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‎data-analysis project - 6 - wine_&_whiskies_bokeh/READ_ME.txt

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Scotch Whisky |
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Scotch whisky is prized for its complexity and variety of flavors.
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And the regions of Scotland where it is produced
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are believed to have distinct flavor profiles.
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In this case study, we will classify scotch whiskies
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based on their flavor characteristics.
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The dataset we'll be using contains a selection of scotch whiskies
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from several distilleries, and we'll attempt to cluster whiskies
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into groups that are similar in flavor.
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This case study will deepen your understanding of Pandas, NumPy,
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and scikit-learn, and perhaps of scotch whisky.
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The dataset we'll be using consists of tasting ratings
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of one readily available single malt scotch whisky
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from almost every active whisky distillery in Scotland.
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The resulting dataset has 86 malt whiskies
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that are scored between 0 and 4 in 12 different taste categories.
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The scores have been aggregated from 10 different tasters.
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The taste categories describe whether the whiskies are sweet, smoky,
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medicinal, spicy, and so on.
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Order of Project subparts :
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1.whiskies_loading_&_inspecting_data.py
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2.whiskies_exploring_correlations.py
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3.clustering_whiskies_by_flavour_profile.py
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4.whiskies_comparing_correlation_matrices.py
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I did this project to accompany my learnings from HarvardX course - PH526x Using Python for Research.
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- Amartya Ranjan Saikia ([email protected]/[email protected])

‎data-analysis project - 6 - wine_&_whiskies_bokeh/clustering_whiskies_by_flavour_profile.py

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from sklearn.cluster.bicluster import SpectralCoclustering
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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model = SpectralCoclustering(n_clusters=6, random_state=0)
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model.fit(corr_whisky)
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model.rows_
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# >>>np.sum(model.rows_, axis=1)
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# >>>np.sum(model.rows_, axis=0)
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model.row_labels_

‎data-analysis project - 6 - wine_&_whiskies_bokeh/code.jpegs_wine_&_whiskies_bokeh/clustering_whiskies_by_flavour_profile.JPG

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/code.jpegs_wine_&_whiskies_bokeh/whiskies_comparing_correlation_matrices.JPG

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/code.jpegs_wine_&_whiskies_bokeh/whiskies_exploring_correlations.JPG

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/code.jpegs_wine_&_whiskies_bokeh/whiskies_loading_&_inspecting_data.JPG

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/corlate-whisky.pdf

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/corlate-whisky_2.pdf

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/figure_1_clustering_whiskies_by_flavour_profile1.png

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/figure_2_clustering_whiskies_by_flavour_profile2.png

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/figure_3_whiskies_comparing_correlation_matrices1.png

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/figures_wine_&_whiskies_bokeh/figure_4_whiskies_comparing_correlation_matrices2.png

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‎data-analysis project - 6 - wine_&_whiskies_bokeh/regions.txt

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#####Regions#####
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Highlands
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Speyside
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Highlands
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Islay
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Highlands
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Islands
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Lowlands
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Speyside
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Speyside
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Highlands
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Speyside
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Speyside
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Highlands
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Speyside
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Speyside
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Speyside
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Lowlands
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Highlands
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Islay
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Islay
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Islay
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Islay
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Speyside
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Highlands
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Speyside
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Speyside
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Speyside
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Highlands
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Highlands
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Highlands
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Speyside
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Highlands
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Speyside
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Speyside
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Highlands
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Speyside
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Speyside
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Speyside
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Highlands
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Campbelltown
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Speyside
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Speyside
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Highlands
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Speyside
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Speyside
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Speyside
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Highlands
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Lowlands
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Speyside
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Speyside
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Highlands
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Speyside
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Highlands
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Islands
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Speyside
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Islands
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Speyside
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Islay
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Islay
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Speyside
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Highlands
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Speyside
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Speyside
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Speyside
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Speyside
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Speyside
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Highlands
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Highlands
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Highlands
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Speyside
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Highlands
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Islands
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Speyside
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Speyside
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Campbelltown
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Speyside
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Speyside
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Islands
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Speyside
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Speyside
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Highlands
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Islands
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Highlands
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Speyside
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Speyside
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Highlands

