%global _empty_manifest_terminate_build 0
Name: python-slicer
Version: 0.0.7
Release: 1
Summary: A small package for big slicing.
License: MIT License
URL: https://github.com/interpretml/slicer
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5f/1a/0388801c10441b1060f7e9dbb540544e602e6fe774359c6fa5c17df9679e/slicer-0.0.7.tar.gz
BuildArch: noarch
%description
# slicer [alpha]
*(Equal Contribution) Samuel Jenkins & Harsha Nori & Scott Lundberg*
**slicer** wraps tensor-like objects and provides a uniform slicing interface via `__getitem__`.
It supports many data types including:
[numpy](https://github.com/numpy/numpy) |
[pandas](https://github.com/pandas-dev/pandas) |
[scipy](https://docs.scipy.org/doc/scipy/reference/sparse.html) |
[pytorch](https://github.com/pytorch/pytorch) |
[list](https://github.com/python/cpython) |
[tuple](https://github.com/python/cpython) |
[dict](https://github.com/python/cpython)
And enables upgraded slicing functionality on its objects:
```python
# Handles non-integer indexes for slicing.
S(df)[:, ["Age", "Income"]]
# Handles nested slicing in one call.
S(nested_list)[..., :5]
```
It can also simultaneously slice many objects at once:
```python
# Gets first elements of both objects.
S(first=df, second=ar)[0, :]
```
This package has **0** dependencies. Not even one.
## Installation
Python 3.6+ | Linux, Mac, Windows
```sh
pip install slicer
```
## Getting Started
Basic anonymous slicing:
```python
from slicer import Slicer as S
li = [[1, 2, 3], [4, 5, 6]]
S(li)[:, 0:2].o
# [[1, 2], [4, 5]]
di = {'x': [1, 2, 3], 'y': [4, 5, 6]}
S(di)[:, 0:2].o
# {'x': [1, 2], 'y': [4, 5]}
```
Basic named slicing:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
ar = np.array([[5, 6], [7, 8]])
sliced = S(first=df, second=ar)[0, :]
sliced.first
# A 1
# B 2
# Name: 0, dtype: int64
sliced.second
# array([5, 6])
```
Real example:
```python
from slicer import Slicer as S
from slicer import Alias as A
data = [[1, 2], [3, 4]]
values = [[5, 6], [7, 8]]
identifiers = ["id1", "id1"]
instance_names = ["r1", "r2"]
feature_names = ["f1", "f2"]
full_name = "A"
slicer = S(
data=data,
values=values,
# Aliases are objects that also function as slicing keys.
# A(obj, dim) where dim informs what dimension it can be sliced on.
identifiers=A(identifiers, 0),
instance_names=A(instance_names, 0),
feature_names=A(feature_names, 1),
full_name=full_name,
)
sliced = slicer[:, 1] # Tensor-like parallel slicing on all objects
assert sliced.data == [2, 4]
assert sliced.instance_names == ["r1", "r2"]
assert sliced.feature_names == "f2"
assert sliced.values == [6, 8]
sliced = slicer["r1", "f2"] # Example use of aliasing
assert sliced.data == 2
assert sliced.feature_names == "f2"
assert sliced.instance_names == "r1"
assert sliced.values == 6
```
## Contact us
Raise an issue on GitHub, or contact us at interpret@microsoft.com
%package -n python3-slicer
Summary: A small package for big slicing.
Provides: python-slicer
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-slicer
# slicer [alpha]
*(Equal Contribution) Samuel Jenkins & Harsha Nori & Scott Lundberg*
**slicer** wraps tensor-like objects and provides a uniform slicing interface via `__getitem__`.
It supports many data types including:
[numpy](https://github.com/numpy/numpy) |
[pandas](https://github.com/pandas-dev/pandas) |
[scipy](https://docs.scipy.org/doc/scipy/reference/sparse.html) |
[pytorch](https://github.com/pytorch/pytorch) |
[list](https://github.com/python/cpython) |
[tuple](https://github.com/python/cpython) |
[dict](https://github.com/python/cpython)
And enables upgraded slicing functionality on its objects:
```python
# Handles non-integer indexes for slicing.
S(df)[:, ["Age", "Income"]]
# Handles nested slicing in one call.
S(nested_list)[..., :5]
```
It can also simultaneously slice many objects at once:
```python
# Gets first elements of both objects.
S(first=df, second=ar)[0, :]
```
This package has **0** dependencies. Not even one.
