C1_4_ManejoArrays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9201e0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbc0d84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\1-Python_para_principiantes'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c0863a",
   "metadata": {},
   "source": [
    "# 4. Creacion de arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c898d6",
   "metadata": {},
   "source": [
    "NumPy es un paquete de Python. Significa *Python Numérico*. Es una biblioteca que consta de objetos de matriz multidimensionales y una colección de rutinas para el procesamiento de matriz.\n",
    "\n",
    "**Operaciones usando NumPy:**\n",
    "\n",
    "* Operaciones matemáticas y lógicas en matrices.\n",
    "* Transformaciones de Fourier y rutinas para la manipulación de formas.\n",
    "* Operaciones relacionadas con el álgebra lineal. NumPy tiene funciones integradas para álgebra lineal y generación de números aleatorios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e55ce77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='arrays.png', width=500, height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64ba7e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.20.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7078894d",
   "metadata": {},
   "source": [
    "## Arrays uni-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3945e700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10, 15])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([5, 10, 15])\n",
    "a\n",
    "\n",
    "type(a)\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "502fa863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim\n",
    "a.shape\n",
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c8d8897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.array(list(range(1, 5)))\n",
    "a2\n",
    "type(a2)\n",
    "a2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fb7411a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.ndim\n",
    "a2.shape\n",
    "a2.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f5b4915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  5. , 10. ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = np.array([2.5, 5, 10])\n",
    "a3\n",
    "type(a3)\n",
    "a3.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60511488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '5', 'A'], dtype='<U11')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('<U11')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4 = np.array([2, 5, 'A'])\n",
    "a4\n",
    "type(a4)\n",
    "a4.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b951af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 9, 0, 6, 5])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2021)\n",
    "\n",
    "x1 = np.random.randint(10, size=6)\n",
    "x1\n",
    "type(x1)\n",
    "x1.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee80f1e",
   "metadata": {},
   "source": [
    "## Arrays multi-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2bdf638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "b\n",
    "type(b)\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "395ec164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.ndim\n",
    "b.shape\n",
    "b.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2363b191",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-3dbce7861276>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  b2 = np.array([[1,2,3], [4,5]])  # cuando no tienen el mismo tamaño automaticamente convierte a arrays de objetos\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([list([1, 2, 3]), list([4, 5])], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2 = np.array([[1,2,3], [4,5]])  # cuando no tienen el mismo tamaño automaticamente convierte a arrays de objetos\n",
    "b2\n",
    "type(b2)\n",
    "b2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bd2c6db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2.ndim\n",
    "b2.shape\n",
    "b2.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b39f4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([[[1,2,3], [4,5,6]], [[7,8,9],[10,11,12]]])\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3d698cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.ndim\n",
    "c.shape\n",
    "c.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eac62b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]],\n",
       "\n",
       "       [[13, 14, 15],\n",
       "        [16, 17, 18]]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d= np.array([[[1,2,3], [4,5,6]], [[7,8,9],[10,11,12]], [[13,14,15],[16,17,18]]])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "983430fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(3, 2, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.ndim\n",
    "d.shape\n",
    "d.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362d798f",
   "metadata": {},
   "source": [
    "## Otras formas de crear arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fec07ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(4)\n",
    "\n",
    "np.zeros((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb467207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(4)\n",
    "\n",
    "np.ones((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3643a4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 3x3 identity matrix\n",
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e758d11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbc17237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ab9457a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 1, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f22fb591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 1.75, 3.5 , 5.25, 7.  ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 7, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "314f0d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5],\n",
       "       [5, 5],\n",
       "       [5, 5]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,2), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c38f426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 9, 0, 6])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[4, 5, 9],\n",
       "       [0, 6, 5]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[[4, 5, 9],\n",
       "        [0, 6, 5]],\n",
       "\n",
       "       [[8, 6, 6],\n",
       "        [6, 6, 1]],\n",
       "\n",
       "       [[5, 7, 1],\n",
       "        [1, 5, 2]]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numeros aleatorios\n",
    "\n",
    "np.random.seed(2021)\n",
    "np.random.randint(10, size=5)\n",
    "\n",
    "np.random.seed(2021)\n",
    "np.random.randint(10, size=(2, 3))  # Two-dimensional array\n",
    "\n",
    "np.random.seed(2021)\n",
