{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 11. Librería Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**pandas** es una librería *open source* que nos proporciona estructuras de datos y herramientas de análisis de datos potentes y fáciles de usar en Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se puede instalar en nuestro entorno virtual con el siguiente comando:\n",
    "\n",
    "```\n",
    "pipenv install pandas\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Se utiliza el alias `pd` como estándar de facto par el uso de **pandas**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Series\n",
    "\n",
    "Una serie representa una secuencia de datos unidimensional, y se crea pasándole a pandas una lista de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DataFrame\n",
    "\n",
    "Un objeto `DataFrame` representa una estructura tabular bi-dimensional que contiene datos potencialmente heterogéneos, con filas etiquetadas.\n",
    "\n",
    "Se pueden crear a partir de un diccionario, o de un `array` de NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'A' : [1., 2., np.nan, None],\n",
    "    'B' : pd.Timestamp('20130102'),\n",
    "    'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "    'D' : np.array([3] * 4,dtype='int32'),\n",
    "    'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "    'F' : 'foo'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>train</td>\n",
       "      <td>foo</td>\n",
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       "      <th>2</th>\n",
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       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  2.0 2013-01-02  1.0  3  train  foo\n",
       "2  NaN 2013-01-02  1.0  3   test  foo\n",
       "3  NaN 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Podemos utilizar una serie para especificar la columna de índice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.679399</td>\n",
       "      <td>-0.564244</td>\n",
       "      <td>-0.395166</td>\n",
       "      <td>-0.004622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>2.147829</td>\n",
       "      <td>-0.991826</td>\n",
       "      <td>-1.004833</td>\n",
       "      <td>0.168517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.398068</td>\n",
       "      <td>-0.536610</td>\n",
       "      <td>-0.773990</td>\n",
       "      <td>-1.075894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.185011</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>-0.770427</td>\n",
       "      <td>-0.472499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.359634</td>\n",
       "      <td>0.338176</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>0.359107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.555880</td>\n",
       "      <td>1.115044</td>\n",
       "      <td>-2.108126</td>\n",
       "      <td>0.139896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894\n",
       "2013-01-04 -1.185011  1.988697 -0.770427 -0.472499\n",
       "2013-01-05 -0.359634  0.338176  0.105786  0.359107\n",
       "2013-01-06 -0.555880  1.115044 -2.108126  0.139896"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ejes\n",
    "\n",
    "En un DataFrame de `pandas` se pueden realizar operaciones a lo largo de los dos ejes, o `axis`.\n",
    "\n",
    "- Si en una operación especificamos `axis=0` nos referimos a loas índices, es decir, estaremos diciendo que la operación se realiza para todas las filas.\n",
    "- Si en una operación especificamos `axis=1` estaremos diciendo que la operación se realiza para todas las columnas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![Axis](./img/axis.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Visualización de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.679399</td>\n",
       "      <td>-0.564244</td>\n",
       "      <td>-0.395166</td>\n",
       "      <td>-0.004622</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>2.147829</td>\n",
       "      <td>-0.991826</td>\n",
       "      <td>-1.004833</td>\n",
       "      <td>0.168517</td>\n",
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       "                   A         B         C         D\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517"
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.359634</td>\n",
       "      <td>0.338176</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>0.359107</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.555880</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-05 -0.359634  0.338176  0.105786  0.359107\n",
       "2013-01-06 -0.555880  1.115044 -2.108126  0.139896"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### DataFrame.to_numpy()\n",
    "\n",
    "El método `.to_numpy()` de un DataFrame nos da una representación en una estructura de datos de `numpy` de los datos del DataFrame,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.67939947, -0.5642441 , -0.39516608, -0.00462202],\n",
       "       [ 2.14782856, -0.99182561, -1.00483345,  0.16851747],\n",
       "       [ 0.39806756, -0.53661026, -0.77399033, -1.07589368],\n",
       "       [-1.18501088,  1.98869725, -0.77042661, -0.47249893],\n",
       "       [-0.35963418,  0.3381756 ,  0.10578614,  0.35910665],\n",
       "       [-0.55588001,  1.11504445, -2.10812582,  0.13989579]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_numpy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Describe\n",
    "\n",
    "El método `.describe()` nos muestra un resumen estadístico de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.039005</td>\n",
       "      <td>0.224873</td>\n",
       "      <td>-0.824459</td>\n",
       "      <td>-0.147582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.188837</td>\n",
       "      <td>1.148846</td>\n",
       "      <td>0.739655</td>\n",
       "      <td>0.534241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.185011</td>\n",
       "      <td>-0.991826</td>\n",
       "      <td>-2.108126</td>\n",
       "      <td>-1.075894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.648520</td>\n",
       "      <td>-0.557336</td>\n",
       "      <td>-0.947123</td>\n",
       "      <td>-0.355530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.457757</td>\n",
       "      <td>-0.099217</td>\n",
       "      <td>-0.772208</td>\n",
       "      <td>0.067637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.208642</td>\n",
       "      <td>0.920827</td>\n",
       "      <td>-0.488981</td>\n",
