Iran_Protests_2022.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/Iran Protests Dataset (Ver 1.6.23).csv\")"
      ],
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      "execution_count": 4,
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 112 entries, 0 to 111\n",
            "Data columns (total 10 columns):\n",
            " #   Column                                        Non-Null Count  Dtype \n",
            "---  ------                                        --------------  ----- \n",
            " 0   Date                                          112 non-null    object\n",
            " 1   Death Toll of Protestors                      112 non-null    int64 \n",
            " 2   Number of Children Killed                     112 non-null    int64 \n",
            " 3   Number of Military-Security Personnel Killed  112 non-null    int64 \n",
            " 4   Number of Individuals Arrested                112 non-null    int64 \n",
            " 5   Number of Detainees Identified                112 non-null    int64 \n",
            " 6   Number of Students Arrested                   112 non-null    int64 \n",
            " 7   Number of Protests                            112 non-null    int64 \n",
            " 8   Number of Cities Involved                     112 non-null    int64 \n",
            " 9   Number of Universities Involved               112 non-null    int64 \n",
            "dtypes: int64(9), object(1)\n",
            "memory usage: 8.9+ KB\n"
          ]
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              "        Date  Death Toll of Protestors  Number of Children Killed  \\\n",
              "0  9/17/2022                         0                          0   \n",
              "1  9/18/2022                         0                          0   \n",
              "2  9/19/2022                         8                          0   \n",
              "3  9/20/2022                        28                          1   \n",
              "4  9/21/2022                        74                          8   \n",
              "5  9/22/2022                       100                         12   \n",
              "6  9/23/2022                       109                         13   \n",
              "7  9/24/2022                       111                         13   \n",
              "8  9/25/2022                       112                         13   \n",
              "9  9/26/2022                       113                         13   \n",
              "\n",
              "   Number of Military-Security Personnel Killed  \\\n",
              "0                                             0   \n",
              "1                                             0   \n",
              "2                                             0   \n",
              "3                                             1   \n",
              "4                                             9   \n",
              "5                                            10   \n",
              "6                                            10   \n",
              "7                                            10   \n",
              "8                                            11   \n",
              "9                                            11   \n",
              "\n",
              "   Number of Individuals Arrested  Number of Detainees Identified  \\\n",
              "0                              78                              13   \n",
              "1                             255                              29   \n",
              "2                             527                              58   \n",
              "3                            1040                             101   \n",
              "4                            1285                             145   \n",
              "5                            1956                             229   \n",
              "6                            2312                             277   \n",
              "7                            2472                             298   \n",
