02_Estadistica_descriptiva.ipynb

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      "name": "02 Estadistica descriptiva.ipynb",
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      "source": [
        "\n",
        "**Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones**\n",
        "\n",
        "**Edición 2021**\n",
        "\n",
        "---\n",
        "\n",
        "# Estadística Descriptiva\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xwdfo7z20TUK"
      },
      "source": [
        "import io\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "import pandas as pd\n",
        "import seaborn\n",
        "\n",
        "seaborn.set_context('talk')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XY2Hl-Ma07Nn"
      },
      "source": [
        "## Lectura del dataset\n",
        "\n",
        "En la notebook 00 se explican los detalles de la siguiente sección."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vviv_sqXdR5W"
      },
      "source": [
        "url = 'https://cs.famaf.unc.edu.ar/~mteruel/datasets/diplodatos/sysarmy_survey_2020_processed.csv'\n",
        "df = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "outputId": "ee815350-7bab-4d0d-f157-3856d05b33a3"
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      "source": [
        "df[:3]"
      ],
      "execution_count": null,
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>profile_gender</th>\n",
              "      <th>profile_age</th>\n",
              "      <th>work_country</th>\n",
              "      <th>work_province</th>\n",
              "      <th>profile_years_experience</th>\n",
              "      <th>work_years_in_company</th>\n",
              "      <th>work_years_in_current_position</th>\n",
              "      <th>work_people_in_charge_of</th>\n",
              "      <th>profile_studies_level</th>\n",
              "      <th>profile_studies_level_state</th>\n",
              "      <th>profile_career</th>\n",
              "      <th>profile_university</th>\n",
              "      <th>profile_specialization_courses</th>\n",
              "      <th>profile_open_source_contributions</th>\n",
              "      <th>profile_programming_as_hobby</th>\n",
              "      <th>work_role</th>\n",
              "      <th>tools_platform</th>\n",
              "      <th>tools_programming_languages</th>\n",
              "      <th>tools_frameworks</th>\n",
              "      <th>tools_data_bases</th>\n",
              "      <th>tools_qa_testing</th>\n",
              "      <th>tools_IDEs</th>\n",
              "      <th>tools_work_pc_os</th>\n",
              "      <th>tools_cellphone_os</th>\n",
              "      <th>work_on_call_duty</th>\n",
              "      <th>salary_on_call_duty_charge</th>\n",
              "      <th>work_on_call_duty_charge_type</th>\n",
              "      <th>work_contract_type</th>\n",
              "      <th>salary_monthly_BRUTO</th>\n",
              "      <th>salary_monthly_NETO</th>\n",
              "      <th>salary_in_usd</th>\n",
              "      <th>salary_satisfaction</th>\n",
              "      <th>salary_comparison_last_semester</th>\n",
              "      <th>salary_has_bonus</th>\n",
              "      <th>salary_bonus_tied_to</th>\n",
              "      <th>salary_inflation_adjustment_2020</th>\n",
              "      <th>salary_percentage_inflation_adjustment_2020</th>\n",
              "      <th>salary_month_last_inflation_adjustment</th>\n",
              "      <th>work_has_violence_situations</th>\n",
              "      <th>profile_sexual_orientation</th>\n",
              "      <th>profile_has_disabilities</th>\n",
              "      <th>profile_has_disabilities_hiring_difficulties</th>\n",
              "      <th>company_employee_number</th>\n",
              "      <th>company_main_activity</th>\n",
              "      <th>company_recommended</th>\n",
              "      <th>company_diversity_policies</th>\n",
              "      <th>company_extra_benefits</th>\n",
              "      <th>company_best_companies_city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mujer</td>\n",
              "      <td>26</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>En curso</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>Sí, los pagó un empleador</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Consultant</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>GNU/Linux</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>53000.0</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No recibo bono</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>En mi trabajo actual</td>\n",
              "      <td>Homosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>501-1000</td>\n",
              "      <td>Servicios / Consultoría de Software / Digital</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Capacitaciones y/o cursos, Comidas pagas / sub...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>29</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Corrientes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>En curso</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>Sí, de forma particular, Sí, los pagó un emple...</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Developer</td>\n",
              "      <td>Docker, Google Cloud Platform/App Engine</td>\n",
              "      <td>HTML, Javascript, Python</td>\n",
              "      <td>Django, jQuery</td>\n",
              "      <td>Informix, MySQL, PostgreSQL</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>IPython / Jupyter, Sublime Text, Visual Studio...</td>\n",
