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indexing.py
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import warnings
import numpy as np
import pandas.util.testing as tm
from pandas import (Series, DataFrame, Panel, MultiIndex,
Int64Index, UInt64Index, Float64Index,
IntervalIndex, CategoricalIndex,
IndexSlice, concat, date_range)
class NumericSeriesIndexing(object):
goal_time = 0.2
params = [
(Int64Index, UInt64Index, Float64Index),
('unique_monotonic_inc', 'nonunique_monotonic_inc'),
]
param_names = ['index_dtype', 'index_structure']
def setup(self, index, index_structure):
N = 10**6
indices = {
'unique_monotonic_inc': index(range(N)),
'nonunique_monotonic_inc': index(
list(range(55)) + [54] + list(range(55, N - 1))),
}
self.data = Series(np.random.rand(N), index=indices[index_structure])
self.array = np.arange(10000)
self.array_list = self.array.tolist()
def time_getitem_scalar(self, index, index_structure):
self.data[800000]
def time_getitem_slice(self, index, index_structure):
self.data[:800000]
def time_getitem_list_like(self, index, index_structure):
self.data[[800000]]
def time_getitem_array(self, index, index_structure):
self.data[self.array]
def time_getitem_lists(self, index, index_structure):
self.data[self.array_list]
def time_iloc_array(self, index, index_structure):
self.data.iloc[self.array]
def time_iloc_list_like(self, index, index_structure):
self.data.iloc[[800000]]
def time_iloc_scalar(self, index, index_structure):
self.data.iloc[800000]
def time_iloc_slice(self, index, index_structure):
self.data.iloc[:800000]
def time_ix_array(self, index, index_structure):
self.data.ix[self.array]
def time_ix_list_like(self, index, index_structure):
self.data.ix[[800000]]
def time_ix_scalar(self, index, index_structure):
self.data.ix[800000]
def time_ix_slice(self, index, index_structure):
self.data.ix[:800000]
def time_loc_array(self, index, index_structure):
self.data.loc[self.array]
def time_loc_list_like(self, index, index_structure):
self.data.loc[[800000]]
def time_loc_scalar(self, index, index_structure):
self.data.loc[800000]
def time_loc_slice(self, index, index_structure):
self.data.loc[:800000]
class NonNumericSeriesIndexing(object):
goal_time = 0.2
params = [
('string', 'datetime'),
('unique_monotonic_inc', 'nonunique_monotonic_inc'),
]
param_names = ['index_dtype', 'index_structure']
def setup(self, index, index_structure):
N = 10**6
indexes = {'string': tm.makeStringIndex(N),
'datetime': date_range('1900', periods=N, freq='s')}
index = indexes[index]
if index_structure == 'nonunique_monotonic_inc':
index = index.insert(item=index[2], loc=2)[:-1]
self.s = Series(np.random.rand(N), index=index)
self.lbl = index[80000]
def time_getitem_label_slice(self, index, index_structure):
self.s[:self.lbl]
def time_getitem_pos_slice(self, index, index_structure):
self.s[:80000]
def time_get_value(self, index, index_structure):
with warnings.catch_warnings(record=True):
self.s.get_value(self.lbl)
def time_getitem_scalar(self, index, index_structure):
self.s[self.lbl]
def time_getitem_list_like(self, index, index_structure):
self.s[[self.lbl]]
class DataFrameStringIndexing(object):
goal_time = 0.2
def setup(self):
index = tm.makeStringIndex(1000)
columns = tm.makeStringIndex(30)
self.df = DataFrame(np.random.randn(1000, 30), index=index,
columns=columns)
self.idx_scalar = index[100]
self.col_scalar = columns[10]
self.bool_indexer = self.df[self.col_scalar] > 0
self.bool_obj_indexer = self.bool_indexer.astype(object)
def time_get_value(self):
with warnings.catch_warnings(record=True):
self.df.get_value(self.idx_scalar, self.col_scalar)
def time_ix(self):
self.df.ix[self.idx_scalar, self.col_scalar]
def time_loc(self):
self.df.loc[self.idx_scalar, self.col_scalar]
def time_getitem_scalar(self):
self.df[self.col_scalar][self.idx_scalar]
def time_boolean_rows(self):
self.df[self.bool_indexer]
def time_boolean_rows_object(self):
self.df[self.bool_obj_indexer]
class DataFrameNumericIndexing(object):
goal_time = 0.2
def setup(self):
self.idx_dupe = np.array(range(30)) * 99
self.df = DataFrame(np.random.randn(10000, 5))
self.df_dup = concat([self.df, 2 * self.df, 3 * self.df])
self.bool_indexer = [True] * 5000 + [False] * 5000
def time_iloc_dups(self):
self.df_dup.iloc[self.idx_dupe]
def time_loc_dups(self):
