baarutil 1.0.0

Utility functions for BAAR developers

This Custom Library is specifically created for the developers/useres who use BAAR. Which is a product of Allied Media Inc. (www.alliedmedia.com)

Author:

Souvik Roy	[sroy-2019]
Zhaoyu (Thomas) Xu	[xuzhaoyu]

Dependencies:

pandas==1.0.3 or above numpy==1.18.4 or above

Additional Info:

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"


Available functions and the examples are listed below:

1. read_convert(string), Output Data Type: list of dictionary 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"}]

2. write_convert(input_list), Output Data Type: string 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"

3. string_to_df(string, rename_cols, drop_dupes), Output Data Type: pandas DataFrame 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: | Column_1 | Column_2

---

0 | abc | def 1 | hello | world

4. df_to_string(input_df, rename_cols, drop_dupes), Output Data Type: string 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: | Column_1 | Column_2

---

0 | abc | def 1 | hello | world Output: "Column_1__=abc$$Column_2=def::Column_1=hello$$Column_2=__world"