‎data-analysis project - 6 - wine_&_whiskies_bokeh/whiskies.txt

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RowID,Distillery,Body,Sweetness,Smoky,Medicinal,Tobacco,Honey,Spicy,Winey,Nutty,Malty,Fruity,Floral,Postcode, Latitude, Longitude
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01,Aberfeldy,2,2,2,0,0,2,1,2,2,2,2,2, PH15 2EB, 286580,749680
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02,Aberlour,3,3,1,0,0,4,3,2,2,3,3,2, AB38 9PJ, 326340,842570
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03,AnCnoc,1,3,2,0,0,2,0,0,2,2,3,2, AB5 5LI, 352960,839320
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04,Ardbeg,4,1,4,4,0,0,2,0,1,2,1,0, PA42 7EB, 141560,646220
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05,Ardmore,2,2,2,0,0,1,1,1,2,3,1,1, AB54 4NH, 355350,829140
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06,ArranIsleOf,2,3,1,1,0,1,1,1,0,1,1,2, KA27 8HJ, 194050,649950
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07,Auchentoshan,0,2,0,0,0,1,1,0,2,2,3,3, G81 4SJ, 247670,672610
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08,Auchroisk,2,3,1,0,0,2,1,2,2,2,2,1, AB55 3XS, 340754,848623
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09,Aultmore,2,2,1,0,0,1,0,0,2,2,2,2, AB55 3QY, 340754,848623
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10,Balblair,2,3,2,1,0,0,2,0,2,1,2,1, IV19 1LB, 270820,885770
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11,Balmenach,4,3,2,0,0,2,1,3,3,0,1,2, PH26 3PF, 307750,827170
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12,Belvenie,3,2,1,0,0,3,2,1,0,2,2,2, AB55 4DH, 332680,840840
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13,BenNevis,4,2,2,0,0,2,2,0,2,2,2,2, PH33 6TJ, 212600,775710
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14,Benriach,2,2,1,0,0,2,2,0,0,2,3,2, IV30 8SJ, 323450,858380
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15,Benrinnes,3,2,2,0,0,3,1,1,2,3,2,2, AB38 9NN, 325800,839920
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16,Benromach,2,2,2,0,0,2,2,1,2,2,2,2, IV36 3EB, 303330,859350
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17,Bladnoch,1,2,1,0,0,0,1,1,0,2,2,3, DG8 9AB, 242260,554260
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18,BlairAthol,2,2,2,0,0,1,2,2,2,2,2,2, PH16 5LY, 294860,757580
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19,Bowmore,2,2,3,1,0,2,2,1,1,1,1,2, PA43 7GS, 131330,659720
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20,Bruichladdich,1,1,2,2,0,2,2,1,2,2,2,2, PA49 7UN, 126680,661400
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21,Bunnahabhain,1,2,1,1,0,1,1,1,1,2,2,3, PA46 7RR, 142210,673170
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22,Caol Ila,3,1,4,2,1,0,2,0,2,1,1,1, PA46 7RL, 142920,670040
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23,Cardhu,1,3,1,0,0,1,1,0,2,2,2,2, AB38 7RY, 318790,843090
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24,Clynelish,3,2,3,3,1,0,2,0,1,1,2,0, KW9 6LB, 290250,904230
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25,Craigallechie,2,2,2,0,1,2,2,1,2,2,1,4, AB38 9ST, 328920,844920
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26,Craigganmore,2,3,2,1,0,0,1,0,2,2,2,2, AB37 9AB, 316600,836370
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27,Dailuaine,4,2,2,0,0,1,2,2,2,2,2,1, AB38 7RE, 323520,841010
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28,Dalmore,3,2,2,1,0,1,2,2,1,2,3,1, IV17 0UT, 266610,868730
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29,Dalwhinnie,2,2,2,0,0,2,1,0,1,2,2,2, PH19 1AB, 263670,785270
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30,Deanston,2,2,1,0,0,2,1,1,1,3,2,1, FK16 6AG, 271570,701570
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31,Dufftown,2,3,1,1,0,0,0,0,1,2,2,2, AB55 4BR, 332360,839200
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32,Edradour,2,3,1,0,0,2,1,1,4,2,2,2, PH16 5JP, 295960,757940
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33,GlenDeveronMacduff,2,3,1,1,1,1,1,2,0,2,0,1, AB4 3JT, 372120,860400
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34,GlenElgin,2,3,1,0,0,2,1,1,1,1,2,3, IV30 3SL, 322640,861040
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35,GlenGarioch,2,1,3,0,0,0,3,1,0,2,2,2, AB51 0ES, 381020,827590
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36,GlenGrant,1,2,0,0,0,1,0,1,2,1,2,1, AB38 7BS, 327610,849570
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37,GlenKeith,2,3,1,0,0,1,2,1,2,1,2,1, AB55 3BU, 340754,848623
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38,GlenMoray,1,2,1,0,0,1,2,1,2,2,2,4, IV30 1YE, 319820,862320
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39,GlenOrd,3,2,1,0,0,1,2,1,1,2,2,2, IV6 7UJ, 251810,850860
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40,GlenScotia,2,2,2,2,0,1,0,1,2,2,1,1, PA28 6DS, 172090,621010
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41,GlenSpey,1,3,1,0,0,0,1,1,1,2,0,2, AB38 7AU, 327760,849140
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42,Glenallachie,1,3,1,0,0,1,1,0,1,2,2,2, AB38 9LR, 326490,841240
