From cc93c9d8d3cbc96a02559401f169d55e00905682 Mon Sep 17 00:00:00 2001 From: Debian Science Team Date: Wed, 26 Aug 2020 22:34:50 +0100 Subject: [PATCH] Use fixed seeds for reproducible pseudorandomness Author: Rebecca N. Palmer Forwarded: no Gbp-Pq: Name fix_random_seeds.patch --- doc/source/getting_started/comparison/comparison_with_r.rst | 1 + doc/source/user_guide/advanced.rst | 1 + doc/source/user_guide/visualization.rst | 1 + 3 files changed, 3 insertions(+) diff --git a/doc/source/getting_started/comparison/comparison_with_r.rst b/doc/source/getting_started/comparison/comparison_with_r.rst index f67f46fc..7280b549 100644 --- a/doc/source/getting_started/comparison/comparison_with_r.rst +++ b/doc/source/getting_started/comparison/comparison_with_r.rst @@ -226,6 +226,7 @@ In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this: import random import string + random.seed(123456) # for reproducibility baseball = pd.DataFrame( {'team': ["team %d" % (x + 1) for x in range(5)] * 5, diff --git a/doc/source/user_guide/advanced.rst b/doc/source/user_guide/advanced.rst index d6f5c0c7..73468d15 100644 --- a/doc/source/user_guide/advanced.rst +++ b/doc/source/user_guide/advanced.rst @@ -584,6 +584,7 @@ they need to be sorted. As with any index, you can use :meth:`~DataFrame.sort_in .. ipython:: python import random + random.seed(123456) # for reproducibility random.shuffle(tuples) s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples)) s diff --git a/doc/source/user_guide/visualization.rst b/doc/source/user_guide/visualization.rst index 4fde053a..91138743 100644 --- a/doc/source/user_guide/visualization.rst +++ b/doc/source/user_guide/visualization.rst @@ -992,6 +992,7 @@ are what constitutes the bootstrap plot. :suppress: np.random.seed(123456) + random.seed(123456) # for reproducibility - bootstrap_plot uses random.sample .. ipython:: python -- 2.30.2