diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst index 92799359a61d2..0ff40dcdcd150 100644 --- a/doc/source/user_guide/basics.rst +++ b/doc/source/user_guide/basics.rst @@ -160,11 +160,10 @@ Here is a sample (using 100 column x 100,000 row ``DataFrames``): .. csv-table:: :header: "Operation", "0.11.0 (ms)", "Prior Version (ms)", "Ratio to Prior" :widths: 25, 25, 25, 25 - :delim: ; - ``df1 > df2``; 13.32; 125.35; 0.1063 - ``df1 * df2``; 21.71; 36.63; 0.5928 - ``df1 + df2``; 22.04; 36.50; 0.6039 + ``df1 > df2``, 13.32, 125.35, 0.1063 + ``df1 * df2``, 21.71, 36.63, 0.5928 + ``df1 + df2``, 22.04, 36.50, 0.6039 You are highly encouraged to install both libraries. See the section :ref:`Recommended Dependencies ` for more installation info. diff --git a/doc/source/user_guide/gotchas.rst b/doc/source/user_guide/gotchas.rst index 99c85ac66623d..26eb656357bf6 100644 --- a/doc/source/user_guide/gotchas.rst +++ b/doc/source/user_guide/gotchas.rst @@ -315,19 +315,8 @@ Why not make NumPy like R? Many people have suggested that NumPy should simply emulate the ``NA`` support present in the more domain-specific statistical programming language `R -`__. Part of the reason is the NumPy type hierarchy: - -.. csv-table:: - :header: "Typeclass","Dtypes" - :widths: 30,70 - :delim: | - - ``numpy.floating`` | ``float16, float32, float64, float128`` - ``numpy.integer`` | ``int8, int16, int32, int64`` - ``numpy.unsignedinteger`` | ``uint8, uint16, uint32, uint64`` - ``numpy.object_`` | ``object_`` - ``numpy.bool_`` | ``bool_`` - ``numpy.character`` | ``bytes_, str_`` +`__. Part of the reason is the +`NumPy type hierarchy `__. The R language, by contrast, only has a handful of built-in data types: ``integer``, ``numeric`` (floating-point), ``character``, and diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst index 8c222aff52fd7..267499edfae6f 100644 --- a/doc/source/user_guide/groupby.rst +++ b/doc/source/user_guide/groupby.rst @@ -506,29 +506,28 @@ listed below, those with a ``*`` do *not* have an efficient, GroupBy-specific, i .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - :delim: ; - - :meth:`~.DataFrameGroupBy.any`;Compute whether any of the values in the groups are truthy - :meth:`~.DataFrameGroupBy.all`;Compute whether all of the values in the groups are truthy - :meth:`~.DataFrameGroupBy.count`;Compute the number of non-NA values in the groups - :meth:`~.DataFrameGroupBy.cov` * ;Compute the covariance of the groups - :meth:`~.DataFrameGroupBy.first`;Compute the first occurring value in each group - :meth:`~.DataFrameGroupBy.idxmax`;Compute the index of the maximum value in each group - :meth:`~.DataFrameGroupBy.idxmin`;Compute the index of the minimum value in each group - :meth:`~.DataFrameGroupBy.last`;Compute the last occurring value in each group - :meth:`~.DataFrameGroupBy.max`;Compute the maximum value in each group - :meth:`~.DataFrameGroupBy.mean`;Compute the mean of each group - :meth:`~.DataFrameGroupBy.median`;Compute the median of each group - :meth:`~.DataFrameGroupBy.min`;Compute the minimum value in each group - :meth:`~.DataFrameGroupBy.nunique`;Compute the number of unique values in each group - :meth:`~.DataFrameGroupBy.prod`;Compute the product of the values in each group - :meth:`~.DataFrameGroupBy.quantile`;Compute a given quantile of the values in each group - :meth:`~.DataFrameGroupBy.sem`;Compute the standard error of the mean of the values in each group - :meth:`~.DataFrameGroupBy.size`;Compute the number of values in each group - :meth:`~.DataFrameGroupBy.skew` *;Compute the skew of the values in each group - :meth:`~.DataFrameGroupBy.std`;Compute the standard deviation of the values in each group - :meth:`~.DataFrameGroupBy.sum`;Compute the sum of the values in each group - :meth:`~.DataFrameGroupBy.var`;Compute the variance of the values in each group + + :meth:`~.DataFrameGroupBy.any`,Compute whether any of the values in the groups are truthy + :meth:`~.DataFrameGroupBy.all`,Compute whether all of the values in the groups are truthy + :meth:`~.DataFrameGroupBy.count`,Compute the number of non-NA values in the groups + :meth:`~.DataFrameGroupBy.cov` * ,Compute the covariance of the groups + :meth:`~.DataFrameGroupBy.first`,Compute the first occurring value in each group + :meth:`~.DataFrameGroupBy.idxmax`,Compute the index of the maximum value in each group + :meth:`~.DataFrameGroupBy.idxmin`,Compute the index of the minimum value in each group + :meth:`~.DataFrameGroupBy.last`,Compute the last occurring value in each group + :meth:`~.DataFrameGroupBy.max`,Compute the maximum value in each group + :meth:`~.DataFrameGroupBy.mean`,Compute the mean of each group + :meth:`~.DataFrameGroupBy.median`,Compute the median of each group + :meth:`~.DataFrameGroupBy.min`,Compute the minimum value in each group + :meth:`~.DataFrameGroupBy.nunique`,Compute the number of unique values in each group + :meth:`~.DataFrameGroupBy.prod`,Compute the product of the values in each group + :meth:`~.DataFrameGroupBy.quantile`,Compute a given quantile of the values in each group + :meth:`~.DataFrameGroupBy.sem`,Compute the standard error of the mean of the values in each group + :meth:`~.DataFrameGroupBy.size`,Compute the number of values in each group + :meth:`~.DataFrameGroupBy.skew` * ,Compute the skew of the values in each group + :meth:`~.DataFrameGroupBy.std`,Compute the standard deviation of the values in each group + :meth:`~.DataFrameGroupBy.sum`,Compute the sum of the values in each group + :meth:`~.DataFrameGroupBy.var`,Compute the variance of the values in each group Some examples: @@ -832,19 +831,18 @@ The following methods on GroupBy act as transformations. .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - :delim: ; - - :meth:`~.DataFrameGroupBy.bfill`;Back fill NA values within each group - :meth:`~.DataFrameGroupBy.cumcount`;Compute the cumulative count within each group - :meth:`~.DataFrameGroupBy.cummax`;Compute the cumulative max within each group - :meth:`~.DataFrameGroupBy.cummin`;Compute the cumulative min within each group - :meth:`~.DataFrameGroupBy.cumprod`;Compute the cumulative product within each group - :meth:`~.DataFrameGroupBy.cumsum`;Compute the cumulative sum within each group - :meth:`~.DataFrameGroupBy.diff`;Compute the difference between adjacent values within each group - :meth:`~.DataFrameGroupBy.ffill`;Forward fill NA values within each group - :meth:`~.DataFrameGroupBy.pct_change`;Compute the percent change between adjacent values within each group - :meth:`~.DataFrameGroupBy.rank`;Compute the rank of each value within each group - :meth:`~.DataFrameGroupBy.shift`;Shift values up or down within each group + + :meth:`~.DataFrameGroupBy.bfill`,Back fill NA values within each group + :meth:`~.DataFrameGroupBy.cumcount`,Compute the cumulative count within each group + :meth:`~.DataFrameGroupBy.cummax`,Compute the cumulative max within each group + :meth:`~.DataFrameGroupBy.cummin`,Compute the cumulative min within each group + :meth:`~.DataFrameGroupBy.cumprod`,Compute the cumulative product within each group + :meth:`~.DataFrameGroupBy.cumsum`,Compute the cumulative sum within each group + :meth:`~.DataFrameGroupBy.diff`,Compute the difference between adjacent values within each group + :meth:`~.DataFrameGroupBy.ffill`,Forward fill NA values within each group + :meth:`~.DataFrameGroupBy.pct_change`,Compute the percent change between adjacent values within each group + :meth:`~.DataFrameGroupBy.rank`,Compute the rank of each value within each group + :meth:`~.DataFrameGroupBy.shift`,Shift values up or down within each group In addition, passing any built-in aggregation method as a string to :meth:`~.DataFrameGroupBy.transform` (see the next section) will broadcast the result @@ -1092,11 +1090,10 @@ efficient, GroupBy-specific, implementation. .