              "      <td>GNU/Linux</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>63000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Menos de un sueldo</td>\n",
              "      <td>Performance de la compañía</td>\n",
              "      <td>Uno</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>Visual</td>\n",
              "      <td>No</td>\n",
              "      <td>201-500</td>\n",
              "      <td>Otras industrias</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>Horarios flexibles, Stock options / RSUs, Viát...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mujer</td>\n",
              "      <td>22</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Secundario</td>\n",
              "      <td>Completado</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sí, de forma particular, Sí, los pagó un emple...</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Infosec</td>\n",
              "      <td>Linux, VMWare</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Windows</td>\n",
              "      <td>iOS</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Porcentaje de mi sueldo</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>160000.0</td>\n",
              "      <td>127000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Un sueldo</td>\n",
              "      <td>Performance individual</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>En un trabajo anterior</td>\n",
              "      <td>Bisexual o queer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>2001-5000</td>\n",
              "      <td>Otras industrias</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>Clases de gimnasia online, Comidas pagas / sub...</td>\n",
              "      <td>NaN</td>\n",
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              "  profile_gender  ...  company_best_companies_city\n",
              "0          Mujer  ...                          NaN\n",
              "1         Hombre  ...                          NaN\n",
              "2          Mujer  ...                          NaN\n",
              "\n",
              "[3 rows x 48 columns]"
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2i_yGkkUs3QA"
      },
      "source": [
        "# Estadística descriptiva\n",
        "\n",
        "Hemos estado organizando y visualizando los datos de distintas maneras, pero ¿qué intuiciones podemos obtener?\n",
        "\n",
        "Las visualizaciones como histogramas o gráficos de conteo muestran la cantidad de veces que se observa cada valor en un conjunto de realizaciones de una variable aleatoria. Esto se denomina análisis de frecuencia, y es parte de la **estadística descriptiva**.\n",
        "\n",
        "El uso de visualizaciones nos limita a estimaciones, pero los datos crudos son demasiado como para intepretarlos en conjunto. Para eso, la estadística descriptiva provee también medidas de tendencia central y de dispersión, que resumen en un valor numérico propiedades de las realizaciones de la variable.\n",
        "\n",
        "Retomemos el problema original con la v.a. `salary_monthly_NETO`, ¿qué información brindan las siguientes métricas y cómo usarlas?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXFDG0eBPDgH"
      },
      "source": [
        "salary_col='salary_monthly_BRUTO'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHre-H9euQv4",
        "outputId": "43415949-2a56-47f3-a2c7-e455aff79587"
      },
      "source": [
        "df[salary_col].describe().apply(lambda s: '{0:.2f}'.format(s))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       6095.00\n",
              "mean      124058.55\n",
              "std       122912.35\n",
              "min            1.00\n",
              "25%        61595.00\n",
              "50%        96000.00\n",
              "75%       145000.00\n",
              "max      2180000.00\n",
              "Name: salary_monthly_BRUTO, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QC-wJbBitzDH"
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      "source": [
        "## Medidas de tendencia central\n",
        "\n",
        "Cuando se quiere obtener un valor representativo de todas las realizaciones de una v.a., o su centro, se utiliza una *medida de tendencia central*.\n",
        "\n",
        "Repasando, dada X una v.a y un conjunto de realizaciones $x = \\{ x_1, x_2 ... \\}$ donde $x_i = X(\\omega)$ para algún $\\omega \\in \\Omega$, y $N = |x|$:\n",
        "\n",
        "* La **media muestral** (aritmética) o promedio se calcula como:\n",
        "\n",
        "$$ \\bar{x} = \\frac{1}{N} \\sum_i^N x_i $$\n",
        "\n",
        "* La **mediana** se calcula:\n",
        "  1. Ordenar las realizaciones tal que $x_j \\leq x_{j+1}$ \n",
        "  2. Si la cantidad de datos $N$ es impar, la mediana es el valor central: $median = x_{\\lfloor N / 2 \\rfloor +1}$\n",
        "  3. Si la cantidad de datos $N$ es par, la mediana es e promedio de los dos valores centrales: $median = \\frac{1}{2} (x_{ N / 2 } + x_{ (N / 2) +1})$\n",
        "\n",
        "* La **moda** son los valores con mayor frecuencia, es decir, los que más se repite.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "VGJfjf-x5TOh",
        "outputId": "28e1d571-a21d-4760-97c9-7fa823ac04ef"
      },
      "source": [
        "df[salary_col].mean(), df[salary_col].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(124058.54962132902, 96000.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDltOaTjnuFd"
      },
      "source": [
        "**¿Por qué las dos medidas son tan distintas?**\n",
        "\n",
        "Propiedades de la media:\n",
