![]() ![]() So what now? Will we go for a hike?Īnother situation: Every week you play the lottery and dream of a far away island. Can we go for a hike in the afternoon or will it rain? The weather forecast tells us, that the probability of precipitation will be 30 %. We have to cope with it whenever we have to make a decision from various options. Statistics and probability calculation is all around us in real-life situations. Whatever his intentions might have been, we quoted him to show a "real" life example of statistics. Usually, there is no context given, so it is not clear, if he might have meant it as a "joke". This saying is attributed to Bill Clinton on umpteen websites. "Every American should have above average income, and my Administration is going to see they get it." ![]() Estimation of Corona cases with Python and Pandas.Net Income Method Example with Numpy, Matplotlib and Scipy.Expenses and income example with Pandas and Python. ![]() Accessing and Changing values of DataFrames.Image Processing Techniques with Python and Matplotlib.Image Processing in Python with Matplotlib.Adding Legends and Annotations in Matplotlib.Reading and Writing Data Files: ndarrays.Matrix Arithmetics under NumPy and Python.Numpy Arrays: Concatenating, Flattening and Adding Dimensions.Instructor-led training courses by Bernd Klein Import numpy as np import matplotlib.pyplot as plt mu = 10.0 sigma = 2.0 data = np.random.randn(100000) * sigma mu hx, hy, _ = plt.hist(data, bins=50, normed=1,color="lightblue") plt.ylim(0.0,max(hx) 0.05) plt.title('Generate random numbers \n from a normal distribution with python') plt.grid() plt.savefig("numpy_random_numbers_normal_distribution.png", bbox_inches='tight') plt.Live Python classes by highly experienced instructors: If we know how to generate random numbers from a standard normal distribution, it is possible to generate random numbers from any normal distribution with the formula $$X = Z * \sigma \mu$$ where Z is random numbers from a standard normal distribution, $\sigma$ the standard deviation $\mu$ the mean. Import numpy as np import matplotlib.pyplot as plt data = np.random.randn(100000) hx, hy, _ = plt.hist(data, bins=50, normed=1,color="lightblue") plt.ylim(0.0,max(hx) 0.05) plt.title('Generate random numbers \n from a standard normal distribution with python') plt.grid() plt.savefig("numpy_random_numbers_stantard_normal_distribution.png", bbox_inches='tight') plt.show() Create a matrix of random numbers from a normal distribution How to generate random numbers from a normal (Gaussian) distribution in python ? To generate a random numbers from a standard normal distribution ($\mu_0=0$, $\sigma=1$) Gives ] Create a matrix of random numbers from a standard normal distribution Note: can be also used to generate random numbers for other range, for example : data = np.random.uniform(-10,5, size=(4,3)) print(data) To create a matrix of negative and positive random floats, a solution is to use data = np.random.uniform(-1,1, size=(6,2)) print(data) Gives for example ] Create a matrix of random floats between -1 and 1 Note: to generate for example random floats between 0 and 100 just multiply the matrix by 100: data = np.random.rand(4,3) * 100.0 print(data) To create a matrix of random floats between 0 and 1, a solution is to use data = np.random.rand(4,3) print(data) Will always gives the same random numbers: Create a matrix of random floats between 0 and 1 Then data = np.random.randint(-10,10,10) print(data) To understand why go see: the "The Hitchhiker's Guide to the Galaxy (travel guide)'s book") You can choose any seed number (It is common to use 42. To do that a solution is to use : np.ed(seed=42) Note: to make your work reproductible it is sometimes important to generate the same random numbers. Gives ] Create always the same random numbers To create a matrix of random integers, a solution is to use import numpy as np data = np.random.randint(-10,10,10) print(data)Īnother example with a matrix of size=(4,3) data = np.random.randint(-10,10,size=(4,3)) print(data) ![]()
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