EDA_Violence_in_lyrics.ipynb

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  "metadata": {
    "colab": {
      "name": "EDA_Violence in lyrics.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPKipiTYt7TogiOTtPD9vf5",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Hecramco/NLP_Violencia-en-musica-/blob/master/EDA_Violence_in_lyrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DKbq2bisFhn"
      },
      "source": [
        "# Tipos de violencia "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4wl-eeokkD9"
      },
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns \n",
        "import plotly as ptly \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIpOJM2elPl0",
        "outputId": "87496c2d-caa2-4f33-ca7f-adcffe6a39ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "hls_df = pd.read_csv(\"/Hot_Latinsongs_lables.csv\")\n",
        "hls_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>artists</th>\n",
              "      <th>title</th>\n",
              "      <th>subgénero</th>\n",
              "      <th>genre</th>\n",
              "      <th>lyric</th>\n",
              "      <th>Violencia verbal</th>\n",
              "      <th>Violencia Física</th>\n",
              "      <th>Violencia Psicológica</th>\n",
              "      <th>Violencia Sexual</th>\n",
              "      <th>VIolencia Económica</th>\n",
              "      <th>Violencia Simbólica</th>\n",
              "      <th>Apto para niñxs</th>\n",
              "      <th>CONTENIDO PARA GENTE ADULTA</th>\n",
              "      <th>OBSERVACION</th>\n",
              "      <th>Fuente</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Maluma</td>\n",
              "      <td>Hawai</td>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>HOT LATIN SONGS</td>\n",
              "      <td>https://www.musixmatch.com/es/letras/Maluma/Ha...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Billboard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Sech, Daddy Yankee, J Balvin Featuring ROSALIA...</td>\n",
              "      <td>Relacion</td>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>HOT LATIN SONGS</td>\n",
              "      <td>https://www.musixmatch.com/es/letras/Sech/Rela...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Billboard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>J Balvin, Dua Lipa, Bad Bunny &amp; Tainy</td>\n",
              "      <td>Un Dia (One Day)</td>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>HOT LATIN SONGS</td>\n",
