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"source": [
"# Got Scotch?"
]
},
{
"cell_type": "code",
"execution_count": 13,
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},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
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}
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}
},
"outputs": [],
"source": [
"import ipywidgets as widgets\n",
"from traitlets import Unicode, List, Instance, link, HasTraits\n",
"from IPython.display import display, clear_output, HTML, Javascript"
]
},
{
"cell_type": "code",
"execution_count": 15,
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"source": [
"\n",
"\n",
"features = [[2, 2, 2, 0, 0, 2, 1, 2, 2, 2, 2, 2],\n",
" [3, 3, 1, 0, 0, 4, 3, 2, 2, 3, 3, 2],\n",
" [1, 3, 2, 0, 0, 2, 0, 0, 2, 2, 3, 1],\n",
" [4, 1, 4, 4, 0, 0, 2, 0, 1, 2, 1, 0],\n",
" [2, 2, 2, 0, 0, 1, 1, 1, 2, 3, 1, 3],\n",
" [2, 3, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n",
" [0, 2, 0, 0, 0, 1, 1, 0, 2, 2, 3, 1],\n",
" [2, 3, 1, 0, 0, 2, 1, 2, 2, 2, 2, 2],\n",
" [2, 2, 1, 0, 0, 1, 0, 0, 2, 2, 2, 1],\n",
" [2, 3, 2, 1, 0, 0, 2, 0, 2, 1, 2, 3],\n",
" [4, 3, 2, 0, 0, 2, 1, 3, 3, 0, 1, 2],\n",
" [3, 2, 1, 0, 0, 3, 2, 1, 0, 2, 2, 2],\n",
" [4, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2],\n",
" [2, 2, 1, 0, 0, 2, 2, 0, 0, 2, 3, 1],\n",
" [3, 2, 2, 0, 0, 3, 1, 1, 2, 3, 2, 2],\n",
" [2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 2],\n",
" [1, 2, 1, 0, 0, 0, 1, 1, 0, 2, 2, 1],\n",
" [2, 2, 2, 0, 0, 1, 2, 2, 2, 2, 2, 2],\n",
" [2, 2, 3, 1, 0, 2, 2, 1, 1, 1, 1, 3],\n",
" [1, 1, 2, 2, 0, 2, 2, 1, 2, 2, 2, 3],\n",
" [1, 2, 1, 1, 0, 1, 1, 1, 1, 2, 2, 1],\n",
" [3, 1, 4, 2, 1, 0, 2, 0, 2, 1, 1, 0],\n",
" [1, 3, 1, 0, 0, 1, 1, 0, 2, 2, 2, 1],\n",
" [3, 2, 3, 3, 1, 0, 2, 0, 1, 1, 2, 0],\n",
" [2, 2, 2, 0, 1, 2, 2, 1, 2, 2, 1, 2],\n",
" [2, 3, 2, 1, 0, 0, 1, 0, 2, 2, 2, 1],\n",
" [4, 2, 2, 0, 0, 1, 2, 2, 2, 2, 2, 2],\n",
" [3, 2, 2, 1, 0, 1, 2, 2, 1, 2, 3, 2],\n",
" [2, 2, 2, 0, 0, 2, 1, 0, 1, 2, 2, 1],\n",
" [2, 2, 1, 0, 0, 2, 1, 1, 1, 3, 2, 2],\n",
" [2, 3, 1, 1, 0, 0, 0, 0, 1, 2, 2, 1],\n",
" [2, 3, 1, 0, 0, 2, 1, 1, 4, 2, 2, 2],\n",
" [2, 3, 1, 1, 1, 1, 1, 2, 0, 2, 0, 3],\n",
" [2, 3, 1, 0, 0, 2, 1, 1, 1, 1, 2, 1],\n",
" [2, 1, 3, 0, 0, 0, 3, 1, 0, 2, 2, 3],\n",
" [1, 2, 0, 0, 0, 1, 0, 1, 2, 1, 2, 1],\n",
" [2, 3, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2],\n",
" [1, 2, 1, 0, 0, 1, 2, 1, 2, 2, 2, 1],\n",
" [3, 2, 1, 0, 0, 1, 2, 1, 1, 2, 2, 2],\n",
" [2, 2, 2, 2, 0, 1, 0, 1, 2, 2, 1, 3],\n",
" [1, 3, 1, 0, 0, 0, 1, 1, 1, 2, 0, 1],\n",
" [1, 3, 1, 0, 0, 1, 1, 0, 1, 2, 2, 1],\n",
" [4, 2, 2, 0, 0, 2, 1, 4, 2, 2, 2, 2],\n",
" [3, 2, 1, 0, 0, 2, 1, 2, 1, 2, 3, 2],\n",
" [2, 4, 1, 0, 0, 1, 2, 3, 2, 3, 2, 2],\n",
" [1, 3, 1, 0, 0, 0, 0, 0, 0, 2, 2, 1],\n",
" [1, 2, 0, 0, 0, 1, 1, 1, 2, 2, 3, 1],\n",
" [1, 2, 1, 0, 0, 1, 2, 0, 0, 2, 2, 1],\n",
" [2, 3, 1, 0, 0, 2, 2, 2, 1, 2, 2, 2],\n",
" [1, 2, 1, 0, 0, 1, 2, 0, 1, 2, 2, 1],\n",
" [2, 2, 1, 1, 0, 1, 2, 0, 2, 1, 2, 1],\n",
" [2, 3, 1, 0, 0, 1, 1, 2, 1, 2, 2, 2],\n",
" [2, 3, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2],\n",
" [2, 2, 3, 1, 0, 2, 1, 1, 1, 2, 1, 3],\n",
" [1, 3, 1, 1, 0, 2, 2, 0, 1, 2, 1, 1],\n",
" [2, 1, 2, 2, 0, 1, 1, 0, 2, 1, 1, 3],\n",
" [2, 3, 1, 0, 0, 2, 2, 1, 2, 1, 2, 2],\n",
