py-toolbelt 0.2.3

PyToolBelt - Extend your built-in methods.

Py ToolBelt

This module contains basic functions for built-in datatypes for execute trivial tasks. This functions are available as built-in datatype methods.

Installation

Install package using pip -> pip install py-toolbelt

Usage

Import the module and activate toolbelt import py_toolbelt py_toolbelt.activate() After activation, custom functions are available as built-in methods. For example - Before activation, in python console, create a list variable list_object = [1, 2, 3] and type list_object. and press tab to get prompt of available methods, you see -

>>> list_object.
l.append(   l.clear(    l.copy(     l.count(    l.extend(   l.index(    l.insert(   l.pop(      l.remove(   l.reverse(  l.sort(

After activation -

>>> list_object.
l.append(         l.count(          l.filter(         l.insert(         l.pop(            l.reverse(        l.to_tuple(
l.clear(          l.count_all(      l.flat_list(      l.intersection(   l.remove(         l.sort(           l.transform(
l.compact(        l.execute(        l.get_index(      l.is_none(        l.remove_index(   l.to_dict(        l.unique(
l.copy(           l.extend(         l.index(          l.is_not_none(    l.remove_values(  l.to_set(

To deactivate the toolbelt py_toolbelt.deactivate()

Available Functions

List

unique()

Eliminate duplicate elements from list.
note - elements should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Example

>>> list_obj = [1, 2, 3, 3, 2]
>>> print(list_obj.unique())
[1, 2, 3]

to_set()

Convert list to set.
note - elements should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Example

>>> list_obj = [1, 2, 3, 3, 2]
>>> print(list_obj.to_set())
{1, 2, 3}

to_tuple()

Convert list to tuple.
note - elements should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Example

>>> list_obj = [1, 2, 3, 3, 2]
>>> print(list_obj.to_tuple())
(1, 2, 3, 3, 2)

to_dict(key_function=None, value_function=None)

Convert list to dict with with keys using key_function and values using value_function. Argument

• key_function- function, any number of argument are accepted. (required)
• value_function - function, any number of arguments are accepted. (required)

Example

>>> list_obj = [1, 2, 3, 4]
>>> list_obj.to_dict(key_function=lambda x: x, value_function=lambda y: y * 2)
{1: 1, 2: 4, 3: 6, 4: 8}

remove_index(index=[], **kwargs)

Remove indexes from list based on given index values in index argument and given condition keyword argument. **note - ** condition is applicable on indexes, not on the elements of list Arguments

• index - list / int, if single int is given then remove that index, if list is given then remove indexes given in list. (optional, default: [])

Keyword arguments

• condition - function, if condition function satisfy for index number then it will be removed.
• cast - data_type, cast the elements of list.

Example

>>> list_obj = ['hello', 'this', 'is', 'a', 'list']
>>> list_obj.remove_index(1)
['hello', 'is', 'a', 'list']

>>> list_obj.remove_index([0, 1, 2])
['a', 'list']

>>> list_obj.remove_index(condition=lambda x: x % 2 == 0) # even indexes will be removed
['is', 'list']

>>> list_obj = [1, 2, 3, 4]
>>> list_obj.remove_index([1, 2], condition=lambda x: x % 2 == 0, cast=str)
['4']

remove_values(values=[], **kwargs)

Remove elements from list based on given values in index argument and given condition keyword argument.

Arguments

• values - list / any data type, if any other data type is given (except list) then it exclude all occurences, if list is given then exclude elements if it exist in given list. (optional, default: [])

Keyword arguments

• condition - function, if condition function satisfy for list element then it will be removed.
• cast - data_type, cast the elements of list.

Example

>>> list_obj = ['hello', 'this', 'is', 'a', 'list']
>>> list_obj.remove_values('this')
['hello', 'is', 'a', 'list']

>>> list_obj.remove_values(['hello', 'this', 'is'])
['a', 'list']

>>> list_obj.remove_values(condition=lambda x: x == 'hello')
['this', 'is', 'a', 'list']

>>> list_obj = [1, 2, 3, 4]
>>> list_obj.remove_values([1, 2], condition=lambda x: x % 2 == 0, cast=str)
['4']

filter(condition, **kwargs)

Filter list with given condition function. It is equivalent to - [element for element in list_object if condition(element)] Arguments

• condition - function, if condition function not satisfy for list element then it will be excluded. (required)

Keyword arguments

• cast - data_type, cast the elements of list.

