# Python – Random Sample Training and Test Data from dictionary

Sometimes, while working with Machine Learning Algorithm, we can have problem in which we need to differentiate the training and testing data randomly. This is very common problem and solution to it is desirable for Machine Learning domains. This article discusses approach to solve this without using external libraries.

Method : Using `keys() + random.randint()` + computations
This problem can be solved by using combination of above functions. In this, we perform the task of extraction of random keys using randint(), from the keys extracted using keys(). The logical computations are performed for getting the separated test and training data.

 `# Python3 code to demonstrate working of  ` `# Random Sample Training and Test Data ` `# Using keys() + randint() + computations ` `import` `random ` ` `  `# initializing dictionary ` `test_dict ``=` `{``'gfg'` `: ``4``, ``'is'` `: ``12``, ``'best'` `: ``6``, ``'for'` `: ``7``, ``'geeks'` `: ``10``} ` ` `  `# printing original dictionary ` `print``(``"The original dictionary is : "` `+` `str``(test_dict)) ` ` `  `# initializing ratio ` `test ``=` `40` `training ``=` `60` ` `  `# Random Sample Training and Test Data ` `# Using keys() + randint() + computations ` `key_list ``=` `list``(test_dict.keys()) ` ` `  `test_key_count ``=` `int``((``len``(key_list) ``/` `100``) ``*` `test) ` `test_keys ``=` `[random.choice(key_list) ``for` `ele ``in` `range``(test_key_count)] ` `train_keys ``=` `[ele ``for` `ele ``in` `key_list ``if` `ele ``not` `in` `test_keys] ` ` `  `testing_dict ``=` `dict``((key, test_dict[key]) ``for` `key ``in` `test_keys  ` `                                        ``if` `key ``in` `test_dict)  ` `training_dict ``=` `dict``((key, test_dict[key]) ``for` `key ``in` `train_keys  ` `                                        ``if` `key ``in` `test_dict)  ` ` `  `# printing result  ` `print``(``"The testing dictionary is : "` `+` `str``(testing_dict))  ` `print``(``"The training dictionary is : "` `+` `str``(training_dict))  `

Output :

The original dictionary is : {‘is’: 12, ‘gfg’: 4, ‘best’: 6, ‘for’: 7, ‘geeks’: 10}
The testing dictionary is : {‘is’: 12, ‘for’: 7}
The training dictionary is : {‘gfg’: 4, ‘best’: 6, ‘geeks’: 10}

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