              "8                            2560                             325   \n",
              "9                            2655                             350   \n",
              "\n",
              "   Number of Students Arrested  Number of Protests  Number of Cities Involved  \\\n",
              "0                            0                   2                          2   \n",
              "1                            1                   7                          4   \n",
              "2                            5                  32                         14   \n",
              "3                            7                  76                         26   \n",
              "4                           12                 129                         43   \n",
              "5                           32                 147                         51   \n",
              "6                           39                 161                         58   \n",
              "7                           43                 179                         72   \n",
              "8                           52                 186                         78   \n",
              "9                           58                 195                         85   \n",
              "\n",
              "   Number of Universities Involved  \n",
              "0                                0  \n",
              "1                                2  \n",
              "2                                7  \n",
              "3                                9  \n",
              "4                               18  \n",
              "5                               35  \n",
              "6                               49  \n",
              "7                               50  \n",
              "8                               52  \n",
              "9                               53  "
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              "      <td>9/23/2022</td>\n",
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              "Date                                            0\n",
              "Death Toll of Protestors                        0\n",
              "Number of Children Killed                       0\n",
              "Number of Military-Security Personnel Killed    0\n",
              "Number of Individuals Arrested                  0\n",
              "Number of Detainees Identified                  0\n",
              "Number of Students Arrested                     0\n",
              "Number of Protests                              0\n",
              "Number of Cities Involved                       0\n",
              "Number of Universities Involved                 0\n",
              "dtype: int64"
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              "Index(['Date', 'Death Toll of Protestors', 'Number of Children Killed',\n",
              "       'Number of Military-Security Personnel Killed',\n",
              "       'Number of Individuals Arrested', 'Number of Detainees Identified',\n",
              "       'Number of Students Arrested', 'Number of Protests',\n",
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              "Date                                            112\n",
              "Death Toll of Protestors                         82\n",
              "Number of Children Killed                        38\n",
              "Number of Military-Security Personnel Killed     35\n",
              "Number of Individuals Arrested                   90\n",
              "Number of Detainees Identified                  111\n",
              "Number of Students Arrested                     100\n",
              "Number of Protests                               96\n",
              "Number of Cities Involved                        44\n",
              "Number of Universities Involved                  44\n",
              "dtype: int64"
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              "       Death Toll of Protestors  Number of Children Killed  \\\n",