       "      <td>0.161362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147829</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>0.359107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.039005  0.224873 -0.824459 -0.147582\n",
       "std    1.188837  1.148846  0.739655  0.534241\n",
       "min   -1.185011 -0.991826 -2.108126 -1.075894\n",
       "25%   -0.648520 -0.557336 -0.947123 -0.355530\n",
       "50%   -0.457757 -0.099217 -0.772208  0.067637\n",
       "75%    0.208642  0.920827 -0.488981  0.161362\n",
       "max    2.147829  1.988697  0.105786  0.359107"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Transposición\n",
    "\n",
    "Podemos obtener el DataFrame transpuesto de uno dado a través del atributo `T`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01</th>\n",
       "      <th>2013-01-02</th>\n",
       "      <th>2013-01-03</th>\n",
       "      <th>2013-01-04</th>\n",
       "      <th>2013-01-05</th>\n",
       "      <th>2013-01-06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.679399</td>\n",
       "      <td>2.147829</td>\n",
       "      <td>0.398068</td>\n",
       "      <td>-1.185011</td>\n",
       "      <td>-0.359634</td>\n",
       "      <td>-0.555880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.564244</td>\n",
       "      <td>-0.991826</td>\n",
       "      <td>-0.536610</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>0.338176</td>\n",
       "      <td>1.115044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.395166</td>\n",
       "      <td>-1.004833</td>\n",
       "      <td>-0.773990</td>\n",
       "      <td>-0.770427</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>-2.108126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.004622</td>\n",
       "      <td>0.168517</td>\n",
       "      <td>-1.075894</td>\n",
       "      <td>-0.472499</td>\n",
       "      <td>0.359107</td>\n",
       "      <td>0.139896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A   -0.679399    2.147829    0.398068   -1.185011   -0.359634   -0.555880\n",
       "B   -0.564244   -0.991826   -0.536610    1.988697    0.338176    1.115044\n",
       "C   -0.395166   -1.004833   -0.773990   -0.770427    0.105786   -2.108126\n",
       "D   -0.004622    0.168517   -1.075894   -0.472499    0.359107    0.139896"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Ordenación\n",
    "\n",
    "Podemos ordenar los datos por alguno de los ejes o por valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>D</th>\n",
       "      <th>C</th>\n",
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       "      <th>A</th>\n",
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       "  </thead>\n",
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       "      <th>2013-01-01</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.168517</td>\n",
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       "      <td>-0.991826</td>\n",
       "      <td>2.147829</td>\n",
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       "      <td>-1.075894</td>\n",
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       "      <td>-0.536610</td>\n",
       "      <td>0.398068</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.472499</td>\n",
       "      <td>-0.770427</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>-1.185011</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.359107</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>0.338176</td>\n",
       "      <td>-0.359634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.139896</td>\n",
       "      <td>-2.108126</td>\n",
       "      <td>1.115044</td>\n",
       "      <td>-0.555880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01 -0.004622 -0.395166 -0.564244 -0.679399\n",
       "2013-01-02  0.168517 -1.004833 -0.991826  2.147829\n",
       "2013-01-03 -1.075894 -0.773990 -0.536610  0.398068\n",
       "2013-01-04 -0.472499 -0.770427  1.988697 -1.185011\n",
       "2013-01-05  0.359107  0.105786  0.338176 -0.359634\n",
       "2013-01-06  0.139896 -2.108126  1.115044 -0.555880"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>2013-01-02</th>\n",
       "      <td>2.147829</td>\n",
       "      <td>-0.991826</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.679399</td>\n",
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       "      <td>-0.395166</td>\n",
       "      <td>-0.004622</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.398068</td>\n",
       "      <td>-0.536610</td>\n",
       "      <td>-0.773990</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.185011</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>-0.770427</td>\n",
       "      <td>-0.472499</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894\n",
       "2013-01-05 -0.359634  0.338176  0.105786  0.359107\n",
       "2013-01-06 -0.555880  1.115044 -2.108126  0.139896\n",
       "2013-01-04 -1.185011  1.988697 -0.770427 -0.472499"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Selección \n",
    "\n",
    "Podemos obtener una selección de los datos usando los métodos estándar de Python o `numpy` para la obtener *slices* en listas o matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Además, `pandas` proporcia métodos especializados (y optimizados) para el acceso a los datos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`.loc`\n",
    "\n",
    "Se utiliza principalmente para acceder por etiqueta. Soporta los siguietnes tipos de entradas:\n",
    "\n",
    "- Una etiqueta única: df.loc['a']\n",
    "- Una lista o array de etiqueta: df.loc[['a', 'b', 'c']]\n",
    "- Un *slice* con etiquetas: df.loc[a':'f']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`.iloc`\n",
    "\n",
    "Se utiliza principalmente para acceder posición. Soporta los siguietnes tipos de entradas:\n",
    "\n",
    "- Una entero: df.iloc[0]\n",
    "- Una lista o array de enteros: df.iloc[[0, 1, 2]]\n",
    "- Un *slice* : df.loc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Tipo de objeto | Selección      | Valor retornado\n",
    "---------------|----------------|-------------------------------------\n",
    "Series         | series[label]  | valor escalar\n",
    "DataFrame      | frame[colname] | La serie correspondiente a `colname`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.679399\n",
       "2013-01-02    2.147829\n",
       "2013-01-03    0.398068\n",
       "2013-01-04   -1.185011\n",
       "2013-01-05   -0.359634\n",
       "2013-01-06   -0.555880\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.679399\n",
