Slides-04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Trabajando con columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Además de las funciones vistas anteriormente __Pandas__ tiene una serie de funciones para trabajar con columnas, tanto para analizar como para crear nuevas columnas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Empezamos por cargar los datos que hemos estado utilizando."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tipos = {'Numero':'int', 'Expediente':'str', 'Nombre':'str', 'Apellido':'str',\n",
    "         'Plan':'str', 'Tarea1':'float', 'Examen1':'float', 'Tarea2':'float',\n",
    "         'Examen2':'float', 'Tarea3':'float', 'Examen3':'float'}\n",
    "\n",
    "df = pd.read_csv('ListaAlumnos.csv', index_col='Expediente', dtype=tipos)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Además, le indicaremos a __matplotlib__ (librería que __Pandas__ utiliza para hacer gráficas) que queremos ver las gráficas en este mismo _notebook_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Análisis por columna\n",
    "\n",
    "El método `value_counts()` nos permite analizar la frecuencia de cada uno de los valores que tiene una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0    8\n",
       "95.0     3\n",
       "90.0     2\n",
       "60.0     2\n",
       "97.5     1\n",
       "Name: Examen2, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Este método es similar a un histograma de frecuencias, que se puede hacer mediante el método `hist()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff474195cc0>"
      ]
     },
     "execution_count": 4,
     "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": [
    "df.Examen2.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "__Nota__ Los valores no coinciden porque el histograma trabaja con intervalos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Si queremos conocer unicamente cuantos valores únicos hay, podemos utilizar el método `nunique()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "El método`describe()` fue mencionado anteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Numero</th>\n",
       "      <th>Tarea1</th>\n",
       "      <th>Examen1</th>\n",
       "      <th>Tarea2</th>\n",
       "      <th>Examen2</th>\n",
       "      <th>Tarea3</th>\n",
       "      <th>Examen3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>83.375000</td>\n",
       "      <td>68.375000</td>\n",
       "      <td>87.562500</td>\n",
       "      <td>92.656250</td>\n",
       "      <td>90.625000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.760952</td>\n",
       "      <td>24.374509</td>\n",
       "      <td>20.710303</td>\n",
       "      <td>20.607341</td>\n",
       "      <td>13.212013</td>\n",
       "      <td>20.155644</td>\n",
       "      <td>33.348663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.750000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>98.750000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.250000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.750000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Numero      Tarea1     Examen1      Tarea2     Examen2      Tarea3  \\\n",
       "count  16.000000   16.000000   16.000000   16.000000   16.000000   16.000000   \n",
       "mean    8.500000   83.375000   68.375000   87.562500   92.656250   90.625000   \n",
       "std     4.760952   24.374509   20.710303   20.607341   13.212013   20.155644   \n",
       "min     1.000000   33.000000   23.000000   33.000000   60.000000   50.000000   \n",
       "25%     4.750000   67.000000   62.250000   67.000000   93.750000  100.000000   \n",
       "50%     8.500000  100.000000   69.000000  100.000000   98.750000  100.000000   \n",
       "75%    12.250000  100.000000   78.750000  100.000000  100.000000  100.000000   \n",
       "max    16.000000  100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "\n",
       "          Examen3  \n",
       "count   16.000000  \n",
       "mean    47.000000  \n",
       "std     33.348663  \n",
       "min      0.000000  \n",
       "25%     22.000000  \n",
       "50%     50.000000  \n",
       "75%     75.000000  \n",
       "max    100.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Los valores que esta tabla automática contiene se pueden conocer con métodos específicos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "El mínimo se puede obtener con el método `min()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "El máximo se puede obtener con el método `max()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "El promedio de una columna se obtiene con `mean()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.65625"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "La varianza usando `var()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174.55729166666666"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "La desviación estándar con `std()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.212013157224249"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Para la suma total de la columna se puede usar `sum()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1482.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se puede contar el número de valores (excluyendo `null` y `NA`) usando `count()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "La mediana se puede calcular con `median()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Los cuantiles se pueden obtener con `quantile()`, indicando con una lista los valores de inicio y final de los intervalos (de 0 a 1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     60.00\n",
