%global _empty_manifest_terminate_build 0
Name: python-sspipe
Version: 0.1.17
Release: 1
Summary: Simple Smart Pipe Operator
License: MIT License
URL: https://sspipe.github.io/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d4/d1/89b35880c8f25d01a1802322567fd831fdd5b7d62ec3649df85bf3e3e05e/sspipe-0.1.17.tar.gz
BuildArch: noarch
Requires: python3-pipe
Requires: python3-check-manifest
Requires: python3-coverage
%description
[](http://pepy.tech/project/sspipe)
[](https://app.circleci.com/pipelines/github/sspipe/sspipe)
[](http://pypi.org/project/sspipe)
# Simple Smart Pipe
SSPipe is a python productivity-tool for rapid data manipulation in python.
It helps you break up any complicated expression into a sequence of
simple transformations, increasing human-readability and decreasing the
need for matching parentheses!
As an example, here is a single line code for reading students' data
from 'data.csv', reporting those in the class 'A19' whose score is more
than the average class score into 'report.csv':
```python
from sspipe import p, px
import pandas as pd
pd.read_csv('data.csv') | px[px['class'] == 'A19'] | px[px.score > px.score.mean()].to_csv('report.csv')
```
As another example, here is a single line code for plotting
sin(x) for points in range(0, 2*pi) where cos(x) is less than 0 in red color:
```python
from sspipe import p, px
import numpy as np
import matplotlib.pyplot as plt
np.linspace(0, 2*np.pi, 100) | px[np.cos(px) < 0] | p(plt.plot, px, np.sin(px), 'r')
# The single-line code above is equivalent to the following code without SSPipe:
# X = np.linspace(0, 2*np.pi, 100)
# X = X[np.cos(X) < 0]
# plt.plot(X, np.sin(X), 'r')
```
If you're familiar with
[`|` operator](https://en.wikipedia.org/wiki/Pipeline_(Unix))
of Unix, or
[`%>%` operator](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)
of R's magrittr, `sspipe` provides the same functionality in python.
### Installation and Usage
Install sspipe using pip:
```bash
pip install --upgrade sspipe
```
Then import it in your scripts.
```python
from sspipe import p, px
```
The whole functionality
of this library is exposed by two objects `p` (as a wrapper for functions to
be called on the piped object) and `px` (as a placeholder for piped object).
### Examples
| Description | Python expression using `p` and `px` | Equivalent python code |
| --- |:--- |:--- |
| Simple
function call | `"hello world!" \| p(print)` | `X = "hello world!"`
`print(X)` |
| Function call
with extra args | `"hello" \| p(print, "world", end='!')` | `X = "hello"`
`print(X, "world", end='!')` |
| Explicitly positioning
piped argument
with `px` placeholder | `"world" \| p(print, "hello", px, "!")` | `X = "world"`
`print("hello", X, "!")` |
| Chaining pipes | `5 \| px + 2 \| px ** 5 + px \| p(print)` | `X = 5`
`X = X + 2`
`X = X ** 5 + X`
`print(X)` |
| Tailored behavior
for builtin `map`
and `filter` | `(`
` range(5)`
` \| p(filter, px % 2 == 0)`
` \| p(map, px + 10)`
` \| p(list) \| p(print)`
`)` | `X = range(5)`
`X = filter((lambda x:x%2==0),X)`
`X = map((lambda x: x + 10), X)`
`X = list(X)`
`print(X)` |
| NumPy expressions | `range(10) \| np.sin(px)+1 \| p(plt.plot)` | `X = range(10)`
`X = np.sin(X) + 1`
`plt.plot(X)` |
| Pandas support | `people_df \| px.loc[px.age > 10, 'name']` | `X = people_df`
`X.loc[X.age > 10, 'name']` |
| Assignment | `people_df['name'] \|= px.str.upper()` | `X = people_df['name']`
`X = X.str.upper()`
`people_df['name'] = X` |
| Pipe as variable | `to_upper = px.strip().upper()`
`to_underscore = px.replace(' ', '_')`
`normalize = to_upper \| to_underscore`
`" ab cde " \| normalize \| p(print)` | `_f1 = lambda x: x.strip().upper()`
`_f2 = lambda x: x.replace(' ','_')`
`_f3 = lambda x: _f2(_f1(x))`
`X = " ab cde "`
`X = _f3(X)`
`print(X)` |
| Builtin
Data Structures | `2 \| p({px-1: p([px, p((px+1, 4))])})` | `X = 2`
`X = {X-1: [X, (X+1, 4)]}` |
### How it works
The expression `p(func, *args, **kwargs)` returns a `Pipe` object that overloads
`__or__` and `__ror__` operators. This object keeps `func` and `args` and `kwargs` until
evaluation of `x | `, when `Pipe.__ror__` is called by python. Then it will evaluate
`func(x, *args, **kwargs)` and return the result.
