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':

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:

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 of Unix, or %>% operator of R's magrittr, sspipe provides the same functionality in python.

Install sspipe using pip:

pip install --upgrade sspipe

Then import it in your scripts.

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).

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 | <Pipe>, 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).

• Incompatibility with dict.items, dict.keys and dict.values:

  The objects returned by dict.keys(), dict.values() and dict.items() are called view objects. Python does not allow classes to override the | operator on these types. As a workaround, the / operator has been implemented for view objects. Example:

  # 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)

This library is inspired by, and depends on, the intelligent and concise work of 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.<original_name> 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 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:

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:

euler2 = (fib() | p.where(px % 2 == 0)
                | p.take_while(px < 4000000)
                | p.add())