       "0.1     75.00\n",
       "0.2     90.00\n",
       "0.3     95.00\n",
       "0.4     95.00\n",
       "0.5     98.75\n",
       "0.6    100.00\n",
       "0.7    100.00\n",
       "0.8    100.00\n",
       "0.9    100.00\n",
       "1.0    100.00\n",
       "Name: Examen2, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Examen2.quantile([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]) # deciles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Para hacer una gráfica con los valores de una columna se puede utilizar simplemente `plot()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff47204e2b0>"
      ]
     },
     "execution_count": 16,
     "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": [
    "df.set_index('Numero').Examen1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff471fd0a90>"
      ]
     },
     "execution_count": 17,
     "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": [
    "df.set_index('Numero')[['Examen1','Examen2','Examen3']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Datos no existentes\n",
    "\n",
    "Un dato faltante puede modificar nuestras conclusiones por lo que es bueno detectarlo y, si fuera necesario, modificar el _dataframe_ para poder trabajar con los datos restantes. El objeto _dataframe_ tiene dos métodos especializados para trabajar con datos faltantes:\n",
    "\n",
    "    * `df.dropna()` borrará las lineas en las que falte un dato.\n",
    "    * `df.fillna(<value>)` colocará <value> para los valores que no existan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creando columnas\n",
    "\n",
    "Para crear una columna basta asignarle un valor a un _nombre_ o _identificador_ nuevo del _dataframe_. Para esto es necesario siempre utilizar la nomenclatura con `['<columna>']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Numero</th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Plan</th>\n",
       "      <th>Tarea1</th>\n",
       "      <th>Examen1</th>\n",
       "      <th>Tarea2</th>\n",
       "      <th>Examen2</th>\n",
       "      <th>Tarea3</th>\n",
       "      <th>Examen3</th>\n",
       "      <th>Tareas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expediente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>297613</th>\n",
       "      <td>1</td>\n",
       "      <td>Margarita</td>\n",
       "      <td>Duarte</td>\n",
       "      <td>LIM2014</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261301</th>\n",
       "      <td>2</td>\n",
       "      <td>Manolo</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>LIM2014</td>\n",
       "      <td>100.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262979</th>\n",
       "      <td>3</td>\n",
       "      <td>Tomas</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>LIM2014</td>\n",
       "      <td>67.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223867</th>\n",
       "      <td>4</td>\n",
       "      <td>Susana</td>\n",
       "      <td>Gonzalez</td>\n",
       "      <td>LIM2014</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>77.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200389</th>\n",
       "      <td>5</td>\n",
       "      <td>Itzel</td>\n",
       "      <td>Hernandez</td>\n",
       "      <td>LIM2014</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Numero     Nombre   Apellido     Plan  Tarea1  Examen1  Tarea2  \\\n",
       "Expediente                                                                   \n",
       "297613           1  Margarita     Duarte  LIM2014   100.0    100.0   100.0   \n",
       "261301           2     Manolo     Garcia  LIM2014   100.0     27.0   100.0   \n",
       "262979           3      Tomas      Gomez  LIM2014    67.0     57.0   100.0   \n",
       "223867           4     Susana   Gonzalez  LIM2014   100.0     60.0    33.0   \n",
       "200389           5      Itzel  Hernandez  LIM2014   100.0     75.0    67.0   \n",
       "\n",
       "            Examen2  Tarea3  Examen3      Tareas  \n",
       "Expediente                                        \n",
       "297613        100.0   100.0     75.0  100.000000  \n",
       "261301         60.0    50.0     13.0   83.333333  \n",
       "262979        100.0   100.0     50.0   89.000000  \n",
       "223867        100.0   100.0     38.0   77.666667  \n",
       "200389        100.0   100.0     50.0   89.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tareas'] = (df.Tarea1 + df.Tarea2 + df.Tarea3) / 3\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Para columnas numéricas se pueden usar los métodos anteriormente presentadas si añadimos `axis=1` en la llamada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <td>5</td>\n",
