Section 7/7.ipynb

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   "source": [
    "## Import modules"
   ]
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "## Read our dataset"
   ]
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   "execution_count": 2,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.49</td>\n",
       "      <td>29.02</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.46</td>\n",
       "      <td>82.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.88</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.31</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.75</td>\n",
       "      <td>11.01</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.96</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Rank                      Name Platform    Year         Genre Publisher  \\\n",
       "0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   \n",
       "1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   \n",
       "2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   \n",
       "3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   \n",
       "4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   \n",
       "\n",
       "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \n",
       "0     41.49     29.02      3.77         8.46         82.74  \n",
       "1     29.08      3.58      6.81         0.77         40.24  \n",
       "2     15.85     12.88      3.79         3.31         35.82  \n",
       "3     15.75     11.01      3.28         2.96         33.00  \n",
       "4     11.27      8.89     10.22         1.00         31.37  "
      ]
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "sales = pd.read_csv('vgsales.csv')\n",
    "sales.shape\n",
    "sales.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(sales.Global_Sales)\n",
    "plt.show()"
   ]
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   "cell_type": "code",
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   "metadata": {},
   "source": [
    "### Data - Regional sales, grouped by Genre "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>877.83</td>\n",
       "      <td>525.00</td>\n",
       "      <td>159.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>105.80</td>\n",
       "      <td>64.13</td>\n",
       "      <td>52.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fighting</td>\n",
       "      <td>223.59</td>\n",
       "      <td>101.32</td>\n",
       "      <td>87.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Misc</td>\n",
