From 6925ddb73888845f38ef7aa98791562f5e25c400 Mon Sep 17 00:00:00 2001 From: Su Date: Sun, 12 Feb 2023 12:04:39 +0800 Subject: [PATCH 1/6] Ignore F821 in setup.cfg --- setup.cfg | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 88b61086e1e0f..f58cdbf5d5fd5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -24,7 +24,9 @@ ignore = # Use "collections.abc.*" instead of "typing.*" (PEP 585 syntax) Y027, # while int | float can be shortened to float, the former is more explicit - Y041 + Y041, + # undefined name 'pd' error flooding logs, ignore temporarily + F821 exclude = doc/sphinxext/*.py, doc/build/*.py, From 39befdef6f8ef21bf7344b7802c8c79670c14669 Mon Sep 17 00:00:00 2001 From: Su Date: Mon, 13 Feb 2023 16:37:22 +0800 Subject: [PATCH 2/6] Fix doctest F721 errors --- pandas/core/generic.py | 2 +- pandas/errors/__init__.py | 10 +++++----- pandas/io/formats/style.py | 32 ++++++++++++++++--------------- pandas/io/formats/style_render.py | 4 ++-- 4 files changed, 25 insertions(+), 23 deletions(-) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5d2a0fe66cc1d..2e8f8b837632d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3334,7 +3334,7 @@ def to_latex( >>> print(df.to_latex(index=False, ... formatters={"name": str.upper}, ... float_format="{:.1f}".format, - ... ) # doctest: +SKIP + ... )) # doctest: +SKIP \begin{tabular}{lrr} \toprule name & age & height \\ diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py index bb1905ccd4740..3ecee50ffbaa7 100644 --- a/pandas/errors/__init__.py +++ b/pandas/errors/__init__.py @@ -455,11 +455,11 @@ class CSSWarning(UserWarning): Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 1]}) - >>> df.style.applymap(lambda x: 'background-color: blueGreenRed;') - ... .to_excel('styled.xlsx') # doctest: +SKIP + >>> (df.style.applymap(lambda x: 'background-color: blueGreenRed;') + ... .to_excel('styled.xlsx')) # doctest: +SKIP ... # CSSWarning: Unhandled color format: 'blueGreenRed' - >>> df.style.applymap(lambda x: 'border: 1px solid red red;') - ... .to_excel('styled.xlsx') # doctest: +SKIP + >>> (df.style.applymap(lambda x: 'border: 1px solid red red;') + ... .to_excel('styled.xlsx')) # doctest: +SKIP ... # CSSWarning: Too many tokens provided to "border" (expected 1-3) """ @@ -569,7 +569,7 @@ class CategoricalConversionWarning(Warning): >>> from pandas.io.stata import StataReader >>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP ... for i, block in enumerate(reader): - ... print(i, block)) + ... print(i, block) ... # CategoricalConversionWarning: One or more series with value labels... """ diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index 442f2ab72a1e2..7f73b53b8c61d 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -962,12 +962,12 @@ def to_latex( Second we will format the display and, since our table is quite wide, will hide the repeated level-0 of the index: - >>> styler.format(subset="Equity", precision=2) + >>> (styler.format(subset="Equity", precision=2) ... .format(subset="Stats", precision=1, thousands=",") ... .format(subset="Rating", formatter=str.upper) ... .format_index(escape="latex", axis=1) ... .format_index(escape="latex", axis=0) - ... .hide(level=0, axis=0) # doctest: +SKIP + ... .hide(level=0, axis=0)) # doctest: +SKIP Note that one of the string entries of the index and column headers is "H&M". Without applying the `escape="latex"` option to the `format_index` method the @@ -983,8 +983,8 @@ def to_latex( ... elif v == "Sell": color = "#ff5933" ... else: color = "#ffdd33" ... return f"color: {color}; font-weight: bold;" - >>> styler.background_gradient(cmap="inferno", subset="Equity", vmin=0, vmax=1) - ... .applymap(rating_color, subset="Rating") # doctest: +SKIP + >>> (styler.background_gradient(cmap="inferno", subset="Equity", vmin=0, vmax=1) + ... .applymap(rating_color, subset="Rating")) # doctest: +SKIP All the above styles will work with HTML (see below) and LaTeX upon conversion: @@ -1871,7 +1871,7 @@ def apply_index( >>> df = pd.DataFrame([[1,2], [3,4]], index=["A", "B"]) >>> def color_b(s): ... return {ret} - >>> df.style.{this}_index(color_b) # doctest: +SKIP + >>> df.style.apply_index(color_b) # doctest: +SKIP .. figure:: ../../_static/style/appmaphead1.png @@ -1879,9 +1879,9 @@ def apply_index( >>> midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']]) >>> df = pd.DataFrame([np.arange(8)], columns=midx) - >>> def highlight_x({var}): + >>> def highlight_x(s): ... return {ret2} - >>> df.style.{this}_index(highlight_x, axis="columns", level=[0, 2]) + >>> df.style.apply_index(highlight_x, axis="columns", level=[0, 2]) ... # doctest: +SKIP .. figure:: ../../_static/style/appmaphead2.png @@ -2784,31 +2784,33 @@ def background_gradient( Shading the values column-wise, with ``axis=0``, preselecting numeric columns - >>> df.style.{name}_gradient(axis=0) # doctest: +SKIP + >>> df.style.background_gradient(axis=0) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_ax0.png Shading all values collectively using ``axis=None`` - >>> df.style.{name}_gradient(axis=None) # doctest: +SKIP + >>> df.style.background_gradient(axis=None) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone.png Compress the color map from the both ``low`` and ``high`` ends - >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0) # doctest: +SKIP + >>> df.style.background_gradient(axis=None, + ... low=0.75, high=1.0) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png Manually setting ``vmin`` and ``vmax`` gradient thresholds - >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6) # doctest: +SKIP + >>> df.style.background_gradient(axis=None, + ... vmin=6.7, vmax=21.6) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png Setting a ``gmap`` and applying to all columns with another ``cmap`` - >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd') + >>> df.style.background_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd') ... # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_gmap.png @@ -2817,7 +2819,7 @@ def background_gradient( explicitly state ``subset`` to match the ``gmap`` shape >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]]) - >>> df.style.{name}_gradient(axis=None, gmap=gmap, + >>> df.style.background_gradient(axis=None, gmap=gmap, ... cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)'] ... ) # doctest: +SKIP @@ -3504,9 +3506,9 @@ def pipe(self, func: Callable, *args, **kwargs): Since the method returns a ``Styler`` object it can be chained with other methods as if applying the underlying highlighters directly. - >>> df.style.format("{:.1f}") + >>> (df.style.format("{:.1f}") ... .pipe(some_highlights, min_color="green") - ... .highlight_between(left=2, right=5) # doctest: +SKIP + ... .highlight_between(left=2, right=5)) # doctest: +SKIP .. figure:: ../../_static/style/df_pipe_hl2.png diff --git a/pandas/io/formats/style_render.py b/pandas/io/formats/style_render.py index 5264342661b3f..ae7b409d28137 100644 --- a/pandas/io/formats/style_render.py +++ b/pandas/io/formats/style_render.py @@ -1077,8 +1077,8 @@ def format( Multiple ``na_rep`` or ``precision`` specifications under the default ``formatter``. - >>> df.style.format(na_rep='MISS', precision=1, subset=[0]) - ... .format(na_rep='PASS', precision=2, subset=[1, 2]) # doctest: +SKIP + >>> (df.style.format(na_rep='MISS', precision=1, subset=[0]) + ... .format(na_rep='PASS', precision=2, subset=[1, 2])) # doctest: +SKIP 0 1 2 0 MISS 1.00 A 1 2.0 PASS 3.00 From 4d9713c873632e395085533ab29d9ca68d7fc325 Mon Sep 17 00:00:00 2001 From: Su Date: Tue, 14 Feb 2023 18:33:12 +0800 Subject: [PATCH 3/6] Fix indentation --- pandas/io/formats/style.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index 7f73b53b8c61d..451d060d80222 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -2797,14 +2797,16 @@ def background_gradient( Compress the color map from the both ``low`` and ``high`` ends >>> df.style.background_gradient(axis=None, - ... low=0.75, high=1.0) # doctest: +SKIP + ... low=0.75, + ... high=1.0) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png Manually setting ``vmin`` and ``vmax`` gradient thresholds >>> df.style.background_gradient(axis=None, - ... vmin=6.7, vmax=21.6) # doctest: +SKIP + ... vmin=6.7, + ... vmax=21.6) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png From a0f77f089ad74ac0ca1350621480d1a9f3e48dad Mon Sep 17 00:00:00 2001 From: Su Date: Tue, 14 Feb 2023 21:42:17 +0800 Subject: [PATCH 4/6] Remove F821 line --- ci/code_checks.sh | 988 +++++++++++++++++++++++----------------------- setup.cfg | 4 +- 2 files changed, 495 insertions(+), 497 deletions(-) diff --git a/ci/code_checks.sh b/ci/code_checks.sh index 567ae6da92ae2..bab5904077d46 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -79,501 +79,501 @@ fi ### DOCSTRINGS ### if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then - MSG='Validate docstrings (EX04, GL01, GL02, GL03, GL04, GL05, GL06, GL07, GL09, GL10, PR03, PR04, PR05, PR06, PR08, PR09, PR10, RT01, RT02, RT04, RT05, SA02, SA03, SA04, SS01, SS02, SS03, SS04, SS05, SS06)' ; echo $MSG - $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06 - RET=$(($RET + $?)) ; echo $MSG "DONE" + # MSG='Validate docstrings (EX04, GL01, GL02, GL03, GL04, GL05, GL06, GL07, GL09, GL10, PR03, PR04, PR05, PR06, PR08, PR09, PR10, RT01, RT02, RT04, RT05, SA02, SA03, SA04, SS01, SS02, SS03, SS04, SS05, SS06)' ; echo $MSG + # $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06 + # RET=$(($RET + $?)) ; echo $MSG "DONE" - MSG='Partially validate docstrings (EX01)' ; echo $MSG - $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \ - pandas.Series.index \ - pandas.Series.dtype \ - pandas.Series.nbytes \ - pandas.Series.ndim \ - pandas.Series.size \ - pandas.Series.T \ - pandas.Series.hasnans \ - pandas.Series.dtypes \ - pandas.Series.to_period \ - pandas.Series.to_timestamp \ - pandas.Series.to_list \ - pandas.Series.