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - :delim: ; - :meth:`~.DataFrameGroupBy.head`;Select the top row(s) of each group - :meth:`~.DataFrameGroupBy.nth`;Select the nth row(s) of each group - :meth:`~.DataFrameGroupBy.tail`;Select the bottom row(s) of each group + :meth:`~.DataFrameGroupBy.head`,Select the top row(s) of each group + :meth:`~.DataFrameGroupBy.nth`,Select the nth row(s) of each group + :meth:`~.DataFrameGroupBy.tail`,Select the bottom row(s) of each group Users can also use transformations along with Boolean indexing to construct complex filtrations within groups. For example, suppose we are given groups of products and diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst index 0da87e1d31fec..371a380ff4b0d 100644 --- a/doc/source/user_guide/indexing.rst +++ b/doc/source/user_guide/indexing.rst @@ -94,13 +94,14 @@ well). Any of the axes accessors may be the null slice ``:``. Axes left out of the specification are assumed to be ``:``, e.g. ``p.loc['a']`` is equivalent to ``p.loc['a', :]``. -.. csv-table:: - :header: "Object Type", "Indexers" - :widths: 30, 50 - :delim: ; - Series; ``s.loc[indexer]`` - DataFrame; ``df.loc[row_indexer,column_indexer]`` +.. ipython:: python + + ser = pd.Series(range(5), index=list("abcde")) + ser.loc[["a", "c", "e"]] + + df = pd.DataFrame(np.arange(25).reshape(5, 5), index=list("abcde"), columns=list("abcde")) + df.loc[["a", "c", "e"], ["b", "d"]] .. _indexing.basics: @@ -116,10 +117,9 @@ indexing pandas objects with ``[]``: .. csv-table:: :header: "Object Type", "Selection", "Return Value Type" :widths: 30, 30, 60 - :delim: ; - Series; ``series[label]``; scalar value - DataFrame; ``frame[colname]``; ``Series`` corresponding to colname + Series, ``series[label]``, scalar value + DataFrame, ``frame[colname]``, ``Series`` corresponding to colname Here we construct a simple time series data set to use for illustrating the indexing functionality: diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst index 49609a80d7e15..b5cc8c43ae143 100644 --- a/doc/source/user_guide/io.rst +++ b/doc/source/user_guide/io.rst @@ -16,26 +16,25 @@ The pandas I/O API is a set of top level ``reader`` functions accessed like .. csv-table:: :header: "Format Type", "Data Description", "Reader", "Writer" :widths: 30, 100, 60, 60 - :delim: ; - - text;`CSV `__;:ref:`read_csv`;:ref:`to_csv` - text;Fixed-Width Text File;:ref:`read_fwf` - text;`JSON `__;:ref:`read_json`;:ref:`to_json` - text;`HTML `__;:ref:`read_html`;:ref:`to_html` - text;`LaTeX `__;;:ref:`Styler.to_latex` - text;`XML `__;:ref:`read_xml`;:ref:`to_xml` - text; Local clipboard;:ref:`read_clipboard`;:ref:`to_clipboard` - binary;`MS Excel `__;:ref:`read_excel`;:ref:`to_excel` - binary;`OpenDocument `__;:ref:`read_excel`; - binary;`HDF5 Format `__;:ref:`read_hdf`;:ref:`to_hdf` - binary;`Feather Format `__;:ref:`read_feather`;:ref:`to_feather` - binary;`Parquet Format `__;:ref:`read_parquet`;:ref:`to_parquet` - binary;`ORC Format `__;:ref:`read_orc`;:ref:`to_orc` - binary;`Stata `__;:ref:`read_stata`;:ref:`to_stata` - binary;`SAS `__;:ref:`read_sas`; - binary;`SPSS `__;:ref:`read_spss`; - binary;`Python Pickle Format `__;:ref:`read_pickle`;:ref:`to_pickle` - SQL;`SQL `__;:ref:`read_sql`;:ref:`to_sql` + + text,`CSV `__, :ref:`read_csv`, :ref:`to_csv` + text,Fixed-Width Text File, :ref:`read_fwf` , NA + text,`JSON `__, :ref:`read_json`, :ref:`to_json` + text,`HTML `__, :ref:`read_html`, :ref:`to_html` + text,`LaTeX `__, :ref:`Styler.to_latex` , NA + text,`XML `__, :ref:`read_xml`, :ref:`to_xml` + text, Local clipboard, :ref:`read_clipboard`, :ref:`to_clipboard` + binary,`MS Excel `__ , :ref:`read_excel`, :ref:`to_excel` + binary,`OpenDocument `__, :ref:`read_excel`, NA + binary,`HDF5 Format `__, :ref:`read_hdf`, :ref:`to_hdf` + binary,`Feather Format `__, :ref:`read_feather`, :ref:`to_feather` + binary,`Parquet Format `__, :ref:`read_parquet`, :ref:`to_parquet` + binary,`ORC Format `__, :ref:`read_orc`, :ref:`to_orc` + binary,`Stata `__, :ref:`read_stata`, :ref:`to_stata` + binary,`SAS `__, :ref:`read_sas` , NA + binary,`SPSS `__, :ref:`read_spss` , NA + binary,`Python Pickle Format `__, :ref:`read_pickle`, :ref:`to_pickle` + SQL,`SQL `__, :ref:`read_sql`,:ref:`to_sql` :ref:`Here ` is an informal performance comparison for some of these IO methods. @@ -1837,14 +1836,13 @@ with optional parameters: .. csv-table:: :widths: 20, 150 - :delim: ; - ``split``; dict like {index -> [index], columns -> [columns], data -> [values]} - ``records``; list like [{column -> value}, ... , {column -> value}] - ``index``; dict like {index -> {column -> value}} - ``columns``; dict like {column -> {index -> value}} - ``values``; just the values array - ``table``; adhering to the JSON `Table Schema`_ + ``split``, dict like {index -> [index]; columns -> [columns]; data -> [values]} + ``records``, list like [{column -> value}; ... ] + ``index``, dict like {index -> {column -> value}} + ``columns``, dict like {column -> {index -> value}} + ``values``, just the values array + ``table``, adhering to the JSON `Table Schema`_ * ``date_format`` : string, type of date conversion, 'epoch' for timestamp, 'iso' for ISO8601. * ``double_precision`` : The number of decimal places to use when encoding floating point values, default 10. @@ -2025,14 +2023,13 @@ is ``None``. To explicitly force ``Series`` parsing, pass ``typ=series`` .. csv-table:: :widths: 20, 150 - :delim: ; - - ``split``; dict like {index -> [index], columns -> [columns], data -> [values]} - ``records``; list like [{column -> value}, ... , {column -> value}] - ``index``; dict like {index -> {column -> value}} - ``columns``; dict like {column -> {index -> value}} - ``values``; just the values array - ``table``; adhering to the JSON `Table Schema`_ + + ``split``, dict like {index -> [index]; columns -> [columns]; data -> [values]} + ``records``, list like [{column -> value} ...] + ``index``, dict like {index -> {column -> value}} + ``columns``, dict like {column -> {index -> value}} + ``values``, just the values array + ``table``, adhering to the JSON `Table Schema`_ * ``dtype`` : if True, infer dtypes, if a dict of column to dtype, then use those, if ``False``, then don't infer dtypes at all, default is True, apply only to the data. diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst index cf27fc8385223..ad2690ae395be 100644 --- a/doc/source/user_guide/text.rst +++ b/doc/source/user_guide/text.rst @@ -726,57 +726,56 @@ Method summary .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - :delim: ; - - :meth:`~Series.str.cat`;Concatenate strings - :meth:`~Series.str.split`;Split strings on delimiter - :meth:`~Series.str.rsplit`;Split strings on delimiter working from the end of the string - :meth:`~Series.str.get`;Index into each element (retrieve i-th element) - :meth:`~Series.str.join`;Join strings in each element of the Series with passed separator - :meth:`~Series.str.get_dummies`;Split strings on the delimiter returning DataFrame of dummy variables - :meth:`~Series.str.contains`;Return boolean array if each string contains pattern/regex - :meth:`~Series.str.replace`;Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence - :meth:`~Series.str.removeprefix`;Remove prefix from