128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
from pandas import (
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api as api,
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arrays as arrays,
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errors as errors,
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io as io,
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plotting as plotting,
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testing as testing,
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tseries as tseries,
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util as util,
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)
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from pandas.core.api import (
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NA as NA,
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ArrowDtype as ArrowDtype,
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BooleanDtype as BooleanDtype,
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Categorical as Categorical,
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CategoricalDtype as CategoricalDtype,
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CategoricalIndex as CategoricalIndex,
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DataFrame as DataFrame,
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DateOffset as DateOffset,
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DatetimeIndex as DatetimeIndex,
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DatetimeTZDtype as DatetimeTZDtype,
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Float32Dtype as Float32Dtype,
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Float64Dtype as Float64Dtype,
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Grouper as Grouper,
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Index as Index,
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IndexSlice as IndexSlice,
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Int8Dtype as Int8Dtype,
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Int16Dtype as Int16Dtype,
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Int32Dtype as Int32Dtype,
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Int64Dtype as Int64Dtype,
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Interval as Interval,
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IntervalDtype as IntervalDtype,
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IntervalIndex as IntervalIndex,
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MultiIndex as MultiIndex,
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NamedAgg as NamedAgg,
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NaT as NaT,
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Period as Period,
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PeriodDtype as PeriodDtype,
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PeriodIndex as PeriodIndex,
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RangeIndex as RangeIndex,
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Series as Series,
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StringDtype as StringDtype,
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Timedelta as Timedelta,
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TimedeltaIndex as TimedeltaIndex,
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Timestamp as Timestamp,
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UInt8Dtype as UInt8Dtype,
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UInt16Dtype as UInt16Dtype,
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UInt32Dtype as UInt32Dtype,
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UInt64Dtype as UInt64Dtype,
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array as array,
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bdate_range as bdate_range,
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date_range as date_range,
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factorize as factorize,
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interval_range as interval_range,
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isna as isna,
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isnull as isnull,
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notna as notna,
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notnull as notnull,
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period_range as period_range,
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set_eng_float_format as set_eng_float_format,
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timedelta_range as timedelta_range,
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to_datetime as to_datetime,
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to_numeric as to_numeric,
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to_timedelta as to_timedelta,
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unique as unique,
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value_counts as value_counts,
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)
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from pandas.core.arrays.sparse import SparseDtype as SparseDtype
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from pandas.core.computation.api import eval as eval
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from pandas.core.reshape.api import (
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concat as concat,
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crosstab as crosstab,
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cut as cut,
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from_dummies as from_dummies,
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get_dummies as get_dummies,
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lreshape as lreshape,
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melt as melt,
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merge as merge,
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merge_asof as merge_asof,
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merge_ordered as merge_ordered,
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pivot as pivot,
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pivot_table as pivot_table,
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qcut as qcut,
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wide_to_long as wide_to_long,
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)
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from pandas._config import (
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describe_option as describe_option,
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get_option as get_option,
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option_context as option_context,
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options as options,
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reset_option as reset_option,
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set_option as set_option,
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)
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from pandas.util._print_versions import show_versions as show_versions
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from pandas.util._tester import test as test
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from pandas.io.api import (
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ExcelFile as ExcelFile,
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ExcelWriter as ExcelWriter,
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HDFStore as HDFStore,
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read_clipboard as read_clipboard,
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read_csv as read_csv,
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read_excel as read_excel,
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read_feather as read_feather,
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read_fwf as read_fwf,
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read_hdf as read_hdf,
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read_html as read_html,
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read_json as read_json,
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read_orc as read_orc,
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read_parquet as read_parquet,
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read_pickle as read_pickle,
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read_sas as read_sas,
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read_spss as read_spss,
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read_sql as read_sql,
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read_sql_query as read_sql_query,
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read_sql_table as read_sql_table,
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read_stata as read_stata,
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read_table as read_table,
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read_xml as read_xml,
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)
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from pandas.io.json._normalize import json_normalize as json_normalize
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from pandas.tseries import offsets as offsets
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from pandas.tseries.api import infer_freq as infer_freq
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__version__: str
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