fCC_data_analysis_python.ipynb

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      "source": [
        "# Data Analysis with Python Course Notes\n",
        "\n",
        "Estas son mis notas y ejercicios de codigo en _Python_ para el curso de _Data Analysis with Python_ en [freeCodeCamp](www.freecodecamp.org).\n",
        "\n",
        "---\n",
        "\n",
        "## Parte I. Usando ```NumPy```.\n",
        "\n",
        "* Es una libreria de computo numerico que acelera el procesado de grandes cantidades de datos. ```matplotlib``` y ```pandas``` funcionan implementando ```NumPy```. \n",
        "\n",
        "* Optimiza el uso de memoria para llevar a cabo calculos complejos.\n",
        "\n",
        "Permite procesar arreglos en una forma optimizada que permite hacer un mejor uso de la memoria alocada para esos arreglos y hacer uso de formas eficientes de llevar a cabo calculos matriciales que usan instrucciones de bajo nivel, lo cual hace el computo mucho mas rapido.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "40pvLYPgwJ72"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import sys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XglpxdsUMb5e"
      },
      "outputs": [],
      "source": [
        "a = np.array([1, 2, 3, 4])\n",
        "b = np.array([0, .5, 1, 1.5, 2])\n",
        "c = np.arange(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ItWcXcFM-5a"
      },
      "source": [
        "El indexado y _slicing_ de los arreglos funciona igual que en las listas, utilizando el operador de indexar (```[]```), y la posicion o posiciones usando ```:```. Los arreglos de ```NumPy``` permiten realizar multi-indexado, al pasar una lista de elementos, y se devuelve un arreglo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "A_BQlhK2OcRK",
        "outputId": "9cfcc7c2-acec-48db-9265-935f59d300a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.5, 1. , 2. ])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "b[[1, 2, -1]] # Ejemplo de multi-indexado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5NgVqj2OpvA"
      },
      "source": [
        "Se puede conocer el tipo de cada arreglo usando el atributo ```dtype```:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "yQFX6XmJPiv7",
        "outputId": "b08ad66d-d4d0-46bb-a790-3109283273d5"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1., 2., 3., 4.])"
            ]
          },
          "execution_count": 4,
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      ],
      "source": [
        "a.dtype, b.dtype # Regresa una tupla con los tipo de datos almacnados por cada array\n",
        "np.array([1, 2, 3, 4], dtype=np.float64) # O puedes asignar el tipo de dato de forma manual al declarar el arreglo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlGj3sATQLfT"
      },
      "source": [
        "El especificar el atributo ```dtype``` es lo que permite mejorar el desempeño en memoria ya que podrian almacenarse enteros de tipo ```int8``` que ocupan menos memoria. \n",
        "Tambien se pueden almacenar objetos regulares en arreglos pero no es para lo que se pretende usarlo, sino para alamcenar datos. \n",
        "\n",
        "También se pueden crear arreglos multidimensionales con la notación usual de lista:"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "A = np.array([\n",
        "  [1, 2, 3], # indice de fila: 0     \n",
        "  [4, 5, 6]  # indice de fila: 1\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INF69-O8RWDP"
      },
      "source": [
        "y se puede obtener las forma (```A.shape```, el cual da una tupla ```(nrows, ncols)```), las dimensiones (```A.ndim```, el cual arroja el número de dimensiones), su tamaño (```A.size```, el número de elementos).\n",
        "\n",
        "Si las dimensiones no cuadran entonces el arreglo se guarda como un objeto regular de _Python_.\n",
        "\n",
        "El indiexado se puede hacer de forma regular (_e.g._ ```A[0][2]``` para el elemento de la primera fila y tercera columna) o usando una tupla:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "JS9cjSL7UMtN",
        "outputId": "2f97f04f-0232-42de-c14e-6c7548275de5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [4, 5]])"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "A[0, 2] # Perimte el slicing\n",
        "A[0:2] # Selecciona todas las filas desde 0 hasta 2 (sin incluir el dos)\n",
        "A[:, :2] # Selecciona todos las filas de las columnas 0 y 1 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jF5d85U4WFcV"
      },
      "source": [
        "```NumPy``` tiene algunos métodos disponibles para realizar estadística descriptiva: ```sum()```, ```mean()```, ```std()``` y ```var()```.\n",