        "* Se puede interpretar como el *centro de masa* del histograma. Es decir, si el histograma fuera una figura de madera, el punto de equilibrio donde podemos apoyarlo y no se cae es la media.\n",
        "* Es muy sensible a valores extremos. [Opcional] ¿Por qué? Ver filminas\n",
        "\n",
        "**¿Se cumple para estos datos?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woWeBF8-0u5Q",
        "outputId": "d4937c54-bf5b-43eb-8061-509e07d2ce9a"
      },
      "source": [
        "max_salaries = [df[salary_col].max(), 10**6, 500000, 400000, 300000, 200000]\n",
        "central_tendency = [\n",
        "    (max_salary, df[df[salary_col] < max_salary][salary_col].mean(),\n",
        "     df[df[salary_col] < max_salary][salary_col].median())\n",
        "    for max_salary in max_salaries\n",
        "]\n",
        "central_tendency"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(2180000.0, 123721.17819855602, 96000.0),\n",
              " (1000000, 120723.13043954605, 96000.0),\n",
              " (500000, 112145.56938829432, 95000.0),\n",
              " (400000, 107991.96966661021, 94000.0),\n",
              " (300000, 102223.41686622657, 92000.0),\n",
              " (200000, 91461.11102857142, 88000.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZrjSY4yPV8-"
      },
      "source": [
        "Se pueden graficar estos números para lograr una mejor intuición de la magnitud de las diferencias. Además, al mostrar una visualización se pueden incluir más puntos.\n",
        "\n",
        "Para poder crear gráficos de seaborn con distintos grupos de datos, muchas veces es necesario cambiar el formato del dataframe de wide a long. Ver [este link](https://anvil.works/blog/tidy-data) para más información."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "MpMJWSNq3Xq_",
        "outputId": "e44dd90c-4628-4d6a-caa6-14a455a477ca"
      },
      "source": [
        "central_tendency_max = [\n",
        "    (max_salary, df[df[salary_col] < max_salary][salary_col].mean(),\n",
        "     df[df[salary_col] < max_salary][salary_col].median())\n",
        "    for max_salary in range(50000, int(df[salary_col].max()), 10**4)\n",
        "]\n",
        "central_tendency_max_df = pd.DataFrame(central_tendency_max,\n",
        "                                   columns=['max_salary', 'mean', 'median'])\\\n",
        "  .melt(id_vars='max_salary', var_name='metric')\n",
        "\n",
        "central_tendency_max_df[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_salary</th>\n",
              "      <th>metric</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50000</td>\n",
              "      <td>mean</td>\n",
              "      <td>30339.534775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>60000</td>\n",
              "      <td>mean</td>\n",
              "      <td>37032.451850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70000</td>\n",
              "      <td>mean</td>\n",
              "      <td>44179.526605</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   max_salary metric         value\n",
              "0       50000   mean  30339.534775\n",
              "1       60000   mean  37032.451850\n",
              "2       70000   mean  44179.526605"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "rJQfOlKV15Z4",
        "outputId": "809470d4-e304-442a-ee1d-3762aecae282"
      },
      "source": [
        "# ¡Podemos ver estos datos visualmente!\n",
        "fig = plt.figure(figsize=(15, 5))\n",
        "seaborn.lineplot(data=central_tendency_max_df, x='max_salary', y='value', hue='metric')\n",
        "plt.ticklabel_format(style='plain', axis='x')\n",
        "seaborn.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "mSyyNgvndRPQ",
        "outputId": "f9f78eed-8da6-4974-81f1-c4287a2a98a5"
      },
      "source": [
        "## La diferencia no siempre se aprencia en los gráficos\n",
        "fig, axes = plt.subplots(nrows=2, figsize=(16, 8))\n",
        "seaborn.histplot(df[salary_col], bins=100, ax=axes[0], color='gray')\n",
        "axes[0].axvline(df[salary_col].mean(), color='orangered',\n",
        "            linestyle='--', label='Media')\n",
        "axes[0].axvline(df[salary_col].median(), color='indigo',\n",
        "            linestyle='-.', label='Mediana')\n",
        "\n",
        "filtered_df = df[df[salary_col] < 200000]\n",
        "seaborn.histplot(filtered_df[salary_col], bins=100, ax=axes[1], color='gray')\n",
        "axes[1].axvline(filtered_df[salary_col].mean(), color='orangered',\n",
        "            linestyle='--', label='Media')\n",
        "axes[1].axvline(filtered_df[salary_col].median(), color='indigo',\n",
        "            linestyle='-.', label='Mediana')\n",
        "\n",
        "axes[0].legend()\n",
        "seaborn.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MdG-7bK8AKR"
      },
      "source": [
        "¿Qué decir de la moda? Sólo que el resultado de la función no es un valor, sino una series de valores, aunque la serie tenga un único elemento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r01xw1q18AmV",
        "outputId": "c660b266-dd56-4abd-b55e-514ab02699e0"
      },
      "source": [
        "df.profile_gender.mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Hombre\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Li3vLv3X8k7Z"
      },
      "source": [
        "## Medidas de dispersión\n",
        "\n",
        "Las medidas de dispersión vistas en el teórico son la desviación estándar, la varianza, y el coeficiente de variación. También permiten representar con un número alguna propiedad de los datos.\n",
        "\n",
        "Por ejemplo, comparemos el salario neto con el salario bruto. A priori, **¿deberíamos ver alguna diferencia?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "504NtuJWjpX0",
