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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
[[ -z "$1" || "$1" == "code" || "$1" == "doctests" || "$1" == "docstrings" || "$1" == "single-docs" || "$1" == "notebooks" ]] || \
{ echo "Unknown command $1. Usage: $0 [code|doctests|docstrings|single-docs|notebooks]"; exit 9999; }
BASE_DIR="$(dirname $0)/.."
RET=0
CHECK=$1
function invgrep {
# grep with inverse exist status and formatting for azure-pipelines
#
# This function works exactly as grep, but with opposite exit status:
# - 0 (success) when no patterns are found
# - 1 (fail) when the patterns are found
#
# This is useful for the CI, as we want to fail if one of the patterns
# that we want to avoid is found by grep.
grep -n "$@" | sed "s/^/$INVGREP_PREPEND/" | sed "s/$/$INVGREP_APPEND/" ; EXIT_STATUS=${PIPESTATUS[0]}
return $((! $EXIT_STATUS))
}
if [[ "$GITHUB_ACTIONS" == "true" ]]; then
INVGREP_PREPEND="##[error]"
fi
### 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 (EX02, 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
$BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX02,EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06
RET=$(($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.backfill \
pandas.Series.ffill \
pandas.Series.pad \
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.IncompatibilityWarning \
pandas.errors.InvalidComparison \
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.asfreq \
pandas.Period.now \
pandas.arrays.PeriodArray \
pandas.Int8Dtype \
pandas.Int16Dtype \
pandas.Int32Dtype \
pandas.Int64Dtype \
pandas.UInt8Dtype \
pandas.UInt16Dtype \
pandas.UInt32Dtype \
pandas.UInt64Dtype \
pandas.NA \
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.fillna \
pandas.Index.dropna \
pandas.Index.astype \
pandas.Index.map \
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.values \
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.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.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.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._hash_pandas_object \
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.columns \
pandas.DataFrame.backfill \
pandas.DataFrame.ffill \
pandas.DataFrame.pad \
pandas.DataFrame.swapaxes \
pandas.DataFrame.attrs \
pandas.DataFrame.plot \
pandas.DataFrame.to_gbq \
pandas.DataFrame.style \
pandas.DataFrame.__dataframe__
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### DOCUMENTATION NOTEBOOKS ###
if [[ -z "$CHECK" || "$CHECK" == "notebooks" ]]; then
MSG='Notebooks' ; echo $MSG
jupyter nbconvert --execute $(find doc/source -name '*.ipynb') --to notebook
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
### SINGLE-PAGE DOCS ###
if [[ -z "$CHECK" || "$CHECK" == "single-docs" ]]; then
python doc/make.py --warnings-are-errors --single pandas.Series.value_counts
python doc/make.py --warnings-are-errors --single pandas.Series.str.split
python doc/make.py clean
fi
exit $RET