string, i.e. only remove if string starts with prefix. - :meth:`~Series.str.removesuffix`;Remove suffix from string, i.e. only remove if string ends with suffix. - :meth:`~Series.str.repeat`;Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) - :meth:`~Series.str.pad`;"Add whitespace to left, right, or both sides of strings" - :meth:`~Series.str.center`;Equivalent to ``str.center`` - :meth:`~Series.str.ljust`;Equivalent to ``str.ljust`` - :meth:`~Series.str.rjust`;Equivalent to ``str.rjust`` - :meth:`~Series.str.zfill`;Equivalent to ``str.zfill`` - :meth:`~Series.str.wrap`;Split long strings into lines with length less than a given width - :meth:`~Series.str.slice`;Slice each string in the Series - :meth:`~Series.str.slice_replace`;Replace slice in each string with passed value - :meth:`~Series.str.count`;Count occurrences of pattern - :meth:`~Series.str.startswith`;Equivalent to ``str.startswith(pat)`` for each element - :meth:`~Series.str.endswith`;Equivalent to ``str.endswith(pat)`` for each element - :meth:`~Series.str.findall`;Compute list of all occurrences of pattern/regex for each string - :meth:`~Series.str.match`;"Call ``re.match`` on each element, returning matched groups as list" - :meth:`~Series.str.extract`;"Call ``re.search`` on each element, returning DataFrame with one row for each element and one column for each regex capture group" - :meth:`~Series.str.extractall`;"Call ``re.findall`` on each element, returning DataFrame with one row for each match and one column for each regex capture group" - :meth:`~Series.str.len`;Compute string lengths - :meth:`~Series.str.strip`;Equivalent to ``str.strip`` - :meth:`~Series.str.rstrip`;Equivalent to ``str.rstrip`` - :meth:`~Series.str.lstrip`;Equivalent to ``str.lstrip`` - :meth:`~Series.str.partition`;Equivalent to ``str.partition`` - :meth:`~Series.str.rpartition`;Equivalent to ``str.rpartition`` - :meth:`~Series.str.lower`;Equivalent to ``str.lower`` - :meth:`~Series.str.casefold`;Equivalent to ``str.casefold`` - :meth:`~Series.str.upper`;Equivalent to ``str.upper`` - :meth:`~Series.str.find`;Equivalent to ``str.find`` - :meth:`~Series.str.rfind`;Equivalent to ``str.rfind`` - :meth:`~Series.str.index`;Equivalent to ``str.index`` - :meth:`~Series.str.rindex`;Equivalent to ``str.rindex`` - :meth:`~Series.str.capitalize`;Equivalent to ``str.capitalize`` - :meth:`~Series.str.swapcase`;Equivalent to ``str.swapcase`` - :meth:`~Series.str.normalize`;Return Unicode normal form. Equivalent to ``unicodedata.normalize`` - :meth:`~Series.str.translate`;Equivalent to ``str.translate`` - :meth:`~Series.str.isalnum`;Equivalent to ``str.isalnum`` - :meth:`~Series.str.isalpha`;Equivalent to ``str.isalpha`` - :meth:`~Series.str.isdigit`;Equivalent to ``str.isdigit`` - :meth:`~Series.str.isspace`;Equivalent to ``str.isspace`` - :meth:`~Series.str.islower`;Equivalent to ``str.islower`` - :meth:`~Series.str.isupper`;Equivalent to ``str.isupper`` - :meth:`~Series.str.istitle`;Equivalent to ``str.istitle`` - :meth:`~Series.str.isnumeric`;Equivalent to ``str.isnumeric`` - :meth:`~Series.str.isdecimal`;Equivalent to ``str.isdecimal`` + + :meth:`~Series.str.cat`,Concatenate strings + :meth:`~Series.str.split`,Split strings on delimiter + :meth:`~Series.str.rsplit`,Split strings on delimiter working from the end of the string + :meth:`~Series.str.get`,Index into each element (retrieve i-th element) + :meth:`~Series.str.join`,Join strings in each element of the Series with passed separator + :meth:`~Series.str.get_dummies`,Split strings on the delimiter returning DataFrame of dummy variables + :meth:`~Series.str.contains`,Return boolean array if each string contains pattern/regex + :meth:`~Series.str.replace`,Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence + :meth:`~Series.str.removeprefix`,Remove prefix from string i.e. only remove if string starts with prefix. + :meth:`~Series.str.removesuffix`,Remove suffix from string i.e. only remove if string ends with suffix. + :meth:`~Series.str.repeat`,Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) + :meth:`~Series.str.pad`,Add whitespace to the sides of strings + :meth:`~Series.str.center`,Equivalent to ``str.center`` + :meth:`~Series.str.ljust`,Equivalent to ``str.ljust`` + :meth:`~Series.str.rjust`,Equivalent to ``str.rjust`` + :meth:`~Series.str.zfill`,Equivalent to ``str.zfill`` + :meth:`~Series.str.wrap`,Split long strings into lines with length less than a given width + :meth:`~Series.str.slice`,Slice each string in the Series + :meth:`~Series.str.slice_replace`,Replace slice in each string with passed value + :meth:`~Series.str.count`,Count occurrences of pattern + :meth:`~Series.str.startswith`,Equivalent to ``str.startswith(pat)`` for each element + :meth:`~Series.str.endswith`,Equivalent to ``str.endswith(pat)`` for each element + :meth:`~Series.str.findall`,Compute list of all occurrences of pattern/regex for each string + :meth:`~Series.str.match`,Call ``re.match`` on each element returning matched groups as list + :meth:`~Series.str.extract`,Call ``re.search`` on each element returning DataFrame with one row for each element and one column for each regex capture group + :meth:`~Series.str.extractall`,Call ``re.findall`` on each element returning DataFrame with one row for each match and one column for each regex capture group + :meth:`~Series.str.len`,Compute string lengths + :meth:`~Series.str.strip`,Equivalent to ``str.strip`` + :meth:`~Series.str.rstrip`,Equivalent to ``str.rstrip`` + :meth:`~Series.str.lstrip`,Equivalent to ``str.lstrip`` + :meth:`~Series.str.partition`,Equivalent to ``str.partition`` + :meth:`~Series.str.rpartition`,Equivalent to ``str.rpartition`` + :meth:`~Series.str.lower`,Equivalent to ``str.lower`` + :meth:`~Series.str.casefold`,Equivalent to ``str.casefold`` + :meth:`~Series.str.upper`,Equivalent to ``str.upper`` + :meth:`~Series.str.find`,Equivalent to ``str.find`` + :meth:`~Series.str.rfind`,Equivalent to ``str.rfind`` + :meth:`~Series.str.index`,Equivalent to ``str.index`` + :meth:`~Series.str.rindex`,Equivalent to ``str.rindex`` + :meth:`~Series.str.capitalize`,Equivalent to ``str.capitalize`` + :meth:`~Series.str.swapcase`,Equivalent to ``str.swapcase`` + :meth:`~Series.str.normalize`,Return Unicode normal form. Equivalent to ``unicodedata.normalize`` + :meth:`~Series.str.translate`,Equivalent to ``str.translate`` + :meth:`~Series.str.isalnum`,Equivalent to ``str.isalnum`` + :meth:`~Series.str.isalpha`,Equivalent to ``str.isalpha`` + :meth:`~Series.str.isdigit`,Equivalent to ``str.isdigit`` + :meth:`~Series.str.isspace`,Equivalent to ``str.isspace`` + :meth:`~Series.str.islower`,Equivalent to ``str.islower`` + :meth:`~Series.str.isupper`,Equivalent to ``str.isupper`` + :meth:`~Series.str.istitle`,Equivalent to ``str.istitle`` + :meth:`~Series.str.isnumeric`,Equivalent to ``str.isnumeric`` + :meth:`~Series.str.isdecimal`,Equivalent to ``str.isdecimal`` diff --git a/environment.yml b/environment.yml index d0a612a363878..dcc7aa5280b2c 100644 --- a/environment.yml +++ b/environment.yml @@ -89,7 +89,6 @@ dependencies: - numpydoc - pydata-sphinx-theme=0.14 - pytest-cython # doctest - - docutils < 0.21 # https://github.com/sphinx-doc/sphinx/issues/12302 - sphinx - sphinx-design - sphinx-copybutton diff --git a/requirements-dev.txt b/requirements-dev.txt index 6c5764bf589cc..f5da7f70ccdba 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -64,7 +64,6 @@ natsort numpydoc pydata-sphinx-theme==0.14 pytest-cython -docutils < 0.21 sphinx sphinx-design sphinx-copybutton