diff --git a/asv_bench/benchmarks/io/excel.py b/asv_bench/benchmarks/io/excel.py new file mode 100644 index 0000000000000..a7c6c43d15026 --- /dev/null +++ b/asv_bench/benchmarks/io/excel.py @@ -0,0 +1,37 @@ +import numpy as np +from pandas import DataFrame, date_range, ExcelWriter, read_excel +from pandas.compat import BytesIO +import pandas.util.testing as tm + +from ..pandas_vb_common import BaseIO, setup # noqa + + +class Excel(object): + + goal_time = 0.2 + params = ['openpyxl', 'xlsxwriter', 'xlwt'] + param_names = ['engine'] + + def setup(self, engine): + N = 2000 + C = 5 + self.df = DataFrame(np.random.randn(N, C), + columns=['float{}'.format(i) for i in range(C)], + index=date_range('20000101', periods=N, freq='H')) + self.df['object'] = tm.makeStringIndex(N) + self.bio_read = BytesIO() + self.writer_read = ExcelWriter(self.bio_read, engine=engine) + self.df.to_excel(self.writer_read, sheet_name='Sheet1') + self.writer_read.save() + self.bio_read.seek(0) + + self.bio_write = BytesIO() + self.bio_write.seek(0) + self.writer_write = ExcelWriter(self.bio_write, engine=engine) + + def time_read_excel(self, engine): + read_excel(self.bio_read) + + def time_write_excel(self, engine): + self.df.to_excel(self.writer_write, sheet_name='Sheet1') + self.writer_write.save() diff --git a/asv_bench/benchmarks/hdfstore_bench.py b/asv_bench/benchmarks/io/hdf.py similarity index 77% rename from asv_bench/benchmarks/hdfstore_bench.py rename to asv_bench/benchmarks/io/hdf.py index d7b3be25a18b9..5c0e9586c1cb5 100644 --- a/asv_bench/benchmarks/hdfstore_bench.py +++ b/asv_bench/benchmarks/io/hdf.py @@ -1,11 +1,11 @@ import numpy as np -from pandas import DataFrame, Panel, date_range, HDFStore +from pandas import DataFrame, Panel, date_range, HDFStore, read_hdf import pandas.util.testing as tm -from .pandas_vb_common import BaseIO, setup # noqa +from ..pandas_vb_common import BaseIO, setup # noqa -class HDF5(BaseIO): +class HDFStoreDataFrame(BaseIO): goal_time = 0.2 @@ -34,9 +34,9 @@ def setup(self): self.df_dc = DataFrame(np.random.randn(N, 10), columns=['C%03d' % i for i in range(10)]) - self.f = '__test__.h5' + self.fname = '__test__.h5' - self.store = HDFStore(self.f) + self.store = HDFStore(self.fname) self.store.put('fixed', self.df) self.store.put('fixed_mixed', self.df_mixed) self.store.append('table', self.df2) @@ -46,7 +46,7 @@ def setup(self): def teardown(self): self.store.close() - self.remove(self.f) + self.remove(self.fname) def time_read_store(self): self.store.get('fixed') @@ -99,25 +99,48 @@ def time_store_info(self): self.store.info() -class HDF5Panel(BaseIO): +class HDFStorePanel(BaseIO): goal_time = 0.2 def setup(self): - self.f = '__test__.h5' + self.fname = '__test__.h5' self.p = Panel(np.random.randn(20, 1000, 25), items=['Item%03d' % i for i in range(20)], major_axis=date_range('1/1/2000', periods=1000), minor_axis=['E%03d' % i for i in range(25)]) - self.store = HDFStore(self.f) + self.store = HDFStore(self.fname) self.store.append('p1', self.p) def teardown(self): self.store.close() - self.remove(self.f) + self.remove(self.fname) def time_read_store_table_panel(self): self.store.select('p1') def time_write_store_table_panel(self): self.store.append('p2', self.p) + + +class HDF(BaseIO): + + goal_time = 0.2 + params = ['table', 'fixed'] + param_names = ['format'] + + def setup(self, format): + self.fname = '__test__.h5' + N = 100000 + C = 5 + self.df = DataFrame(np.random.randn(N, C), + columns=['float{}'.format(i) for i in range(C)], + index=date_range('20000101', periods=N, freq='H')) + self.df['object'] = tm.makeStringIndex(N) + self.df.to_hdf(self.fname, 