01_Probabilidad.ipynb

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      "name": "01 Probabilidad.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",
        "# Variables Aleatorias y Probabilidad\n",
        "\n",
        "En esta notebook, vamos a realizar una primera aproximación al conjunto de datos. \n",
        "\n",
        "* Variables aleatorias y sus distintos tipos\n",
        "* Probabilidad"
      ]
    },
    {
      "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": "5d0828d3-7199-43ad-eb74-9bd1d4ff7c14"
      },
      "source": [
        "df[:3]"
      ],
      "execution_count": null,
      "outputs": [
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              "      <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",
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              "      <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",
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              "      <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",
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              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>Otras industrias</td>\n",
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              "      <td>9</td>\n",
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              "      <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",
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              "      <td>Completado</td>\n",
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              "      <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",
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              "      <td>127000.0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>9</td>\n",
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            "text/plain": [
              "  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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI51zs3MoPKI"
      },
      "source": [
        "# Análisis de salarios\n",
        "\n",
        "La primera pregunta que se nos ocurre al ver esta encuenta es: **\"¿Y cuánto cobran los programadores en Argentina?\"**.\n",
        "\n",
        "Este es un punto de partida para el análisis del conjunto de datos. El proceso total constará de varias iteraciones: a medida que se obtengan conclusiones, se descrubrirán otros aspectos relevantes de los datos, lo cual disparará nuevas preguntas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uX_twatXtp9a"
      },
      "source": [
        "Para conocer más sobre la distribución de los salarios, es necesario elegir una columna de la encuesta para analizar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lFCvZP_tj7U"
      },
      "source": [
        "salary_col = 'salary_monthly_NETO'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rx7D2-rgIioD"
      },
      "source": [
        "Una buena forma de comenzar una exploración es a través de la visualización. Seaborn nos provee un tipo de gráfico específico para graficar columnas que contienen números, llamado `displot`. (No confundir con `distplot`, que está deprecado). \n",
        "\n",
        "El gráfico generado es un **histograma** de frecuencias. En el eje x se grafican los valores que toma la columna, divididos en intervalos o bins. En el eje y se grafica el conteo de ocurrencias de valores en cada intervalo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EXC0yLXoC5k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94c908be-07f2-434a-bd07-96c23564486f"
      },
      "source": [
        "seaborn.displot(df[salary_col], aspect=2)\n",
        "## para evitar la notación científica en las etiquetas\n",
        "plt.ticklabel_format(style='plain', axis='x')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gq8L1PQOvJOm"
      },
      "source": [
        "### ¿Qué estamos viendo?\n",
        "\n",
        "Las visualizaciones simples son prácticas para conocer la forma de los datos rápidamente, porque condensan mucha información. Por ejemplo:\n",
        "* El rango de valores tomados por la columna va desde 0 hasta aproximadamente 2M.\n",
        "* La mayoría de ls valores se condensa por debajo de los 250K, y pocos superan los 500K.\n",
        "* Los valores más frencuentes aparentan estar cerca de los 100K.\n",
        "* Hay un pico de ocurrencias en el valor 0.\n",
        " y brindan poco detalle.\n",
        "\n",
        "## Ejercicio: ¿Qué otro tipo de preguntas nos podemos hacer en este punto que no se responden con un histograma?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxhUA46yw6RS"
      },
      "source": [
        "### Análisis, ¡fundamentado!\n",
        "\n",
        "Para continuar el análisis, es necesario aplicar herramientas teóricas que nos brinda la Estadística y la Probabilidad."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElD6s__FHyw1"
      },
      "source": [
        "## Variables aleatorias y sus tipos\n",
        "\n",
