Metadata-Version: 2.1
Name: baarutil
Version: 1.2.3
Summary: Utility functions for BAAR developers
Home-page: https://github.com/Allied-Media/baarutil
Author: Zhaoyu Xu, Souvik Roy
Author-email: zhaoyu.xu@alliedmedia.com, souvik.roy@alliedmedia.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/Allied-Media/baarutil/issues
Description: # Baarutil
        
        **This Custom Library is specifically created for the developers/users who use BAAR. Which is a product of [Allied Media Inc](https://www.alliedmedia.com/).**
        
        <h2>
        Authors:
        </h2>
        
        
        **Souvik Roy  [sroy-2019](https://github.com/sroy-2019)**
        
        **Zhaoyu (Thomas) Xu  [xuzhaoyu](https://github.com/xuzhaoyu)**
        
        
        
        <h2>
        Additional Info:
        </h2>
        
        The string structure that follows is a streamline structure that the developers/users follow throughout an automation workflow designed in BAAR:
        ~~~
        "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
        ~~~
        
        <h2>
        Available functions and the examples are listed below:
        </h2>
        
        <h3>
        1.  read_convert(string), Output Data Type: list of dictionary
        </h3>
        
        **Attributes:**
        
          *i.  **string:** Input String, Data Type = String*
        
        ~~~
        Input:  "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
        Output: [{"Column_1":"abc", "Column_2":"def"}, {"Column_1":"hello", "Column_2":"world"}]
        ~~~
        
        <h3>
        2.  write_convert(input_list), Output Data Type: string
        </h3>
        
        **Attributes:**
        
          *i.  **input_list:** List that contains the Dictionaries of Data, Data Type = List*
        
        ~~~
        Input:  [{"Column_1":"abc", "Column_2":"def"}, {"Column_1":"hello", "Column_2":"world"}]
        Output: "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
        ~~~
        
        <h3>
        3.  string_to_df(string, rename_cols, drop_dupes), Output Data Type: pandas DataFrame
        </h3>
        
        **Attributes:**
        
          *i.  **string:** Input String, Data Type = String*
        
          *ii. **rename_cols:**  Dictionary that contains old column names and new column names mapping, Data Type = Dictionary, Default Value = {}*
        
          *iii.  **drop_dupes:** Drop duplicate rows from the final dataframe, Data Type = Bool, Default Value = False*
        
        ~~~
        Input:  "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
        ~~~
        
        Output:
        <table>
          <thead>
            <tr>
              <th>Column_1</th>
              <th>Column_2</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>abc</td>
              <td>def</td>
            </tr>
            <tr>
              <td>hello</td>
              <td>world</td>
            </tr>
          </tbody>
        </table>
        
        <h3>
        4.  df_to_string(input_df, rename_cols, drop_dupes), Output Data Type: string
        </h3>
        
        **Attributes:**
        
          *i. **input_df:** Input DataFrame, Data Type = pandas DataFrame*
        
          *ii. **rename_cols:**  Dictionary that contains old column names and new column names mapping, Data Type = Dictionary, Default Value = {}*
        
          *iii. **drop_dupes:** Drop duplicate rows from the final dataframe, Data Type = Bool, Default Value = False*
        
        Input:
        <table>
          <thead>
            <tr>
              <th>Column_1</th>
              <th>Column_2</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>abc</td>
              <td>def</td>
            </tr>
            <tr>
              <td>hello</td>
              <td>world</td>
            </tr>
          </tbody>
        </table>
          
        ~~~
        Output: "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
        ~~~
        
        <h3>
        5.  df_to_listdict(input_df, rename_cols, drop_dupes), Output Data Type: list
        </h3>
        
        **Attributes:**
        
          *i. **input_df:** Input DataFrame, Data Type = pandas DataFrame*
        
          *ii. **rename_cols:**  Dictionary that contains old column names and new column names mapping, Data Type = Dictionary, Default Value = {}*
        
          *iii. **drop_dupes:** Drop duplicate rows from the final dataframe, Data Type = Bool, Default Value = False*
        
        Input:
        <table>
          <thead>
            <tr>
              <th>Column_1</th>
              <th>Column_2</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>abc</td>
              <td>def</td>
            </tr>
            <tr>
              <td>hello</td>
              <td>world</td>
            </tr>
          </tbody>
        </table>
        
        ~~~
        Output: [{"Column_1":"abc", "Column_2":"def"}, {"Column_1":"hello", "Column_2":"world"}]
        ~~~
        
        <h3>
        6.  decrypt_text(encrypted_message, config_file), Output Data Type: string
        </h3>
        
        **Attributes:**
        
          *i. **encrypted_message:** Encrypted Baar Vault Data, Data Type = string*
        
          *ii. **config_file:**  Keys, that needs to be provided by [Allied Media](https://www.alliedmedia.com/).*
        
        ~~~
        Input:  <<Encrypted Text>>
        Output: <<Decrypted Text>>
        ~~~
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6.8
Description-Content-Type: text/markdown