'df', format=format) + + def time_read_hdf(self, format): + read_hdf(self.fname, 'df') + + def time_write_hdf(self, format): + self.df.to_hdf(self.fname, 'df', format=format) diff --git a/asv_bench/benchmarks/io/msgpack.py b/asv_bench/benchmarks/io/msgpack.py new file mode 100644 index 0000000000000..8ccce01117ca4 --- /dev/null +++ b/asv_bench/benchmarks/io/msgpack.py @@ -0,0 +1,26 @@ +import numpy as np +from pandas import DataFrame, date_range, read_msgpack +import pandas.util.testing as tm + +from ..pandas_vb_common import BaseIO, setup # noqa + + +class MSGPack(BaseIO): + + goal_time = 0.2 + + def setup(self): + self.fname = '__test__.msg' + N = 100000 + C = 5 + self.df = DataFrame(np.random.randn(N, C), + columns=['float{}'.format(i) for i in range(C)], + index=date_range('20000101', periods=N, freq='H')) + self.df['object'] = tm.makeStringIndex(N) + self.df.to_msgpack(self.fname) + + def time_read_msgpack(self): + read_msgpack(self.fname) + + def time_write_msgpack(self): + self.df.to_msgpack(self.fname) diff --git a/asv_bench/benchmarks/io/pickle.py b/asv_bench/benchmarks/io/pickle.py new file mode 100644 index 0000000000000..2ad0fcca6eb26 --- /dev/null +++ b/asv_bench/benchmarks/io/pickle.py @@ -0,0 +1,26 @@ +import numpy as np +from pandas import DataFrame, date_range, read_pickle +import pandas.util.testing as tm + +from ..pandas_vb_common import BaseIO, setup # noqa + + +class Pickle(BaseIO): + + goal_time = 0.2 + + def setup(self): + self.fname = '__test__.pkl' + N = 100000 + C = 5 + self.df = DataFrame(np.random.randn(N, C), + columns=['float{}'.format(i) for i in range(C)], + index=date_range('20000101', periods=N, freq='H')) + self.df['object'] = tm.makeStringIndex(N) + self.df.to_pickle(self.fname) + + def time_read_pickle(self): + read_pickle(self.fname) + + def time_write_pickle(self): + self.df.to_pickle(self.fname) diff --git a/asv_bench/benchmarks/io/sas.py b/asv_bench/benchmarks/io/sas.py new file mode 100644 index 0000000000000..526c524de7fff --- /dev/null +++ b/asv_bench/benchmarks/io/sas.py @@ -0,0 +1,21 @@ +import os + +from pandas import read_sas + + +class SAS(object): + + goal_time = 0.2 + params = ['sas7bdat', 'xport'] + param_names = ['format'] + + def setup(self, format): + # Read files that are located in 'pandas/io/tests/sas/data' + files = {'sas7bdat': 'test1.sas7bdat', 'xport': 'paxraw_d_short.xpt'} + file = files[format] + paths = [os.path.dirname(__file__), '..', '..', '..', 'pandas', + 'tests', 'io', 'sas', 'data', file] + self.f = os.path.join(*paths) + + def time_read_msgpack(self, format): + read_sas(self.f, format=format) diff --git a/asv_bench/benchmarks/io/sql.py b/asv_bench/benchmarks/io/sql.py new file mode 100644 index 0000000000000..ef4e501e5f3b9 --- /dev/null +++ b/asv_bench/benchmarks/io/sql.py @@ -0,0 +1,132 @@ +import sqlite3 + +import numpy as np +import pandas.util.testing as tm +from pandas import DataFrame, date_range, read_sql_query, read_sql_table +from sqlalchemy import create_engine + +from ..pandas_vb_common import setup # noqa + + +class SQL(object): + + goal_time = 0.2 + params = ['sqlalchemy', 'sqlite'] + param_names = ['connection'] + + def setup(self, connection): + N = 10000 + con = {'sqlalchemy': create_engine('sqlite:///:memory:'), + 'sqlite': sqlite3.connect(':memory:')} + self.table_name = 'test_type' + self.query_all = 'SELECT * FROM {}'.format(self.table_name) + self.con = con[connection] + self.df = DataFrame({'float': np.random.randn(N), + 'float_with_nan': np.random.randn(N), + 'string': ['foo'] * N, + 'bool': [True] * N, + 