    "np.random.randint(10, size=(3, 2, 3))  # Three-dimensional array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f22557",
   "metadata": {},
   "source": [
    "## Seleccion de valores en arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1d3c5b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.arange(5, 10)\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "883a9790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[0]\n",
    "a1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98fc0797",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([8, 9])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([7, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[:3]\n",
    "a1[3:]\n",
    "a1[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2284eca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 9])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccionar 5, 7 y 9\n",
    "a1[[0, 2, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4f2d821a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09a539e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccion de filas\n",
    "a2[0,:]\n",
    "a2[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "436d5604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([2, 5])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([3, 6])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccion de columnas\n",
    "a2[:,0]\n",
    "a2[:,1]\n",
    "a2[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ab45b8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccionar 1, 2\n",
    "a2[0][0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "21563a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59212eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccionar el valor de 4 de a2\n",
    "a2[1,0]\n",
    "a2[-1,-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eb7caed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 6])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccionar el valor de 2 y 6 de a2\n",
    "# indices de 2: (0, 1)\n",
    "# indices de 6: (1, 2)\n",
    "\n",
    "a2[[0,1],[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "622702dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "565a5a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99,  6,  7,  8,  9])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reemplazar valores\n",
    "a1[0] = 99\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fa6e2d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[7, 8, 9],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2\n",
    "\n",
    "a1[2:]\n",
    "a2[0,:] = a1[2:]\n",
    "\n",
    "a2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e42134c",
   "metadata": {},
   "source": [
    "## Operaciones basicas entre arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b3e960d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([9, 9, 9, 9])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.full(4, 3)\n",
    "a2 = np.full(4, 9)\n",
    "a1 ; a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "55cfb778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, 8])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([9, 9, 9, 9])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 + 5\n",
    "a1 * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5a5a3c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 12, 12, 12])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([-6, -6, -6, -6])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([27, 27, 27, 27])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0.33333333, 0.33333333, 0.33333333, 0.33333333])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 + a2\n",
    "a1 - a2\n",
    "a1 * a2\n",
    "a1 / a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "37e2baaa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20.08553692, 20.08553692, 20.08553692, 20.08553692])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "49005f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.19722458, 2.19722458, 2.19722458, 2.19722458])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ca6dcdee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 3., 3., 3.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "27409e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3],\n",
       "       [3, 3],\n",
       "       [3, 3]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[9, 9],\n",
       "       [9, 9],\n",
       "       [9, 9]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = np.full((3,2), 3)\n",
    "a4 = np.full((3,2), 9)\n",
    "a3 ; a4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1c345564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 12],\n",
       "       [12, 12],\n",
       "       [12, 12]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-6, -6],\n",
       "       [-6, -6],\n",
       "       [-6, -6]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[27, 27],\n",
       "       [27, 27],\n",
       "       [27, 27]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.33333333, 0.33333333],\n",
       "       [0.33333333, 0.33333333],\n",
       "       [0.33333333, 0.33333333]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 + a4\n",
    "a3 - a4\n",
    "a3 * a4\n",
    "a3 / a4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fba86ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20.08553692, 20.08553692],\n",
       "       [20.08553692, 20.08553692],\n",
       "       [20.08553692, 20.08553692]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[2.19722458, 2.19722458],\n",
       "       [2.19722458, 2.19722458],\n",
       "       [2.19722458, 2.19722458]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[3., 3.],\n",
       "       [3., 3.],\n",
       "       [3., 3.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a3)\n",
    "np.log(a4)\n",
    "np.sqrt(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "389daa3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 9],\n",
       "       [9, 9],\n",
       "       [9, 9]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[9, 9, 9],\n",
       "       [9, 9, 9]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpuesta\n",
    "a4\n",
    "np.transpose(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fcf2cbbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 6, 1],\n",
       "       [7, 6, 9],\n",
       "       [7, 7, 7]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.5       ,  2.5       , -3.42857143],\n",
       "       [-1.        , -2.        ,  2.71428571],\n",
       "       [-0.5       , -0.5       ,  0.85714286]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2021)\n",
    "a6 = np.random.randint(1,10, size=(3, 3))\n",
    "a6\n",
    "\n",
    "# Inversa\n",
    "\n",
    "np.linalg.inv(a6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6bb7280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5       ,  2.5       , -3.42857143],\n",
