Adding sample datasets to be used in the documentation

doc/source/whatsnew/v0.23.0.txt

@@ -306,6 +306,7 @@ Other Enhancements
 - Added :func:`SeriesGroupBy.is_monotonic_increasing` and :func:`SeriesGroupBy.is_monotonic_decreasing` (:issue:`17015`)
 - For subclassed ``DataFrames``, :func:`DataFrame.apply` will now preserve the ``Series`` subclass (if defined) when passing the data to the applied function (:issue:`19822`)
 - :func:`DataFrame.from_dict` now accepts a ``columns`` argument that can be used to specify the column names when ``orient='index'`` is used (:issue:`18529`)
+- Added sample datasets in ``pandas.io.samples`` to be used mainly in the documentation (:issue:`19710`)
 
 
 .. _whatsnew_0230.api_breaking:

pandas/io/samples.py

@@ -0,0 +1,48 @@
+import numpy
+import pandas
+
+
+def _countries_with_penguins():
+    """Main dataset to be used in pandas documentation examples."""
+    columns = ('Code', 'Name', 'Capital', 'Continent',
+               'Penguin species', 'Avg. temperature')
+    data = [
+        ('AO', 'Angola', 'Luanda', 'AF', 1, 21.55),
+        ('AQ', 'Antarctica', numpy.nan, 'AN', 7, numpy.nan),
+        ('AR', 'Argentina', 'Buenos Aires', 'SA', 7, 14.8),
+        ('AU', 'Australia', 'Canberra', 'OC', 10, 21.65),
+        ('BR', 'Brazil', 'Brasilia', 'SA', 1, 24.95),
+        ('CL', 'Chile', 'Santiago', 'SA', 10, 8.45),
+        ('EC', 'Ecuador', 'Quito', 'SA', 2, 21.85),
+        ('MZ', 'Mozambique', 'Maputo', 'AF', 1, 23.8),
+        ('NA', 'Namibia', 'Windhoek', 'AF', 2, 19.95),
+        ('NZ', 'New Zealand', 'Wellington', 'OC', 7, 10.55),
+        ('PE', 'Peru', 'Lima', 'SA', 2, 19.6),
+        ('UY', 'Uruguay', 'Montevideo', 'SA', 3, 17.55),
+        ('ZA', 'South Africa', 'Pretoria', 'AF', 4, 17.75),
+    ]
+    df = pandas.DataFrame(data, columns=columns).set_index('Code')
+    df['Continent'] = df['Continent'].astype('category')
+    return df
+
+
+def _stocks():
+    """Sample dataset to be used in time series examples in pandas docs."""
+    data = {'MSFT': [62.9423, 62.2900, 64.5091, 67.0557, 68.4074, 67.9031,
+                     71.6169, 73.6561, 73.7712, 82.3774, 83.3578, 85.1401],
+            'RHT': [75.8800, 82.8100, 86.5000, 88.0800, 89.5700, 95.7500,
+                    98.8700, 107.5000, 110.8600, 120.8300, 126.7600, 120.1000],
+            'AAPL': [118.9445, 134.2745, 141.4228, 141.4129, 150.3810,
+                     142.3624, 147.0181, 162.1124, 152.9444, 167.7505,
+                     170.5391, 168.5428]}
+    index = pandas.to_datetime(['2017-01-01', '2017-02-01', '2017-03-01',
+                                '2017-04-01', '2017-05-01', '2017-06-01',
+                                '2017-07-01', '2017-08-01', '2017-09-01',
+                                '2017-10-01', '2017-11-01', '2017-12-01'])
+    index.name = 'Date'
+    df = pandas.DataFrame(data, index=index)
+    return df
+
+
+countries_with_penguins = _countries_with_penguins()
+stocks = _stocks()