Introduction-to-Pandas--solution.ipynb

{
 "cells": [
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     "task"
    ]
   },
   "source": [
    "# Data Analysis and Plotting in Python with Pandas\n",
    "\n",
    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 2 June 2021_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "onlysolution",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Version: Solutions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Task Outline\n",
    "\n",
    "* [Task 1](#task1)\n",
    "* [Task 2](#task2)\n",
    "* [Task 3](#task3)\n",
    "* [Task 4](#task4)\n",
    "* [Task 5](#task5)\n",
    "* [Task 6](#task6)\n",
    "* [Task 7](#task7)\n",
    "* [Task 7B](#task7b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 1\n",
    "<a name=\"task1\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "* Create data frame with\n",
    "    - 6 names of dinosaurs, \n",
    "    - their favourite prime number, \n",
    "    - and their favorite color.\n",
    "* Play around with the frame\n",
    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "nopresentation",
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Jupyter Notebook 101:\n",
    "\n",
    "* Execute cell: `shift+enter`\n",
    "* New cell in front of current cell: `a`\n",
    "* New cell after current cell: `b`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "happy_dinos = {\n",
    "    \"Dinosaur Name\": [],\n",
    "    \"Favourite Prime\": [],\n",
    "    \"Favourite Color\": []\n",
    "}\n",
    "#df_dinos = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Dinosaur Name</th>\n",
       "      <th>Aegyptosaurus</th>\n",
       "      <th>Tyrannosaurus</th>\n",
       "      <th>Panoplosaurus</th>\n",
       "      <th>Isisaurus</th>\n",
       "      <th>Triceratops</th>\n",
       "      <th>Velociraptor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Favourite Prime</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favourite Color</th>\n",
       "      <td>blue</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "      <td>purple</td>\n",
       "      <td>violet</td>\n",
       "      <td>gray</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n",
       "Favourite Prime             4             8            15        16   \n",
       "Favourite Color          blue         white          blue    purple   \n",
       "\n",
       "Dinosaur Name   Triceratops Velociraptor  \n",
       "Favourite Prime          23           42  \n",
       "Favourite Color      violet         gray  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy_dinos = {\n",
    "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n",
    "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n",
    "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n",
    "}\n",
    "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n",
    "df_dinos.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 2\n",
    "<a name=\"task2\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "* Read in `data-nest.csv` to `DataFrame`; call it `df`  \n",
    "  *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n",
    "* Get to know it and play a bit with it\n",
    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "exercise": "task"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n",
      "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n",
      "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n",
      "5,1,2,8,202.15,10,true,0.28,47.98,48.48,0.70,1.20,7.95,142.81,47699384.00,802865,7.03,112500,1265738500,1.5,1.5\n",
      "5,1,4,8,89.57,10,true,0.15,20.41,23.21,0.23,3.04,3.19,60.31,46813040.00,821491,7.23,112500,1265738500,1.5,1.5\n",
      "5,2,2,4,164.16,10,true,0.20,40.03,41.09,0.52,1.58,6.08,114.88,46937216.00,802865,7.03,112500,1265738500,1.5,1.5\n",
      "5,2,4,4,77.68,10,true,0.13,20.93,21.22,0.16,0.46,3.12,52.05,47362064.00,821491,7.23,112500,1265738500,1.5,1.5\n",
      "5,2,2,8,79.60,10,true,0.20,21.63,21.91,0.19,0.47,2.98,53.12,46847168.00,821491,7.23,112500,1265738500,1.5,1.5\n",
      "5,2,4,8,37.20,10,true,0.13,10.08,11.60,0.10,1.63,1.24,23.29,47065232.00,818198,7.33,112500,1265738500,1.5,1.5\n",
      "5,3,2,4,96.51,10,true,0.15,26.54,27.41,0.36,1.22,3.33,64.28,52256880.00,813743,7.27,112500,1265738500,1.5,1.5\n"
     ]
    }
   ],
   "source": [
    "!head data-nest.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
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       "      <th>id</th>\n",
       "      <th>Nodes</th>\n",
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       "      <th>Runtime Program / s</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Plastic</th>\n",
       "      <th>Avg. Neuron Build Time / s</th>\n",
       "      <th>Min. Edge Build Time / s</th>\n",
       "      <th>Max. Edge Build Time / s</th>\n",
       "      <th>...</th>\n",
       "      <th>Max. Init. Time / s</th>\n",
       "      <th>Presim. Time / s</th>\n",
       "      <th>Sim. Time / s</th>\n",
       "      <th>Virt. Memory (Sum) / kB</th>\n",
       "      <th>Local Spike Counter (Sum)</th>\n",
       "      <th>Average Rate (Sum)</th>\n",
       "      <th>Number of Neurons</th>\n",
       "      <th>Number of Connections</th>\n",
       "      <th>Min. Delay</th>\n",
       "      <th>Max. Delay</th>\n",
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       "      <th>0</th>\n",
       "      <td>5</td>\n",
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       "      <td>88.18</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20</td>\n",
       "      <td>17.26</td>\n",
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       "      <td>825499</td>\n",
       "      <td>7.48</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>200.84</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.15</td>\n",
       "      <td>46.03</td>\n",
       "      <td>46.34</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01</td>\n",
