baarutil 1.5.0

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 streamlined 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 function, 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>>

7. generate_password(password_size, upper, lower, digits, symbols, exclude_chars), Output Data Type: string

Attributes:

i. password_size: Password Length, Data Type = int, Default Value = 10, (Should be greater than 4)

ii. upper: Are Uppercase characters required?, Data Type = Bool (True/False), Default Value = True

iii. lower: Are Lowercase characters required?, Data Type = Bool (True/False), Default Value = True

iv. digits: Are Digits characters required?, Data Type = Bool (True/False), Default Value = True

v. symbols: Are Symbols/ Special characters required?, Data Type = Bool (True/False), Default Value = True

vi. exclude_chars: List of characters to be excluded from the final password, Data Type = List, Default Value = []

This function can also be called from a Robot Framework Script by importing the baarutil library and using Generate Password keyword. Upon initiation of this function, 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 (Optional):  <<Password Length>>, <<Uppercase Required?>>, <<Lowercase Required?>>, <<Digits Required?>>, <<Symbols Required?>>
Output: <<Password String>>

8. generate_report(data_df, file_name, path, file_type, detailed_report, replace_old_file, final_file_name_case, time_stamp, encoding, index, engine, max_new_files_count, sheet_name), Output Data Type: Bool or, Dictionary (based on the input value of detailed_report)

Attributes:

i. data_df: Input Dataframe, Data Type = pandas.DataFrame()

ii. file_name: Final file name, Data Type = str

iii. path: Final file path, Data Type = str, Default Value = Current working directory

iv. file_type: Final file extension/ file type, Data Type = str, Default Value = 'csv', Available Options = 'csv' or, 'xlsx'

v. detailed_report: Is detailed status of the run required?, Data Type = Bool (True/False), Default Value = False

vi. replace_old_file: Should the program replace the old files after each run? or keep creating new files (only works if the final file name is the same each time), Data Type = Bool (True/False)

vii. final_file_name_case: Font case of the final file name, Data Type = str, Default Value = 'unchanged', Available Options = 'upper' or, 'lower' or, 'unchanged'

viii. time_stamp: Time stamp at the end of the filename to make each file unique, Data Type = Bool (True/False), Default Value = False

ix. encoding: Encoding of the file, Data Type = str, Default Value = 'utf-8'

x. index: Dataframe index in the final file, Data Type = Bool (True/False), Default Value = False

xi. engine: Engine of the excelwriter for pandas to_excel function, Data Type = str, Default Value = 'openpyxl'

xii. max_new_files_count: Count of maximum new files if the replace_old_file is False, Data Type = int, Default Value = 100

xiii. sheet_name: Sheet name in the final excel, Data Type = str, Default Value = 'Sheet1'

This function can also be called from a Robot Framework Script by importing the baarutil library and using Generate Report.

Input:  Mandetory arguments ->  data_df, file_name
Output (if detailed_report==False):  True/ False
Output (if detailed_report==True):  {'file_path': <<Absolute path of the newly generated file>>, 'file_generation_status': True/ False, 'message': <<Detailed message>>, 'start_time': <<Start time when the function was initiated>>, 'end_time': <<End time when the function was completed>>} 

9. string_to_report(data, file_name, path, file_type, detailed_report, replace_old_file, final_file_name_case, time_stamp, encoding, index, engine, max_new_files_count, sheet_name), Output Data Type: Bool or, Dictionary (based on the input value of detailed_report, rename_cols, drop_dupes)

Attributes:

i. data: Input BAAR string, Data Type = str

ii. file_name: Final file name, Data Type = str

iii. path: Final file path, Data Type = str, Default Value = Current working directory

iv. file_type: Final file extension/ file type, Data Type = str, Default Value = 'csv', Available Options = 'csv' or, 'xlsx'

v. detailed_report: Is detailed status of the run required?, Data Type = Bool (True/False), Default Value = False

vi. replace_old_file: Should the program replace the old files after each run? or keep creating new files (only works if the final file name is the same each time), Data Type = Bool (True/False)

vii. final_file_name_case: Font case of the final file name, Data Type = str, Default Value = 'unchanged', Available Options = 'upper' or, 'lower' or, 'unchanged'

viii. time_stamp: Time stamp at the end of the filename to make each file unique, Data Type = Bool (True/False), Default Value = False

ix. encoding: Encoding of the file, Data Type = str, Default Value = 'utf-8'

x. index: Dataframe index in the final file, Data Type = Bool (True/False), Default Value = False

xi. engine: Engine of the excelwriter for pandas to_excel function, Data Type = str, Default Value = 'openpyxl'

xii. max_new_files_count: Count of maximum new files if the replace_old_file is False, Data Type = int, Default Value = 100

xiii. sheet_name: Sheet name in the final excel, Data Type = str, Default Value = 'Sheet1'

xiv. rename_cols: Dictionary that contains old column names and new column names mapping, Data Type = Dictionary, Default Value = {}

xv. drop_dupes: Drop duplicate rows from the final dataframe, Data Type = Bool, Default Value = False

This function can also be called from a Robot Framework Script by importing the baarutil library and using String To Report.

Input:  Mandetory arguments ->  data (BAAR String: Column_1__=__abc__$$__Column_2__=__def__::__Column_1__=__hello__$$__Column_2__=__world), file_name
Output (if detailed_report==False):  True/ False
Output (if detailed_report==True):  {'file_path': <<Absolute path of the newly generated file>>, 'file_generation_status': True/ False, 'message': <<Detailed message>>, 'start_time': <<Start time when the function was initiated>>, 'end_time': <<End time when the function was completed>>} 

10. clean_directory(path, remove_directory), Output Data Type: boolean

Attributes:

i. path: Absolute paths of the target directories seperated by "|", Data Type = str

ii. remove_directory: Should the nested directories be deleted?, Data Type = Bool (True/False), Default Value = False

This function can also be called from a Robot Framework Script by importing the baarutil library and using Clean Directory keyword.

Input:  "C:/Path1|C:/Path2|C:/Path3"
Output: True/False