              "      <td>https://www.musixmatch.com/es/letras/J-Balvin-...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Billboard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Ozuna</td>\n",
              "      <td>Caramelo</td>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>HOT LATIN SONGS</td>\n",
              "      <td>https://www.musixmatch.com/es/letras/Ozuna/Car...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Billboard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Nio Garcia x Anuel AA x Myke Towers x Brray x ...</td>\n",
              "      <td>La Jeepeta</td>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>HOT LATIN SONGS</td>\n",
              "      <td>https://www.musixmatch.com/es/letras/Nio-Garci...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Billboard</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rank  ...     Fuente \n",
              "0     1  ...  Billboard \n",
              "1     2  ...  Billboard \n",
              "2     3  ...  Billboard \n",
              "3     4  ...  Billboard \n",
              "4     5  ...  Billboard \n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efT9i2hrnL4t",
        "outputId": "8e6d21e7-f703-445b-9d45-e8cbb47b8f1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "violence_label = hls_df[['subgénero','Violencia verbal'\t,'Violencia Física'\t,'Violencia Psicológica'\t,'Violencia Sexual'\t,'VIolencia Económica'\t,'Violencia Simbólica']]\n",
        "violence_label.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subgénero</th>\n",
              "      <th>Violencia verbal</th>\n",
              "      <th>Violencia Física</th>\n",
              "      <th>Violencia Psicológica</th>\n",
              "      <th>Violencia Sexual</th>\n",
              "      <th>VIolencia Económica</th>\n",
              "      <th>Violencia Simbólica</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Reggeaton</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    subgénero  Violencia verbal  ...  VIolencia Económica  Violencia Simbólica\n",
              "0  Reggeaton                0.0  ...                  0.0                  0.0\n",
              "1  Reggeaton                0.0  ...                  0.0                  0.0\n",
              "2  Reggeaton                0.0  ...                  0.0                  0.0\n",
              "3  Reggeaton                0.0  ...                  0.0                  0.0\n",
