{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Information\n",
    "__Section__: Plot graphics  \n",
    "__Goal__: Get some tools to create plots with Python and Pandas.  \n",
    "__Time needed__: 15 min  \n",
    "__Prerequisites__: Curiosity\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot graphics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we work with data in this course, it is important to be able to represent graphically the data and results we will be working with.\n",
    "\n",
    "This notebook provides you with some basic methods to represent the basic information about a dataset.\n",
    "\n",
    "To know more about the different types of graphs, visit the page __TODO: add link to graphs page__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the distribution of the attributes of a dataset (with Pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The library ``Pandas`` comes with a method to easily represent some basic features about a Dataframe. The method [plot()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html) used on a Series or a Dataframe allows you to create several types of plots on the attributes.\n",
    "\n",
    "As an example, let's import an easy dataset and use the method ``plot()`` on the attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the data\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./trip9.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method ``hist()`` plots the histogram of the Series. The histogram represents the distribution of the values of the attribute in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63c76d780>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# plot the histogram of the attribute 'LAT'\n",
    "\n",
    "df['LAT'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method ``box()`` creates the boxplot of the attribute. The boxplot is another representation of the distribution of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63c7e7f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fr2hbKemopF+PiFcr2p+S9JfZ5hci4uPzWSswWwzLYCnZI+lS2z+1fZ/tj0v6DUn/XRnsmXFJl2fLM/NcJzBrhDuWjIh4XdJHJW2VNCnpG5K6JdX689W12m3/g+0Dtv99LmsFZosxdywpEXFK0vclfd/2jyR9VtJ626si4rWKrhslfTtb/0zF8XdlY/W8RgwLGnfuWDJsX2Z7Q0XTlZJ+IukBSX9nuyXrd4eklZK+ly0rbG+rOG7lPJUMzBh37lhKfk3SoO3Vkt6WdFinh2hek/S3kn5q+1eSnpP06chmG9j+lKS/t/05nR7O+YWkzxdQP5Abs2UAIEEMywBAggh3AEgQ4Q4ACSLcASBBhDsAJIhwB4AEEe4AkCDCHQAS9P+YsK6r9WOC0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SOG'].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method ``scatter()`` plots the values of 2 attributes against each other. For example, here we represent the attributes ``LON`` and ``LAT`` together to visualize the path of the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a63cdf7e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot.scatter('LON', 'LAT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other types of plots are possible with this method, visit the documentation for more information: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot some lists of values (with Matplotlib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Matplotlib](https://matplotlib.org/index.html) is a library that provides a lot of visualization tools for Python. In this course, we want to keep it simple and will only use a few methods of the [pyplot](https://matplotlib.org/api/pyplot_api.html) API.\n",
    "\n",
    "First, the library has to be imported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we need to create the figure with the method ``figure()``. We can specify the size of the figure we want to create:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot any value we want, for example, we plot again the attributes ``LAT`` and ``LON`` of the previously used dataset. We use the method [plot()](https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.plot.html), which comes with a lot of parameters. Here, we specify an ``'x'`` for the type of marker we want to plot, and ``'orange'`` for the color. We can specify a parameter ``label`` that can be used later for the legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a63eb7c5c0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(df['LON'], df['LAT'], marker = 'x', color = 'orange', label = 'Path taken by ship 09')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to add other plots to the graph. For example, we want to add a point with the coordinates [-122.72, 45.75]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a63ec88ac8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(-122.72, 45.75, marker = 'o', color = 'purple', label = 'Single point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can combine the last 3 cells to create our final plot. We print the legend with the method ``legend()``, specify a title with the method ``title`` and add the names of the two axes with the methods ``xlabel()`` and ``ylabel()``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Latitude')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(df['LON'], df['LAT'], marker = 'x', color = 'orange', label = 'Path taken by ship 09')\n",
    "plt.plot(-122.72, 45.75, marker = 'o', color = 'purple', label = 'Single point')\n",
    "plt.legend()\n",
    "plt.title('Example of figure')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')"
   ]
  }
 ],
 "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": "3.7.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}