        "En base a la definición de variable aleatoria que discutimos, se puede hablar de que cada columna de nuestro dataset es un **variable aleatoria**, y que su valor en cada respuesta es una **realización** de dicha variable. Pero, ¿qué tipo tienen esas variables?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDT1wPpmIXPU"
      },
      "source": [
        "### V.A. numéricas\n",
        "\n",
        "El salario, la edad, los años de experiencia, son variables aleatorias cuyo rango es un conjunto numérico. Podemos clasificarlas en **continuas** o **discretas**, aunque esa distinción se vuelve difusa cuando trabajamos con datos computacionalmente. ¿Por qué? \n",
        "\n",
        "* Datos que en teoría son continuos, se miden de manera discreta. Por ejemplo, los *años* de experiencia, la altura de una persona en *centímetros*.\n",
        "* Datos que en teoría son continuos, se discretizan a fines prácticos. Por ejemplo, la edad, el salario en pesos argentinos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBUC2KrxLrf4"
      },
      "source": [
        "Para analizar datos continuos se usan frecuentemente los histogramas, como en el caso anterior de los sueldos.\n",
        "\n",
        "**¡Tip!** Antes de graficar, controlar el rango (ya que seaborn intentará crear miles de segmentos si el rango es muy grande) y remover los valores nulos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xF9tJgmnJmzf",
        "outputId": "79538e83-f152-412c-94aa-0d4a00e08571"
      },
      "source": [
        "# Obtenemos el rango de valores observados de la variable\n",
        "df.profile_age.min(), df.profile_age.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18, 52000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98sP34xDixWP",
        "outputId": "dd82c11f-a473-43a2-c9c5-5a204772e0d9"
      },
      "source": [
        "seaborn.displot(df.profile_age[df.profile_age < 100].dropna(),\n",
        "                 stat='count', aspect=4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe05af47510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HK_1M3qQ43_1"
      },
      "source": [
        "Sin embargo, los histogramas pueden ocultar información. ¿Por qué? Porque agrupan rangos de valores en intervalos inferidos automáticamente. Como resultado, la visualización varía de con distintas longitudes de segmentos. Comparemos los siguientes histogramas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BR9K373qujYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "0b6de0e3-8b52-4336-b7b0-2ab6a8cdcee0"
      },
      "source": [
        "# Un ejemplo más avanzado\n",
        "fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,10), sharey='row')\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[0,0],\n",
        "                 stat='count')\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[0,1],\n",
        "                 bins=20, stat='count')\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[0,2],\n",
        "                 bins=5, stat='count')\n",
        "\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[1,0],\n",
        "                 stat='frequency')\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[1,1],\n",
        "                 bins=20, stat='frequency')\n",
        "seaborn.histplot(df.profile_age[df.profile_age < 100].dropna(), ax=ax[1,2],\n",
        "                 bins=5, stat='frequency')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kNU3ISA46NE"
      },
      "source": [
        "Para variables discretas puede usarse un gráfico de línea, que permite visualizar el conteo de cada uno de los puntos en el rango observado.\n",
        "\n",
        "**¿Se puede usar un gráfico de líneas para la variable `salary_montly_NETO`? ¿Tiene sentido?** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "z9oJLWSLWpic",
        "outputId": "a40c3693-e539-4ad0-c876-43fc678f967e"
      },
      "source": [
        "fig = plt.figure(figsize=(16,4))\n",
        "age_counts = df[df.profile_age < 100].profile_age.value_counts()\n",
        "seaborn.lineplot(age_counts.index, age_counts.values, color='steelblue')\n",
        "plt.xticks(fontsize=14)  # Achicamos la letra para que se vea mejor\n",
        "seaborn.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rq3ALV5OKWAA"
      },
      "source": [
        "### V.A. categóricas\n",
        "\n",
        "Las variables categóricas toman valores de un conjunto pre-definido, usualmente pero no necesariamente finito. Para visualizarlas, puede usarse un gráfico de barras, que representa cada valor observado con una columna, y el conteo de ese valor con la altura de la columna.\n",
        "\n",
        "Las variables numéricas discretas, ¿son categóricas?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UN1AOH3DakIw",
        "outputId": "46a0a90a-04ec-4c73-e7f3-c0a68984a34d"
      },
      "source": [
        "df.profile_gender.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mujer', 'Hombre', 'Otros'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzI0G2jEaeH-",
        "outputId": "a6562558-fedc-4414-9079-cb33763cb8e6"
      },