              "count                112.000000                 112.000000   \n",
              "mean                 343.142857                  48.482143   \n",
              "std                  143.906367                  19.872106   \n",
              "min                    0.000000                   0.000000   \n",
              "25%                  243.000000                  36.000000   \n",
              "50%                  337.500000                  52.000000   \n",
              "75%                  482.000000                  68.000000   \n",
              "max                  517.000000                  70.000000   \n",
              "\n",
              "       Number of Military-Security Personnel Killed  \\\n",
              "count                                    112.000000   \n",
              "mean                                      41.964286   \n",
              "std                                       20.564523   \n",
              "min                                        0.000000   \n",
              "25%                                       26.000000   \n",
              "50%                                       39.000000   \n",
              "75%                                       62.000000   \n",
              "max                                       68.000000   \n",
              "\n",
              "       Number of Individuals Arrested  Number of Detainees Identified  \\\n",
              "count                      112.000000                      112.000000   \n",
              "mean                     13248.714286                     2236.625000   \n",
              "std                       6170.059099                     1512.028666   \n",
              "min                         78.000000                       13.000000   \n",
              "25%                       7770.000000                      727.500000   \n",
              "50%                      15219.500000                     1976.500000   \n",
              "75%                      18256.000000                     3660.250000   \n",
              "max                      19262.000000                     4721.000000   \n",
              "\n",
              "       Number of Students Arrested  Number of Protests  \\\n",
              "count                   112.000000          112.000000   \n",
              "mean                    397.392857          805.142857   \n",
              "std                     231.592036          388.939928   \n",
              "min                       0.000000            2.000000   \n",
              "25%                     173.750000          446.750000   \n",
              "50%                     441.500000          911.500000   \n",
              "75%                     613.250000         1170.000000   \n",
              "max                     693.000000         1241.000000   \n",
              "\n",
              "       Number of Cities Involved  Number of Universities Involved  \n",
              "count                 112.000000                       112.000000  \n",
              "mean                  130.732143                       112.348214  \n",
              "std                    35.992738                        40.724234  \n",
              "min                     2.000000                         0.000000  \n",
              "25%                   112.000000                        72.250000  \n",
              "50%                   139.000000                       137.000000  \n",
              "75%                   160.000000                       143.000000  \n",
              "max                   163.000000                       144.000000  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Death Toll of Protestors</th>\n",
              "      <th>Number of Children Killed</th>\n",
              "      <th>Number of Military-Security Personnel Killed</th>\n",
              "      <th>Number of Individuals Arrested</th>\n",
              "      <th>Number of Detainees Identified</th>\n",
              "      <th>Number of Students Arrested</th>\n",