       "      <td>410.24</td>\n",
       "      <td>215.98</td>\n",
       "      <td>107.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Platform</td>\n",
       "      <td>447.05</td>\n",
       "      <td>201.63</td>\n",
       "      <td>130.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Puzzle</td>\n",
       "      <td>123.78</td>\n",
       "      <td>50.78</td>\n",
       "      <td>57.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Racing</td>\n",
       "      <td>359.42</td>\n",
       "      <td>238.39</td>\n",
       "      <td>56.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>327.28</td>\n",
       "      <td>188.06</td>\n",
       "      <td>352.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>582.60</td>\n",
       "      <td>313.27</td>\n",
       "      <td>38.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Simulation</td>\n",
       "      <td>183.31</td>\n",
       "      <td>113.38</td>\n",
       "      <td>63.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sports</td>\n",
       "      <td>683.35</td>\n",
       "      <td>376.85</td>\n",
       "      <td>135.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>68.70</td>\n",
       "      <td>45.34</td>\n",
       "      <td>49.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Genre  NA_Sales  EU_Sales  JP_Sales\n",
       "0         Action    877.83    525.00    159.95\n",
       "1      Adventure    105.80     64.13     52.07\n",
       "2       Fighting    223.59    101.32     87.35\n",
       "3           Misc    410.24    215.98    107.76\n",
       "4       Platform    447.05    201.63    130.77\n",
       "5         Puzzle    123.78     50.78     57.31\n",
       "6         Racing    359.42    238.39     56.69\n",
       "7   Role-Playing    327.28    188.06    352.31\n",
       "8        Shooter    582.60    313.27     38.28\n",
       "9     Simulation    183.31    113.38     63.70\n",
       "10        Sports    683.35    376.85    135.37\n",
       "11      Strategy     68.70     45.34     49.46"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre = sales[[ \"Genre\", 'NA_Sales','EU_Sales','JP_Sales']].groupby(['Genre'], as_index=False).sum()\n",
    "genre"
   ]
  },
  {
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   "execution_count": 4,