## Installation
Python 3.6+ | Linux, Mac, Windows
```sh
pip install slicer
```
## Getting Started
Basic anonymous slicing:
```python
from slicer import Slicer as S
li = [[1, 2, 3], [4, 5, 6]]
S(li)[:, 0:2].o
# [[1, 2], [4, 5]]
di = {'x': [1, 2, 3], 'y': [4, 5, 6]}
S(di)[:, 0:2].o
# {'x': [1, 2], 'y': [4, 5]}
```
Basic named slicing:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
ar = np.array([[5, 6], [7, 8]])
sliced = S(first=df, second=ar)[0, :]
sliced.first
# A 1
# B 2
# Name: 0, dtype: int64
sliced.second
# array([5, 6])
```
Real example:
```python
from slicer import Slicer as S
from slicer import Alias as A
data = [[1, 2], [3, 4]]
values = [[5, 6], [7, 8]]
identifiers = ["id1", "id1"]
instance_names = ["r1", "r2"]
feature_names = ["f1", "f2"]
full_name = "A"
slicer = S(
data=data,
values=values,
# Aliases are objects that also function as slicing keys.
# A(obj, dim) where dim informs what dimension it can be sliced on.
identifiers=A(identifiers, 0),
instance_names=A(instance_names, 0),
feature_names=A(feature_names, 1),
full_name=full_name,
)
sliced = slicer[:, 1] # Tensor-like parallel slicing on all objects
assert sliced.data == [2, 4]
assert sliced.instance_names == ["r1", "r2"]
assert sliced.feature_names == "f2"
assert sliced.values == [6, 8]
sliced = slicer["r1", "f2"] # Example use of aliasing
assert sliced.data == 2
assert sliced.feature_names == "f2"
assert sliced.instance_names == "r1"
assert sliced.values == 6
```
## Contact us
Raise an issue on GitHub, or contact us at interpret@microsoft.com
%package help
Summary: Development documents and examples for slicer
Provides: python3-slicer-doc
%description help
# slicer [alpha]
*(Equal Contribution) Samuel Jenkins & Harsha Nori & Scott Lundberg*
**slicer** wraps tensor-like objects and provides a uniform slicing interface via `__getitem__`.
It supports many data types including:
[numpy](https://github.com/numpy/numpy) |
[pandas](https://github.com/pandas-dev/pandas) |
[scipy](https://docs.scipy.org/doc/scipy/reference/sparse.html) |
[pytorch](https://github.com/pytorch/pytorch) |
[list](https://github.com/python/cpython) |
[tuple](https://github.com/python/cpython) |
[dict](https://github.com/python/cpython)
And enables upgraded slicing functionality on its objects:
```python
# Handles non-integer indexes for slicing.
S(df)[:, ["Age", "Income"]]
# Handles nested slicing in one call.
S(nested_list)[..., :5]
```
It can also simultaneously slice many objects at once:
```python
# Gets first elements of both objects.
S(first=df, second=ar)[0, :]
```
This package has **0** dependencies. Not even one.
## Installation
Python 3.6+ | Linux, Mac, Windows
```sh
pip install slicer
```
## Getting Started
Basic anonymous slicing:
```python
from slicer import Slicer as S
li = [[1, 2, 3], [4, 5, 6]]
S(li)[:, 0:2].o
# [[1, 2], [4, 5]]
di = {'x': [1, 2, 3], 'y': [4, 5, 6]}
S(di)[:, 0:2].o
# {'x': [1, 2], 'y': [4, 5]}
```
Basic named slicing:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
ar = np.array([[5, 6], [7, 8]])
sliced = S(first=df, second=ar)[0, :]
sliced.first
# A 1
# B 2
# Name: 0, dtype: int64
sliced.second
# array([5, 6])
```
Real example:
```python
from slicer import Slicer as S
from slicer import Alias as A
data = [[1, 2], [3, 4]]
values = [[5, 6], [7, 8]]
identifiers = ["id1", "id1"]
instance_names = ["r1", "r2"]
feature_names = ["f1", "f2"]
full_name = "A"
slicer = S(
data=data,
values=values,
# Aliases are objects that also function as slicing keys.
# A(obj, dim) where dim informs what dimension it can be sliced on.
identifiers=A(identifiers, 0),
instance_names=A(instance_names, 0),
feature_names=A(feature_names, 1),
full_name=full_name,
)
sliced = slicer[:, 1] # Tensor-like parallel slicing on all objects
assert sliced.data == [2, 4]
assert sliced.instance_names == ["r1", "r2"]
assert sliced.feature_names == "f2"
assert sliced.values == [6, 8]
sliced = slicer["r1", "f2"] # Example use of aliasing
assert sliced.data == 2
assert sliced.feature_names == "f2"
assert sliced.instance_names == "r1"
assert sliced.values == 6
```
## Contact us
Raise an issue on GitHub, or contact us at interpret@microsoft.com
%prep
%autosetup -n slicer-0.0.7
%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-slicer -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot - 0.0.7-1
- Package Spec generated