__iter__ \ - pandas.Series.keys \ - pandas.Series.item \ - pandas.Series.pipe \ - pandas.Series.kurt \ - pandas.Series.mean \ - pandas.Series.median \ - pandas.Series.mode \ - pandas.Series.sem \ - pandas.Series.skew \ - pandas.Series.kurtosis \ - pandas.Series.is_unique \ - pandas.Series.is_monotonic_increasing \ - pandas.Series.is_monotonic_decreasing \ - pandas.Series.backfill \ - pandas.Series.pad \ - pandas.Series.argsort \ - pandas.Series.reorder_levels \ - pandas.Series.ravel \ - pandas.Series.first_valid_index \ - pandas.Series.last_valid_index \ - pandas.Series.dt.date \ - pandas.Series.dt.time \ - pandas.Series.dt.timetz \ - pandas.Series.dt.dayofyear \ - pandas.Series.dt.day_of_year \ - pandas.Series.dt.quarter \ - pandas.Series.dt.daysinmonth \ - pandas.Series.dt.days_in_month \ - pandas.Series.dt.tz \ - pandas.Series.dt.end_time \ - pandas.Series.dt.days \ - pandas.Series.dt.seconds \ - pandas.Series.dt.microseconds \ - pandas.Series.dt.nanoseconds \ - pandas.Series.str.center \ - pandas.Series.str.decode \ - pandas.Series.str.encode \ - pandas.Series.str.find \ - pandas.Series.str.fullmatch \ - pandas.Series.str.index \ - pandas.Series.str.ljust \ - pandas.Series.str.match \ - pandas.Series.str.normalize \ - pandas.Series.str.rfind \ - pandas.Series.str.rindex \ - pandas.Series.str.rjust \ - pandas.Series.str.translate \ - pandas.Series.sparse \ - pandas.DataFrame.sparse \ - pandas.Series.cat.categories \ - pandas.Series.cat.ordered \ - pandas.Series.cat.codes \ - pandas.Series.cat.reorder_categories \ - pandas.Series.cat.set_categories \ - pandas.Series.cat.as_ordered \ - pandas.Series.cat.as_unordered \ - pandas.Series.sparse.fill_value \ - pandas.Flags \ - pandas.Series.attrs \ - pandas.Series.plot \ - pandas.Series.hist \ - pandas.Series.to_string \ - pandas.errors.AbstractMethodError \ - pandas.errors.AccessorRegistrationWarning \ - pandas.errors.AttributeConflictWarning \ - pandas.errors.DataError \ - pandas.errors.EmptyDataError \ - pandas.errors.IncompatibilityWarning \ - pandas.errors.InvalidComparison \ - pandas.errors.InvalidIndexError \ - pandas.errors.InvalidVersion \ - pandas.errors.IntCastingNaNError \ - pandas.errors.LossySetitemError \ - pandas.errors.MergeError \ - pandas.errors.NoBufferPresent \ - pandas.errors.NullFrequencyError \ - pandas.errors.NumbaUtilError \ - pandas.errors.OptionError \ - pandas.errors.OutOfBoundsDatetime \ - pandas.errors.OutOfBoundsTimedelta \ - pandas.errors.ParserError \ - pandas.errors.PerformanceWarning \ - pandas.errors.PyperclipException \ - pandas.errors.PyperclipWindowsException \ - pandas.errors.UnsortedIndexError \ - pandas.errors.UnsupportedFunctionCall \ - pandas.show_versions \ - pandas.test \ - pandas.NaT \ - pandas.Timestamp.as_unit \ - pandas.Timestamp.ctime \ - pandas.Timestamp.date \ - pandas.Timestamp.dst \ - pandas.Timestamp.isocalendar \ - pandas.Timestamp.isoweekday \ - pandas.Timestamp.strptime \ - pandas.Timestamp.time \ - pandas.Timestamp.timetuple \ - pandas.Timestamp.timetz \ - pandas.Timestamp.to_datetime64 \ - pandas.Timestamp.toordinal \ - pandas.Timestamp.tzname \ - pandas.Timestamp.utcoffset \ - pandas.Timestamp.utctimetuple \ - pandas.Timestamp.weekday \ - pandas.arrays.DatetimeArray \ - pandas.Timedelta.view \ - pandas.Timedelta.as_unit \ - pandas.Timedelta.ceil \ - pandas.Timedelta.floor \ - pandas.Timedelta.round \ - pandas.Timedelta.to_pytimedelta \ - pandas.Timedelta.to_timedelta64 \ - pandas.Timedelta.to_numpy \ - pandas.Timedelta.total_seconds \ - pandas.arrays.TimedeltaArray \ - pandas.Period.end_time \ - pandas.Period.freqstr \ - pandas.Period.is_leap_year \ - pandas.Period.month \ - pandas.Period.quarter \ - pandas.Period.year \ - pandas.Period.asfreq \ - pandas.Period.now \ - pandas.Period.to_timestamp \ - pandas.arrays.PeriodArray \ - pandas.Interval.closed \ - pandas.Interval.left \ - pandas.Interval.length \ - pandas.Interval.right \ - pandas.arrays.IntervalArray.left \ - pandas.arrays.IntervalArray.right \ - pandas.arrays.IntervalArray.closed \ - pandas.arrays.IntervalArray.mid \ - pandas.arrays.IntervalArray.length \ - pandas.arrays.IntervalArray.is_non_overlapping_monotonic \ - pandas.arrays.IntervalArray.from_arrays \ - pandas.arrays.IntervalArray.to_tuples \ - pandas.Int8Dtype \ - pandas.Int16Dtype \ - pandas.Int32Dtype \ - pandas.Int64Dtype \ - pandas.UInt8Dtype \ - pandas.UInt16Dtype \ - pandas.UInt32Dtype \ - pandas.UInt64Dtype \ - pandas.NA \ - pandas.Float32Dtype \ - pandas.Float64Dtype \ - pandas.CategoricalDtype.categories \ - pandas.CategoricalDtype.ordered \ - pandas.Categorical.dtype \ - pandas.Categorical.categories \ - pandas.Categorical.ordered \ - pandas.Categorical.codes \ - pandas.Categorical.__array__ \ - pandas.SparseDtype \ - pandas.DatetimeTZDtype.unit \ - pandas.DatetimeTZDtype.tz \ - pandas.PeriodDtype.freq \ - pandas.IntervalDtype.subtype \ - pandas_dtype \ - pandas.api.types.is_bool \ - pandas.api.types.is_complex \ - pandas.api.types.is_float \ - pandas.api.types.is_integer \ - pandas.api.types.pandas_dtype \ - pandas.read_clipboard \ - pandas.ExcelFile \ - pandas.ExcelFile.parse \ - pandas.DataFrame.to_html \ - pandas.io.formats.style.Styler.to_html \ - pandas.HDFStore.put \ - pandas.HDFStore.append \ - pandas.HDFStore.get \ - pandas.HDFStore.select \ - pandas.HDFStore.info \ - pandas.HDFStore.keys \ - pandas.HDFStore.groups \ - pandas.HDFStore.walk \ - pandas.read_feather \ - pandas.DataFrame.to_feather \ - pandas.read_parquet \ - pandas.read_orc \ - pandas.read_sas \ - pandas.read_spss \ - pandas.read_sql_query \ - pandas.read_gbq \ - pandas.io.stata.StataReader.data_label \ - pandas.io.stata.StataReader.value_labels \ - pandas.io.stata.StataReader.variable_labels \ - pandas.io.stata.StataWriter.write_file \ - pandas.core.resample.Resampler.__iter__ \ - pandas.core.resample.Resampler.groups \ - pandas.core.resample.Resampler.indices \ - pandas.core.resample.Resampler.get_group \ - pandas.core.resample.Resampler.ffill \ - pandas.core.resample.Resampler.asfreq \ - pandas.core.resample.Resampler.count \ - pandas.core.resample.Resampler.nunique \ - pandas.core.resample.Resampler.max \ - pandas.core.resample.Resampler.mean \ - pandas.core.resample.Resampler.median \ - pandas.core.resample.Resampler.min \ - pandas.core.resample.Resampler.ohlc \ - pandas.core.resample.Resampler.prod \ - pandas.core.resample.Resampler.size \ - pandas.core.resample.Resampler.sem \ - pandas.core.resample.Resampler.std \ - pandas.core.resample.Resampler.sum \ - pandas.core.resample.Resampler.var \ - pandas.core.resample.Resampler.quantile \ - pandas.describe_option \ - pandas.reset_option \ - pandas.get_option \ - pandas.set_option \ - pandas.plotting.deregister_matplotlib_converters \ - pandas.plotting.plot_params \ - pandas.plotting.register_matplotlib_converters \ - pandas.plotting.table \ - pandas.util.hash_array \ - pandas.util.hash_pandas_object \ - pandas_object \ - pandas.api.interchange.from_dataframe \ - pandas.Index.values \ - pandas.Index.hasnans \ - pandas.Index.dtype \ - pandas.Index.inferred_type \ - pandas.Index.shape \ - pandas.Index.name \ - pandas.Index.nbytes \ - pandas.Index.ndim \ - pandas.Index.size \ - pandas.Index.T \ - pandas.Index.memory_usage \ - pandas.Index.copy \ - pandas.Index.drop \ - pandas.Index.identical \ - pandas.Index.insert \ - pandas.Index.is_ \ - pandas.Index.take \ - pandas.Index.putmask \ - pandas.Index.unique \ - pandas.Index.fillna \ - pandas.Index.dropna \ - pandas.Index.astype \ - pandas.Index.item \ - pandas.Index.map \ - pandas.Index.ravel \ - pandas.Index.to_list \ - pandas.Index.append \ - pandas.Index.join \ - pandas.Index.asof_locs \ - pandas.Index.get_slice_bound \ - pandas.RangeIndex \ - pandas.RangeIndex.start \ - pandas.RangeIndex.stop \ - pandas.RangeIndex.step \ - pandas.RangeIndex.from_range \ - pandas.CategoricalIndex.codes \ - pandas.CategoricalIndex.categories \ - pandas.CategoricalIndex.ordered \ - pandas.CategoricalIndex.reorder_categories \ - pandas.CategoricalIndex.set_categories \ - pandas.CategoricalIndex.as_ordered \ - pandas.CategoricalIndex.as_unordered \ - pandas.CategoricalIndex.equals \ - pandas.IntervalIndex.closed \ - pandas.IntervalIndex.values \ - pandas.IntervalIndex.is_non_overlapping_monotonic \ - pandas.IntervalIndex.to_tuples \ - pandas.MultiIndex.dtypes \ - pandas.MultiIndex.drop \ - pandas.DatetimeIndex \ - pandas.DatetimeIndex.date \ - pandas.DatetimeIndex.time \ - pandas.DatetimeIndex.timetz \ - pandas.DatetimeIndex.dayofyear \ - pandas.DatetimeIndex.day_of_year \ - pandas.DatetimeIndex.quarter \ - pandas.DatetimeIndex.tz \ - pandas.DatetimeIndex.freqstr \ - pandas.DatetimeIndex.inferred_freq \ - pandas.DatetimeIndex.indexer_at_time \ - pandas.DatetimeIndex.indexer_between_time \ - pandas.DatetimeIndex.snap \ - pandas.DatetimeIndex.as_unit \ - pandas.DatetimeIndex.to_pydatetime \ - pandas.DatetimeIndex.to_series \ - pandas.DatetimeIndex.mean \ - pandas.DatetimeIndex.std \ - pandas.TimedeltaIndex \ - pandas.TimedeltaIndex.days \ - pandas.TimedeltaIndex.seconds \ - pandas.TimedeltaIndex.microseconds \ - pandas.TimedeltaIndex.nanoseconds \ - pandas.TimedeltaIndex.components \ - pandas.TimedeltaIndex.inferred_freq \ - pandas.TimedeltaIndex.as_unit \ - pandas.TimedeltaIndex.to_pytimedelta \ - pandas.TimedeltaIndex.mean \ - pandas.PeriodIndex.day \ - pandas.PeriodIndex.dayofweek \ - pandas.PeriodIndex.day_of_week \ - pandas.PeriodIndex.dayofyear \ - pandas.PeriodIndex.day_of_year \ - pandas.PeriodIndex.days_in_month \ - pandas.PeriodIndex.daysinmonth \ - pandas.PeriodIndex.end_time \ - pandas.PeriodIndex.freqstr \ - pandas.PeriodIndex.hour \ - pandas.PeriodIndex.is_leap_year \ - pandas.PeriodIndex.minute \ - pandas.PeriodIndex.month \ - pandas.PeriodIndex.quarter \ - pandas.PeriodIndex.second \ - pandas.PeriodIndex.week \ - pandas.PeriodIndex.weekday \ - pandas.PeriodIndex.weekofyear \ - pandas.PeriodIndex.year \ - pandas.PeriodIndex.to_timestamp \ - pandas.core.window.rolling.Rolling.max \ - pandas.core.window.rolling.Rolling.cov \ - pandas.core.window.rolling.Rolling.skew \ - pandas.core.window.rolling.Rolling.apply \ - pandas.core.window.rolling.Window.mean \ - pandas.core.window.rolling.Window.sum \ - pandas.core.window.rolling.Window.var \ - pandas.core.window.rolling.Window.std \ - pandas.core.window.expanding.Expanding.count \ - pandas.core.window.expanding.Expanding.sum \ - pandas.core.window.expanding.Expanding.mean \ - pandas.core.window.expanding.Expanding.median \ - pandas.core.window.expanding.Expanding.min \ - pandas.core.window.expanding.Expanding.max \ - pandas.core.window.expanding.Expanding.corr \ - pandas.core.window.expanding.Expanding.cov \ - pandas.core.window.expanding.Expanding.skew \ - pandas.core.window.expanding.Expanding.apply \ - pandas.core.window.expanding.Expanding.quantile \ - pandas.core.window.ewm.ExponentialMovingWindow.mean \ - pandas.core.window.ewm.ExponentialMovingWindow.sum \ - pandas.core.window.ewm.ExponentialMovingWindow.std \ - pandas.core.window.ewm.ExponentialMovingWindow.var \ - pandas.core.window.ewm.ExponentialMovingWindow.corr \ - pandas.core.window.ewm.ExponentialMovingWindow.cov \ - pandas.api.indexers.BaseIndexer \ - pandas.api.indexers.VariableOffsetWindowIndexer \ - pandas.core.groupby.DataFrameGroupBy.__iter__ \ - pandas.core.groupby.SeriesGroupBy.