        "outputId": "2cd2b93a-ea7c-4110-e4df-b2572385d1ec"
      },
      "source": [
        "df[['salary_monthly_NETO', 'salary_monthly_BRUTO']].describe().round()"
      ],
      "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",
              "    }\n",
              "\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>salary_monthly_NETO</th>\n",
              "      <th>salary_monthly_BRUTO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5883.0</td>\n",
              "      <td>6095.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94908.0</td>\n",
              "      <td>124059.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>96862.0</td>\n",
              "      <td>122912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>51480.0</td>\n",
              "      <td>61595.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>78000.0</td>\n",
              "      <td>96000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>106000.0</td>\n",
              "      <td>145000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2080000.0</td>\n",
              "      <td>2180000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       salary_monthly_NETO  salary_monthly_BRUTO\n",
              "count               5883.0                6095.0\n",
              "mean               94908.0              124059.0\n",
              "std                96862.0              122912.0\n",
              "min                    1.0                   1.0\n",
              "25%                51480.0               61595.0\n",
              "50%                78000.0               96000.0\n",
              "75%               106000.0              145000.0\n",
              "max              2080000.0             2180000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6dcAgVYlUWK"
      },
      "source": [
        "Claramente, ambas distribuciones están centradas en valores distintos, pero ¿podemos decir algo sobre su dispersión?\n",
        "\n",
        "Como estamos hablando de dos v.a. distintas, no podemos comparar directamente los valores de las desviaciones estándar, sino que tenemos que usar el coeficiente de variación."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ga3FpQalrCm",
        "outputId": "74e208ab-e189-4668-b796-0bc0daa30def"
      },
      "source": [
        "import scipy\n",
        "\n",
        "print(\"Coeficiente de variación salario bruto\",\n",
        "      scipy.stats.variation(df.salary_monthly_BRUTO))\n",
        "print(\"Coeficiente de variación salario neto\",\n",
        "      scipy.stats.variation(df.salary_monthly_NETO.dropna()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coeficiente de variación salario bruto 0.9906795521353624\n",
            "Coeficiente de variación salario neto 1.0205048799041485\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVG6Ro-6ao3j"
      },
      "source": [
        "## Percentiles y gráficos de caja\n",
        "\n",
        "Los gráficos de caja son otra forma de representar la distribución de las realizaciones de una v.a. numérica, de una forma más condensada que un histograma.\n",
        "\n",
        "Son muy útiles para comparar muchas distribuciones, pero sólo cuando son muy distintas entre ellas, ya que oscurecen algunas sutilezas. Otros problema de este tipo de gráficos es que *no todo el mundo recuerda cómo leerlos*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "5dbBiShrasMI",
        "outputId": "48979844-fc0d-4c5e-f3bb-a9c7ff76a4ac"
      },
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "seaborn.boxplot(x=df[salary_col])\n",
        "seaborn.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "GdK00mpDa7Nz",
        "outputId": "c5431d14-a5f9-472c-f6f8-e7e0be5be086"
      },
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "seaborn.boxplot(x=df[df.profile_age < 100].profile_age)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c6c6e7c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyx3Pmk-dJL4"
      },
      "source": [
        "Por ejemplo, podemos comparar la distribución de los salarios netos con respecto al nivel de estudios alcanzado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "W1dKgRP9gkHj",
        "outputId": "bc5078c3-3a8d-4136-9b49-99e04eac18ea"
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "seaborn.boxplot(data=df, x=salary_col, y='profile_studies_level',\n",
        "                color='orangered')\n",
        "plt.ticklabel_format(style='plain', axis='x')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HClH-TMBhMfg"
      },
      "source": [
        "**¿Qué puede estar causando una diferencia tan grande en la distribución para las respuestas que dicen Posdoctorado?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqHgU6mUhXSi"
      },
      "source": [
        "### Boxenplots\n",
        "\n",
        "Los boxplots tienen una gran desventaja: ocultan mucha información en la distribución de las colas. Por ejemplo, para la categoría Posdoctorado, sabemos que el 25% de los valores de sueldo neto es mayor que los ~650000 pesos. Pero no conocemos cómo se distribuyen. Para conjuntos de datos de gran tamaño, el 25% de los datos contiene mucha información.\n",
        "\n",
        "Un gráfico más informativo es el **boxenplot**, que visualiza más percentiles. Otra ventaja es la percepción del mismo debido al peso visual de las cajas: los datos en el rango intercuartílico no parecen muuuucho más importantes que los datos en las colas.\n",
        "\n",