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43,Glendronach,4,2,2,0,0,2,1,4,2,2,2,0, AB54 6DA, 361200,844930
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44,Glendullan,3,2,1,0,0,2,1,2,1,2,3,2, AB55 4DJ, 333000,840300
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45,Glenfarclas,2,4,1,0,0,1,2,3,2,3,2,2, AB37 9BD, 320950,838160
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46,Glenfiddich,1,3,1,0,0,0,0,0,0,2,2,2, AB55 4DH, 332680,840840
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47,Glengoyne,1,2,0,0,0,1,1,1,2,2,3,2, G63 9LB, 252810,682750
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48,Glenkinchie,1,2,1,0,0,1,2,0,0,2,2,2, EH34 5ET, 344380,666690
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49,Glenlivet,2,3,1,0,0,2,2,2,1,2,2,3, AB37 9DB, 319560,828780
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50,Glenlossie,1,2,1,0,0,1,2,0,1,2,2,2, IV30 3SS, 322640,861040
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51,Glenmorangie,2,2,1,1,0,1,2,0,2,1,2,2, IV19 1PZ, 276750,883450
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52,Glenrothes,2,3,1,0,0,1,1,2,1,2,2,0, AB38 7AA, 327650,849170
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53,Glenturret,2,3,1,0,0,2,2,2,2,2,1,2, PH7 4HA, 285630,723580
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54,Highland Park,2,2,3,1,0,2,1,1,1,2,1,1, KW15 1SU, 345340,1009260
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55,Inchgower,1,3,1,1,0,2,2,0,1,2,1,2, AB56 5AB, 342610,863970
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56,Isle of Jura,2,1,2,2,0,1,1,0,2,1,1,1, PA60 7XT, 152660,667040
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57,Knochando,2,3,1,0,0,2,2,1,2,1,2,2, AB38 7RT, 319470,841570
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58,Lagavulin,4,1,4,4,1,0,1,2,1,1,1,0, PA42 7DZ, 140430,645730
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59,Laphroig,4,2,4,4,1,0,0,1,1,1,0,0, PA42 7DU, 138680,645160
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60,Linkwood,2,3,1,0,0,1,1,2,0,1,3,2, IV30 3RD, 322640,861040
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61,Loch Lomond,1,1,1,1,0,1,1,0,1,2,1,2, G83 0TL, 239370,680920
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62,Longmorn,3,2,1,0,0,1,1,1,3,3,2,3, IV30 3SJ, 322640,861040
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63,Macallan,4,3,1,0,0,2,1,4,2,2,3,1, AB38 9RX, 327710,844480
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64,Mannochmore,2,1,1,0,0,1,1,1,2,1,2,2, IV30 3SS, 322640,861040
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65,Miltonduff,2,4,1,0,0,1,0,0,2,1,1,2, IV30 3TQ, 322640,861040
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66,Mortlach,3,2,2,0,0,2,3,3,2,1,2,2, AB55 4AQ, 332950,839850
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67,Oban,2,2,2,2,0,0,2,0,2,2,2,0, PA34 5NH, 185940,730190
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68,OldFettercairn,1,2,2,0,1,2,2,1,2,3,1,1, AB30 1YE, 370860,772900
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69,OldPulteney,2,1,2,2,1,0,1,1,2,2,2,2, KW1 5BA, 336730,950130
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70,RoyalBrackla,2,3,2,1,1,1,2,1,0,2,3,2, IV12 5QY, 286040,851320
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71,RoyalLochnagar,3,2,2,0,0,2,2,2,2,2,3,1, AB35 5TB, 326140,794370
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72,Scapa,2,2,1,1,0,2,1,1,2,2,2,2, KW15 1SE, 342850,1008930
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73,Speyburn,2,4,1,0,0,2,1,0,0,2,1,2, AB38 7AG, 326930,851430
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74,Speyside,2,2,1,0,0,1,0,1,2,2,2,2, PH21 1NS, 278740,800600
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75,Springbank,2,2,2,2,0,2,2,1,2,1,0,1, PA28 6EJ, 172280,620910
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76,Strathisla,2,2,1,0,0,2,2,2,3,3,3,2, AB55 3BS, 340754,848623
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77,Strathmill,2,3,1,0,0,0,2,0,2,1,3,2, AB55 5DQ,342650,850500
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78,Talisker,4,2,3,3,0,1,3,0,1,2,2,0, IV47 8SR, 137950,831770
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79,Tamdhu,1,2,1,0,0,2,0,1,1,2,2,2, AB38 7RP, 319210,841760
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80,Tamnavulin,1,3,2,0,0,0,2,0,2,1,2,3, AB37 9JA, 321180,826110
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81,Teaninich,2,2,2,1,0,0,2,0,0,0,2,2, IV17 0XB, 265360,869120
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82,Tobermory,1,1,1,0,0,1,0,0,1,2,2,2, PA75 6NR, 150450,755070
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83,Tomatin,2,3,2,0,0,2,2,1,1,2,0,1, IV13 7YT, 279120,829630
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84,Tomintoul,0,3,1,0,0,2,2,1,1,2,1,2, AB37 9AQ, 315100,825560
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85,Tormore,2,2,1,0,0,1,0,1,2,1,0,0, PH26 3LR, 315180,834960
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86,Tullibardine,2,3,0,0,1,0,2,1,1,2,2,1, PH4 1QG, 289690,708850

