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code_checks.sh
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#!/bin/bash
#
# Run checks related to code quality.
#
# This script is intended for both the CI and to check locally that code standards are
# respected. We run doctests here (currently some files only), and we
# validate formatting error in docstrings.
#
# Usage:
# $ ./ci/code_checks.sh # run all checks
# $ ./ci/code_checks.sh code # checks on imported code
# $ ./ci/code_checks.sh doctests # run doctests
# $ ./ci/code_checks.sh docstrings # validate docstring errors
# $ ./ci/code_checks.sh single-docs # check single-page docs build warning-free
# $ ./ci/code_checks.sh notebooks # check execution of documentation notebooks
set -uo pipefail
if [[ -v 1 ]]; then
CHECK=$1
else
# script will fail if it uses an unset variable (i.e. $1 is not provided)
CHECK=""
fi
[[ -z "$CHECK" || "$CHECK" == "code" || "$CHECK" == "doctests" || "$CHECK" == "docstrings" || "$CHECK" == "single-docs" || "$CHECK" == "notebooks" ]] || \
{ echo "Unknown command $1. Usage: $0 [code|doctests|docstrings|single-docs|notebooks]"; exit 9999; }
BASE_DIR="$(dirname $0)/.."
RET=0
### CODE ###
if [[ -z "$CHECK" || "$CHECK" == "code" ]]; then
MSG='Check import. No warnings, and blocklist some optional dependencies' ; echo $MSG
python -W error -c "
import sys
import pandas
blocklist = {'bs4', 'gcsfs', 'html5lib', 'http', 'ipython', 'jinja2', 'hypothesis',
'lxml', 'matplotlib', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
'tables', 'urllib.request', 'xlrd', 'xlsxwriter'}
# GH#28227 for some of these check for top-level modules, while others are
# more specific (e.g. urllib.request)
import_mods = set(m.split('.')[0] for m in sys.modules) | set(sys.modules)
mods = blocklist & import_mods
if mods:
sys.stderr.write('err: pandas should not import: {}\n'.format(', '.join(mods)))
sys.exit(len(mods))
"
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### DOCTESTS ###
if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
MSG='Python and Cython Doctests' ; echo $MSG
python -c 'import pandas as pd; pd.test(run_doctests=True)'
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### DOCSTRINGS ###
if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
MSG='Validate Docstrings' ; echo $MSG
$BASE_DIR/scripts/validate_docstrings.py \
--format=actions \
-i ES01 `# For now it is ok if docstrings are missing the extended summary` \
-i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-i "pandas.Categorical.__array__ SA01" \
-i "pandas.Categorical.codes SA01" \
-i "pandas.Categorical.dtype SA01" \
-i "pandas.Categorical.from_codes SA01" \
-i "pandas.Categorical.ordered SA01" \
-i "pandas.CategoricalDtype.categories SA01" \
-i "pandas.CategoricalDtype.ordered SA01" \
-i "pandas.CategoricalIndex.codes SA01" \
-i "pandas.CategoricalIndex.ordered SA01" \
-i "pandas.DataFrame.__dataframe__ SA01" \
-i "pandas.DataFrame.__iter__ SA01" \
-i "pandas.DataFrame.assign SA01" \
-i "pandas.DataFrame.at_time PR01" \
-i "pandas.DataFrame.axes SA01" \
-i "pandas.DataFrame.backfill PR01,SA01" \
-i "pandas.DataFrame.bfill SA01" \
-i "pandas.DataFrame.columns SA01" \
-i "pandas.DataFrame.copy SA01" \
-i "pandas.DataFrame.droplevel SA01" \
-i "pandas.DataFrame.dtypes SA01" \
-i "pandas.DataFrame.ffill SA01" \
-i "pandas.DataFrame.first_valid_index SA01" \
-i "pandas.DataFrame.get SA01" \
-i "pandas.DataFrame.hist RT03" \
-i "pandas.DataFrame.infer_objects RT03" \
-i "pandas.DataFrame.keys SA01" \
-i "pandas.DataFrame.kurt RT03,SA01" \
-i "pandas.DataFrame.kurtosis RT03,SA01" \
-i "pandas.DataFrame.last_valid_index SA01" \
-i "pandas.DataFrame.max RT03" \
-i "pandas.DataFrame.mean RT03,SA01" \
-i "pandas.DataFrame.median RT03,SA01" \
-i "pandas.DataFrame.min RT03" \
-i "pandas.DataFrame.pad PR01,SA01" \
-i "pandas.DataFrame.plot PR02,SA01" \
-i "pandas.DataFrame.pop SA01" \
-i "pandas.DataFrame.prod RT03" \