The `px` object is simply `p(lambda x: x)`.
Please notice that SSPipe does not wrap piped objects. On the other hand, it just wraps transforming functions. Therefore, when a variable like `x` is not an instance of `Pipe` class, after python evaluates `y = x | p(func)`, the resulting variable `y` has absolutely no trace of Pipe. Thus, it will be exactly the same object as if we have originally evaluated `y = func(x)`.
### Common Gotchas
* Incompatibility with `dict.items`, `dict.keys` and `dict.values`:
The objects returned by dict.keys(), dict.values() and dict.items() are
called [view objects](https://docs.python.org/3.3/library/stdtypes.html#dict-views).
Python does not allow classes to override the `|` operator on these types. As a workaround,
the `/` operator has been implemented for view objects. Example:
```python3
# WRONG ERRONEOUS CODE:
{1: 2, 3: 4}.items() | p(list) | p(print)
# CORRECT CODE (With / operator):
{1: 2, 3: 4}.items() / p(list) | p(print)
```
### Compatibility with JulienPalard/Pipe
This library is inspired by, and depends on, the intelligent and concise work of
[JulienPalard/Pipe](https://github.com/JulienPalard/Pipe). If you want
a single `pipe.py` script or a lightweight library that implements core
functionality and logic of SSPipe, Pipe is perfect.
SSPipe is focused on facilitating usage of pipes, by integration with
popular libraries and introducing `px` concept and overriding python
operators to make pipe a first-class citizen.
Every existing pipe implemented by JulienPalard/Pipe
library is accessible through `p.` and is compatible with SSPipe.
SSPipe does not implement any specific pipe function and delegates
implementation and naming of pipe functions to JulienPalard/Pipe.
For example, JulienPalard/Pipe's [example](https://github.com/JulienPalard/Pipe#introduction)
for solving "Find the sum of all the even-valued terms in Fibonacci which do not exceed four million."
can be re-written using sspipe:
```python
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
from sspipe import p, px
euler2 = (fib() | p.where(lambda x: x % 2 == 0)
| p.take_while(lambda x: x < 4000000)
| p.add())
```
You can also pass `px` shorthands to JulienPalard/Pipe API:
```python
euler2 = (fib() | p.where(px % 2 == 0)
| p.take_while(px < 4000000)
| p.add())
```
%package -n python3-sspipe
Summary: Simple Smart Pipe Operator
Provides: python-sspipe
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-sspipe
[](http://pepy.tech/project/sspipe)
[](https://app.circleci.com/pipelines/github/sspipe/sspipe)
[](http://pypi.org/project/sspipe)
# Simple Smart Pipe
SSPipe is a python productivity-tool for rapid data manipulation in python.
It helps you break up any complicated expression into a sequence of
simple transformations, increasing human-readability and decreasing the
need for matching parentheses!
As an example, here is a single line code for reading students' data
from 'data.csv', reporting those in the class 'A19' whose score is more
than the average class score into 'report.csv':
```python
from sspipe import p, px
import pandas as pd
pd.read_csv('data.csv') | px[px['class'] == 'A19'] | px[px.score > px.score.mean()].to_csv('report.csv')
```
As another example, here is a single line code for plotting
sin(x) for points in range(0, 2*pi) where cos(x) is less than 0 in red color:
```python
from sspipe import p, px
import numpy as np
import matplotlib.pyplot as plt
np.linspace(0, 2*np.pi, 100) | px[np.cos(px) < 0] | p(plt.plot, px, np.sin(px), 'r')
# The single-line code above is equivalent to the following code without SSPipe:
# X = np.linspace(0, 2*np.pi, 100)
# X = X[np.cos(X) < 0]
# plt.plot(X, np.sin(X), 'r')
```
If you're familiar with
[`|` operator](https://en.wikipedia.org/wiki/Pipeline_(Unix))
of Unix, or
[`%>%` operator](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)
of R's magrittr, `sspipe` provides the same functionality in python.
