# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.14.4
#   kernelspec:
#     display_name: Python 3
#     language: python
#     name: python3
# ---

import numpy as np

np.random.uniform(0,1)

# +
# np.random.uniform?
# -

np.random.normal(0.75)

# +
# np.random.normal?
# -

distribution=np.random.normal(0.75,size=1000)

np.sqrt(np.sum((np.mean(distribution)-distribution)**2)/len(distribution))

distribution=np.random.normal(0.75,size=1000)
np.sqrt(np.sum((np.mean(distribution)-distribution)**2)/len(distribution))

np.std(distribution)

import scipy.stats as stats
stats.kurtosis(distribution)

stats.kurtosis(distribution)

# ### Chi squared

chi_squared_df2=np.random.chisquare(2,size=1000)
stats.skew(chi_squared_df2)

chi_squared_df5=np.random.chisquare(5,size=10000)
stats.skew(chi_squared_df5)

# %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
output=plt.hist([chi_squared_df2,chi_squared_df5],bins=100,histtype='step',label=['2 degree of freedom','5 degree of freedom'])
plt.legend(loc='right upper')

# # hypothesis Testing

import pandas as pd
df=pd.read_csv('grades.csv')



StarcoderdataJupyterScriptsDedupFiltered