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code_checks.sh
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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.at_time PR01" \
-i "pandas.DataFrame.hist RT03" \
-i "pandas.DataFrame.infer_objects RT03" \
-i "pandas.DataFrame.kurt RT03,SA01" \
-i "pandas.DataFrame.kurtosis RT03,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.plot PR02,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" \
-i "pandas.DataFrame.std PR01,RT03,SA01" \
-i "pandas.DataFrame.sum RT03" \
-i "pandas.DataFrame.swaplevel SA01" \
-i "pandas.DataFrame.to_markdown SA01" \
-i "pandas.DataFrame.to_parquet RT03" \
-i "pandas.DataFrame.var PR01,RT03,SA01" \
-i "pandas.DatetimeIndex.day_of_year SA01" \
-i "pandas.DatetimeIndex.dayofyear SA01" \
-i "pandas.DatetimeIndex.freqstr 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.snap PR01,RT03" \
-i "pandas.DatetimeIndex.std PR01,RT03" \
-i "pandas.DatetimeIndex.to_period RT03" \
-i "pandas.DatetimeIndex.to_pydatetime RT03,SA01" \
-i "pandas.DatetimeIndex.tz_convert RT03" \
-i "pandas.DatetimeTZDtype SA01" \
-i "pandas.DatetimeTZDtype.tz SA01" \
-i "pandas.Index PR07" \
-i "pandas.Index.T SA01" \
-i "pandas.Index.append PR07,RT03,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.duplicated RT03" \
-i "pandas.Index.empty GL08" \
-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.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.memory_usage RT03" \
-i "pandas.Index.names GL08" \
-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.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.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" \
-i "pandas.Period.year SA01" \
-i "pandas.PeriodDtype SA01" \
-i "pandas.PeriodDtype.freq SA01" \
-i "pandas.PeriodIndex.day SA01" \
-i "pandas.PeriodIndex.day_of_week SA01" \
-i "pandas.PeriodIndex.day_of_year SA01" \
-i "pandas.PeriodIndex.dayofweek SA01" \
-i "pandas.PeriodIndex.dayofyear SA01" \
-i "pandas.PeriodIndex.days_in_month SA01" \
-i "pandas.PeriodIndex.daysinmonth SA01" \
-i "pandas.PeriodIndex.freqstr SA01" \
-i "pandas.PeriodIndex.from_fields PR07,SA01" \
-i "pandas.PeriodIndex.from_ordinals SA01" \
-i "pandas.PeriodIndex.hour SA01" \
-i "pandas.PeriodIndex.is_leap_year SA01" \
-i "pandas.PeriodIndex.minute SA01" \
-i "pandas.PeriodIndex.month SA01" \
-i "pandas.PeriodIndex.quarter SA01" \
-i "pandas.PeriodIndex.qyear GL08" \
-i "pandas.PeriodIndex.second SA01" \
-i "pandas.PeriodIndex.to_timestamp RT03,SA01" \
-i "pandas.PeriodIndex.week SA01" \
-i "pandas.PeriodIndex.weekday SA01" \
-i "pandas.PeriodIndex.weekofyear SA01" \
-i "pandas.PeriodIndex.year SA01" \
-i "pandas.RangeIndex PR07" \
-i "pandas.RangeIndex.from_range PR01,SA01" \
-i "pandas.RangeIndex.start SA01" \
-i "pandas.RangeIndex.step SA01" \
-i "pandas.RangeIndex.stop SA01" \
-i "pandas.Series SA01" \
-i "pandas.Series.T SA01" \
-i "pandas.Series.__iter__ RT03,SA01" \
-i "pandas.Series.add PR07" \
-i "pandas.Series.at_time PR01" \
-i "pandas.Series.backfill PR01,SA01" \
-i "pandas.Series.case_when RT03" \
-i "pandas.Series.cat PR07,SA01" \
-i "pandas.Series.cat.add_categories PR01,PR02" \
-i "pandas.Series.cat.as_ordered PR01" \
-i "pandas.Series.cat.as_unordered PR01" \
-i "pandas.Series.cat.codes SA01" \
-i "pandas.Series.cat.ordered SA01" \
-i "pandas.Series.cat.remove_categories PR01,PR02" \
-i "pandas.Series.cat.remove_unused_categories PR01" \
-i "pandas.Series.cat.rename_categories PR01,PR02" \
-i "pandas.Series.cat.reorder_categories PR01,PR02" \
-i "pandas.Series.cat.set_categories PR01,PR02" \
-i "pandas.Series.div PR07" \
-i "pandas.Series.dt.as_unit PR01,PR02" \
-i "pandas.Series.dt.ceil PR01,PR02" \
-i "pandas.Series.dt.components SA01" \
-i "pandas.Series.dt.day_name PR01,PR02" \
-i "pandas.Series.dt.day_of_year SA01" \
-i "pandas.Series.dt.dayofyear SA01" \
-i "pandas.Series.dt.days SA01" \
-i "pandas.Series.dt.days_in_month SA01" \
-i "pandas.Series.dt.daysinmonth SA01" \
-i "pandas.Series.dt.floor PR01,PR02" \
-i "pandas.Series.dt.freq GL08" \
-i "pandas.Series.dt.is_leap_year SA01" \
-i "pandas.Series.dt.microseconds SA01" \
-i "pandas.Series.dt.month_name PR01,PR02" \
-i "pandas.Series.dt.nanoseconds SA01" \
-i "pandas.Series.dt.normalize PR01" \
-i "pandas.Series.dt.qyear GL08" \
-i "pandas.Series.dt.round PR01,PR02" \
-i "pandas.Series.dt.seconds SA01" \
-i "pandas.Series.dt.strftime PR01,PR02" \
-i "pandas.Series.dt.to_period PR01,PR02,RT03" \
-i "pandas.Series.dt.total_seconds PR01" \
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-i "pandas.tseries.offsets.BusinessHour.name SA01" \
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-i "pandas.tseries.offsets.Easter.nanos GL08" \
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-i "pandas.tseries.offsets.Hour.n GL08" \
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-i "pandas.tseries.offsets.Hour.nanos SA01" \
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-i "pandas.tseries.offsets.LastWeekOfMonth.week GL08" \
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-i "pandas.tseries.offsets.Micro.is_on_offset GL08" \
-i "pandas.tseries.offsets.Micro.kwds SA01" \
-i "pandas.tseries.offsets.Micro.n GL08" \
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-i "pandas.tseries.offsets.Micro.nanos SA01" \
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-i "pandas.tseries.offsets.Milli.kwds SA01" \
-i "pandas.tseries.offsets.Milli.n GL08" \
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-i "pandas.tseries.offsets.Milli.nanos SA01" \
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-i "pandas.tseries.offsets.Minute.is_on_offset GL08" \
-i "pandas.tseries.offsets.Minute.kwds SA01" \
-i "pandas.tseries.offsets.Minute.n GL08" \
-i "pandas.tseries.offsets.Minute.name SA01" \
-i "pandas.tseries.offsets.Minute.nanos SA01" \
-i "pandas.tseries.offsets.Minute.normalize GL08" \
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