-i "pandas.DataFrame.product RT03" \
-i "pandas.DataFrame.reorder_levels SA01" \
-i "pandas.DataFrame.sem PR01,RT03,SA01" \
-i "pandas.DataFrame.skew RT03,SA01" \
-i "pandas.DataFrame.sparse PR01,SA01" \
-i "pandas.DataFrame.sparse.density SA01" \
-i "pandas.DataFrame.sparse.from_spmatrix SA01" \
-i "pandas.DataFrame.sparse.to_coo SA01" \
-i "pandas.DataFrame.sparse.to_dense SA01" \
-i "pandas.DataFrame.std PR01,RT03,SA01" \
-i "pandas.DataFrame.sum RT03" \
-i "pandas.DataFrame.swapaxes PR01,SA01" \
-i "pandas.DataFrame.swaplevel SA01" \
-i "pandas.DataFrame.to_feather SA01" \
-i "pandas.DataFrame.to_markdown SA01" \
-i "pandas.DataFrame.to_parquet RT03" \
-i "pandas.DataFrame.to_period SA01" \
-i "pandas.DataFrame.to_timestamp SA01" \
-i "pandas.DataFrame.tz_convert SA01" \
-i "pandas.DataFrame.tz_localize SA01" \
-i "pandas.DataFrame.unstack RT03" \
-i "pandas.DataFrame.value_counts RT03" \
-i "pandas.DataFrame.var PR01,RT03,SA01" \
-i "pandas.DatetimeIndex.ceil SA01" \
-i "pandas.DatetimeIndex.date SA01" \
-i "pandas.DatetimeIndex.day SA01" \
-i "pandas.DatetimeIndex.day_name SA01" \
-i "pandas.DatetimeIndex.day_of_year SA01" \
-i "pandas.DatetimeIndex.dayofyear SA01" \
-i "pandas.DatetimeIndex.floor SA01" \
-i "pandas.DatetimeIndex.freqstr SA01" \
-i "pandas.DatetimeIndex.hour SA01" \
-i "pandas.DatetimeIndex.indexer_at_time PR01,RT03" \
-i "pandas.DatetimeIndex.indexer_between_time RT03" \
-i "pandas.DatetimeIndex.inferred_freq SA01" \
-i "pandas.DatetimeIndex.is_leap_year SA01" \
-i "pandas.DatetimeIndex.microsecond SA01" \
-i "pandas.DatetimeIndex.minute SA01" \
-i "pandas.DatetimeIndex.month SA01" \
-i "pandas.DatetimeIndex.month_name SA01" \
-i "pandas.DatetimeIndex.nanosecond SA01" \
-i "pandas.DatetimeIndex.quarter SA01" \
-i "pandas.DatetimeIndex.round SA01" \
-i "pandas.DatetimeIndex.second SA01" \
-i "pandas.DatetimeIndex.snap PR01,RT03,SA01" \
-i "pandas.DatetimeIndex.std PR01,RT03" \
-i "pandas.DatetimeIndex.time SA01" \
-i "pandas.DatetimeIndex.timetz SA01" \
-i "pandas.DatetimeIndex.to_period RT03" \
-i "pandas.DatetimeIndex.to_pydatetime RT03,SA01" \
-i "pandas.DatetimeIndex.tz SA01" \
-i "pandas.DatetimeIndex.tz_convert RT03" \
-i "pandas.DatetimeIndex.year SA01" \
-i "pandas.DatetimeTZDtype SA01" \
-i "pandas.DatetimeTZDtype.tz SA01" \
-i "pandas.DatetimeTZDtype.unit SA01" \
-i "pandas.ExcelFile PR01,SA01" \
-i "pandas.ExcelFile.parse PR01,SA01" \
-i "pandas.ExcelWriter SA01" \
-i "pandas.Float32Dtype SA01" \
-i "pandas.Float64Dtype SA01" \
-i "pandas.Grouper PR02,SA01" \
-i "pandas.HDFStore.append PR01,SA01" \
-i "pandas.HDFStore.get SA01" \
-i "pandas.HDFStore.groups SA01" \
-i "pandas.HDFStore.info RT03,SA01" \
-i "pandas.HDFStore.keys SA01" \
-i "pandas.HDFStore.put PR01,SA01" \
-i "pandas.HDFStore.select SA01" \
-i "pandas.HDFStore.walk SA01" \
-i "pandas.Index PR07" \
-i "pandas.Index.T SA01" \
-i "pandas.Index.append PR07,RT03,SA01" \
-i "pandas.Index.astype SA01" \
-i "pandas.Index.copy PR07,SA01" \
-i "pandas.Index.difference PR07,RT03,SA01" \
-i "pandas.Index.drop PR07,SA01" \
-i "pandas.Index.drop_duplicates RT03" \
-i "pandas.Index.droplevel RT03,SA01" \
-i "pandas.Index.dropna RT03,SA01" \
-i "pandas.Index.dtype SA01" \
-i "pandas.Index.duplicated RT03" \
-i "pandas.Index.empty GL08" \
-i "pandas.Index.equals SA01" \
-i "pandas.Index.fillna RT03" \
-i "pandas.Index.get_indexer PR07,SA01" \
-i "pandas.Index.get_indexer_for PR01,SA01" \
-i "pandas.Index.get_indexer_non_unique PR07,SA01" \
-i "pandas.Index.get_loc PR07,RT03,SA01" \
-i "pandas.Index.get_slice_bound PR07" \
-i "pandas.Index.hasnans SA01" \
-i "pandas.Index.identical PR01,SA01" \
-i "pandas.Index.inferred_type SA01" \
-i "pandas.Index.insert PR07,RT03,SA01" \
-i "pandas.Index.intersection PR07,RT03,SA01" \
-i "pandas.Index.item SA01" \
-i "pandas.Index.join PR07,RT03,SA01" \
-i "pandas.Index.map SA01" \
-i "pandas.Index.memory_usage RT03" \
-i "pandas.Index.name SA01" \