        "Sin embargo, es aún más difícil de leer si buscamos exactitud, ya que los percentiles que definen el límite de cada caja se definen recursivamente y no decrecen linealmente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "evtF2AFChc06",
        "outputId": "b6f3b780-066e-4802-d8d5-3172dbba40af"
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "seaborn.boxenplot(data=df, x=salary_col, y='profile_studies_level',\n",
        "                color='orangered')\n",
        "plt.ticklabel_format(style='plain', axis='x')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81z4Ue6PkEZr"
      },
      "source": [
        "## Eliminación de valores extremos\n",
        "\n",
        "### Usando percentiles \n",
        "\n",
        "Una forma conservadora de eliminar valores extremos que estén afectando a la media, el rango y las visualizaciones es seleccionar un cierto porcentaje más extremo. Para eso, usamos los percentiles.\n",
        "\n",
        "Por ejemplo, podemos elegir quedarnos con el 99% de salarios más bajos, eliminando el 1%. Podemos calcular todos los percentiles para decidir cuál sería el más apropiado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWEgaBVvka9p",
        "outputId": "e7baf270-f52d-4ec8-8aa6-dce26d5584dd"
      },
      "source": [
        "df[salary_col].quantile([.95, .98, .99, .995, .998])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.950     307150.0\n",
              "0.980     480000.0\n",
              "0.990     630600.0\n",
              "0.995     796240.0\n",
              "0.998    1032671.0\n",
              "Name: salary_monthly_BRUTO, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "A-2cG3unruwo",
        "outputId": "2e8308b6-f072-40ae-94c2-a84c123e8618"
      },
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "max_limit = df[salary_col].quantile(.98)\n",
        "seaborn.boxenplot(x=df[df[salary_col] < max_limit][salary_col])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c6c5dc410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "MWmor0akspwt",
        "outputId": "c07bcbd1-fb54-4b28-97a8-63df74c03b08"
      },
      "source": [
        "fig, axes = plt.subplots(figsize=(12, 12), nrows=3)\n",
        "max_limit = df[salary_col].quantile(.98)\n",
        "data = df[df[salary_col] < max_limit][salary_col]\n",
        "seaborn.histplot(x=data, ax=axes[0])\n",
        "seaborn.boxplot(x=data, ax=axes[1])\n",
        "seaborn.boxenplot(x=data, ax=axes[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c6c45ee50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBkKwLFltJwg"
      },
      "source": [
        "### Rango itercuartílico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RPNlz5-kjgD"
      },
      "source": [
        "def clean_outliers(dataset, column_name):\n",
        "    \"\"\"Returns dataset removing the outlier rows from column @column_name.\"\"\"\n",
        "    interesting_col = dataset[column_name]\n",
        "    # Here we can remove the outliers from both ends, or even add more restrictions.\n",
        "    mask_outlier = (\n",
        "        numpy.abs(interesting_col - interesting_col.mean()) <= (2.5 * interesting_col.std()))\n",
        "    return dataset[mask_outlier]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "MaZj8_fatXgo",
        "outputId": "92836670-cb13-468a-977a-d14ff14b7aae"
      },
      "source": [
        "fig, axes = plt.subplots(figsize=(12, 12), nrows=3)\n",
        "max_limit = df[salary_col].quantile(.98)\n",
        "data = clean_outliers(df, salary_col)[salary_col]\n",
        "seaborn.histplot(x=data, ax=axes[0])\n",
        "seaborn.boxplot(x=data, ax=axes[1])\n",
        "seaborn.boxenplot(x=data, ax=axes[2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c6c224990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuDscbVqttGZ"
      },
      "source": [
        "### ¡Mirando los datos!\n",
        "\n",
        "¿Quiénes son los que cobran tanto?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "id": "zIt2nJXvtx3g",
        "outputId": "68b04aad-5eca-4b3f-c3eb-4fdd61e5387f"
      },
      "source": [
        "df[df[salary_col] > df[salary_col].quantile(0.98)]"
      ],
      "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",
              "    }\n",
              "\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>profile_gender</th>\n",
              "      <th>profile_age</th>\n",
              "      <th>work_country</th>\n",
              "      <th>work_province</th>\n",
              "      <th>profile_years_experience</th>\n",
              "      <th>work_years_in_company</th>\n",
              "      <th>work_years_in_current_position</th>\n",
              "      <th>work_people_in_charge_of</th>\n",
              "      <th>profile_studies_level</th>\n",
              "      <th>profile_studies_level_state</th>\n",
              "      <th>profile_career</th>\n",
              "      <th>profile_university</th>\n",
              "      <th>profile_specialization_courses</th>\n",
              "      <th>profile_open_source_contributions</th>\n",
              "      <th>profile_programming_as_hobby</th>\n",
              "      <th>work_role</th>\n",
              "      <th>tools_platform</th>\n",
              "      <th>tools_programming_languages</th>\n",
              "      <th>tools_frameworks</th>\n",
              "      <th>tools_data_bases</th>\n",
              "      <th>tools_qa_testing</th>\n",
              "      <th>tools_IDEs</th>\n",
              "      <th>tools_work_pc_os</th>\n",
              "      <th>tools_cellphone_os</th>\n",
              "      <th>work_on_call_duty</th>\n",
              "      <th>salary_on_call_duty_charge</th>\n",
              "      <th>work_on_call_duty_charge_type</th>\n",