        "Estas funcionan tambien en matrices, y se pueden especificar sobre que columnas o filas realizar la operacion pasando el argumento ```axis``` (0 para filas, 1 para columnas, etc.) como en ```A.mean(axis=0)```.\n",
        "\n",
        "### Operaciones vectorizadas y _broadcasting_.\n",
        "\n",
        "Las operaciones vectorizadas crean y regresan un arreglo nuevo (el arreglo original no se modifica. Aunque este comportamiento se puede modificar). \n",
        "Las versiones reducidas de los operadores escritos de la forma ```op=``` si modifican el arreglo orginal ya que se esta asignando a este la operación ```op```.\n",
        "\n",
        "La vectorización permite el indexado usando valores booleanos o expresiones lógicas (como ```a > a.mean()```) cocatenadas por operadores logicos de disjunción y conjunción. \n",
        "Esto también es llamado filtrado de valores o _query_."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "eada98be-6fff-4a98-dbe6-5b74ef583c61"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([3, 4])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a[a > a.mean()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-wc8HNtNu0E"
      },
      "source": [
        "### Tamaño de objetos y manejo de memoria.\n",
        "\n",
        "En general se requiere un mayor número de bytes para almacenar objetos regulares de _Python_, que los creados usando ```NumPy```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uMVGAXOOYvj",
        "outputId": "ae44982d-6c19-4bea-9a3a-120893df3239"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(28, 72)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Un entero en Python, un long en Python\n",
        "sys.getsizeof(1), sys.getsizeof(10 ** 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "p8O4zrWeOdN0",
        "outputId": "06e0415c-fa35-46cd-f5e1-9feb2b7db43f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(8, 8)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# En NumPy son mucho mas pequeños\n",
        "np.dtype(int).itemsize, np.dtype(float).itemsize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "rlfUB-kMPF0z",
        "outputId": "db6285f0-7708-494a-915e-9d239a58b3ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lista regular\n",
        "sys.getsizeof([1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RD9Suou-POVt",
        "outputId": "92045437-367d-49d0-fc1a-4c7fd33c5a2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Y una lista en NumPy\n",
        "np.array([1]).nbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROuDOZBuPr8M"
      },
      "source": [
        "También se hace bastante clara la diferencia en desempeño:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RpF83pPkP2MX"
      },
      "outputs": [],
      "source": [
        "l = list(range(1000))\n",
        "a = np.arange(1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YGOteImQCye",
        "outputId": "6e2b0505-eae7-44a0-9471-b00f004dffba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 1.49 ms, sys: 44 µs, total: 1.54 ms\n",
            "Wall time: 1.54 ms\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "332833500"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculo el perfomrance de la misma operacion sobre ambos objetos.\n",
        "%time np.sum(a ** 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzHjGVsOQac1",
        "outputId": "72e0a671-268c-47a6-cdce-61b8d2d08703"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 461 µs, sys: 0 ns, total: 461 µs\n",
            "Wall time: 475 µs\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "332833500"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        " %time sum([x ** 2 for x in l])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFawZ-yDQp_0"
      },
      "source": [
        "### Funciones útiles\n",
        "\n",
        "Entre estas estan las de generar numeros aleatorios, ```arange```, ```reshape```, ```linspace```, ```zeros```, ```ones``` y ```empty```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4FMfYBpQv-r",
        "outputId": "6bacc40d-15df-49d3-cadc-584a093fa66c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.2783611, 0.9553966])"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Genera numeros aleatorios entre 0 y 1\n",
        "np.random.random(size=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39neTJFHeTkP"
      },
      "source": [
        "## Parte II. Usando ```pandas```.\n",
        "\n",
        "Biblioteca que ayuda al análisis de datos (desde obtención de datos, visualización y análisis)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wbNaVtR83-b3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruMuAbydfsO_"
      },
      "source": [
        "### ```Series```\n",
        "\n",
        "La primera son las ```Series```, que almacena datos indexados en filas mostrando el tipo de datos,\n",
