Metadata-Version: 2.1
Name: mcmder
Version: 0.2.0
Summary: Yet Another Wrapper of Nysol M-Command in Python.
Home-page: https://github.com/yhay81/mcmder
Author: Yusuke Hayashi
Author-email: yusuke8h@gmail.com
License: MIT
Description: 
        # mcmder
        Yet Another Wrapper of Nysol M-Command in Python.
        
        
        ## Description
        
        High-speed processing (CSV) of large-scale structured data tables.
        
        To know more about Nysol and M-Command, see the official documents.
        
        
        ### What is Nysol and M-Command?
        [Nysol Official Page](http://www.nysol.jp/en/home)
        
        [M-Command GitHub](https://github.com/nysol/mcmd)
        
        
        ### How fast Nysol is?
        [Nysol Biz Page (Japanese)](http://www.nysol.biz/)
        
        
        ## Features
        - Create M-Command easily in python with method chaining. 
        - Execute M-Command without putting large data on memory.(csv to csv)
        - Optionally use pandas DataFrame as input and output.
        
        
        ## Requirement
        - [nysol/mcmd](https://github.com/nysol/mcmd#installation)
        - [pandas](https://pandas.pydata.org/)
        
        
        ## Usage
        
        ### From CSV File
        sample.csv
        ```
        a,b,c
        x,1,4
        y,2,9
        z,3,3
        ```
        
        ```
        >>> from mcmder import Mcmder
        >>> m = Mcmder('sample.csv')
        >>> mc = m.mcut(['a','c'])
        >>> mc.save('cut.csv')
        ```
        
        cut.csv
        ```
        a,c
        x,4
        y,9
        z,3
        ```
        
        ```
        >>> mc.dataframe
           a  c
        0  x  4
        1  y  9
        2  z  3
        ```
        
        ### From pandas DataFrame
        ```
        >>> from mcmder import Mcmder
        >>> import pandas as pd
        >>> import numpy as np
        >>> df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))
        >>> m = Mcmder(df)
        >>> mc = m.mcut(['A','C','D']).msel('${A}>0')
        >>> mc.dataframe
                  A         C         D
        0  0.251857  0.080099 -1.211923
        1  0.100167 -1.824585  0.051611
        2  0.890079  1.440997 -0.298709
        ```
        
        ## License
        
        [MIT](https://github.com/yhay81/mcmder/blob/master/LICENSE)
        
        
        ## Author
        
        [Yusuke Hayashi](https://github.com/yahy81)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
