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io_bench.py
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from .pandas_vb_common import *
from pandas import concat, Timestamp, compat
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
import timeit
class frame_to_csv(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(3000, 30))
def time_frame_to_csv(self):
self.df.to_csv('__test__.csv')
class frame_to_csv2(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame({'A': range(50000), })
self.df['B'] = (self.df.A + 1.0)
self.df['C'] = (self.df.A + 2.0)
self.df['D'] = (self.df.A + 3.0)
def time_frame_to_csv2(self):
self.df.to_csv('__test__.csv')
class frame_to_csv_date_formatting(object):
goal_time = 0.2
def setup(self):
self.rng = date_range('1/1/2000', periods=1000)
self.data = DataFrame(self.rng, index=self.rng)
def time_frame_to_csv_date_formatting(self):
self.data.to_csv('__test__.csv', date_format='%Y%m%d')
class frame_to_csv_mixed(object):
goal_time = 0.2
def setup(self):
self.df_float = DataFrame(np.random.randn(5000, 5), dtype='float64', columns=self.create_cols('float'))
self.df_int = DataFrame(np.random.randn(5000, 5), dtype='int64', columns=self.create_cols('int'))
self.df_bool = DataFrame(True, index=self.df_float.index, columns=self.create_cols('bool'))
self.df_object = DataFrame('foo', index=self.df_float.index, columns=self.create_cols('object'))
self.df_dt = DataFrame(Timestamp('20010101'), index=self.df_float.index, columns=self.create_cols('date'))
self.df_float.ix[30:500, 1:3] = np.nan
self.df = concat([self.df_float, self.df_int, self.df_bool, self.df_object, self.df_dt], axis=1)
def time_frame_to_csv_mixed(self):
self.df.to_csv('__test__.csv')
def create_cols(self, name):
return [('%s%03d' % (name, i)) for i in range(5)]
class read_csv_infer_datetime_format_custom(object):
goal_time = 0.2
def setup(self):
self.rng = date_range('1/1/2000', periods=1000)
self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%m/%d/%Y %H:%M:%S.%f'))))
def time_read_csv_infer_datetime_format_custom(self):
read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True)
class read_csv_infer_datetime_format_iso8601(object):
goal_time = 0.2
def setup(self):
self.rng = date_range('1/1/2000', periods=1000)
self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))))
def time_read_csv_infer_datetime_format_iso8601(self):
read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True)
class read_csv_infer_datetime_format_ymd(object):
goal_time = 0.2
def setup(self):
self.rng = date_range('1/1/2000', periods=1000)
self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y%m%d'))))
def time_read_csv_infer_datetime_format_ymd(self):
read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True)
class read_csv_skiprows(object):
goal_time = 0.2
def setup(self):
self.index = tm.makeStringIndex(20000)
self.df = DataFrame({'float1': randn(20000), 'float2': randn(20000), 'string1': (['foo'] * 20000), 'bool1': ([True] * 20000), 'int1': np.random.randint(0, 200000, size=20000), }, index=self.index)
self.df.to_csv('__test__.csv')
def time_read_csv_skiprows(self):
read_csv('__test__.csv', skiprows=10000)
class read_csv_standard(object):
goal_time = 0.2
def setup(self):
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_csv('__test__.csv')
def time_read_csv_standard(self):
read_csv('__test__.csv')
class read_parse_dates_iso8601(object):
goal_time = 0.2
def setup(self):
self.rng = date_range('1/1/2000', periods=1000)
self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))))
def time_read_parse_dates_iso8601(self):
read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'])
class read_uint64_integers(object):
goal_time = 0.2
def setup(self):
self.na_values = [2**63 + 500]
self.arr1 = np.arange(10000).astype('uint64') + 2**63
self.data1 = '\n'.join(map(lambda x: str(x), self.arr1))
self.arr2 = self.arr1.copy().astype(object)
self.arr2[500] = -1
self.data2 = '\n'.join(map(lambda x: str(x), self.arr2))
def time_read_uint64(self):
read_csv(StringIO(self.data1), header=None)
def time_read_uint64_neg_values(self):
read_csv(StringIO(self.data2), header=None)
def time_read_uint64_na_values(self):
read_csv(StringIO(self.data1), header=None, na_values=self.na_values)
class write_csv_standard(object):
goal_time = 0.2
def setup(self):
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_write_csv_standard(self):
self.df.to_csv('__test__.csv')
class read_csv_from_s3(object):
# Make sure that we can read part of a file from S3 without
# needing to download the entire thing. Use the timeit.default_timer
# to measure wall time instead of CPU time -- we want to see
# how long it takes to download the data.
timer = timeit.default_timer
params = ([None, "gzip", "bz2"], ["python", "c"])
param_names = ["compression", "engine"]
def setup(self, compression, engine):
if compression == "bz2" and engine == "c" and compat.PY2:
# The Python 2 C parser can't read bz2 from open files.
raise NotImplementedError
try:
import s3fs
except ImportError:
# Skip these benchmarks if `boto` is not installed.
raise NotImplementedError
self.big_fname = "s3://pandas-test/large_random.csv"
def time_read_nrows(self, compression, engine):
# Read a small number of rows from a huge (100,000 x 50) table.
ext = ""
if compression == "gzip":
ext = ".gz"
elif compression == "bz2":
ext = ".bz2"
pd.read_csv(self.big_fname + ext, nrows=10,
compression=compression, engine=engine)