a) + >>> f(a) #doctest: +SKIP 148293216 # may vary View or Shallow Copy @@ -1272,6 +1272,7 @@ set <https://en.wikipedia.org/wiki/Mandelbrot_set>`__: ... z[diverge] = r # avoid diverging too much ... ... return divtime + >>> plt.clf() >>> plt.imshow(mandelbrot(400, 400)) The second way of indexing with booleans is more similar to integer @@ -1468,9 +1469,10 @@ that ``pylab.hist`` plots the histogram automatically, while >>> v = rg.normal(mu, sigma, 10000) >>> # Plot a normalized histogram with 50 bins >>> plt.hist(v, bins=50, density=True) # matplotlib version (plot) + (array...) >>> # Compute the histogram with numpy and then plot it >>> (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) - >>> plt.plot(.5 * (bins[1:] + bins[:-1]), n) + >>> plt.plot(.5 * (bins[1:] + bins[:-1]), n) #doctest: +SKIP With Matplotlib >=3.4 you can also use ``plt.stairs(n, bins)``. |