       "      <td>7.87</td>\n",
       "      <td>142.97</td>\n",
       "      <td>46903088.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>202.15</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.28</td>\n",
       "      <td>47.98</td>\n",
       "      <td>48.48</td>\n",
       "      <td>...</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.95</td>\n",
       "      <td>142.81</td>\n",
       "      <td>47699384.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>89.57</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.15</td>\n",
       "      <td>20.41</td>\n",
       "      <td>23.21</td>\n",
       "      <td>...</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.19</td>\n",
       "      <td>60.31</td>\n",
       "      <td>46813040.0</td>\n",
       "      <td>821491</td>\n",
       "      <td>7.23</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>164.16</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.20</td>\n",
       "      <td>40.03</td>\n",
       "      <td>41.09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.58</td>\n",
       "      <td>6.08</td>\n",
       "      <td>114.88</td>\n",
       "      <td>46937216.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
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       "<p>5 rows \u00d7 21 columns</p>\n",
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      "text/plain": [
       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
       "0   5      1           2             4               420.42     10     True   \n",
       "1   5      1           4             4               200.84     10     True   \n",
       "2   5      1           2             8               202.15     10     True   \n",
       "3   5      1           4             8                89.57     10     True   \n",
       "4   5      2           2             4               164.16     10     True   \n",
       "\n",
       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
       "0                        0.29                     88.12   \n",
       "1                        0.15                     46.03   \n",
       "2                        0.28                     47.98   \n",
       "3                        0.15                     20.41   \n",
       "4                        0.20                     40.03   \n",
       "\n",
       "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n",
       "0                     88.18  ...                 1.20             17.26   \n",
       "1                     46.34  ...                 1.01              7.87   \n",
       "2                     48.48  ...                 1.20              7.95   \n",
       "3                     23.21  ...                 3.04              3.19   \n",
       "4                     41.09  ...                 1.58              6.08   \n",
       "\n",
       "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
       "0         311.52               46560664.0                     825499   \n",
       "1         142.97               46903088.0                     802865   \n",
       "2         142.81               47699384.0                     802865   \n",
       "3          60.31               46813040.0                     821491   \n",
       "4         114.88               46937216.0                     802865   \n",
       "\n",
       "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n",
       "0                7.48             112500             1265738500         1.5   \n",
       "1                7.03             112500             1265738500         1.5   \n",
       "2                7.03             112500             1265738500         1.5   \n",
       "3                7.23             112500             1265738500         1.5   \n",
       "4                7.03             112500             1265738500         1.5   \n",
       "\n",
       "   Max. Delay  \n",
       "0         1.5  \n",
       "1         1.5  \n",
       "2         1.5  \n",
       "3         1.5  \n",
       "4         1.5  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data-nest.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Task 3\n",
    "<a name=\"task3\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "* Add a column to the Nest data frame form Task 2 called `Threads` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <th>Avg. Neuron Build Time / s</th>\n",
       "      <th>Min. Edge Build Time / s</th>\n",
       "      <th>Max. Edge Build Time / s</th>\n",
       "      <th>...</th>\n",
       "      <th>Presim. Time / s</th>\n",
       "      <th>Sim. Time / s</th>\n",
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       "      <th>Average Rate (Sum)</th>\n",
       "      <th>Number of Neurons</th>\n",
       "      <th>Number of Connections</th>\n",
       "      <th>Min. Delay</th>\n",
       "      <th>Max. Delay</th>\n",
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       "      <th>0</th>\n",
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       "      <td>825499</td>\n",
       "      <td>7.48</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>200.84</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.15</td>\n",
       "      <td>46.03</td>\n",
       "      <td>46.34</td>\n",
       "      <td>...</td>\n",
       "      <td>7.87</td>\n",
       "      <td>142.97</td>\n",
       "      <td>46903088.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>202.15</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.28</td>\n",
       "      <td>47.98</td>\n",
       "      <td>48.48</td>\n",
       "      <td>...</td>\n",
       "      <td>7.95</td>\n",
       "      <td>142.81</td>\n",
       "      <td>47699384.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>89.57</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.15</td>\n",
       "      <td>20.41</td>\n",
       "      <td>23.21</td>\n",
