you can get cdf easily. so pdf via cdf import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
import scipy.stats
def setGridLine(ax):
#http://jonathansoma.com/lede/data-studio/matplotlib/adding-grid-lines-to-a-matplotlib-chart/
ax.set_axisbelow(True)
ax.minorticks_on()
ax.grid(which='major', linestyle='-', linewidth=0.5, color='grey')
ax.grid(which='minor', linestyle=':', linewidth=0.5, color='#a6a6a6')
ax.tick_params(which='both', # Options for both major and minor ticks
top=False, # turn off top ticks
left=False, # turn off left ticks
right=False, # turn off right ticks
bottom=False) # turn off bottom ticks
data1 = np.random.normal(0,1,1000000)
x=np.sort(data1)
y=np.arange(x.shape[0])/(x.shape[0]+1)
f2 = scipy.interpolate.interp1d(x, y,kind='linear')
x2 = np.linspace(x[0],x[-1],1001)
y2 = f2(x2)
y2b = np.diff(y2)/np.diff(x2)
x2b=(x2[1:]+x2[:-1])/2.
f3 = scipy.interpolate.interp1d(x, y,kind='cubic')
x3 = np.linspace(x[0],x[-1],1001)
y3 = f3(x3)
y3b = np.diff(y3)/np.diff(x3)
x3b=(x3[1:]+x3[:-1])/2.
bins=np.arange(-4,4,0.1)
bins_centers=0.5*(bins[1:]+bins[:-1])
cdf = scipy.stats.norm.cdf(bins_centers)
pdf = scipy.stats.norm.pdf(bins_centers)
plt.rcParams["font.size"] = 18
fig, ax = plt.subplots(3,1,figsize=(10,16))
ax[0].set_title("cdf")
ax[0].plot(x,y,label="data")
ax[0].plot(x2,y2,label="linear")
ax[0].plot(x3,y3,label="cubic")
ax[0].plot(bins_centers,cdf,label="ans")
ax[1].set_title("pdf:linear")
ax[1].plot(x2b,y2b,label="linear")
ax[1].plot(bins_centers,pdf,label="ans")
ax[2].set_title("pdf:cubic")
ax[2].plot(x3b,y3b,label="cubic")
ax[2].plot(bins_centers,pdf,label="ans")
for idx in range(3):
ax[idx].legend()
setGridLine(ax[idx])
plt.show()
plt.clf()
plt.close()
View Discussion Improve Article Save Article ReadDiscussView Discussion Improve Article Save Article Prerequisites: - Matplotlib
- Numpy
- Scipy
- Statistics
Normal Distribution is a probability function used in statistics that tells about how the data values are distributed. It is the most
important probability distribution function used in statistics because of its advantages in real case scenarios. For example, the height of the population, shoe size, IQ level, rolling a die, and many more. The probability density function of normal or Gaussian distribution is given by: Probability Density Function Where, x is the variable, mu is the mean, and sigma standard deviation Modules Needed- Matplotlib is python’s data visualization library which is widely used for the
purpose of data visualization.
- Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.
- Scipy is a python library that is useful in solving many mathematical equations and algorithms.
- Statistics module provides
functions for calculating mathematical statistics of numeric data.
Functions used- To calculate mean of the data
Syntax: mean(data) - To calculate standard deviation of the data
Syntax: stdev(data) - To calculate normal probability density of the data norm.pdf is used, it refers to the normal probability density function which is a module in scipy library that uses the above probability density function to
calculate the value.
Syntax: norm.pdf(Data, loc, scale)
Here, loc parameter is also known as the mean and the scale parameter is also known as standard deviation. Approach- Import module
- Create data
- Calculate mean and deviation
- Calculate normal probability density
- Plot using above calculated values
- Display plot
Below is the implementation. Python3
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import statistics
x_axis = np.arange(-20, 20, 0.01)
mean = statistics.mean(x_axis)
sd = statistics.stdev(x_axis)
plt.plot(x_axis, norm.pdf(x_axis, mean, sd))
plt.show()
Output: The output of above code
How do you plot a Gaussian function in Python?
SOLUTION:. # normal_curve.py import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm # if using a Jupyter notebook, inlcude: %matplotlib inline.. # define constants mu = 998.8 sigma = 73.10 x1 = 900 x2 = 1100.. # calculate the z-transform z1 = ( x1 - mu ) / sigma z2 = ( x2 - mu ) / sigma.. x = np.. How do you plot a Gaussian distribution curve?
Now that you know the essentials, let's move from theory to practice.. Getting Started.. Step #1: Find the mean.. Step #2: Find the standard deviation.. Step #3: Set up the x-axis values for the curve.. Step #4: Compute the normal distribution values for every x-axis value.. Step #5: Create a scatter plot with smooth lines.. What is Gaussian distribution Python?
Normal Distribution with Python Example
A normal distribution can be thought of as a bell curve or Gaussian Distribution which typically has two parameters: mean and standard deviation (SD). The parameter used to measure the variability of observations around the mean is called standard deviation.
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