        "ya que la representacion interna utiliza un arreglo ```NumPy```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TII4c2AVe34s"
      },
      "outputs": [],
      "source": [
        "g7_pop = pd.Series([35.467, 63.951, 80.948])\n",
        "g7_pop.name = \"G7 Population in millions\" # Documentacion que describe la Serie"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svvgQP6vfSYr",
        "outputId": "9320c274-59b8-46ff-bece-9dd9b57eb03d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    35.467\n",
              "1    63.951\n",
              "2    80.948\n",
              "Name: G7 Population in millions, dtype: float64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c49Xo3j6geE4",
        "outputId": "00d3d243-5396-4b55-ad15-17cf85a91a0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([35.467, 63.951, 80.948])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Otros atributos de la series son\n",
        "g7_pop.dtype\n",
        "g7_pop.values # Deuelve un NumPy Array"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLMSRYqRhAQz"
      },
      "source": [
        "Al igual que en listas regulares, \n",
        "se puede acceder a elementos de la serie usando los índices entre corchetes.\n",
        "O también se le pueden asignar índices personalizados:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7ppRzX-hgZw",
        "outputId": "973aa9d8-68f3-43cf-f33f-c1ba102bd986"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Canada     35.467\n",
              "France     63.951\n",
              "Germany    80.948\n",
              "Name: G7 Population in millions, dtype: float64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop.index = ['Canada', 'France', 'Germany']\n",
        "g7_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uF5QHp_CiPvN"
      },
      "source": [
        "O, durante la creación de la serie puedes asignar índices usando un diccionario:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_IcDmdkBilOI"
      },
      "outputs": [],
      "source": [
        "g7_pop = pd.Series({\n",
        "  'Canada': 35.467, \n",
        "  'France': 63.951, \n",
        "  'Germany': 80.948})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVQOupyxjY7M"
      },
      "source": [
        "Por ejemplo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPK_Phv67K-I",
        "outputId": "df369f3b-f0ab-475b-8382-955b6403c560"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(35.467, 63.951)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop['Canada'], g7_pop['France']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILz5LNCT7241"
      },
      "source": [
        "O usando su posición en la serie:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qLf4A6878k5",
        "outputId": "70a473ad-407d-4554-bdc7-8f3b9898671e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(35.467, 80.948)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop.iloc[0], g7_pop.iloc[-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Vgh54jL8Jat"
      },
      "source": [
        "O seleccionar varios elementos (aqui el resultado es otra ```Series```):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPq0qaDu8Mgx",
        "outputId": "151e6625-1eb7-4c00-ff45-c1ebf6b6d6a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "France     63.951\n",
              "Germany    80.948\n",
              "dtype: float64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop[['France', 'Germany']] # o g7_pop.iloc[[0, 1]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vZ8IlYU8sDO"
      },
      "source": [
        "Y además, puedes hacer _slicing_, pero en este caso, el elemento representando el limite superior si es incluido:"
      ]
    },
    {
      "cell_type": "code",
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        "id": "dTcs4bO585sX",
        "outputId": "90436f3c-848a-4f24-ebfd-4aa368bc2ad7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Canada     35.467\n",
              "France     63.951\n",
              "Germany    80.948\n",
              "dtype: float64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop['Canada':'Germany']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KybHxEQE9Wyq"
      },
      "source": [
        "Otra forma de acceder a los elementos de una serie es por medio del indexado selectivo usando expresiones booleanas. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyC929dk9an-",
        "outputId": "9c0b8999-b2e8-4850-f158-8e9b4517220d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Canada     False\n",
              "France     False\n",
              "Germany     True\n",
              "dtype: bool"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop > 70"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YUgD_LI9n1b",
