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from vbench.benchmark import Benchmark
from datetime import datetime
common_setup = """from pandas_vb_common import *
"""
setup = common_setup + """
level1 = np.array([rands(10) for _ in xrange(10)], dtype='O')
level2 = np.array([rands(10) for _ in xrange(1000)], dtype='O')
label1 = np.arange(10).repeat(1000)
label2 = np.tile(np.arange(1000), 10)
key1 = np.tile(level1.take(label1), 10)
key2 = np.tile(level2.take(label2), 10)
shuf = np.arange(100000)
random.shuffle(shuf)
try:
index2 = MultiIndex(levels=[level1, level2], labels=[label1, label2])
index3 = 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)])
df_multi = DataFrame(np.random.randn(len(index2), 4), index=index2,
columns=['A', 'B', 'C', 'D'])
except: # pre-MultiIndex
pass
try:
DataFrame = DataMatrix
except:
pass
df = DataFrame({'data1' : np.random.randn(100000),
'data2' : np.random.randn(100000),
'key1' : key1,
'key2' : key2})
df_key1 = DataFrame(np.random.randn(len(level1), 4), index=level1,
columns=['A', 'B', 'C', 'D'])
df_key2 = DataFrame(np.random.randn(len(level2), 4), index=level2,
columns=['A', 'B', 'C', 'D'])
df_shuf = df.reindex(df.index[shuf])
"""
#----------------------------------------------------------------------
# DataFrame joins on key
join_dataframe_index_single_key_small = \
Benchmark("df.join(df_key1, on='key1')", setup,
name='join_dataframe_index_single_key_small')
join_dataframe_index_single_key_bigger = \
Benchmark("df.join(df_key2, on='key2')", setup,
name='join_dataframe_index_single_key_bigger')
join_dataframe_index_single_key_bigger_sort = \
Benchmark("df_shuf.join(df_key2, on='key2', sort=True)", setup,
name='join_dataframe_index_single_key_bigger_sort',
start_date=datetime(2012, 2, 5))
join_dataframe_index_multi = \
Benchmark("df.join(df_multi, on=['key1', 'key2'])", setup,
name='join_dataframe_index_multi',
start_date=datetime(2011, 10, 20))
#----------------------------------------------------------------------
# Joins on integer keys
setup = common_setup + """
df = DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2),
'key2': np.tile(np.arange(250).repeat(10), 4),
'value': np.random.randn(10000)})
df2 = DataFrame({'key1': np.arange(500), 'value2': randn(500)})
df3 = df[:5000]
"""
join_dataframe_integer_key = Benchmark("merge(df, df2, on='key1')", setup,
start_date=datetime(2011, 10, 20))
join_dataframe_integer_2key = Benchmark("merge(df, df3)", setup,
start_date=datetime(2011, 10, 20))
#----------------------------------------------------------------------
# DataFrame joins on index
#----------------------------------------------------------------------
# Merges
setup = common_setup + """
N = 10000
indices = np.array([rands(10) for _ in xrange(N)], dtype='O')
indices2 = np.array([rands(10) for _ in xrange(N)], dtype='O')
key = np.tile(indices[:8000], 10)
key2 = np.tile(indices2[:8000], 10)
left = DataFrame({'key' : key, 'key2':key2,
'value' : np.random.randn(80000)})
right = DataFrame({'key': indices[2000:], 'key2':indices2[2000:],
'value2' : np.random.randn(8000)})
"""
merge_2intkey_nosort = Benchmark('merge(left, right, sort=False)', setup,
start_date=datetime(2011, 10, 20))
merge_2intkey_sort = Benchmark('merge(left, right, sort=True)', setup,
start_date=datetime(2011, 10, 20))
#----------------------------------------------------------------------
# Appending DataFrames
setup = common_setup + """
df1 = DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D'])
df2 = df1.copy()
df2.index = np.arange(10000, 20000)
mdf1 = df1.copy()
mdf1['obj1'] = 'bar'
mdf1['obj2'] = 'bar'
mdf1['int1'] = 5
try:
mdf1.consolidate(inplace=True)
except:
pass
mdf2 = mdf1.copy()
mdf2.index = df2.index
"""
stmt = "df1.append(df2)"
append_frame_single_homogenous = \
Benchmark(stmt, setup, name='append_frame_single_homogenous',
ncalls=500, repeat=1)
stmt = "mdf1.append(mdf2)"
append_frame_single_mixed = Benchmark(stmt, setup,
name='append_frame_single_mixed',
ncalls=500, repeat=1)
#----------------------------------------------------------------------
# data alignment
setup = common_setup + """n = 1000000
# indices = Index([rands(10) for _ in xrange(n)])
def sample(values, k):
sampler = np.random.permutation(len(values))
return values.take(sampler[:k])
sz = 500000
rng = np.arange(0, 10000000000000, 10000000)
stamps = np.datetime64(datetime.now()).view('i8') + rng
idx1 = np.sort(sample(stamps, sz))
idx2 = np.sort(sample(stamps, sz))
ts1 = Series(np.random.randn(sz), idx1)
ts2 = Series(np.random.randn(sz), idx2)
"""
stmt = "ts1 + ts2"
series_align_int64_index = \
Benchmark(stmt, setup,
name="series_align_int64_index",
start_date=datetime(2010, 6, 1), logy=True)
stmt = "ts1.align(ts2, join='left')"
series_align_left_monotonic = \
Benchmark(stmt, setup,
name="series_align_left_monotonic",
start_date=datetime(2011, 12, 1), logy=True)
#----------------------------------------------------------------------
# Concat Series axis=1
setup = common_setup + """
n = 1000
indices = Index([rands(10) for _ in xrange(1000)])
s = Series(n, index=indices)
pieces = [s[i:-i] for i in range(1, 10)]
pieces = pieces * 50
"""
concat_series_axis1 = Benchmark('concat(pieces, axis=1)', setup,
start_date=datetime(2012, 2, 27))
setup = common_setup + """
df = DataFrame(randn(5, 4))
"""
concat_small_frames = Benchmark('concat([df] * 1000)', setup,
start_date=datetime(2012, 1, 1))
#----------------------------------------------------------------------
# Ordered merge
setup = common_setup + """
groups = np.array([rands(10) for _ in xrange(10)], dtype='O')
left = DataFrame({'group': groups.repeat(5000),
'key' : np.tile(np.arange(0, 10000, 2), 10),
'lvalue': np.random.randn(50000)})
right = DataFrame({'key' : np.arange(10000),
'rvalue' : np.random.randn(10000)})
"""
stmt = "ordered_merge(left, right, on='key', left_by='group')"
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