advanced-topics/data-visualization.ipynb

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   "source": [
    "# Data Visualization\n",
    "\n",
    "You can use `matplotlib` and `seaborn` to visualize data. For better performance, you should use `numpy` to convert list data to `ndarray` collection."
   ],
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   "source": [
    "## 1 Install Python packages\n",
    "\n",
    "You may need to install the following libraries if you haven't installed the Python packages. \n",
    "\n",
    "- In Windows, run commands:  `pip install matplotlib` and `pip install seaborn` \n",
    "- In MacOS, run commands: `pip3 install matplotlib` and `pip3 install seaborn`"
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   "source": [
    "## 2 Display Dice Rolling"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import random\n",
    "\n",
    "rolls = [random.randrange(1, 7) for i in range(600)]\n",
    "values, frequencies = np.unique(rolls, return_counts = True)\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "axes = sns.barplot(x=values, y=frequencies, palette='bright')\n",
    "\n",
    "title = f'Rolling a Six-sided Dice {len(rolls):,} times'\n",
    "axes.set_title(title)\n",
    "axes.set(xlabel='Dice Value', ylabel='Frequency')\n",
    "\n",
    "# make some space for text above the bar top\n",
    "axes.set_ylim(top = max(frequencies) * 1.10)\n",
    "\n",
    "# show frequency and % for each bar\n",
    "for bar, frequency in zip(axes.patches, frequencies):\n",
    "    text_x = bar.get_x() + bar.get_width() / 2.0\n",
    "    text_y = bar.get_height()\n",
    "    text = f'{frequency:,}\\n{frequency / len(rolls):.3%}'\n",
    "    axes.text(text_x, text_y, text, fontsize=11, ha='center', va='bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 3 Dynamic Visualization\n",
    "\n",
    "The following animation code doesn't work as a Python note code in VS Code. Please copy, paste the code and run it from a separate `.py` file. \n",
    "\n",
    "```python\n",
    "from matplotlib import animation\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import seaborn as sns\n",
    "import sys\n",
    "\n",
    "NUMBER_OF_FRAMES = 600\n",
    "ROLLS_PER_FRAME = 10\n",
    "\n",
    "# this function is called by animation to render each frame\n",
    "# the first argument frame_number is provided by animation\n",
    "def update(frame_number, faces, frequencies):\n",
    "    for _ in range(ROLLS_PER_FRAME):  # roll the dice for the specified times\n",
    "        dice_key = random.randrange(1, 7) - 1\n",
    "        frequencies[dice_key] += 1\n",
    "    \n",
    "    plt.cla() # clear old content\n",
    "    axes = sns.barplot(x=faces, y=frequencies, palette='bright')\n",
    "    total_rolls = sum(frequencies)\n",
    "    title = f'Dice frequencies for {total_rolls:,} rolls'\n",
    "    axes.set_title(title)\n",
    "    axes.set(xlabel='Dice Value', ylabel='Frequency')\n",
    "    \n",
    "    # make some space for text above the bar top\n",
    "    axes.set_ylim(top = max(frequencies) * 1.10)\n",
    "    \n",
    "    # show frequency and % for each bar\n",
    "    for bar, frequency in zip(axes.patches, frequencies):\n",
    "        text_x = bar.get_x() + bar.get_width() / 2.0\n",
    "        text_y = bar.get_height()\n",
    "        text = f'{frequency:,}\\n{frequency / total_rolls:.3%}'\n",
    "        axes.text(text_x, text_y, text, fontsize=11, ha='center', va='bottom')\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "figure = plt.figure('Rolling a Sixe-sided Dice')\n",
    "values = list(range(1, 7))\n",
    "frequencies = [0] * 6\n",
    "\n",
    "die_animation = animation.FuncAnimation(\n",
    "    figure, update, repeat=False, frames=NUMBER_OF_FRAMES, interval=33,\n",
    "    fargs=(values, frequencies)\n",
    ")\n",
    "\n",
    "plt.show()\n",
    "```"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}