              "      <th>Number of Protests</th>\n",
              "      <th>Number of Cities Involved</th>\n",
              "      <th>Number of Universities Involved</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>112.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>343.142857</td>\n",
              "      <td>48.482143</td>\n",
              "      <td>41.964286</td>\n",
              "      <td>13248.714286</td>\n",
              "      <td>2236.625000</td>\n",
              "      <td>397.392857</td>\n",
              "      <td>805.142857</td>\n",
              "      <td>130.732143</td>\n",
              "      <td>112.348214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>143.906367</td>\n",
              "      <td>19.872106</td>\n",
              "      <td>20.564523</td>\n",
              "      <td>6170.059099</td>\n",
              "      <td>1512.028666</td>\n",
              "      <td>231.592036</td>\n",
              "      <td>388.939928</td>\n",
              "      <td>35.992738</td>\n",
              "      <td>40.724234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>243.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>7770.000000</td>\n",
              "      <td>727.500000</td>\n",
              "      <td>173.750000</td>\n",
              "      <td>446.750000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>72.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>337.500000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>15219.500000</td>\n",
              "      <td>1976.500000</td>\n",
              "      <td>441.500000</td>\n",
              "      <td>911.500000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>137.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>482.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>18256.000000</td>\n",
              "      <td>3660.250000</td>\n",
              "      <td>613.250000</td>\n",
              "      <td>1170.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>143.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>517.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>19262.000000</td>\n",
              "      <td>4721.000000</td>\n",
              "      <td>693.000000</td>\n",
              "      <td>1241.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>144.000000</td>\n",
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              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
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              "          document.querySelector('#df-6394f972-db3b-4e3a-b023-9f0e5029f5d5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6394f972-db3b-4e3a-b023-9f0e5029f5d5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.lineplot(data=df['Death Toll of Protestors'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "iDXOzTrgHfka",
        "outputId": "1b142eed-32a7-4e02-db53-f67bbac61e08"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f574ff079a0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = df[\"Death Toll of Protestors\"].to_numpy()"
      ],
      "metadata": {
        "id": "43uM-hQAJElP"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHbQMU0IJei8",
        "outputId": "00107e53-8cdc-427c-b5a4-d871b2137631"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0,   0,   8,  28,  74, 100, 109, 111, 112, 113, 113, 116, 122,\n",
              "       206, 210, 219, 220, 220, 221, 221, 222, 230, 232, 234, 234, 243,\n",
              "       243, 243, 243, 243, 245, 245, 247, 247, 247, 249, 249, 250, 252,\n",
              "       252, 266, 272, 283, 284, 287, 287, 298, 300, 314, 318, 319, 321,\n",
              "       328, 328, 330, 336, 339, 341, 344, 348, 362, 381, 402, 410, 419,\n",
              "       434, 437, 440, 445, 448, 450, 451, 451, 455, 459, 462, 469, 470,\n",
              "       471, 473, 473, 475, 478, 481, 485, 488, 490, 493, 494, 495, 495,\n",
              "       496, 502, 503, 506, 506, 506, 506, 506, 507, 507, 507, 508, 508,\n",