       "      <td>Itzel</td>\n",
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       "      <td>75.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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      "text/plain": [
       "            Numero     Nombre   Apellido     Plan  Tarea1  Examen1  Tarea2  \\\n",
       "Expediente                                                                   \n",
       "297613           1  Margarita     Duarte  LIM2014   100.0    100.0   100.0   \n",
       "261301           2     Manolo     Garcia  LIM2014   100.0     27.0   100.0   \n",
       "262979           3      Tomas      Gomez  LIM2014    67.0     57.0   100.0   \n",
       "223867           4     Susana   Gonzalez  LIM2014   100.0     60.0    33.0   \n",
       "200389           5      Itzel  Hernandez  LIM2014   100.0     75.0    67.0   \n",
       "\n",
       "            Examen2  Tarea3  Examen3      Tareas   Examenes  \n",
       "Expediente                                                   \n",
       "297613        100.0   100.0     75.0  100.000000  91.666667  \n",
       "261301         60.0    50.0     13.0   83.333333  33.333333  \n",
       "262979        100.0   100.0     50.0   89.000000  69.000000  \n",
       "223867        100.0   100.0     38.0   77.666667  66.000000  \n",
       "200389        100.0   100.0     50.0   89.000000  75.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Examenes'] = df[['Examen1','Examen2','Examen3']].mean(axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "            Numero     Nombre   Apellido     Plan  Tarea1  Examen1  Tarea2  \\\n",
       "Expediente                                                                   \n",
       "297613           1  Margarita     Duarte  LIM2014   100.0    100.0   100.0   \n",
       "261301           2     Manolo     Garcia  LIM2014   100.0     27.0   100.0   \n",
       "262979           3      Tomas      Gomez  LIM2014    67.0     57.0   100.0   \n",
       "223867           4     Susana   Gonzalez  LIM2014   100.0     60.0    33.0   \n",
       "200389           5      Itzel  Hernandez  LIM2014   100.0     75.0    67.0   \n",
       "\n",
       "            Examen2  Tarea3  Examen3      Tareas   Examenes      Final  \n",
       "Expediente                                                              \n",
       "297613        100.0   100.0     75.0  100.000000  91.666667  94.166667  \n",
       "261301         60.0    50.0     13.0   83.333333  33.333333  48.333333  \n",
       "262979        100.0   100.0     50.0   89.000000  69.000000  75.000000  \n",
       "223867        100.0   100.0     38.0   77.666667  66.000000  69.500000  \n",
       "200389        100.0   100.0     50.0   89.000000  75.000000  79.200000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Final'] = 0.7 * df.Examenes + 0.3 * df.Tareas\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Claro, además siempre es posible asignar con un ciclo `for`, aunque normalmente será más tardado. Para esto utilizamos el método `iterrow()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df['Anotacion'] = ''\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "    if row.Final > 90.0:\n",
    "        df.loc[i,'Anotacion'] = 'Muy buena nota.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
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      ],
      "text/plain": [
       "            Numero     Nombre   Apellido     Plan  Tarea1  Examen1  Tarea2  \\\n",
       "Expediente                                                                   \n",
       "297613           1  Margarita     Duarte  LIM2014   100.0    100.0   100.0   \n",
       "261301           2     Manolo     Garcia  LIM2014   100.0     27.0   100.0   \n",
       "262979           3      Tomas      Gomez  LIM2014    67.0     57.0   100.0   \n",
       "223867           4     Susana   Gonzalez  LIM2014   100.0     60.0    33.0   \n",
       "200389           5      Itzel  Hernandez  LIM2014   100.0     75.0    67.0   \n",
       "\n",
       "            Examen2  Tarea3  Examen3      Tareas   Examenes      Final  \\\n",
       "Expediente                                                               \n",
       "297613        100.0   100.0     75.0  100.000000  91.666667  94.166667   \n",
       "261301         60.0    50.0     13.0   83.333333  33.333333  48.333333   \n",
       "262979        100.0   100.0     50.0   89.000000  69.000000  75.000000   \n",
       "223867        100.0   100.0     38.0   77.666667  66.000000  69.500000   \n",
       "200389        100.0   100.0     50.0   89.000000  75.000000  79.200000   \n",
       "\n",
       "                  Anotacion  \n",
       "Expediente                   \n",
       "297613      Muy buena nota.  \n",
       "261301                       \n",
       "262979                       \n",
       "223867                       \n",
       "200389                       "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  }
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