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|
Description: Use fixed seeds for reproducible pseudorandomness
Author: Rebecca N. Palmer <rebecca_palmer@zoho.com>
Forwarded: no
--- a/doc/source/getting_started/comparison/comparison_with_r.rst
+++ b/doc/source/getting_started/comparison/comparison_with_r.rst
@@ -237,6 +237,7 @@ In pandas we may use :meth:`~pandas.pivo
import random
import string
+ random.seed(123456) # for reproducibility
baseball = pd.DataFrame(
{
--- a/doc/source/user_guide/10min.rst
+++ b/doc/source/user_guide/10min.rst
@@ -701,6 +701,11 @@ We use the standard convention for refer
The ``plt.close`` method is used to `close <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.close.html>`__ a figure window:
.. ipython:: python
+ :suppress:
+
+ np.random.seed(123456) # for reproducibility
+
+.. ipython:: python
ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
ts = ts.cumsum()
@@ -717,6 +722,11 @@ The ``plt.close`` method is used to `clo
:meth:`~DataFrame.plot` plots all columns:
.. ipython:: python
+ :suppress:
+
+ np.random.seed(123456) # for reproducibility
+
+.. ipython:: python
df = pd.DataFrame(
np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"]
--- a/doc/source/user_guide/advanced.rst
+++ b/doc/source/user_guide/advanced.rst
@@ -590,6 +590,7 @@ they need to be sorted. As with any inde
import random
+ random.seed(123456) # for reproducibility
random.shuffle(tuples)
s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples))
s
--- a/doc/source/user_guide/visualization.rst
+++ b/doc/source/user_guide/visualization.rst
@@ -1090,6 +1090,7 @@ are what constitutes the bootstrap plot.
.. ipython:: python
from pandas.plotting import bootstrap_plot
+ random.seed(123456) # for reproducibility - bootstrap_plot uses random.sample
data = pd.Series(np.random.rand(1000))
--- a/pandas/plotting/_core.py
+++ b/pandas/plotting/_core.py
@@ -604,6 +604,7 @@ def boxplot_frame_groupby(
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> import itertools
>>> tuples = [t for t in itertools.product(range(1000), range(4))]
>>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
@@ -1328,6 +1329,7 @@ class PlotAccessor(PandasObject):
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> data = np.random.randn(25, 4)
>>> df = pd.DataFrame(data, columns=list('ABCD'))
>>> ax = df.plot.box()
@@ -1392,6 +1394,7 @@ class PlotAccessor(PandasObject):
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
>>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)
@@ -1811,6 +1814,7 @@ class PlotAccessor(PandasObject):
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> n = 10000
>>> df = pd.DataFrame({'x': np.random.randn(n),
... 'y': np.random.randn(n)})
--- a/pandas/plotting/_misc.py
+++ b/pandas/plotting/_misc.py
@@ -204,6 +204,7 @@ def scatter_matrix(
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> pd.plotting.scatter_matrix(df, alpha=0.2)
array([[<Axes: xlabel='A', ylabel='A'>, <Axes: xlabel='B', ylabel='A'>,
@@ -438,6 +439,8 @@ def bootstrap_plot(
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
+ >>> random.seed(1234) # for reproducibility
>>> s = pd.Series(np.random.uniform(size=100))
>>> pd.plotting.bootstrap_plot(s) # doctest: +SKIP
<Figure size 640x480 with 6 Axes>
@@ -597,6 +600,7 @@ def autocorrelation_plot(series: Series,
.. plot::
:context: close-figs
+ >>> np.random.seed(1234)
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
>>> pd.plotting.autocorrelation_plot(s) # doctest: +SKIP
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -78,8 +78,37 @@
"source": [
"import pandas as pd\n",
"import numpy as np\n",
- "import matplotlib as mpl\n",
- "\n",
+ "import matplotlib as mpl\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "nbsphinx": "hidden"
+ },
+ "outputs": [],
+ "source": [
+ "# For reproducibility - this doesn't respect uuid_len or positionally-passed uuid but the places here that use that coincidentally bypass this anyway\n",
+ "from pandas.io.formats.style import Styler\n",
+ "next_uuid = 1000\n",
+ "class StylerReproducible(Styler):\n",
+ " def __init__(self, *args, uuid=None, **kwargs):\n",
+ " global next_uuid\n",
+ " if uuid is None:\n",
+ " uuid = str(next_uuid)\n",
+ " next_uuid = next_uuid + 1\n",
+ " super().__init__(*args, uuid=uuid, **kwargs)\n",
+ "Styler = StylerReproducible\n",
+ "pd.DataFrame.style = property(lambda self: StylerReproducible(self))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"df = pd.DataFrame({\n",
" \"strings\": [\"Adam\", \"Mike\"],\n",
" \"ints\": [1, 3],\n",
@@ -104,6 +133,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "np.random.seed(25) # for reproducibility\n",
"weather_df = pd.DataFrame(np.random.rand(10,2)*5, \n",
" index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
" columns=[\"Tokyo\", \"Beijing\"])\n",
@@ -1394,7 +1424,6 @@
"outputs": [],
"source": [
"# Hide the construction of the display chart from the user\n",
- "import pandas as pd\n",
"from IPython.display import HTML\n",
"\n",
"# Test series\n",
@@ -1926,6 +1955,18 @@
]
},
{
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "nbsphinx": "hidden"
+ },
+ "outputs": [],
+ "source": [
+ "# For reproducibility\n",
+ "Styler = StylerReproducible\n"
+ ]
+ },
+ {
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -2126,7 +2167,8 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
- }
+ },
+ "record_timing": false
},
"nbformat": 4,
"nbformat_minor": 1
|
