Simple Analysis of Beer Data from the Craft Beers Data Set

Accompanies post on Practical Business Python

In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px
In [2]:
%matplotlib inline
In [3]:
sns.set_style('whitegrid')
In [4]:
df_beers = pd.read_csv('https://github.com/nickhould/craft-beers-dataset/blob/master/data/processed/beers.csv?raw=True', index_col=0)
df_breweries = pd.read_csv('https://github.com/nickhould/craft-beers-dataset/blob/master/data/processed/breweries.csv?raw=True', index_col=0)
In [5]:
df_beers.head()
Out[5]:
abv ibu id name style brewery_id ounces
0 0.050 NaN 1436 Pub Beer American Pale Lager 408 12.0
1 0.066 NaN 2265 Devil's Cup American Pale Ale (APA) 177 12.0
2 0.071 NaN 2264 Rise of the Phoenix American IPA 177 12.0
3 0.090 NaN 2263 Sinister American Double / Imperial IPA 177 12.0
4 0.075 NaN 2262 Sex and Candy American IPA 177 12.0
In [6]:
df_beers.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2410 entries, 0 to 2409
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   abv         2348 non-null   float64
 1   ibu         1405 non-null   float64
 2   id          2410 non-null   int64  
 3   name        2410 non-null   object 
 4   style       2405 non-null   object 
 5   brewery_id  2410 non-null   int64  
 6   ounces      2410 non-null   float64
dtypes: float64(3), int64(2), object(2)
memory usage: 150.6+ KB
In [7]:
df_breweries.head()
Out[7]:
name city state id
0 NorthGate Brewing Minneapolis MN 0
1 Against the Grain Brewery Louisville KY 1
2 Jack's Abby Craft Lagers Framingham MA 2
3 Mike Hess Brewing Company San Diego CA 3
4 Fort Point Beer Company San Francisco CA 4
In [8]:
df_breweries.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 558 entries, 0 to 557
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   name    558 non-null    object
 1   city    558 non-null    object
 2   state   558 non-null    object
 3   id      558 non-null    int64 
dtypes: int64(1), object(3)
memory usage: 21.8+ KB
In [9]:
all_beer = pd.merge(df_beers, df_breweries, how='left', 
                               left_on="brewery_id", 
                               right_on="id", 
                               suffixes=('_beer', '_brewery'))
In [10]:
all_beer.head()
Out[10]:
abv ibu id_beer name_beer style brewery_id ounces name_brewery city state id_brewery
0 0.050 NaN 1436 Pub Beer American Pale Lager 408 12.0 10 Barrel Brewing Company Bend OR 408
1 0.066 NaN 2265 Devil's Cup American Pale Ale (APA) 177 12.0 18th Street Brewery Gary IN 177
2 0.071 NaN 2264 Rise of the Phoenix American IPA 177 12.0 18th Street Brewery Gary IN 177
3 0.090 NaN 2263 Sinister American Double / Imperial IPA 177 12.0 18th Street Brewery Gary IN 177
4 0.075 NaN 2262 Sex and Candy American IPA 177 12.0 18th Street Brewery Gary IN 177

Check for empty values

In [11]:
all_beer.isna().sum()
Out[11]:
abv               62
ibu             1005
id_beer            0
name_beer          0
style              5
brewery_id         0
ounces             0
name_brewery       0
city               0
state              0
id_brewery         0
dtype: int64
In [12]:
all_beer.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2410 entries, 0 to 2409
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   abv           2348 non-null   float64
 1   ibu           1405 non-null   float64
 2   id_beer       2410 non-null   int64  
 3   name_beer     2410 non-null   object 
 4   style         2405 non-null   object 
 5   brewery_id    2410 non-null   int64  
 6   ounces        2410 non-null   float64
 7   name_brewery  2410 non-null   object 
 8   city          2410 non-null   object 
 9   state         2410 non-null   object 
 10  id_brewery    2410 non-null   int64  
dtypes: float64(3), int64(3), object(5)
memory usage: 225.9+ KB
In [13]:
all_beer['ounces'].plot(kind='hist')
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f994180a2e0>
In [14]:
all_beer['style'].value_counts()
Out[14]:
American IPA                      424
American Pale Ale (APA)           245
American Amber / Red Ale          133
American Blonde Ale               108
American Double / Imperial IPA    105
                                 ... 
Braggot                             1
Kristalweizen                       1
Other                               1
Wheat Ale                           1
Flanders Oud Bruin                  1
Name: style, Length: 99, dtype: int64
In [15]:
all_beer['IPA'] = all_beer['style'].str.contains('IPA', case=False)
In [16]:
all_beer['IPA'].value_counts()
Out[16]:
False    1834
True      571
Name: IPA, dtype: int64
In [17]:
sns.catplot(data=all_beer, x='IPA', y='ibu', kind='box')
Out[17]:
<seaborn.axisgrid.FacetGrid at 0x7f9941047ee0>
In [18]:
sns.catplot(data=all_beer, x='IPA', y='ibu', kind='swarm')
Out[18]:
<seaborn.axisgrid.FacetGrid at 0x7f9940f186a0>
In [19]:
fig = px.scatter(all_beer, x="abv", y="ibu")
fig.show()