NumPy is a Python library that is used for scientific computing. It provides support for arrays and matrices, which are used for storing and manipulating large amounts of data. NumPy also provides a number of functions for working with data distributions. In this article, we will discuss NumPy data distribution and its various functions.
NumPy data distribution is a set of functions that are used for generating random numbers. These functions are used for simulating real-world scenarios where random events occur. The functions in NumPy data distribution are based on probability distributions. A probability distribution is a function that describes the likelihood of obtaining the possible values of a random variable. The functions in NumPy data distribution are used for generating random numbers that follow a specific probability distribution.
NumPy data distribution provides a number of functions for generating random numbers. Some of the commonly used functions are:
Let's take a look at some code examples to understand how NumPy data distribution works.
The numpy.random.normal() function is used for generating random numbers that follow a normal distribution. The normal distribution is a probability distribution that is symmetric around the mean. The function takes three arguments: loc, scale, and size. The loc argument specifies the mean of the distribution, the scale argument specifies the standard deviation of the distribution, and the size argument specifies the size of the output array.
<p>import numpy as np</p>
<p>import matplotlib.pyplot as plt</p>
<p>mu, sigma = 0, 0.1 # mean and standard deviation</p>
<p>s = np.random.normal(mu, sigma, 1000)</p>
<p>count, bins, ignored = plt.hist(s, 30, density=True)</p>
<p>plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ),</p>
<p>linewidth=2, color='r')</p>
<p>plt.show()</p>
The numpy.random.uniform() function is used for generating random numbers that follow a uniform distribution. The uniform distribution is a probability distribution where all values have an equal probability of occurring. The function takes three arguments: low, high, and size. The low argument specifies the lower bound of the distribution, the high argument specifies the upper bound of the distribution, and the size argument specifies the size of the output array.
<p>import numpy as np</p>
<p>s = np.random.uniform(0, 1, 1000)</p>
<p>print(s)</p>
The numpy.random.binomial() function is used for generating random numbers that follow a binomial distribution. The binomial distribution is a probability distribution that describes the number of successes in a fixed number of trials. The function takes three arguments: n, p, and size. The n argument specifies the number of trials, the p argument specifies the probability of success, and the size argument specifies the size of the output array.
<p>import numpy as np</p>
<p>s = np.random.binomial(10, 0.5, 1000)</p>
<p>print(s)</p>
The numpy.random.poisson() function is used for generating random numbers that follow a Poisson distribution. The Poisson distribution is a probability distribution that describes the number of events occurring in a fixed interval of time or space. The function takes two arguments: lam and size. The lam argument specifies the expected number of events in the interval, and the size argument specifies the size of the output array.
<p>import numpy as np</p>
<p>s = np.random.poisson(5, 1000)</p>
<p>print(s)</p>
NumPy data distribution provides a number of functions for generating random numbers that follow specific probability distributions. These functions are useful for simulating real-world scenarios where random events occur. By using NumPy data distribution, we can generate random numbers that follow a specific probability distribution and use them for various applications.