San Diego Burrito Analytics: Correlations

Scott Cole

21 May 2016

This notebook investigates the correlations between different burrito measures.

imports



In [1]:

    
%config InlineBackend.figure_format = 'retina'
%matplotlib inline

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd

import seaborn as sns
sns.set_style("white")

Load data



In [2]:

    
import util
df = util.load_burritos()
N = df.shape[0]

Correlation matrix

Note that many dimensions of the burrito are correlated with each other. This lack of independence across dimensions means we should be careful while interpreting the presence of a correlation between two dimensions.



In [3]:

    
m_corr = ['Google','Yelp','Hunger','Cost','Volume','Tortilla','Temp','Meat','Fillings','Meat:filling',
               'Uniformity','Salsa','Synergy','Wrap','overall']
M = len(m_corr)
dfcorr = df[m_corr].corr()



In [4]:

    
from matplotlib import cm

clim1 = (-1,1)
plt.figure(figsize=(12,10))
cax = plt.pcolor(range(M+1), range(M+1), dfcorr, cmap=cm.bwr)
cbar = plt.colorbar(cax, ticks=(-1,-.5,0,.5,1))
cbar.ax.set_ylabel('Pearson correlation (r)', size=30)
plt.clim(clim1)
cbar.ax.set_yticklabels((-1,-.5,0,.5,1),size=20)
ax = plt.gca()
ax.set_yticks(np.arange(M)+.5)
ax.set_yticklabels(m_corr,size=25)
ax.set_xticks(np.arange(M)+.5)
ax.set_xticklabels(m_corr,size=25)
plt.xticks(rotation='vertical')
plt.tight_layout()
plt.xlim((0,M))
plt.ylim((0,M))

figname = 'metriccorrmat'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')

Correlation: Cost and volume



In [12]:

    
plt.figure(figsize=(4,4))
ax = plt.gca()
df.plot(kind='scatter',x='Cost',y='Volume',ax=ax,**{'s':40,'color':'k'})
plt.xlabel('Cost ($)',size=20)
plt.ylabel('Volume (L)',size=20)
plt.xticks(np.arange(3,11),size=15)
plt.yticks(np.arange(.4,1.4,.1),size=15)
plt.tight_layout()
print df.corr()['Cost']['Volume']
from tools.misc import pearsonp
print pearsonp(df.corr()['Cost']['Volume'],len(df[['Cost','Volume']].dropna()))

figname = 'corr-volume-cost'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')









    



0.257364952291
0.0434447301506

Positive correlation: Meat and overall rating



In [6]:

    
# Visualize some correlations

from tools.plt import scatt_corr
scatt_corr(df['overall'].values,df['Meat'].values,
          xlabel = 'overall rating', ylabel='meat rating', xlim = (-.5,5.5),ylim = (-.5,5.5),xticks=range(6),yticks=range(6))
          #showline = True)
figname = 'corr-meat-overall'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')

Positive correlation: Meat and Non-meat fillings



In [7]:

    
plt.figure(figsize=(4,4))
ax = plt.gca()
df.plot(kind='scatter',x='Meat',y='Fillings',ax=ax,**{'s':40,'color':'k','alpha':.1})
plt.xlabel('Meat rating',size=20)
plt.ylabel('Non-meat rating',size=20)
plt.xticks(np.arange(0,6),size=15)
plt.yticks(np.arange(0,6),size=15)
dfMF = df[['Meat','Fillings']].dropna()
print sp.stats.spearmanr(dfMF.Meat,dfMF.Fillings)

figname = 'corr-meat-filling'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')









    



SpearmanrResult(correlation=0.51866754590616759, pvalue=5.2228394488281898e-11)

Positive correlation: Meat and non-meat fillings at The Taco Stand

The positive correlation here indicates that the correlation between these two features is not simply due to a restaurant having good meat tending to have good non-meat fillings



In [8]:

    
# Restrict analysis to burritos at the taco stand
restrictCali = False
import re
reTS = re.compile('.*taco stand.*', re.IGNORECASE)
reCali = re.compile('.*cali.*', re.IGNORECASE)
locTS = np.ones(len(df))
for i in range(len(df)):
    mat = reTS.match(df['Location'][i])
    if mat is None:
        locTS[i] = 0
    else:
        if restrictCali:
            mat = reCali.match(df['Burrito'][i])
            if mat is None:
                locTS[i] = 0
temp = np.arange(len(df))
dfTS = df.loc[temp[locTS==1]]



In [9]:

    
plt.figure(figsize=(4,4))
ax = plt.gca()
dfTS.plot(kind='scatter',x='Meat',y='Fillings',ax=ax,**{'s':40,'color':'k','alpha':.1})
plt.xlabel('Meat rating',size=20)
plt.ylabel('Non-meat rating',size=20)
plt.xticks(np.arange(0,6),size=15)
plt.yticks(np.arange(0,6),size=15)
dfTSMF = dfTS[['Meat','Fillings']].dropna()
print sp.stats.spearmanr(dfTSMF.Meat,dfTSMF.Fillings)

figname = 'corr-meat-filling-TS'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')









    



SpearmanrResult(correlation=0.55094901777070204, pvalue=0.021897138606074399)

Correlation: Hunger level and overall rating



In [10]:

    
plt.figure(figsize=(4,4))
ax = plt.gca()
df.plot(kind='scatter',x='Hunger',y='overall',ax=ax,**{'s':40,'color':'k'})
plt.xlabel('Hunger',size=20)
plt.ylabel('Overall rating',size=20)
plt.xticks(np.arange(0,6),size=15)
plt.yticks(np.arange(0,6),size=15)
print df.corr()['Hunger']['overall']
from tools.misc import pearsonp
print pearsonp(df.corr()['Hunger']['overall'],len(df[['Hunger','overall']].dropna()))

figname = 'corr-hunger-overall'
plt.savefig('C:/gh/fig/burrito/'+figname + '.png')









    



0.257662390685
0.00196344010702



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