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" <td>True</td>\n",
" <td>0.220000</td>\n",
" <td>42.040000</td>\n",
" <td>42.838333</td>\n",
" <td>0.583333</td>\n",
" <td>...</td>\n",
" <td>7.226667</td>\n",
" <td>132.061667</td>\n",
" <td>4.806585e+07</td>\n",
" <td>816298.000000</td>\n",
" <td>7.215000</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>2.891667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.333333</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>73.601667</td>\n",
" <td>10.0</td>\n",
" <td>True</td>\n",
" <td>0.168333</td>\n",
" <td>19.628333</td>\n",
" <td>20.313333</td>\n",
" <td>0.191667</td>\n",
" <td>...</td>\n",
" <td>2.725000</td>\n",
" <td>48.901667</td>\n",
" <td>4.975288e+07</td>\n",
" <td>818151.000000</td>\n",
" <td>7.210000</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.986667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.333333</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>43.990000</td>\n",
" <td>10.0</td>\n",
" <td>True</td>\n",
" <td>0.138333</td>\n",
" <td>12.810000</td>\n",
" <td>13.305000</td>\n",
" <td>0.135000</td>\n",
" <td>...</td>\n",
" <td>1.426667</td>\n",
" <td>27.735000</td>\n",
" <td>5.511165e+07</td>\n",
" <td>820465.666667</td>\n",
" <td>7.253333</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.745000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.333333</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>31.225000</td>\n",
" <td>10.0</td>\n",
" <td>True</td>\n",
" <td>0.116667</td>\n",
" <td>9.325000</td>\n",
" <td>9.740000</td>\n",
" <td>0.088333</td>\n",
" <td>...</td>\n",
" <td>1.066667</td>\n",
" <td>19.353333</td>\n",
" <td>5.325783e+07</td>\n",
" <td>819558.166667</td>\n",
" <td>7.288333</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.275000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.333333</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>24.896667</td>\n",
" <td>10.0</td>\n",
" <td>True</td>\n",
" <td>0.140000</td>\n",
" <td>7.468333</td>\n",
" <td>7.790000</td>\n",
" <td>0.070000</td>\n",
" <td>...</td>\n",
" <td>0.771667</td>\n",
" <td>14.950000</td>\n",
" <td>6.075634e+07</td>\n",
" <td>815307.666667</td>\n",
" <td>7.225000</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.496667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5.333333</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>20.215000</td>\n",
" <td>10.0</td>\n",
" <td>True</td>\n",
" <td>0.106667</td>\n",
" <td>6.165000</td>\n",
" <td>6.406667</td>\n",
" <td>0.051667</td>\n",
" <td>...</td>\n",
" <td>0.630000</td>\n",
" <td>12.271667</td>\n",
" <td>6.060652e+07</td>\n",
" <td>815456.333333</td>\n",
" <td>7.201667</td>\n",
" <td>112500.0</td>\n",
" <td>1.265738e+09</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>0.990000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" id Tasks/Node Threads/Task Runtime Program / s Scale \\\n",
"Nodes \n",
"1 5.333333 3.0 8.0 185.023333 10.0 \n",
"2 5.333333 3.0 8.0 73.601667 10.0 \n",
"3 5.333333 3.0 8.0 43.990000 10.0 \n",
"4 5.333333 3.0 8.0 31.225000 10.0 \n",
"5 5.333333 3.0 8.0 24.896667 10.0 \n",
"6 5.333333 3.0 8.0 20.215000 10.0 \n",
"\n",
" Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n",
"Nodes \n",
"1 True 0.220000 42.040000 \n",
"2 True 0.168333 19.628333 \n",
"3 True 0.138333 12.810000 \n",
"4 True 0.116667 9.325000 \n",
"5 True 0.140000 7.468333 \n",
"6 True 0.106667 6.165000 \n",
"\n",
" Max. Edge Build Time / s Min. Init. Time / s ... Presim. Time / s \\\n",
"Nodes ... \n",
"1 42.838333 0.583333 ... 7.226667 \n",
"2 20.313333 0.191667 ... 2.725000 \n",
"3 13.305000 0.135000 ... 1.426667 \n",
"4 9.740000 0.088333 ... 1.066667 \n",
"5 7.790000 0.070000 ... 0.771667 \n",
"6 6.406667 0.051667 ... 0.630000 \n",
"\n",
" Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) \\\n",
"Nodes \n",
"1 132.061667 4.806585e+07 816298.000000 \n",
"2 48.901667 4.975288e+07 818151.000000 \n",
"3 27.735000 5.511165e+07 820465.666667 \n",
