Python uses Statsmodes to perform probability distribution fitting and parameter estimation, including Normal distribution, Poisson distribution, gamma distribution, etc
Environmental construction:
1. Install Statsmodes library: Use the pip command to install the Statsmodes library.
pip install statsmodels
Dependent class libraries:
-Pandas: Used for data processing and conversion.
-Numpy: Used for numerical calculations.
Dataset:
In this example, we will use the dataset 'long' that comes with Statsmodes. This dataset contains US economic data from 1947 to 1962.
python
import statsmodels.api as sm
#Loading the Longley dataset
data = sm.datasets.macrodata.load_pandas().data
Sample data:
`Data 'is a DataFrame that contains multiple variables, with the most important variable being' unemp '(unemployment rate).
The complete sample code is as follows:
python
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy.stats import t
def fit_normal_distribution(data):
mu, std = norm.fit(data)
return mu, std
def fit_poisson_distribution(data):
mu = poisson.fit(data)
return mu
def fit_gamma_distribution(data):
a, loc, scale = gamma.fit(data)
return a, loc, scale
#Loading the Longley dataset
data = sm.datasets.macrodata.load_pandas().data
#Select the data to be fitted
unemployment_rate = data["unemp"]
#Fitting Normal distribution
mu, std = fit_normal_distribution(unemployment_rate)
#Fitting Poisson distribution
mu_poisson = fit_poisson_distribution(unemployment_rate)
#Fitting gamma distribution
a, loc, scale = fit_gamma_distribution(unemployment_rate)
#Print fitting results
Print ("Normal distribution:")
Print ("mean:", mu)
Print ("Standard deviation:", std)
print("
")
Print ("Poisson distribution:")
Print ("mean:", mu_poisson)
print("
")
Print ("Gamma distribution:")
Print ("Shape parameters:", a)
Print ("positional parameter:", loc)
Print ("Scale parameter:", scale)
Please ensure that the required libraries are installed before running the code.