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from vbench.benchmark import Benchmark
from datetime import datetime
common_setup = """from pandas_vb_common import *
"""
#----------------------------------------------------------------------
# nanops
setup = common_setup + """
s = Series(np.random.randn(100000), index=np.arange(100000))
s[::2] = np.nan
"""
stat_ops_series_std = Benchmark("s.std()", setup)
#----------------------------------------------------------------------
# ops by level
setup = common_setup + """
index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)],
labels=[np.arange(10).repeat(10000),
np.tile(np.arange(100).repeat(100), 10),
np.tile(np.tile(np.arange(100), 100), 10)])
random.shuffle(index.values)
df = DataFrame(np.random.randn(len(index), 4), index=index)
df_level = DataFrame(np.random.randn(100, 4), index=index.levels[1])
"""
stat_ops_level_frame_sum = \
Benchmark("df.sum(level=1)", setup,
start_date=datetime(2011, 11, 15))
stat_ops_level_frame_sum_multiple = \
Benchmark("df.sum(level=[0, 1])", setup, repeat=1,
start_date=datetime(2011, 11, 15))
stat_ops_level_series_sum = \
Benchmark("df[1].sum(level=1)", setup,
start_date=datetime(2011, 11, 15))
stat_ops_level_series_sum_multiple = \
Benchmark("df[1].sum(level=[0, 1])", setup, repeat=1,
start_date=datetime(2011, 11, 15))
sum_setup = common_setup + """
df = DataFrame(np.random.randn(100000, 4))
dfi = DataFrame(np.random.randint(1000, size=df.shape))
"""
stat_ops_frame_sum_int_axis_0 = \
Benchmark("dfi.sum()", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_sum_float_axis_0 = \
Benchmark("df.sum()", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_mean_int_axis_0 = \
Benchmark("dfi.mean()", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_mean_float_axis_0 = \
Benchmark("df.mean()", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_sum_int_axis_1 = \
Benchmark("dfi.sum(1)", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_sum_float_axis_1 = \
Benchmark("df.sum(1)", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_mean_int_axis_1 = \
Benchmark("dfi.mean(1)", sum_setup, start_date=datetime(2013, 7, 25))
stat_ops_frame_mean_float_axis_1 = \
Benchmark("df.mean(1)", sum_setup, start_date=datetime(2013, 7, 25))
#----------------------------------------------------------------------
# rank
setup = common_setup + """
values = np.concatenate([np.arange(100000),
np.random.randn(100000),
np.arange(100000)])
s = Series(values)
"""
stats_rank_average = Benchmark('s.rank()', setup,
start_date=datetime(2011, 12, 12))
setup = common_setup + """
values = np.random.randint(0, 100000, size=200000)
s = Series(values)
"""
stats_rank_average_int = Benchmark('s.rank()', setup,
start_date=datetime(2011, 12, 12))
setup = common_setup + """
df = DataFrame(np.random.randn(5000, 50))
"""
stats_rank2d_axis1_average = Benchmark('df.rank(1)', setup,
start_date=datetime(2011, 12, 12))
stats_rank2d_axis0_average = Benchmark('df.rank()', setup,
start_date=datetime(2011, 12, 12))
# rolling functions
setup = common_setup + """
arr = np.random.randn(100000)
"""
stats_rolling_mean = Benchmark('rolling_mean(arr, 100)', setup,
start_date=datetime(2011, 6, 1))
# spearman correlation
setup = common_setup + """
df = DataFrame(np.random.randn(1000, 30))
"""
stats_corr_spearman = Benchmark("df.corr(method='spearman')", setup,
start_date=datetime(2011, 12, 4))
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