              "      <th>work_contract_type</th>\n",
              "      <th>salary_monthly_BRUTO</th>\n",
              "      <th>salary_monthly_NETO</th>\n",
              "      <th>salary_in_usd</th>\n",
              "      <th>salary_satisfaction</th>\n",
              "      <th>salary_comparison_last_semester</th>\n",
              "      <th>salary_has_bonus</th>\n",
              "      <th>salary_bonus_tied_to</th>\n",
              "      <th>salary_inflation_adjustment_2020</th>\n",
              "      <th>salary_percentage_inflation_adjustment_2020</th>\n",
              "      <th>salary_month_last_inflation_adjustment</th>\n",
              "      <th>work_has_violence_situations</th>\n",
              "      <th>profile_sexual_orientation</th>\n",
              "      <th>profile_has_disabilities</th>\n",
              "      <th>profile_has_disabilities_hiring_difficulties</th>\n",
              "      <th>company_employee_number</th>\n",
              "      <th>company_main_activity</th>\n",
              "      <th>company_recommended</th>\n",
              "      <th>company_diversity_policies</th>\n",
              "      <th>company_extra_benefits</th>\n",
              "      <th>company_best_companies_city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>37</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>21.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>Incompleto</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Sí</td>\n",
              "      <td>VP / C-Level</td>\n",
              "      <td>Docker, Firebase, Google Cloud Platform/App En...</td>\n",
              "      <td>Bash/Shell, Go, HTML, Javascript, Perl, PHP, P...</td>\n",
              "      <td>jQuery, Rails, React.js</td>\n",
              "      <td>Google Cloud Storage, MariaDB, MySQL, Percona ...</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Vi, Vim</td>\n",
              "      <td>GNU/Linux</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Porcentaje de mi sueldo</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>800000.0</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3+ sueldos</td>\n",
              "      <td>Mix de las anteriores</td>\n",
              "      <td>Uno</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5001-10000</td>\n",
              "      <td>Producto basado en Software</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>Abono de celular, Capacitaciones y/o cursos, C...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>40</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Posdoctorado</td>\n",
              "      <td>Completado</td>\n",
              "      <td>Licenciatura en Ciencias dela Computación</td>\n",
              "      <td>UNR - Universidad Nacional de Rosario</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Developer</td>\n",
              "      <td>Linux</td>\n",
              "      <td>C++</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>google test</td>\n",
              "      <td>Vim</td>\n",
              "      <td>GNU/Linux</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Neto</td>\n",
              "      <td>Remoto (empresa de otro país)</td>\n",
              "      <td>650000.0</td>\n",
              "      <td>422500.0</td>\n",
              "      <td>Mi sueldo está dolarizado</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>No</td>\n",
              "      <td>No recibo bono</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>¿Qué les importa?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1001-2000</td>\n",
              "      <td>Servicios / Consultoría de Software / Digital</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>Capacitaciones y/o cursos, Horarios flexibles,...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>36</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>17.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>Incompleto</td>\n",
              "      <td>Ingeniería en Informática</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>Sí, de forma particular</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Manager / Director</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>Java</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>PostgreSQL</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>IntelliJ</td>\n",
              "      <td>macOS</td>\n",
              "      <td>iOS</td>\n",
              "      <td>Sí, pasiva</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Porcentaje de mi sueldo</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>540000.0</td>\n",
              "      <td>380000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Un sueldo</td>\n",
              "      <td>Performance de la compañía</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>En un trabajo anterior</td>\n",
              "      <td>¿Qué les importa?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>1001-2000</td>\n",
              "      <td>Producto basado en Software</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>Clases de gimnasia online, Comidas pagas / sub...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>34</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>GBA</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>Completado</td>\n",
              "      <td>Ingeniería en Informática</td>\n",
              "      <td>UM - Universidad de Morón</td>\n",
              "      <td>Sí, de forma particular</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>SysAdmin / DevOps / SRE</td>\n",
              "      <td>Amazon Web Services, Docker, Kubernetes, Linux...</td>\n",
              "      <td>Bash/Shell, Terraform</td>\n",