        "outputId": "ef4c31bd-8ef6-494a-d372-c29e6c0d0770"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Germany    80.948\n",
              "dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "g7_pop[g7_pop > 70] # regresa una Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CaaJEKaI_xeQ"
      },
      "source": [
        "### ```DataFrame```\n",
        "\n",
        "Estructura de datos en formato tabular."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WDAEwOxVAHk-"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({\n",
        "   'Population' : [35.467, 63.951, 80.94, 60.065, 127.021, 60.45, 318.23],\n",
        "   'GDP' : [1785387, 2833687, 3874437, 2167744, 4602367, 2950039, 17348075],\n",
        "   'Surface Area' : [9984670, 64069, 357114, 301336, 377930, 24245, 9525067],\n",
        "   'HDI' : [0.913, 0.888, 0.916, 0.873, 0.981, 0.907, 0.915],\n",
        "    'Continent' : ['America', 'Europe', 'Europe','Europe', 'Asia', 'Europe', 'America']\n",
        "  }, columns=['Population', 'GDP', 'Surface Area', 'HDI', 'Continent']\n",
        ")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HM-2-Q85CGqb"
      },
      "outputs": [],
      "source": [
        " df.index = [ # Cada columna es una Series\n",
        "  'Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kongdom', 'Uniated States']\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4TzTaxbCmax",
        "outputId": "3a8d8f53-c8cb-4906-f659-39c0f9f519ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'], dtype='object')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NuKwD5y7CoeM",
        "outputId": "072af418-5f4a-4333-d74d-09a2aa389961"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kongdom',\n",
              "       'Uniated States'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fh9BGavpCp-a",
        "outputId": "dda22210-bd49-49ee-b272-9ad13fd6d0d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 7 entries, Canada to Uniated States\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Population    7 non-null      float64\n",
            " 1   GDP           7 non-null      int64  \n",
            " 2   Surface Area  7 non-null      int64  \n",
            " 3   HDI           7 non-null      float64\n",
            " 4   Continent     7 non-null      object \n",
            "dtypes: float64(2), int64(2), object(1)\n",
            "memory usage: 336.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JPXgMU7CrEV",
        "outputId": "1c48b2ad-a0e2-41ca-e3e5-fa54f4388e8a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THQ4rBSNCs5j",
        "outputId": "b7903083-d0ab-4eff-f957-0361321b2722"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7, 5)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "TGY9DaGhCu9U",
        "outputId": "4e09a85f-2cf3-49ee-8ab1-da53f647571b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP</th>\n",
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              "      <th>HDI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.000000</td>\n",
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              "      <td>7.000000e+00</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>106.589143</td>\n",
              "      <td>5.080248e+06</td>\n",
              "      <td>2.947776e+06</td>\n",
              "      <td>0.913286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>97.499469</td>\n",
              "      <td>5.494020e+06</td>\n",
              "      <td>4.654043e+06</td>\n",
              "      <td>0.033905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>35.467000</td>\n",
              "      <td>1.785387e+06</td>\n",
              "      <td>2.424500e+04</td>\n",
              "      <td>0.873000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>60.257500</td>\n",
              "      <td>2.500716e+06</td>\n",
              "      <td>1.827025e+05</td>\n",
              "      <td>0.897500</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>63.951000</td>\n",
              "      <td>2.950039e+06</td>\n",
              "      <td>3.571140e+05</td>\n",
              "      <td>0.913000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>103.980500</td>\n",
              "      <td>4.238402e+06</td>\n",
              "      <td>4.951498e+06</td>\n",
              "      <td>0.915500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>318.230000</td>\n",
              "      <td>1.734808e+07</td>\n",
              "      <td>9.984670e+06</td>\n",
              "      <td>0.981000</td>\n",
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            "text/plain": [
              "       Population           GDP  Surface Area       HDI\n",
              "count    7.000000  7.000000e+00  7.000000e+00  7.000000\n",
              "mean   106.589143  5.080248e+06  2.947776e+06  0.913286\n",