-i "pandas.Index.names GL08" \
-i "pandas.Index.nbytes SA01" \
-i "pandas.Index.ndim SA01" \
-i "pandas.Index.nunique RT03" \
-i "pandas.Index.putmask PR01,RT03" \
-i "pandas.Index.ravel PR01,RT03" \
-i "pandas.Index.reindex PR07" \
-i "pandas.Index.shape SA01" \
-i "pandas.Index.size SA01" \
-i "pandas.Index.slice_indexer PR07,RT03,SA01" \
-i "pandas.Index.slice_locs RT03" \
-i "pandas.Index.str PR01,SA01" \
-i "pandas.Index.symmetric_difference PR07,RT03,SA01" \
-i "pandas.Index.take PR01,PR07" \
-i "pandas.Index.to_list RT03" \
-i "pandas.Index.union PR07,RT03,SA01" \
-i "pandas.Index.unique RT03" \
-i "pandas.Index.value_counts RT03" \
-i "pandas.Index.view GL08" \
-i "pandas.Int16Dtype SA01" \
-i "pandas.Int32Dtype SA01" \
-i "pandas.Int64Dtype SA01" \
-i "pandas.Int8Dtype SA01" \
-i "pandas.Interval PR02" \
-i "pandas.Interval.closed SA01" \
-i "pandas.Interval.left SA01" \
-i "pandas.Interval.mid SA01" \
-i "pandas.Interval.right SA01" \
-i "pandas.IntervalDtype PR01,SA01" \
-i "pandas.IntervalDtype.subtype SA01" \
-i "pandas.IntervalIndex.closed SA01" \
-i "pandas.IntervalIndex.contains RT03" \
-i "pandas.IntervalIndex.get_indexer PR07,SA01" \
-i "pandas.IntervalIndex.get_loc PR07,RT03,SA01" \
-i "pandas.IntervalIndex.is_non_overlapping_monotonic SA01" \
-i "pandas.IntervalIndex.left GL08" \
-i "pandas.IntervalIndex.length GL08" \
-i "pandas.IntervalIndex.mid GL08" \
-i "pandas.IntervalIndex.right GL08" \
-i "pandas.IntervalIndex.set_closed RT03,SA01" \
-i "pandas.IntervalIndex.to_tuples RT03,SA01" \
-i "pandas.MultiIndex PR01" \
-i "pandas.MultiIndex.append PR07,SA01" \
-i "pandas.MultiIndex.copy PR07,RT03,SA01" \
-i "pandas.MultiIndex.drop PR07,RT03,SA01" \
-i "pandas.MultiIndex.droplevel RT03,SA01" \
-i "pandas.MultiIndex.dtypes SA01" \
-i "pandas.MultiIndex.get_indexer PR07,SA01" \
-i "pandas.MultiIndex.get_level_values SA01" \
-i "pandas.MultiIndex.get_loc PR07" \
-i "pandas.MultiIndex.get_loc_level PR07" \
-i "pandas.MultiIndex.levels SA01" \
-i "pandas.MultiIndex.levshape SA01" \
-i "pandas.MultiIndex.names SA01" \
-i "pandas.MultiIndex.nlevels SA01" \
-i "pandas.MultiIndex.remove_unused_levels RT03,SA01" \
-i "pandas.MultiIndex.reorder_levels RT03,SA01" \
-i "pandas.MultiIndex.set_codes SA01" \
-i "pandas.MultiIndex.set_levels RT03,SA01" \
-i "pandas.MultiIndex.sortlevel PR07,SA01" \
-i "pandas.MultiIndex.to_frame RT03" \
-i "pandas.MultiIndex.truncate SA01" \
-i "pandas.NA SA01" \
-i "pandas.NaT SA01" \
-i "pandas.NamedAgg SA01" \
-i "pandas.Period SA01" \
-i "pandas.Period.asfreq SA01" \
-i "pandas.Period.freq GL08" \
-i "pandas.Period.freqstr SA01" \
-i "pandas.Period.is_leap_year SA01" \
-i "pandas.Period.month SA01" \
-i "pandas.Period.now SA01" \
-i "pandas.Period.ordinal GL08" \
-i "pandas.Period.quarter SA01" \
-i "pandas.Period.strftime PR01,SA01" \
-i "pandas.Period.to_timestamp SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.max SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.skew RT03" \
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-i "pandas.core.groupby.SeriesGroupBy.cummax RT03" \
-i "pandas.core.groupby.SeriesGroupBy.cummin RT03" \
-i "pandas.core.groupby.SeriesGroupBy.cumprod RT03" \
-i "pandas.core.groupby.SeriesGroupBy.cumsum RT03" \
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-i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
-i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
-i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
-i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
-i "pandas.core.groupby.SeriesGroupBy.max SA01" \
-i "pandas.core.groupby.SeriesGroupBy.mean RT03" \
-i "pandas.core.groupby.SeriesGroupBy.median SA01" \
-i "pandas.core.groupby.SeriesGroupBy.min SA01" \
-i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
-i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
-i "pandas.core.groupby.SeriesGroupBy.plot PR02,SA01" \