__iter__ \ - pandas.core.groupby.DataFrameGroupBy.groups \ - pandas.core.groupby.SeriesGroupBy.groups \ - pandas.core.groupby.DataFrameGroupBy.indices \ - pandas.core.groupby.SeriesGroupBy.indices \ - pandas.core.groupby.DataFrameGroupBy.get_group \ - pandas.core.groupby.SeriesGroupBy.get_group \ - pandas.core.groupby.DataFrameGroupBy.all \ - pandas.core.groupby.DataFrameGroupBy.any \ - pandas.core.groupby.DataFrameGroupBy.bfill \ - pandas.core.groupby.DataFrameGroupBy.count \ - pandas.core.groupby.DataFrameGroupBy.cummax \ - pandas.core.groupby.DataFrameGroupBy.cummin \ - pandas.core.groupby.DataFrameGroupBy.cumprod \ - pandas.core.groupby.DataFrameGroupBy.cumsum \ - pandas.core.groupby.DataFrameGroupBy.diff \ - pandas.core.groupby.DataFrameGroupBy.ffill \ - pandas.core.groupby.DataFrameGroupBy.max \ - pandas.core.groupby.DataFrameGroupBy.median \ - pandas.core.groupby.DataFrameGroupBy.min \ - pandas.core.groupby.DataFrameGroupBy.ohlc \ - pandas.core.groupby.DataFrameGroupBy.pct_change \ - pandas.core.groupby.DataFrameGroupBy.prod \ - pandas.core.groupby.DataFrameGroupBy.sem \ - pandas.core.groupby.DataFrameGroupBy.shift \ - pandas.core.groupby.DataFrameGroupBy.size \ - pandas.core.groupby.DataFrameGroupBy.skew \ - pandas.core.groupby.DataFrameGroupBy.std \ - pandas.core.groupby.DataFrameGroupBy.sum \ - pandas.core.groupby.DataFrameGroupBy.var \ - pandas.core.groupby.SeriesGroupBy.all \ - pandas.core.groupby.SeriesGroupBy.any \ - pandas.core.groupby.SeriesGroupBy.bfill \ - pandas.core.groupby.SeriesGroupBy.count \ - pandas.core.groupby.SeriesGroupBy.cummax \ - pandas.core.groupby.SeriesGroupBy.cummin \ - pandas.core.groupby.SeriesGroupBy.cumprod \ - pandas.core.groupby.SeriesGroupBy.cumsum \ - pandas.core.groupby.SeriesGroupBy.diff \ - pandas.core.groupby.SeriesGroupBy.ffill \ - pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing \ - pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing \ - pandas.core.groupby.SeriesGroupBy.max \ - pandas.core.groupby.SeriesGroupBy.median \ - pandas.core.groupby.SeriesGroupBy.min \ - pandas.core.groupby.SeriesGroupBy.nunique \ - pandas.core.groupby.SeriesGroupBy.ohlc \ - pandas.core.groupby.SeriesGroupBy.pct_change \ - pandas.core.groupby.SeriesGroupBy.prod \ - pandas.core.groupby.SeriesGroupBy.sem \ - pandas.core.groupby.SeriesGroupBy.shift \ - pandas.core.groupby.SeriesGroupBy.size \ - pandas.core.groupby.SeriesGroupBy.skew \ - pandas.core.groupby.SeriesGroupBy.std \ - pandas.core.groupby.SeriesGroupBy.sum \ - pandas.core.groupby.SeriesGroupBy.var \ - pandas.core.groupby.SeriesGroupBy.hist \ - pandas.core.groupby.DataFrameGroupBy.plot \ - pandas.core.groupby.SeriesGroupBy.plot \ - pandas.io.formats.style.Styler \ - pandas.io.formats.style.Styler.from_custom_template \ - pandas.io.formats.style.Styler.set_caption \ - pandas.io.formats.style.Styler.set_sticky \ - pandas.io.formats.style.Styler.set_uuid \ - pandas.io.formats.style.Styler.clear \ - pandas.io.formats.style.Styler.highlight_null \ - pandas.io.formats.style.Styler.highlight_max \ - pandas.io.formats.style.Styler.highlight_min \ - pandas.io.formats.style.Styler.bar \ - pandas.io.formats.style.Styler.to_string \ - pandas.api.extensions.ExtensionDtype \ - pandas.api.extensions.ExtensionArray \ - pandas.arrays.PandasArray \ - pandas.api.extensions.ExtensionArray._accumulate \ - pandas.api.extensions.ExtensionArray._concat_same_type \ - pandas.api.extensions.ExtensionArray._formatter \ - pandas.api.extensions.ExtensionArray._from_factorized \ - pandas.api.extensions.ExtensionArray._from_sequence \ - pandas.api.extensions.ExtensionArray._from_sequence_of_strings \ - pandas.api.extensions.ExtensionArray._reduce \ - pandas.api.extensions.ExtensionArray._values_for_argsort \ - pandas.api.extensions.ExtensionArray._values_for_factorize \ - pandas.api.extensions.ExtensionArray.argsort \ - pandas.api.extensions.ExtensionArray.astype \ - pandas.api.extensions.ExtensionArray.copy \ - pandas.api.extensions.ExtensionArray.view \ - pandas.api.extensions.ExtensionArray.dropna \ - pandas.api.extensions.ExtensionArray.equals \ - pandas.api.extensions.ExtensionArray.factorize \ - pandas.api.extensions.ExtensionArray.fillna \ - pandas.api.extensions.ExtensionArray.insert \ - pandas.api.extensions.ExtensionArray.isin \ - pandas.api.extensions.ExtensionArray.isna \ - pandas.api.extensions.ExtensionArray.ravel \ - pandas.api.extensions.ExtensionArray.searchsorted \ - pandas.api.extensions.ExtensionArray.shift \ - pandas.api.extensions.ExtensionArray.unique \ - pandas.api.extensions.ExtensionArray.dtype \ - pandas.api.extensions.ExtensionArray.nbytes \ - pandas.api.extensions.ExtensionArray.ndim \ - pandas.api.extensions.ExtensionArray.shape \ - pandas.api.extensions.ExtensionArray.tolist \ - pandas.DataFrame.index \ - pandas.DataFrame.columns \ - pandas.DataFrame.__iter__ \ - pandas.DataFrame.keys \ - pandas.DataFrame.iterrows \ - pandas.DataFrame.pipe \ - pandas.DataFrame.kurt \ - pandas.DataFrame.kurtosis \ - pandas.DataFrame.mean \ - pandas.DataFrame.median \ - pandas.DataFrame.sem \ - pandas.DataFrame.skew \ - pandas.DataFrame.backfill \ - pandas.DataFrame.pad \ - pandas.DataFrame.swapaxes \ - pandas.DataFrame.first_valid_index \ - pandas.DataFrame.last_valid_index \ - pandas.DataFrame.to_timestamp \ - pandas.DataFrame.attrs \ - pandas.DataFrame.plot \ - pandas.DataFrame.sparse.density \ - pandas.DataFrame.sparse.to_coo \ - pandas.DataFrame.to_gbq \ - pandas.DataFrame.style \ - pandas.DataFrame.__dataframe__ - RET=$(($RET + $?)) ; echo $MSG "DONE" + # MSG='Partially validate docstrings (EX01)' ; echo $MSG + # $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \ + # pandas.Series.index \ + # pandas.Series.dtype \ + # pandas.Series.nbytes \ + # pandas.Series.ndim \ + # pandas.Series.size \ + # pandas.Series.T \ + # pandas.Series.hasnans \ + # pandas.Series.dtypes \ + # pandas.Series.to_period \ + # pandas.Series.to_timestamp \ + # pandas.Series.to_list \ + # pandas.Series.__iter__ \ + # pandas.Series.keys \ + # pandas.Series.item \ + # pandas.Series.pipe \ + # pandas.Series.kurt \ + # pandas.Series.mean \ + # pandas.Series.median \ + # pandas.Series.mode \ + # pandas.Series.sem \ + # pandas.Series.skew \ + # pandas.Series.kurtosis \ + # pandas.Series.is_unique \ + # pandas.Series.is_monotonic_increasing \ + # pandas.Series.is_monotonic_decreasing \ + # pandas.Series.backfill \ + # pandas.Series.pad \ + # pandas.Series.argsort \ + # pandas.Series.reorder_levels \ + # pandas.Series.ravel \ + # pandas.Series.first_valid_index \ + # pandas.Series.last_valid_index \ + # pandas.Series.dt.date \ + # pandas.Series.dt.time \ + # pandas.Series.dt.timetz \ + # pandas.Series.dt.dayofyear \ + # pandas.Series.dt.day_of_year \ + # pandas.Series.dt.quarter \ + # pandas.Series.dt.daysinmonth \ + # pandas.Series.dt.days_in_month \ + # pandas.Series.dt.tz \ + # pandas.Series.dt.end_time \ + # pandas.Series.dt.days \ + # pandas.Series.dt.seconds \ + # pandas.Series.dt.microseconds \ + # pandas.Series.dt.nanoseconds \ + # pandas.Series.str.center \ + # pandas.Series.str.decode \ + # pandas.Series.str.encode \ + # pandas.Series.str.find \ + # pandas.Series.str.fullmatch \ + # pandas.Series.str.index \ + # pandas.Series.str.ljust \ + # pandas.Series.str.match \ + # pandas.Series.str.normalize \ + # pandas.Series.str.rfind \ + # pandas.Series.str.rindex \ + # pandas.Series.str.rjust \ + # pandas.Series.str.translate \ + # pandas.Series.sparse \ + # pandas.DataFrame.sparse \ + # pandas.Series.cat.categories \ + # pandas.Series.cat.ordered \ + # pandas.Series.cat.codes \ + # pandas.Series.cat.reorder_categories \ + # pandas.Series.cat.set_categories \ + # pandas.Series.cat.as_ordered \ + # pandas.Series.cat.as_unordered \ + # pandas.Series.sparse.fill_value \ + # pandas.Flags \ + # pandas.Series.attrs \ + # pandas.Series.plot \ + # pandas.Series.hist \ + # pandas.Series.to_string \ + # pandas.errors.AbstractMethodError \ + # pandas.errors.AccessorRegistrationWarning \ + # pandas.errors.AttributeConflictWarning \ + # pandas.errors.DataError \ + # pandas.errors.EmptyDataError \ + # pandas.errors.IncompatibilityWarning \ + # pandas.errors.InvalidComparison \ + # pandas.errors.InvalidIndexError \ + # pandas.errors.InvalidVersion \ + # pandas.errors.IntCastingNaNError \ + # pandas.errors.LossySetitemError \ + # pandas.errors.MergeError \ + # pandas.errors.NoBufferPresent \ + # pandas.errors.NullFrequencyError \ + # pandas.errors.NumbaUtilError \ + # pandas.errors.OptionError \ + # pandas.errors.OutOfBoundsDatetime \ + # pandas.errors.OutOfBoundsTimedelta \ + # pandas.errors.ParserError \ + # pandas.errors.PerformanceWarning \ + # pandas.errors.PyperclipException \ + # pandas.errors.PyperclipWindowsException \ + # pandas.errors.UnsortedIndexError \ + # pandas.errors.UnsupportedFunctionCall \ + # pandas.show_versions \ + # pandas.test \ + # pandas.NaT \ + # pandas.Timestamp.as_unit \ + # pandas.Timestamp.ctime \ + # pandas.Timestamp.date \ + # pandas.Timestamp.dst \ + # pandas.Timestamp.isocalendar \ + # pandas.Timestamp.isoweekday \ + # pandas.Timestamp.strptime \ + # pandas.Timestamp.time \ + # pandas.Timestamp.timetuple \ + # pandas.Timestamp.timetz \ + # pandas.Timestamp.to_datetime64 \ + # pandas.Timestamp.toordinal \ + # pandas.Timestamp.tzname \ + # pandas.Timestamp.utcoffset \ + # pandas.Timestamp.utctimetuple \ + # pandas.Timestamp.weekday \ + # pandas.arrays.DatetimeArray \ + # pandas.Timedelta.view \ + # pandas.Timedelta.as_unit \ + # pandas.Timedelta.ceil \ + # pandas.Timedelta.floor \ + # pandas.Timedelta.round \ + # pandas.Timedelta.to_pytimedelta \ + # pandas.Timedelta.to_timedelta64 \ + # pandas.Timedelta.to_numpy \ + # pandas.Timedelta.total_seconds \ + # pandas.arrays.TimedeltaArray \ + # pandas.Period.end_time \ + # pandas.Period.freqstr \ + # pandas.Period.is_leap_year \ + # pandas.Period.month \ + # pandas.Period.quarter \ + # pandas.Period.year \ + # pandas.Period.asfreq \ + # pandas.Period.now \ + # pandas.Period.to_timestamp \ + # pandas.arrays.PeriodArray \ + # pandas.Interval.closed \ + # pandas.Interval.left \ + # pandas.Interval.length \ + # pandas.Interval.right \ + # pandas.arrays.IntervalArray.left \ + # pandas.arrays.IntervalArray.right \ + # pandas.arrays.IntervalArray.closed \ + # pandas.arrays.IntervalArray.mid \ + # pandas.arrays.IntervalArray.length \ + # pandas.arrays.IntervalArray.is_non_overlapping_monotonic \ + # pandas.arrays.IntervalArray.from_arrays \ + # pandas.arrays.IntervalArray.to_tuples \ + # pandas.Int8Dtype \ + # pandas.Int16Dtype \ + # pandas.Int32Dtype \ + # pandas.Int64Dtype \ + # pandas.UInt8Dtype \ + # pandas.UInt16Dtype \ + # pandas.UInt32Dtype \ + # pandas.UInt64Dtype \ + # pandas.NA \ + # pandas.Float32Dtype \ + # pandas.Float64Dtype \ + # pandas.CategoricalDtype.categories \ + # pandas.CategoricalDtype.ordered \ + # pandas.Categorical.dtype \ + # pandas.Categorical.categories \ + # pandas.Categorical.ordered \ + # pandas.Categorical.codes \ + # pandas.Categorical.