In [1]:

    

import numpy as np
import pandas as pd

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

pd.options.display.max_columns=1000


    

In [2]:

    

# Load data of Chipotle orders
df = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv', sep='\t')


    

In [26]:

    

df.sample(20)


    

    
Out[26]:







  

    

      

      
order_id
      
quantity
      
item_name
      
choice_description
      
item_price
    
  
  

    

      
3842
      
1539
      
1
      
Steak Burrito
      
[Fresh Tomato Salsa, [Pinto Beans, Cheese, Sou...
      
$11.75
    
    

      
231
      
102
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...
      
$8.75
    
    

      
2517
      
1000
      
1
      
Barbacoa Bowl
      
[Roasted Chili Corn Salsa, [Rice, Black Beans,...
      
$9.25
    
    

      
4589
      
1825
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...
      
$11.25
    
    

      
2856
      
1135
      
1
      
Steak Bowl
      
[[Roasted Chili Corn Salsa (Medium), Tomatillo...
      
$8.99
    
    

      
23
      
12
      
1
      
Chicken Burrito
      
[[Tomatillo-Green Chili Salsa (Medium), Tomati...
      
$10.98
    
    

      
1895
      
764
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Rice, Black Beans, Cheese]]
      
$8.75
    
    

      
553
      
230
      
1
      
6 Pack Soft Drink
      
[Diet Coke]
      
$6.49
    
    

      
1277
      
520
      
1
      
Chips and Guacamole
      
NaN
      
$4.45
    
    

      
3796
      
1520
      
1
      
Steak Soft Tacos
      
[Roasted Chili Corn Salsa, [Rice, Pinto Beans,...
      
$9.25
    
    

      
3717
      
1485
      
1
      
Chicken Bowl
      
[Tomatillo-Red Chili Salsa (Hot), [Black Beans...
      
$8.49
    
    

      
3950
      
1584
      
1
      
Steak Crispy Tacos
      
[Fresh Tomato Salsa, [Sour Cream, Lettuce, Ric...
      
$9.25
    
    

      
775
      
320
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Rice, Black Beans, Chees...
      
$8.75
    
    

      
4296
      
1714
      
1
      
Chicken Burrito
      
[Roasted Chili Corn Salsa, [Rice, Pinto Beans,...
      
$8.75
    
    

      
4474
      
1783
      
1
      
Chicken Soft Tacos
      
[Roasted Chili Corn Salsa, [Rice, Fajita Veget...
      
$8.75
    
    

      
3034
      
1207
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Rice, Cheese, Lettuce]]
      
$8.75
    
    

      
3647
      
1458
      
1
      
Chicken Bowl
      
[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream...
      
$11.25
    
    

      
1364
      
557
      
1
      
Chips and Fresh Tomato Salsa
      
NaN
      
$2.95
    
    

      
4008
      
1604
      
1
      
Canned Soda
      
[Diet Coke]
      
$1.09
    
    

      
538
      
225
      
1
      
Carnitas Bowl
      
[Tomatillo Green Chili Salsa, [Pinto Beans, Bl...
      
$9.25
    
  

In [8]:

    

df['item_name'].value_counts()


    

    
Out[8]:





Chicken Bowl                             726
Chicken Burrito                          553
Chips and Guacamole                      479
Steak Burrito                            368
Canned Soft Drink                        301
Chips                                    211
Steak Bowl                               211
Bottled Water                            162
Chicken Soft Tacos                       115
Chips and Fresh Tomato Salsa             110
Chicken Salad Bowl                       110
Canned Soda                              104
Side of Chips                            101
Veggie Burrito                            95
Barbacoa Burrito                          91
Veggie Bowl                               85
Carnitas Bowl                             68
Barbacoa Bowl                             66
Carnitas Burrito                          59
Steak Soft Tacos                          55
6 Pack Soft Drink                         54
Chips and Tomatillo Red Chili Salsa       48
Chicken Crispy Tacos                      47
Chips and Tomatillo Green Chili Salsa     43
Carnitas Soft Tacos                       40
Steak Crispy Tacos                        35
Chips and Tomatillo-Green Chili Salsa     31
Steak Salad Bowl                          29
Nantucket Nectar                          27
Barbacoa Soft Tacos                       25
Chips and Roasted Chili Corn Salsa        22
Chips and Tomatillo-Red Chili Salsa       20
Izze                                      20
Chips and Roasted Chili-Corn Salsa        18
Veggie Salad Bowl                         18
Barbacoa Crispy Tacos                     11
Barbacoa Salad Bowl                       10
Chicken Salad                              9
Veggie Soft Tacos                          7
Carnitas Crispy Tacos                      7
Veggie Salad                               6
Burrito                                    6
Carnitas Salad Bowl                        6
Steak Salad                                4
Bowl                                       2
Crispy Tacos                               2
Salad                                      2
Carnitas Salad                             1
Chips and Mild Fresh Tomato Salsa          1
Veggie Crispy Tacos                        1
Name: item_name, dtype: int64

In [4]:

    

df_plt = df_orders.set_index('Veggie')
df_plt = df_plt.stack().reset_index().rename(columns={'level_1': 'item', 0: 'value'})
df_plt = df_plt[df_plt['item'].isin(['Chips', 'Bottled Water', 'soda'])]
plt.figure(figsize=(5,5))
with sns.plotting_context('poster'):
    sns.barplot(x='item', hue='Veggie', y='value', data=df_plt)
plt.ylabel('Fraction of orders with item')


    

    




/Users/scott/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
  stat_data = remove_na(group_data[hue_mask])






    
Out[4]:





<matplotlib.text.Text at 0x112120be0>






    

In [5]:

    

# Differentiate orders for bowls and burritos
cols_keep = ['Veggie', 'Chips', 'Bottled Water', 'soda']
df_bowl = df_orders[df_orders['Bowl']][cols_keep]
df_bowl['main_item'] = 'Bowl'
df_burrito = df_orders[df_orders['Burrito']][cols_keep]
df_burrito['main_item'] = 'Burrito'
df_bb = pd.concat([df_bowl, df_burrito])

df_plt = df_bb.set_index('main_item')
df_plt = df_plt.stack().reset_index().rename(columns={'level_1': 'side_item', 0: 'value'})
df_plt = df_plt[df_plt['side_item'].isin(cols_keep)]
plt.figure(figsize=(8,5))
with sns.plotting_context('poster'):
    sns.barplot(x='side_item', hue='main_item', y='value', data=df_plt)
plt.ylabel('Fraction of orders with side item')


    

    




/Users/scott/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.
  stat_data = remove_na(group_data[hue_mask])






    
Out[5]:





<matplotlib.text.Text at 0x113463828>