Various dictionary functionalities with dicter.

Name : dicter.py Author : E.Taskesen Contact : erdogant@gmail.com github : https://github.com/erdogant/XXX Licence : See licences

dicter.dicter.check_logger(verbose: [<class 'str'>, <class 'int'>] = 'info')

Check the logger.

dicter.dicter.clean_filename(name)

Clean filename.

dicter.dicter.compare(d1: dict, d2: dict)

Compare two dictionaries.

The second dictionary is compared with the first one, and results are shown accordingly.

Parameters:

  • d1 (dict) – Dictionary.

  • d2 (dict) – Dictionary.

Returns:

  • added (list) – added keys in d1 compared with d2.

  • removed (list) – Removed keys in d1 compared with d2.

  • modified (list) – Modified keys in d1 compared with d2.

  • same (list) – Similar keys in d1 compared with d2.

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> Example: Add
>>> d1 = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 'new in d2'}
>>> d2 = {'level_a': 1, 'level_b': {'a': 'hello world'}}
>>> out = dt.compare(d1, d2)
>>> print(out)
>>> #
>>> Example: Remove
>>> d1 = {'level_a': 1, 'level_b': {'a': 'hello world'}}
>>> d2 = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 'new in d2'}
>>> out = dt.compare(d1, d2)
>>> print(out)
>>> #
>>> Example: Modified
>>> d1 = {'level_a': 1, 'level_b': {'a': 'hello world'}}
>>> d2 = {'level_a': 1, 'level_b': {'a': 'modified'}}
>>> out = dt.compare(d1, d2)
>>> print(out['modified'])
>
dicter.dicter.depth(d: dict)

Depth of dictionary.

Parameters:

d (dict) – Input dictionary.

Returns:

depth – Depth of dictionary.

Return type:

int

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> Example dict
>>> d = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 3, 'level_d': {'a': 1, 'b': 2, 'c': {'e': 10}}, 'level_e': 2}
>>> n = dt.depth(d)
dicter.dicter.disable_tqdm()

Set the logger for verbosity messages.

dicter.dicter.flatten(d: dict)

Flatten dictionary to the deepest level and return key-value.

Parameters:

d (dict) – Input dictionary.

Returns:

d_flatten – Flattened dictionary containing key and value.

Return type:

list

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> # Example dict
>>> d = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 3, 'level_d': {'a': 1, 'b': 2, 'c': {'e': 10}}, 'level_e': 2}
>>> # Flatten dictionary
>>> dflat = dt.flatten(d)
>>> #
>>> print(d_flat)
dicter.dicter.get_nested(d: dict, path: list)

Get nested value from dictionary.

Parameters:

  • d (dict) – Input dictionary.

  • path (list) – The nested path of keys in a ordered list. Example: [‘level 1’, ‘level 2’]

Returns:

value –

  • value belonging to the key.

  • None: if the path does not exists.

Return type:

string or numerical

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> # Example dictionary
>>> d = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 3, 'level_d': {'a': 1, 'b': 2, 'c': {'e': 10}}, 'level_e': 2}
>>> # Get the value for the nested path for:
>>> value = dt.get_nested(d, ["level_a"])
>>> print(value)  # 1
>>> #
>>> # Get the value for the nested path for:
>>> value = dt.get_nested(d, ["level_b","a"])
>>> print(value)  # hello world
>>> #
>>> # Get the value for the nested path for:
>>> value = dt.get_nested(d, ["level_d","c", "e"])
>>> print(value)  # 10
>>> #
>>> # Get the value for the nested path for a list:
>>> l = [[[1,2,3],[10,20,30]]]
>>> value = dt.get_nested(l, [0,1,2])    # =>>> 30
>>> print(value)  # 30
dicter.dicter.is_key(d: dict, path: list) → bool

Check whether the dictionary contains the key.

Parameters:

  • d (dict) – Input dictionary.

  • path (list) – The nested path of keys in a ordered list. Example: [‘level 1’, ‘level 2’]

Returns:

True: Key exists. False: Key does not exist.

Return type:

Bool

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> # Example dictionary
>>> d = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 3, 'level_d': {'a': 1, 'b': 2, 'c': {'e': 10}}, 'level_e': 2}
>>> #
>>> # Get the value for the nested path for:
>>> value = dt.is_key(d, ["level_b","a"])
dicter.dicter.load(filepath: str, verbose: [<class 'str'>, <class 'int'>] = 'info')

Load dictionary from json file.

Parameters:

  • filepath (str) – path location to json.