self.df_dup.loc[self.idx_dupe]
def time_iloc(self):
self.df.iloc[:100, 0]
def time_loc(self):
self.df.loc[:100, 0]
def time_bool_indexer(self):
self.df[self.bool_indexer]
class Take(object):
goal_time = 0.2
params = ['int', 'datetime']
param_names = ['index']
def setup(self, index):
N = 100000
indexes = {'int': Int64Index(np.arange(N)),
'datetime': date_range('2011-01-01', freq='S', periods=N)}
index = indexes[index]
self.s = Series(np.random.rand(N), index=index)
self.indexer = [True, False, True, True, False] * 20000
def time_take(self, index):
self.s.take(self.indexer)
class MultiIndexing(object):
goal_time = 0.2
def setup(self):
mi = MultiIndex.from_product([range(1000), range(1000)])
self.s = Series(np.random.randn(1000000), index=mi)
self.df = DataFrame(self.s)
n = 100000
self.mdt = DataFrame({'A': np.random.choice(range(10000, 45000, 1000),
n),
'B': np.random.choice(range(10, 400), n),
'C': np.random.choice(range(1, 150), n),
'D': np.random.choice(range(10000, 45000), n),
'x': np.random.choice(range(400), n),
'y': np.random.choice(range(25), n)})
self.idx = IndexSlice[20000:30000, 20:30, 35:45, 30000:40000]
self.mdt = self.mdt.set_index(['A', 'B', 'C', 'D']).sort_index()
def time_series_ix(self):
self.s.ix[999]
def time_frame_ix(self):
self.df.ix[999]
def time_index_slice(self):
self.mdt.loc[self.idx, :]
class IntervalIndexing(object):
goal_time = 0.2
def setup_cache(self):
idx = IntervalIndex.from_breaks(np.arange(1000001))
monotonic = Series(np.arange(1000000), index=idx)
return monotonic
def time_getitem_scalar(self, monotonic):
monotonic[80000]
def time_loc_scalar(self, monotonic):
monotonic.loc[80000]
def time_getitem_list(self, monotonic):
monotonic[80000:]
def time_loc_list(self, monotonic):
monotonic.loc[80000:]
class CategoricalIndexIndexing(object):
goal_time = 0.2
params = ['monotonic_incr', 'monotonic_decr', 'non_monotonic']
param_names = ['index']
def setup(self, index):
N = 10**5
values = list('a' * N + 'b' * N + 'c' * N)
indices = {
'monotonic_incr': CategoricalIndex(values),
'monotonic_decr': CategoricalIndex(reversed(values)),
'non_monotonic': CategoricalIndex(list('abc' * N))}
self.data = indices[index]
self.int_scalar = 10000
self.int_list = list(range(10000))
self.cat_scalar = 'b'
self.cat_list = ['a', 'c']
def time_getitem_scalar(self, index):
self.data[self.int_scalar]
def time_getitem_slice(self, index):
self.data[:self.int_scalar]
def time_getitem_list_like(self, index):
self.data[[self.int_scalar]]
def time_getitem_list(self, index):
self.data[self.int_list]
def time_getitem_bool_array(self, index):
self.data[self.data == self.cat_scalar]
def time_get_loc_scalar(self, index):
self.data.get_loc(self.cat_scalar)
def time_get_indexer_list(self, index):
self.data.get_indexer(self.cat_list)
class PanelIndexing(object):
goal_time = 0.2
def setup(self):
with warnings.catch_warnings(record=True):
self.p = Panel(np.random.randn(100, 100, 100))
self.inds = range(0, 100, 10)
def time_subset(self):
with warnings.catch_warnings(record=True):
self.p.ix[(self.inds, self.inds, self.inds)]
class MethodLookup(object):
goal_time = 0.2
def setup_cache(self):
s = Series()
return s
def time_lookup_iloc(self, s):
s.iloc
def time_lookup_ix(self, s):
s.ix
def time_lookup_loc(self, s):
s.loc
class GetItemSingleColumn(object):
goal_time = 0.2
def setup(self):
self.df_string_col = DataFrame(np.random.randn(3000, 1), columns=['A'])
self.df_int_col = DataFrame(np.random.randn(3000, 1))
def time_frame_getitem_single_column_label(self):
self.df_string_col['A']
def time_frame_getitem_single_column_int(self):
self.df_int_col[0]
class AssignTimeseriesIndex(object):
goal_time = 0.2
def setup(self):
N = 100000
idx = date_range('1/1/2000', periods=N, freq='H')
self.df = DataFrame(np.random.randn(N, 1), columns=['A'], index=idx)
def time_frame_assign_timeseries_index(self):
self.df['date'] = self.df.index
class InsertColumns(object):
goal_time = 0.2
def setup(self):
self.N = 10**3
self.df = DataFrame(index=range(self.N))
def time_insert(self):
np.random.seed(1234)
for i in range(100):
self.df.insert(0, i, np.random.randn(self.N),
allow_duplicates=True)
def time_assign_with_setitem(self):
np.random.seed(1234)
for i in range(100):
self.df[i] = np.random.randn(self.N)
from .pandas_vb_common import setup # noqa: F401