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    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(15, 5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.13, 1.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1: Change the size of the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(1, figsize=(15, 5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.13, 1.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2: Save Plot images to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(genre['NA_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(genre['NA_Sales'])\n",
    "plt.savefig('out.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3: Move legend out of the plot area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot with legend inside the lot area\n",
    "f, ax = plt.subplots(1, figsize=(12,5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot with legend inside the lot area\n",
    "f, ax = plt.subplots(1, figsize=(12,5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "plt.legend();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot with legend outside the lot area\n",
    "f, ax = plt.subplots(1, figsize=(12,5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.11, 1.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5: clear a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(sales.Global_Sales)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.cla()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.6: Change font sizes of plot elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(15, 5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.13, 1.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family' : 'sans-serif',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(15, 5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\")\n",
    "ax.set_xlabel(\"Genre\");\n",
    "ax.set_ylabel(\"Sales\");\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.13, 1.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r3aMt41Scuo1tdaiIeElb7mc/tYNeprlzvq/K37e/rXI9T4aqxOB6tnsqmWKxo50vKTfrwn8U2Z/7Dh/axiRT7ljfb7voLSK2x6kNiYI0yZNr/5hSs7rY3l7JgI5S0osEAFAlSBQAQBvY3s32hbaHNlNuXyXjFEjJKPxvZXbnBpY7rMQtBudoy69xrRIRD0t6JF39L9tlLwbSC4aS60VkkwNvtD5CScngZDOLxGIliQgrmVrt18Uqp7dG5MYh+Iykf02ft3ZKxEJXacuI+F+y/fW2jsUQES9LuiFdPadgPIWt2N6l1EVdJ8klgA5NB9osyfaAdPDI1sid86UuxL+vZPT+jvJHJTN9SNLFZb53ozowBklSRPxTW3oVTC9yblypJBHWU9I1zc08YnuPgk2522aGqsg0pOk5/f3Wxp2R69Gzm4oP1GoltyflZv34ZWEZAEDXRaIAANqmTtI3JL1i+7e2P2F7L9vDbA+3PdX2t5V00e+jZLCu8wvayF1AjpN0q+0D07p72/6JkoEEn1HbfUrJvcTDJT1q+5u297E91PZ2tvezfabtv0j6e0Hdn9l+Mr1InmZ7h7TeXrbPk3RRWu7h9Jfotlgs6bPpNHm5uA6QdLOSWSMk6ftlpo+TtlysvDs9ztA2JgrSe8lP0JbR3C+S9Jjts9I4R9oebHtX2x+2/Ttt6c1QrDv92ZKWKDkP7rH9PdsHpJ/1cNsTbZ9q+xZJi1R+0LeO9nNJf0mf/8D2DbYPs7297SG2d7d9rO0rldxvXzh1ZXNy5/y7bV9re9/0O3OA7euVDGS4Led8WRGxXlt+vX+npLtsH5rGsGc6I8OFajqoZUeaqaSnkZT8e5KNdZmkL6Sr0yTNtf2pNEk52PaOaez/YfspSd8rqD9bW6ZX/ZbtH6Xf36G2D5R0i5KxUxa3MfYblQxsKiXJyB8WtH+zksEqJemSiGhu9gUAQFcSESwsLCwsrVwk7SFpo5IL0+aWVZKOK9JGjaT/LVPvISV/yOfWRxdpI7fv1BJxHqDkgq65GN8oqDe7BXWel7RbG967xWn985XcPlCq/Zsl9WymLSu5uM7V+Us7fsYDJV2rJMnTks/5Dknjy5wv81vQxiZJQwrqXp3um10m1tGZNt7V0n0l2uqv5CKwJcd8dCvf0/6SHivT3h8knZ5bL1L/XeW+D0XO36tL7L+iTAz3SzqjVAytPMdLfmaZsldmXnvfIvs/I2lDCz6L64vUHdrM+32Bkh47Iel/i9T/dO68LBH7MCXTM5aL6xoV+R5Lel+mzDvKvD8PpmWuaq/vNgsLCwtL8ws9CgCgDSLiOSXTGH5c0k8lPapk/IFNSv6oX6Lkl9lzJO0RETcWaWOzkjnSz1Py6/VGSW9KmiPpy0ouitoycn/2NR5Vcg/yF9J4XlPyC2a9kgvs30s6UVvml885RdL/U/IL8FNKBi/bpOQ2g/vT+CZFxPPbEp+S2wXOVPL+rVZyvI8quTj6SERsaub4Qk0HLry6eMnWi4jVEfEJSZOU/Mr8sJL53hvSOP8p6U5JMySNjYgjImJBibaek7Svkvf1tkw7G5RcVN6m5Ji3j2QMiIqJiLURcZyS6TCvVTKoXr2S82aZpPuUnLNjI+KPrW1bya/j31WSaHpLybn1oJIeMMepeK+MdhURn5N0vJJjyZ13T0j6mqT3asvYAZ3hO9rSq+DrhTsj4udKuvd/R9JcSSuVJK9WS3pc0n8rueg+sUjdFUp6TvyHkn9jNih5v++RdExEzNA2zEoQEW8o6VVyhpLpLlekx7JUaY+FiDilue8xAKDrcfI3FgAA1cn2V5R0u16j5EK7vsIhAVUjnapxgqRLI2KrsQYAAG9P9CgAAFS7U9PH60kSAC1ne1dJe6WrcysZCwCgayFRAACoWrbfrS0zQ/y8krEAXY3tgel0j8X21SiZlcBKbkm4vTNjAwB0bUX/8wAAoKtKL3BqlIwdcEW6+YF0PAYAWxwg6Qrb/y3pbiXjavSRNFXJ+CmHpOV+GBGrijcBoKuZO3fukB49epxSU1NzUkQMV5LwA1oibL++efPm6xobG6+ZMmVKyX/7GaMAAFBVbM+WND2zaaOkgyJiXmUiArom2++TdFczxW6QdHJEdOYAjgDaaO7cub169ux5w9ChQycNHz58bZ8+fRps8gRomYjQhg0bei1fvnzAypUrn9i0adPxU6ZMKfrvP7ceAACq1Rolo9a/lyQBUNQcSWcpmVVjkZKZEhqUzMpyi5KZDz5GkgCoKv86cODASe94xztW1NXVkSRAq9hWXV1dw0477fTGwIEDJymZvat4WXoUdK7hw4fH6NGjKx0GAAAAgCpz5pln6sADD1RNTU2lQ9kmGzdu3DR58uTHKx3H29mbb77Z/8UXX3xs7733LposYIyCTjZ69GjNmTOn0mEAAAAAqDILFy7U7rvvXvWJgvnz59OTqcL69eu3XtLepfZz6wEAAAAAVIHGxkb16MElHLZdTU3N5ojoW2o/ZxkAAAAAVAnGJUB7aO48IlEAAAAAAADySBQAAAAAAIA8EgUAAAAAgKpne0p22X333Sds2rRpq3Jf+tKXdsyWu+yyy4aVa3fKlCnjCts+88wz39FhB9IFkCgAAAAAgO7Aropl4qRJfWVP6ei34/nnn+9z+eWXD9+WNp566qne8+bN61+4/aabbhpaLAnRXZAoAAAAAAB0SxdffPGOa9eubfMIkD//+c+L9jZYvnx57S233DKw7ZF1bSQKAAAAAADd0rJly2pnzpw5si11GxsbdcMNN+QTBT179ozs/muuuabsLQvVjEQBAAAAAKDbuvzyy7dftmxZTWvr3XHHHQOWLFnSK7f+oQ99aMUuu+yyMbd+1113DXnjjTda3W41IFEAAAAAAOhWtttuu7dyF/Vr1qyp+eY3v7lDa9u4+uqrm/QYOPHEE1cce+yxK3LrGzdu9NVXXz1k26PtekgUAAAAAAC6lZ49e8a3vvWtV3Lr11xzzXbPPfdcr3J1stasWdNj1qxZ+STA4MGDN334wx9e/clPfnJFttxvfvObbnn7Qc9KB4CuwRe0eXyPLilmRPOFAAAAAHRbp59++spLLrmkfv78+X0bGhr8ta99bcc//OEPi1tS91e/+tXg+vr6/A/rRxxxxMra2lpNnjx5w7hx49YvXLiwTpLmzZvXf/78+b0nTpy4sXRr1YceBQAAAACAbmnmzJkv557fcsstw/7+97/3aUm9X//6102mVTz55JPzPQk++tGPNulVcNVVV3W7XgUkCgAAAAAA3dJRRx21Ztq0aaulZBaDc8455x3N1Vm0aFHtI488MiC3PnLkyLcOP/zwtbn1U045ZYW9pUf2DTfcMKyxsbGdI6+sTk0U2N7V9hds/8