### Installation and Usage
Install sspipe using pip:
```bash
pip install --upgrade sspipe
```
Then import it in your scripts.
```python
from sspipe import p, px
```
The whole functionality
of this library is exposed by two objects `p` (as a wrapper for functions to
be called on the piped object) and `px` (as a placeholder for piped object).
### Examples
| Description | Python expression using `p` and `px` | Equivalent python code |
| --- |:--- |:--- |
| Simple
function call | `"hello world!" \| p(print)` | `X = "hello world!"`
`print(X)` |
| Function call
with extra args | `"hello" \| p(print, "world", end='!')` | `X = "hello"`
`print(X, "world", end='!')` |
| Explicitly positioning
piped argument
with `px` placeholder | `"world" \| p(print, "hello", px, "!")` | `X = "world"`
`print("hello", X, "!")` |
| Chaining pipes | `5 \| px + 2 \| px ** 5 + px \| p(print)` | `X = 5`
`X = X + 2`
`X = X ** 5 + X`
`print(X)` |
| Tailored behavior
for builtin `map`
and `filter` | `(`
` range(5)`
` \| p(filter, px % 2 == 0)`
` \| p(map, px + 10)`
` \| p(list) \| p(print)`
`)` | `X = range(5)`
`X = filter((lambda x:x%2==0),X)`
`X = map((lambda x: x + 10), X)`
`X = list(X)`
`print(X)` |
| NumPy expressions | `range(10) \| np.sin(px)+1 \| p(plt.plot)` | `X = range(10)`
`X = np.sin(X) + 1`
`plt.plot(X)` |
| Pandas support | `people_df \| px.loc[px.age > 10, 'name']` | `X = people_df`
`X.loc[X.age > 10, 'name']` |
| Assignment | `people_df['name'] \|= px.str.upper()` | `X = people_df['name']`
`X = X.str.upper()`
`people_df['name'] = X` |
| Pipe as variable | `to_upper = px.strip().upper()`
`to_underscore = px.replace(' ', '_')`
`normalize = to_upper \| to_underscore`
`" ab cde " \| normalize \| p(print)` | `_f1 = lambda x: x.strip().upper()`
`_f2 = lambda x: x.replace(' ','_')`
`_f3 = lambda x: _f2(_f1(x))`
`X = " ab cde "`
`X = _f3(X)`
`print(X)` |
| Builtin
Data Structures | `2 \| p({px-1: p([px, p((px+1, 4))])})` | `X = 2`
`X = {X-1: [X, (X+1, 4)]}` |
### How it works
The expression `p(func, *args, **kwargs)` returns a `Pipe` object that overloads
`__or__` and `__ror__` operators. This object keeps `func` and `args` and `kwargs` until
evaluation of `x | `, when `Pipe.__ror__` is called by python. Then it will evaluate
`func(x, *args, **kwargs)` and return the result.
The `px` object is simply `p(lambda x: x)`.
Please notice that SSPipe does not wrap piped objects. On the other hand, it just wraps transforming functions. Therefore, when a variable like `x` is not an instance of `Pipe` class, after python evaluates `y = x | p(func)`, the resulting variable `y` has absolutely no trace of Pipe. Thus, it will be exactly the same object as if we have originally evaluated `y = func(x)`.
### Common Gotchas
* Incompatibility with `dict.items`, `dict.keys` and `dict.values`:
The objects returned by dict.keys(), dict.values() and dict.items() are
called [view objects](https://docs.python.org/3.3/library/stdtypes.html#dict-views).