" [4, 1, 4, 4, 1, 0, 1, 2, 1, 1, 1, 0],\n",
" [4, 2, 4, 4, 1, 0, 0, 1, 1, 1, 0, 0],\n",
" [2, 3, 1, 0, 0, 1, 1, 2, 0, 1, 3, 1],\n",
" [1, 1, 1, 1, 0, 1, 1, 0, 1, 2, 1, 1],\n",
" [3, 2, 1, 0, 0, 1, 1, 1, 3, 3, 2, 2],\n",
" [4, 3, 1, 0, 0, 2, 1, 4, 2, 2, 3, 2],\n",
" [2, 1, 1, 0, 0, 1, 1, 1, 2, 1, 2, 1],\n",
" [2, 4, 1, 0, 0, 1, 0, 0, 2, 1, 1, 1],\n",
" [3, 2, 2, 0, 0, 2, 3, 3, 2, 1, 2, 2],\n",
" [2, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 3],\n",
" [1, 2, 2, 0, 1, 2, 2, 1, 2, 3, 1, 3],\n",
" [2, 1, 2, 2, 1, 0, 1, 1, 2, 2, 2, 3],\n",
" [2, 3, 2, 1, 1, 1, 2, 1, 0, 2, 3, 1],\n",
" [3, 2, 2, 0, 0, 2, 2, 2, 2, 2, 3, 2],\n",
" [2, 2, 1, 1, 0, 2, 1, 1, 2, 2, 2, 2],\n",
" [2, 4, 1, 0, 0, 2, 1, 0, 0, 2, 1, 1],\n",
" [2, 2, 1, 0, 0, 1, 0, 1, 2, 2, 2, 1],\n",
" [2, 2, 2, 2, 0, 2, 2, 1, 2, 1, 0, 3],\n",
" [2, 2, 1, 0, 0, 2, 2, 2, 3, 3, 3, 2],\n",
" [2, 3, 1, 0, 0, 0, 2, 0, 2, 1, 3, 1],\n",
" [4, 2, 3, 3, 0, 1, 3, 0, 1, 2, 2, 0],\n",
" [1, 2, 1, 0, 0, 2, 0, 1, 1, 2, 2, 1],\n",
" [1, 3, 2, 0, 0, 0, 2, 0, 2, 1, 2, 1],\n",
" [2, 2, 2, 1, 0, 0, 2, 0, 0, 0, 2, 3],\n",
" [1, 1, 1, 0, 0, 1, 0, 0, 1, 2, 2, 1],\n",
" [2, 3, 2, 0, 0, 2, 2, 1, 1, 2, 0, 3],\n",
" [0, 3, 1, 0, 0, 2, 2, 1, 1, 2, 1, 1],\n",
" [2, 2, 1, 0, 0, 1, 0, 1, 2, 1, 0, 3],\n",
" [2, 3, 0, 0, 1, 0, 2, 1, 1, 2, 2, 1]]\n",
"\n",
"feature_names = ['Body', 'Sweetness', 'Smoky', \n",
" 'Medicinal', 'Tobacco', 'Honey',\n",
" 'Spicy', 'Winey', 'Nutty',\n",
" 'Malty', 'Fruity', 'cluster']\n",
"\n",
"brand_names = ['Aberfeldy',\n",
" 'Aberlour',\n",
" 'AnCnoc',\n",
" 'Ardbeg',\n",
" 'Ardmore',\n",
" 'ArranIsleOf',\n",
" 'Auchentoshan',\n",
" 'Auchroisk',\n",
" 'Aultmore',\n",
" 'Balblair',\n",
" 'Balmenach',\n",
" 'Belvenie',\n",
" 'BenNevis',\n",
" 'Benriach',\n",
" 'Benrinnes',\n",
" 'Benromach',\n",
" 'Bladnoch',\n",
" 'BlairAthol',\n",
" 'Bowmore',\n",
" 'Bruichladdich',\n",
" 'Bunnahabhain',\n",
" 'Caol Ila',\n",
" 'Cardhu',\n",
" 'Clynelish',\n",
" 'Craigallechie',\n",
" 'Craigganmore',\n",
" 'Dailuaine',\n",
" 'Dalmore',\n",
" 'Dalwhinnie',\n",
" 'Deanston',\n",
" 'Dufftown',\n",
" 'Edradour',\n",
" 'GlenDeveronMacduff',\n",
" 'GlenElgin',\n",
" 'GlenGarioch',\n",
" 'GlenGrant',\n",
" 'GlenKeith',\n",
" 'GlenMoray',\n",
" 'GlenOrd',\n",
" 'GlenScotia',\n",
" 'GlenSpey',\n",
" 'Glenallachie',\n",
" 'Glendronach',\n",
" 'Glendullan',\n",
" 'Glenfarclas',\n",
" 'Glenfiddich',\n",
" 'Glengoyne',\n",
" 'Glenkinchie',\n",
" 'Glenlivet',\n",
" 'Glenlossie',\n",
" 'Glenmorangie',\n",
" 'Glenrothes',\n",
" 'Glenturret',\n",
" 'Highland Park',\n",
" 'Inchgower',\n",
" 'Isle of Jura',\n",
" 'Knochando',\n",
" 'Lagavulin',\n",
" 'Laphroig',\n",
" 'Linkwood',\n",
" 'Loch Lomond',\n",
" 'Longmorn',\n",
" 'Macallan',\n",
" 'Mannochmore',\n",
" 'Miltonduff',\n",
" 'Mortlach',\n",
" 'Oban',\n",
" 'OldFettercairn',\n",
" 'OldPulteney',\n",
" 'RoyalBrackla',\n",
" 'RoyalLochnagar',\n",
" 'Scapa',\n",
" 'Speyburn',\n",
" 'Speyside',\n",
" 'Springbank',\n",
" 'Strathisla',\n",
" 'Strathmill',\n",
" 'Talisker',\n",
" 'Tamdhu',\n",
" 'Tamnavulin',\n",
" 'Teaninich',\n",
" 'Tobermory',\n",
" 'Tomatin',\n",
" 'Tomintoul',\n",
" 'Tormore',\n",
" 'Tullibardine']"
]
},
{
"cell_type": "code",
"execution_count": 16,
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}
}
}
},
"outputs": [],
"source": [
"features_df = pd.DataFrame(features, columns=feature_names, index=brand_names)\n",