-i "pandas.core.groupby.SeriesGroupBy.prod SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.resample RT03" \
-i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
-i "pandas.core.groupby.SeriesGroupBy.skew RT03" \
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-i "pandas.core.groupby.SeriesGroupBy.transform RT03" \
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-i "pandas.core.resample.Resampler.get_group RT03,SA01" \
-i "pandas.core.resample.Resampler.groups SA01" \
-i "pandas.core.resample.Resampler.indices SA01" \
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-i "pandas.core.resample.Resampler.ohlc SA01" \
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-i "pandas.date_range RT03" \
-i "pandas.describe_option SA01" \
-i "pandas.errors.AbstractMethodError PR01,SA01" \
-i "pandas.errors.AttributeConflictWarning SA01" \
-i "pandas.errors.CSSWarning SA01" \
-i "pandas.errors.CategoricalConversionWarning SA01" \
-i "pandas.errors.ChainedAssignmentError SA01" \
-i "pandas.errors.ClosedFileError SA01" \
-i "pandas.errors.DataError SA01" \
-i "pandas.errors.DuplicateLabelError SA01" \
-i "pandas.errors.EmptyDataError SA01" \
-i "pandas.errors.IntCastingNaNError SA01" \
-i "pandas.errors.InvalidIndexError SA01" \
-i "pandas.errors.InvalidVersion SA01" \
-i "pandas.errors.MergeError SA01" \
-i "pandas.errors.NullFrequencyError SA01" \
-i "pandas.errors.NumExprClobberingError SA01" \
-i "pandas.errors.NumbaUtilError SA01" \
-i "pandas.errors.OptionError SA01" \
-i "pandas.errors.OutOfBoundsDatetime SA01" \
-i "pandas.errors.OutOfBoundsTimedelta SA01" \
-i "pandas.errors.PerformanceWarning SA01" \
-i "pandas.errors.PossibleDataLossError SA01" \
-i "pandas.errors.PossiblePrecisionLoss SA01" \
-i "pandas.errors.SpecificationError SA01" \
-i "pandas.errors.UndefinedVariableError PR01,SA01" \
-i "pandas.errors.UnsortedIndexError SA01" \
-i "pandas.errors.UnsupportedFunctionCall SA01" \
-i "pandas.errors.ValueLabelTypeMismatch SA01" \
-i "pandas.get_option SA01" \
-i "pandas.infer_freq SA01" \
-i "pandas.interval_range RT03" \
-i "pandas.io.formats.style.Styler.apply RT03" \
-i "pandas.io.formats.style.Styler.apply_index RT03" \
-i "pandas.io.formats.style.Styler.background_gradient RT03" \
-i "pandas.io.formats.style.Styler.bar RT03,SA01" \
-i "pandas.io.formats.style.Styler.clear SA01" \
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-i "pandas.io.formats.style.Styler.from_custom_template SA01" \
-i "pandas.io.formats.style.Styler.hide RT03,SA01" \
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-i "pandas.io.formats.style.Styler.highlight_min RT03" \
-i "pandas.io.formats.style.Styler.highlight_null RT03" \
-i "pandas.io.formats.style.Styler.highlight_quantile RT03" \
-i "pandas.io.formats.style.Styler.map RT03" \
-i "pandas.io.formats.style.Styler.map_index RT03" \
-i "pandas.io.formats.style.Styler.set_caption RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_properties RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_sticky RT03,SA01" \
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-i "pandas.io.formats.style.Styler.set_td_classes RT03" \
-i "pandas.io.formats.style.Styler.set_tooltips RT03,SA01" \
-i "pandas.io.formats.style.Styler.set_uuid PR07,RT03,SA01" \
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-i "pandas.io.formats.style.Styler.to_excel PR01" \
-i "pandas.io.formats.style.Styler.to_string SA01" \
-i "pandas.io.formats.style.Styler.use RT03" \
-i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
-i "pandas.io.stata.StataReader.data_label SA01" \
-i "pandas.io.stata.StataReader.value_labels RT03,SA01" \
-i "pandas.io.stata.StataReader.variable_labels RT03,SA01" \
-i "pandas.io.stata.StataWriter.write_file SA01" \
-i "pandas.json_normalize RT03,SA01" \
-i "pandas.merge PR07" \
-i "pandas.merge_asof PR07,RT03" \
-i "pandas.merge_ordered PR07" \
-i "pandas.option_context SA01" \
-i "pandas.period_range RT03,SA01" \
-i "pandas.pivot PR07" \