              "       508, 511, 512, 516, 516, 516, 516, 517])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dt, linestyle = 'dotted')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JJ2TB-5dJgy3",
        "outputId": "52bda120-c622-42fd-f26c-aa12e5a24105"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f574fe26a30>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dc = df[\"Number of Children Killed\"].to_numpy()"
      ],
      "metadata": {
        "id": "ij5ec3V9KDky"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dc, ls=\"--\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3HFswSxrKgKZ",
        "outputId": "1893e5b5-2ff9-431b-c7fc-542f8ad6315f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f574fe15940>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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oCvQEfg1kmllqtBU+GNgZvzIlUbk7dzz9Pve9upXrz81lwqBeQZck0mE02QJ39++5+2B3zwWuAp5392uApcBnorvNBhbFrUpJWHc+t4Hfv7iJa84Zyr9fMV6zDYq0o9ZcSn8b8IiZ/Rh4G7inbUqSRFNT18Cu0oOHl1M6GUP6dOP1TR9w15INXJk3mB/NPE3hLdLOrD0n2M/Ly/P8/Px2+zxpvaLyaq7+4xts3Hvg8LpBmem8OvdC3J1/vruHS8bnkNJJ4S0SL2a2wt3zjl6vyazkpJ5ZW8jOkoP8x8xT6dE18nVJT4s8mxkzNE2sSGAU4MLaXfvZXlx5zPrzx/TlmnOGMX1sPwZlpgdQmYicjAJceHXjPn7y1Lpj1r986wV065Oq8BZJUArwDqyiuo6MLql8cdpwpo7KwjiyHzunZ9eAKhORWCjAQ6i+wfnT61vZf7COqaOyOCu3D2WVtdz/2tZj9p0+NpuJQzIpKq/moWXbD68/WFvPY/k7+NMXz+a0Qb04daDGb4uEjQI8hBat3Mntf18LQFrq2EiAH6zlzufWH7Nv74w0Jg7JZN+B6mO2n5Xbm6FZ3dqlZhFpexpGGDL1Dc5H73yRtJRO/O83z6OTRUaDuDvHO5V2ku2HtolIYtMwwiTx9JpCNhVV8D+fm3zE2Gsz42RZ3NR2EQkf3ZEnZHqlp3HZhP5cqnm2RTo8tcBDZtrovkwb3TfoMkQkASjAE5i7c9eSjSxaGZnocfzAnvxk1gTdY1JEAHWhJLRfPLOeO59bT07Prpw6qBdmxppdZUGXJSIJQi3wBFLf4NQ3OKmdjPLqOhat2snVZw/lp5/UTH8iciwFeILYW17Fxb94kf1Vdcy/Lo+Lx+fwt6+eS+9unRXeInJcCvAE8fSaQvZX1fHV6SMZ2a87AFnduwRclYgkMgV4gvjH6kJGZmdw64xxQZciIiGhHzETQHFFDcu2fKCx3SLSLGqBJ4CUTsb3Lx/Pv4zR+G4RiZ0CPAH0Sk/jhmnDgy5DREKmyS4UM+tqZsvNbJWZvWtmt0fXDzezZWa20cweNbPO8S83+eyvquUvKwrYX1UbdCkiEjKx9IFXAxe6+0RgEjDDzKYAdwB3uvsooAS4IX5lJq8l6/Zwy+Or2LDnQNM7i4g00mSAe8ShdEmLPhy4EFgYXb8AmBWXCpPc/76zm5yeXZg8JDPoUkQkZGIahWJmKWa2EtgLPAtsAkrdvS66SwEw6ATvnWNm+WaWX1RU1BY1J43fv7iJ59bt5bNnDqFTJ12sIyLNE1OAu3u9u08CBgNnAzEPVnb3ee6e5+552dnZLSwz+TyWv4Of/eM9Pj5xIN+6ZEzQ5YhICDVrFIq7l5rZUuAjQKaZpUZb4YOBnfEoMMz+fdEatuyrOGLd2Jwe/OCK8Zw/Opsbpw3ntkvHHXFjBhGRWMUyCiXbzDKjr9OBS4B1wFLgM9HdZgOL4lVkWLg781/eTHl0RMnBmnoqquuOeFTV1QPQv1dXfnDFeNJSdC2ViLRMLC3wAcACM0shEviPufuTZrYWeMTMfgy8DdwTxzoTnrtz+9/Xcv9rW+mSlsK1U4bx35+dGHRZIpLEmgxwd38HmHyc9ZuJ9Id3aHc+u56Hlm+nocH5oKKGG6YN5/PnDA26LBHpAHQlZiuNyenBxafkRF935wtTczX9q4i0CwV4C7y2cR8PLd/OL66cyOWnD+Dy0zUJlYi0PwV4M1XV1vOlB1YwMDOdgzX1dElNCbokEemgNASimYrKqymvruOG84aT2U3Tv4hIcBTgzVRSWQNAb4W3iARMAd5MJZWRMd69u6UFXImIdHQK8GbKTE/j8gkD6N+ra9CliEgHpx8xm2nikEx+c80ZQZchIqIWeHO5e9AliIgACvBmu/3vaznvv54PugwREQV4c31QUUOKrrQUkQSgAG+m0soajf8WkYSgAG+mksoaDSEUkYSgAG+mkopaXcQjIglBwwib6ROTBjI2p0fQZYiIKMCb67YZMd8OVEQkrtSF0gz1DU5VbX3QZYiIAArwZtmwt5xx//Y0T63eHXQpIiIK8OYoqYhMZJWZrlEoIhK8WO5KP8TMlprZWjN718xujq7vY2bPmtmG6HPv+JcbrNLoVLIaBy4iiSCWFngdcIu7jwemAF8zs/HAXGCJu48GlkSXk9rhqWQz1AIXkeA1GeDuvtvd34q+LgfWAYOAmcCC6G4LgFnxKjJR6GYOIpJImtUHbma5wGRgGZDj7od+zSsEck7wnjlmlm9m+UVFRa0oNXhnDO3N1y4YSdc03QdTRIJnsU6PambdgReBn7j7E2ZW6u6ZjbaXuPtJ+8Hz8vI8Pz+/VQWLiHQ0ZrbC3fOOXh9TC9zM0oC/AH929yeiq/eY2YDo9gHA3rYqNlHtLa+ivKo26DJERIDYRqEYcA+wzt1/2WjTYmB29PVsYFHbl5dYvvLgW3zpgRVBlyEiAsTWAj8XuBa40MxWRh+XAT8DLjGzDcDF0eWkVlJRQ+8M/YApIomhyblQ3P0V4ER3MLiobctJbJpKVkQSia7EjFFDg1N2UFPJikjiUIDHaH9VLQ2uqzBFJHFoOtkYpaV04ocfH885w7OCLkVEBFCAxyyjSyrXnzs86DJERA5TF0qMSitrWL+nnJq6hqBLEREBFOAxe27dXj5650vsLjsYdCkiIoACPGaaSlZEEo0CPEYllTWkdDJ6dtXPBiKSGBTgMSqprCUzPY3IzAIiIsFTgMeotLKGTF2FKSIJRP0BMbruI7mUHdRMhCKSOBTgMaipa2DKCF3AIyKJRV0oTXhzazEX/PwF1u7aH3QpIiJH6BAt8LLKWhat2sl1H8kF4ME3trG9uPKIffr16MKN540A4N5XtlC4v4qGBueRN3fQr2cXsnt0ae+yRUROKukDvLyqluvuW05h2UGunTIMM+O5dXtYtrn4iP3GDehxOMD/sWY3a3ZGWtwjsjO4Z/ZZCnARSThJHeAV1XVcf9+bvLuzjN99/szDQwDvv/7sk77v8S9PbY/yRERaJWkD3N358oMreHtHKXdfPZlLxucEXZKISJtK2h8xtxdX8vKGfXz7kjFcNmFA0OWIiLS5pG2B9+/VlYduOodR2d2DLkVEJC6SNsC7pKYwdWTfoMsQEYmbJrtQzOxeM9trZmsaretjZs+a2Yboc+/4ltl8v3thE2t2lgVdhohI3MTSB34/MOOodXOBJe4+GlgSXU4Yu8sOcsfT77FsS3HTO4uIhFSTAe7uLwFHJ+FMYEH09QJgVhvX1SrLo8F9zvA+AVciIhI/LR2FkuPuu6OvC4ETjtEzszlmlm9m+UVFRS38uOZZvqWY