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "seaborn.countplot(df.profile_gender, color='steelblue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe05a61ab90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNKEYz22aZLz"
      },
      "source": [
        "Las variables categóricas pueden ser *ordinales*, si existe un orden lógico entre sus valores. Esto es independiente de que sean numéricas. En caso de que un orden exista, es adecuado incluirlo en el gráfico."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Es9a9vAKLKr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "9637a694-1c45-402b-8ad9-ec074cc6d4e2"
      },
      "source": [
        "sorted_studies_levels = ['Primario', 'Secundario', 'Terciario', 'Universitario',\n",
        "                         'Posgrado', 'Doctorado', 'Posdoctorado']\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(15,6))\n",
        "g = seaborn.countplot(df.profile_studies_level, color='steelblue', ax=axes[0])\n",
        "g = seaborn.countplot(df.profile_studies_level, color='steelblue', ax=axes[1],\n",
        "                  order=sorted_studies_levels)\n",
        "for ax in axes:\n",
        "  ax.tick_params(labelrotation=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1spi9v6-dLSh"
      },
      "source": [
        "### Tipos de variables vs tipos de datos\n",
        "\n",
        "Tenemos que distinguir dos conceptos con el mismo nombre y significado similar, pero que no son iguales:\n",
        " - **tipo de la variable aleatoria** es el tipo de valores con los que decidimos *intepretar* las realizaciones\n",
        " - **tipo de datos** es un concepto de programación que indica en qué formato se representa la información. Cuando asignamos a una variable `age` *del programa de Python* una realización de una variable aleatoria conceptual `profile_age`, esa variable `age` también tiene un *tipo de Python*, por ejemplo `int` o `float`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4EfQjqte1hl",
        "outputId": "f1457bbb-1c99-4435-abef-acbc559206ac"
      },
      "source": [
        "age = df.profile_age.iloc[0]\n",
        "type(age)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P96ppb5pfY77"
      },
      "source": [
        "*¡Importante!* Hay que tener en cuenta también los límites de la capacidad computacional al momento de representar entidades matemáticas.\n",
        "* Los números reales siempre son \"redondeados\" a una representación racional.\n",
        "* Los tipos básicos como `Int` sólo pueden representar números en un rango, por ejemplo `(-2^31, 2^31)`. Exceder el rango puede tener consecuencias inesperadas, como `integer overflow`.\n",
        "\n",
        "¿Por qué es importante saberlo? Porque se pueden producir errores de redondeo u obtener resultados aproximados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBuFctfvZtAL",
        "outputId": "a78b8d72-f25a-4371-d481-d77bfc80c674"
      },
      "source": [
        "print(type(3), type(3.44), type(1/3))  # 1/3 es un numero irracional\n",
        "import numpy\n",
        "print(numpy.iinfo('int64').min, numpy.iinfo('int64').max)\n",
        "numpy.int64(numpy.iinfo('int64').max) + 1\n",
        "# Traten de hacer numpy.int64(numpy.iinfo('int64').max + 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'int'> <class 'float'> <class 'float'>\n",
            "-9223372036854775808 9223372036854775807\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in long_scalars\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-9223372036854775808"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SX7jH3IOhMk8"
      },
      "source": [
        "Se puede acceder a los tipos de datos del DataFrame. El tipo `object` se utiliza para representar cualquier variable que no sea numérica, como por ejemplo los `str`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49W3njDLhOHe",
        "outputId": "b2339188-c95e-4aa2-c17d-75ec76bd99ae"
      },
      "source": [
        "df.dtypes[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "profile_gender                     object\n",
              "profile_age                         int64\n",
              "work_country                       object\n",
              "work_province                      object\n",
              "profile_years_experience          float64\n",
              "work_years_in_company             float64\n",
              "work_years_in_current_position    float64\n",
              "work_people_in_charge_of            int64\n",
              "profile_studies_level              object\n",
              "profile_studies_level_state        object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kF5u79olaP_C"
      },
      "source": [
        "Hay que tener en cuenta que las librerías de gráficos nos permitirán crear las visualizaciones que querramos, mientras los tipos de datos sean los adecuados.\n",
        "\n",
        "Por ejemplo, podemos hacer un histograma con la variable `profile_open_source_contributions` si la transformamos a tipo `bool` (que se representa internamente como un tipo entero). Sin embargo, esto no tiene ningún sentido."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_NtWA6KjZUY",