-i "pandas.pivot_table PR07" \
-i "pandas.plotting.andrews_curves RT03,SA01" \
-i "pandas.plotting.autocorrelation_plot RT03,SA01" \
-i "pandas.plotting.lag_plot RT03,SA01" \
-i "pandas.plotting.parallel_coordinates PR07,RT03,SA01" \
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-i "pandas.plotting.scatter_matrix PR07,SA01" \
-i "pandas.plotting.table PR07,RT03,SA01" \
-i "pandas.qcut PR07,SA01" \
-i "pandas.read_feather SA01" \
-i "pandas.read_orc SA01" \
-i "pandas.read_sas SA01" \
-i "pandas.read_spss SA01" \
-i "pandas.reset_option SA01" \
-i "pandas.set_eng_float_format RT03,SA01" \
-i "pandas.set_option SA01" \
-i "pandas.show_versions SA01" \
-i "pandas.test SA01" \
-i "pandas.testing.assert_extension_array_equal SA01" \
-i "pandas.testing.assert_index_equal PR07,SA01" \
-i "pandas.testing.assert_series_equal PR07,SA01" \
-i "pandas.timedelta_range SA01" \
-i "pandas.tseries.api.guess_datetime_format SA01" \
-i "pandas.tseries.offsets.BDay PR02,SA01" \
-i "pandas.tseries.offsets.BMonthBegin PR02" \
-i "pandas.tseries.offsets.BMonthEnd PR02" \
-i "pandas.tseries.offsets.BQuarterBegin PR02" \
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-i "pandas.tseries.offsets.BQuarterBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.kwds SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.n GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.name SA01" \
-i "pandas.tseries.offsets.BQuarterBegin.nanos GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.normalize GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BQuarterBegin.startingMonth GL08" \
-i "pandas.tseries.offsets.BQuarterEnd PR02" \
-i "pandas.tseries.offsets.BQuarterEnd.copy SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.kwds SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.n GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.name SA01" \
-i "pandas.tseries.offsets.BQuarterEnd.nanos GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.normalize GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.rule_code GL08" \
-i "pandas.tseries.offsets.BQuarterEnd.startingMonth GL08" \
-i "pandas.tseries.offsets.BYearBegin PR02" \
-i "pandas.tseries.offsets.BYearBegin.copy SA01" \
-i "pandas.tseries.offsets.BYearBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BYearBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BYearBegin.kwds SA01" \
-i "pandas.tseries.offsets.BYearBegin.month GL08" \
-i "pandas.tseries.offsets.BYearBegin.n GL08" \
-i "pandas.tseries.offsets.BYearBegin.name SA01" \
-i "pandas.tseries.offsets.BYearBegin.nanos GL08" \
-i "pandas.tseries.offsets.BYearBegin.normalize GL08" \
-i "pandas.tseries.offsets.BYearBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BYearEnd PR02" \
-i "pandas.tseries.offsets.BYearEnd.copy SA01" \
-i "pandas.tseries.offsets.BYearEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BYearEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BYearEnd.kwds SA01" \
-i "pandas.tseries.offsets.BYearEnd.month GL08" \
-i "pandas.tseries.offsets.BYearEnd.n GL08" \
-i "pandas.tseries.offsets.BYearEnd.name SA01" \
-i "pandas.tseries.offsets.BYearEnd.nanos GL08" \
-i "pandas.tseries.offsets.BYearEnd.normalize GL08" \
-i "pandas.tseries.offsets.BYearEnd.rule_code GL08" \
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-i "pandas.tseries.offsets.BusinessDay.copy SA01" \
-i "pandas.tseries.offsets.BusinessDay.freqstr SA01" \
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-i "pandas.tseries.offsets.BusinessDay.n GL08" \
-i "pandas.tseries.offsets.BusinessDay.name SA01" \
-i "pandas.tseries.offsets.BusinessDay.nanos GL08" \
-i "pandas.tseries.offsets.BusinessDay.normalize GL08" \
-i "pandas.tseries.offsets.BusinessDay.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessDay.weekmask GL08" \
-i "pandas.tseries.offsets.BusinessHour PR02,SA01" \
-i "pandas.tseries.offsets.BusinessHour.calendar GL08" \
