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Merge branch 'master' into fix_docstring_validation
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‎asv_bench/benchmarks/categoricals.py

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@@ -282,4 +282,18 @@ def time_sort_values(self):
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self.index.sort_values(ascending=False)
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285+
class SearchSorted:
286+
def setup(self):
287+
N = 10 ** 5
288+
self.ci = tm.makeCategoricalIndex(N).sort_values()
289+
self.c = self.ci.values
290+
self.key = self.ci.categories[1]
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292+
def time_categorical_index_contains(self):
293+
self.ci.searchsorted(self.key)
294+
295+
def time_categorical_contains(self):
296+
self.c.searchsorted(self.key)
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298+
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from .pandas_vb_common import setup # noqa: F401 isort:skip

‎asv_bench/benchmarks/ctors.py

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@@ -67,7 +67,7 @@ class SeriesConstructors:
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def setup(self, data_fmt, with_index, dtype):
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if data_fmt in (gen_of_str, gen_of_tuples) and with_index:
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raise NotImplementedError(
70-
"Series constructors do not support " "using generators with indexes"
70+
"Series constructors do not support using generators with indexes"
7171
)
7272
N = 10 ** 4
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if dtype == "float":

‎asv_bench/benchmarks/eval.py

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@@ -27,7 +27,7 @@ def time_add(self, engine, threads):
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def time_and(self, engine, threads):
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pd.eval(
30-
"(self.df > 0) & (self.df2 > 0) & " "(self.df3 > 0) & (self.df4 > 0)",
30+
"(self.df > 0) & (self.df2 > 0) & (self.df3 > 0) & (self.df4 > 0)",
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engine=engine,
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)
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‎asv_bench/benchmarks/io/hdf.py

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@@ -88,11 +88,11 @@ def time_write_store_table_dc(self):
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def time_query_store_table_wide(self):
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self.store.select(
91-
"table_wide", where="index > self.start_wide and " "index < self.stop_wide"
91+
"table_wide", where="index > self.start_wide and index < self.stop_wide"
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)
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def time_query_store_table(self):
95-
self.store.select("table", where="index > self.start and " "index < self.stop")
95+
self.store.select("table", where="index > self.start and index < self.stop")
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def time_store_repr(self):
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repr(self.store)

‎ci/code_checks.sh

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@@ -122,7 +122,11 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
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MSG='Check for non-standard imports' ; echo $MSG
123123
invgrep -R --include="*.py*" -E "from pandas.core.common import " pandas
124124
invgrep -R --include="*.py*" -E "from collections.abc import " pandas
125-
# invgrep -R --include="*.py*" -E "from numpy import nan " pandas # GH#24822 not yet implemented since the offending imports have not all been removed
125+
invgrep -R --include="*.py*" -E "from numpy import nan " pandas
126+
RET=$(($RET + $?)) ; echo $MSG "DONE"
127+
128+
MSG='Check for use of exec' ; echo $MSG
129+
invgrep -R --include="*.py*" -E "[^a-zA-Z0-9_]exec\(" pandas
126130
RET=$(($RET + $?)) ; echo $MSG "DONE"
127131

128132
MSG='Check for pytest warns' ; echo $MSG
@@ -184,7 +188,7 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
184188
invgrep -R --include="*.rst" ".. ipython ::" doc/source
185189
RET=$(($RET + $?)) ; echo $MSG "DONE"
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187-
MSG='Check that no file in the repo contains tailing whitespaces' ; echo $MSG
191+
MSG='Check that no file in the repo contains trailing whitespaces' ; echo $MSG
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set -o pipefail
189193
if [[ "$AZURE" == "true" ]]; then
190194
# we exclude all c/cpp files as the c/cpp files of pandas code base are tested when Linting .c and .h files
@@ -262,13 +266,17 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
262266
-k"-from_arrays -from_breaks -from_intervals -from_tuples -set_closed -to_tuples -interval_range"
263267
RET=$(($RET + $?)) ; echo $MSG "DONE"
264268