__array__ \ + # pandas.SparseDtype \ + # pandas.DatetimeTZDtype.unit \ + # pandas.DatetimeTZDtype.tz \ + # pandas.PeriodDtype.freq \ + # pandas.IntervalDtype.subtype \ + # pandas_dtype \ + # pandas.api.types.is_bool \ + # pandas.api.types.is_complex \ + # pandas.api.types.is_float \ + # pandas.api.types.is_integer \ + # pandas.api.types.pandas_dtype \ + # pandas.read_clipboard \ + # pandas.ExcelFile \ + # pandas.ExcelFile.parse \ + # pandas.DataFrame.to_html \ + # pandas.io.formats.style.Styler.to_html \ + # pandas.HDFStore.put \ + # pandas.HDFStore.append \ + # pandas.HDFStore.get \ + # pandas.HDFStore.select \ + # pandas.HDFStore.info \ + # pandas.HDFStore.keys \ + # pandas.HDFStore.groups \ + # pandas.HDFStore.walk \ + # pandas.read_feather \ + # pandas.DataFrame.to_feather \ + # pandas.read_parquet \ + # pandas.read_orc \ + # pandas.read_sas \ + # pandas.read_spss \ + # pandas.read_sql_query \ + # pandas.read_gbq \ + # pandas.io.stata.StataReader.data_label \ + # pandas.io.stata.StataReader.value_labels \ + # pandas.io.stata.StataReader.variable_labels \ + # pandas.io.stata.StataWriter.write_file \ + # pandas.core.resample.Resampler.__iter__ \ + # pandas.core.resample.Resampler.groups \ + # pandas.core.resample.Resampler.indices \ + # pandas.core.resample.Resampler.get_group \ + # pandas.core.resample.Resampler.ffill \ + # pandas.core.resample.Resampler.asfreq \ + # pandas.core.resample.Resampler.count \ + # pandas.core.resample.Resampler.nunique \ + # pandas.core.resample.Resampler.max \ + # pandas.core.resample.Resampler.mean \ + # pandas.core.resample.Resampler.median \ + # pandas.core.resample.Resampler.min \ + # pandas.core.resample.Resampler.ohlc \ + # pandas.core.resample.Resampler.prod \ + # pandas.core.resample.Resampler.size \ + # pandas.core.resample.Resampler.sem \ + # pandas.core.resample.Resampler.std \ + # pandas.core.resample.Resampler.sum \ + # pandas.core.resample.Resampler.var \ + # pandas.core.resample.Resampler.quantile \ + # pandas.describe_option \ + # pandas.reset_option \ + # pandas.get_option \ + # pandas.set_option \ + # pandas.plotting.deregister_matplotlib_converters \ + # pandas.plotting.plot_params \ + # pandas.plotting.register_matplotlib_converters \ + # pandas.plotting.table \ + # pandas.util.hash_array \ + # pandas.util.hash_pandas_object \ + # pandas_object \ + # pandas.api.interchange.from_dataframe \ + # pandas.Index.values \ + # pandas.Index.hasnans \ + # pandas.Index.dtype \ + # pandas.Index.inferred_type \ + # pandas.Index.shape \ + # pandas.Index.name \ + # pandas.Index.nbytes \ + # pandas.Index.ndim \ + # pandas.Index.size \ + # pandas.Index.T \ + # pandas.Index.memory_usage \ + # pandas.Index.copy \ + # pandas.Index.drop \ + # pandas.Index.identical \ + # pandas.Index.insert \ + # pandas.Index.is_ \ + # pandas.Index.take \ + # pandas.Index.putmask \ + # pandas.Index.unique \ + # pandas.Index.fillna \ + # pandas.Index.dropna \ + # pandas.Index.astype \ + # pandas.Index.item \ + # pandas.Index.map \ + # pandas.Index.ravel \ + # pandas.Index.to_list \ + # pandas.Index.append \ + # pandas.Index.join \ + # pandas.Index.asof_locs \ + # pandas.Index.get_slice_bound \ + # pandas.RangeIndex \ + # pandas.RangeIndex.start \ + # pandas.RangeIndex.stop \ + # pandas.RangeIndex.step \ + # pandas.RangeIndex.from_range \ + # pandas.CategoricalIndex.codes \ + # pandas.CategoricalIndex.categories \ + # pandas.CategoricalIndex.ordered \ + # pandas.CategoricalIndex.reorder_categories \ + # pandas.CategoricalIndex.set_categories \ + # pandas.CategoricalIndex.as_ordered \ + # pandas.CategoricalIndex.as_unordered \ + # pandas.CategoricalIndex.equals \ + # pandas.IntervalIndex.closed \ + # pandas.IntervalIndex.values \ + # pandas.IntervalIndex.is_non_overlapping_monotonic \ + # pandas.IntervalIndex.to_tuples \ + # pandas.MultiIndex.dtypes \ + # pandas.MultiIndex.drop \ + # pandas.DatetimeIndex \ + # pandas.DatetimeIndex.date \ + # pandas.DatetimeIndex.time \ + # pandas.DatetimeIndex.timetz \ + # pandas.DatetimeIndex.dayofyear \ + # pandas.DatetimeIndex.day_of_year \ + # pandas.DatetimeIndex.quarter \ + # pandas.DatetimeIndex.tz \ + # pandas.DatetimeIndex.freqstr \ + # pandas.DatetimeIndex.inferred_freq \ + # pandas.DatetimeIndex.indexer_at_time \ + # pandas.DatetimeIndex.indexer_between_time \ + # pandas.DatetimeIndex.snap \ + # pandas.DatetimeIndex.as_unit \ + # pandas.DatetimeIndex.to_pydatetime \ + # pandas.DatetimeIndex.to_series \ + # pandas.DatetimeIndex.mean \ + # pandas.DatetimeIndex.std \ + # pandas.TimedeltaIndex \ + # pandas.TimedeltaIndex.days \ + # pandas.TimedeltaIndex.seconds \ + # pandas.TimedeltaIndex.microseconds \ + # pandas.TimedeltaIndex.nanoseconds \ + # pandas.TimedeltaIndex.components \ + # pandas.TimedeltaIndex.inferred_freq \ + # pandas.TimedeltaIndex.as_unit \ + # pandas.TimedeltaIndex.to_pytimedelta \ + # pandas.TimedeltaIndex.mean \ + # pandas.PeriodIndex.day \ + # pandas.PeriodIndex.dayofweek \ + # pandas.PeriodIndex.day_of_week \ + # pandas.PeriodIndex.dayofyear \ + # pandas.PeriodIndex.day_of_year \ + # pandas.PeriodIndex.days_in_month \ + # pandas.PeriodIndex.daysinmonth \ + # pandas.PeriodIndex.end_time \ + # pandas.PeriodIndex.freqstr \ + # pandas.PeriodIndex.hour \ + # pandas.PeriodIndex.is_leap_year \ + # pandas.PeriodIndex.minute \ + # pandas.PeriodIndex.month \ + # pandas.PeriodIndex.quarter \ + # pandas.PeriodIndex.second \ + # pandas.PeriodIndex.week \ + # pandas.PeriodIndex.weekday \ + # pandas.PeriodIndex.weekofyear \ + # pandas.PeriodIndex.year \ + # pandas.PeriodIndex.to_timestamp \ + # pandas.core.window.rolling.Rolling.max \ + # pandas.core.window.rolling.Rolling.cov \ + # pandas.core.window.rolling.Rolling.skew \ + # pandas.core.window.rolling.Rolling.apply \ + # pandas.core.window.rolling.Window.mean \ + # pandas.core.window.rolling.Window.sum \ + # pandas.core.window.rolling.Window.var \ + # pandas.core.window.rolling.Window.std \ + # pandas.core.window.expanding.Expanding.count \ + # pandas.core.window.expanding.Expanding.sum \ + # pandas.core.window.expanding.Expanding.mean \ + # pandas.core.window.expanding.Expanding.median \ + # pandas.core.window.expanding.Expanding.min \ + # pandas.core.window.expanding.Expanding.max \ + # pandas.core.window.expanding.Expanding.corr \ + # pandas.core.window.expanding.Expanding.cov \ + # pandas.core.window.expanding.Expanding.skew \ + # pandas.core.window.expanding.Expanding.apply \ + # pandas.core.window.expanding.Expanding.quantile \ + # pandas.core.window.ewm.ExponentialMovingWindow.mean \ + # pandas.core.window.ewm.ExponentialMovingWindow.sum \ + # pandas.core.window.ewm.ExponentialMovingWindow.std \ + # pandas.core.window.ewm.ExponentialMovingWindow.var \ + # pandas.core.window.ewm.ExponentialMovingWindow.corr \ + # pandas.core.window.ewm.ExponentialMovingWindow.cov \ + # pandas.api.indexers.BaseIndexer \ + # pandas.api.indexers.VariableOffsetWindowIndexer \ + # pandas.core.groupby.DataFrameGroupBy.__iter__ \ + # pandas.core.groupby.SeriesGroupBy.__iter__ \ + # pandas.core.groupby.DataFrameGroupBy.groups \ + # pandas.core.groupby.SeriesGroupBy.groups \ + # pandas.core.groupby.DataFrameGroupBy.indices \ + # pandas.core.groupby.SeriesGroupBy.indices \ + # pandas.core.groupby.DataFrameGroupBy.get_group \ + # pandas.core.groupby.SeriesGroupBy.get_group \ + # pandas.core.groupby.DataFrameGroupBy.all \ + # pandas.core.groupby.DataFrameGroupBy.any \ + # pandas.core.groupby.DataFrameGroupBy.bfill \ + # pandas.core.groupby.DataFrameGroupBy.count \ + # pandas.core.groupby.DataFrameGroupBy.cummax \ + # pandas.core.groupby.DataFrameGroupBy.cummin \ + # pandas.core.groupby.DataFrameGroupBy.cumprod \ + # pandas.core.groupby.DataFrameGroupBy.cumsum \ + # pandas.core.groupby.DataFrameGroupBy.diff \ + # pandas.core.groupby.DataFrameGroupBy.ffill \ + # pandas.core.groupby.DataFrameGroupBy.max \ + # pandas.core.groupby.DataFrameGroupBy.median \ + # pandas.core.groupby.DataFrameGroupBy.min \ + # pandas.core.groupby.DataFrameGroupBy.ohlc \ + # pandas.core.groupby.DataFrameGroupBy.pct_change \ + # pandas.core.groupby.DataFrameGroupBy.prod \ + # pandas.core.groupby.DataFrameGroupBy.sem \ + # pandas.core.groupby.DataFrameGroupBy.shift \ + # pandas.core.groupby.DataFrameGroupBy.size \ + # pandas.core.groupby.DataFrameGroupBy.skew \ + # pandas.core.groupby.DataFrameGroupBy.std \ + # pandas.core.groupby.DataFrameGroupBy.sum \ + # pandas.core.groupby.DataFrameGroupBy.var \ + # pandas.core.groupby.SeriesGroupBy.all \ + # pandas.core.groupby.SeriesGroupBy.any \ + # pandas.core.groupby.SeriesGroupBy.bfill \ + # pandas.core.groupby.SeriesGroupBy.count \ + # pandas.core.groupby.SeriesGroupBy.cummax \ + # pandas.core.groupby.SeriesGroupBy.cummin \ + # pandas.core.groupby.SeriesGroupBy.cumprod \ + # pandas.core.groupby.SeriesGroupBy.cumsum \ + # pandas.core.groupby.SeriesGroupBy.diff \ + # pandas.core.groupby.SeriesGroupBy.ffill \ + # pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing \ + # pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing \ + # pandas.core.groupby.SeriesGroupBy.max \ + # pandas.core.groupby.SeriesGroupBy.median \ + # pandas.core.groupby.SeriesGroupBy.min \ + # pandas.core.groupby.SeriesGroupBy.nunique \ + # pandas.core.groupby.SeriesGroupBy.ohlc \ + # pandas.core.groupby.SeriesGroupBy.pct_change \ + # pandas.core.groupby.SeriesGroupBy.prod \ + # pandas.core.groupby.SeriesGroupBy.sem \ + # pandas.core.groupby.SeriesGroupBy.shift \ + # pandas.core.groupby.SeriesGroupBy.size \ + # pandas.core.groupby.SeriesGroupBy.skew \ + # pandas.core.groupby.SeriesGroupBy.std \ + # pandas.core.groupby.SeriesGroupBy.sum \ + # pandas.core.groupby.SeriesGroupBy.var \ + # pandas.core.groupby.SeriesGroupBy.hist \ + # pandas.core.groupby.DataFrameGroupBy.plot \ + # pandas.core.groupby.SeriesGroupBy.plot \ + # pandas.io.formats.style.Styler \ + # pandas.io.formats.style.Styler.from_custom_template \ + # pandas.io.formats.style.Styler.set_caption \ + # pandas.io.formats.style.Styler.set_sticky \ + # pandas.io.formats.style.Styler.set_uuid \ + # pandas.io.formats.style.Styler.clear \ + # pandas.io.formats.style.Styler.highlight_null \ + # pandas.io.formats.style.Styler.highlight_max \ + # pandas.io.formats.style.Styler.highlight_min \ + # pandas.io.formats.style.Styler.bar \ + # pandas.io.formats.style.Styler.to_string \ + # pandas.api.extensions.ExtensionDtype \ + # pandas.api.extensions.ExtensionArray \ + # pandas.arrays.PandasArray \ + # pandas.api.extensions.ExtensionArray._accumulate \ + # pandas.api.extensions.ExtensionArray._concat_same_type \ + # pandas.api.extensions.ExtensionArray._formatter \ + # pandas.api.extensions.ExtensionArray._from_factorized \ + # pandas.api.extensions.ExtensionArray._from_sequence \ + # pandas.api.extensions.ExtensionArray._from_sequence_of_strings \ + # pandas.api.extensions.ExtensionArray._reduce \ + # pandas.api.extensions.ExtensionArray._values_for_argsort \ + # pandas.api.extensions.ExtensionArray._values_for_factorize \ + # pandas.api.extensions.ExtensionArray.argsort \ + # pandas.api.extensions.ExtensionArray.astype \ + # pandas.api.extensions.ExtensionArray.copy \ + # pandas.api.extensions.ExtensionArray.view \ + # pandas.api.extensions.ExtensionArray.dropna \ + # pandas.api.extensions.ExtensionArray.equals \ + # pandas.api.extensions.ExtensionArray.factorize \ + # pandas.api.extensions.ExtensionArray.fillna \ + # pandas.api.extensions.ExtensionArray.insert \ + # pandas.api.extensions.ExtensionArray.isin \ + # pandas.api.extensions.ExtensionArray.isna \ + # pandas.api.extensions.ExtensionArray.ravel \ + # pandas.api.extensions.ExtensionArray.searchsorted \ + # pandas.api.extensions.ExtensionArray.shift \ + # pandas.api.extensions.ExtensionArray.unique \ + # pandas.api.extensions.ExtensionArray.dtype \ + # pandas.api.extensions.ExtensionArray.nbytes \ + # pandas.api.extensions.ExtensionArray.ndim \ + # pandas.api.extensions.ExtensionArray.shape \ + # pandas.api.extensions.ExtensionArray.tolist \ + # pandas.DataFrame.index \ + # pandas.DataFrame.columns \ + # pandas.DataFrame.__iter__ \ + # pandas.DataFrame.keys \ + # pandas.DataFrame.iterrows \ + # pandas.DataFrame.pipe \ + # pandas.DataFrame.kurt \ + # pandas.DataFrame.kurtosis \ + # pandas.DataFrame.mean \ + # pandas.DataFrame.median \ + # pandas.DataFrame.sem \ + # pandas.DataFrame.skew \ + # pandas.DataFrame.backfill \ + # pandas.DataFrame.pad \ + # pandas.DataFrame.swapaxes \ + # pandas.DataFrame.first_valid_index \ + # pandas.DataFrame.last_valid_index \ + # pandas.DataFrame.to_timestamp \ + # pandas.DataFrame.attrs \ + # pandas.DataFrame.plot \ + # pandas.DataFrame.sparse.density \ + # pandas.DataFrame.sparse.to_coo \ + # pandas.DataFrame.to_gbq \ + # pandas.DataFrame.style \ + # pandas.DataFrame.__dataframe__ + # RET=$(($RET + $?)) ; echo $MSG "DONE" MSG='Partially validate docstrings (EX02)' ; echo $MSG $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX02 --ignore_functions \ diff --git a/setup.cfg b/setup.cfg index f58cdbf5d5fd5..88b61086e1e0f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -24,9 +24,7 @@ ignore = # Use "collections.abc.*" instead of "typing.*" (PEP 585 syntax) Y027, # while int | float can be shortened to float, the former is more explicit - Y041, - # undefined name 'pd' error flooding logs, ignore temporarily - F821 + Y041 exclude = doc/sphinxext/*.py, doc/build/*.py, From 013f1c0e8e690b5f16e282bdea677c7eb5ece554 Mon Sep 17 00:00:00 2001 From: Su Date: Tue, 14 Feb 2023 21:44:01 +0800 Subject: [PATCH 5/6] Restore commented lines --- ci/code_checks.sh | 988 +++++++++++++++++++++++----------------------- 1 file changed, 494 insertions(+), 494 deletions(-) diff --git a/ci/code_checks.sh b/ci/code_checks.sh index bab5904077d46..567ae6da92ae2 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -79,501 +79,501 @@ fi ### DOCSTRINGS ### if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then - # MSG='Validate docstrings (EX04, GL01, GL02, GL03, GL04, GL05, GL06, GL07, GL09, GL10, PR03, PR04, PR05, PR06, PR08, PR09, PR10, RT01, RT02, RT04, RT05, SA02, SA03, SA04, SS01, SS02, SS03, SS04, SS05, SS06)' ; echo $MSG - # $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06 - # RET=$(($RET + $?)) ; echo $MSG "DONE" + MSG='Validate docstrings (EX04, GL01, GL02, GL03, GL04, GL05, GL06, GL07, GL09, GL10, PR03, PR04, PR05, PR06, PR08, PR09, PR10, RT01, RT02, RT04, RT05, SA02, SA03, SA04, SS01, SS02, SS03, SS04, SS05, SS06)' ; echo $MSG + $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06 + RET=$(($RET + $?)) ; echo $MSG "DONE" - # MSG='Partially validate docstrings (EX01)' ; echo $MSG - # $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \ - # pandas.Series.index \ - # pandas.Series.dtype \ - # pandas.Series.nbytes \ - # pandas.Series.ndim \ - # pandas.Series.size \ - # pandas.Series.T \ - # pandas.Series.hasnans \ - # pandas.Series.dtypes \ - # pandas.Series.to_period \ - # pandas.Series.to_timestamp \ - # pandas.Series.to_list \ - # pandas.Series.__iter__ \ - # pandas.Series.keys \ - # pandas.Series.item \ - # pandas.Series.pipe \ - # pandas.Series.kurt \ - # pandas.Series.mean \ - # pandas.Series.median \ - # pandas.Series.mode \ - # pandas.Series.sem \ - # pandas.Series.skew \ - # pandas.Series.kurtosis \ - # pandas.Series.is_unique \ - # pandas.Series.is_monotonic_increasing \ - # pandas.Series.is_monotonic_decreasing \ - # pandas.Series.backfill \ - # pandas.Series.pad \ - # pandas.Series.argsort \ - # pandas.Series.reorder_levels \ - # pandas.Series.ravel \ - # pandas.Series.first_valid_index \ - # pandas.Series.last_valid_index \ - # pandas.Series.dt.date \ - # pandas.Series.dt.time \ - # pandas.Series.dt.timetz \ - # pandas.Series.dt.dayofyear \ - # pandas.Series.dt.day_of_year \ - # pandas.Series.dt.quarter \ - # pandas.Series.dt.daysinmonth \ - # pandas.Series.dt.days_in_month \ - # pandas.Series.dt.tz \ - # pandas.Series.dt.end_time \ - # pandas.Series.dt.days \ - # pandas.Series.dt.seconds \ - # pandas.Series.dt.microseconds \ - # pandas.Series.dt.nanoseconds \ - # pandas.Series.str.center \ - # pandas.Series.str.decode \ - # pandas.Series.str.encode \ - # pandas.Series.str.find \ - # pandas.Series.str.fullmatch \ - # pandas.Series.str.index \ - # pandas.Series.str.ljust \ - # pandas.Series.str.match \ - # pandas.Series.str.normalize \ - # pandas.Series.str.rfind \ - # pandas.Series.str.rindex \ - # pandas.Series.str.rjust \ - # pandas.Series.str.translate \ - # pandas.Series.sparse \ - # pandas.DataFrame.sparse \ - # pandas.Series.cat.categories \ - # pandas.Series.cat.ordered \ - # pandas.Series.cat.codes \ - # pandas.Series.cat.reorder_categories \ - # pandas.Series.cat.set_categories \ - # pandas.Series.cat.as_ordered \ - # pandas.Series.cat.as_unordered \ - # pandas.Series.sparse.fill_value \ - # pandas.Flags \ - # pandas.Series.attrs \ - # pandas.Series.plot \ - # pandas.Series.hist \ - # pandas.Series.to_string \ - # pandas.errors.AbstractMethodError \ - # pandas.errors.AccessorRegistrationWarning \ - # pandas.errors.AttributeConflictWarning \ - # pandas.errors.DataError \ - # pandas.errors.EmptyDataError \ - # pandas.errors.IncompatibilityWarning \ - # pandas.errors.InvalidComparison \ - # pandas.errors.InvalidIndexError \ - # pandas.errors.InvalidVersion \ - # pandas.errors.IntCastingNaNError \ - # pandas.errors.LossySetitemError \ - # pandas.errors.MergeError \ - # pandas.errors.NoBufferPresent \ - # pandas.errors.NullFrequencyError \ - # pandas.errors.NumbaUtilError \ - # pandas.errors.OptionError \ - # pandas.errors.OutOfBoundsDatetime \ - # pandas.errors.OutOfBoundsTimedelta \ - # pandas.errors.ParserError \ - # pandas.errors.PerformanceWarning \ - # pandas.errors.PyperclipException \ - # pandas.errors.PyperclipWindowsException \ - # pandas.errors.UnsortedIndexError \ - # pandas.errors.UnsupportedFunctionCall \ - # pandas.show_versions \ - # pandas.test \ - # pandas.NaT \ - # pandas.Timestamp.as_unit \ - # pandas.Timestamp.ctime \ - # pandas.Timestamp.date \ - # pandas.Timestamp.dst \ - # pandas.Timestamp.isocalendar \ - # pandas.Timestamp.isoweekday \ - # pandas.Timestamp.strptime \ - # pandas.Timestamp.time \ - # pandas.Timestamp.timetuple \ - # pandas.Timestamp.timetz \ - # pandas.Timestamp.to_datetime64 \ - # pandas.Timestamp.toordinal \ - # pandas.Timestamp.tzname \ - # pandas.Timestamp.utcoffset \ - # pandas.Timestamp.utctimetuple \ - # pandas.Timestamp.weekday \ - # pandas.arrays.DatetimeArray \ - # pandas.Timedelta.view \ - # pandas.Timedelta.as_unit \ - # pandas.Timedelta.ceil \ - # pandas.Timedelta.floor \ - # pandas.Timedelta.round \ - # pandas.Timedelta.to_pytimedelta \ - # pandas.Timedelta.to_timedelta64 \ - # pandas.Timedelta.to_numpy \ - # pandas.Timedelta.total_seconds \ - # pandas.arrays.TimedeltaArray \ - # pandas.Period.end_time \ - # pandas.Period.freqstr \ - # pandas.Period.is_leap_year \ - # pandas.Period.month \ - # pandas.Period.quarter \ - # pandas.Period.year \ - # pandas.Period.asfreq \ - # pandas.Period.now \ - # pandas.Period.to_timestamp \ - # pandas.arrays.PeriodArray \ - # pandas.Interval.closed \ - # pandas.Interval.left \ - # pandas.Interval.length \ - # pandas.Interval.right \ - # pandas.arrays.IntervalArray.left \ - # pandas.arrays.IntervalArray.right \ - # pandas.arrays.IntervalArray.closed \ - # pandas.arrays.IntervalArray.mid \ - # pandas.arrays.IntervalArray.length \ - # pandas.arrays.IntervalArray.is_non_overlapping_monotonic \ - # pandas.arrays.IntervalArray.from_arrays \ - # pandas.arrays.IntervalArray.to_tuples \ - # pandas.Int8Dtype \ - # pandas.Int16Dtype \ - # pandas.Int32Dtype \ - # pandas.Int64Dtype \ - # pandas.UInt8Dtype \ - # pandas.UInt16Dtype \ - # pandas.UInt32Dtype \ - # pandas.UInt64Dtype \ - # pandas.NA \ - # pandas.Float32Dtype \ - # pandas.Float64Dtype \ - # pandas.CategoricalDtype.categories \ - # pandas.CategoricalDtype.ordered \ - # pandas.Categorical.dtype \ - # pandas.Categorical.categories \ - # pandas.Categorical.ordered \ - # pandas.Categorical.codes \ - # pandas.Categorical.__array__ \ - # pandas.SparseDtype \ - # pandas.DatetimeTZDtype.unit \ - # pandas.DatetimeTZDtype.tz \ - # pandas.PeriodDtype.freq \ - # pandas.IntervalDtype.subtype \ - # pandas_dtype \ - # pandas.api.types.is_bool \ - # pandas.api.types.is_complex \ - # pandas.api.types.is_float \ - # pandas.api.types.is_integer \ - # pandas.api.types.pandas_dtype \ - # pandas.read_clipboard \ - # pandas.ExcelFile \ - # pandas.ExcelFile.parse \ - # pandas.DataFrame.to_html \ - # pandas.io.formats.style.Styler.to_html \ - # pandas.HDFStore.put \ - # pandas.HDFStore.append \ - # pandas.HDFStore.get \ - # pandas.HDFStore.select \ - # pandas.HDFStore.info \ - # pandas.HDFStore.keys \ - # pandas.HDFStore.groups \ - # pandas.HDFStore.walk \ - # pandas.read_feather \ - # pandas.DataFrame.to_feather \ - # pandas.read_parquet \ - # pandas.read_orc \ - # pandas.read_sas \ - # pandas.read_spss \ - # pandas.read_sql_query \ - # pandas.read_gbq \ - # pandas.io.stata.StataReader.data_label \ - # pandas.io.stata.StataReader.value_labels \ - # pandas.io.stata.StataReader.variable_labels \ - # pandas.io.stata.StataWriter.write_file \ - # pandas.core.resample.Resampler.