<jupyter_start><jupyter_text>Project for Programming   Creating a dataset  I decided to simulate a dataset about wiskeys ![imagewhiskey.jpg](attachment:imagewhiskey.jpg)    Research I looked at a number of exisiting datasets out there about wines. These datasets are interesting as wines like spirits are somewhat impervious to the usual reguations on listings its ingredients, even the alcohol content is observed to be more of an estimation than anything else. I looked at one particular dataset about wine quality. The author input physiochemical properties and the outputs are sensory data. Two sets were created using white and red wines. A panel of experts scored the quality of the wines between 0 and 10. O being the worst and 10 being excellent. A number of methods were applied and many beyond my limited abilities but I found this dataset to be a good starting as its interesting/relatable                    Wine Dataset Reference  https://www.kaggle.com/piyushgoyal443/red-wine-datasetwineQualityInfo.txt        <NAME>, <NAME>, <NAME>, <NAME> and <NAME>.   Modeling wine preferences by data mining from physicochemical properties.  In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236          Whiskey Dataset https://www.kaggle.com/koki25ando/scotch-whisky-dataset This dataset looks at scotch whiskys, with a focus on flavours and preference by the expert panel. Its not a highly complex dataset but having looked at a few I feel its best to start with something simple for the purposes of this assigment. I also really liked this analysis - the author used R and GGplots. The plots look great and its a nicely explained exercise for a total novice http://www.drbunsen.org/whisky-analysis/  Focus Rather than focusing on qulity per se, this dataset will hopefully provide an  overview of current preferences/tastes.   Are increasingly sweet tastes killing tradition? A recent article noted how the traditional herbaceous flavours of Gin are lessening in favour of sweetened pink gins. insert link  https://www.theguardian.com/food/2018/dec/06/pink-terror-how-sweet-gins-muscled-in-on-the-artisan-market-pink-ginIt is genreally accpeted that aged whiskeys with a dark colour and the barrel conditions etc  are what makes a great, high quality whisky and likely the preference for a panel of experts. These types of whiskys are often complex, hot, smoky and deep.  They require little embellishment other than air and a little water.  To try to ascertain what appeals to a more general audience the panel for this simualted dataset have all identifed as non experts.   The Whiskeys I took a list from a search located here at http://www.dcs.ed.ac.uk/home/jhb/whisky/spread.htmlI saved to excel and used RAND(0) to reshuffle   The Variables -  Brand  taken from dataset reference above -  Years Aged  up 7-  VOL between 40 & 45 -  Sweetness 1 to 10 1 indicating little or none, 10 the strongest-  Shade  1 to 10 1 indicating little or none, 10 the strongest-  Heat   1 to 10 1 indicating little or none, 10 the strongest -  Smoke  1 to 10 1 indicating little or none, 10 the strongest-  Age Participant-  Graden 1 to 10, 1 being the worste, 10 being excellent<jupyter_code>import pandas as pd

import numpy as np
import numpy as np
np.random.seed(45)
pd.options.display.max_rows= 101

df = pd.DataFrame({'Brands':['Ardmore','LeaValley','Glengalwan','Tullibardine','Braeval','Glengoblin','Glengalwan',
                    
'Glengalwan',
'Braeval',
'Tullibardine',
'Thomas Street',
'Braes of Glenlivet',
'<NAME>',
'Rosebank',
'Glentarras',
'Pitilie',
'Hillside',
'Campbeltown',
'Inisfinn',
'Scotia',
'Springbank',
'Dalaruan',
'Benmore',
'Scapa',
'Glenesk',
'Glenfarclas',
'Dufftown',
'Auchinblae',
'Glentauchers',
'Brora',
'Inchmurrin',
'Dean',
'Devanha',
'Lochnagar',
'Speyside',
'Kininvie',
'Bankier',
'Isla',
'Bowmore',
'Boness',
'Macallan',
'Glenlochy',
'Glenallachie',
'LomondLoch',
'BlairAthol',
'Imperial',
'Dalwhinnie',
'Avoniel',
'<NAME>',
'Burnside',
'Yamazaki',
'Cameronbridge',
'Lochruan',
'Karuizawa',
'Tomintoul',
'Provanmill',
'Glenglassaugh',
'Bushmills',
'Mannochmore',
'Ballechin',
'Glenside',
'Lagavulin',
'Kirkliston',
'Longmorn',
'Vauxhall'
'Highland Park'
'Glenaden',
'Aberfeldy',
'AlltBhainne',
'Knockando',
'Bladnoch',
'Tomatin',
'Convalmore',
'Parkmore',
'<NAME>',
'Glencraig',
'Glen',
'Clydesdale',
'Strathmore',
'GlenSpey',
'Tamnavulin',
'GlenScotia',
'Mortlach',
'Ladyburn',
'Glenfoyle',
'Strathclyde',
'Carsebridge',
'Ardlussa',
'Annandale',
'<NAME>',
'Lochindaal',
'Inchgower',
'Abbey Street',
'Lo<NAME>',
'Auchroisk',
'Adelphi',
'Dumbarton',
'Teaninich',
'Glenturret',
'Hokkaido',
'Craigellachie',
'Laphroaig',
'Tamdhu'],
                  
     'Years Aged':np.random.randint(low=3, high=12,size=101), #avoiding negative numbers used low/high
     'VOL':np.random.randint(low=40,high=50,size=101),
    'Sweetness':np.random.normal(7,1,size=101),
 
    'Shade':np.random.randint(1,3,size=101)*2,  #using randint*2- trying to manipulate a little
     "Heat":np.random.randint(1,10,size=101),              
     "Smoke": np.random.randint(1,5,size=101),
    "Age Participant":np.random.randint(low=25,high=35,size=101),
     "Grade":np.random.randint(low=1,high=10,size=101)})
    

df.set_index('VOL','Brands')
df.columns

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns;sns.set() 
ax=sns.scatterplot(x='Sweetness',y='Grade',data=df)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns;sns.set() 
ax=sns.violinplot(x='Grade',y='VOL',data=df)
df
import matplotlib.pyplot as plt
import seaborn as sns 
w=sns.catplot("VOL","Years Aged",data=df, kind="bar",height=6,aspect=3,palette="autumn",legend=False)



# Show plot
plt.show()
df.boxplot(column='Years Aged',by='VOL')
plt.show()
 np.random.multinomial(20, [1/6.]*6, size=1)
df.boxplot(column='Grade',by='VOL')
plt.show()<jupyter_output><empty_output>
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