              "4  Reggeaton                0.0  ...                  0.0                  0.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeDtqNo2oKTk",
        "outputId": "3a35551a-4104-4531-e6b2-a41d709012cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "Violence_type=violence_label.sum().sort_values()\n",
        "print(Violence_type)\n",
        "Violence_type.plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Violencia Física          1.0\n",
            "VIolencia Económica       1.0\n",
            "Violencia Psicológica     5.0\n",
            "Violencia verbal          7.0\n",
            "Violencia Sexual          7.0\n",
            "Violencia Simbólica      17.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcec46ec940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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goBMRCcFAJyISgoFORCQEA52ISAjVbv1vSq5urdGqpXWlW3Nr7+2SctwqKLbq/RpK8tiIqHHZZaC3aumIwGnbm+z9di4PRlOtJiF5bETUuDjlQkQkBAOdiEgIBjoRkRAMdCIiIRjoRERCMNCJiIRgoBMRCcFAJyISwuZAz87OxqhRozBkyBCMGjUKv/76qwplERFRQ9kc6PPmzcOYMWOwa9cujBkzBnPnzlWjLiIiaiCbbv03mUw4efIkPvnkEwBAQEAAFi1ahGvXrqFdu3b1eg0HB4NV792hbWurvs9a1tZpDcljAzg+tXF86tL7+O713xsURVGsLSQjIwOzZs1CWlqa5bFXXnkFCQkJ6Nevn7UvS0REVuBFUSIiIWwKdE9PT1y5cgVmsxkAYDabcfXqVXh6eqpSHBER1Z9Nge7h4YE+ffogNTUVAJCamoo+ffrUe/6ciIjUY9McOgBkZWVh9uzZKCgogJubG+Lj4/HII4+oVR8REdWTzYFORET6wIuiRERCMNCJiIRgoBMRCcFAJyISgoFORCQEA52ISAibFufSu8uXLyMhIQGZmZkoKSmxPL5v3z4Nq1LP+fPnkZmZidLSUstjISEhGlaknvLycmzduhWnTp2q8Xe3ZMkSDauyXVJS0j2fHzt2bBNV0jikj686PX7+RAd6dHQ0XnnlFWRmZmLZsmXYvHkzunbtqnVZqti4cSO2bNmCvLw8PPHEE/jpp5/w1FNPaf4PSi1z586F2WzG4cOHER4ejtTUVPj4+Ghdls0yMjK0LqFRSR9fFd1+/hTBgoODFUVRlICAAEVRFMVsNivDhw/XsiTV+Pv7K0VFRUpQUJCiKIpy+vRpJSoqSuOq1FP1d1b1/wUFBcq4ceO0LInIQq+fP9FH6A888AAAoE2bNrh06RLat2+Pa9euaVyVOpycnNCmTRtUVFRAURT07NlT1G5RLVu2BAC0aNECxcXFcHV1hclk0rgqdenxlF1Nksen18+f6ED38fHBjRs3EB4ejtDQUDg5OWHIkCFal6WK1q1bo6ysDL1790ZCQgI8PT1RUVGhdVmqefDBB3Hz5k0MGjQIb731Ftq2bYuOHTtqXZZqdHvKrhLp49Pt50/rU4Sm8ttvvymnT5/WugzVnD59WikqKlLy8/OV6OhoZfLkycrJkye1Lks15eXliqJUTpOlpKQomzZtUm7duqVxVerR6ym7WqSPT6+fP9FH6AcPHsQTTzwBV1dX/OEPf0BBQQEOHTqEgQMHal2azXr27Amgcjpp8eLFGlejvps3b8LFxQVOTk4IDg5GaWkpCgsLtS5LNXo9ZVeL9PHp9fMnug996dKlcHFxsXzt4uKCpUuXaliReiZPnowbN25Yvr5+/TqmTJmiYUXq+utf/2rZOAWobGOcMGGChhWp685T9k2bNunjlF0l0sen18+f6EBXFAUGw783VHVwcKgREvYsJycH7u7ulq/btm2LixcvaliRukpLS9G69b83623Tpk2NfnR7N2/ePJSVlWH27Nm4efMmfvzxRzEHG4D88en18yd6ysXZ2RnHjx9H//79AQDHjx9HmzZtNK5KHWazGWazGS1atAAAlJWV1egmkODatWuW3a9MJpOoIzy9nrKrRfr49Pr5Ex3oM2bMwDvvvIPHHnsMAHDu3DmsWrVK46rU8ec//xlTp07Fa6+9BqCyq2DQoEEaV6WeV199FeHh4QgODgYAbN++HZGRkRpXpZ6rV68iLi4Ohw8fBgAMHDgQ0dHR6NChg8aVqSMqKqrG2XGVFStWaFCN+vT6+RO/Y9HNmzdx7NgxAICXlxcefPBBjStSR1lZGRITE/Hdd98BAJ5//nlERkbCyclJ28JUdPjwYaSnpwOoHN/TTz+tcUXqef311+Hj44MRI0YAALZu3YojR47g008/1bYwlWzbts3y55KSEuzatQuPPvooYmJiNKxKPXr9/IkPdCI98vf3R1pa2n0fk6K0tBTjx4/Hpk2btC5FNJFTLv/xH/+Bzz77DM8880yN076qi6SHDh3SsDrbfPPNNxg6dOhdF0Gy98WPEhISMGPGDPGn7F27dsWFCxfQrVs3AMDFixfx8MMPa1tUIzIYDLhy5YrWZdhM758/kYGekJAAoPI0VpqzZ89i6NChYhdB8vb2BgC88MILGlfSOKp+UZWUlCA4ONgy3l9++QV/+tOfNK5OPdV/ISuKgtOnT+PZZ5/VuCrb6f3z12ymXEwmE3JycuDl5aV1KdSMVZ9brsuwYcOaqJLGVX2cLVq0QLdu3SzdZtR4RAf6mDFjkJiYCEVR4O/vDzc3NwwePBizZs3SujRVHDp0CBcvXkR5ebnlMa1P+dRS15SLq6srvLy8EBoaCgcH+72Fwmw246OPPkJUVJTWpTSa33//vVaL8JUrV+x+PZ6qi/R385e//KWJKqmbyCmXKr///jtcXV2xfft2BAYGYvr06QgODhYR6NOnT8eZM2fQu3dvSy+sJEajERkZGfD39wcAfP3113B1dcU333yDzMxMu+6WaNGiBQ4cOCA60IcPH473338fvXv3BgAcOHAA8+fPx/79+zWuzDbr1q2763MGg4GB3piqGv0PHz4Mf39/ODg4iAm/jIwMpKWliRnPnTIzM7Fp0yZLG9ioUaPw+uuv47PPPhOxYt/zzz+P9evXIyQkpMaRbPW7Y+1ZbGws3nnnHbzxxhvIzc3Ft99+i8TERK3Lspneu3REB/rTTz+NV155BWazGQsWLEBBQYFdn6pX17VrVxQXF9dYq0aS/Px8y3r2AODo6Ijr16/DyclJ815fNVTd4JaQkACDwWDpwDp16pTGlalj4MCB+Pvf/47w8HC0a9cOaWlpcHNz07osVf3P//wPDh48CKDyRqPnnntO44oEzqEXFBRY/uEoioLMzEx06dIFLi4uuHbtGnJzc9G3b1+Nq7RdVlYWpk+fDm9v7xoBN3PmTA2rUk9sbCxyc3Mtd4ru3LkTRqMR7777LiIiIvDFF19oXCHdy759+7B48WJMnjwZJ06cwC+//IIPPvjArreAPHXqFPr06QOgcuolJSWlxpRgSEgIxo8fr2WJ8gL9s88+Q0lJCSIjI1FcXFznfyPhtDYiIgItW7ZEnz59aky7TJo0ScOq1FNWVoZ//OMfOHLkCIDKs63Ro0fXOGq3d9nZ2cjKysKLL76IoqIilJWV1VjwyZ4FBQXhgw8+wCOPPAIA2LNnD9577z18++23Gldmvb179+LkyZOIiopCYGAgNm/ebDlDLiwsRHh4OHbu3KlpjeICHQD+67/+C+3bt0dsbGyN01lJp7VDhw7FN998o3UZZKXk5GSsXbsWZWVl2LdvH86fP4+FCxeKufW/pKTEso1gld9++w2dOnXSqCJ1JCcnIzQ0FIGBgbXCu67HmprIOfS3334bxcXFlnUyJOrVqxeuXr0qZjGnO127dg2LFi2y3NX73HPPYc6cOZbVF+3dxo0bsXXrVkub6SOPPIL8/HyNq1JPYWEh5syZg8uXLyMpKQmZmZk4evQowsPDtS7NJqGhoQCAxx9/HO+++64lY7766is8/vjjWpYGQOh66EeOHBExrXIvt27dQmBgICZMmIApU6ZY/mfPkpOTLaE2b948PPzww9i+fTtSUlLQrVs3zJ07V+MK1fPAAw/A2dm5xmOSOpZiYmLg7e2NgoICAJW/sD7//HONq1JPbGws2rVrh7i4OMTFxaFdu3aIjY3VuiyZR+hLliyx3Kk2atQobNmyReOK1BcQEICAgACty1DVgAEDsGjRIqxYsQIXLlzAypUrLc9FRUVZLpBK4O7ujuzsbMvNU9u3b8dDDz2kcVXquXLlCsLDwy2fPScnJzEdZkDlOu8zZszQuoxaRAZ69csCkna5qU7KLeLVderUydKloygKTCYTPDw8AMjb4CI6OhrTpk1DdnY2fH190apVK6xZs0brslTj6FgzWgoKCiDpcl1hYSFWr16NH374AUBlm+bbb7+teRuxyECvfst4XSv2SSB1jrnqotn48eMREhKC559/HkDlLdfTpk3TsDJ1de/eHV9++SV+/fVXKIqC7t27i5pyeemllzB37lwUFRUhOTkZn3/+OcLCwrQuSzXR0dFwcXGx3LGcnJyM6OhofPjhh5rWJbLLxcvLq8YuRVV/rvLVV19pUZbNkpOTMXjwYLRv3x6TJ0/GY489htGjR0NRFHzxxRc4c+aMmB2ZAODMmTOWtsUBAwagR48eGleknujoaISFhVlWW5Rox44d2L9/PxRFga+vr6gps7q6zPTQeSbyCH3t2rVal9AomtMcM1C5L2XV3pTS9OvXD4sXL0ZhYSGGDRuGYcOGiZpDByp70YOCgrQuo1F06NChxp63169f18XCYyIDXdJWZdU1hznmsLCwe06T2evZ1Z3Gjh2LsWPH4vTp00hJScHIkSPRo0cPrF+/XuvSbJKWloYnnnjCckfo/PnzkZqais6dOyMhIcHuz7KWLl0KAGjbti2Cg4Mt6/Z/99138PHx0bI0AEIDXTLpc8wSVsJsiB49euDpp5/GhQsXLNNL9mzNmjWWZRn27t2L9PR0bNiwARkZGViyZAk2bNigcYW2qVpI7bHHHqsxlTty5EitSqpB5Bx6cyF5jrnK77//DgC11ta2d6dPn8a2bduQmpqKnj17YtiwYXjppZfQqlUrrUuzSVBQEHbs2AGg8l4Co9FoWY4iODgY27dv17I88XiEbsckzzHn5ORg2rRpOHXqFAwGA/r27YuEhAR06dJF69JUERUVhWHDhuHLL7+Ep6en1uWopvq039GjRzF79uw6n7N3t2/fRmpqaq0NZrReHK9ZBLrJZKrRj/6HP/xBw2rU8csvvyAhIQE5OTkwm80iNsCubu7cuRg5cqSl1S05ORlz587FJ598onFl6ti1a5fWJTSKp556Cv/5n/+J9u3b4/r165Z55WvXrtXqTbdnkyZNgoODA/r166er5ZxFT7kcOnQIs2fPhslkgoODg2U1OwmhN3ToUEycOBFeXl417sCz98WPqtR1es5Tdv0rKyvDxo0bkZubixEjRljOIH/66Sf89ttvYjqx/P39kZaWpnUZtcj5lVmHhIQEfPrpp5g6dSq2bduGr776Cv/3f/+ndVmqaNWqFQIDA7Uuo9E4ODjg/PnzluVXs7OzRd14I9UDDzxQ55rgPj4+uugCUUuPHj10uTie6EAHKu/IKy8vh8FgwIgRIxAaGoqpU6dqXZbNBg8ejPT0dM33MGwsU6dOxdixYy0bCmRmZlpaxoi0NmnSJIwcORK9e/eusUzwihUrNKxKeKBXzdl17NgR+/fvR6dOnXDz5k2Nq1LHli1bkJiYCGdnZzg5OYmbQx88eDDS0tJw/PhxAED//v3tflmDuki8vtMczJw5E76+vujbt6+uzhxFz6GnpqZi0KBBuHDhAqZNm4Zbt27h3XffFTGP99tvv9X5uJQ59DNnzqBz586WdsWioiJcunRJTGum5Os7zYEeNrOoi+hAl668vBzZ2dkAKqeWJHURhIaGYsuWLZYt50pLSzF69GgkJydrXJk6QkNDsXz58lrXdyRMB1Yn9QwkNjYW48aNQ69evbQupQY5CVDNzz//DG9vb6Snp9f5vIR553/961+IioqyTLeUl5dj5cqV6Nevn9alqcJsNtfYP9TJyQlms1nDitQn9foOIP8M5H//938RFhaG7t2715hD13ppCpGBvm3bNnh7e2PdunW1njMYDCICffHixXjvvfcwcOBAAJUfoEWLFuEf//iHxpWpw9HRETk5OZYbiS5evKiruUpbSb6+A8juMAOAOXPmaF1C3RSyS0FBQfV6zF7t379fee6555To6GglOjpaee6555Rvv/1W67JUs3PnTuXGjRvK8ePHlRdffFEZMGCAkpKSonVZqhk2bJiiKIri7+9f6zFqPCKP0KukpKTghRdewIMPPggAuHHjBg4cOCBiSc/WrVvj8OHDGDBgAAB5+6i+8MIL2LRpEw4ePAgAiIyMRLdu3TSuSj1V2wc++eST2LNnj8bVqE/qGUhCQgJmzJiBqKioOlcFZdtiI9qwYQNCQkIsX7u7u2PDhg0iAj06OhpTpkyx3HZcVlam+W4pauvevTu6d++udTZFI/AAAA4YSURBVBmqag7XdwDgtddew82bNzFlypQaHWb2rmpDkqplc/VGdJdL9ZXfqui13cgaZWVlNbpcql9EtFczZsxAQkLCXddF1/qik61iYmIQFxeHV199tdZzBoMBGzdu1KAqkkJ0oI8fPx6jRo3Cyy+/DKByQaTNmzfj008/1bYwFRw8eBBPPPEEXF1dAVRuwnvixAnLRVJ7lZGRgccff/yua4NL3bxECulnIHrfwEN0oGdlZWHixImWZTtbtGiB1atXW9YHsWchISHYtm2b5Si2oqICYWFh2LZtm8aVqa+0tBQ3b96E0WjUuhTVSL2+I/0MJDAwEF988QVat26NvXv3YvHixVixYgUyMjKwd+9e7Tfw0PKKbFMoLy9Xzp49q5w9e1YpLy/XuhzV1NXREhgYqEEljeNvf/ubUlBQoBQXFysvv/yy8tRTTynr1q3TuizV1PV3FRwcrEEl1BDV/97mzp2rrFy50vK1HrrMHO4f+fattLTU0r+cnZ2Nc+fOaVyROpydnS3rnADA8ePHRe3qk52dDVdXV3z33XcYMGAA0tPTkZKSonVZjUrSjVMpKSk1ulpu3LhR63qWPbpzA48//elPdT6nFdFdLklJSVi2bBnc3d0tUxMGgwH79u3TuDLbzZgxA++8845lX8Nz585h1apVGlelnqpdYH788Uf85S9/QevWrWus+27vjEYjdu/eXeP6TtWG3xJI7TDT+wYe2lfQiDZs2IDU1FQxC1ZV98c//hFpaWk4duwYAMDLy8syHyvBo48+ivHjxyM7OxvTpk3D7du3tS5JVdHR0Zg4cSISEhIA/Pv6jmQSzkCio6MtG3isX7/e0jZ8/vx5vP7669oWB+EXRUePHi3mVvgqly5duusCRydOnBCzlsvt27fx/fffo1evXujSpQuuXLmC06dPY/DgwVqXphqz2Vyj7VTS0gaSO8z0THSgf/jhh7h9+zb8/f1rLKBTNU1hj4YNG2bpZBk+fHiNvuzqz9mrS5cuYcmSJfj111/Rp08fzJ49W+Q66ABQXFyM3NzcGkeu9vxvszrJHWZ6JnrKpeoi2n//939bHrP3OfTqv3+r7zZ+53P2KjY2Fj169MDw4cOxe/duxMfHIz4+XuuyVCf5+g5QOWX29ddfiz0D0SvRgb5//36tS1Bd9bsn77yTsq47K+3N1atXsX79egDAoEGDEBoaqnFFjUPy9Z0qVR1m1aeWpJyB6JXoQAcql5XNysrCuHHjYDKZUFBQYNfrg5SUlCArKwuKotT4c9Vz9q56p4CDg4OIX1J1MRqNosNc+hlIFb1t4CF6Dn3t2rVIT09HXl4edu/ejdzcXEydOhWbN2/WujSr+fr63vU5CR8YLy+vGkdx586dq/G1va/lUkXi9Z3q/Pz8sHHjRrG/tPS6gYfoI/TU1FRs3boVI0aMAAA89NBDKCws1Lgq20icRqpu7dq1WpfQJCRe36lO+hmIXjfwEB3orVq1qrUCodRTeCmay+Jb0n8xP/vss1i6dKnYMxBAn1sIig70hx56CD/99BMMBgMqKiqwZs0azVdDI6oi7fpOddLPQPS6gYfoOfS8vDzMmjULR44cgcFggI+PD5YtWybqFmuyTxKv7zQnqampGDRoEC5cuFBjA4/g4GBN6xId6FWKi4tRUVEBZ2dnrUshAlC5+UrV9Z2qo1lJm68Ass9A9ErklMv9VlSUMo/3/fff49SpUzXapiZNmqRhRerTW1uYWqRf36l+BjJu3DiUlZUhOjra7s9A9L6Bh8hAj4yMvOtzUubxli1bhn/96184d+4c/Pz8sG/fPrvfrag6vbaFqUX69R2JHWYAsG3bNnh7e2PdunW1njMYDJoHuvgNLqQKCAhQysrKLAvu5+bmKm+++abGValn2LBhyvnz55Xg4GCloqJC+eKLL5T3339f67JUc/XqVSUiIkLp16+f8vjjjyuvv/66kp+fr3VZqhkxYoSiKDU37ZC0AYteiTxCr+7cuXM4fPgwAOCZZ57Bo48+qnFF6nBycoKjoyMMBgPKysrQsWNH5Obmal2WqvTYFqYWo9GIDRs2iL2+I/0MRK9bCIoO9JSUFCxfvtxyGpSYmIjp06dr/kNXg7OzM4qLi/HHP/4Rs2fPhtFoRKtWrbQuSzV6bQuzVXO5vhMbG4tZs2bh7Nmz6N+/v6XDTAq9buAhusslKCgI69evt2wunJeXh/Hjx4vYCis/Px9ubm4wm8345JNPcOvWLbz66qtiLhrqtS3MVtKXbriT1DOQoKCgWjmihy4l0UfoAGrsFC9p1/j27dtb/jxx4kQNK2kcAQEBAIAnn3wSe/bs0bga9Ui/Q7S5nIHodQtB0UfokyZNQs+ePTFq1CgAwJdffolTp07ho48+0rgy6yUkJGDGjBmIioqqs81txYoVGlSlHr23halJ4vWd5nIGotcNPEQHuslkQlxcHA4ePAiDwYBnn30Wc+bM0cVvUmvt378fvr6+d92ZaNiwYU1ckbpiYmIQFxeHV199tdZzBoMBGzdu1KAq9d15fefAgQNiru80F3rcQlB0oBPpleTrO1UknoFUp8ctBB00ffdG8vPPPwMA0tPT6/yfBJMnT8aNGzcsX1+/fh1TpkzRsCJ1paSk1OhquXHjhqiwA+Re3wEq//4iIiJw6tQpnDp1ChEREaL+/pKSkvDss8/ijTfeQGRkJCIjI/HXv/5V67JkXhRNSUnR991cKsjJyYG7u7vl67Zt2+LixYsaVqQuvbaFqaVr16748MMPa1zf6dKli8ZVqWfDhg1ITk6udQYi5e9Pr1sIigz0qta2TZs2aVxJ4zGbzTCbzZZ5u7KyMpSWlmpcVeOqfmpr7xYsWIC4uDgEBQVZru8sXLhQ67JUJfkMRK8beIgM9NmzZ6NFixYICwtDSEgIOnTooHVJqvvzn/+MqVOn4rXXXgMAbNy4EYMGDdK4KvXotS1MLR4eHvj73/+udRmNRvoZiF438BB7UfSHH37Atm3bsHfvXnh7eyMsLAx+fn41NiG2Z2VlZUhMTMR3330HAHj++ecRGRkJJycnbQtTiV7bwmzVXNoyJXaYVVdXe6Ye2jLFBnqVoqIifP3110hOTsaFCxcQGBiId999V+uyqB702BZmq9jYWCxatEh8WyZpQ3ygA4CiKEhPT8fKlSuRlZWFY8eOaV2SzUpKSrBjxw7k5OSgvLzc8vjMmTM1rEpdemwLs9VPP/0EHx8frctoNM3lDATQ5wYeMuYf7uL8+fPYunUrduzYgQ4dOiA0NBSBgYFal6WKKVOmoKysDE8++aSYaZbqkpKSsGzZMri7u1vuiNXDKa2tpF/faQ4dZoB+N/AQGehbtmxBcnIyLl68iMDAQHz88cfo3bu31mWp6sKFC/jmm2+0LqPR6LUtzFZ79+61XN8ZOnSouOs7zaHDDNDvBh72/y+oDnv37kVERAT8/PxqbfMlRZcuXVBYWAgXFxetS2kUem0LU8MzzzyDZ555xnJ959NPP8WCBQtEXN+RfgZSRa9bCIoM9I8//ljrEhqdq6srwsLCMGjQoBpTLlLm0PXaFqYmZ2dnDB8+HEajEStXrsSWLVvsPtCln4FU0esGHs3ioqhEq1atqvNxKZtE67UtTC13u77j5uamdWmqkdxhlpeXh1mzZuHIkSMwGAyWDTy0bstkoBM1oTuv74SGhoq7vlOdxA6z6vS2gYecc6BmxmQyYcmSJbh8+TKSkpKQmZmJo0ePIjw8XOvSVKPHtjBbNYfrO4DcDjO9b+DBI3Q79fbbb2Pw4MH4/PPPsXPnTpSWliIsLEzzLbDUUr0tbPfu3cjNzcXUqVM1bwuje5N+BqL3DTx4hG6nrly5gvDwcGzZsgUA4OTkBAcHOash67UtjO5N+hmI3rcQZKDbqTs7BgoKCiDpZEuvbWF0b82hw6yKHjfwkHNI18y89NJLmDt3LoqKipCcnIw33ngDYWFhWpelmjvbwlavXq2LtjAiQL8beHAO3Y7t2LED+/fvh6Io8PX1tdylJ4Fe28KIAP1uIchAJ13TW1sYEVAZ6HeGd12PNTXOoduZpUuX3vN5e79TVO9tYUSAfjfwYKDbmTZt2mhdQqOKjIy863N6aAsjAvS7hSCnXIiIhOARup0qLi7G6tWrcfDgQQCVe4xOmDABrVu31rgy9eixLYyaN71v4MEjdDsVHR0Ns9mMkSNHAgC++uorAMCSJUu0LEs1KSkpWL58ueUDcuDAAUyfPh1BQUEaV0bNmd63EGSg26nAwMAat/krioKgoCAxt/7rtS2Mmje9byHIG4vs2O+//275c3FxsYaVNI6qML/zz0RamT17NoYMGYK1a9fi6tWrWpdTC+fQ7VRgYCBGjRoFf39/AMDXX38t6sYivbaFUfOm9w08OOVix9LT0/HDDz8AAAYOHIjBgwdrXJF6TCYT4uLicPDgQUtb2Jw5c3inKOmGHjfwYKATEVlJbxt4aH+OQA0SFRV1z1UHV6xY0YTVqE/vbWFEgH438GCg25kXXnhB6xIaVUpKCry9vbFu3bpazxkMBgY6aerODTw+/vhjXW3gwSkXO6P3tilbSR8f2be33nrLchFUjxt4MNDtzIsvvogWLVogNDQUISEh6Nixo9YlqapqfGFhYQgJCUGHDh20LonIbjDQ7VBV29TevXt11zalBunjI2osDHQ7pse2KTVJHx+R2hjodk5vbVNqkz4+IjXxHNZO6bVtSi3Sx0fUGHiEbmfubJsKDQ3VVduUraSPj6gx8QjdzuzduxcRERG6bZuylfTxETUmHqETEQnB5XOJiIRgoBMRCcFAJyISgoFORCTE/wPCocaJ9XEnuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M-5w-UEqzS4",
        "outputId": "45794bf5-813a-4f0b-c44d-f97935e1cbe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "violence_label.groupby(by='subgénero').sum().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcec418e828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZHTD4Cmv8tS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Y4JMqPwzjAN"
      },
      "source": [
        "# Palabras Recurrentes "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-vgB7xUzmZR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}