7l1SOWVAz3b5PBGRILR6GKG7O+An2T7P3fPcPS87O7u1HxeT5VuKycvtTUonzd0tIsmrpQG+x8wGAESf97ZdSa1TUV1H4f4qzlb3iYgkuZYOI1wMzAZ+Fn1e1GYVtVJGl1RW/vtHdfd4EUl6sQwjfBh4HRhrZgVmdgOR4L7EzDYAF0eXE0ZKJyO9c0rQZYiIxFWTLXB3v/oEmy5q41raxHceX8W4/h/OLCgikqyS7krMF97fS6p+vBSRDiCpJrM6UF3HvgM1DM3qFnQpIiJxl1QBvu2DCgByszICrkREJP6SLMAjt0kb2kctcBFJfkkV4ACj+nVnmLpQRKQDSKofMS+bMEA3bxCRDiPpWuAiIh1FUgX4p377Kr9ZujHoMkRE2kXSBHhVbT1v7yiltl6X0ItIx5A0AV5QUom7hhCKSMeRNAF+eAihRqCISAeRNAG+NRrgaoGLSEeRNAGe07MLl4zPoXe3tKBLERFpF0kzDvyK0wdyxekDgy5DRKTdJE0LvL7hhHd1ExFJSkkR4HX1DZz6w6f5w4ubgi5FRKTdJEWA7yqtoqq2gUz1f4tIB5IUAb6tODKN7DCNQBGRDiQpAnz9ngOAhhCKSMfSqgA3sxlm9r6ZbTSzuW1VVHMUlVfzy2feZ1z/HvTr0SWIEkREAtHiYYRmlgL8BrgEKADeNLPF7r62rYqLRXaPLvzkkxOYOjKLTroXpoh0IK0ZB342sNHdNwOY2SPATCAuAf7xu1+hqrb+8LIDc2eM4+LxOcyaPCgeHykiktBaE+CDgB2NlguAc47eyczmAHMAhg4d2uIPG5mdQc1RMw2+uL6IC8b1I0UtbxHpgOJ+Jaa7zwPmAeTl5bX4aptfXTW5zWoSEUkGrfkRcycwpNHy4Og6ERFpB60J8DeB0WY23Mw6A1cBi9umLBERaUqLu1Dcvc7Mvg78E0gB7nX3d9usMhEROalW9YG7+1PAU21Ui4iINENSXIkpItIRKcBFREJKAS4iElIKcBGRkDL39ruTjZkVAdta+Pa+wL42LCeR6NjCKVmPLVmPC8J7bMPcPfvole0a4K1hZvnunhd0HfGgYwunZD22ZD0uSL5jUxeKiEhIKcBFREIqTAE+L+gC4kjHFk7JemzJelyQZMcWmj5wERE5Upha4CIi0ogCXEQkpEIR4Ilw8+S2YGZDzGypma01s3fN7Obo+j5m9qyZbYg+9w661pYysxQze9vMnowuDzezZdFz92h06uHQMbNMM1toZu+Z2Toz+0iynDcz+1b0+7jGzB42s65hPW9mdq+Z7TWzNY3WHfc8WcRd0WN8x8zOCK7ylkn4AG908+RLgfHA1WY2PtiqWqwOuMXdxwNTgK9Fj2UusMTdRwNLosthdTOwrtHyHcCd7j4KKAFuCKSq1vs18LS7jwMmEjnG0J83MxsEfBPIc/fTiEwNfRXhPW/3AzOOWnei83QpMDr6mAP8rp1qbDMJH+A0unmyu9cAh26eHDruvtvd34q+LicSAoOIHM+C6G4LgFnBVNg6ZjYYuByYH1024EJgYXSXUB6bmfUCzgfuAXD3GncvJUnOG5FppdPNLBXoBuwmpOfN3V8Cio9afaLzNBP4k0e8AWSa2YD2qbRthCHAj3fz5NDfht7McoHJwDIgx913RzcVAjkBldVavwJuBQ7dfToLKHX3uuhyWM/dcKAIuC/aPTTfzDJIgvPm7juBnwPbiQR3GbCC5Dhvh5zoPIU+W8IQ4EnHzLoDfwH+1d33N97mkXGdoRvbaWZXAHvdfUXQtcRBKnAG8Dt3nwxUcFR3SYjPW28iLdHhwEAgg2O7IJJGWM/TiYQhwJPq5slmlkYkvP/s7k9EV+859Kdb9HlvUPW1wrnAJ8xsK5FurguJ9BtnRv80h/CeuwKgwN2XRZcXEgn0ZDhvFwNb3L3I3WuBJ4icy2Q4b4ec6DyFPlvCEOBJc/PkaJ/wPcA6d/9lo02LgdnR17OBRe1dW2u5+/fcfbC75xI5R8+7+zXAUuAz0d3CemyFwA4zGxtddRGwliQ4b0S6TqaYWbfo9/PQsYX+vDVyovO0GLguOhplClDWqKslHNw94R/AZcB6YBPw/aDracVxTCPy59s7wMro4zIifcVLgA3Ac0CfoGtt5XFOB56Mvh4BLAc2Ao8DXYKur4XHNAnIj567vwG9k+W8AbcD7wFrgAeALmE9b8DDRPrya4n85XTDic4TYERGuG0CVhMZiRP4MTTnoUvpRURCKgxdKCIichwKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISP0fAXlDy93ktvcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dms = df[\"Number of Military-Security Personnel Killed\"].to_numpy()"
      ],
      "metadata": {
        "id": "hVWCkl5HKs1W"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dms, ls=\"--\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "isDOFHLjLA9X",
        "outputId": "eaff771e-6bd9-4a8e-c008-52648e4659c1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f574fd1a730>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXd5Hm8jMsxD",
        "outputId": "fae66e9a-165a-42ec-feb0-31f0525a8320"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=112, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.set_index('Date', inplace=True)"
      ],
      "metadata": {
        "id": "5m_y2_v1fLf9"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "fig1 = px.line(df, x=df.index, y=\"Death Toll of Protestors\", title=\"Death Tolls\")\n",
        "fig1.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "NDGLjKExMq04",
        "outputId": "264968da-db56-4429-d562-6c9a9c827beb"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "fig2 = px.line(df, x=\"Date\", y=\"Number of Children Killed\", title=\"Children Killed\")\n",
        "fig3 = px.line(df, x=\"Date\", y=\"Number of Military-Security Personnel Killed\", title=\"Military Personnel Killed\")\n",
        "fig2.show()\n",
        "fig3.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N7hta9p1NWcr",
        "outputId": "09fa2639-12da-4fbb-a02c-010636a4f0a1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
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    {
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      "source": [
        "fig = px.bar(df, x= \"Date\", y = \"Death Toll of Protestors\", text_auto=True,\n",
        " title='Death Toll of Protestors')\n",
