{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/financieras/pyCourse/blob/main/jupyter/calisto3/0080_numpy6_ejes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dcc3965b",
      "metadata": {
        "id": "dcc3965b"
      },
      "source": [
        "# NumPy [6] Ejes\n",
        "Para matrices 2D:\n",
        "* axis 0 para las columnas\n",
        "* axis 1 para las filas\n",
        "\n",
        "Para matrices en una dimensión se trabaja con un vector fila:\n",
        "* En este caso el eje siempre será axis 0"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "50573a39",
      "metadata": {
        "id": "50573a39"
      },
      "source": [
        "## Trabajando con matrices 2D"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "343b8eca",
      "metadata": {
        "id": "343b8eca"
      },
      "source": [
        "### Suma en un eje"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce88184e",
      "metadata": {
        "id": "ce88184e",
        "outputId": "58cf460b-5466-4913-db4b-515f103ffd01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "m = np.array([[1,2,3], [4,5,6]])\n",
        "print(m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81b6729d",
      "metadata": {
        "id": "81b6729d",
        "outputId": "1983cee6-4fd1-4ac4-9116-e82b3debf7a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[5 7 9]\n"
          ]
        }
      ],
      "source": [
        "print(np.sum(m, axis=0))    # sumando columnas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ba3bc6f",
      "metadata": {
        "id": "1ba3bc6f",
        "outputId": "2a3cf240-c374-4c4e-894f-3f423fda0c59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 6 15]\n"
          ]
        }
      ],
      "source": [
        "print(np.sum(m, axis=1))    # sumando filas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79f72f70",
      "metadata": {
        "id": "79f72f70"
      },
      "source": [
        "### Concatenar por ejes\n",
        "Antes de concatenar preparamos las matrices m1 y m2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f29106d1",
      "metadata": {
        "id": "f29106d1",
        "outputId": "fc77540e-935e-4075-8f90-da9d0b624be4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "\n",
            "[[10 20 30]\n",
            " [40 50 60]]\n"
          ]
        }
      ],
      "source": [
        "m1 = np.array([[1,2,3], [4,5,6]])\n",
        "m2 = np.array([[10,20,30], [40,50,60]])\n",
        "print(m1)                               # mostramos m1\n",
        "print('')\n",
        "print(m2)                               # mostramos m2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57cefda0",
      "metadata": {
        "id": "57cefda0"
      },
      "source": [
        "#### Concatenamos por columnas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d025e55",
      "metadata": {
        "id": "3d025e55",
        "outputId": "56dae580-d76d-44cb-b4b8-b22d9405d814"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 1  2  3]\n",
            " [ 4  5  6]\n",
            " [10 20 30]\n",
            " [40 50 60]]\n"
          ]
        }
      ],
      "source": [
        "print(np.concatenate([m1, m2], axis=0))   # concatenamos por columnas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61f4642b",
      "metadata": {
        "id": "61f4642b"
      },
      "source": [
        "#### Concatenamos por filas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a376fd30",
      "metadata": {
        "id": "a376fd30",
        "outputId": "7f1b9cd3-b5ce-45b8-8e5f-007847d319df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 1  2  3 10 20 30]\n",
            " [ 4  5  6 40 50 60]]\n"
          ]
        }
      ],
      "source": [
        "print(np.concatenate([m1, m2], axis=1))   # concatenamos por filas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5940f6ea",
      "metadata": {
        "id": "5940f6ea"
      },
      "source": [
        "## Trabajando con un vector\n",
        "Trabajando con una matriz de una dimensión o vector fila. El único eje que existe es el axis 0."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0363877e",
      "metadata": {
        "id": "0363877e"
      },
      "source": [
        "### Concatenar vectores\n",
        "Los vectores siempre son de tipo fila.  \n",
        "Se trata de concatenar dos vectores fila."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "946c108c",
      "metadata": {
        "id": "946c108c",
        "outputId": "89ce1bdd-bfbf-4c11-cf51-ce729444b158"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6]\n"
          ]
        }
      ],
      "source": [
        "m1 = np.array([1, 2, 3])\n",
        "m2 = np.array([4, 5, 6])\n",
        "print(np.concatenate([m1, m2]))         # sin poner lo del eje"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15914d4e",
      "metadata": {
        "id": "15914d4e",
        "outputId": "3f0e86f3-7ba4-4931-ad1c-ada968effb42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6]\n"
          ]
        }
      ],
      "source": [
        "print(np.concatenate([m1, m2], axis=0)) # con el axis 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e87969a",
      "metadata": {
        "id": "3e87969a"
      },
      "outputs": [],
      "source": [
        "#print(np.concatenate([m1,m2], axis=1)) # ERROR: con el axis 1"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.11"
    },
    "colab": {
      "name": "0080_numpy6_ejes.ipynb",
      "provenance": [],
      "include_colab_link": true
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  "nbformat": 4,
  "nbformat_minor": 5
}