269+
MSG='Doctests arrays/string_.py' ; echo $MSG
270+
pytest -q --doctest-modules pandas/core/arrays/string_.py
271+
RET=$(($RET + $?)) ; echo $MSG "DONE"
272+
265273
fi
266274

267275
### DOCSTRINGS ###
268276
if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
269277

270-
MSG='Validate docstrings (GL03, GL04, GL05, GL06, GL07, GL09, GL10, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT01, RT04, RT05, SA05)' ; echo $MSG
271-
$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
278+
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
279+
$BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL04,GL05,GL06,GL07,GL09,GL10,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT01,RT04,RT05,SA01,SA02,SA03,SA05
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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274282
fi

‎ci/deps/azure-36-32bit.yaml

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@@ -3,6 +3,7 @@ channels:
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- defaults
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- conda-forge
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dependencies:
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- attrs=19.1.0
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- gcc_linux-32
78
- gcc_linux-32
89
- gxx_linux-32
@@ -11,7 +12,7 @@ dependencies:
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- python=3.6.*
1213
- pytz=2017.2
1314
# universal
14-
- pytest>=4.0.2,<5.0.0
15+
- pytest
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- pytest-xdist
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- pytest-mock
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- pytest-azurepipelines

‎ci/run_tests.sh

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@@ -43,10 +43,9 @@ do
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# if no tests are found (the case of "single and slow"), pytest exits with code 5, and would make the script fail, if not for the below code
4444
sh -c "$PYTEST_CMD; ret=\$?; [ \$ret = 5 ] && exit 0 || exit \$ret"
4545

46-
# 2019-08-21 disabling because this is hitting HTTP 400 errors GH#27602
47-
# if [[ "$COVERAGE" && $? == 0 && "$TRAVIS_BRANCH" == "master" ]]; then
48-
# echo "uploading coverage for $TYPE tests"
49-
# echo "bash <(curl -s https://codecov.io/bash) -Z -c -F $TYPE -f $COVERAGE_FNAME"
50-
# bash <(curl -s https://codecov.io/bash) -Z -c -F $TYPE -f $COVERAGE_FNAME
51-
# fi
46+
if [[ "$COVERAGE" && $? == 0 && "$TRAVIS_BRANCH" == "master" ]]; then
47+
echo "uploading coverage for $TYPE tests"
48+
echo "bash <(curl -s https://codecov.io/bash) -Z -c -F $TYPE -f $COVERAGE_FNAME"
49+
bash <(curl -s https://codecov.io/bash) -Z -c -F $TYPE -f $COVERAGE_FNAME
50+
fi
5251
done

‎doc/.gitignore

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@@ -0,0 +1,4 @@
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data/
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timeseries.csv
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timeseries.parquet
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timeseries_wide.parquet

‎doc/redirects.csv

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@@ -6,6 +6,7 @@ whatsnew,whatsnew/index
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release,whatsnew/index
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# getting started
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install,getting_started/install
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10min,getting_started/10min
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basics,getting_started/basics
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comparison_with_r,getting_started/comparison/comparison_with_r

‎doc/source/conf.py

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# The theme to use for HTML and HTML Help pages. Major themes that come with
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# Sphinx are currently 'default' and 'sphinxdoc'.
194-
html_theme = "nature_with_gtoc"
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html_theme = "pandas_sphinx_theme"
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196196
# The style sheet to use for HTML and HTML Help pages. A file of that name
197197
# must exist either in Sphinx' static/ path, or in one of the custom paths
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# html_theme_options = {}
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206206
# Add any paths that contain custom themes here, relative to this directory.
207-
html_theme_path = ["themes"]
207+
# html_theme_path = ["themes"]
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209209
# The name for this set of Sphinx documents. If None, it defaults to
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# "<project> v<release> documentation".
@@ -628,11 +628,11 @@ def linkcode_resolve(domain, info):
628628
fn = os.path.relpath(fn, start=os.path.dirname(pandas.__file__))
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630630
if "+" in pandas.__version__:
631-
return "http://github.com/pandas-dev/pandas/blob/master/pandas/" "{}{}".format(
631+
return "http://github.com/pandas-dev/pandas/blob/master/pandas/{}{}".format(
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fn, linespec
633633
)
634634
else:
635-
return "http://github.com/pandas-dev/pandas/blob/" "v{}/pandas/{}{}".format(
635+
return "http://github.com/pandas-dev/pandas/blob/v{}/pandas/{}{}".format(
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pandas.__version__, fn, linespec
637637
)
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‎doc/source/development/contributing.rst

