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")


    

In [2]:

    

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


    

In [3]:

    

m_Location = ['Location','N','Yelp','Google','Hunger','Cost','Volume','Tortilla','Temp','Meat','Fillings','Meat:filling',
               'Uniformity','Salsa','Synergy','Wrap','overall']

# Calculate the mean of each of the metrics above for each taco shop
tacoshops = df.Location.unique()
TS = len(tacoshops)
dfmean = pd.DataFrame(np.nan, index=range(TS), columns=m_Location)
for ts in range(TS):
    dfmean.loc[ts] = df.loc[df.Location==tacoshops[ts]].mean()
    dfmean['N'][ts] = sum(df.Location == tacoshops[ts])
dfmean.Location = tacoshops


    

In [4]:

    

Ncutoff = 5
dfToRank = dfmean.loc[dfmean.N>=Ncutoff]


    

In [5]:

    

dfToRank


    

    
Out[5]:






  

    

      

      
Location
      
N
      
Yelp
      
Google
      
Hunger
      
Cost
      
Volume
      
Tortilla
      
Temp
      
Meat
      
Fillings
      
Meat:filling
      
Uniformity
      
Salsa
      
Synergy
      
Wrap
      
overall
    
  
  

    

      
4
      
los primos mexican food
      
8.0
      
3.000000
      
3.700000
      
3.500000
      
7.200000
      
0.822500
      
3.187500
      
3.625000
      
3.125000
      
3.250000
      
2.000000
      
2.428571
      
2.875000
      
2.562500
      
3.125000
      
2.750000
    
    

      
7
      
taco stand
      
16.0
      
4.500000
      
4.400000
      
3.125000
      
7.558750
      
0.787143
      
3.593750
      
2.968750
      
4.187500
      
3.875000
      
3.843750
      
3.781250
      
3.718750
      
4.125000
      
3.875000
      
4.018750
    
    

      
9
      
rigoberto's taco shop
      
13.0
      
3.769231
      
4.338462
      
3.538462
      
6.761538
      
0.890000
      
3.500000
      
3.807692
      
3.557692
      
3.500000
      
4.000000
      
3.653846
      
3.225000
      
3.818182
      
3.923077
      
3.650000
    
    

      
10
      
lolita's taco shop
      
12.0
      
4.000000
      
4.400000
      
3.141667
      
7.225000
      
0.747778
      
2.983333
      
3.275000
      
3.363636
      
3.641667
      
3.354545
      
2.991667
      
2.854167
      
3.437500
      
3.916667
      
3.283333
    
    

      
17
      
vallarta express
      
9.0
      
3.500000
      
4.000000
      
3.500000
      
6.933333
      
0.880000
      
2.916667
      
4.357143
      
3.277778
      
3.444444
      
3.611111
      
3.166667
      
3.500000
      
3.000000
      
3.611111
      
3.500000
    
    

      
22
      
california burritos
      
12.0
      
4.500000
      
4.400000
      
3.916667
      
6.187500
      
0.843750
      
4.108333
      
3.608333
      
4.166667
      
3.833333
      
4.125000
      
3.791667
      
3.681818
      
4.166667
      
4.458333
      
4.254167
    
  

In [6]:

    

m_Rank = ['Location','Cost','Volume','Tortilla','Temp','Meat','Fillings','Meat:filling', 'Uniformity','Salsa','Synergy','Wrap','overall']
TS = len(dfToRank)
dfRanked = pd.DataFrame(np.nan, index=range(TS), columns=m_Rank)
dfRanked.Location[:] = dfToRank.Location
for m in m_Rank[1:]:
    if m == 'Cost':
        dfRanked[m][:] = dfToRank[m].rank(ascending=1)
    else:
        dfRanked[m][:] = dfToRank[m].rank(ascending=0)


    

    




C:\Users\Scott\Anaconda2\lib\site-packages\ipykernel\__main__.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
C:\Users\Scott\Anaconda2\lib\site-packages\ipykernel\__main__.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

In [7]:

    

dfRanked


    

    
Out[7]:






  

    

      

      
Location
      
Cost
      
Volume
      
Tortilla
      
Temp
      
Meat
      
Fillings
      
Meat:filling
      
Uniformity
      
Salsa
      
Synergy
      
Wrap
      
overall
    
  
  

    

      
0
      
los primos mexican food
      
4.0
      
4.0
      
4.0
      
3.0
      
6.0
      
6.0
      
6.0
      
6.0
      
5.0
      
6.0
      
6.0
      
6.0
    
    

      
1
      
taco stand
      
6.0
      
5.0
      
2.0
      
6.0
      
1.0
      
1.0
      
3.0
      
2.0
      
1.0
      
2.0
      
4.0
      
2.0
    
    

      
2
      
rigoberto's taco shop
      
2.0
      
1.0
      
3.0
      
2.0
      
3.0
      
4.0
      
2.0
      
3.0
      
4.0
      
3.0
      
2.0
      
3.0
    
    

      
3
      
lolita's taco shop
      
5.0
      
6.0
      
5.0
      
5.0
      
4.0
      
3.0
      
5.0
      
5.0
      
6.0
      
4.0
      
3.0
      
5.0
    
    

      
4
      
vallarta express
      
3.0
      
2.0
      
6.0
      
1.0
      
5.0
      
5.0
      
4.0
      
4.0
      
3.0
      
5.0
      
5.0
      
4.0
    
    

      
5
      
california burritos
      
1.0
      
3.0
      
1.0
      
4.0
      
2.0
      
2.0
      
1.0
      
1.0
      
2.0
      
1.0
      
1.0
      
1.0
    
  

In [8]:

    

#TODO