Updated examples 21 June 2018

Author: ryankarlos

Makefile

@@ -23,3 +23,4 @@ doc:
 	cd doc; \
 	python make.py clean; \
 	python make.py html
+	python make.py spellcheck

asv_bench/benchmarks/categoricals.py

@@ -193,3 +193,20 @@ def time_categorical_series_is_monotonic_increasing(self):
 
     def time_categorical_series_is_monotonic_decreasing(self):
         self.s.is_monotonic_decreasing
+
+
+class Contains(object):
+
+    goal_time = 0.2
+
+    def setup(self):
+        N = 10**5
+        self.ci = tm.makeCategoricalIndex(N)
+        self.c = self.ci.values
+        self.key = self.ci.categories[0]
+
+    def time_categorical_index_contains(self):
+        self.key in self.ci
+
+    def time_categorical_contains(self):
+        self.key in self.c

asv_bench/benchmarks/frame_methods.py

@@ -512,3 +512,21 @@ def time_nlargest(self, keep):
 
     def time_nsmallest(self, keep):
         self.df.nsmallest(100, 'A', keep=keep)
+
+
+class Describe(object):
+
+    goal_time = 0.2
+
+    def setup(self):
+        self.df = DataFrame({
+            'a': np.random.randint(0, 100, int(1e6)),
+            'b': np.random.randint(0, 100, int(1e6)),
+            'c': np.random.randint(0, 100, int(1e6))
+        })
+
+    def time_series_describe(self):
+        self.df['a'].describe()
+
+    def time_dataframe_describe(self):
+        self.df.describe()

asv_bench/benchmarks/groupby.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 from pandas import (DataFrame, Series, MultiIndex, date_range, period_range,
-                    TimeGrouper, Categorical)
+                    TimeGrouper, Categorical, Timestamp)
 import pandas.util.testing as tm
 
 from .pandas_vb_common import setup  # noqa
@@ -385,6 +385,25 @@ def time_dtype_as_field(self, dtype, method, application):
         self.as_field_method()
 
 
+class RankWithTies(object):
+    # GH 21237
+    goal_time = 0.2
+    param_names = ['dtype', 'tie_method']
+    params = [['float64', 'float32', 'int64', 'datetime64'],
+              ['first', 'average', 'dense', 'min', 'max']]
+
+    def setup(self, dtype, tie_method):
+        N = 10**4
+        if dtype == 'datetime64':
+            data = np.array([Timestamp("2011/01/01")] * N, dtype=dtype)
+        else:
+            data = np.array([1] * N, dtype=dtype)
+        self.df = DataFrame({'values': data, 'key': ['foo'] * N})
+
+    def time_rank_ties(self, dtype, tie_method):
+        self.df.groupby('key').rank(method=tie_method)
+
+
 class Float32(object):
     # GH 13335
     goal_time = 0.2

ci/environment-dev.yaml

@@ -13,3 +13,4 @@ dependencies:
   - pytz
   - setuptools>=24.2.0
   - sphinx
+  - sphinxcontrib-spelling

ci/requirements_dev.txt

@@ -9,3 +9,4 @@ python-dateutil>=2.5.0
 pytz
 setuptools>=24.2.0
 sphinx
+sphinxcontrib-spelling

doc/make.py

@@ -224,8 +224,9 @@ def _sphinx_build(self, kind):
         --------
         >>> DocBuilder(num_jobs=4)._sphinx_build('html')
         """
-        if kind not in ('html', 'latex'):
-            raise ValueError('kind must be html or latex, not {}'.format(kind))
+        if kind not in ('html', 'latex', 'spelling'):
+            raise ValueError('kind must be html, latex or '
+                             'spelling, not {}'.format(kind))
 
         self._run_os('sphinx-build',
                      '-j{}'.format(self.num_jobs),
@@ -304,6 +305,18 @@ def zip_html(self):
                      '-q',
                      *fnames)
 
+    def spellcheck(self):
+        """Spell check the documentation."""
+        self._sphinx_build('spelling')
+        output_location = os.path.join('build', 'spelling', 'output.txt')
+        with open(output_location) as output:
+            lines = output.readlines()
+            if lines:
+                raise SyntaxError(
+                    'Found misspelled words.'
+                    ' Check pandas/doc/build/spelling/output.txt'
+                    ' for more details.')
+
 
 def main():
     cmds = [method for method in dir(DocBuilder) if not method.startswith('_')]

doc/source/_static/favicon.ico

@@ -342,7 +342,7 @@ As usual, **both sides** of the slicers are included as this is label indexing.
                        columns=micolumns).sort_index().sort_index(axis=1)
    dfmi
 
-Basic multi-index slicing using slices, lists, and labels.
+Basic MultiIndex slicing using slices, lists, and labels.
 
 .. ipython:: python
 
@@ -1039,7 +1039,7 @@ On the other hand, if the index is not monotonic, then both slice bounds must be
     KeyError: 'Cannot get right slice bound for non-unique label: 3'
 
 :meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` only check that
-an index is weakly monotonic. To check for strict montonicity, you can combine one of those with
+an index is weakly monotonic. To check for strict monotonicity, you can combine one of those with
 :meth:`Index.is_unique`
 
 .. ipython:: python

doc/source/advanced.rst

@@ -1200,9 +1200,9 @@ Attributes and underlying data
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 **Axes**
 
-  * **items**: axis 0; each item corresponds to a DataFrame contained inside
-  * **major_axis**: axis 1; the index (rows) of each of the DataFrames
-  * **minor_axis**: axis 2; the columns of each of the DataFrames
+* **items**: axis 0; each item corresponds to a DataFrame contained inside
+* **major_axis**: axis 1; the index (rows) of each of the DataFrames
+* **minor_axis**: axis 2; the columns of each of the DataFrames
 
 .. autosummary::
    :toctree: generated/