  • verbose (str, optional) –

    Set the verbose messages using string or integer values.

    • [0, None, ‘silent’, ‘off’, ‘no’]: No message.

    • [10, ‘debug’]: Messages from debug level and higher.

    • [20, ‘info’]: Messages from info level and higher.

    • [30, ‘warning’]: Messages from warning level and higher.

    • [40, ‘critical’]: Messages from critical level and higher.

Returns:

dictionary – None or Dictionary.

Return type:

dict

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> d = {'level_a': None, 'level_b': {'a': 'hello world'}, 'level_c': True, 'level_d': 2.3, 'level_e': [[1,2,3], [1,2]]}
>>> filepath='c:/temp/test/dicter_save.json'
>>> # First save
>>> dt.save(d, filepath=filepath, overwrite=True)
>>> # Load
>>> d = dt.load(filepath)
dicter.dicter.mkdir(dirpath)

Create directory.

dicter.dicter.save(d: dict, filepath: str, overwrite: bool = False, verbose: [<class 'str'>, <class 'int'>] = 'info')

Save dictionary to json.

Parameters:

  • d (dict) – Dictionary.

  • filepath (str) – File path to store the dictionary.

  • overwrite (bool, default: False) – True: Overwrite if file exists. False: Do not overwrite existing files.

  • verbose (int, default is 'info' or 20) –

    Set the verbose messages using string or integer values.

    • 0, None, ‘silent’, ‘off’, ‘no’: No message.

    • 10,’debug’: Messages from debug level and higher.

    • 20,’info’: Messages from info level and higher.

    • 30,’warning’: Messages from warning level and higher.

    • 40,’critical’: Messages from critical level and higher.

Returns:

writeok – True: Succesful writing to json on disk. False: Nothing is written to disk.

Return type:

bool

Examples

>>> # Import dicter
>>> import dicter as dt
>
>>> d = {'level_a': None, 'level_b': {'a': 'hello world'}, 'level_c': True, 'level_d': 2.3, 'level_e': [[1,2,3], [1,2]]}
>>> dt.save(d, filepath='c:/temp/test/dicter_save.json', overwrite=True)
dicter.dicter.set_logger(verbose: [<class 'str'>, <class 'int'>] = 'info')

Set the logger for verbosity messages.

Parameters:

verbose ([str, int], default is 'info' or 20) –

Set the verbose messages using string or integer values.

  • [10, ‘debug’]: Messages from debug level and higher.

  • [20, ‘info’]: Messages from info level and higher.

  • [30, ‘warning’]: Messages from warning level and higher.

  • [50, ‘critical’]: Messages from critical level and higher.

  • [60, 0, None, ‘silent’, ‘off’, ‘no’]: No message.

Return type:

None.

Examples

>>> # Set the logger to warning
>>> set_logger(verbose='warning')
>>> # Test with different messages
>>> logger.debug("Hello debug")
>>> logger.info("Hello info")
>>> logger.warning("Hello warning")
>>> logger.critical("Hello critical")
dicter.dicter.set_nested(d: dict, path: list, value: str) → dict

Set nested value in dictionary.

Parameters:

  • d (dict) – Input dictionary.

  • path (list) – The nested path of keys in a ordered list.

  • value (str) – The value that needs to be set.

Return type:

Dictionary is updated without a return.

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> # Example: New path and value in dictionary.
>>> d = {}
>>> path = ['person', 'address', 'city']
>>> dt.set_nested(d, path, 'New York')
>>> # Print updated dictionary
>>> print(d)
>>> # {'person': {'address': {'city': 'New York'}}}
>>> #
>>> # Example: Update value in path.
>>> d = {'person': {'address': {'city': 'New York'}}}
>>> path = ['person', 'address', 'city']
>>> dt.set_nested(d, path, 'Amsterdam')
>>> # Print updated dictionary
>>> print(d)
>>> # {'person': {'address': {'city': 'Amsterdam'}}, 'person_2': {'address': {'city': 'Amsterdam'}}}
>>> #
>>> # Example: New path with value.
>>> d = {'person': {'address': {'city': 'New York'}}}
>>> path = ['person_2', 'address', 'city']
>>> dt.set_nested(d, path, 'Amsterdam')
>>> # Print updated dictionary
>>> print(d)
>>> # {'person': {'address': {'city': 'New York'}}, 'person_2': {'address': {'city': 'Amsterdam'}}}
dicter.dicter.to_df()

Convert to dataFrame.

Parameters:

  • d (dict) – Input dictionary.