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yum groupinstall "Development Tools"
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For other Linux distributions, consult your favourite search engine for
175-
commpiler installation instructions.
175+
compiler installation instructions.
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177177
Let us know if you have any difficulties by opening an issue or reaching out on
178178
`Gitter`_.
@@ -952,7 +952,7 @@ the expected correct result::
952952
Transitioning to ``pytest``
953953
~~~~~~~~~~~~~~~~~~~~~~~~~~~
954954

955-
*pandas* existing test structure is *mostly* classed based, meaning that you will typically find tests wrapped in a class.
955+
*pandas* existing test structure is *mostly* class-based, meaning that you will typically find tests wrapped in a class.
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.. code-block:: python
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‎doc/source/ecosystem.rst

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:orphan:
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.. _ecosystem:
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{{ header }}

‎doc/source/getting_started/basics.rst

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tsdf.agg({'A': ['mean', 'min'], 'B': 'sum'})
988988
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.. _basics.aggregation.mixed_dtypes:
989+
.. _basics.aggregation.mixed_string:
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Mixed dtypes
992992
++++++++++++
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17041704

17051705
.. ipython:: python
17061706
1707-
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
1707+
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
1708+
dtype="string")
17081709
s.str.lower()
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17101711
Powerful pattern-matching methods are provided as well, but note that
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pattern-matching generally uses `regular expressions
17121713
<https://docs.python.org/3/library/re.html>`__ by default (and in some cases
17131714
always uses them).
17141715

1716+
.. note::
1717+
1718+
Prior to pandas 1.0, string methods were only available on ``object`` -dtype
1719+
``Series``. Pandas 1.0 added the :class:`StringDtype` which is dedicated
1720+
to strings. See :ref:`text.types` for more.
1721+
17151722
Please see :ref:`Vectorized String Methods <text.string_methods>` for a complete
17161723
description.
17171724

@@ -1925,9 +1932,15 @@ period (time spans) :class:`PeriodDtype` :class:`Period` :class:`arrays.
19251932
sparse :class:`SparseDtype` (none) :class:`arrays.SparseArray` :ref:`sparse`
19261933
intervals :class:`IntervalDtype` :class:`Interval` :class:`arrays.IntervalArray` :ref:`advanced.intervalindex`
19271934
nullable integer :class:`Int64Dtype`, ... (none) :class:`arrays.IntegerArray` :ref:`integer_na`
1935+
Strings :class:`StringDtype` :class:`str` :class:`arrays.StringArray` :ref:`text`
19281936
=================== ========================= ================== ============================= =============================
19291937

1930-
Pandas uses the ``object`` dtype for storing strings.
1938+
Pandas has two ways to store strings.
1939+
1940+
1. ``object`` dtype, which can hold any Python object, including strings.
1941+
2. :class:`StringDtype`, which is dedicated to strings.
1942+
1943+
Generally, we recommend using :class:`StringDtype`. See :ref:`text.types` fore more.
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19321945
Finally, arbitrary objects may be stored using the ``object`` dtype, but should
19331946
be avoided to the extent possible (for performance and interoperability with

‎doc/source/getting_started/index.rst

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.. toctree::
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:maxdepth: 2
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install
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overview
1617
10min
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basics

‎doc/source/install.rst renamed to ‎doc/source/getting_started/install.rst

File renamed without changes.

‎doc/source/index.rst.template

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@@ -40,10 +40,8 @@ See the :ref:`overview` for more detail about what's in the library.
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{% endif %}
4141
{% if not single_doc %}
4242
What's New in 1.0.0 <whatsnew/v1.0.0>
43-
install
4443
getting_started/index
4544
user_guide/index
46-
ecosystem
4745
{% endif -%}
4846
{% if include_api -%}
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reference/index
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5452