"features_df = features_df.drop('cluster', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
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}
},
"outputs": [],
"source": [
"norm = (features_df ** 2).sum(axis=1).apply('sqrt')\n",
"normed_df = features_df.divide(norm, axis=0)\n",
"sim_df = normed_df.dot(normed_df.T)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"extensions": {
"jupyter_dashboards": {
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}
}
}
}
},
"outputs": [],
"source": [
"def radar(df, ax=None):\n",
" # calculate evenly-spaced axis angles\n",
" num_vars = len(df.columns)\n",
" theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars)\n",
" # rotate theta such that the first axis is at the top\n",
" theta += np.pi/2\n",
" if not ax:\n",
" fig = plt.figure(figsize=(4, 4))\n",
"\n",
" ax = fig.add_subplot(1,1,1, projection='polar')\n",
" else:\n",
" ax.clear()\n",
" for d, color in zip(df.itertuples(), sns.color_palette()):\n",
" ax.plot(theta, d[1:], color=color, alpha=0.7)\n",
" ax.fill(theta, d[1:], facecolor=color, alpha=0.5)\n",
" ax.set_xticklabels(df.columns)\n",
"\n",
" legend = ax.legend(df.index, loc=(0.9, .95))\n",
" return ax"
]
},
{
"cell_type": "code",
"execution_count": 19,
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},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [],
"source": [
"def get_similar(name, n, top=True):\n",
" a = sim_df[name].sort_values(ascending=False)\n",
" a.name = 'Similarity'\n",
" df = pd.DataFrame(a) #.join(features_df).iloc[start:end]\n",
" return df.head(n) if top else df.tail(n)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
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"col": null,
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"hidden": true,
"locked": false,
"row": null,
"width": 2
},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [],
"source": [
"def on_pick_scotch(Scotch):\n",
" name = Scotch\n",
" # Get top 6 similar whiskeys, and remove this one\n",
" top_df = get_similar(name, 6).iloc[1:]\n",
" # Get bottom 5 similar whiskeys\n",
" df = top_df\n",
" \n",
" # Make table index a set of links that the radar widget will watch\n",
" df.index = ['''<a class=\"scotch\" href=\"#\" data-factors_keys='[\"{}\",\"{}\"]'>{}</a>'''.format(name, i, i) for i in df.index]\n",
" \n",
" tmpl = f'''<p>If you like {name} you might want to try these five brands. Click one to see how its taste profile compares.</p>'''\n",
" prompt_w.value = tmpl\n",
" table.value = df.to_html(escape=False)\n",
" lines.x = features_df.loc[Scotch].index.values\n",
" lines.y = features_df.loc[Scotch].values"
]
},
{
"cell_type": "code",
"execution_count": 21,
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}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9793f6c5c68647e29bbfa50519370ea2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HTML(value='Aberfeldy')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"prompt_w = widgets.HTML(value='Aberfeldy')\n",
"display(prompt_w)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"extensions": {
"jupyter_dashboards": {
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"row": 7,
"width": 4