              "std     97.499469  5.494020e+06  4.654043e+06  0.033905\n",
              "min     35.467000  1.785387e+06  2.424500e+04  0.873000\n",
              "25%     60.257500  2.500716e+06  1.827025e+05  0.897500\n",
              "50%     63.951000  2.950039e+06  3.571140e+05  0.913000\n",
              "75%    103.980500  4.238402e+06  4.951498e+06  0.915500\n",
              "max    318.230000  1.734808e+07  9.984670e+06  0.981000"
            ]
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      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzQwPVOUDFhJ",
        "outputId": "b8f7205e-3fc0-4231-c36d-afe5c443d6d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "float64    2\n",
              "int64      2\n",
              "object     1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzJWET1YEN3T"
      },
      "source": [
        "En el caso del indexado, como cada columna es una ```Series```, entonces se puede indexar de la misma forma que para esta clase de objetos.\n",
        "Se puede usar ```df['Population']```, que devuelve una serie, o  ```df['Population'].to_frame()```, que devuelve una presentacion tabular. \n",
        "\n",
        "También es posible indexar una lista de elementos (o multiples columnas) ```df[['Population', 'GDP']]``` o usar _slicing_ ```df[1:3]``` (seleccion de multiples filas). \n",
        "Aunque la selección a nivel de filas es mejor (y se recomienda, ya que reduce la ambiguedad) con ``loc`` y ``iloc``.\n",
        "\n",
        "Para seleccionar multiples filas, en una o mas columnas, se usa algo similar a ```df.loc['Canada':'France', ['Population', 'GDP']]```"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "PzL4-jasEM3Y",
        "outputId": "d3a6e0ab-aff6-4f0e-f3b0-c78e2e3b08b8"
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9c5e025-421d-49e4-bb5a-651a62404204 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9c5e025-421d-49e4-bb5a-651a62404204');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Population      GDP\n",
              "Canada      35.467  1785387\n",
              "France      63.951  2833687"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc['Canada':'France', ['Population', 'GDP']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOd41sYqIbuq"
      },
      "source": [
        "Tambien es posible realizar selecciones basadas en expresiones booleanas,\n",
        " y modificar el ```DataFrame``` usnado estas selecciones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 143
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        "id": "oK2W7vHfKXUj",
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      },
      "outputs": [
        {
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              "      <th>Population</th>\n",
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              "    <tr>\n",
              "      <th>Uniated States</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "                Population       GDP\n",
              "Germany             80.940   3874437\n",
              "Japan              127.021   4602367\n",
              "Uniated States     318.230  17348075"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[df[\"Population\"] > 70, [\"Population\", \"GDP\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caP_WgG9Di66"
      },
      "source": [
        "Se pueden eliminar piezas especificas con el metodo ```drop```:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "POjUWudpDq41",
        "outputId": "8ab96ca6-7d5d-40d4-a3ac-e305657e9cf6"
      },
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        {
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              "          const element = document.querySelector('#df-0be6e91f-21bb-41cb-b7c0-7060290879f0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "                Population       GDP  Surface Area    HDI Continent\n",
              "France              63.951   2833687         64069  0.888    Europe\n",
              "Germany             80.940   3874437        357114  0.916    Europe\n",
              "Italy               60.065   2167744        301336  0.873    Europe\n",
              "United Kongdom      60.450   2950039         24245  0.907    Europe\n",
              "Uniated States     318.230  17348075       9525067  0.915   America"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop([\"Canada\", \"Japan\"]) # Devuelve todas las netradas menos aquellas para Canada y Japan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wqv1glk_D9uo"
      },
      "source": [
        "o devolver columnas especificas (el argumento ```columns``` funciopna con este proposito auqneu tambien es posible solo dar una lista y especificfar de donde quitar elementos (filas o columnas) usando el argumento ```axis```):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "aMgsO8tQECID",