"4 19.353333 5.325783e+07 819558.166667 \n",
"5 14.950000 6.075634e+07 815307.666667 \n",
"6 12.271667 6.060652e+07 815456.333333 \n",
"\n",
" Average Rate (Sum) Number of Neurons Number of Connections \\\n",
"Nodes \n",
"1 7.215000 112500.0 1.265738e+09 \n",
"2 7.210000 112500.0 1.265738e+09 \n",
"3 7.253333 112500.0 1.265738e+09 \n",
"4 7.288333 112500.0 1.265738e+09 \n",
"5 7.225000 112500.0 1.265738e+09 \n",
"6 7.201667 112500.0 1.265738e+09 \n",
"\n",
" Min. Delay Max. Delay Unaccounted Time / s \n",
"Nodes \n",
"1 1.5 1.5 2.891667 \n",
"2 1.5 1.5 1.986667 \n",
"3 1.5 1.5 1.745000 \n",
"4 1.5 1.5 1.275000 \n",
"5 1.5 1.5 1.496667 \n",
"6 1.5 1.5 0.990000 \n",
"\n",
"[6 rows x 21 columns]"
]
},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(\"Nodes\").mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Pivoting\n",
"\n",
"* Combine categorically-similar columns\n",
"* Creates hierarchical index\n",
"* Respected during plotting!\n",
"* A pivot table has three *layers*; if confused, think about these questions\n",
" - `index`: »What's on the `x` axis?«\n",
" - `values`: »What value do I want to plot?«\n",
" - `columns`: »What categories do I want [to be in the legend]?«\n",
"* All can be populated from base data frame\n",
"* Might be aggregated, if needed"
]
},
{
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"df_demo[\"H\"] = [(-1)**n for n in range(5)]"
]
},
{
"cell_type": "code",
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"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>H</th>\n",
" <th>-1</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>F</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>-3.918282</th>\n",
" <td>NaN</td>\n",
" <td>7.389056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-2.504068</th>\n",
" <td>NaN</td>\n",
" <td>1.700594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-1.918282</th>\n",
" <td>NaN</td>\n",
" <td>0.515929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-0.213769</th>\n",
" <td>0.972652</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.518282</th>\n",
" <td>2.952492</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"H -1 1\n",
"F \n",
"-3.918282 NaN 7.389056\n",
"-2.504068 NaN 1.700594\n",
"-1.918282 NaN 0.515929\n",
"-0.213769 0.972652 NaN\n",
" 0.518282 2.952492 NaN"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pivot = df_demo.pivot_table(\n",
" index=\"F\",\n",
" values=\"G\",\n",
" columns=\"H\"\n",
")\n",
"df_pivot"
]
},
{
"cell_type": "code",
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"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_pivot.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Task 7\n",
"<a name=\"task7\"></a>\n",
"\n",
"* Create a pivot table based on the NEST `df` data frame\n",
"* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n",
"* Please plot a bar plot\n",
"* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"
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"metadata": {
"exercise": "solution",
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.pivot_table(\n",
" index=[\"Nodes\"],\n",
" columns=[\"Tasks/Node\", \"Threads/Task\"],\n",
" values=\"Sim. Time / s\",\n",
").plot(kind=\"bar\", figsize=(12, 4));"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<a name=\"taskb\"></a>\n",
"\n",
"* Bonus task\n",
" - Use `Sim. Time / s` and `Presim. Time / s` as values to show\n",
" - Show a stack of those two values inside the pivot table"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The End\n",
"\n",
"* Pandas works on data frames\n",
"* Slice frames to your likings\n",
"* Plot frames\n",
" - Together with Matplotlib, Seaborn, others\n",
"* Pivot tables are next level greatness\n",
"* Thanks for being here! 😍"
]
},
{
"cell_type": "markdown",
"metadata": {
"exercise": "task"
},
"source": [
"<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>"
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}