1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
import string
import numpy as np
import pandas as pd
from pandas import (
DataFrame,
Index,
)
import pandas._testing as tm
from pandas.api.types import (
is_extension_array_dtype,
pandas_dtype,
)
from .pandas_vb_common import (
datetime_dtypes,
extension_dtypes,
numeric_dtypes,
string_dtypes,
)
_numpy_dtypes = [
np.dtype(dtype) for dtype in (numeric_dtypes + datetime_dtypes + string_dtypes)
]
_dtypes = _numpy_dtypes + extension_dtypes
class Dtypes:
params = _dtypes + [dt.name for dt in _dtypes]
param_names = ["dtype"]
def time_pandas_dtype(self, dtype):
pandas_dtype(dtype)
class DtypesInvalid:
param_names = ["dtype"]
params = ["scalar-string", "scalar-int", "list-string", "array-string"]
data_dict = {
"scalar-string": "foo",
"scalar-int": 1,
"list-string": ["foo"] * 1000,
"array-string": np.array(["foo"] * 1000),
}
def time_pandas_dtype_invalid(self, dtype):
try:
pandas_dtype(self.data_dict[dtype])
except TypeError:
pass
class SelectDtypes:
try:
params = [
tm.ALL_INT_NUMPY_DTYPES
+ tm.ALL_INT_EA_DTYPES
+ tm.FLOAT_NUMPY_DTYPES
+ tm.COMPLEX_DTYPES
+ tm.DATETIME64_DTYPES
+ tm.TIMEDELTA64_DTYPES
+ tm.BOOL_DTYPES
]
except AttributeError:
params = [
tm.ALL_INT_DTYPES
+ tm.ALL_EA_INT_DTYPES
+ tm.FLOAT_DTYPES
+ tm.COMPLEX_DTYPES
+ tm.DATETIME64_DTYPES
+ tm.TIMEDELTA64_DTYPES
+ tm.BOOL_DTYPES
]
param_names = ["dtype"]
def setup(self, dtype):
N, K = 5000, 50
self.index = Index([f"i-{i}" for i in range(N)], dtype=object)
self.columns = Index([f"i-{i}" for i in range(K)], dtype=object)
def create_df(data):
return DataFrame(data, index=self.index, columns=self.columns)
self.df_int = create_df(np.random.randint(low=100, size=(N, K)))
self.df_float = create_df(np.random.randn(N, K))
self.df_bool = create_df(np.random.choice([True, False], size=(N, K)))
self.df_string = create_df(
np.random.choice(list(string.ascii_letters), size=(N, K))
)
def time_select_dtype_int_include(self, dtype):
self.df_int.select_dtypes(include=dtype)
def time_select_dtype_int_exclude(self, dtype):
self.df_int.select_dtypes(exclude=dtype)
def time_select_dtype_float_include(self, dtype):
self.df_float.select_dtypes(include=dtype)
def time_select_dtype_float_exclude(self, dtype):
self.df_float.select_dtypes(exclude=dtype)
def time_select_dtype_bool_include(self, dtype):
self.df_bool.select_dtypes(include=dtype)
def time_select_dtype_bool_exclude(self, dtype):
self.df_bool.select_dtypes(exclude=dtype)
def time_select_dtype_string_include(self, dtype):
self.df_string.select_dtypes(include=dtype)
def time_select_dtype_string_exclude(self, dtype):
self.df_string.select_dtypes(exclude=dtype)
class CheckDtypes:
def setup(self):
self.ext_dtype = pd.Int64Dtype()
self.np_dtype = np.dtype("int64")
def time_is_extension_array_dtype_true(self):
is_extension_array_dtype(self.ext_dtype)
def time_is_extension_array_dtype_false(self):
is_extension_array_dtype(self.np_dtype)
from .pandas_vb_common import setup # noqa: F401 isort:skip
|
