Introduction-to-Pandas--master.ipynb

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   "source": [
    "* Merge on common column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      Key  Value_x  Value_y\n",
       "0   First        1        2\n",
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    "pd.merge(df_1, df_2, on=\"Key\")"
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   "source": [
    "## Task 3\n",
    "<a name=\"task3\"></a>\n",
    "\n",
    "* Add a column to the Nest data frame called `Virtual Processes` 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",
    "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
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       "      <td>23.21</td>\n",
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       "   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  \\\n",
       "0             112500             1265738500         1.5         1.5   \n",
       "1             112500             1265738500         1.5         1.5   \n",
       "2             112500             1265738500         1.5         1.5   \n",
       "3             112500             1265738500         1.5         1.5   \n",
       "4             112500             1265738500         1.5         1.5   \n",
       "\n",
       "   Virtual Processes  \n",
       "0                  8  \n",
       "1                 16  \n",
       "2                 16  \n",
       "3                 32  \n",
       "4                 16  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Virtual Processes\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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', 'Virtual Processes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Aside: Plotting without Pandas\n",
    "\n",
    "### Matplotlib 101\n",
    "\n",
    "* Matplotlib: de-facto standard for plotting in Python\n",
    "* Main interface: `pyplot`; provides MATLAB-like interface\n",
    "* Better: Use object-oriented API with `Figure` and `Axis`\n",
    "* Great integration into Jupyter Notebooks\n",
    "* Since v. 3: Only support for Python 3\n",
    "* → https://matplotlib.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0, 2*np.pi, 400)\n",
    "y = np.sin(x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.set_title('Use like this')\n",
    "ax.set_xlabel(\"Numbers again\");\n",
    "ax.set_ylabel(\"$\\sqrt{x}$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Plot multiple lines into one canvas\n",
    "* Call `ax.plot()` multiple times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "y2 = y/np.exp(y*1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, label=\"y\")\n",
    "ax.plot(x, y2, label=\"y2\")\n",
    "ax.legend()\n",
    "ax.set_title(\"This plot makes no sense\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 4\n",
    "<a name=\"task4\"></a>\n",
    "\n",
    "* Sort the data frame by the virtual proccesses\n",
    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\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\n",
    "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df.sort_values([\"Virtual Processes\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df[\"Virtual Processes\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\")\n",
    "ax.plot(df[\"Virtual Processes\"], df[\"Sim. Time / s\"], \"-b\")\n",
    "ax.set_xlabel(\"Virtual Process\")\n",
    "ax.set_ylabel(\"Time / s\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting with Pandas\n",
    "\n",
    "* Each data frame hast a `.plot()` function (see [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html))\n",
    "* Plots with Matplotlib\n",
    "* Important API options:\n",
    "    - `kind`: `line` (default), `bar[h]`, `hist`, `box`, `kde`, `scatter`, `hexbin`\n",
    "    - `subplots`: Make a sub-plot for each column (good together with `sharex`, `sharey`)\n",
    "    - `figsize`\n",
    "    - `grid`: Add a grid to plot (use Matplotlib options)\n",
    "    - `style`: Line style per column (accepts list or dict)\n",
    "    - `logx`, `logy`, `loglog`: Logarithmic plots\n",
    "    - `xticks`, `yticks`: Use values for ticks\n",
    "    - `xlim`, `ylim`: Limits of axes\n",
    "    - `yerr`, `xerr`: Add uncertainty to data points\n",
    "    - `stacked`: Stack a bar plot\n",
    "    - `secondary_y`: Use a secondary `y` axis for this plot\n",
    "    - Labeling\n",
    "        * `title`: Add title to plot (Use a list of strings if `subplots=True`)\n",
    "        * `legend`: Add a legend\n",
    "        * `table`: If `true`, add table of data under plot\n",
    "    - `**kwds`: Every non-parsed keyword is passed through to Matplotlib's plotting methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Either slice and plot…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[\"C\"].plot(figsize=(10, 2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* … or plot and select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo.plot(y=\"C\", figsize=(10, 2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* I prefer slicing first, as it allows for further operations on the sliced data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[\"C\"].plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are pseudo-sub-functions for each of the plot `kind`s\n",
    "* I prefer to just call `.plot(kind=\"smthng\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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_demo[\"C\"].plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "exercise": "task",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Task 5\n",
    "<a name=\"task5\"></a>\n",
    "\n",
    "Use the NEST data frame `df` to:\n",
    "\n",
    "1. Make the virtual processes the index of the data frame (`.set_index()`)\n",
    "2. Plot `\"Presim. Program / 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 labels\n",
    "\n",
    "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "exercise": "solution",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df.set_index(\"Virtual Processes\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Presim. Time / s\"].plot(figsize=(10, 3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "exercise": "solution"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Sim. Time / s\"].plot(figsize=(10, 3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Presim. Time / s\"].plot();\n",
    "df[\"Sim. Time / s\"].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n",
    "ax.set_ylabel(\"Time / s\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## More Plotting with Pandas\n",
    "### Our first proper Pandas plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **That's why I think Pandas is great!**\n",
    "* It has great defaults to quickly plot data\n",
    "* Plotting functionality is very versatile\n",
    "* Before plotting, data can be *massaged* within data frames, if needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## More Plotting with Pandas\n",
    "### Some versatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n",
    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots\", figsize=(12, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"