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sitc/ml21/visualization/02_01_More_Comparison_Charts.ipynb

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Added visualization notebooks
2024-04-03 20:53:02 +00:00
{
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"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
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"cell_type": "markdown",
"metadata": {
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},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
]
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"cell_type": "markdown",
"metadata": {
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"source": [
"## [Introduction to Visualization](00_Intro_Visualization.ipynb)"
]
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"# More Comparison Charts\n",
"\n",
"Other charts can be created with libraries such as **dash plotly**.\n",
"\n",
"First, you should install dash:\n",
"\n",
"**conda install -c plotly plotly**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Radar Charts"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"df = sns.load_dataset('tips')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Calculate the average tip per day."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
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"data": {
"text/plain": [
"day\n",
"Thur 2.771452\n",
"Fri 2.734737\n",
"Sat 2.993103\n",
"Sun 3.255132\n",
"Name: tip, dtype: float64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Get a Pandas Series\n",
"avg_tip = df.groupby('day')['tip'].mean()\n",
"avg_tip"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"outputs": [
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"data": {
"text/plain": [
"['Thur', 'Fri', 'Sat', 'Sun']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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"source": [
"days = list(avg_tip.keys())\n",
"days"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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},
"outputs": [
{
"data": {
"text/plain": [
"[2.7714516129032254, 2.734736842105263, 2.993103448275862, 3.2551315789473683]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"avg_tips = avg_tip.to_list()\n",
"avg_tips"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Thur', 'Fri', 'Sat', 'Sun']\n",
"[2.7714516129032254, 2.734736842105263, 2.993103448275862, 3.2551315789473683]\n"
]
}
],
"source": [
"#A simpler alternative\n",
"days = list(avg_tip.index)\n",
"avg_tips = list(avg_tip.values)\n",
"print(days)\n",
"print(avg_tips)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"df_avg = pd.DataFrame(dict(\n",
" r=avg_tips,\n",
" theta=days))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
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},
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}
],
"source": [
"import plotly.express as px\n",
"\n",
"fig = px.line_polar(df_avg, r='r', theta='theta', line_close=True)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
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},
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.line_polar(df_avg, r='r', theta='theta', line_close=True)\n",
"fig.update_traces(fill='toself')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"day\n",
"Thur 17.682742\n",
"Fri 17.151579\n",
"Sat 20.441379\n",
"Sun 21.410000\n",
"Name: total_bill, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Get a Pandas Series\n",
"avg_bill = df.groupby('day')['total_bill'].mean()\n",
"avg_bill"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[17.682741935483868, 17.15157894736842, 20.44137931034483, 21.41]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_bills = avg_bill.to_list()\n",
"avg_bills"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
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{
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure()\n",
"\n",
"fig.add_trace(go.Scatterpolar(\n",
" r=avg_bills,\n",
" theta=days,\n",
" fill='toself',\n",
" name='Bills'\n",
"))\n",
"fig.add_trace(go.Scatterpolar(\n",
" r=avg_tips,\n",
" theta=days,\n",
" fill='toself',\n",
" name='Tips'\n",
"))\n",
"\n",
"fig.update_layout(\n",
" polar=dict(\n",
" radialaxis=dict(\n",
" visible=True,\n",
" range=[0, 25]\n",
" )),\n",
" showlegend=False\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# References\n",
"* [Seaborn](http://seaborn.pydata.org/index.html) documentation\n",
"\n",
"* [plotly](https://plot.ly/) documentation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## Licence\n",
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
],
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