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The normal way of distributing Python applications is with distutils. It's made both for distributing library type python modules, and python applications, although I don't know how it works on Windows. You would on Windows have to install Python separately if you use distutils, in any case.
- I highly recommend Pyinstaller , which supports all major platforms pretty seamlessly. Like py2exe and py2app, it produces a standard executable on...
- I think it’s also worth mentioning PEX (considering more the attention this question received and less the question itself). According to its own d...
- Fredrik Lundh's squeeze.py can create a single file that does not contain the Python interpreter, but instead contains bytecode. With the right arg...
- I think IronPython has a built in compiler for windows: I tried out Cx_Freeze and think it is by far the best .py to .exe (plus a few .dlls) compil...
- If you are distributing on windows, use an installer to install all the relevant files/interpeter whatever is needed. Distribute a setup.exe. That...
- The most convenient* cross-platform way of distributing python desktop applications is to rely on cross-platform conda package manager. There are s...
- For Windows, I found py2exe extremely easy to use. pip install distutils pip install py2exe All it takes is a few lines of code in setup.py: from...
- Uniform Distribution Function
- Uniform Distribution in Python
- Normal Distribution Function
- Normal Distribution in Python
- Gamma Distribution Function
- Gamma Distribution in Python
- Exponential Distribution Function
- Exponential Distribution in Python
- Poisson Distribution
- Poisson Distribution in Python
Perhaps one of the simplest and useful distribution is the uniform distribution. The probability distribution function of the continuous uniform distribution is: Since any interval of numbers of equal width has an equal probability of being observed, the curve describing the distribution is a rectangle, with constant height across the interval and ...
You can visualize uniform distribution in python with the help of a random number generator acting over an interval of numbers (a,b). You need to import the uniform function from scipy.statsmodule. The uniform function generates a uniform continuous variable between the specified interval via its loc and scale arguments. This distribution is consta...
Normal Distribution, also known as Gaussian distribution, is ubiquitous in Data Science. You will encounter it at many places especially in topics of statistical inference. It is one of the assumptions of many data science algorithms too. A normal distribution has a bell-shaped density curve described by its mean $μ$ and standard deviation $σ$. The...
You can generate a normally distributed random variable using scipy.stats module's norm.rvs() method. The loc argument corresponds to the mean of the distribution. scale corresponds to standard deviation and size to the number of random variates. If you want to maintain reproducibility, include a random_stateargument assigned to a number. You can v...
The gamma distribution is a two-parameter family of continuous probability distributions. While it is used rarely in its raw form but other popularly used distributions like exponential, chi-squared, erlang distributions are special cases of the gamma distribution. The gamma distribution can be parameterized in terms of a shape parameter $α = k$ an...
You can generate a gamma distributed random variable using scipy.stats module's gamma.rvs() method which takes shape parameter $a$ as its argument. When $a$ is an integer, gamma reduces to the Erlang distribution, and when $a=1$ to the exponential distribution. To shift distribution use the loc argument, to scale use scale argument, size decides th...
The exponential distribution describes the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. It has a parameter $λ$ called rate parameter, and its equation is described as : A decreasing exponential distribution looks like :
You can generate an exponentially distributed random variable using scipy.stats module's expon.rvs() method which takes shape parameter scale as its argument which is nothing but 1/lambda in the equation. To shift distribution use the loc argument, size decides the number of random variates in the distribution. If you want to maintain reproducibili...
Poisson random variable is typically used to model the number of times an event happened in a time interval. For example, the number of users visited on a website in an interval can be thought of a Poisson process. Poisson distribution is described in terms of the rate ($μ$) at which the events happen. An event can occur 0, 1, 2, … times in an inte...
You can generate a poisson distributed discrete random variable using scipy.stats module's poisson.rvs() method which takes $μ$ as a shape parameter and is nothing but the $λ$ in the equation. To shift distribution use the loc parameter. size decides the number of random variates in the distribution. If you want to maintain reproducibility, include...
Oct 11, 2023 · Learn how to measure data variability using Mean Absolute Deviation (MAD), Variance, and Median Absolute Deviation (MedAD) in Python. Explore their calculations, pros, and practical applications.
Dec 8, 2022 · In this article, some of the most useful probability distributions are introduced, but the focus will be on providing an intuitive understanding of each distribution and its mathematical properties. You will also learn how to generate different probability distributions in Python using the SciPy library. Update
- Reza Bagheri
May 17, 2021 · Probability distribution is a function that gives the probabilities of occurrence of different possible outcomes for an experiment. To illustrate, given a 6-sided dice, there are 6 possible outcomes it can be rolled: 1,2,3,4,5,6. If the dice is fair, then the probability across all possible outcomes is identical: 1/6.
Nov 30, 2020 · Probability distributions help model random phenomena, enabling us to obtain estimates of the probability that a certain event may occur. In this article, we’ll implement and visualize some of the commonly used probability distributions using Python.
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Aug 29, 2023 · A fundamental concept in probability theory, marginal probability shows the probability distribution of a single variable in a multidimensional system. Marginal probability isolates a variable’s solo likelihood as opposed to joint probability, which takes numerous factors into consideration.