'int': np.random.randint(0, N, size=N), + 'datetime': date_range('2000-01-01', + periods=N, + freq='s')}, + index=tm.makeStringIndex(N)) + self.df.loc[1000:3000, 'float_with_nan'] = np.nan + self.df['datetime_string'] = self.df['datetime'].astype(str) + self.df.to_sql(self.table_name, self.con, if_exists='replace') + + def time_to_sql_dataframe(self, connection): + self.df.to_sql('test1', self.con, if_exists='replace') + + def time_read_sql_query(self, connection): + read_sql_query(self.query_all, self.con) + + +class WriteSQLDtypes(object): + + goal_time = 0.2 + params = (['sqlalchemy', 'sqlite'], + ['float', 'float_with_nan', 'string', 'bool', 'int', 'datetime']) + param_names = ['connection', 'dtype'] + + def setup(self, connection, dtype): + N = 10000 + con = {'sqlalchemy': create_engine('sqlite:///:memory:'), + 'sqlite': sqlite3.connect(':memory:')} + self.table_name = 'test_type' + self.query_col = 'SELECT {} FROM {}'.format(dtype, self.table_name) + self.con = con[connection] + self.df = DataFrame({'float': np.random.randn(N), + 'float_with_nan': np.random.randn(N), + 'string': ['foo'] * N, + 'bool': [True] * N, + 'int': np.random.randint(0, N, size=N), + 'datetime': date_range('2000-01-01', + periods=N, + freq='s')}, + index=tm.makeStringIndex(N)) + self.df.loc[1000:3000, 'float_with_nan'] = np.nan + self.df['datetime_string'] = self.df['datetime'].astype(str) + self.df.to_sql(self.table_name, self.con, if_exists='replace') + + def time_to_sql_dataframe_column(self, connection, dtype): + self.df[[dtype]].to_sql('test1', self.con, if_exists='replace') + + def time_read_sql_query_select_column(self, connection, dtype): + read_sql_query(self.query_col, self.con) + + +class ReadSQLTable(object): + + goal_time = 0.2 + + def setup(self): + N = 10000 + self.table_name = 'test' + self.con = create_engine('sqlite:///:memory:') + self.df = DataFrame({'float': np.random.randn(N), + 'float_with_nan': np.random.randn(N), + 'string': ['foo'] * N, + 'bool': [True] * N, + 'int': np.random.randint(0, N, size=N), + 'datetime': date_range('2000-01-01', + periods=N, + freq='s')}, + index=tm.makeStringIndex(N)) + self.df.loc[1000:3000, 'float_with_nan'] = np.nan + self.df['datetime_string'] = self.df['datetime'].astype(str) + self.df.to_sql(self.table_name, self.con, if_exists='replace') + + def time_read_sql_table_all(self): + read_sql_table(self.table_name, self.con) + + def time_read_sql_table_parse_dates(self): + read_sql_table(self.table_name, self.con, columns=['datetime_string'], + parse_dates=['datetime_string']) + + +class ReadSQLTableDtypes(object): + + goal_time = 0.2 + + params = ['float', 'float_with_nan', 'string', 'bool', 'int', 'datetime'] + param_names = ['dtype'] + + def setup(self, dtype): + N = 10000 + self.table_name = 'test' + self.con = create_engine('sqlite:///:memory:') + self.df = DataFrame({'float': np.random.randn(N), + 'float_with_nan': np.random.randn(N), + 'string': ['foo'] * N, + 'bool': [True] * N, + 'int': np.random.randint(0, N, size=N), + 'datetime': date_range('2000-01-01', + periods=N, + freq='s')}, + index=tm.makeStringIndex(N)) + self.df.loc[1000:3000, 'float_with_nan'] = np.nan + self.df['datetime_string'] = self.df['datetime'].astype(str) + self.df.to_sql(self.table_name, self.con, if_exists='replace') + + def time_read_sql_table_column(self, dtype): + read_sql_table(self.table_name, self.con, columns=[dtype]) diff --git a/asv_bench/benchmarks/io/stata.py b/asv_bench/benchmarks/io/stata.py new file mode 100644 index 0000000000000..e0f5752ca930f --- /dev/null +++ b/asv_bench/benchmarks/io/stata.py @@ -0,0 +1,37 @@ +import numpy as np +from pandas import DataFrame, date_range, read_stata +import pandas.util.testing as tm + +from ..pandas_vb_common import BaseIO, setup # noqa + + +class Stata(BaseIO): + + goal_time = 0.2 + params = ['tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'] + param_names = ['convert_dates'] + + def setup(self, convert_dates): + self.fname = '__test__.dta' + N = 100000 + C = 5 + self.df = DataFrame(np.random.randn(N, C), + columns=['float{}'.format(i) for i in range(C)], + index=date_range('20000101', periods=N, freq='H')) + self.df['object'] = tm.makeStringIndex(N) + self.df['int8_'] = np.random.randint(np.iinfo(np.int8).min, + np.iinfo(np.int8).max - 27, N) + self.df['int16_'] = np.random.randint(np.iinfo(np.int16).min, + np.iinfo(np.int16).max - 27, N) + self.df['int32_'] = np.random.randint(np.iinfo(np.int32).min, + np.iinfo(np.int32).max - 27, N) + self.df['float32_'] = np.array(np.random.randn(N), + dtype=np.float32) + self.convert_dates = {'index': convert_dates} + self.df.to_stata(self.fname, self.convert_dates) + + def time_read_stata(self, convert_dates): + read_stata(self.fname) + + def time_write_stata(self, convert_dates): + self.df.to_stata(self.fname, self.convert_dates) diff --git a/asv_bench/benchmarks/io_sql.py b/asv_bench/benchmarks/io_sql.py deleted file mode 100644 index ec855e5d33525..0000000000000 --- a/asv_bench/benchmarks/io_sql.py +++ /dev/null @@ -1,105 +0,0 @@ -import sqlalchemy -from .pandas_vb_common import * -import sqlite3 -from sqlalchemy import create_engine - - -#------------------------------------------------------------------------------- -# to_sql - -class WriteSQL(object): - goal_time = 0.2 - - def setup(self): - self.engine = create_engine('sqlite:///:memory:') - self.con = sqlite3.connect(':memory:') - self.index = tm.makeStringIndex(10000) - self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) - - def time_fallback(self): - self.df.to_sql('test1', self.con, if_exists='replace') - - def time_sqlalchemy(self): - self.df.to_sql('test1', self.engine, if_exists='replace') - - -#------------------------------------------------------------------------------- -# read_sql - -class ReadSQL(object): - goal_time = 0.2 - - def setup(self): - self.engine = create_engine('sqlite:///:memory:') - self.con = sqlite3.connect(':memory:') - self.index = tm.makeStringIndex(10000) - self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) - self.df.to_sql('test2', self.engine, if_exists='replace') - self.df.to_sql('test2', self.con, if_exists='replace') - - def time_read_query_fallback(self): - read_sql_query('SELECT * FROM test2', self.con) - - def time_read_query_sqlalchemy(self): - read_sql_query('SELECT * FROM test2', self.engine) - - def time_read_table_sqlalchemy(self): - read_sql_table('test2', self.engine) - - -#------------------------------------------------------------------------------- -# type specific write - -class WriteSQLTypes(object): - goal_time = 0.2 - - def setup(self): - self.engine = create_engine('sqlite:///:memory:') - self.con = sqlite3.connect(':memory:') - self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) - self.df.loc[1000:3000, 'float'] = np.nan - - def time_string_fallback(self): - self.df[['string']].to_sql('test_string', self.con, if_exists='replace') - - def time_string_sqlalchemy(self): - self.df[['string']].to_sql('test_string', self.engine, if_exists='replace') - - def time_float_fallback(self): - self.df[['float']].to_sql('test_float', self.con, if_exists='replace') - - def time_float_sqlalchemy(self): - self.df[['float']].to_sql('test_float', self.engine, if_exists='replace') - - def