-i "pandas.tseries.offsets.BusinessHour.copy SA01" \
-i "pandas.tseries.offsets.BusinessHour.end GL08" \
-i "pandas.tseries.offsets.BusinessHour.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessHour.holidays GL08" \
-i "pandas.tseries.offsets.BusinessHour.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessHour.kwds SA01" \
-i "pandas.tseries.offsets.BusinessHour.n GL08" \
-i "pandas.tseries.offsets.BusinessHour.name SA01" \
-i "pandas.tseries.offsets.BusinessHour.nanos GL08" \
-i "pandas.tseries.offsets.BusinessHour.normalize GL08" \
-i "pandas.tseries.offsets.BusinessHour.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessHour.start GL08" \
-i "pandas.tseries.offsets.BusinessHour.weekmask GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin PR02" \
-i "pandas.tseries.offsets.BusinessMonthBegin.copy SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.kwds SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.n GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.name SA01" \
-i "pandas.tseries.offsets.BusinessMonthBegin.nanos GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.normalize GL08" \
-i "pandas.tseries.offsets.BusinessMonthBegin.rule_code GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd PR02" \
-i "pandas.tseries.offsets.BusinessMonthEnd.copy SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.freqstr SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.is_on_offset GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.kwds SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.n GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.name SA01" \
-i "pandas.tseries.offsets.BusinessMonthEnd.nanos GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.normalize GL08" \
-i "pandas.tseries.offsets.BusinessMonthEnd.rule_code GL08" \
-i "pandas.tseries.offsets.CBMonthBegin PR02" \
-i "pandas.tseries.offsets.CBMonthEnd PR02" \
-i "pandas.tseries.offsets.CDay PR02,SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay PR02,SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.freqstr SA01" \
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-i "pandas.tseries.offsets.CustomBusinessDay.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessDay.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessDay.weekmask GL08" \
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-i "pandas.tseries.offsets.CustomBusinessHour.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.copy SA01" \
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-i "pandas.tseries.offsets.CustomBusinessHour.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.is_on_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessHour.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.start GL08" \
-i "pandas.tseries.offsets.CustomBusinessHour.weekmask GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin PR02" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.is_on_offset SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.m_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.name SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.nanos GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.normalize GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.rule_code GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthBegin.weekmask GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd PR02" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.calendar GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.copy SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.freqstr SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.holidays GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.is_on_offset SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.kwds SA01" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.m_offset GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.n GL08" \
-i "pandas.tseries.offsets.CustomBusinessMonthEnd.name SA01" \