        "fig.show()"
      ],
      "metadata": {
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        "id": "C8etddy0O-_y",
        "outputId": "e8d032ca-65dc-4ddd-aee3-1809ff980591"
      },
      "execution_count": 32,
      "outputs": [
        {
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      "source": [
        "fig = px.bar(df, x= df.index, y = \"Number of Children Killed\", text_auto=True,\n",
        " title='Number of Children Killed')\n",
        "fig.show()"
      ],
      "metadata": {
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        "id": "aqcYnHIsPIWI",
        "outputId": "8fa8ac73-78ef-400e-9791-49b25bcbadab"
      },
      "execution_count": 33,
      "outputs": [
        {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "di = df[\"Number of Individuals Arrested\"].to_numpy()\n",
        "dd = df[\"Number of Detainees Identified\"].to_numpy()\n",
        "ds = df[\"Number of Students Arrested\"].to_numpy()\n",
        "\n"
      ],
      "metadata": {
        "id": "7bOj_l-5PhdS"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(di, ls=\"--\")\n",
        "plt.plot(dd, ls=\"--\")\n",
        "plt.plot(ds, ls=\"--\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "cxGFPu-sP_vo",
        "outputId": "1a880454-fe5e-48af-cf57-c536295f21b9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f574dfe3400>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(df, x = \"Date\", y=\"Number of Individuals Arrested\", title='Individuals arrested')\n",
        "fig1 = px.line(df, x = \"Date\", y=\"Number of Detainees Identified\", title = \"Detainees Identified\")\n",
        "fig2 = px.line(df, x = \"Date\", y=\"Number of Students Arrested\", title = \"Students Arrested\")\n",
        "fig.show()\n",
        "fig1.show()\n",
        "fig2.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eHqf27DVQ7T9",
        "outputId": "8ed5cc36-2e64-4495-cb63-9bce0027ec8d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
      "cell_type": "code",
      "source": [
        "corr = df.corr()\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "9EVKb1yqnYGW",
        "outputId": "6eb95097-2789-4a99-a6aa-a58b24ee8228"
      },
      "execution_count": 46,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "                                              Death Toll of Protestors  \\\n",
              "Death Toll of Protestors                                      1.000000   \n",
              "Number of Children Killed                                     0.990735   \n",
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              "Number of Protests                                            0.977519   \n",
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              "Number of Universities Involved                               0.923719   \n",
              "\n",
              "                                              Number of Children Killed  \\\n",
              "Death Toll of Protestors                                       0.990735   \n",
              "Number of Children Killed                                      1.000000   \n",
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              "\n",
              "                                              Number of Military-Security Personnel Killed  \\\n",
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              "Number of Children Killed                                                         0.982973   \n",
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              "                                              Number of Individuals Arrested  \\\n",
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              "                                              Number of Detainees Identified  \\\n",
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              "Number of Universities Involved                                  0.925488   \n",
              "\n",
              "                                              Number of Protests  \\\n",
