Merge branch 'master' of https://github.com/pandas-dev/pandas into Test-PivotTable-Categoricaldata

Author: ryankarlos

asv_bench/benchmarks/categoricals.py

@@ -282,4 +282,18 @@ def time_sort_values(self):
         self.index.sort_values(ascending=False)
 
 
+class SearchSorted:
+    def setup(self):
+        N = 10 ** 5
+        self.ci = tm.makeCategoricalIndex(N).sort_values()
+        self.c = self.ci.values
+        self.key = self.ci.categories[1]
+
+    def time_categorical_index_contains(self):
+        self.ci.searchsorted(self.key)
+
+    def time_categorical_contains(self):
+        self.c.searchsorted(self.key)
+
+
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

ci/code_checks.sh

@@ -266,13 +266,17 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
         -k"-from_arrays -from_breaks -from_intervals -from_tuples -set_closed -to_tuples -interval_range"
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
+    MSG='Doctests arrays/string_.py' ; echo $MSG
+    pytest -q --doctest-modules pandas/core/arrays/string_.py
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
 fi
 
 ### DOCSTRINGS ###
 if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
 
-    MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT01, RT04, RT05, SA05)' ; echo $MSG
-    $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL04,GL05,GL06,GL07,GL09,GL10,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT01,RT04,RT05,SA05
+    MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT01, RT04, RT05, SA01, SA02, SA03, SA05)' ; echo $MSG
+    $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL04,GL05,GL06,GL07,GL09,GL10,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT01,RT04,RT05,SA01,SA02,SA03,SA05
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
 fi

doc/source/getting_started/basics.rst

@@ -986,7 +986,7 @@ not noted for a particular column will be ``NaN``:
 
    tsdf.agg({'A': ['mean', 'min'], 'B': 'sum'})
 
-.. _basics.aggregation.mixed_dtypes:
+.. _basics.aggregation.mixed_string:
 
 Mixed dtypes
 ++++++++++++
@@ -1704,14 +1704,21 @@ built-in string methods. For example:
 
  .. ipython:: python
 
-  s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
+  s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
+                dtype="string")
   s.str.lower()
 
 Powerful pattern-matching methods are provided as well, but note that
 pattern-matching generally uses `regular expressions
 <https://docs.python.org/3/library/re.html>`__ by default (and in some cases
 always uses them).
 
+.. note::
+
+   Prior to pandas 1.0, string methods were only available on ``object`` -dtype
+   ``Series``. Pandas 1.0 added the :class:`StringDtype` which is dedicated
+   to strings. See :ref:`text.types` for more.
+
 Please see :ref:`Vectorized String Methods <text.string_methods>` for a complete
 description.
 
@@ -1925,9 +1932,15 @@ period (time spans) :class:`PeriodDtype`      :class:`Period`    :class:`arrays.
 sparse              :class:`SparseDtype`      (none)             :class:`arrays.SparseArray`   :ref:`sparse`
 intervals           :class:`IntervalDtype`    :class:`Interval`  :class:`arrays.IntervalArray` :ref:`advanced.intervalindex`
 nullable integer    :class:`Int64Dtype`, ...  (none)             :class:`arrays.IntegerArray`  :ref:`integer_na`
+Strings             :class:`StringDtype`      :class:`str`       :class:`arrays.StringArray`   :ref:`text`
 =================== ========================= ================== ============================= =============================
 
-Pandas uses the ``object`` dtype for storing strings.
+Pandas has two ways to store strings.
+
+1. ``object`` dtype, which can hold any Python object, including strings.
+2. :class:`StringDtype`, which is dedicated to strings.
+
+Generally, we recommend using :class:`StringDtype`. See :ref:`text.types` fore more.
 
 Finally, arbitrary objects may be stored using the ``object`` dtype, but should
 be avoided to the extent possible (for performance and interoperability with

doc/source/reference/arrays.rst

@@ -24,6 +24,7 @@ Intervals           :class:`IntervalDtype`    :class:`Interval`  :ref:`api.array
 Nullable Integer    :class:`Int64Dtype`, ...  (none)             :ref:`api.arrays.integer_na`
 Categorical         :class:`CategoricalDtype` (none)             :ref:`api.arrays.categorical`
 Sparse              :class:`SparseDtype`      (none)             :ref:`api.arrays.sparse`
+Strings             :class:`StringDtype`      :class:`str`       :ref:`api.arrays.string`
 =================== ========================= ================== =============================
 
 Pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
@@ -460,6 +461,29 @@ and methods if the :class:`Series` contains sparse values. See
 :ref:`api.series.sparse` for more.
 
 
+.. _api.arrays.string:
+
+Text data
+---------
+
+When working with text data, where each valid element is a string or missing,
+we recommend using :class:`StringDtype` (with the alias ``"string"``).
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   arrays.StringArray
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   StringDtype
+
+The ``Series.str`` accessor is available for ``Series`` backed by a :class:`arrays.StringArray`.
+See :ref:`api.series.str` for more.
+
 
 .. Dtype attributes which are manually listed in their docstrings: including
 .. it here to make sure a docstring page is built for them
@@ -471,4 +495,4 @@ and methods if the :class:`Series` contains sparse values. See
       DatetimeTZDtype.unit
       DatetimeTZDtype.tz
       PeriodDtype.freq
-      IntervalDtype.subtype
+      IntervalDtype.subtype