编程语言
首页 > 编程语言> > Python for Data Science - Delving into non-parametric methods using pandas and scipy

Python for Data Science - Delving into non-parametric methods using pandas and scipy

作者：互联网

Chapter 5 - Basic Math and Statistics

Segment 6 - Delving into non-parametric methods using pandas and scipy

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sb
from pylab import rcParams

import scipy
from scipy.stats import spearmanr

%matplotlib inline
rcParams['figure.figsize'] = 14, 7
plt.style.use('seaborn-whitegrid')

The Spearman Rank Correlation

address = '~/Data/mtcars.csv'

cars = pd.read_csv(address)
cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']

cars.head()
car_names mpg cyl disp hp drat wt qsec vs am gear carb
0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 
sb.pairplot(cars)

<seaborn.axisgrid.PairGrid at 0x7f1891238e80>

output_6_1--

X = cars[['cyl','vs','am','gear']]
sb.pairplot(X)

<seaborn.axisgrid.PairGrid at 0x7f188b9b8ba8>

output_7_1--

cyl = cars['cyl']
vs = cars['vs']
am = cars['am']
gear = cars['gear']

spearmanr_coefficient, p_value = spearmanr(cyl,vs)

print('Spearman Rank Correlation Coefficient %0.3f' % (spearmanr_coefficient))

Spearman Rank Correlation Coefficient -0.814

spearmanr_coefficient, p_value = spearmanr(cyl,am)

print('Spearman Rank Correlation Coefficient %0.3f' % (spearmanr_coefficient))

Spearman Rank Correlation Coefficient -0.522

spearmanr_coefficient, p_value = spearmanr(cyl,gear)

print('Spearman Rank Correlation Coefficient %0.3f' % (spearmanr_coefficient))

Spearman Rank Correlation Coefficient -0.564

Chi-square test for independence

table = pd.crosstab(cyl, am)

from scipy.stats import chi2_contingency
chi2, p, dof, expected = chi2_contingency(table.values)
print('Chi-square statistic %0.3f p_value %0.3f' % (chi2,p))

Chi-square statistic 8.741 p_value 0.013

table = pd.crosstab(cyl, vs)

from scipy.stats import chi2_contingency
chi2, p, dof, expected = chi2_contingency(table.values)
print('Chi-square statistic %0.3f p_value %0.3f' % (chi2,p))

Chi-square statistic 21.340 p_value 0.000

table = pd.crosstab(cyl, gear)

from scipy.stats import chi2_contingency
chi2, p, dof, expected = chi2_contingency(table.values)
print('Chi-square statistic %0.3f p_value %0.3f' % (chi2,p))

Chi-square statistic 18.036 p_value 0.001

标签：spearmanr,non,methods,Python,cars,value,chi2,import,cyl
来源： https://www.cnblogs.com/keepmoving1113/p/14285316.html