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}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "219ba9be2b984eaf9aa3432da29c2aae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HTML(value='Hello <b>World</b>')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table = widgets.HTML(\n",
" value=\"Hello <b>World</b>\"\n",
")\n",
"display(table)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
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"col": 4,
"height": 9,
"hidden": false,
"locked": false,
"row": 4,
"width": 8
},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51195e849bb740e9a6cfe5edcb244c96",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Figure(animation_duration=500, axes=[Axis(scale=OrdinalScale(), tick_rotate=45, tick_style={'font-size': 20}),…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from bqplot import (OrdinalScale, LinearScale, Bars, Lines,\n",
" Figure, Axis, ColorScale, ColorAxis, CATEGORY10)\n",
"x_ord = OrdinalScale()\n",
"y_sc = LinearScale()\n",
"\n",
"lines = Lines(x=features_df.loc['Aberfeldy'].index.values,\n",
" y=features_df.loc['Aberfeldy'].values, scales={'x': x_ord, 'y': y_sc},\n",
" fill='bottom', fill_colors=['#aaaaff'], fill_opacities=[0.4],\n",
" stroke_width=3)\n",
"ax_x = Axis(scale=x_ord, tick_rotate=45, tick_style={'font-size': 20})\n",
"ax_y = Axis(scale=y_sc, tick_format='0.2f', orientation='vertical')\n",
"\n",
"Figure(marks=[lines], axes=[ax_x, ax_y], animation_duration=500)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
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"col": 0,
"height": 3,
"hidden": false,
"locked": false,
"row": 4,
"width": 4
},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c3cce0f1be5540c999a2975031ae887e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='Scotch', options=('Aberfeldy', 'Aberlour', 'AnCnoc', 'Ardbeg', 'Ar…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"picker_w = widgets.interact(on_pick_scotch, Scotch=list(sim_df.index))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
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}
}
}
}
},
"source": [
"Powered by data from https://www.strath.ac.uk and inspired by analysis from http://blog.revolutionanalytics.com/2013/12/k-means-clustering-86-single-malt-scotch-whiskies.html. This dashboard originated as a Jupyter Notebook."
]
}
],
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"anaconda-cloud": {},
"celltoolbar": "Edit Metadata",
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
},
"voila": {
"template": "gridstack"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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jupyter_config.json
{
"LabApp": { "expose_app_in_browser": true }
}
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lorenz.ipynb
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lsp.ipynb
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postBuild
# generate non minified assets so it's easier to step into the code on Binder
# jupyter lab build --minimize=False
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requirements.txt
ipykernel>=6
jupyterlab
jupyter-collaboration
jupyterlab-lsp
voila-gridstack
jupyterlab-link-share
xeus-python
ipywidgets
flake8
pylint
python-lsp-server
pylsp-mypy
ruamel_yaml
black
isort
mypy
ipyleaflet
numpy
bqplot
matplotlib
pandas
scikit-learn
seaborn
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