        "outputId": "87c15b7f-0557-4abe-9043-32350f3623ce"
      },
      "source": [
        "df.loc[:,'salary_in_usd_bool'] = \\\n",
        "  df.salary_in_usd.replace({'Mi sueldo está dolarizado': True}).fillna(False)\n",
        "print(df.salary_in_usd.unique(), df.salary_in_usd_bool.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nan 'Mi sueldo está dolarizado'] [False  True]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "KIyV5sezkSTE",
        "outputId": "f829e498-8831-4e9f-f3fb-625705196635"
      },
      "source": [
        "seaborn.histplot(df.salary_in_usd_bool, bins=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<string>:6: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
            "<string>:6: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe05a04ff10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0HKdM8olrwE"
      },
      "source": [
        "También podemos graficar la frecuencia de una variable categórica utilizando un gráfico de líneas. **¿Por qué esta visualización no es correcta?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "KB_ogJIzkOSk",
        "outputId": "122d84f2-85f4-43c3-b33d-3eafd5b3c23b"
      },
      "source": [
        "count_by_province = df.work_province.value_counts()\n",
        "fig = plt.figure(figsize=(16, 4))\n",
        "seaborn.lineplot(x=count_by_province.index, y=count_by_province.values)\n",
        "plt.xticks(rotation=45)\n",
        "seaborn.despine()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PaQBY7c3oGk7"
      },
      "source": [
        "# Análisis del impacto de los años de experiencia\n",
        "\n",
        "Ahora que ya sabemos aproximadamente la forma de nuestros datos, podemos pasar a realizar otra pregunta (otra iteración del proceso de análisis): \n",
        "\n",
        "**¿Tener más años de experiencia significa que se cobra más?**\n",
        "\n",
        "Para responder a esta pregunta, analizamos la probabilidad de que un programador tenga un salario mensual mayor que el promedio, cuando tiene una experiencia mayor que 5 años.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXQCb250t94J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12fbe8e0-d89d-4ac6-e158-f733a14948dd"
      },
      "source": [
        "avg_salary = df[salary_col].mean()\n",
        "avg_salary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94907.9491018188"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRssCsuhbzpd"
      },
      "source": [
        "## Medida de probabilidad\n",
        "\n",
        "En el teórico vimos que si cada una de nuestros eventos es independiente e idénticamente distribuido, es decir, que $P(\\{\\omega_i\\})=1/k$, entonces la probabilidad de un conjunto $A \\subset \\Omega$ es la proporción de $A$, donde .\n",
        "\n",
        "\n",
        "$$P(\\{\\omega_i\\})=1/k \\implies P(A)=|A|/|\\Omega|=|A|/k$$\n",
        "\n",
        "\n",
        "En este problema en particular, $\\Omega$ son todas las respuestas del dataset, cada $a_i$ es una variable que representa una respuesta, y el conjunto $A$ son las respuestas (filas) en la que la columna `salary_col` tiene un valor mayor que el promedio\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxN28Ib3uQl2",
        "outputId": "b5f20835-687b-4d93-c040-49d43a9fe071"
      },
      "source": [
        "p_above_avg = len(df[df[salary_col] >= avg_salary]) / len(df)\n",
        "p_above_avg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.33125512715340444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EC-NAnOuy_s"
      },
      "source": [
        "* ¿Por qué podemos usar la teoría de la probabilidad?\n",
        "* ¿Por qué calculamos una probabilidad con esta fórmula?\n",
        "* ¿Cómo podemos interpretar esta probabilidad?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4KuOb_tn2zG"
      },
      "source": [
        "## Probabilidad condicional\n",
        "\n",
        "Ahora podemos pasar a hablar de la probabilidad condicional entre los dos eventos. La definimos como\n",
        "\n",
        "$$P(A|B) = \\frac{P(A \\cap B)}{P(B)}$$\n",
        "\n",
        "Esto es equivalente a:\n",
        "\n",
        "$$P(A|B) = \\frac{|A \\cap B|}{|B|}$$\n",
        "\n",
        "## Ejercicio\n",
        "\n",
        "Reponder: **¿Si uno tiene más de 5 años de experiencia, la probabilidad de cobrar más que el promedio aumenta? ¿Estos eventos, son independientes?**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lzmzK1NuPNT"
      },
      "source": [
        "is_above_avg = df[salary_col] > avg_salary\n",
        "experience_greater_5 = df.profile_years_experience > 5\n",
        "intersection_count = len(df[is_above_avg & experience_greater_5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaINF9wSp8bk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "423b6b3a-3730-491b-c58d-9da756394b36"
      },
      "source": [
        "p_above_avg_given_experience = 0\n",
        "p_above_avg_given_experience"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BA7YyOp7IIrb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}