intento-1.html

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INTENTO CON PYTHON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HELLO WORLD\n",
    "\n",
    "El primer codigo \n",
    "que usaremos como lenguaje de programación es `Hello World`. \n",
    "Usando la funcion: `print()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola\n"
     ]
    }
   ],
   "source": [
    "print(\"¡hola!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## introduccion a JupyterHub\n",
    "\n",
    "* Para crear una nueva celda presionar la tecla B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operaciones aritméticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 3\n",
    "x = 7\n",
    "a + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 8\n",
    "a + x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gráfico de densidad "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instalamos la librería seaborn para visualizar y dotar de una interfaz al gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in ./.local/lib/python3.8/site-packages (0.11.2)\n",
      "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.3.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.21.1)\n",
      "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.7.0)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.8/dist-packages (from seaborn) (3.3.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->seaborn) (2020.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (8.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (2020.6.20)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos una **dataframe (DF)**\n",
    "\n",
    "Importamos seaborn como SNS para introducir los datos del **archivo iris**\n",
    "\n",
    "El dataframe tiene que ser igual a la visualización de los datos del anterior archivo. Utilizamos seaborn (tenemos que importarlo como **sns**)\n",
    "\n",
    "Mostramos el DF con la línea de texto: sns.kdeplot (df[\"sepal_width\"]). Prestamos atención a la variable que queremos mostrar en la X del gráfico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_width', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library & dataset\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')\n",
    "\n",
    "# Make default density plot\n",
    "sns.kdeplot(df['sepal_width'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con PWD podremos conocer en que directorio estamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/100386874@alumnos.uc3m.es'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}