Python does not allow classes to override the `|` operator on these types. As a workaround,
the `/` operator has been implemented for view objects. Example:
```python3
# WRONG ERRONEOUS CODE:
{1: 2, 3: 4}.items() | p(list) | p(print)
# CORRECT CODE (With / operator):
{1: 2, 3: 4}.items() / p(list) | p(print)
```
### Compatibility with JulienPalard/Pipe
This library is inspired by, and depends on, the intelligent and concise work of
[JulienPalard/Pipe](https://github.com/JulienPalard/Pipe). If you want
a single `pipe.py` script or a lightweight library that implements core
functionality and logic of SSPipe, Pipe is perfect.
SSPipe is focused on facilitating usage of pipes, by integration with
popular libraries and introducing `px` concept and overriding python
operators to make pipe a first-class citizen.
Every existing pipe implemented by JulienPalard/Pipe
library is accessible through `p.` and is compatible with SSPipe.
SSPipe does not implement any specific pipe function and delegates
implementation and naming of pipe functions to JulienPalard/Pipe.
For example, JulienPalard/Pipe's [example](https://github.com/JulienPalard/Pipe#introduction)
for solving "Find the sum of all the even-valued terms in Fibonacci which do not exceed four million."
can be re-written using sspipe:
```python
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
from sspipe import p, px
euler2 = (fib() | p.where(lambda x: x % 2 == 0)
| p.take_while(lambda x: x < 4000000)
| p.add())
```
You can also pass `px` shorthands to JulienPalard/Pipe API:
```python
euler2 = (fib() | p.where(px % 2 == 0)
| p.take_while(px < 4000000)
| p.add())
```
%package help
Summary: Development documents and examples for sspipe
Provides: python3-sspipe-doc
%description help
[](http://pepy.tech/project/sspipe)
[](https://app.circleci.com/pipelines/github/sspipe/sspipe)
[](http://pypi.org/project/sspipe)
# Simple Smart Pipe
SSPipe is a python productivity-tool for rapid data manipulation in python.
It helps you break up any complicated expression into a sequence of
simple transformations, increasing human-readability and decreasing the
need for matching parentheses!
As an example, here is a single line code for reading students' data
from 'data.csv', reporting those in the class 'A19' whose score is more
than the average class score into 'report.csv':
```python
from sspipe import p, px
import pandas as pd
pd.read_csv('data.csv') | px[px['class'] == 'A19'] | px[px.score > px.score.mean()].to_csv('report.csv')
```
As another example, here is a single line code for plotting
sin(x) for points in range(0, 2*pi) where cos(x) is less than 0 in red color:
```python
from sspipe import p, px
import numpy as np
import matplotlib.pyplot as plt
np.linspace(0, 2*np.pi, 100) | px[np.cos(px) < 0] | p(plt.plot, px, np.sin(px), 'r')
# The single-line code above is equivalent to the following code without SSPipe:
# X = np.linspace(0, 2*np.pi, 100)
# X = X[np.cos(X) < 0]
# plt.plot(X, np.sin(X), 'r')
```
If you're familiar with
[`|` operator](https://en.wikipedia.org/wiki/Pipeline_(Unix))
of Unix, or
[`%>%` operator](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)
of R's magrittr, `sspipe` provides the same functionality in python.
### Installation and Usage
Install sspipe using pip:
```bash
pip install --upgrade sspipe
```
Then import it in your scripts.
```python
from sspipe import p, px
```
The whole functionality
of this library is exposed by two objects `p` (as a wrapper for functions to
be called on the piped object) and `px` (as a placeholder for piped object).
