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import random
import timeit
import string
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Categorical, date_range, read_csv
from pandas.compat import PY2
from pandas.compat import cStringIO as StringIO
from ..pandas_vb_common import setup, BaseIO # noqa
class ToCSV(BaseIO):
goal_time = 0.2
fname = '__test__.csv'
params = ['wide', 'long', 'mixed']
param_names = ['kind']
def setup(self, kind):
wide_frame = DataFrame(np.random.randn(3000, 30))
long_frame = DataFrame({'A': np.arange(50000),
'B': np.arange(50000) + 1.,
'C': np.arange(50000) + 2.,
'D': np.arange(50000) + 3.})
mixed_frame = DataFrame({'float': np.random.randn(5000),
'int': np.random.randn(5000).astype(int),
'bool': (np.arange(5000) % 2) == 0,
'datetime': date_range('2001',
freq='s',
periods=5000),
'object': ['foo'] * 5000})
mixed_frame.loc[30:500, 'float'] = np.nan
data = {'wide': wide_frame,
'long': long_frame,
'mixed': mixed_frame}
self.df = data[kind]
def time_frame(self, kind):
self.df.to_csv(self.fname)
class ToCSVDatetime(BaseIO):
goal_time = 0.2
fname = '__test__.csv'
def setup(self):
rng = date_range('1/1/2000', periods=1000)
self.data = DataFrame(rng, index=rng)
def time_frame_date_formatting(self):
self.data.to_csv(self.fname, date_format='%Y%m%d')
class ReadCSVDInferDatetimeFormat(object):
goal_time = 0.2
params = ([True, False], ['custom', 'iso8601', 'ymd'])
param_names = ['infer_datetime_format', 'format']
def setup(self, infer_datetime_format, format):
rng = date_range('1/1/2000', periods=1000)
formats = {'custom': '%m/%d/%Y %H:%M:%S.%f',
'iso8601': '%Y-%m-%d %H:%M:%S',
'ymd': '%Y%m%d'}
dt_format = formats[format]
self.data = StringIO('\n'.join(rng.strftime(dt_format).tolist()))
def time_read_csv(self, infer_datetime_format, format):
read_csv(self.data, header=None, names=['foo'], parse_dates=['foo'],
infer_datetime_format=infer_datetime_format)
class ReadCSVSkipRows(BaseIO):
goal_time = 0.2
fname = '__test__.csv'
params = [None, 10000]
param_names = ['skiprows']
def setup(self, skiprows):
N = 20000
index = tm.makeStringIndex(N)
df = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N),
'string1': ['foo'] * N,
'bool1': [True] * N,
'int1': np.random.randint(0, N, size=N)},
index=index)
df.to_csv(self.fname)
def time_skipprows(self, skiprows):
read_csv(self.fname, skiprows=skiprows)
class ReadUint64Integers(object):
goal_time = 0.2
def setup(self):
self.na_values = [2**63 + 500]
arr = np.arange(10000).astype('uint64') + 2**63
self.data1 = StringIO('\n'.join(arr.astype(str).tolist()))
arr = arr.astype(object)
arr[500] = -1
self.data2 = StringIO('\n'.join(arr.astype(str).tolist()))
def time_read_uint64(self):
read_csv(self.data1, header=None, names=['foo'])
def time_read_uint64_neg_values(self):
read_csv(self.data2, header=None, names=['foo'])
def time_read_uint64_na_values(self):
read_csv(self.data1, header=None, names=['foo'],
na_values=self.na_values)
class ReadCSVThousands(BaseIO):
goal_time = 0.2
fname = '__test__.csv'
params = ([',', '|'], [None, ','])
param_names = ['sep', 'thousands']
def setup(self, sep, thousands):
N = 10000
K = 8
data = np.random.randn(N, K) * np.random.randint(100, 10000, (N, K))
df = DataFrame(data)
if thousands is not None:
fmt = ':{}'.format(thousands)
fmt = '{' + fmt + '}'
df = df.applymap(lambda x: fmt.format(x))
df.to_csv(self.fname, sep=sep)
def time_thousands(self, sep, thousands):
read_csv(self.fname, sep=sep, thousands=thousands)
class ReadCSVComment(object):
goal_time = 0.2
def setup(self):
data = ['A,B,C'] + (['1,2,3 # comment'] * 100000)
self.s_data = StringIO('\n'.join(data))
def time_comment(self):
read_csv(self.s_data, comment='#', header=None, names=list('abc'))
class ReadCSVFloatPrecision(object):
goal_time = 0.2
params = ([',', ';'], ['.', '_'], [None, 'high', 'round_trip'])
param_names = ['sep', 'decimal', 'float_precision']
def setup(self, sep, decimal, float_precision):
floats = [''.join(random.choice(string.digits) for _ in range(28))
for _ in range(15)]
rows = sep.join(['0{}'.format(decimal) + '{}'] * 3) + '\n'
data = rows * 5
data = data.format(*floats) * 200 # 1000 x 3 strings csv
self.s_data = StringIO(data)
def time_read_csv(self, sep, decimal, float_precision):
read_csv(self.s_data, sep=sep, header=None, names=list('abc'),
float_precision=float_precision)
def time_read_csv_python_engine(self, sep, decimal, float_precision):
read_csv(self.s_data, sep=sep, header=None, engine='python',
float_precision=None, names=list('abc'))
class ReadCSVCategorical(BaseIO):
goal_time = 0.2
fname = '__test__.csv'
def setup(self):
N = 100000
group1 = ['aaaaaaaa', 'bbbbbbb', 'cccccccc', 'dddddddd', 'eeeeeeee']
df = DataFrame(np.random.choice(group1, (N, 3)), columns=list('abc'))
df.to_csv(self.fname, index=False)
def time_convert_post(self):
read_csv(self.fname).apply(Categorical)
def time_convert_direct(self):
read_csv(self.fname, dtype='category')
class ReadCSVParseDates(object):
goal_time = 0.2
def setup(self):
data = """{},19:00:00,18:56:00,0.8100,2.8100,7.2000,0.0000,280.0000\n
{},20:00:00,19:56:00,0.0100,2.2100,7.2000,0.0000,260.0000\n
{},21:00:00,20:56:00,-0.5900,2.2100,5.7000,0.0000,280.0000\n
{},21:00:00,21:18:00,-0.9900,2.0100,3.6000,0.0000,270.0000\n
{},22:00:00,21:56:00,-0.5900,1.7100,5.1000,0.0000,290.0000\n
"""
two_cols = ['KORD,19990127'] * 5
data = data.format(*two_cols)
self.s_data = StringIO(data)
def time_multiple_date(self):
read_csv(self.s_data, sep=',', header=None,
names=list(string.digits[:9]), parse_dates=[[1, 2], [1, 3]])
def time_baseline(self):
read_csv(self.s_data, sep=',', header=None, parse_dates=[1],
names=list(string.digits[:9]))
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