diff options
| -rw-r--r-- | numpy/lib/function_base.py | 31 |
1 files changed, 19 insertions, 12 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index dee64c671..03019f1b5 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -4084,7 +4084,7 @@ def percentile(a, array([7., 2.]) >>> assert not np.all(a == b) - The different types of interpolation can be visualized graphically: + The different methods can be visualized graphically: .. plot:: @@ -4094,20 +4094,25 @@ def percentile(a, p = np.linspace(0, 100, 6001) ax = plt.gca() lines = [ - ('linear', None), - ('higher', '--'), - ('lower', '--'), - ('nearest', '-.'), - ('midpoint', '-.'), - ] - for interpolation, style in lines: + ('linear', '-', 'C0'), + ('inverted_cdf', ':', 'C1'), + # Almost the same as `inverted_cdf`: + ('averaged_inverted_cdf', '-.', 'C1'), + ('closest_observation', ':', 'C2'), + ('interpolated_inverted_cdf', '--', 'C1'), + ('hazen', '--', 'C3'), + ('weibull', '-.', 'C4'), + ('median_unbiased', '--', 'C5'), + ('normal_unbiased', '-.', 'C6'), + ] + for method, style, color in lines: ax.plot( - p, np.percentile(a, p, interpolation=interpolation), - label=interpolation, linestyle=style) + p, np.percentile(a, p, method=method), + label=method, linestyle=style, color=color) ax.set( - title='Result for the data: ' + str(a), + title='Percentiles for different methods and data: ' + str(a), xlabel='Percentile', - ylabel='List item returned', + ylabel='Estimated percentile value', yticks=a) ax.legend() plt.show() @@ -4347,6 +4352,8 @@ def quantile(a, array([7., 2.]) >>> assert not np.all(a == b) + See also `numpy.percentile` for a visualization of most methods. + References ---------- .. [1] R. J. Hyndman and Y. Fan, |