‎data-analysis project - 6 - wine_&_whiskies_bokeh/whiskies_comparing_correlation_matrices.py

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from sklearn.cluster.bicluster import SpectralCoclustering
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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whisky['Group'] = pd.Series(model.row_labels_, index = whisky.index)
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whisky = whisky.ix[np.argsort(model.row_labels_)]
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whisky = whisky.reset_index(drop=True)
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correlations = pd.DataFrame.corr(whisky.iloc[:,2:14].transpose())
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correlations = np.array(correlations)
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plt.figure(figsize = (14,7))
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plt.subplot(121)
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plt.pcolor(corr_whisky)
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plt.title("Original")
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plt.axis("tight")
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plt.subplot(122)
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plt.pcolor(correlations)
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plt.title("Rearranged")
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plt.axis("tight")
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plt.show()
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plt.savefig("correlations.pdf")

‎data-analysis project - 6 - wine_&_whiskies_bokeh/whiskies_exploring_correlations.py

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import matplotlib.pyplot as plt
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plt.figure(figsize=(10,10))
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plt.pcolor(corr_flavors)
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plt.colorbar()
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# >>>plt.savefig("corlate-whisky1.pdf")
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corr_whisky = pd.DataFrame.corr(flavors.transpose())
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plt.figure(figsize=(10,10))
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plt.pcolor(corr_whisky)
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plt.axis("tight")
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plt.colorbar()
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# >>>plt.savefig("corlate-whisky2.pdf")
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plt.show()

‎data-analysis project - 6 - wine_&_whiskies_bokeh/whiskies_loading_&_inspecting_data.py

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import numpy as np
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import pandas as pd
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whisky = pd.read_csv("whiskies.txt")
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whisky["Region"] = pd.read_csv("regions.txt")
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# >>>whisky.head() #iloc method to index a data frame by location.
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# >>>whisky.iloc[0:10] #we specified the rows from 0 - 9
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# >>>whisky.iloc[0:10,0:5] #we specified the rows from 0 - 9 & columns from 0-5
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# >>>whisky.columns
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flavors=whisky.iloc[:,2:14]
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corr_flavors = pd.DataFrame.corr(flavors)
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print(corr_flavors)

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