       "      <td>...</td>\n",
       "      <td>3.19</td>\n",
       "      <td>60.31</td>\n",
       "      <td>46813040.0</td>\n",
       "      <td>821491</td>\n",
       "      <td>7.23</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>164.16</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.20</td>\n",
       "      <td>40.03</td>\n",
       "      <td>41.09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.08</td>\n",
       "      <td>114.88</td>\n",
       "      <td>46937216.0</td>\n",
       "      <td>802865</td>\n",
       "      <td>7.03</td>\n",
       "      <td>112500</td>\n",
       "      <td>1265738500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows \u00d7 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
       "0   5      1           2             4               420.42     10     True   \n",
       "1   5      1           4             4               200.84     10     True   \n",
       "2   5      1           2             8               202.15     10     True   \n",
       "3   5      1           4             8                89.57     10     True   \n",
       "4   5      2           2             4               164.16     10     True   \n",
       "\n",
       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
       "0                        0.29                     88.12   \n",
       "1                        0.15                     46.03   \n",
       "2                        0.28                     47.98   \n",
       "3                        0.15                     20.41   \n",
       "4                        0.20                     40.03   \n",
       "\n",
       "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n",
       "0                     88.18  ...             17.26         311.52   \n",
       "1                     46.34  ...              7.87         142.97   \n",
       "2                     48.48  ...              7.95         142.81   \n",
       "3                     23.21  ...              3.19          60.31   \n",
       "4                     41.09  ...              6.08         114.88   \n",
       "\n",
       "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n",
       "0               46560664.0                     825499                7.48   \n",
       "1               46903088.0                     802865                7.03   \n",
       "2               47699384.0                     802865                7.03   \n",
       "3               46813040.0                     821491                7.23   \n",
       "4               46937216.0                     802865                7.03   \n",
       "\n",
       "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  Threads  \n",
       "0             112500             1265738500         1.5         1.5        8  \n",
       "1             112500             1265738500         1.5         1.5       16  \n",
       "2             112500             1265738500         1.5         1.5       16  \n",
       "3             112500             1265738500         1.5         1.5       32  \n",
       "4             112500             1265738500         1.5         1.5       16  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Threads\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n",
       "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n",
       "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n",
       "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n",
       "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n",
       "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n",
       "       'Min. Delay', 'Max. Delay', 'Threads'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 4\n",
    "<a name=\"task4\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "\n",
    "* Sort the Nest data frame by threads\n",
    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n",
    "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n",
    "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n",
    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df.sort_values([\"Threads\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)  # multi-level sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df[\"Threads\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\", label=\"Presim. Time / s\")\n",
    "ax.plot(df[\"Threads\"], df[\"Sim. Time / s\"], \"-b\", label=\"Sim. Time / s\")\n",
    "ax.set_xlabel(\"Threads\")\n",
    "ax.set_ylabel(\"Time / s\")\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 5\n",
    "<a name=\"task5\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "Use the Nest data frame `df` to:\n",
    "\n",
    "1. Make threads index of the data frame (`.set_index()`)\n",
    "2. Plot `\"Presim. Time / s\"` and `\"Sim. Time / s`\" individually\n",
    "3. Plot them onto one common canvas!\n",
    "4. Make them have the same line colors and styles as before\n",
    "5. Add a legend, add missing axes labels\n",
    "6. Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df.set_index(\"Threads\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Presim. Time / s\"].plot(figsize=(10, 3), style=\"--\", color=\"red\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Sim. Time / s\"].plot(figsize=(10, 3), style=\"-b\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Presim. Time / s\"].plot(style=\"--r\");\n",
    "df[\"Sim. Time / s\"].plot(style=\"-b\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"]);\n",
    "ax.set_ylabel(\"Time / s\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 6\n",
    "<a name=\"task6\"></a>\n",
    "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
    "\n",
    "* To your `df` Nest data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n",
    "(*I know this is technically not super correct, but it will do for our example.*)\n",
    "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n",