Utility functions for BAAR developers
Project description
Baarutil
This Custom Library is specifically created for the developers/users who use BAAR. Which is a product of Allied Media Inc.
Authors:
Souvik Roy sroy-2019
Zhaoyu (Thomas) Xu xuzhaoyu
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 |
---|---|
abc | def |
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 |
---|---|
abc | def |
hello | world |
Output: "Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world"
5. df_to_listdict(input_df, rename_cols, drop_dupes), Output Data Type: list
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 |
---|---|
abc | def |
hello | world |
Output: [{"Column_1":"abc", "Column_2":"def"}, {"Column_1":"hello", "Column_2":"world"}]
6. decrypt_text(encrypted_message, config_file), Output Data Type: string
Attributes:
i. encrypted_message: Encrypted Baar Vault Data, Data Type = string
ii. config_file: Keys, that needs to be provided by Allied Media.
Input: <<Encrypted Text>>
Output: <<Decrypted Text>>
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