  • sep (str, default is '->') – The seperator should be unique and is used to normalize the dictionary.

Return type:

None.

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> # Example 1
>>> d = {'Person 1': {'Country': 'NL', 'Income': {'Amsterdam': 100, 'The Haque': 50}, 'Age': 33, 'Type': 'bicycle'},
         'Person 2': {'Country': 'USA', 'Income': {'NY': 150, 'The Haque': 50}, 'Age': 23, 'Type': 'Taxi'},
         'Person 3': {'Country': 'DE', 'Income': {'BE': 90}, 'Age': 43, 'Type': 'Car'} }
>>> #
>>> # Convert to DataFrame
>>> df = dt.to_df(d)
>>> #
>>> #
>>> # Example 2
>>> d = {'Country': 'NL', 'Weight': {'Amsterdam': 100, 'The Haque': 50}, 'Age': 33}
>>> df = dt.to_df(d)
>>> #
dicter.dicter.traverse(d: dict, sep: str = '$->$', keys_as_list: bool = True, verbose: [<class 'str'>, <class 'int'>] = 'info') → list

Traverse through all paths in dictionary.

Parameters:

  • d (dict) – Input dictionary.

  • sep (str, default is '$->$') – The seperator should be unique and is used to normalize the dictionary. Change the sep in case a string exists with such pattern in the input dictionary.

  • keys_as_list (bool, default is True) – True: Output is structured list with keys. False: Output is list with keys seperated with the “sep” string.

  • verbose (int, default is 'info' or 20) –

    Set the verbose messages using string or integer values.

    • [0, None, ‘silent’, ‘off’, ‘no’]: No message.

    • [10, ‘debug’]: Messages from debug level and higher.

    • [20, ‘info’]: Messages from info level and higher.

    • [30, ‘warning’]: Messages from warning level and higher.

    • [40, ‘critical’]: Messages from critical level and higher.

Returns:

dic – list containing two columns: [[key], value].

Return type:

list

Examples

>>> # Import dicter
>>> import dicter as dt
>>> #
>>> Example dict
>>> d = {'level_a': 1, 'level_b': {'a': 'hello world'}, 'level_c': 3, 'level_d': {'a': 1, 'b': 2, 'c': {'e': 10}}, 'level_e': 2}
>>> # Traverse all paths in dictionary
>>> dlist = dt.traverse(d)
>>> #
>>> print(dlist)
>>> # [[['level_a'], 1],
>>> # [['level_c'], 3],
>>> # [['level_e'], 2],
>>> # [['level_b', 'a'], 'hello world'],
>>> # [['level_d', 'a'], 1],
>>> # [['level_d', 'b'], 2],
>>> # [['level_d', 'c', 'e'], 10]]
>>> #
>>> # Example to retrieve value from a dictionary
>>> value = dt.get_nested(d, dlist[3])
>>> # Look up for ['level_b', 'a']
>>> print(value)  # hello world
dicter.dicter.update(dictionary, key_to_update, new_value)

Update a nested dictionary for a specific key with a new value.

Parameters:

  • dictionary (dict) – The nested dictionary to update.

  • key_to_update (str) – The key to update.

  • new_value (any) – The new value to assign to the specified key.

Examples

>>> data_dict_template = {
...     'DEPARTURE': {
...         'SLOPE': 3,
...         'INTERSECTION': 'V4',
...         'TORA': '1234',
...         'CIRCUIT_ALTITUDE': '1000',
...         'TOWER': '122.108',
...         'alignment': 'concrete'
...     }
... }
>>> update(data_dict_template['DEPARTURE'], 'SLOPE', 5)
>>> data_dict_template['DEPARTURE']['SLOPE']
5

>>> update(data_dict_template['DEPARTURE'], 'INTERSECTION', 'V5')
>>> data_dict_template['DEPARTURE']['INTERSECTION']
'V5'

>>> update(data_dict_template['DEPARTURE'], 'TORA', '2000')
>>> data_dict_template['DEPARTURE']['TORA']
'2000'

>>> update(data_dict_template['DEPARTURE'], 'CIRCUIT_ALTITUDE', '1500')
>>> data_dict_template['DEPARTURE']['CIRCUIT_ALTITUDE']
'1500'

>>> update(data_dict_template['DEPARTURE'], 'TOWER', '122.109')
>>> data_dict_template['DEPARTURE']['TOWER']
'122.109'

>>> update(data_dict_template['DEPARTURE'], 'alignment', 'asphalt')
>>> data_dict_template['DEPARTURE']['alignment']
'asphalt'