You are here : python_3 -> random## random

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random.betavariate() | Beta distribution |

random.choice() | Return a random element from the non-empty sequence seq |

random.expovariate() | Exponential distribution |

random.gammavariate() | Gamma distribution |

random.gauss() | Gaussian distribution |

random.getrandbits() | Returns a Python integer with k random bits |

random.getstate() | Return an object capturing the current internal state of the generator |

random.lognormvariate() | Log normal distribution |

random.normalvariate() | Normal distribution |

random.paretovariate() | Pareto distribution |

random.randint() | Return a random integer N such that a <= N <= b |

random.random() | Return the next random floating point number in the range [0 |

random.randrange() | Return a randomly selected element from range(start, stop, step) |

random.sample() | Return a k length list of unique elements chosen from the population sequence or set |

random.seed() | Initialize the random number generator |

random.setstate() | state should have been obtained from a previous call to getstate(), and setstate() restores the internal state of the generator to what it was at the time getstate() was called |

random.shuffle() | Shuffle the sequence x in place |

random.triangular() | Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds |

random.uniform() | Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a |

random.vonmisesvariate() | mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero |

random.weibullvariate() | Weibull distribution |