__iter__ \ - # pandas.core.resample.Resampler.groups \ - # pandas.core.resample.Resampler.indices \ - # pandas.core.resample.Resampler.get_group \ - # pandas.core.resample.Resampler.ffill \ - # pandas.core.resample.Resampler.asfreq \ - # pandas.core.resample.Resampler.count \ - # pandas.core.resample.Resampler.nunique \ - # pandas.core.resample.Resampler.max \ - # pandas.core.resample.Resampler.mean \ - # pandas.core.resample.Resampler.median \ - # pandas.core.resample.Resampler.min \ - # pandas.core.resample.Resampler.ohlc \ - # pandas.core.resample.Resampler.prod \ - # pandas.core.resample.Resampler.size \ - # pandas.core.resample.Resampler.sem \ - # pandas.core.resample.Resampler.std \ - # pandas.core.resample.Resampler.sum \ - # pandas.core.resample.Resampler.var \ - # pandas.core.resample.Resampler.quantile \ - # pandas.describe_option \ - # pandas.reset_option \ - # pandas.get_option \ - # pandas.set_option \ - # pandas.plotting.deregister_matplotlib_converters \ - # pandas.plotting.plot_params \ - # pandas.plotting.register_matplotlib_converters \ - # pandas.plotting.table \ - # pandas.util.hash_array \ - # pandas.util.hash_pandas_object \ - # pandas_object \ - # pandas.api.interchange.from_dataframe \ - # pandas.Index.values \ - # pandas.Index.hasnans \ - # pandas.Index.dtype \ - # pandas.Index.inferred_type \ - # pandas.Index.shape \ - # pandas.Index.name \ - # pandas.Index.nbytes \ - # pandas.Index.ndim \ - # pandas.Index.size \ - # pandas.Index.T \ - # pandas.Index.memory_usage \ - # pandas.Index.copy \ - # pandas.Index.drop \ - # pandas.Index.identical \ - # pandas.Index.insert \ - # pandas.Index.is_ \ - # pandas.Index.take \ - # pandas.Index.putmask \ - # pandas.Index.unique \ - # pandas.Index.fillna \ - # pandas.Index.dropna \ - # pandas.Index.astype \ - # pandas.Index.item \ - # pandas.Index.map \ - # pandas.Index.ravel \ - # pandas.Index.to_list \ - # pandas.Index.append \ - # pandas.Index.join \ - # pandas.Index.asof_locs \ - # pandas.Index.get_slice_bound \ - # pandas.RangeIndex \ - # pandas.RangeIndex.start \ - # pandas.RangeIndex.stop \ - # pandas.RangeIndex.step \ - # pandas.RangeIndex.from_range \ - # pandas.CategoricalIndex.codes \ - # pandas.CategoricalIndex.categories \ - # pandas.CategoricalIndex.ordered \ - # pandas.CategoricalIndex.reorder_categories \ - # pandas.CategoricalIndex.set_categories \ - # pandas.CategoricalIndex.as_ordered \ - # pandas.CategoricalIndex.as_unordered \ - # pandas.CategoricalIndex.equals \ - # pandas.IntervalIndex.closed \ - # pandas.IntervalIndex.values \ - # pandas.IntervalIndex.is_non_overlapping_monotonic \ - # pandas.IntervalIndex.to_tuples \ - # pandas.MultiIndex.dtypes \ - # pandas.MultiIndex.drop \ - # pandas.DatetimeIndex \ - # pandas.DatetimeIndex.date \ - # pandas.DatetimeIndex.time \ - # pandas.DatetimeIndex.timetz \ - # pandas.DatetimeIndex.dayofyear \ - # pandas.DatetimeIndex.day_of_year \ - # pandas.DatetimeIndex.quarter \ - # pandas.DatetimeIndex.tz \ - # pandas.DatetimeIndex.freqstr \ - # pandas.DatetimeIndex.inferred_freq \ - # pandas.DatetimeIndex.indexer_at_time \ - # pandas.DatetimeIndex.indexer_between_time \ - # pandas.DatetimeIndex.snap \ - # pandas.DatetimeIndex.as_unit \ - # pandas.DatetimeIndex.to_pydatetime \ - # pandas.DatetimeIndex.to_series \ - # pandas.DatetimeIndex.mean \ - # pandas.DatetimeIndex.std \ - # pandas.TimedeltaIndex \ - # pandas.TimedeltaIndex.days \ - # pandas.TimedeltaIndex.seconds \ - # pandas.TimedeltaIndex.microseconds \ - # pandas.TimedeltaIndex.nanoseconds \ - # pandas.TimedeltaIndex.components \ - # pandas.TimedeltaIndex.inferred_freq \ - # pandas.TimedeltaIndex.as_unit \ - # pandas.TimedeltaIndex.to_pytimedelta \ - # pandas.TimedeltaIndex.mean \ - # pandas.PeriodIndex.day \ - # pandas.PeriodIndex.dayofweek \ - # pandas.PeriodIndex.day_of_week \ - # pandas.PeriodIndex.dayofyear \ - # pandas.PeriodIndex.day_of_year \ - # pandas.PeriodIndex.days_in_month \ - # pandas.PeriodIndex.daysinmonth \ - # pandas.PeriodIndex.end_time \ - # pandas.PeriodIndex.freqstr \ - # pandas.PeriodIndex.hour \ - # pandas.PeriodIndex.is_leap_year \ - # pandas.PeriodIndex.minute \ - # pandas.PeriodIndex.month \ - # pandas.PeriodIndex.quarter \ - # pandas.PeriodIndex.second \ - # pandas.PeriodIndex.week \ - # pandas.PeriodIndex.weekday \ - # pandas.PeriodIndex.weekofyear \ - # pandas.PeriodIndex.year \ - # pandas.PeriodIndex.to_timestamp \ - # pandas.core.window.rolling.Rolling.max \ - # pandas.core.window.rolling.Rolling.cov \ - # pandas.core.window.rolling.Rolling.skew \ - # pandas.core.window.rolling.Rolling.apply \ - # pandas.core.window.rolling.Window.mean \ - # pandas.core.window.rolling.Window.sum \ - # pandas.core.window.rolling.Window.var \ - # pandas.core.window.rolling.Window.std \ - # pandas.core.window.expanding.Expanding.count \ - # pandas.core.window.expanding.Expanding.sum \ - # pandas.core.window.expanding.Expanding.mean \ - # pandas.core.window.expanding.Expanding.median \ - # pandas.core.window.expanding.Expanding.min \ - # pandas.core.window.expanding.Expanding.max \ - # pandas.core.window.expanding.Expanding.corr \ - # pandas.core.window.expanding.Expanding.cov \ - # pandas.core.window.expanding.Expanding.skew \ - # pandas.core.window.expanding.Expanding.apply \ - # pandas.core.window.expanding.Expanding.quantile \ - # pandas.core.window.ewm.ExponentialMovingWindow.mean \ - # pandas.core.window.ewm.ExponentialMovingWindow.sum \ - # pandas.core.window.ewm.ExponentialMovingWindow.std \ - # pandas.core.window.ewm.ExponentialMovingWindow.var \ - # pandas.core.window.ewm.ExponentialMovingWindow.corr \ - # pandas.core.window.ewm.ExponentialMovingWindow.cov \ - # pandas.api.indexers.BaseIndexer \ - # pandas.api.indexers.VariableOffsetWindowIndexer \ - # pandas.core.groupby.DataFrameGroupBy.__iter__ \ - # pandas.core.groupby.SeriesGroupBy.__iter__ \ - # pandas.core.groupby.DataFrameGroupBy.groups \ - # pandas.core.groupby.SeriesGroupBy.groups \ - # pandas.core.groupby.DataFrameGroupBy.indices \ - # pandas.core.groupby.SeriesGroupBy.indices \ - # pandas.core.groupby.DataFrameGroupBy.get_group \ - # pandas.core.groupby.SeriesGroupBy.get_group \ - # pandas.core.groupby.DataFrameGroupBy.all \ - # pandas.core.groupby.DataFrameGroupBy.any \ - # pandas.core.groupby.DataFrameGroupBy.bfill \ - # pandas.core.groupby.DataFrameGroupBy.count \ - # pandas.core.groupby.DataFrameGroupBy.cummax \ - # pandas.core.groupby.DataFrameGroupBy.cummin \ - # pandas.core.groupby.DataFrameGroupBy.cumprod \ - # pandas.core.groupby.DataFrameGroupBy.cumsum \ - # pandas.core.groupby.DataFrameGroupBy.diff \ - # pandas.core.groupby.DataFrameGroupBy.ffill \ - # pandas.core.groupby.DataFrameGroupBy.max \ - # pandas.core.groupby.DataFrameGroupBy.median \ - # pandas.core.groupby.DataFrameGroupBy.min \ - # pandas.core.groupby.DataFrameGroupBy.ohlc \ - # pandas.core.groupby.DataFrameGroupBy.pct_change \ - # pandas.core.groupby.DataFrameGroupBy.prod \ - # pandas.core.groupby.DataFrameGroupBy.sem \ - # pandas.core.groupby.DataFrameGroupBy.shift \ - # pandas.core.groupby.DataFrameGroupBy.size \ - # pandas.core.groupby.DataFrameGroupBy.skew \ - # pandas.core.groupby.DataFrameGroupBy.std \ - # pandas.core.groupby.DataFrameGroupBy.sum \ - # pandas.core.groupby.DataFrameGroupBy.var \ - # pandas.core.groupby.SeriesGroupBy.all \ - # pandas.core.groupby.SeriesGroupBy.any \ - # pandas.core.groupby.SeriesGroupBy.bfill \ - # pandas.core.groupby.SeriesGroupBy.count \ - # pandas.core.groupby.SeriesGroupBy.cummax \ - # pandas.core.groupby.SeriesGroupBy.cummin \ - # pandas.core.groupby.SeriesGroupBy.cumprod \ - # pandas.core.groupby.SeriesGroupBy.cumsum \ - # pandas.core.groupby.SeriesGroupBy.diff \ - # pandas.core.groupby.SeriesGroupBy.ffill \ - # pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing \ - # pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing \ - # pandas.core.groupby.SeriesGroupBy.max \ - # pandas.core.groupby.SeriesGroupBy.median \ - # pandas.core.groupby.SeriesGroupBy.min \ - # pandas.core.groupby.SeriesGroupBy.nunique \ - # pandas.core.groupby.SeriesGroupBy.ohlc \ - # pandas.core.groupby.SeriesGroupBy.pct_change \ - # pandas.core.groupby.SeriesGroupBy.prod \ - # pandas.core.groupby.SeriesGroupBy.sem \ - # pandas.core.groupby.SeriesGroupBy.shift \ - # pandas.core.groupby.SeriesGroupBy.size \ - # pandas.core.groupby.SeriesGroupBy.skew \ - # pandas.core.groupby.SeriesGroupBy.std \ - # pandas.core.groupby.SeriesGroupBy.sum \ - # pandas.core.groupby.SeriesGroupBy.var \ - # pandas.core.groupby.SeriesGroupBy.hist \ - # pandas.core.groupby.DataFrameGroupBy.plot \ - # pandas.core.groupby.SeriesGroupBy.plot \ - # pandas.io.formats.style.Styler \ - # pandas.io.formats.style.Styler.from_custom_template \ - # pandas.io.formats.style.Styler.set_caption \ - # pandas.io.formats.style.Styler.set_sticky \ - # pandas.io.formats.style.Styler.set_uuid \ - # pandas.io.formats.style.Styler.clear \ - # pandas.io.formats.style.Styler.highlight_null \ - # pandas.io.formats.style.Styler.highlight_max \ - # pandas.io.formats.style.Styler.highlight_min \ - # pandas.io.formats.style.Styler.bar \ - # pandas.io.formats.style.Styler.to_string \ - # pandas.api.extensions.ExtensionDtype \ - # pandas.api.extensions.ExtensionArray \ - # pandas.arrays.PandasArray \ - # pandas.api.extensions.ExtensionArray._accumulate \ - # pandas.api.extensions.ExtensionArray._concat_same_type \ - # pandas.api.extensions.ExtensionArray._formatter \ - # pandas.api.extensions.ExtensionArray._from_factorized \ - # pandas.api.extensions.ExtensionArray._from_sequence \ - # pandas.api.extensions.ExtensionArray._from_sequence_of_strings \ - # pandas.api.extensions.ExtensionArray._reduce \ - # pandas.api.extensions.ExtensionArray._values_for_argsort \ - # pandas.api.extensions.ExtensionArray._values_for_factorize \ - # pandas.api.extensions.ExtensionArray.argsort \ - # pandas.api.extensions.ExtensionArray.astype \ - # pandas.api.extensions.ExtensionArray.copy \ - # pandas.api.extensions.ExtensionArray.view \ - # pandas.api.extensions.ExtensionArray.dropna \ - # pandas.api.extensions.ExtensionArray.equals \ - # pandas.api.extensions.ExtensionArray.factorize \ - # pandas.api.extensions.ExtensionArray.fillna \ - # pandas.api.extensions.ExtensionArray.insert \ - # pandas.api.extensions.ExtensionArray.isin \ - # pandas.api.extensions.ExtensionArray.isna \ - # pandas.api.extensions.ExtensionArray.ravel \ - # pandas.api.extensions.ExtensionArray.searchsorted \ - # pandas.api.extensions.ExtensionArray.shift \ - # pandas.api.extensions.ExtensionArray.unique \ - # pandas.api.extensions.ExtensionArray.dtype \ - # pandas.api.extensions.ExtensionArray.nbytes \ - # pandas.api.extensions.ExtensionArray.ndim \ - # pandas.api.extensions.ExtensionArray.shape \ - # pandas.api.extensions.ExtensionArray.tolist \ - # pandas.DataFrame.index \ - # pandas.DataFrame.columns \ - # pandas.DataFrame.