              "      <td>Terrafor</td>\n",
              "      <td>Amazon DynamoDB, Amazon RDS/Aurora, ElasticSea...</td>\n",
              "      <td>Ninguna</td>\n",
              "      <td>IntelliJ, Vim</td>\n",
              "      <td>macOS</td>\n",
              "      <td>iOS</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Remoto (empresa de otro país)</td>\n",
              "      <td>800000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Un sueldo</td>\n",
              "      <td>Performance individual</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>En un trabajo anterior</td>\n",
              "      <td>Oveja</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>11-50</td>\n",
              "      <td>Servicios / Consultoría de Software / Digital</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>Vacaciones flexibles (adicionales a las reglam...</td>\n",
              "      <td>Airbnb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>26</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Córdoba</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>Completado</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>Sí, los pagó un empleador</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Developer</td>\n",
              "      <td>Amazon Web Services, Docker, Linux</td>\n",
              "      <td>Go, Python, Ruby</td>\n",
              "      <td>Flask, Rails</td>\n",
              "      <td>Amazon DynamoDB, Amazon RDS/Aurora, ElasticSea...</td>\n",
              "      <td>RSpec</td>\n",
              "      <td>IntelliJ, Visual Studio Code</td>\n",
              "      <td>macOS</td>\n",
              "      <td>iOS</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Porcentaje de mi sueldo</td>\n",
              "      <td>Remoto (empresa de otro país)</td>\n",
              "      <td>515000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>No</td>\n",
              "      <td>No recibo bono</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>201-500</td>\n",
              "      <td>Producto basado en Software</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>Horarios flexibles, Vacaciones flexibles (adic...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5819</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>35</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>En curso</td>\n",
              "      <td>Tecnicatura Superior en Análisis de Sistemas</td>\n",
              "      <td>UAI - Universidad Abierta Interamericana</td>\n",
              "      <td>Sí, de forma particular, Sí, los pagó un emple...</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>HelpDesk</td>\n",
              "      <td>Google Cloud Platform/App Engine, Linux, Windo...</td>\n",
              "      <td>Java, Python, SQL</td>\n",
              "      <td>jQuery, WordPress</td>\n",
              "      <td>Google Cloud Storage, Microsoft SQL Server, Or...</td>\n",
              "      <td>Ninguna de las anteriores</td>\n",
              "      <td>Android Studio, IPython / Jupyter, Visual Stud...</td>\n",
              "      <td>Windows</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Part-Time</td>\n",
              "      <td>986840.0</td>\n",
              "      <td>836400.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>Menos de un sueldo</td>\n",
              "      <td>Mix de las anteriores</td>\n",
              "      <td>Dos</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2001-5000</td>\n",
              "      <td>Otras industrias</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>Abono de Internet, Capacitaciones y/o cursos, ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5844</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>28</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Universitario</td>\n",
              "      <td>En curso</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Developer</td>\n",
              "      <td>Firebase</td>\n",
              "      <td>Objective-C, Swift</td>\n",
              "      <td>Ninguno de los anteriores</td>\n",
              "      <td>SQLite</td>\n",
              "      <td>Postman</td>\n",
              "      <td>Xcode</td>\n",
              "      <td>macOS</td>\n",
              "      <td>iOS</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>2000000.0</td>\n",
              "      <td>150000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>De uno a tres sueldos</td>\n",
              "      <td>Mix de las anteriores</td>\n",
              "      <td>Uno</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "      <td>En un trabajo anterior</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>201-500</td>\n",
              "      <td>Producto basado en Software</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>Capacitaciones y/o cursos, Clases de gimnasia ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5878</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>54</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>36.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2500</td>\n",
              "      <td>Posgrado</td>\n",
              "      <td>Completado</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UAI - Universidad Abierta Interamericana</td>\n",
              "      <td>Sí, de forma particular, Sí, los pagó un emple...</td>\n",
              "      <td>No</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Manager / Director</td>\n",
              "      <td>Amazon Web Services, Azure, Docker, IBM Cloud ...</td>\n",
              "      <td>.NET, C#, COBOL, HTML, Java, Ruby, SQL</td>\n",
