Piper

Piper is a python module to simplify data wrangling with pandas.

Combined with a Jupyter notebook a 'magic' command (%%piper), provides an SQL like syntax - similar to R's tidyverse and magrittr libraries.

The main functions are:

• select()
• where()
• group_by()
• summarise()
• order_by()

For other piper functionality, please see the Features section.

Alternatives

For a comprehensive alternative, please check out Michael Chow's siuba package.

Table of contents

• Installation
• Basic use
• Documentation
• Features
• Status
• Inspiration
• Contact

Installation

To install the package, enter the following:

pip install dpiper

Basic use

Within a Jupyter notebook cell, add the function below to returned for a given dataframe trimmed column text data.

def trim_columns(df):
    ''' Trim blanks for given dataframe '''

    str_cols = df.select_dtypes(include='object').columns

    for col in str_cols:
        df[col] = df[col].str.strip()

    return df

In standard pandas, we can combine the new function in a pipeline, along with filtering the input data as follows:

import pandas as pd
from piper.factory import get_sample_data

df = get_sample_data()

# Select all columns EXCEPT 'dates'
subset_cols = ['order_dates', 'regions', 'countries', 'values_1', 'values_2']

criteria1 = ~df['countries'].isin(['Italy', 'Portugal'])
criteria2 = df['values_1'] > 40
criteria3 = df['values_2'] < 25

df2 = (df[subset_cols][criteria1 & criteria2 & criteria3]
       .pipe(trim_columns)
       .sort_values('countries', ascending=False))

df2.head()

Result:

Using piper's %%piper magic command and using piper 'verbs'. Let's import the necessary functions:

from piper import piper
from piper.verbs import head, select, where, group_by, summarise, order_by

Using %%piper magic function, piper verbs can be 'piped' together along with standard functions like trim_columns() using the linking symbol '>>'

%%piper
get_sample_data()
>> trim_columns()
>> select('-regions')
>> where(""" ~countries.isin(['Italy', 'Portugal']) &
              values_1 > 40 &
              values_2 < 25 """)
>> order_by('countries', ascending=False)
>> head(5)

Features

• Simplifies working with data pipelines by implementing a set of common wrapper functions.
• Additional wrappers for exporting data to Excel files (using xlsxwriter)
• Provide access to databases with support for SQL based scripting and connections.

To-do list:

• TBD

Documentation

Further examples are available in these jupyter notebooks:

• piper demo jupyter notebooks

Status

Project has just started. I welcome any and all help to improve etc.

Inspiration

Pandas, numpy are amazing data analysis libraries. That said, I'm disappointed that I do not feel as productive in Python in terms of the ease of use of the R language and tidyverse suite of packages.

Contact

This is very much a personal library, in that its highly opinionated, flawed and probably of no use to anyone else :). However if it helps anyone else in their endeavours, that would be fantastic to hear about.

If you'd like to contact me, I'm miketarpey@gmx.net.