              "Death Toll of Protestors                                0.977519   \n",
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              "\n",
              "                                              Number of Cities Involved  \\\n",
              "Death Toll of Protestors                                       0.947009   \n",
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              "                                              Number of Universities Involved  \n",
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              "Number of Cities Involved                                            0.959246  \n",
              "Number of Universities Involved                                      1.000000  "
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              "      <td>0.950166</td>\n",
              "      <td>0.978311</td>\n",
              "      <td>0.987820</td>\n",
              "      <td>0.985627</td>\n",
              "      <td>0.925056</td>\n",
              "      <td>0.922275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Individuals Arrested</th>\n",
              "      <td>0.938075</td>\n",
              "      <td>0.964254</td>\n",
              "      <td>0.950166</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.896989</td>\n",
              "      <td>0.946333</td>\n",
              "      <td>0.975909</td>\n",
              "      <td>0.926220</td>\n",
              "      <td>0.968566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Detainees Identified</th>\n",
              "      <td>0.964997</td>\n",
              "      <td>0.935614</td>\n",
              "      <td>0.978311</td>\n",
              "      <td>0.896989</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.982558</td>\n",
              "      <td>0.955834</td>\n",
              "      <td>0.850226</td>\n",
              "      <td>0.855206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Students Arrested</th>\n",
              "      <td>0.978865</td>\n",
              "      <td>0.970931</td>\n",
              "      <td>0.987820</td>\n",
              "      <td>0.946333</td>\n",
              "      <td>0.982558</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.989693</td>\n",
              "      <td>0.901361</td>\n",
              "      <td>0.925488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Protests</th>\n",
              "      <td>0.977519</td>\n",
              "      <td>0.984937</td>\n",
              "      <td>0.985627</td>\n",
              "      <td>0.975909</td>\n",
              "      <td>0.955834</td>\n",
              "      <td>0.989693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.933887</td>\n",
              "      <td>0.964385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Cities Involved</th>\n",
              "      <td>0.947009</td>\n",
              "      <td>0.963946</td>\n",
              "      <td>0.925056</td>\n",
              "      <td>0.926220</td>\n",
              "      <td>0.850226</td>\n",
              "      <td>0.901361</td>\n",
              "      <td>0.933887</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.959246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Universities Involved</th>\n",
              "      <td>0.923719</td>\n",
              "      <td>0.956846</td>\n",
              "      <td>0.922275</td>\n",
              "      <td>0.968566</td>\n",
              "      <td>0.855206</td>\n",
              "      <td>0.925488</td>\n",
              "      <td>0.964385</td>\n",
              "      <td>0.959246</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d8ab3c4-004c-42eb-aa2c-f637c4425d77')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d8ab3c4-004c-42eb-aa2c-f637c4425d77 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d8ab3c4-004c-42eb-aa2c-f637c4425d77');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "sns.heatmap(corr, annot = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "YZdbi4HenZDH",
        "outputId": "9828274d-38b4-41ef-b317-e58c06840c2f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}