def create_data():
data = {<EOL>'<STR_LIT:A>': [<NUM_LIT:0.>, <NUM_LIT:1.>, <NUM_LIT>, <NUM_LIT>, np.nan],<EOL>'<STR_LIT:B>': [<NUM_LIT:0>, <NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:1>, <NUM_LIT:0>],<EOL>'<STR_LIT:C>': ['<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>'],<EOL>'<STR_LIT:D>': date_range('<STR_LIT>', periods=<NUM_LIT:5>),<EOL>'<STR_LIT:E>': [<NUM_LIT:0.>, <NUM_LIT:1>, Timestamp('<STR_LIT>'), '<STR_LIT:foo>', <NUM_LIT>]<EOL>}<EOL>scalars = dict(timestamp=Timestamp('<STR_LIT>'),<EOL>period=Period('<STR_LIT>', '<STR_LIT:M>'))<EOL>index = dict(int=Index(np.arange(<NUM_LIT:10>)),<EOL>date=date_range('<STR_LIT>', periods=<NUM_LIT:10>),<EOL>period=period_range('<STR_LIT>', freq='<STR_LIT:M>', periods=<NUM_LIT:10>),<EOL>float=Index(np.arange(<NUM_LIT:10>, dtype=np.float64)),<EOL>uint=Index(np.arange(<NUM_LIT:10>, dtype=np.uint64)),<EOL>timedelta=timedelta_range('<STR_LIT>', freq='<STR_LIT>', periods=<NUM_LIT:10>))<EOL>if _loose_version >= LooseVersion('<STR_LIT>'):<EOL><INDENT>from pandas import RangeIndex<EOL>index['<STR_LIT>'] = RangeIndex(<NUM_LIT:10>)<EOL><DEDENT>if _loose_version >= LooseVersion('<STR_LIT>'):<EOL><INDENT>from pandas import interval_range<EOL>index['<STR_LIT>'] = interval_range(<NUM_LIT:0>, periods=<NUM_LIT:10>)<EOL><DEDENT>mi = dict(reg2=MultiIndex.from_tuples(<EOL>tuple(zip(*[['<STR_LIT:bar>', '<STR_LIT:bar>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT:foo>',<EOL>'<STR_LIT:foo>', '<STR_LIT>', '<STR_LIT>'],<EOL>['<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>',<EOL>'<STR_LIT>', '<STR_LIT>', '<STR_LIT>']])),<EOL>names=['<STR_LIT>', '<STR_LIT>']))<EOL>series = dict(float=Series(data['<STR_LIT:A>']),<EOL>int=Series(data['<STR_LIT:B>']),<EOL>mixed=Series(data['<STR_LIT:E>']),<EOL>ts=Series(np.arange(<NUM_LIT:10>).astype(np.int64),<EOL>index=date_range('<STR_LIT>', periods=<NUM_LIT:10>)),<EOL>mi=Series(np.arange(<NUM_LIT:5>).astype(np.float64),<EOL>index=MultiIndex.from_tuples(<EOL>tuple(zip(*[[<NUM_LIT:1>, <NUM_LIT:1>, <NUM_LIT:2>, <NUM_LIT:2>, <NUM_LIT:2>],<EOL>[<NUM_LIT:3>, <NUM_LIT:4>, <NUM_LIT:3>, <NUM_LIT:4>, <NUM_LIT:5>]])),<EOL>names=['<STR_LIT>', '<STR_LIT>'])),<EOL>dup=Series(np.arange(<NUM_LIT:5>).astype(np.float64),<EOL>index=['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>', '<STR_LIT:D>', '<STR_LIT:A>']),<EOL>cat=Series(Categorical(['<STR_LIT:foo>', '<STR_LIT:bar>', '<STR_LIT>'])),<EOL>dt=Series(date_range('<STR_LIT>', periods=<NUM_LIT:5>)),<EOL>dt_tz=Series(date_range('<STR_LIT>', periods=<NUM_LIT:5>,<EOL>tz='<STR_LIT>')),<EOL>period=Series([Period('<STR_LIT>')] * <NUM_LIT:5>))<EOL>mixed_dup_df = DataFrame(data)<EOL>mixed_dup_df.columns = list("<STR_LIT>")<EOL>frame = dict(float=DataFrame({'<STR_LIT:A>': series['<STR_LIT:float>'],<EOL>'<STR_LIT:B>': series['<STR_LIT:float>'] + <NUM_LIT:1>}),<EOL>int=DataFrame({'<STR_LIT:A>': series['<STR_LIT:int>'],<EOL>'<STR_LIT:B>': series['<STR_LIT:int>'] + <NUM_LIT:1>}),<EOL>mixed=DataFrame({k: data[k]<EOL>for k in ['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>', '<STR_LIT:D>']}),<EOL>mi=DataFrame({'<STR_LIT:A>': np.arange(<NUM_LIT:5>).astype(np.float64),<EOL>'<STR_LIT:B>': np.arange(<NUM_LIT:5>).astype(np.int64)},<EOL>index=MultiIndex.from_tuples(<EOL>tuple(zip(*[['<STR_LIT:bar>', '<STR_LIT:bar>', '<STR_LIT>',<EOL>'<STR_LIT>', '<STR_LIT>'],<EOL>['<STR_LIT>', '<STR_LIT>', '<STR_LIT>',<EOL>'<STR_LIT>', '<STR_LIT>']])),<EOL>names=['<STR_LIT>', '<STR_LIT>'])),<EOL>dup=DataFrame(np.arange(<NUM_LIT:15>).reshape(<NUM_LIT:5>, <NUM_LIT:3>).astype(np.float64),<EOL>columns=['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:A>']),<EOL>cat_onecol=DataFrame({'<STR_LIT:A>': Categorical(['<STR_LIT:foo>', '<STR_LIT:bar>'])}),<EOL>cat_and_float=DataFrame({<EOL>'<STR_LIT:A>': Categorical(['<STR_LIT:foo>', '<STR_LIT:bar>', '<STR_LIT>']),<EOL>'<STR_LIT:B>': np.arange(<NUM_LIT:3>).astype(np.int64)}),<EOL>mixed_dup=mixed_dup_df,<EOL>dt_mixed_tzs=DataFrame({<EOL>'<STR_LIT:A>': Timestamp('<STR_LIT>', tz='<STR_LIT>'),<EOL>'<STR_LIT:B>': Timestamp('<STR_LIT>', tz='<STR_LIT>')}, index=range(<NUM_LIT:5>)),<EOL>dt_mixed2_tzs=DataFrame({<EOL>'<STR_LIT:A>': Timestamp('<STR_LIT>', tz='<STR_LIT>'),<EOL>'<STR_LIT:B>': Timestamp('<STR_LIT>', tz='<STR_LIT>'),<EOL>'<STR_LIT:C>': Timestamp('<STR_LIT>', tz='<STR_LIT>')}, index=range(<NUM_LIT:5>))<EOL>)<EOL>cat = dict(int8=Categorical(list('<STR_LIT>')),<EOL>int16=Categorical(np.arange(<NUM_LIT:1000>)),<EOL>int32=Categorical(np.arange(<NUM_LIT>)))<EOL>timestamp = dict(normal=Timestamp('<STR_LIT>'),<EOL>nat=NaT,<EOL>tz=Timestamp('<STR_LIT>', tz='<STR_LIT>'))<EOL>if _loose_version < LooseVersion('<STR_LIT>'):<EOL><INDENT>timestamp['<STR_LIT>'] = Timestamp('<STR_LIT>', offset='<STR_LIT:D>')<EOL>timestamp['<STR_LIT>'] = Timestamp('<STR_LIT>', tz='<STR_LIT>',<EOL>offset='<STR_LIT:M>')<EOL><DEDENT>else:<EOL><INDENT>timestamp['<STR_LIT>'] = Timestamp('<STR_LIT>', freq='<STR_LIT:D>')<EOL>timestamp['<STR_LIT>'] = Timestamp('<STR_LIT>', tz='<STR_LIT>',<EOL>freq='<STR_LIT:M>')<EOL><DEDENT>off = {'<STR_LIT>': DateOffset(years=<NUM_LIT:1>),<EOL>'<STR_LIT>': DateOffset(hour=<NUM_LIT:6>, nanoseconds=<NUM_LIT>),<EOL>'<STR_LIT>': BusinessDay(offset=timedelta(seconds=<NUM_LIT:9>)),<EOL>'<STR_LIT>': BusinessHour(normalize=True, n=<NUM_LIT:6>, end='<STR_LIT>'),<EOL>'<STR_LIT>': CustomBusinessDay(weekmask='<STR_LIT>'),<EOL>'<STR_LIT>': SemiMonthBegin(day_of_month=<NUM_LIT:9>),<EOL>'<STR_LIT>': SemiMonthEnd(day_of_month=<NUM_LIT>),<EOL>'<STR_LIT>': MonthBegin(<NUM_LIT:1>),<EOL>'<STR_LIT>': MonthEnd(<NUM_LIT:1>),<EOL>'<STR_LIT>': QuarterBegin(<NUM_LIT:1>),<EOL>'<STR_LIT>': QuarterEnd(<NUM_LIT:1>),<EOL>'<STR_LIT>': Day(<NUM_LIT:1>),<EOL>'<STR_LIT>': YearBegin(<NUM_LIT:1>),<EOL>'<STR_LIT>': YearEnd(<NUM_LIT:1>),<EOL>'<STR_LIT>': Week(<NUM_LIT:1>),<EOL>'<STR_LIT>': Week(<NUM_LIT:2>, normalize=False, weekday=<NUM_LIT:1>),<EOL>'<STR_LIT>': WeekOfMonth(week=<NUM_LIT:3>, weekday=<NUM_LIT:4>),<EOL>'<STR_LIT>': LastWeekOfMonth(n=<NUM_LIT:1>, weekday=<NUM_LIT:3>),<EOL>'<STR_LIT>': FY5253(n=<NUM_LIT:2>, weekday=<NUM_LIT:6>, startingMonth=<NUM_LIT:7>, variation="<STR_LIT>"),<EOL>'<STR_LIT>': Easter(),<EOL>'<STR_LIT>': Hour(<NUM_LIT:1>),<EOL>'<STR_LIT>': Minute(<NUM_LIT:1>)}<EOL>return dict(series=series,<EOL>frame=frame,<EOL>index=index,<EOL>scalars=scalars,<EOL>mi=mi,<EOL>sp_series=dict(float=_create_sp_series(),<EOL>ts=_create_sp_tsseries()),<EOL>sp_frame=dict(float=_create_sp_frame()),<EOL>cat=cat,<EOL>timestamp=timestamp,<EOL>offsets=off)<EOL>
create the pickle/msgpack data