Example

>>> list_obj = [1, 2, 3, 4, 5, 6]
>>> list_obj.filter(lambda x: x > 3)
[4, 5, 6]

>>> list_obj.filter(lambda x: x <= 3, cast=float)
[1.0. 2.0. 3.0]

compact()

Exclude None or empty list / dict / set / tuple from list.

Example

>>> list_obj = [1, 2, 3, None, [], {}]
>>> list_obj.compact()
[1, 2, 3]

transform(element_function)

Transform the elements of list.

Arguments

• element_function - function, list elements are passed to given function. (required)

Example

>>> list_obj = [1, 2, 3, 4]
>>> list_obj.transform(lambda x: x ** 2)
[1, 4, 9, 16]

>>> list_obj = [*enumerate(list_object)]
>>> list_obj.transform(lambda x, y: x + y)
[1, 3, 5, 7]

count_all()

Count occurences of elements in list.
note - elements should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Example

>>> list_obj = [1, 2, 2, 3, 3, 4]
>>> list_obj.count_all()
{1: 1, 2: 2, 3: 2, 4: 1}

intersection(target_list)

Give list of common elements.
note - elements should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Example

>>> list_obj = [1, 2, 2, 3, 3, 4]
>>> list_obj.intersection([2, 4, 5, 6, 7])
[2, 4]

flatten()

Convert N-Dimention list into single flat 1-D list.

Example

>>> list_obj = [1, [2, 2], 3, [[[3]]], 4]
>>> list_obj.flatten()
[1, 2, 2, 3, 3, 4]

get_index(index, default_value=None)

Fetch given index from list, if given index is out of range then return default_value.

Arguments

• index - int, (required)
• default_value - any data type, (optional, default: None)

Example

>>> list_obj = [1, 2, 3, 4, 5]
>>> list_obj.get_index(2)
3

>>> list_obj.get_index(10, 'N/A')
N/A

execute(execute_function)

Execute function using elements of list.

Arguments

• execute_function - function, it can accept any number of arguments (required)

Example

>>> list_obj = [1, 2, 3, 4, 5]
>>> new_list = []
>>> list_obj.execute(lambda x: new_list.append(x * 3))
>>> new_list
[3, 6, 9, 12, 15]

>>> list_obj.execute(lambda x: print(x))
1
2
3
4
5

Dict

filter(condition, **kwargs)

Filter dict based on keys and values. Arguments

• condition - function, function must accept 2 parameters, 1st parameter for key and 2nd parameter for value. (required)

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3}
>>> dict_obj.filter(lambda key, value: value > 1)
{'b': 2, 'c': 3}

transform(key_function=None, value_function=None)

Transform dict keys and values according to given functions. Arguments

• key_function - function, function can accept any number of arguments. (optional, default: None)
• value_function - function, function can accept any number of arguments. (optional, default: None)

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3}
>>> dict_obj.transform(key_function=lambda x: 'a' + x)
{'aa': 1, 'ab': 2, 'ac': 3}

>>> dict_obj.transform(value_function=lambda x: x ** 2)
{'a': 1, 'b': 4, 'c': 9}

>>> dict_obj = {'a': (1, 2), 'b': (3, 4), 'c': (5, 6)}
>>> dict_obj.transform(key_function=lambda key: key + '_sum', value_function=lambda x, y: x + y)
{'a_sum': 3, 'b_sum': 7, 'c_sum': 11}

keys_list()

Return keys of dict as list.

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3}
>>> dict_obj.keys_list()
['a', 'b', 'c']

values_list()

Return values of dict as list.

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3}
>>> dict_obj.values_list()
[1, 2, 3]

remove(key_function=None, value_function=None, operator='and')

Remove dict keys based on given key_function and value_fucntion and operation between key_funciton and value_function which is by default and operation. Arguments

• key_function - function, function can accept any number of arguments. (optional, default: None)
• value_function - function, function can accept any number of arguments. (optional, default: None)
• operator - str, accepted string - ['and', 'or'], (optional, default='and')

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3}
>>> dict_obj.remove(key_function=lambda x: x == 'b')
{'a': 1, 'c': 3}

>>> dict_object.remove(key_function=lambda key: key in ['b', 'c'], value_fucntion=lambda value: value % 2 == 1)
{'a': 1, 'b': 2}

>>> dict_object.remove(key_function=lambda key: key in ['b', 'c'], value_fucntion=lambda value: value % 2 == 1, operator='or')
{'a': 1}

reverse(keep_duplicate=False)

Reverse the dict, set values as keys and keys as values. If keep_duplicate is set true then it will merge duplicate value as list.
note - dict values should be hashable datatype i.e elements must be str, int, float, tuple, bool only.