time_datetime_sqlalchemy(self): - self.df[['datetime']].to_sql('test_datetime', self.engine, if_exists='replace') - - -#------------------------------------------------------------------------------- -# type specific read - -class ReadSQLTypes(object): - goal_time = 0.2 - - def setup(self): - self.engine = create_engine('sqlite:///:memory:') - self.con = sqlite3.connect(':memory:') - self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) - self.df['datetime_string'] = self.df['datetime'].map(str) - self.df.to_sql('test_type', self.engine, if_exists='replace') - self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') - - def time_datetime_read_and_parse_sqlalchemy(self): - read_sql_table('test_type', self.engine, columns=['datetime_string'], parse_dates=['datetime_string']) - - def time_datetime_read_as_native_sqlalchemy(self): - read_sql_table('test_type', self.engine, columns=['datetime']) - - def time_float_read_query_fallback(self): - read_sql_query('SELECT float FROM test_type', self.con) - - def time_float_read_query_sqlalchemy(self): - read_sql_query('SELECT float FROM test_type', self.engine) - - def time_float_read_table_sqlalchemy(self): - read_sql_table('test_type', self.engine, columns=['float']) diff --git a/asv_bench/benchmarks/packers.py b/asv_bench/benchmarks/packers.py deleted file mode 100644 index 7b6cefc56f0da..0000000000000 --- a/asv_bench/benchmarks/packers.py +++ /dev/null @@ -1,243 +0,0 @@ -from .pandas_vb_common import * -from numpy.random import randint -import pandas as pd -from collections import OrderedDict -from pandas.compat import BytesIO -import sqlite3 -import os -from sqlalchemy import create_engine -import numpy as np -from random import randrange - - -class _Packers(object): - goal_time = 0.2 - - def _setup(self): - self.f = '__test__.msg' - self.N = 100000 - self.C = 5 - self.index = date_range('20000101', periods=self.N, freq='H') - self.df = DataFrame({'float{0}'.format(i): randn(self.N) for i in range(self.C)}, index=self.index) - self.df2 = self.df.copy() - self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] - self.remove(self.f) - - def remove(self, f): - try: - os.remove(f) - except: - pass - - def teardown(self): - self.remove(self.f) - - -class Packers(_Packers): - - def setup(self): - self._setup() - self.df.to_csv(self.f) - - def time_packers_read_csv(self): - pd.read_csv(self.f) - - -class packers_read_excel(_Packers): - - def setup(self): - self._setup() - self.bio = BytesIO() - self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlsxwriter') - self.df[:2000].to_excel(self.writer) - self.writer.save() - - def time_packers_read_excel(self): - self.bio.seek(0) - pd.read_excel(self.bio) - - -class packers_read_hdf_store(_Packers): - - def setup(self): - self._setup() - self.df2.to_hdf(self.f, 'df') - - def time_packers_read_hdf_store(self): - pd.read_hdf(self.f, 'df') - - -class packers_read_hdf_table(_Packers): - - def setup(self): - self._setup() - self.df2.to_hdf(self.f, 'df', format='table') - - def time_packers_read_hdf_table(self): - pd.read_hdf(self.f, 'df') - - -class packers_read_pack(_Packers): - - def setup(self): - self._setup() - self.df2.to_msgpack(self.f) - - def time_packers_read_pack(self): - pd.read_msgpack(self.f) - - -class packers_read_pickle(_Packers): - - def setup(self): - self._setup() - self.df2.to_pickle(self.f) - - def time_packers_read_pickle(self): - pd.read_pickle(self.f) - - -class packers_read_sql(_Packers): - - def setup(self): - self._setup() - self.engine = create_engine('sqlite:///:memory:') - self.df2.to_sql('table', self.engine, if_exists='replace') - - def time_packers_read_sql(self): - pd.read_sql_table('table', self.engine) - - -class packers_read_stata(_Packers): - - def setup(self): - self._setup() - self.df.to_stata(self.f, {'index': 'tc', }) - - def time_packers_read_stata(self): - pd.read_stata(self.f) - - -class packers_read_stata_with_validation(_Packers): - - def setup(self): - self._setup() - self.df['int8_'] = [randint(np.iinfo(np.int8).min, (np.iinfo(np.int8).max - 27)) for _ in range(self.N)] - self.df['int16_'] = [randint(np.iinfo(np.int16).min, (np.iinfo(np.int16).max - 27)) for _ in range(self.N)] - self.df['int32_'] = [randint(np.iinfo(np.int32).min, (np.iinfo(np.int32).max - 27)) for _ in range(self.N)] - self.df['float32_'] = np.array(randn(self.N), dtype=np.float32) - self.df.to_stata(self.f, {'index': 'tc', }) - - def time_packers_read_stata_with_validation(self): - pd.read_stata(self.f) - - -class packers_read_sas(_Packers): - - def setup(self): - - testdir = os.path.join(os.path.dirname(__file__), '..', '..', - 'pandas', 'tests', 'io', 'sas') - if not os.path.exists(testdir): - testdir = os.path.join(os.path.dirname(__file__), '..', '..', - 'pandas', 'io', 'tests', 'sas') - self.f = os.path.join(testdir, 'data', 'test1.sas7bdat') - self.f2 = os.path.join(testdir, 'data', 'paxraw_d_short.xpt') - - def time_read_sas7bdat(self): - pd.read_sas(self.f, format='sas7bdat') - - def time_read_xport(self): - pd.read_sas(self.f2, format='xport') - - -class CSV(_Packers): - - def setup(self): - self._setup() - - def time_write_csv(self): - self.df.to_csv(self.f) - - -class Excel(_Packers): - - def setup(self): - self._setup() - self.bio = BytesIO() - - def time_write_excel_openpyxl(self): - self.bio.seek(0) - self.writer = pd.io.excel.ExcelWriter(self.bio, engine='openpyxl') - self.df[:2000].to_excel(self.writer) - self.writer.save() - - def time_write_excel_xlsxwriter(self): - self.bio.seek(0) - self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlsxwriter') - self.df[:2000].to_excel(self.writer) - self.writer.save() - - def time_write_excel_xlwt(self): - self.bio.seek(0) - self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlwt') - self.df[:2000].to_excel(self.writer) - self.writer.save() - - -class HDF(_Packers): - - def setup(self): - self._setup() - - def time_write_hdf_store(self): - self.df2.to_hdf(self.f, 'df') - - def time_write_hdf_table(self): - self.df2.to_hdf(self.f, 'df', table=True) - - -class MsgPack(_Packers): - - def setup(self): - self._setup() - - def time_write_msgpack(self): - self.df2.to_msgpack(self.f) - - -class Pickle(_Packers): - - def setup(self): - self._setup() - - def time_write_pickle(self): - self.df2.to_pickle(self.f) - - -class SQL(_Packers): - - def setup(self): - self._setup() - self.engine = create_engine('sqlite:///:memory:') - - def time_write_sql(self): - self.df2.to_sql('table', self.engine, if_exists='replace') - - -class STATA(_Packers): - - def setup(self): - self._setup() - - self.df3=self.df.copy() - self.df3['int8_'] = [randint(np.iinfo(np.int8).min, (np.iinfo(np.int8).max - 27)) for _ in range(self.N)] - self.df3['int16_'] = [randint(np.iinfo(np.int16).min, (np.iinfo(np.int16).max - 27)) for _ in range(self.N)] - self.df3['int32_'] = [randint(np.iinfo(np.int32).min, (np.iinfo(np.int32).max - 27)) for _ in range(self.N)] - self.df3['float32_'] = np.array(randn(self.N), dtype=np.float32) - - def time_write_stata(self): - self.df.to_stata(self.f, {'index': 'tc', }) - - def time_write_stata_with_validation(self): - self.df3.to_stata(self.f, {'index': 'tc', })