r2AtuNtiOzjC5R710F5UotG8q8dj/bX7P9iO0VtjfYftH2dbYPaiZu2z7J9izbS203pI+zbJ/o7FkCAAAAAOgyvve9773co0dy6Tt79uxBDz30UP9y5a+66qomF/6rVq3qucMOO+w9cuTIvUeOHLn3tGnTxmcvAZcsWdLrjjvuGFCsrWrV2T0KPinpMkmfkDROUqdcYNseJ+lpSTMlHSBpiKTeknaWdJKkh2x/p0Td/pLuknSdpA9IGimpNn38gKRfS7rTdr8OPgwAAAAAQCsdeOCB648++ug3cuuPPvpo2Yv666+/vsmtBBs3bvRrr71Wm10KexAUzpBQ7So5mOEGSSGprg11/1Bie0PhBtsDJP1ZSVIg50lJL0uaJimXTTrP9ssRcUVBE7+S9N7M+iuSHpO0j6Rct5X3S/qlpI+14hgAAAAAAJ3g4osvXnLHHXcMbWhoKPtj9d13391v8eLFLRrHIGvWrFlDVq9e/dLAgQO7xT0Ind2j4CFJn5E0WdIASY+2pZGIOK7EcmKR4l9V0yTBTyNi74g4Io1jbWbfd9PEgiTJ9nskHZvZP0/S2Ig4SkmPiHmZfcfbnt6W4wEAAAAAdJyxY8c2fOITn3ituXK/+MUvmvQM+OpXv7okIuYWW6ZPn/5mrlx9fX2Pa6+9dnBHxF4JnZooiIi7IuKqiHgsIjZ10sueXrB+USaeRZJ+n9k3QNLxZepeEhH1ad16SZcW7P/UtoUKAAAAAOgIF1544av9+/ffXGr/+vXrffvttw/NbsvOdlDouOOOW5ldL5wpoZpV5awHti9KByH8le3v2j7a9la3UdgeI2lUZtPSiHi5oFhhr4ZpJZ5L0t9bURcAAAAA0EVsv/32m88666ylpfb/9re/Hbx69eqa3Pr48ePX77333htLlT/xxBNX9urVK3LrjzzyyIDnnnuuV/tFXDmVHKNgW3y9yLbFtj8ZEQ9kto0vKLOkSL1XC9bHS5LtOjW9ZaFY/cK6o233iYiSsy8AAAAAQIeIaL5MFzB//vz6iRMnPtPe7UbE3ObKzJw5c+nMmTOLJgtOP/30laeffnqzbeQMHTq0cePGjfOaL1l9qrJHQQmjJf2v7X0z2wrvEVlXpF7htiEl6hYrW6y9rerZPsP2HNtzli9fXqQKAAAAAABdQ7UkCjZIukXJ9IoTlMxUsKOSMQTeyJTrK6noNIepYiNcFm4rl4YrLFusva3qR8SVETE1IqaOGDGiTPMAAAAAAFRWVdx6EBF/U9PZB6Tk1/xf2l4p6ebM9vfa7hURDZJWFdTpW6T5wm2rCh4Ly65ppr03i2wDAAAAAKAqVEuPgnLuLljvJSk3pcXCgn2jtLXCbQslKSLWS/pnM2UL119kfAIAAAAAQDWrikRBsRkNMsYU2bZakiLiBUmvZLaPtF04QOEBBev3l3herGy5ugAAAAAAVJ2qSBRIetD2Oba3y260PVLSFQVl/xER2UEGf1Gw/xuZ+mMlHZfZt0bSDWXqnm27b1q3n6SzC/b/T9mjAAAAAACgi+vUMQpsHynpm5lNexUUudl2bp7KVyMiNy7B9pIuljTT9lOSXlQyO8FkbT1OwLcL1r8n6RRtmerwDNv/IullSYd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AQNvQowAAAAAAAOTRowAAAAAAcs7vRj2yZlQ6AFQrEgVAii7HAAAAAMCtBwAAAAAAIINEAQAAAAAAyCNRAAAAAAAA8kgUAAAAAACAPBIFAAAAAAAgj1kPAAAAACDFTFgAPQoAAAAAAEAGiQIAAAAAAJBHogAAAAAAAOSRKGgh20favsn2K7Y32l5ue7btz9murXR8AAAAAAC0BwYzbIbtnpKulnRSwa7hkqany6dtfyAiXuvk8AAAAAAAaFf0KGjef6lpkuANSX+StCizbbKkW23zfgIAAAAAqhoXtmXY3kPSFzOb/ilpfEQcJWm8pNsz+94p6ROdGB4AAAAAAO2OREF5p6jpe3RlRLwuSRGxWUlvg6xPdVZgAAAAAAB0BBIF5U0rWP97kfXIrB9ou1fHhgQAAAAAQMchUVDe+IL1JdmViNggaVVmUy9JYzo6KAAAAAAAOgqJgvIGF6yvK1KmcNuQDooFAAAAAIAOx/SIreMWbIutCthnSDojXV1re2F7B4YCLvZRdRnDJb3eoa/QtY+/c3Tt94BzoDN07feAc6AzdO33gHOgM3Tt94BzoDN03feg4z9/qSsf/y6VDgDlOWKr61qkbC+TtF1m094R8WRBmRVq2otgfESQCEBJtudExNRKx4HK4RwA5wA4B8A58PbG54+ujlsPyiu84B+VXbHdV02TBA2S/q+jgwIAAAAAoKOQKCjv/oL1AwrW9y9YfyQiGjowHgAAAAAAOhSJgvKukdSYWf+M7RGSZLunpHMLyv9PZwWGqnZlpQNAxXEOgHMAnAPgHHh74/NHl8YYBc2w/UNJ/57Z9Iakv0kaJ2n3zPZHJB0UEdnEAgAAAAAAVYVEQTPSngPXSDqxTLHHJH0gIpZ1TlQAAAAAAHQMbj1oRkRsioiTJH1I0q2Slkp6S9IKJWMYfF7SASQJUMh2L9uH2f5P23+xvdD2StsNtpfavtP2abZrKh0rOoftvrYX2Y7sUum40Dlsf9j29bZftL3e9mrbz9v+k+1zbfeqdIzoGLZ3tf0D23PT/wc22a63/YLtG2x/uNIxYtukn/EXbP/K9gLbjQX/1o9upv7Otn9k+xnba9PladuX2t6pc44C26Kt54DtCbbPSv9/eMz2q+nfim+m/2Z81/aoYnWBjkSPAqCD2J4q6e8tKPqIpA9GxMoODgkVZvtySWcVbo+ILjvJMbad7aGSbpT07maKjoiIjp9TG53K9lGSbpDUp5miv5N0YvCHWVWyfb6kGWWKjImIxSXqfkTSdZLqStRdL+njEfHHbYkRHaut54Dt+ZImNNP8GknHR8SdbQ4QaCV6FACdo0HSXyXNkvRywb4DJf200yNCp7L9biU9kPA2YruPpP/f3r2HWlaWcRz//vKeY95GMyyi0Rq74a0LaaUFZaYUopQpBDKJNlRQURF0VbSyoguVmnYTuoyYJZhEilh5yVCTKEjLytQmayzvk43N0x9r733WWZ4Zp/Hstefs8/3AZvb7rnet88Dec87ez3rf572S2UmCh4EbgR8BNw3amkKDWSLfYHaS4G7gMprXvu144E09habx+jfNl/vHleQgYBUzSYL1NDNWf8pMQe0dgIuS7D/PcWp8Nvk90HErzd+GGzr9OwEXJtnriQYmbSoTBdJ43UVzB3lpVR1aVa8HlgHndsYdm2T33qNTL5LsRPNlIcB9wL2TjUg9+ijQ/nC/iuau0ouq6uiqOhhYChwDPDSJADVWL6B5fYf+AOxTVUcNXvszOuNf0Vtkmm/XACcDB9J8qfvlJp73eWDrVvuEqjqsqg5ndn2sbQZjteXa3PfAozQ7IDy3qpYP/ja8GHgNzXLnoafQJBSlXmz9+EMkbaZbgeVVNevDf1WtS/JuYAUz/we3otlF455+Q1RPPgs8c/D8XcBpwC6TC0d9SLIEWNnq+i3w1qr6T3tcVa0FfthnbOrNuk77152/Cdd3jj8w5ng0JlV1ebudPP6KsiT7Mjs5dFtVrWpdc1WSM2luMAAcnmSfqrptHkLWPNuc98DA66vqr3Nc74okFwIntrqXb36E0v/HGQXSmJF0ZRoAAAc5SURBVFTV/d0kQevYWh57V/m+8UelviU5guYOA8AlVXXBJONRrw6juQM09B3gRUk+k+Q7Sc5NckqSXScUn8bvd8DtrfZrk7wuyQ5JljF7++X1wIW9RqdJ684gmauuUfeu9MvHFIsmZK4kQcvqTtvPiuqNMwqkCRgUOmxPR72LZkqqpkiSXYCvDZprgFMmGI76d3CnvYLHTjUHOCvJiqq6qIeY1KPBDLK30MwY2RNYQlOrpmsNsLKqftVnfJq4/Trtub4wdr8ods/RlEryJOCITvdVEwhFi5QzCqSeJdkZ+Hqn+4yqenQS8WisvgAMtzR6u9uoLjp7dtrL5hzVzDr43qDgpaZMVV0HvATYUBLgEeATNAXMtLh0l6DNNQux2+cMpMXjY8ALW+3rAXc9UG9MFEg9SvJUmgro7V/851fV2RMKSWOS5A3AWwfN73q3eFHado6+zwN70CSQ2tPMtwLO7CMo9SvJsTT1KQ4cdP0N+DEzux5sR1PH5Ooke/QfobYgcy1q7/a5feYikOR04MOtrtuBY90+VX0yUSD1JMk+NBVxD2p1n4/T0afVcGeL1TQ7X2jxub/T/gfwvqpaM1iTeioz258BvHSwQ4amRJJn09Sm2HHQdSWwrKqOHOx68LbW8AOB03sOUZPVrVX05DnGdPvcNWeKJdkqyfnAh1rdfwReVVV3TSgsLVImCqQeJDkYuBbYp9V9RlWdXFXrN3CaFrbhXse7AbcmWTN8AM9oD2wdO7L3KDVO3crkt7WXGFXVv2iSB0PBacXT5nhmzyw5e1DMFoCq+hrwYOv4UX0Fpi3CLZ323nOM6fZ1z9GUSLID8AOaejZDNwGHVNWfJhOVFjOLGUpjluQ1wMU0RawA/gu8o6rOmVxU6tF2g8fG7N4aq+lxTae9e7sxKFTVXqO8nqaonabH0zvtuaYNt/tcerC4/LzTfvEcY7p93XM0BZLsBlwKvKzV/ROa5QYPzn2WNF7OKJDGKMkJNAWqhkmCtTS/9E0SSFOuqn7NzDp0gGcneXWrfRKzk0PXVdXDvQSnvtzRaZ+SZPthI8kKoL3c5I+9RKUtQlX9ntlf/JcNdskARp8hntU6flVVdWcqaYFL8gzgamYnCS4AjjZJoEmKNTGk8UhyEHADswsR3QL8ZgOnfKmqrhp3XJq8JH8GnjlsV9VcBaw0BZK8kmY7q+FrvI5mpsG2NB8Kh/0FHFFVl/cdo8YnyXNofudv0+peDdxMsytGdwvND1bVJ3sKT/MoyVHMLj73PGYngW6m2eECYHVVHTM472DgF8zM8l3PTPLgFczc1FsHvKSqbp7/6DUfnsB74GZg/9a4f7PhXVB+W1UfnZ+IpY1z6YE0Pk/hsdWKlw8ec7l0vOFI6ltV/SzJScB5NF8WtwEO7wz7D/