Arguments

• keep_duplicate - bool, (optional, default: False)

Example

>>> dict_obj = {'a': 1, 'b': 2, 'c': 3, 'd': 3}
>>> dict_obj.reverse()
{1: 'a', 2: 'b', 3: 'd'}

>>> dict_obj.reverse(keep_duplicate=True)
{1: 'a', 2: 'b', 3: ['c', 'd']}

deep_merge(target_dict, max_depth=None)

It will merge given target_dict with given max_depth. If max_depth is None then it go down to maximum depth for merging. Arguments

• target_dict - dict, (required)
• max_depth - int, int greater than 0, (optional, default: None)

Example

>>> dict_obj = {'a': 1, 'b': {'c': {'d': 3}, 'e': 2}}
>>> dict_obj.deep_merge({'g': 4, 'b': {'c': {'d': 5}}})
{'a': 1, 'b': {'c': {'d': [3, 5]}, 'e': 2}, 'g': 4}

>>> dict_obj.deep_merge({'g': 4, 'b': {'c': {'d': 5}}}, max_depth=1)
{'a': 1, 'b': [{'c': {'d': 3}, 'e': 2}, {'c': {'d': 5}}], 'g': 4}

Set

to_list()

Convert set to list.

Tuple

to_list()

Convert tuple to list.

Int & Float

is_positive(**kwargs)

Return True if number is positive else return False. If on_true or on_false keyword arguments are present then it will return keyword argument value respectively.

Keyword arguments

• on_true - any data type, (optional, default: True)
• on_false - any data type, (optional, default: False)

Example

>>> num = 1
>>> num.is_positive()
True
>>> num.is_positive(on_true='Yes')
Yes
>>> num = -9
>>> num.is_positive(on_false='No')
No
>>> num.is_positive(on_true='Yes')
False

is_negetive(**kwargs)

Return True if number is negetive else return False. If on_true or on_false keyword arguments are present then it will return keyword argument value respectively.

Keyword arguments

• on_true - any data type, (optional, default: True)
• on_false - any data type, (optional, default: False)

Example

>>> num = 1
>>> num.is_negetive()
False
>>> num.is_negetive(on_true='Yes')
False
>>> num = -9
>>> num.is_negetive(on_false='No')
True
>>> num.is_negetive(on_true='Yes', on_false='No')
Yes

is_zero(**kwargs)

Return True if number is equal to zero else return False. If on_true or on_false keyword arguments are present then it will return keyword argument value respectively.

Keyword arguments

• on_true - any data type, (optional, default: True)
• on_false - any data type, (optional, default: False)

Example

>>> num = 1
>>> num.is_zero()
False
>>> num.is_zero(on_true='Yes')
False
>>> num = 0
>>> num.is_zero(on_false='No')
True
>>> num.is_zero(on_true='Yes', on_false='No')
Yes

safe_divide(denominator, default=math.nan)

It ensure if denominator is zero then return default value otherwise return divided value. Arguments

• denominator - int / float, (required)
• default - any data type, (optional, default: math.nan)

Example

>>> a = 4
>>> a.safe_divide(2)
2.0
>>> a.safe_divide(0)
nan
>>> a.safe_divide(0, 0)
0
>>> a.safe_divde(0, 'N/A')
N/A

Others

This is common to all data types -

is_none()

Return True if object is None else return False. If on_true or on_false keyword arguments are present then it will return keyword argument value respectively.

Keyword arguments

• on_true - any data type, (optional, default: True)
• on_false - any data type, (optional, default: False)

Example

>>> a, b, c, d = 2, 'hello', [], None
>>> a.is_none()
False
>>> a.is_none(on_false=a + 1)
3
>>> b.is_none(on_true="Yes")
False
>>> d.is_none()
True

is_not_none()

Return True if object is not None else return False. If on_true or on_false keyword arguments are present then it will return keyword argument value respectively.

Keyword arguments

• on_true - any data type, (optional, default: True)
• on_false - any data type, (optional, default: False)

Example

>>> a, b, c, d = 2, 'hello', [], None
>>> a.is_not_none()
True
>>> a.is_not_none(on_false=a + 1)
True
>>> b.is_not_none(on_true="Yes")
Yes
>>> d.is_not_none()
False

MIT License

Copyright (c) 2021 Soami Charan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.