        "outputId": "c57f3800-46a0-4458-fa80-2a658b60a7cf"
      },
      "outputs": [
        {
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              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1fd4e711-6352-4e19-9b1c-cc08c7509862 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1fd4e711-6352-4e19-9b1c-cc08c7509862');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                     GDP  Surface Area Continent\n",
              "Canada           1785387       9984670   America\n",
              "France           2833687         64069    Europe\n",
              "Germany          3874437        357114    Europe\n",
              "Italy            2167744        301336    Europe\n",
              "Japan            4602367        377930      Asia\n",
              "United Kongdom   2950039         24245    Europe\n",
              "Uniated States  17348075       9525067   America"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(columns=[\"Population\", \"HDI\"]) # o df.drop([\"Population\", \"HDI\"], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7D-Wps0MJWj"
      },
      "source": [
        "Las operaciones con ```drop``` asi como otras muchas operaciones, son inmutables y no modifican el ```DataFrame``` sobre el que se aplican."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8aWwPvdHY2V"
      },
      "source": [
        "### Operaciones de _broadcasting_.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "jM12ccwMHkVR",
        "outputId": "8bafd561-e20c-4133-f2ea-a8b92ee77629"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>GDP</th>\n",
              "      <th>HDI</th>\n",
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              "  </thead>\n",
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              "      <th>Canada</th>\n",
              "      <td>785387.0</td>\n",
              "      <td>0.613</td>\n",
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              "      <td>0.616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>1167744.0</td>\n",
              "      <td>0.573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>3602367.0</td>\n",
              "      <td>0.681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kongdom</th>\n",
              "      <td>1950039.0</td>\n",
              "      <td>0.607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uniated States</th>\n",
              "      <td>16348075.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                       GDP    HDI\n",
              "Canada            785387.0  0.613\n",
              "France           1833687.0  0.588\n",
              "Germany          2874437.0  0.616\n",
              "Italy            1167744.0  0.573\n",
              "Japan            3602367.0  0.681\n",
              "United Kongdom   1950039.0  0.607\n",
              "Uniated States  16348075.0  0.615"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crisis = pd.Series([-1_000_000, -0.3], index=['GDP', 'HDI'])\n",
        "df[['GDP', 'HDI']] + crisis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OvPdxtgMh2q"
      },
      "source": [
        "### Modificaciones de un ```DataFrame```.\n",
        "\n",
        "Se pede modificar un ```DataFrame``` usando operaciones de asignacion de la siguiente forma:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4COQGxZOM3Xv"
      },
      "outputs": [],
      "source": [
        "langs = pd.Series(['French', 'German', 'Italian'],\n",
        "  index=['France', 'Germany', 'Italy'], name='Language')\n",
        "df['Language'] = langs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o12qcS56iGlB"
      },
      "outputs": [],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClUihhufjIkR"
      },
      "source": [
        "Se pueden crear columnas ademas a partir de otras columnas, como por ejemplo:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "971fxKe7nT7O"
      },
      "outputs": [],
      "source": [
        "df['GDP per capita'] = df['GDP'] / df['Population']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creando un `DataFrame` importando datos de un archivo CSV\n",
        "\n"
      ],
      "metadata": {
        "id": "epBkgWP4BarF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parte III. Manejando y Limpiando Datos.\n",
        "\n"
      ],
      "metadata": {
        "id": "mgRBQ03YBBU9"
      }
    }
  ],
  "metadata": {
    "colab": {
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      "authorship_tag": "ABX9TyOHArd3rmWqk7QY3CGG8YtM",
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    },
    "kernelspec": {
      "display_name": "Python 3",
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