baarutil 1.2.6

Utility functions for BAAR developers

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_vault(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.

This function can also be called from a Robot Framework Script by importing the baarutil library and using Decrypt Vault keyword. Upon initiation of this fuction, this will set the Log Level of the Robot Framework script to NONE for security reasons. The Developers have to use Set Log Level INFO in the robot script in order to restart the Log.

Input:  <<Encrypted Text>>
Output: <<Decrypted Text>>