{% endif %}
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* :doc:`whatsnew/v1.0.0`
57-
* :doc:`install`
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* :doc:`getting_started/index`
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* :doc:`getting_started/install`
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* :doc:`getting_started/overview`
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* :doc:`getting_started/10min`
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* :doc:`getting_started/basics`
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* :doc:`user_guide/style`
8482
* :doc:`user_guide/options`
8583
* :doc:`user_guide/enhancingperf`
84+
* :doc:`user_guide/scale`
8685
* :doc:`user_guide/sparse`
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* :doc:`user_guide/gotchas`
8887
* :doc:`user_guide/cookbook`

‎doc/source/reference/arrays.rst

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@@ -24,6 +24,7 @@ Intervals :class:`IntervalDtype` :class:`Interval` :ref:`api.array
2424
Nullable Integer :class:`Int64Dtype`, ... (none) :ref:`api.arrays.integer_na`
2525
Categorical :class:`CategoricalDtype` (none) :ref:`api.arrays.categorical`
2626
Sparse :class:`SparseDtype` (none) :ref:`api.arrays.sparse`
27+
Strings :class:`StringDtype` :class:`str` :ref:`api.arrays.string`
2728
=================== ========================= ================== =============================
2829

2930
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
460461
:ref:`api.series.sparse` for more.
461462

462463

464+
.. _api.arrays.string:
465+
466+
Text data
467+
---------
468+
469+
When working with text data, where each valid element is a string or missing,
470+
we recommend using :class:`StringDtype` (with the alias ``"string"``).
471+
472+
.. autosummary::
473+
:toctree: api/
474+
:template: autosummary/class_without_autosummary.rst
475+
476+
arrays.StringArray
477+
478+
.. autosummary::
479+
:toctree: api/
480+
:template: autosummary/class_without_autosummary.rst
481+
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StringDtype
483+
484+
The ``Series.str`` accessor is available for ``Series`` backed by a :class:`arrays.StringArray`.
485+
See :ref:`api.series.str` for more.
486+
463487

464488
.. Dtype attributes which are manually listed in their docstrings: including
465489
.. 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
471495
DatetimeTZDtype.unit
472496
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473497
PeriodDtype.freq
474-
IntervalDtype.subtype
498+
IntervalDtype.subtype

‎doc/source/reference/indexing.rst

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166166
Index.get_slice_bound
167167
Index.get_value
168168
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169-
Index.set_value
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‎doc/source/user_guide/index.rst

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style
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options
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enhancingperf
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scale
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sparse
4243
gotchas
4344
cookbook

‎doc/source/user_guide/io.rst

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47104710
indexes. This extra column can cause problems for non-Pandas consumers that are not expecting it. You can
47114711
force including or omitting indexes with the ``index`` argument, regardless of the underlying engine.
47124712
* Index level names, if specified, must be strings.
4713-
* Categorical dtypes can be serialized to parquet, but will de-serialize as ``object`` dtype.
4713+
* In the ``pyarrow`` engine, categorical dtypes for non-string types can be serialized to parquet, but will de-serialize as their primitive dtype.
4714+
* The ``pyarrow`` engine preserves the ``ordered`` flag of categorical dtypes with string types. ``fastparquet`` does not preserve the ``ordered`` flag.
47144715
* Non supported types include ``Period`` and actual Python object types. These will raise a helpful error message
47154716
on an attempt at serialization.
47164717

@@ -4734,7 +4735,9 @@ See the documentation for `pyarrow <https://arrow.apache.org/docs/python/>`__ an
47344735
'd': np.arange(4.0, 7.0, dtype='float64'),
47354736
'e': [True, False, True],
47364737
'f': pd.date_range('20130101', periods=3),
4737-
'g': pd.date_range('20130101', periods=3, tz='US/Eastern')})
4738+
'g': pd.date_range('20130101', periods=3, tz='US/Eastern'),
4739+
'h': pd.Categorical(list('abc')),
4740+
'i': pd.Categorical(list('abc'), ordered=True)})
47384741
47394742
df
47404743
df.dtypes

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