frame_methods.py

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))

# 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)

## 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)
"""

mask_bools = Benchmark('bools.mask(mask)', setup,
                         start_date=datetime(2013,1,1))

mask_floats  = Benchmark('bools.astype(float).mask(mask)', setup,
                         start_date=datetime(2013,1,1))