text_plain_output_1.png image_output_1.png
from scipy.stats import norm

from scipy.stats import norm

from scipy.stats import uniform

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})

# import uniform distribution
from scipy.stats import uniform

# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)

# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])

# plot a historgram of the output
ax = sns.distplot(data_uniform,
                  bins=100,
                  kde_kws={"label": "KDE"},
                  hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');

from scipy.stats import norm

# generate random numbers from a normal distribution

sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)

# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])

# plot a histogram of the output
ax = sns.distplot(data_normal,
                  bins=100,
                  kde_kws={"label": "KDE"},
                  hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');

from scipy.stats import norm
data_normal = norm.rvs(size=100, loc=3, scale=2)
print(data_normal)