semvec/pypi-packages huggingface dataset:
SpliceURL 1.1
Splice, Split and Modify URL
License: MIT
Keywords: URL

# SpliceURL
Splice, Split and Modify URL's python package

# Usage:

```
    一、安装
    #pip install SpliceURL

    二、使用
    """
    1、第一个参数必须是域名,domain=?;
    2、第二个参数是域名协议,scheme=?,默认是http;
    3、第三个参数是路径,path=?,默认是/;
    4、第四个参数是参数,params=?,默认是None;
    5、第五个参数是查询,query=?,默认是None,可传入字符串(如user=xxx&passwd=xxx)或字典(如{"user": "xxxx", "passwd": ""xxxx"});
    6、第六个参数是段位,fragment=?,默认是None.

    7、额外参数：
        接收ip,port组合字典,拼接ip格式的url,port默认80.
    Everything is ok, now run do().
    """

    >>> s=SpliceURL.Splice(domain='saintic.com')
    >>> s.do()
    'http://saintic.com/'

    >>> s=SpliceURL.Splice(ip="127.0.0.1").do()
    'http://127.0.0.1/'

    >>> SpliceURL.Splice(domain='saintic.com', query={"username": "tcw", "password": "xxx", "id": True}).do()
    'http://saintic.com/?username=tcw&password=xxx&id=True'

    >>> SpliceURL.Splice('saintic.com', "https", "api/blog", '', 'api=true&token=true', '20').do()
    'https://saintic.com/api/blog?api=true&token=true#20'

    可以参考demo.py
```

# ChangeLog
> v0.5
1. 增加domain+port形式URL。
2. 根据scheme设定默认port, http=80, https=443, 默认port不显示在URL中。

> v1.0
1. support python3
cmcl 0.1.2
A python library providing various utilities for use with CMCL software
License: MIT
Keywords: cmcl software utilities

CMCL Python Utilities
=======================

A package to provide various python routines relevant to CMCL's software packages, particularly the SRM Engine Suite and MoDS.

----

Contents as of 09/09/2016:
- Some elements of IO routines for SRM and MoDS files.
merge_closest 0.1b160919-3
Merge two pandas DataFrames using closest match
License: MIT
Keywords: pandas merge join closest approximate

## merge_closest
### Merge column(s) into DataFrame using closest-without-going-over lookup.

The `merge_closest` function mimics Excel's VLOOKUP function in approximate
match (range lookup) mode, with added benefits of ensuring the lookup table is
sorted and merging any subset of columns from the lookup table. It's
similar to a left join based on the `lookup_field` value that is closest to
the `data_field` value without going over. Only the first matching row from
`lookup` is merged; if `lookup_field` contains duplicates, it's up to the user
to drop duplicates in advance as appropriate. For any row in `data` whose
`data_field` value is less than all values in `lookup_field`, `lookup` values
in `result` will be missing.

By default, all columns in `lookup` other than `lookup_field` will be merged
with `data`. A specific list of columns from `lookup` to include or exclude
can be passed. If both `include_cols` and `exclude_cols` are provided,
`exclude_cols` is ignored. To include `lookup_field` in `result`, `lookup_field`
must be passed in `include_cols`, perhaps as `include_cols=lookup.columns`.

### This is my first Python package and first GitHub repo...

...and any comments or suggestions are most welcome.

### Use

    from merge_closest import merge_closest
    
    merge_closest(data, lookup, data_field, lookup_field,
                  include_cols=None, exclude_cols=None,
                  presorted=False)

### Parameters

**`data`** : pandas DataFrame

**`lookup`** : pandas DataFrame

**`data_field`** : name of column in `data`

**`lookup_field`** : name of column in `lookup`

**`include_cols`** : list, pandas Index, or single value; columns from
    `lookup` to include in `result`.
    If neither `include_cols` nor `exclude_cols` is provided, all columns
    from `lookup` except `lookup_field` are included in `result`.

**`exclude_cols`** : list, pandas Index, or single value; columns from
    `lookup` to exclude from `result`.
    By default, `lookup_field` will be excluded even if not in `exclude_cols`.
    To include `lookup_field`, pass it in `include_cols`.
    If both `include_cols` and `exclude_cols` are provided, `exclude_cols` is
    ignored.

**`presorted`** : boolean; unless set to True, `lookup` will be sorted
    ascending on `lookup_field` prior to merging.

### Returns

**`result`** : pandas DataFrame