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frame_methods.py
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from vbench.api import Benchmark
from datetime import datetime
common_setup = """from pandas_vb_common import *
"""
#----------------------------------------------------------------------
# lookup
setup = common_setup + """
df = DataFrame(np.random.randn(10000, 8), columns=list('abcdefgh'))
df['foo'] = 'bar'
row_labels = list(df.index[::10])[:900]
col_labels = list(df.columns) * 100
row_labels_all = np.array(list(df.index) * len(df.columns), dtype='object')
col_labels_all = np.array(list(df.columns) * len(df.index), dtype='object')
"""
frame_fancy_lookup = Benchmark('df.lookup(row_labels, col_labels)', setup,
start_date=datetime(2012, 1, 12))
frame_fancy_lookup_all = Benchmark('df.lookup(row_labels_all, col_labels_all)',
setup,
start_date=datetime(2012, 1, 12))
#----------------------------------------------------------------------
# fillna in place
setup = common_setup + """
df = DataFrame(randn(10000, 100))
df.values[::2] = np.nan
"""
frame_fillna_inplace = Benchmark('df.fillna(0, inplace=True)', setup,
start_date=datetime(2012, 4, 4))
#----------------------------------------------------------------------
# reindex both axes
setup = common_setup + """
df = DataFrame(randn(10000, 10000))
idx = np.arange(4000, 7000)
"""
frame_reindex_axis0 = Benchmark('df.reindex(idx)', setup)
frame_reindex_axis1 = Benchmark('df.reindex(columns=idx)', setup)
frame_reindex_both_axes = Benchmark('df.reindex(index=idx, columns=idx)',
setup, start_date=datetime(2011, 1, 1))
frame_reindex_both_axes_ix = Benchmark('df.ix[idx, idx]', setup,
start_date=datetime(2011, 1, 1))
#----------------------------------------------------------------------
# reindex with upcasts
setup = common_setup + """
df=DataFrame(dict([(c, {
0: randint(0, 2, 1000).astype(np.bool_),
1: randint(0, 1000, 1000).astype(np.int16),
2: randint(0, 1000, 1000).astype(np.int32),
3: randint(0, 1000, 1000).astype(np.int64)
}[randint(0, 4)]) for c in range(1000)]))
"""
frame_reindex_upcast = Benchmark('df.reindex(permutation(range(1200)))', setup)
#----------------------------------------------------------------------
# boolean indexing
setup = common_setup + """
df = DataFrame(randn(10000, 100))
bool_arr = np.zeros(10000, dtype=bool)
bool_arr[:1000] = True
"""
frame_boolean_row_select = Benchmark('df[bool_arr]', setup,
start_date=datetime(2011, 1, 1))
#----------------------------------------------------------------------
# iteritems (monitor no-copying behaviour)
setup = common_setup + """
df = DataFrame(randn(10000, 1000))
df2 = DataFrame(randn(3000,1),columns=['A'])
df3 = DataFrame(randn(3000,1))
def f():
if hasattr(df, '_item_cache'):
df._item_cache.clear()
for name, col in df.iteritems():
pass
def g():
for name, col in df.iteritems():
pass
def h():
for i in xrange(10000):
df2['A']
def j():
for i in xrange(10000):
df3[0]
"""
# as far back as the earliest test currently in the suite
frame_iteritems = Benchmark('f()', setup,
start_date=datetime(2010, 6, 1))
frame_iteritems_cached = Benchmark('g()', setup,
start_date=datetime(2010, 6, 1))
frame_getitem_single_column = Benchmark('h()', setup,
start_date=datetime(2010, 6, 1))
frame_getitem_single_column2 = Benchmark('j()', setup,
start_date=datetime(2010, 6, 1))
#----------------------------------------------------------------------
# assignment
setup = common_setup + """
idx = date_range('1/1/2000', periods=100000, freq='D')
df = DataFrame(randn(100000, 1),columns=['A'],index=idx)
def f(x):
x = x.copy()
x['date'] = x.index
"""
frame_assign_timeseries_index = Benchmark('f(df)', setup,
start_date=datetime(2013, 10, 1))
#----------------------------------------------------------------------
# to_string
setup = common_setup + """
df = DataFrame(randn(100, 10))
"""
frame_to_string_floats = Benchmark('df.to_string()', setup,
start_date=datetime(2010, 6, 1))
#----------------------------------------------------------------------
# to_html
setup = common_setup + """