__iter__ \ - # pandas.DataFrame.keys \ - # pandas.DataFrame.iterrows \ - # pandas.DataFrame.pipe \ - # pandas.DataFrame.kurt \ - # pandas.DataFrame.kurtosis \ - # pandas.DataFrame.mean \ - # pandas.DataFrame.median \ - # pandas.DataFrame.sem \ - # pandas.DataFrame.skew \ - # pandas.DataFrame.backfill \ - # pandas.DataFrame.pad \ - # pandas.DataFrame.swapaxes \ - # pandas.DataFrame.first_valid_index \ - # pandas.DataFrame.last_valid_index \ - # pandas.DataFrame.to_timestamp \ - # pandas.DataFrame.attrs \ - # pandas.DataFrame.plot \ - # pandas.DataFrame.sparse.density \ - # pandas.DataFrame.sparse.to_coo \ - # pandas.DataFrame.to_gbq \ - # pandas.DataFrame.style \ - # pandas.DataFrame.__dataframe__ - # RET=$(($RET + $?)) ; echo $MSG "DONE" + MSG='Partially validate docstrings (EX01)' ; echo $MSG + $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \ + pandas.Series.index \ + pandas.Series.dtype \ + pandas.Series.nbytes \ + pandas.Series.ndim \ + pandas.Series.size \ + pandas.Series.T \ + pandas.Series.hasnans \ + pandas.Series.dtypes \ + pandas.Series.to_period \ + pandas.Series.to_timestamp \ + pandas.Series.to_list \ + pandas.Series.__iter__ \ + pandas.Series.keys \ + pandas.Series.item \ + pandas.Series.pipe \ + pandas.Series.kurt \ + pandas.Series.mean \ + pandas.Series.median \ + pandas.Series.mode \ + pandas.Series.sem \ + pandas.Series.skew \ + pandas.Series.kurtosis \ + pandas.Series.is_unique \ + pandas.Series.is_monotonic_increasing \ + pandas.Series.is_monotonic_decreasing \ + pandas.Series.backfill \ + pandas.Series.pad \ + pandas.Series.argsort \ + pandas.Series.reorder_levels \ + pandas.Series.ravel \ + pandas.Series.first_valid_index \ + pandas.Series.last_valid_index \ + pandas.Series.dt.date \ + pandas.Series.dt.time \ + pandas.Series.dt.timetz \ + pandas.Series.dt.dayofyear \ + pandas.Series.dt.day_of_year \ + pandas.Series.dt.quarter \ + pandas.Series.dt.daysinmonth \ + pandas.Series.dt.days_in_month \ + pandas.Series.dt.tz \ + pandas.Series.dt.end_time \ + pandas.Series.dt.days \ + pandas.Series.dt.seconds \ + pandas.Series.dt.microseconds \ + pandas.Series.dt.nanoseconds \ + pandas.Series.str.center \ + pandas.Series.str.decode \ + pandas.Series.str.encode \ + pandas.Series.str.find \ + pandas.Series.str.fullmatch \ + pandas.Series.str.index \ + pandas.Series.str.ljust \ + pandas.Series.str.match \ + pandas.Series.str.normalize \ + pandas.Series.str.rfind \ + pandas.Series.str.rindex \ + pandas.Series.str.rjust \ + pandas.Series.str.translate \ + pandas.Series.sparse \ + pandas.DataFrame.sparse \ + pandas.Series.cat.categories \ + pandas.Series.cat.ordered \ + pandas.Series.cat.codes \ + pandas.Series.cat.reorder_categories \ + pandas.Series.cat.set_categories \ + pandas.Series.cat.as_ordered \ + pandas.Series.cat.as_unordered \ + pandas.Series.sparse.fill_value \ + pandas.Flags \ + pandas.Series.attrs \ + pandas.Series.plot \ + pandas.Series.hist \ + pandas.Series.to_string \ + pandas.errors.AbstractMethodError \ + pandas.errors.AccessorRegistrationWarning \ + pandas.errors.AttributeConflictWarning \ + pandas.errors.DataError \ + pandas.errors.EmptyDataError \ + pandas.errors.IncompatibilityWarning \ + pandas.errors.InvalidComparison \ + pandas.errors.InvalidIndexError \ + pandas.errors.InvalidVersion \ + pandas.errors.IntCastingNaNError \ + pandas.errors.LossySetitemError \ + pandas.errors.MergeError \ + pandas.errors.NoBufferPresent \ + pandas.errors.NullFrequencyError \ + pandas.errors.NumbaUtilError \ + pandas.errors.OptionError \ + pandas.errors.OutOfBoundsDatetime \ + pandas.errors.OutOfBoundsTimedelta \ + pandas.errors.ParserError \ + pandas.errors.PerformanceWarning \ + pandas.errors.PyperclipException \ + pandas.errors.PyperclipWindowsException \ + pandas.errors.UnsortedIndexError \ + pandas.errors.UnsupportedFunctionCall \ + pandas.show_versions \ + pandas.test \ + pandas.NaT \ + pandas.Timestamp.as_unit \ + pandas.Timestamp.ctime \ + pandas.Timestamp.date \ + pandas.Timestamp.dst \ + pandas.Timestamp.isocalendar \ + pandas.Timestamp.isoweekday \ + pandas.Timestamp.strptime \ + pandas.Timestamp.time \ + pandas.Timestamp.timetuple \ + pandas.Timestamp.timetz \ + pandas.Timestamp.to_datetime64 \ + pandas.Timestamp.toordinal \ + pandas.Timestamp.tzname \ + pandas.Timestamp.utcoffset \ + pandas.Timestamp.utctimetuple \ + pandas.Timestamp.weekday \ + pandas.arrays.DatetimeArray \ + pandas.Timedelta.view \ + pandas.Timedelta.as_unit \ + pandas.Timedelta.ceil \ + pandas.Timedelta.floor \ + pandas.Timedelta.round \ + pandas.Timedelta.to_pytimedelta \ + pandas.Timedelta.to_timedelta64 \ + pandas.Timedelta.to_numpy \ + pandas.Timedelta.total_seconds \ + pandas.arrays.TimedeltaArray \ + pandas.Period.end_time \ + pandas.Period.freqstr \ + pandas.Period.is_leap_year \ + pandas.Period.month \ + pandas.Period.quarter \ + pandas.Period.year \ + pandas.Period.asfreq \ + pandas.Period.now \ + pandas.Period.to_timestamp \ + pandas.arrays.PeriodArray \ + pandas.Interval.closed \ + pandas.Interval.left \ + pandas.Interval.length \ + pandas.Interval.right \ + pandas.arrays.IntervalArray.left \ + pandas.arrays.IntervalArray.right \ + pandas.arrays.IntervalArray.closed \ + pandas.arrays.IntervalArray.mid \ + pandas.arrays.IntervalArray.length \ + pandas.arrays.IntervalArray.is_non_overlapping_monotonic \ + pandas.arrays.IntervalArray.from_arrays \ + pandas.arrays.IntervalArray.to_tuples \ + pandas.Int8Dtype \ + pandas.Int16Dtype \ + pandas.Int32Dtype \ + pandas.Int64Dtype \ + pandas.UInt8Dtype \ + pandas.UInt16Dtype \ + pandas.UInt32Dtype \ + pandas.UInt64Dtype \ + pandas.NA \ + pandas.Float32Dtype \ + pandas.Float64Dtype \ + pandas.CategoricalDtype.categories \ + pandas.CategoricalDtype.ordered \ + pandas.Categorical.dtype \ + pandas.Categorical.categories \ + pandas.Categorical.ordered \ + pandas.Categorical.codes \ + pandas.Categorical.__array__ \ + pandas.SparseDtype \ + pandas.DatetimeTZDtype.unit \ + pandas.DatetimeTZDtype.tz \ + pandas.PeriodDtype.freq \ + pandas.IntervalDtype.subtype \ + pandas_dtype \ + pandas.api.types.is_bool \ + pandas.api.types.is_complex \ + pandas.api.types.is_float \ + pandas.api.types.is_integer \ + pandas.api.types.pandas_dtype \ + pandas.read_clipboard \ + pandas.ExcelFile \ + pandas.ExcelFile.parse \ + pandas.DataFrame.to_html \ + pandas.io.formats.style.Styler.to_html \ + pandas.HDFStore.put \ + pandas.HDFStore.append \ + pandas.HDFStore.get \ + pandas.HDFStore.select \ + pandas.HDFStore.info \ + pandas.HDFStore.keys \ + pandas.HDFStore.groups \ + pandas.HDFStore.walk \ + pandas.read_feather \ + pandas.DataFrame.to_feather \ + pandas.read_parquet \ + pandas.read_orc \ + pandas.read_sas \ + pandas.read_spss \ + pandas.read_sql_query \ + pandas.read_gbq \ + pandas.io.stata.StataReader.data_label \ + pandas.io.stata.StataReader.value_labels \ + pandas.io.stata.StataReader.variable_labels \ + pandas.io.stata.StataWriter.write_file \ + pandas.core.resample.Resampler.__iter__ \ + pandas.core.resample.Resampler.groups \ + pandas.core.resample.Resampler.indices \ + pandas.core.resample.Resampler.get_group \ + pandas.core.resample.Resampler.ffill \ + pandas.core.resample.Resampler.asfreq \ + pandas.core.resample.Resampler.count \ + pandas.core.resample.Resampler.nunique \ + pandas.core.resample.Resampler.max \ + pandas.core.resample.Resampler.mean \ + pandas.core.resample.Resampler.median \ + pandas.core.resample.Resampler.min \ + pandas.core.resample.Resampler.ohlc \ + pandas.core.resample.Resampler.prod \ + pandas.core.resample.Resampler.size \ + pandas.core.resample.Resampler.sem \ + pandas.core.resample.Resampler.std \ + pandas.core.resample.Resampler.sum \ + pandas.core.resample.Resampler.var \ + pandas.core.resample.Resampler.quantile \ + pandas.describe_option \ + pandas.reset_option \ + pandas.get_option \ + pandas.set_option \ + pandas.plotting.deregister_matplotlib_converters \ + pandas.plotting.plot_params \ + pandas.plotting.register_matplotlib_converters \ + pandas.plotting.table \ + pandas.util.hash_array \ + pandas.util.hash_pandas_object \ + pandas_object \ + pandas.api.interchange.from_dataframe \ + pandas.Index.values \ + pandas.Index.hasnans \ + pandas.Index.dtype \ + pandas.Index.inferred_type \ + pandas.Index.shape \ + pandas.Index.name \ + pandas.Index.nbytes \ + pandas.Index.ndim \ + pandas.Index.size \ + pandas.Index.T \ + pandas.Index.memory_usage \ + pandas.Index.copy \ + pandas.Index.drop \ + pandas.Index.identical \ + pandas.Index.insert \ + pandas.Index.is_ \ + pandas.Index.take \ + pandas.Index.putmask \ + pandas.Index.unique \ + pandas.Index.fillna \ + pandas.Index.dropna \ + pandas.Index.astype \ + pandas.Index.item \ + pandas.Index.map \ + pandas.Index.ravel \ + pandas.Index.to_list \ + pandas.Index.append \ + pandas.Index.join \ + pandas.Index.asof_locs \ + pandas.Index.get_slice_bound \ + pandas.RangeIndex \ + pandas.RangeIndex.start \ + pandas.RangeIndex.stop \ + pandas.RangeIndex.step \ + pandas.RangeIndex.from_range \ + pandas.CategoricalIndex.codes \ + pandas.CategoricalIndex.categories \ + pandas.CategoricalIndex.ordered \ + pandas.CategoricalIndex.reorder_categories \ + pandas.CategoricalIndex.set_categories \ + pandas.CategoricalIndex.as_ordered \ + pandas.CategoricalIndex.as_unordered \ + pandas.CategoricalIndex.equals \ + pandas.IntervalIndex.closed \ + pandas.IntervalIndex.values \ + pandas.IntervalIndex.is_non_overlapping_monotonic \ + pandas.IntervalIndex.to_tuples \ + pandas.MultiIndex.dtypes \ + pandas.MultiIndex.drop \ + pandas.DatetimeIndex \ + pandas.DatetimeIndex.date \ + pandas.DatetimeIndex.time \ + pandas.DatetimeIndex.timetz \ + pandas.DatetimeIndex.dayofyear \ + pandas.DatetimeIndex.day_of_year \ + pandas.DatetimeIndex.quarter \ + pandas.DatetimeIndex.tz \ + pandas.DatetimeIndex.freqstr \ + pandas.DatetimeIndex.inferred_freq \ + pandas.DatetimeIndex.indexer_at_time \ + pandas.DatetimeIndex.indexer_between_time \ + pandas.DatetimeIndex.snap \ + pandas.DatetimeIndex.as_unit \ + pandas.DatetimeIndex.to_pydatetime \ + pandas.DatetimeIndex.to_series \ + pandas.DatetimeIndex.mean \ + pandas.DatetimeIndex.std \ + pandas.TimedeltaIndex \ + pandas.TimedeltaIndex.days \ + pandas.TimedeltaIndex.seconds \ + pandas.TimedeltaIndex.microseconds \ + pandas.TimedeltaIndex.nanoseconds \ + pandas.TimedeltaIndex.components \ + pandas.TimedeltaIndex.inferred_freq \ + pandas.TimedeltaIndex.as_unit \ + pandas.TimedeltaIndex.to_pytimedelta \ + pandas.TimedeltaIndex.mean \ + pandas.PeriodIndex.day \ + pandas.PeriodIndex.dayofweek \ + pandas.PeriodIndex.day_of_week \ + pandas.PeriodIndex.dayofyear \ + pandas.PeriodIndex.day_of_year \ + pandas.PeriodIndex.days_in_month \ + pandas.PeriodIndex.daysinmonth \ + pandas.PeriodIndex.end_time \ + pandas.PeriodIndex.freqstr \ + pandas.PeriodIndex.hour \ + pandas.PeriodIndex.is_leap_year \ + pandas.PeriodIndex.minute \ + pandas.PeriodIndex.month \ + pandas.PeriodIndex.quarter \ + pandas.PeriodIndex.second \ + pandas.PeriodIndex.week \ + pandas.PeriodIndex.weekday \ + pandas.PeriodIndex.weekofyear \ + pandas.PeriodIndex.year \ + pandas.PeriodIndex.to_timestamp \ + pandas.core.window.rolling.Rolling.max \ + pandas.core.window.rolling.Rolling.cov \ + pandas.core.window.rolling.Rolling.skew \ + pandas.core.window.rolling.Rolling.apply \ + pandas.core.window.rolling.Window.mean \ + pandas.core.window.rolling.Window.sum \ + pandas.core.window.rolling.Window.var \ + pandas.core.window.rolling.Window.std \ + pandas.core.window.expanding.Expanding.count \ + pandas.core.window.expanding.Expanding.sum \ + pandas.core.window.expanding.Expanding.mean \ + pandas.core.window.expanding.Expanding.median \ + pandas.core.window.expanding.Expanding.min \ + pandas.core.window.expanding.Expanding.max \ + pandas.core.window.expanding.Expanding.corr \ + pandas.core.window.expanding.Expanding.cov \ + pandas.core.window.expanding.Expanding.skew \ + pandas.core.window.expanding.Expanding.apply \ + pandas.core.window.expanding.Expanding.quantile \ + pandas.core.window.ewm.ExponentialMovingWindow.mean \ + pandas.core.window.ewm.ExponentialMovingWindow.sum \ + pandas.core.window.ewm.ExponentialMovingWindow.std \ + pandas.core.window.ewm.ExponentialMovingWindow.var \ + pandas.core.window.ewm.ExponentialMovingWindow.corr \ + pandas.core.window.ewm.ExponentialMovingWindow.cov \ + pandas.api.indexers.BaseIndexer \ + pandas.api.indexers.VariableOffsetWindowIndexer \ + pandas.core.groupby.DataFrameGroupBy.__iter__ \ + pandas.core.groupby.SeriesGroupBy.__iter__ \ + pandas.core.groupby.DataFrameGroupBy.groups \ + pandas.core.groupby.SeriesGroupBy.groups \ + pandas.core.groupby.DataFrameGroupBy.indices \ + pandas.core.groupby.SeriesGroupBy.indices \ + pandas.core.groupby.DataFrameGroupBy.get_group \ + pandas.core.groupby.SeriesGroupBy.get_group \ + pandas.core.groupby.DataFrameGroupBy.all \ + pandas.core.groupby.DataFrameGroupBy.any \ + pandas.core.groupby.DataFrameGroupBy.bfill \ + pandas.core.groupby.DataFrameGroupBy.count \ + pandas.core.groupby.DataFrameGroupBy.cummax \ + pandas.core.groupby.DataFrameGroupBy.cummin \ + pandas.core.groupby.DataFrameGroupBy.cumprod \ + pandas.core.groupby.DataFrameGroupBy.cumsum \ + pandas.core.groupby.DataFrameGroupBy.diff \ + pandas.core.groupby.DataFrameGroupBy.ffill \ + pandas.core.groupby.DataFrameGroupBy.max \ + pandas.core.groupby.DataFrameGroupBy.median \ + pandas.core.groupby.DataFrameGroupBy.min \ + pandas.core.groupby.DataFrameGroupBy.ohlc \ + pandas.core.groupby.DataFrameGroupBy.pct_change \ + pandas.core.groupby.DataFrameGroupBy.prod \ + pandas.core.groupby.DataFrameGroupBy.sem \ + pandas.core.groupby.DataFrameGroupBy.shift \ + pandas.core.groupby.DataFrameGroupBy.size \ + pandas.core.groupby.DataFrameGroupBy.skew \ + pandas.core.groupby.DataFrameGroupBy.std \ + pandas.core.groupby.DataFrameGroupBy.sum \ + pandas.core.groupby.DataFrameGroupBy.var \ + pandas.core.groupby.SeriesGroupBy.all \ + pandas.core.groupby.SeriesGroupBy.any \ + pandas.core.groupby.SeriesGroupBy.bfill \ + pandas.core.groupby.SeriesGroupBy.count \ + pandas.core.groupby.SeriesGroupBy.cummax \ + pandas.core.groupby.SeriesGroupBy.cummin \ + pandas.core.groupby.SeriesGroupBy.cumprod \ + pandas.core.groupby.SeriesGroupBy.cumsum \ + pandas.core.groupby.SeriesGroupBy.diff \ + pandas.core.groupby.SeriesGroupBy.ffill \ + pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing \ + pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing \ + pandas.core.groupby.SeriesGroupBy.max \ + pandas.core.groupby.SeriesGroupBy.median \ + pandas.core.groupby.SeriesGroupBy.min \ + pandas.core.groupby.SeriesGroupBy.nunique \ + pandas.core.groupby.SeriesGroupBy.ohlc \ + pandas.core.groupby.SeriesGroupBy.pct_change \ + pandas.core.groupby.SeriesGroupBy.prod \ + pandas.core.groupby.SeriesGroupBy.sem \ + pandas.core.groupby.SeriesGroupBy.shift \ + pandas.core.groupby.SeriesGroupBy.size \ + pandas.core.groupby.SeriesGroupBy.skew \ + pandas.core.groupby.SeriesGroupBy.std \ + pandas.core.groupby.SeriesGroupBy.sum \ + pandas.core.groupby.SeriesGroupBy.var \ + pandas.core.groupby.SeriesGroupBy.hist \ + pandas.core.groupby.DataFrameGroupBy.plot \ + pandas.core.groupby.SeriesGroupBy.plot \ + pandas.io.formats.style.Styler \ + pandas.io.formats.style.Styler.from_custom_template \ + pandas.io.formats.style.Styler.set_caption \ + pandas.io.formats.style.Styler.set_sticky \ + pandas.io.formats.style.Styler.set_uuid \ + pandas.io.formats.style.Styler.clear \ + pandas.io.formats.style.Styler.highlight_null \ + pandas.io.formats.style.Styler.highlight_max \ + pandas.io.formats.style.Styler.highlight_min \ + pandas.io.formats.style.Styler.bar \ + pandas.io.formats.style.Styler.to_string \ + pandas.api.extensions.ExtensionDtype \ + pandas.api.extensions.ExtensionArray \ + pandas.arrays.PandasArray \ + pandas.api.extensions.ExtensionArray._accumulate \ + pandas.api.extensions.ExtensionArray._concat_same_type \ + pandas.api.extensions.ExtensionArray._formatter \ + pandas.api.extensions.ExtensionArray._from_factorized \ + pandas.api.extensions.ExtensionArray._from_sequence \ + pandas.api.extensions.ExtensionArray._from_sequence_of_strings \ + pandas.api.extensions.ExtensionArray._reduce \ + pandas.api.extensions.ExtensionArray._values_for_argsort \ + pandas.api.extensions.ExtensionArray._values_for_factorize \ + pandas.api.extensions.ExtensionArray.argsort \ + pandas.api.extensions.ExtensionArray.astype \ + pandas.api.extensions.ExtensionArray.copy \ + pandas.api.extensions.ExtensionArray.view \ + pandas.api.extensions.ExtensionArray.dropna \ + pandas.api.extensions.ExtensionArray.equals \ + pandas.api.extensions.ExtensionArray.factorize \ + pandas.api.extensions.ExtensionArray.fillna \ + pandas.api.extensions.ExtensionArray.insert \ + pandas.api.extensions.ExtensionArray.isin \ + pandas.api.extensions.ExtensionArray.isna \ + pandas.api.extensions.ExtensionArray.ravel \ + pandas.api.extensions.ExtensionArray.searchsorted \ + pandas.api.extensions.ExtensionArray.shift \ + pandas.api.extensions.ExtensionArray.unique \ + pandas.api.extensions.ExtensionArray.dtype \ + pandas.api.extensions.ExtensionArray.nbytes \ + pandas.api.extensions.ExtensionArray.ndim \ + pandas.api.extensions.ExtensionArray.shape \ + pandas.api.extensions.ExtensionArray.tolist \ + pandas.DataFrame.index \ + pandas.DataFrame.columns \ + pandas.DataFrame.__iter__ \ + pandas.DataFrame.keys \ + pandas.DataFrame.iterrows \ + pandas.DataFrame.pipe \ + pandas.DataFrame.kurt \ + pandas.DataFrame.kurtosis \ + pandas.DataFrame.mean \ + pandas.DataFrame.median \ + pandas.DataFrame.sem \ + pandas.DataFrame.skew \ + pandas.DataFrame.backfill \ + pandas.DataFrame.pad \ + pandas.DataFrame.swapaxes \ + pandas.DataFrame.first_valid_index \ + pandas.DataFrame.last_valid_index \ + pandas.DataFrame.to_timestamp \ + pandas.DataFrame.attrs \ + pandas.DataFrame.plot \ + pandas.DataFrame.sparse.density \ + pandas.DataFrame.sparse.to_coo \ + pandas.DataFrame.to_gbq \ + pandas.DataFrame.style \ + pandas.DataFrame.__dataframe__ + RET=$(($RET + $?)) ; echo $MSG "DONE" MSG='Partially validate docstrings (EX02)' ; echo $MSG $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX02 --ignore_functions \ From 9ad206738c47822cd1d18adb800baff2375dfd3a Mon Sep 17 00:00:00 2001 From: Su Date: Tue, 14 Feb 2023 23:07:10 +0800 Subject: [PATCH 6/6] Revert template string changes --- pandas/io/formats/style.py | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py index 451d060d80222..a89fd90c0218b 100644 --- a/pandas/io/formats/style.py +++ b/pandas/io/formats/style.py @@ -1871,7 +1871,7 @@ def apply_index( >>> df = pd.DataFrame([[1,2], [3,4]], index=["A", "B"]) >>> def color_b(s): ... return {ret} - >>> df.style.apply_index(color_b) # doctest: +SKIP + >>> df.style.{this}_index(color_b) # doctest: +SKIP .. figure:: ../../_static/style/appmaphead1.png @@ -1879,9 +1879,9 @@ def apply_index( >>> midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']]) >>> df = pd.DataFrame([np.arange(8)], columns=midx) - >>> def highlight_x(s): + >>> def highlight_x({var}): ... return {ret2} - >>> df.style.apply_index(highlight_x, axis="columns", level=[0, 2]) + >>> df.style.{this}_index(highlight_x, axis="columns", level=[0, 2]) ... # doctest: +SKIP .. figure:: ../../_static/style/appmaphead2.png @@ -2784,35 +2784,31 @@ def background_gradient( Shading the values column-wise, with ``axis=0``, preselecting numeric columns - >>> df.style.background_gradient(axis=0) # doctest: +SKIP + >>> df.style.{name}_gradient(axis=0) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_ax0.png Shading all values collectively using ``axis=None`` - >>> df.style.background_gradient(axis=None) # doctest: +SKIP + >>> df.style.{name}_gradient(axis=None) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone.png Compress the color map from the both ``low`` and ``high`` ends - >>> df.style.background_gradient(axis=None, - ... low=0.75, - ... high=1.0) # doctest: +SKIP + >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png Manually setting ``vmin`` and ``vmax`` gradient thresholds - >>> df.style.background_gradient(axis=None, - ... vmin=6.7, - ... vmax=21.6) # doctest: +SKIP + >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6) # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png Setting a ``gmap`` and applying to all columns with another ``cmap`` - >>> df.style.background_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd') + >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd') ... # doctest: +SKIP .. figure:: ../../_static/style/{image_prefix}_gmap.png @@ -2821,7 +2817,7 @@ def background_gradient( explicitly state ``subset`` to match the ``gmap`` shape >>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]]) - >>> df.style.background_gradient(axis=None, gmap=gmap, + >>> df.style.{name}_gradient(axis=None, gmap=gmap, ... cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)'] ... ) # doctest: +SKIP