              "      <td>.NET Core, Angular, Bootstrap, Cordova, Hibernate</td>\n",
              "      <td>IBM Db2, Microsoft SQL Server, Oracle</td>\n",
              "      <td>HP LoadRunner, Puppeteer, Visual Studio Coded UI</td>\n",
              "      <td>Android Studio, Eclipse, Visual Studio</td>\n",
              "      <td>Windows</td>\n",
              "      <td>iOS</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Porcentaje de mi sueldo</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>750000.0</td>\n",
              "      <td>480000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>No</td>\n",
              "      <td>No recibo bono</td>\n",
              "      <td>Dos</td>\n",
              "      <td>20.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10001+</td>\n",
              "      <td>Otras industrias</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>Abono de celular, Capacitaciones y/o cursos, C...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5880</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>36</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30</td>\n",
              "      <td>Posgrado</td>\n",
              "      <td>Completado</td>\n",
              "      <td>Ingeniería en Sistemas de Información</td>\n",
              "      <td>UTN - Universidad Tecnológica Nacional</td>\n",
              "      <td>Sí, de forma particular, Sí, los pagó un emple...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Manager / Director</td>\n",
              "      <td>Amazon Web Services, Docker, Kubernetes, Linux</td>\n",
              "      <td>Java</td>\n",
              "      <td>Hibernate, Spring</td>\n",
              "      <td>Cassandra, PostgreSQL, Redis</td>\n",
              "      <td>JUnit, Postman, Selenium</td>\n",
              "      <td>IntelliJ</td>\n",
              "      <td>macOS</td>\n",
              "      <td>Android</td>\n",
              "      <td>Sí, activa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>540000.0</td>\n",
              "      <td>320000.0</td>\n",
              "      <td>Mi sueldo está dolarizado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Un sueldo</td>\n",
              "      <td>Performance de la compañía</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Jamás</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2001-5000</td>\n",
              "      <td>Producto basado en Software</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>Clases de gimnasia online, Clases de idiomas, ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6080</th>\n",
              "      <td>Hombre</td>\n",
              "      <td>30</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>San Luis</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Terciario</td>\n",
              "      <td>Incompleto</td>\n",
              "      <td>Tecnicatura en Programación</td>\n",
              "      <td>Universidad de la punta</td>\n",
              "      <td>Sí, de forma particular</td>\n",
              "      <td>Sí</td>\n",
              "      <td>Sí</td>\n",
              "      <td>SysAdmin / DevOps / SRE</td>\n",
              "      <td>Docker, Kubernetes, Linux, OpenShift, Windows ...</td>\n",
              "      <td>Assembler, Bash/Shell, C++, HTML, Java, PHP, P...</td>\n",
              "      <td>Ninguno de los anteriores</td>\n",
              "      <td>ElasticSearch, MongoDB, Percona Server</td>\n",
              "      <td>Ninguna de las anteriores</td>\n",
              "      <td>NetBeans, Notepad++, Sublime Text, Vim</td>\n",
              "      <td>GNU/Linux</td>\n",
              "      <td>Android</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Bruto</td>\n",
              "      <td>Full-Time</td>\n",
              "      <td>580000.0</td>\n",
              "      <td>391000.0</td>\n",
              "      <td>Mi sueldo está dolarizado</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Menos de un sueldo</td>\n",
              "      <td>Mix de las anteriores</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>En un trabajo anterior</td>\n",
              "      <td>Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>501-1000</td>\n",
              "      <td>Servicios / Consultoría de Software / Digital</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>Clases de idiomas, Estacionamiento gratuito / ...</td>\n",
              "      <td>Init ag, Computercenter, DKB, n26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>118 rows × 48 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     profile_gender  ...        company_best_companies_city\n",
              "261          Hombre  ...                                NaN\n",
              "318          Hombre  ...                                NaN\n",
              "437          Hombre  ...                                NaN\n",
              "453          Hombre  ...                             Airbnb\n",
              "467          Hombre  ...                                NaN\n",
              "...             ...  ...                                ...\n",
              "5819         Hombre  ...                                NaN\n",
              "5844         Hombre  ...                                NaN\n",
              "5878         Hombre  ...                                NaN\n",
              "5880         Hombre  ...                                NaN\n",
              "6080         Hombre  ...  Init ag, Computercenter, DKB, n26\n",
              "\n",
              "[118 rows x 48 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lzmzK1NuPNT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}