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
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
|
%global _empty_manifest_terminate_build 0
Name: python-mapply
Version: 0.1.21
Release: 1
Summary: Sensible multi-core apply function for Pandas
License: MIT
URL: https://github.com/ddelange/mapply
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d5/91/10e4fc128231b330a30d15e2e209c31c0c8151cca19487fd6b01264eb6b1/mapply-0.1.21.tar.gz
BuildArch: noarch
Requires: python3-pathos
Requires: python3-psutil
Requires: python3-tqdm
%description
# mapply
[](https://github.com/ddelange/mapply/actions?query=branch%3Amaster)
[](https://codecov.io/gh/ddelange/mapply)
[](https://pypi.org/project/mapply/)
[](https://pypi.org/project/mapply/)
[](https://pypistats.org/packages/mapply)
[](https://github.com/python/black)
[`mapply`](https://github.com/ddelange/mapply) provides a sensible multi-core apply function for Pandas.
### mapply vs. pandarallel vs. swifter
Where [`pandarallel`](https://pypi.org/project/pandarallel) relies on in-house multiprocessing and progressbars, and hard-codes 1 chunk per worker (which will cause idle CPUs when one chunk happens to be more expensive than the others), [`swifter`](https://pypi.org/project/swifter) relies on the heavy [`dask`](https://pypi.org/project/dask) framework for multiprocessing (converting to Dask DataFrames and back). In an attempt to find the golden mean, `mapply` is highly customizable and remains lightweight, using [`tqdm`](https://pypi.org/project/tqdm) for progressbars and leveraging the powerful [`pathos`](https://pypi.org/project/pathos) framework, which shadows Python's built-in multiprocessing module using [`dill`](https://pypi.org/project/dill) for universal pickling.
## Installation
This pure-Python, OS independent package is available on [PyPI](https://pypi.org/project/mapply):
```sh
$ pip install mapply
```
## Usage
[](https://mapply.readthedocs.io)
For documentation, see [mapply.readthedocs.io](https://mapply.readthedocs.io/en/stable/_code_reference/mapply.html).
```py
import pandas as pd
import mapply
mapply.init(
n_workers=-1,
chunk_size=100,
max_chunks_per_worker=8,
progressbar=False
)
df = pd.DataFrame({"A": list(range(100))})
# avoid unnecessary multiprocessing:
# due to chunk_size=100, this will act as regular apply.
# set chunk_size=1 to skip this check and let max_chunks_per_worker decide.
df["squared"] = df.A.mapply(lambda x: x ** 2)
```
## Development
[](https://github.com/carloscuesta/gitmoji-cli)
[](https://github.com/pre-commit/pre-commit)
Run `make help` for options like installing for development, linting, testing, and building docs.
%package -n python3-mapply
Summary: Sensible multi-core apply function for Pandas
Provides: python-mapply
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-mapply
# mapply
[](https://github.com/ddelange/mapply/actions?query=branch%3Amaster)
[](https://codecov.io/gh/ddelange/mapply)
[](https://pypi.org/project/mapply/)
[](https://pypi.org/project/mapply/)
[](https://pypistats.org/packages/mapply)
[](https://github.com/python/black)
[`mapply`](https://github.com/ddelange/mapply) provides a sensible multi-core apply function for Pandas.
### mapply vs. pandarallel vs. swifter
Where [`pandarallel`](https://pypi.org/project/pandarallel) relies on in-house multiprocessing and progressbars, and hard-codes 1 chunk per worker (which will cause idle CPUs when one chunk happens to be more expensive than the others), [`swifter`](https://pypi.org/project/swifter) relies on the heavy [`dask`](https://pypi.org/project/dask) framework for multiprocessing (converting to Dask DataFrames and back). In an attempt to find the golden mean, `mapply` is highly customizable and remains lightweight, using [`tqdm`](https://pypi.org/project/tqdm) for progressbars and leveraging the powerful [`pathos`](https://pypi.org/project/pathos) framework, which shadows Python's built-in multiprocessing module using [`dill`](https://pypi.org/project/dill) for universal pickling.
## Installation
This pure-Python, OS independent package is available on [PyPI](https://pypi.org/project/mapply):
```sh
$ pip install mapply
```
## Usage
[](https://mapply.readthedocs.io)
For documentation, see [mapply.readthedocs.io](https://mapply.readthedocs.io/en/stable/_code_reference/mapply.html).
```py
import pandas as pd
import mapply
mapply.init(
n_workers=-1,
chunk_size=100,
max_chunks_per_worker=8,
progressbar=False
)
df = pd.DataFrame({"A": list(range(100))})
# avoid unnecessary multiprocessing:
# due to chunk_size=100, this will act as regular apply.
# set chunk_size=1 to skip this check and let max_chunks_per_worker decide.
df["squared"] = df.A.mapply(lambda x: x ** 2)
```
## Development
[](https://github.com/carloscuesta/gitmoji-cli)
[](https://github.com/pre-commit/pre-commit)
Run `make help` for options like installing for development, linting, testing, and building docs.
%package help
Summary: Development documents and examples for mapply
Provides: python3-mapply-doc
%description help
# mapply
[](https://github.com/ddelange/mapply/actions?query=branch%3Amaster)
[](https://codecov.io/gh/ddelange/mapply)
[](https://pypi.org/project/mapply/)
[](https://pypi.org/project/mapply/)
[](https://pypistats.org/packages/mapply)
[](https://github.com/python/black)
[`mapply`](https://github.com/ddelange/mapply) provides a sensible multi-core apply function for Pandas.
### mapply vs. pandarallel vs. swifter
Where [`pandarallel`](https://pypi.org/project/pandarallel) relies on in-house multiprocessing and progressbars, and hard-codes 1 chunk per worker (which will cause idle CPUs when one chunk happens to be more expensive than the others), [`swifter`](https://pypi.org/project/swifter) relies on the heavy [`dask`](https://pypi.org/project/dask) framework for multiprocessing (converting to Dask DataFrames and back). In an attempt to find the golden mean, `mapply` is highly customizable and remains lightweight, using [`tqdm`](https://pypi.org/project/tqdm) for progressbars and leveraging the powerful [`pathos`](https://pypi.org/project/pathos) framework, which shadows Python's built-in multiprocessing module using [`dill`](https://pypi.org/project/dill) for universal pickling.
## Installation
This pure-Python, OS independent package is available on [PyPI](https://pypi.org/project/mapply):
```sh
$ pip install mapply
```
## Usage
[](https://mapply.readthedocs.io)
For documentation, see [mapply.readthedocs.io](https://mapply.readthedocs.io/en/stable/_code_reference/mapply.html).
```py
import pandas as pd
import mapply
mapply.init(
n_workers=-1,
chunk_size=100,
max_chunks_per_worker=8,
progressbar=False
)
df = pd.DataFrame({"A": list(range(100))})
# avoid unnecessary multiprocessing:
# due to chunk_size=100, this will act as regular apply.
# set chunk_size=1 to skip this check and let max_chunks_per_worker decide.
df["squared"] = df.A.mapply(lambda x: x ** 2)
```
## Development
[](https://github.com/carloscuesta/gitmoji-cli)
[](https://github.com/pre-commit/pre-commit)
Run `make help` for options like installing for development, linting, testing, and building docs.
%prep
%autosetup -n mapply-0.1.21
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-mapply -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.21-1
- Package Spec generated
|
