Chapter 5 - Basic Math and Statistics

Segment 5 - Starting with parametric methods in pandas and scipy

import pandas as pd
import numpy as np

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

import scipy
from scipy.stats.stats import pearsonr

%matplotlib inline
rcParams['figure.figsize'] = 8,4
plt.style.use('seaborn-whitegrid')

The Pearson 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']

sb.pairplot(cars)

<seaborn.axisgrid.PairGrid at 0x7ff9164e46d8>

X = cars[['mpg','hp','qsec','wt']]
sb.pairplot(X)

<seaborn.axisgrid.PairGrid at 0x7ff91133c438>

Using scipy to calculate the Pearson correlation coefficient

mpg = cars['mpg']
hp = cars['hp']
qsec = cars['qsec']
wt = cars['wt']

pearsonr_coefficient, p_value = pearsonr(mpg, hp)
print('PeasonR Correlation Coefficient %0.3f'%(pearsonr_coefficient))

PeasonR Correlation Coefficient -0.776

pearsonr_coefficient, p_value = pearsonr(mpg, qsec)
print('PeasonR Correlation Coefficient %0.3f'%(pearsonr_coefficient))

PeasonR Correlation Coefficient 0.419

pearsonr_coefficient, p_value = pearsonr(mpg, wt)
print('PeasonR Correlation Coefficient %0.3f'%(pearsonr_coefficient))

PeasonR Correlation Coefficient -0.868

Using pandas to calculate the Pearson correlation coefficient

corr = X.corr()
corr

	mpg	hp	qsec	wt
mpg	1.000000	-0.776168	0.418684	-0.867659
hp	-0.776168	1.000000	-0.708223	0.658748
qsec	0.418684	-0.708223	1.000000	-0.174716
wt	-0.867659	0.658748	-0.174716	1.000000

Using Seaborn to visualize the Pearson correlation coefficient

sb.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)

<matplotlib.axes._subplots.AxesSubplot at 0x7ff90c978358>