### Examples
| Description | Python expression using `p` and `px` | Equivalent python code |
| --- |:--- |:--- |
| Simple
function call | `"hello world!" \| p(print)` | `X = "hello world!"`
`print(X)` |
| Function call
with extra args | `"hello" \| p(print, "world", end='!')` | `X = "hello"`
`print(X, "world", end='!')` |
| Explicitly positioning
piped argument
with `px` placeholder | `"world" \| p(print, "hello", px, "!")` | `X = "world"`
`print("hello", X, "!")` |
| Chaining pipes | `5 \| px + 2 \| px ** 5 + px \| p(print)` | `X = 5`
`X = X + 2`
`X = X ** 5 + X`
`print(X)` |
| Tailored behavior
for builtin `map`
and `filter` | `(`
` range(5)`
` \| p(filter, px % 2 == 0)`
` \| p(map, px + 10)`
` \| p(list) \| p(print)`
`)` | `X = range(5)`
`X = filter((lambda x:x%2==0),X)`
`X = map((lambda x: x + 10), X)`
`X = list(X)`
`print(X)` |
| NumPy expressions | `range(10) \| np.sin(px)+1 \| p(plt.plot)` | `X = range(10)`
`X = np.sin(X) + 1`
`plt.plot(X)` |
| Pandas support | `people_df \| px.loc[px.age > 10, 'name']` | `X = people_df`
`X.loc[X.age > 10, 'name']` |
| Assignment | `people_df['name'] \|= px.str.upper()` | `X = people_df['name']`
`X = X.str.upper()`
`people_df['name'] = X` |
| Pipe as variable | `to_upper = px.strip().upper()`
`to_underscore = px.replace(' ', '_')`
`normalize = to_upper \| to_underscore`
`" ab cde " \| normalize \| p(print)` | `_f1 = lambda x: x.strip().upper()`
`_f2 = lambda x: x.replace(' ','_')`
`_f3 = lambda x: _f2(_f1(x))`
`X = " ab cde "`
`X = _f3(X)`
`print(X)` |
| Builtin
Data Structures | `2 \| p({px-1: p([px, p((px+1, 4))])})` | `X = 2`
`X = {X-1: [X, (X+1, 4)]}` |
### How it works
The expression `p(func, *args, **kwargs)` returns a `Pipe` object that overloads
`__or__` and `__ror__` operators. This object keeps `func` and `args` and `kwargs` until
evaluation of `x | `, when `Pipe.__ror__` is called by python. Then it will evaluate
`func(x, *args, **kwargs)` and return the result.
The `px` object is simply `p(lambda x: x)`.
Please notice that SSPipe does not wrap piped objects. On the other hand, it just wraps transforming functions. Therefore, when a variable like `x` is not an instance of `Pipe` class, after python evaluates `y = x | p(func)`, the resulting variable `y` has absolutely no trace of Pipe. Thus, it will be exactly the same object as if we have originally evaluated `y = func(x)`.
### Common Gotchas
* Incompatibility with `dict.items`, `dict.keys` and `dict.values`:
The objects returned by dict.keys(), dict.values() and dict.items() are
called [view objects](https://docs.python.org/3.3/library/stdtypes.html#dict-views).
Python does not allow classes to override the `|` operator on these types. As a workaround,
the `/` operator has been implemented for view objects. Example:
```python3
# WRONG ERRONEOUS CODE:
{1: 2, 3: 4}.items() | p(list) | p(print)
# CORRECT CODE (With / operator):
{1: 2, 3: 4}.items() / p(list) | p(print)
```
### Compatibility with JulienPalard/Pipe
This library is inspired by, and depends on, the intelligent and concise work of
[JulienPalard/Pipe](https://github.com/JulienPalard/Pipe). If you want
a single `pipe.py` script or a lightweight library that implements core
functionality and logic of SSPipe, Pipe is perfect.
SSPipe is focused on facilitating usage of pipes, by integration with
popular libraries and introducing `px` concept and overriding python
operators to make pipe a first-class citizen.
Every existing pipe implemented by JulienPalard/Pipe
library is accessible through `p.` and is compatible with SSPipe.
SSPipe does not implement any specific pipe function and delegates
implementation and naming of pipe functions to JulienPalard/Pipe.
For example, JulienPalard/Pipe's [example](https://github.com/JulienPalard/Pipe#introduction)
for solving "Find the sum of all the even-valued terms in Fibonacci which do not exceed four million."
can be re-written using sspipe:
```python
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
from sspipe import p, px
euler2 = (fib() | p.where(lambda x: x % 2 == 0)
| p.take_while(lambda x: x < 4000000)
| p.add())
```
You can also pass `px` shorthands to JulienPalard/Pipe API:
```python
euler2 = (fib() | p.where(px % 2 == 0)
| p.take_while(px < 4000000)
| p.add())
```
%prep
%autosetup -n sspipe-0.1.17
%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-sspipe -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot - 0.1.17-1
- Package Spec generated