exercise_13_matplotlib_seaborn.ipynb

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    "# Exercise 13 - Matplotlib & Seaborn\n",
    "\n",
    "## Plotting data\n",
    "\n",
    "In this exercise you will load some data and use various techniques to plot it.\n",
    "\n",
    "**Exercise 1:**\n",
    "\n",
    "In this excercise you will create a figure with 3 `Axes`, one per row, with `Matplotlib`. All axes will share their x-axis. The figure size will be set to `(6, 6)`. You will then plot data from ` lecture_13/station.dat` with line plots, add axis labels, grids, and add a vertical line.\n",
    "\n",
    "1. Import the necessary packages"
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    "2. Load the data from \"lecture_13/station.dat\""
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    "3. Recreate the following figure\n",
    "![](lecture_13/excercise_task_1_figure.png)"
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    "**Excercise 2:**\n",
    "\n",
    "In this exercise you will create some randomly distributed data with `NumPy`. You will then plot this data with `Seaborn`.\n",
    "\n",
    "1. Import the necessary packages"
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    "2. Create three randomly distributed datatasets\n",
    "\n",
    "- x = 500 samples, low=-6, high=8, uniform distribution\n",
    "- x2 = 1000 samples, Mean of the distribution = 5.0, normal distribution\n",
    "- x3 = 2000 samples, Mean of the distribution = -3.5, normal distribution"
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    "# DO NOT CHANGE. This is so that you get the same results as in the figure.\n",
    "import numpy as np\n",
    "np.random.seed(0)"
   ]
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    "3. Recreate the following figure\n",
    "\n",
    "![](lecture_13/excercise_task_2_figure.png)\n",
    "\n",
    "Note: Bins are set to auto."
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    "You can see that green and orange are normal, also called gaussian, distributed and that the blue data is uniformly distributed across the value range."
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