nrows=500
df = DataFrame(randn(nrows, 10))
df[0]=period_range("2000","2010",nrows)
df[1]=range(nrows)
"""
frame_to_html_mixed = Benchmark('df.to_html()', setup,
start_date=datetime(2011, 11, 18))
# truncated repr_html, single index
setup = common_setup + """
nrows=10000
data=randn(nrows,10)
idx=MultiIndex.from_arrays(np.tile(randn(3,nrows/100),100))
df=DataFrame(data,index=idx)
"""
frame_html_repr_trunc_mi = Benchmark('df._repr_html_()', setup,
start_date=datetime(2013, 11, 25))
# truncated repr_html, MultiIndex
setup = common_setup + """
nrows=10000
data=randn(nrows,10)
idx=randn(nrows)
df=DataFrame(data,index=idx)
"""
frame_html_repr_trunc_si = Benchmark('df._repr_html_()', setup,
start_date=datetime(2013, 11, 25))
# insert many columns
setup = common_setup + """
N = 1000
def f(K=500):
df = DataFrame(index=range(N))
new_col = np.random.randn(N)
for i in range(K):
df[i] = new_col
"""
frame_insert_500_columns_end = Benchmark('f()', setup, start_date=datetime(2011, 1, 1))
setup = common_setup + """
N = 1000
def f(K=100):
df = DataFrame(index=range(N))
new_col = np.random.randn(N)
for i in range(K):
df.insert(0,i,new_col)
"""
frame_insert_100_columns_begin = Benchmark('f()', setup, start_date=datetime(2011, 1, 1))
#----------------------------------------------------------------------
# strings methods, #2602
setup = common_setup + """
s = Series(['abcdefg', np.nan]*500000)
"""
series_string_vector_slice = Benchmark('s.str[:5]', setup,
start_date=datetime(2012, 8, 1))
#----------------------------------------------------------------------
# df.info() and get_dtype_counts() # 2807
setup = common_setup + """
df = pandas.DataFrame(np.random.randn(10,10000))
"""
frame_get_dtype_counts = Benchmark('df.get_dtype_counts()', setup,
start_date=datetime(2012, 8, 1))
##
setup = common_setup + """
df = pandas.DataFrame(np.random.randn(10,10000))
"""
frame_repr_wide = Benchmark('repr(df)', setup,
start_date=datetime(2012, 8, 1))
##
setup = common_setup + """
df = pandas.DataFrame(np.random.randn(10000, 10))
"""
frame_repr_tall = Benchmark('repr(df)', setup,
start_date=datetime(2012, 8, 1))
##
setup = common_setup + """
df = DataFrame(randn(100000, 1))
"""
frame_xs_row = Benchmark('df.xs(50000)', setup)
##
setup = common_setup + """
df = DataFrame(randn(1,100000))
"""
frame_xs_col = Benchmark('df.xs(50000,axis = 1)', setup)
#----------------------------------------------------------------------
# nulls/masking
## masking
setup = common_setup + """
data = np.random.randn(1000, 500)
df = DataFrame(data)
df = df.where(df > 0) # create nans
bools = df > 0
mask = isnull(df)
"""
frame_mask_bools = Benchmark('bools.mask(mask)', setup,
start_date=datetime(2013,1,1))
frame_mask_floats = Benchmark('bools.astype(float).mask(mask)', setup,
start_date=datetime(2013,1,1))
## isnull
setup = common_setup + """
data = np.random.randn(1000, 1000)
df = DataFrame(data)
"""
frame_isnull = Benchmark('isnull(df)', setup,
start_date=datetime(2012,1,1))
## dropna
setup = common_setup + """
data = np.random.randn(10000, 1000)
df = DataFrame(data)
df.ix[50:1000,20:50] = np.nan
df.ix[2000:3000] = np.nan
df.ix[:,60:70] = np.nan
"""
frame_dropna_axis0_any = Benchmark('df.dropna(how="any",axis=0)', setup,
start_date=datetime(2012,1,1))
frame_dropna_axis0_all = Benchmark('df.dropna(how="all",axis=0)', setup,
start_date=datetime(2012,1,1))
setup = common_setup + """
data = np.random.randn(10000, 1000)
df = DataFrame(data)
df.ix[50:1000,20:50] = np.nan
df.ix[2000:3000] = np.nan
df.ix[:,60:70] = np.nan
"""
frame_dropna_axis1_any = Benchmark('df.dropna(how="any",axis=1)', setup,
start_date=datetime(2012,1,1))
frame_dropna_axis1_all = Benchmark('df.dropna(how="all",axis=1)', setup,
start_date=datetime(2012,1,1))
#----------------------------------------------------------------------
# apply
setup = common_setup + """
s = Series(np.arange(1028.))
df = DataFrame({ i:s for i in range(1028) })
"""
frame_apply_user_func = Benchmark('df.apply(lambda x: np.corrcoef(x,s)[0,1])', setup,
start_date=datetime(2012,1,1))