{
"info": {
"author": "Mohammad Hossein Sekhavat",
"author_email": "sekhavat17@gmail.com",
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"classifiers": [
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"description": "![](\"https://sspipe.github.io/img/icon.png\")
\n\n[](http://pepy.tech/project/sspipe)\n[](https://travis-ci.org/sspipe/sspipe)\n[](http://pypi.org/project/sspipe)\n\n# Simple Smart Pipe\n\nSSPipe is a python productivity-tool for rapid data manipulation in python.\n\nIt helps you break up any complicated expression into a sequence of\nsimple transformations, increasing human-readability and decreasing the\nneed for matching parentheses! \n\nAs an example, here is a single line code for reading students' data\nfrom 'data.csv', reporting those in the class 'A19' whose score is more\nthan the average class score into 'report.csv':\n\n```python\nfrom sspipe import p, px\nimport pandas as pd\n\npd.read_csv('data.csv') | px[px['class'] == 'A19'] | px[px.score > px.score.mean()].to_csv('report.csv')\n```\n\nAs another example, here is a single line code for plotting\nsin(x) for points in range(0, 2*pi) where cos(x) is less than 0 in red color:\n\n```python\nfrom sspipe import p, px\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnp.linspace(0, 2*np.pi, 100) | px[np.cos(px) < 0] | p(plt.plot, px, np.sin(px), 'r')\n\n# The single-line code above is equivalent to the following code without SSPipe:\n# X = np.linspace(0, 2*np.pi, 100)\n# X = X[np.cos(X) < 0]\n# plt.plot(X, np.sin(X), 'r')\n```\n\nIf you're familiar with\n[`|` operator](https://en.wikipedia.org/wiki/Pipeline_(Unix))\nof Unix, or\n[`%>%` operator](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)\nof R's magrittr, `sspipe` provides the same functionality in python.\n\n### Installation and Usage\nInstall sspipe using pip:\n```bash\npip install --upgrade sspipe\n```\nThen import it in your scripts.\n\n```python\nfrom sspipe import p, px\n```\n\nThe whole functionality\nof this library is exposed by two objects `p` (as a wrapper for functions to\nbe called on the piped object) and `px` (as a placeholder for piped object).\n\n### Examples\n\n| Description | Python expression using `p` and `px` | Equivalent python code |\n| --- |:--- |:--- |\n| Simple
function call | `\"hello world!\" \\| p(print)` | `X = \"hello world!\"`
`print(X)` |\n| Function call
with extra args | `\"hello\" \\| p(print, \"world\", end='!')` | `X = \"hello\"`
`print(X, \"world\", end='!')` |\n| Explicitly positioning
piped argument
with `px` placeholder | `\"world\" \\| p(print, \"hello\", px, \"!\")` | `X = \"world\"`
`print(\"hello\", X, \"!\")` |\n| Chaining pipes | `5 \\| px + 2 \\| px ** 5 + px \\| p(print)` | `X = 5`
`X = X + 2`
`X = X ** 5 + X`
`print(X)` |\n| 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)` |\n| NumPy expressions | `range(10) \\| np.sin(px)+1 \\| p(plt.plot)` | `X = range(10)`
`X = np.sin(X) + 1`
`plt.plot(X)` |\n| Pandas support | `people_df \\| px.loc[px.age > 10, 'name']` | `X = people_df`
`X.loc[X.age > 10, 'name']` |\n| Assignment | `people_df['name'] \\|= px.str.upper()` | `X = people_df['name']`
`X = X.str.upper()`
`people_df['name'] = X` |\n| 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)` |\n| Builtin
Data Structures | `2 \\| p({px-1: p([px, p((px+1, 4))])})` | `X = 2`
`X = {X-1: [X, (X+1, 4)]}` |\n\n### How it works\n\nThe expression `p(func, *args, **kwargs)` returns a `Pipe` object that overloads \n`__or__` and `__ror__` operators. This object keeps `func` and `args` and `kwargs` until\nevaluation of `x | `, when `Pipe.__ror__` is called by python. Then it will evaluate\n`func(x, *args, **kwargs)` and return the result.\n\nThe `px` object is simply `p(lambda x: x)`.\n\n### Compatibility with JulienPalard/Pipe\n\nThis library is inspired by, and depends on, the intelligent and concise work of\n [JulienPalard/Pipe](https://github.com/JulienPalard/Pipe). If you want\n a single `pipe.py` script or a lightweight library that implements core\n functionality and logic of SSPipe, Pipe is perfect.\n\nSSPipe is focused on facilitating usage of pipes, by integration with\n popular libraries and introducing `px` concept and overriding python\n operators to make pipe a first-class citizen.\n\n Every existing pipe implemented by JulienPalard/Pipe\n library is accessible through `p.` and is compatible with SSPipe.\n SSPipe does not implement any specific pipe function and delegates\nimplementation and naming of pipe functions to JulienPalard/Pipe.\n\nFor example, JulienPalard/Pipe's [example](https://github.com/JulienPalard/Pipe#introduction)\nfor solving \"Find the sum of all the even-valued terms in Fibonacci which do not exceed four million.\"\ncan be re-written using sspipe:\n\n```python\ndef fib():\n a, b = 0, 1\n while True:\n yield a\n a, b = b, a + b\n\nfrom sspipe import p, px\n\neuler2 = (fib() | p.where(lambda x: x % 2 == 0)\n | p.take_while(lambda x: x < 4000000)\n | p.add())\n```\n\nYou can also pass `px` shorthands to JulienPalard/Pipe API:\n```python\neuler2 = (fib() | p.where(px % 2 == 0)\n | p.take_while(px < 4000000)\n | p.add())\n```\n\n\n",
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