Metadata-Version: 2.1
Name: jtatisctic
Version: 0.0.2
Summary: data analysis and statictic tools
Home-page: https://github.com/cavadsalman/jtatistic
Author: Javad Salman
Author-email: aetimpani@yandex.com
License: UNKNOWN
Description: # jtatistic is a Python module for solving problems relevant to statistic topics
        ## BASIC USAGE
        ```python3
        # ------------------- Code ------------------- #
        data = [ random.randint(1, 10)  for i in range(10)]
        dataset = Dataset(data)
        print(data)
        print(dataset)
        print(f'{dataset.variance=},\n{dataset.deviation=},\n{dataset.coefficient=}')
        
        # ------------------- Output ------------------- #
        [5, 9, 5, 6, 10, 4, 6, 10, 2, 8]
        <Dataset [N=10 Mean=6.500 Median=6.000 Mode=False]>
        dataset.variance=7.166666666666667,
        dataset.deviation=2.6770630673681683,
        dataset.coefficient=0.41185585651817974
        ```
        ## COEFFICIENT
        ```python3
        # ------------------- Code ------------------- #
        l = [1,2,3,4,5,6]
        v = [i*2 for i in l]
        dl = Dataset(l)
        dv = Dataset(v)
        print(f'{dl.variance=} {dv.variance=}')
        print(f'{dl.coefficient=} {dv.coefficient=}')
        
        # ------------------- Output ------------------- #
        dl.variance=3.5 dv.variance=14.0
        dl.coefficient=0.5345224838248488 dv.coefficient=0.5345224838248488
        ```
        
        ## SKEW
        ```python3
        # ------------------- Code ------------------- #
        data = [1,2,2,2,3,3,3,4,4,5]
        dataset = Dataset(data)
        print(f'{dataset.mean=},\n{dataset.median=}')
        print(f'{dataset.is_right_skew()=},\n{dataset.is_left_skew()=},\n{dataset.is_skew()=}')
        
        # ------------------- Output ------------------- #
        dataset.mean=2.9,
        dataset.median=3.0
        dataset.is_right_skew()=False,
        dataset.is_left_skew()=True,
        dataset.is_skew()=True
        ```
        
        ## COVVARIANCE
        ```python3
        # ------------------- Code ------------------- #
        l = [2,4,6,8,10]
        v = [1,3,5,7,9]
        v = [9,7,5,3,1]
        v = [9,1,5,3,7]
        dl = Dataset(l)
        dv = Dataset(v)
        
        print(f'{covariance(dl, dv)=}')
        print(f'{correlation(dl, dv)=}')
        print(dl/dv, dl//dv)
        
        # ------------------- Output ------------------- #
        covariance(dl, dv)=-1.8
        correlation(dl, dv)=-0.060000000000000005
        -1.8 -0.060000000000000005
        ```
        
        ## NORMAL DISTRIBUTION
        ```python3
        # ------------------- Code ------------------- #
        data =   [1,2,2,3,3,3,4,4,4,4,5,5,5,6,6,7]
        dataset = Dataset(data)
        print(f'{dataset.mean=} {dataset.median=} {dataset.mode}')
        print(f'{dataset.is_normal_distribution()=}')
        
        # ------------------- Output ------------------- #
        dataset.mean=4.0 dataset.median=4.0 4
        dataset.is_normal_distribution()=True
        ```
        
        ## STANDART NORMAL DISTRIBUTION 
        ```python3
        # ------------------- Code ------------------- #
        l = [1,2,2,3,3,3,4,4,5]
        dataset = Dataset(l)
        standart_dataset = dataset.get_standart()
        
        print(f'{dataset.args=}\n{standart_dataset.args=}')
        print(f'{dataset.mean=} {standart_dataset.mean=}')
        print(f'{dataset.median=} {standart_dataset.median=}')
        print(f'{dataset.mode=} {standart_dataset.mode=}')
        print(f'{dataset.variance=} {standart_dataset.variance=}')
        
        # ------------------- Output ------------------- #
        dataset.args=[1, 2, 2, 3, 3, 3, 4, 4, 5]
        standart_dataset.args=[-1.6329931618554523, -0.8164965809277261, -0.8164965809277261, 0.0, 0.0, 0.0, 0.8164965809277261, 0.8164965809277261, 1.6329931618554523]
        dataset.mean=3.0 standart_dataset.mean=0.0
        dataset.median=3 standart_dataset.median=0.0
        dataset.mode=3 standart_dataset.mode=0.0
        dataset.variance=1.5 standart_dataset.variance=1.0
        ```
        
        ## PROVING CETNRAL LIMIT THEOREM WITH JTATISTIC
        ```python3
        # ------------------- Code ------------------- #
        student_heights = [random.randint(150, 195) for i in range(10000)]
        pop = Dataset(student_heights)
        sample_distribution = [Dataset(random.sample(pop.args, 100)) for i in range(10000)]
        sd_dataset = SampleSet(sample_distribution)
        print(sd_dataset.mean, pop.mean, '\n', sd_dataset.variance, pop.variance)
        print(sd_dataset, sd_dataset.standart_error)
        
        # ------------------- Output ------------------- #
        172.53898100000046 172.5413
        1.783505122151223 178.9789922092179
        <SampleSet [N=10000 Mean=172.53898 Standart Error=1.15563]> 1.1556294217595244
        ```
        
Keywords: python,statistic,dataset
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