BOkwTTp6puTXIqcA4zyYKnDR5d36fZ/UAL0x7ASzdy/IDW89uHT6rqxiRvBr4NbE+TGDisc+5a4ASTBFu8zXoP8NhtMrcHjt3ANZZuRlzSZnHpgSRJY1RV3wJeQLMTxm00d4vW0swwOhvYv6q+OrkINU5V9XWa1/9zNEtR7qOpVbOWZqnBhcAbquq4qlo3sUA1MVV1MbAf8EWa3wsPDx63DPr2q6ofTi5CSYuRSw8kSZIkSdKIMwokSZIkSdKIiQJJkiRJkjRiokCSJEmSJI2YKJAkSZIkSSMmCiRJkiRJ0oiJAkmSJEmSNGKiQJIkSZIkjZgokCRJkiRJIyYKJElaYJIcn+T6JA8l+fvg+cokmXRskiRp4TNRIEnSApLkvcAXgE8DewFPBU4FDgW2neeftdV8Xk+SJC0MJgokSVogkuwMnAasrKqLquqBavyqqk6sqkeSbJfkM0n+kuTuJOck2WFw/uFJ7kzy3sFMhNVJTmpd/5tJzk5yWZKHgFdt7HqSJGk6mSiQJGnheBmwHXDJRsZ8CngOcACwL7A38JHW8b2AnQf9K4AvJ9m1dfwE4AxgJ+DqTbieJEmaMiYKJElaOJYCa6rq0WFHkmuT3JtkbZLDgJOBd1fVP6vqAeBM4PjWNdYBp1XVuqq6DHgQWN46fklVXVNV64FHNuF6kiRpymw96QAkSdImuwdYmmTrYbKgqg4BSHInTb2CJwM3tuoaBmjXGrinnWgAHgaWtNp3tJ7vsQnXkyRJU8YZBZIkLRzX0dzlf+MGjq8B1gLPr6pdBo+dq2rJBsbPpeb5epIkaYExUSBJ0gJRVfcCHwe+kuS4JEuSPCnJAcCOwHrgPOBzSfYESLJ3kiM28+fN6/UkSdLCYKJAkqQFpKrOAt4DvB/4O3A3cC7wAeDawb9/AH6R5H7gCmbXIPh/zff1JEnSFi5V9fijJEmSJEnSouCMAkmSJEmSNGKiQJIkSZIkjZgokCRJkiRJIyYKJEmSJEnSiIkCSZIkSZI0YqJAkiRJkiSNmCiQJEmSJEkjJgokSZIkSdKIiQJJkiRJkjTyPywp3w7/q1eaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(15, 5))\n",
    "\n",
    "bar_l = [i+1 for i in range(len(genre['NA_Sales']))]\n",
    "ax.bar(bar_l, genre['NA_Sales'], label='NA', color='red')\n",
    "ax.bar(bar_l, genre['EU_Sales'], bottom=genre['NA_Sales'], label='EU',color='blue')\n",
    "ax.bar(bar_l, genre['JP_Sales'], bottom=[i+j for i,j in zip(genre['NA_Sales'],genre['EU_Sales'])], label='Japan',color='green')\n",
    "\n",
    "ax.set_title(\"Sales by Genre and Region\", fontsize=26)\n",
    "ax.set_xlabel(\"Genre\", fontsize=12);\n",
    "ax.set_ylabel(\"Sales\", fontsize=12);\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.13, 1.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.7: Troubleshoot Value Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.2'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/opt/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/harish/.config/matplotlib'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.get_configdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/harish/.cache/matplotlib'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.get_cachedir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "# set DEBUG for everything\n",
    "logging.basicConfig(level=logging.DEBUG)\n",
    "logger = logging.getLogger('matplotlib')\n",
    "# set WARNING for Matplotlib\n",
    "\n",
    "logger.setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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