Metadata-Version: 2.1
Name: truefalsepython
Version: 0.1.2
Summary: PyPI package with some better syntax tools for python
Home-page: https://github.com/PasaOpasen/true-false-python
Author: Demetry Pascal
Author-email: qtckpuhdsa@gmail.com
Maintainer: ['Demetry Pascal']
License: UNKNOWN
Description: [![PyPI
        version](https://badge.fury.io/py/truefalsepython.svg)](https://pypi.org/project/truefalsepython/)
        [![GitHub issues](https://img.shields.io/github/issues/Naereen/StrapDown.js.svg)](https://github.com/PasaOpasen/true-false-python/issues) 
        [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](https://github.com/PasaOpasen/true-false-python/pulls)
        
        
        # true-false-python
        
        PyPI package with some better syntax tools for python
        
        ```
        pip install truefalsepython
        ```
        
        - [true-false-python](#true-false-python)
          - [Logical tools](#logical-tools)
          - [Little functions](#little-functions)
          - [R-like functions](#r-like-functions)
        
        ## Logical tools
        
        For `True` and `False` values there are equal constants (like it is in C-like languages or R) `TRUE, T, true` and `FALSE, F, false`; for `None` there is `NULL` constant:
        
        ```python
        from truefalsepython import TRUE, FALSE, T, F, true, false, NULL
        
        print(True == T) # True
        print(True == TRUE) # True
        print(True == true) # True
        
        
        print(False == F) # True
        print(False == FALSE) # True
        print(False == false) # True
        
        print(NULL) # None
        ```
        
        ## Little functions
        
        * `is_odd(number)`
        * `is_even(number)`
        * `is_number(object)`
        
        ## R-like functions
        
        For arrays there are several R-like functions:
        * `nrow` вЂ” returns number of rows
        * `ncol` вЂ” returns number of columns
        * `colMeans` вЂ” returns average for each column
        * `rowMeans` вЂ” returns average for each row
        * `colSums` вЂ” returns sums for each column
        * `rowSums` вЂ” returns sums for each row
        * `apply` вЂ” applies function `FUN` to dimension of `arr2D` array (for rows if `MARGIN == 1` and columns if `MARGIN == 2`) 
        * `lapply` вЂ” applies function `func` for each element in `array` (array/list or something else)
        * `sapply` вЂ” like `lapply` but returns numpy array
        * `sample` вЂ” it is `np.random.choice` but `replace = False` by default
        * `sample_int` вЂ” sample numbers from `0` to `n-1`
        
        Example of usage:
        
        ```python
        import numpy as np
        from truefalsepython import nrow, ncol, colMeans, rowMeans, colSums, rowSums, apply, lapply, sapply, sample, sample_int
        
        
        np.random.seed(1)
        
        # some 2D array
        random_matrix = np.random.randint(8, size = (5, 3))
        
        
        # how to get rows and cols counts
        print(nrow(random_matrix)) # 5
        
        print(ncol(random_matrix)) # 3
        
        # operations for each row/column
        print(rowMeans(random_matrix))
        # [4.         2.66666667 5.         0.33333333 5.33333333]
        
        print(colMeans(random_matrix))
        # [2.4 4.4 3.6]
        
        print(rowSums(random_matrix))
        # [12  8 15  1 16]
        
        print(colSums(random_matrix))
        # [12 22 18]
        
        # apply function (MARGIN is 1 for rows and 2 for columns)
        print(apply(random_matrix, MARGIN = 1, FUN = np.min))
        # [3 0 3 0 4]
        
        # as u can see, it's not necessary to use FUN returns only 1 number by vector
        print(apply(random_matrix, MARGIN = 2, FUN = np.sqrt))
        #[[2.23606798 0.         1.73205081 0.         2.        ]
        # [1.73205081 2.64575131 2.23606798 0.         2.64575131]
        # [2.         1.         2.64575131 1.         2.23606798]]
        
        some_arr = np.array([1, 2, 3, 5, 4, 3, 2])
        
        # returns list
        print(lapply(some_arr, lambda x: -x))
        # [-1, -2, -3, -5, -4, -3, -2]
        
        # returns numpy array
        print(sapply(some_arr, lambda x: -x))
        # [-1 -2 -3 -5 -4 -3 -2]
        
        # like np.random.choice but replace = False by default
        print(sample(some_arr, 4))
        # [5 3 2 1]
        
        # sample numbers from 0 to n-1
        print(sample_int(n = 100, size = 10))
        # [69 46 58 12 73 98 31 53 65 96]
        ```
        
        
Keywords: true,false,C,fun,funny,shit,r,tools
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