       "       [-1.        , -2.        ,  2.71428571],\n",
       "       [-0.5       , -0.5       ,  0.85714286]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.linalg import inv # De esta manera, se puede luego solo usar la función inv()\n",
    "inv(a6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697b195a",
   "metadata": {},
   "source": [
    "## Reorganizacion de arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "df3a1944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.arange(8)\n",
    "a1\n",
    "a1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e1bb86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = a1.reshape(2, 4)\n",
    "a2\n",
    "a2.ndim\n",
    "\n",
    "a1.reshape(4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b303d2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb7918f",
   "metadata": {},
   "source": [
    "## Concatenacion de arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "23bbb0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 3, 2, 1])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "y = np.array([3, 2, 1])\n",
    "x ; y\n",
    "\n",
    "np.concatenate([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1a65b0af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = np.array([[1, 2, 3],\n",
    "                 [4, 5, 6]])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e5a93b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate along the first axis\n",
    "np.concatenate([grid, grid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a5ad8738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 1, 2, 3],\n",
       "       [4, 5, 6, 4, 5, 6]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate along the second axis (zero-indexed)\n",
    "np.concatenate([grid, grid], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c3c1d62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[9, 8, 7],\n",
       "       [6, 5, 4]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [9, 8, 7],\n",
       "       [6, 5, 4]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stack arrays\n",
    "\n",
    "x = np.array([1, 2, 3])\n",
    "x\n",
    "y = np.array([[9, 8, 7], [6, 5, 4]])\n",
    "y\n",
    "\n",
    "# vertically stack the arrays\n",
    "np.vstack([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6f8cba51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 1, 2, 3])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# horizontally stack the arrays\n",
    "np.hstack([y[0,:], x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "598572ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  8,  7, 99],\n",
       "       [ 6,  5,  4, 99]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = np.array([[99],\n",
    "              [99]])\n",
    "np.hstack([y, h])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbb0028",
   "metadata": {},
   "source": [
    "## Arrays booleanos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a1d265bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4, 5])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e3794b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True,  True])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False, False])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False, False])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True,  True])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x == 3\n",
    "x != 3\n",
    "x < 3\n",
    "x <= 3\n",
    "x > 3\n",
    "x >= 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2c83726c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('bool')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = x == 3\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3f85ffcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 5, 9, 0],\n",
       "       [6, 5, 8, 6],\n",
       "       [6, 6, 6, 1]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2021)\n",
    "a = np.random.randint(10, size=(3, 4)) \n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3533bb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True, False,  True],\n",
       "       [False,  True, False, False],\n",
       "       [False, False, False,  True]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a < 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b387c085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many values less than 6?\n",
    "\n",
    "np.count_nonzero(a < 6)\n",
    "np.sum(a < 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e77d0aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 2])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many values less than 6 in each ...\n",
    "\n",
    "np.sum(a < 6, axis=1) # fila\n",
    "np.sum(a < 6, axis=0) # columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "55642d9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True, False, False])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are there any values greater than 8?\n",
    "\n",
    "np.any(a > 8)\n",
    "np.any(a > 8, 1) # fila\n",
    "np.any(a > 8, 0) # columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "74ae0664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are all values less than 10?\n",
    "\n",
    "np.all(a < 10)\n",
    "np.all(a < 10, 1) # fila\n",
    "np.all(a < 10, 0) # columna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ec35d6",
   "metadata": {},
   "source": [
    "## Ordenamiento de Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8f1cb943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 4, 3, 5])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([2, 1, 4, 3, 5])\n",
    "x\n",
    "np.sort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f4a42dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 4, 3, 5])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# argsort: returns the indices of the sorted elements:\n",
    "\n",
    "x = np.array([2, 1, 4, 3, 5])\n",
    "x\n",
    "\n",
    "i = np.argsort(x)\n",
    "i\n",
    "\n",
    "x[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5f1d2b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  9,  0],\n",
       "       [13, 14,  6, 12],\n",
       "       [14, 11, 13,  5]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2021)\n",
    "a = np.random.randint(15, size=(3, 4)) \n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e6491f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  0],\n",
       "       [13, 11,  9,  5],\n",
       "       [14, 14, 13, 12]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  5,  9],\n",
       "       [ 6, 12, 13, 14],\n",
       "       [ 5, 11, 13, 14]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, 0) # ordenar por columna\n",
    "np.sort(a, 1) # ordenar por fila"
   ]
  }
 ],
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