       "2013-01-02    2.147829\n",
       "2013-01-03    0.398068\n",
       "2013-01-04   -1.185011\n",
       "2013-01-05   -0.359634\n",
       "2013-01-06   -0.555880\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>2013-01-02</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>2.147829</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.398068</td>\n",
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       "      <td>-0.773990</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.185011</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894\n",
       "2013-01-04 -1.185011  1.988697 -0.770427 -0.472499"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['20130102':'20130104']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.679399\n",
       "B   -0.564244\n",
       "C   -0.395166\n",
       "D   -0.004622\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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       "                   A         B\n",
       "2013-01-01 -0.679399 -0.564244\n",
       "2013-01-02  2.147829 -0.991826\n",
       "2013-01-03  0.398068 -0.536610\n",
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   "execution_count": 29,
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     "slide_type": "slide"
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       "                   A         B\n",
       "2013-01-02  2.147829 -0.991826\n",
       "2013-01-03  0.398068 -0.536610\n",
       "2013-01-04 -1.185011  1.988697"
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    "slideshow": {
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   "outputs": [
    {
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       "A    2.147829\n",
       "B   -0.991826\n",
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   "source": [
    "df2.loc['20130102', ['A', 'B']]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.185011\n",
       "B    1.988697\n",
       "C   -0.770427\n",
       "D   -0.472499\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "                   A         B\n",
       "2013-01-04 -1.185011  1.988697\n",
       "2013-01-05 -0.359634  0.338176"
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     "execution_count": 32,
     "metadata": {},
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   "source": [
    "df2.iloc[3:5, 0:2]"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Indexación condicional\n",
    "\n",
    "Se puede acceder a las columnas que cumplan una condición concreta, indicando la condición en el selector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>2013-01-02</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894"
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     "execution_count": 33,
     "metadata": {},
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   ],
   "source": [
    "df2[df2.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Operaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se pueden realizar operaciones estadísticas básicas llamando a los métodos correspondientes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.039005\n",
       "B    0.224873\n",
       "C   -0.824459\n",
       "D   -0.147582\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.03900473868952752"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['A'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.410858\n",
       "2013-01-02    0.079922\n",
       "2013-01-03   -0.497107\n",
       "2013-01-04   -0.109810\n",
       "2013-01-05    0.110859\n",
       "2013-01-06   -0.352266\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Se pueden aplicar funciones a los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.679399</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.468429</td>\n",
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       "      <td>-1.400000</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.866497</td>\n",
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       "      <td>-2.173990</td>\n",
       "      <td>-0.911998</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.681486</td>\n",
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       "      <td>-2.944416</td>\n",
       "      <td>-1.384497</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.321852</td>\n",
       "      <td>0.234193</td>\n",
       "      <td>-2.838630</td>\n",
       "      <td>-1.025391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.234028</td>\n",
       "      <td>1.349237</td>\n",
       "      <td>-4.946756</td>\n",
       "      <td>-0.885495</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622\n",
       "2013-01-02  1.468429 -1.556070 -1.400000  0.163895\n",
       "2013-01-03  1.866497 -2.092680 -2.173990 -0.911998\n",
       "2013-01-04  0.681486 -0.103983 -2.944416 -1.384497\n",
       "2013-01-05  0.321852  0.234193 -2.838630 -1.025391\n",
       "2013-01-06 -0.234028  1.349237 -4.946756 -0.885495"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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    {
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       "      <td>-0.004622</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>2.147829</td>\n",
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       "      <td>-1.004833</td>\n",
       "      <td>0.168517</td>\n",
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       "      <th>2013-01-03</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.185011</td>\n",
       "      <td>1.988697</td>\n",
       "      <td>-0.770427</td>\n",
       "      <td>-0.472499</td>\n",
       "      <td>3.173708</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.359634</td>\n",
       "      <td>0.338176</td>\n",
       "      <td>0.105786</td>\n",
       "      <td>0.359107</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.555880</td>\n",
       "      <td>1.115044</td>\n",
       "      <td>-2.108126</td>\n",
       "      <td>0.139896</td>\n",
       "      <td>3.223170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                   A         B         C         D         E\n",
       "2013-01-01 -0.679399 -0.564244 -0.395166 -0.004622  0.674777\n",
       "2013-01-02  2.147829 -0.991826 -1.004833  0.168517  3.152662\n",
       "2013-01-03  0.398068 -0.536610 -0.773990 -1.075894  1.473961\n",
       "2013-01-04 -1.185011  1.988697 -0.770427 -0.472499  3.173708\n",
       "2013-01-05 -0.359634  0.338176  0.105786  0.359107  0.718741\n",
       "2013-01-06 -0.555880  1.115044 -2.108126  0.139896  3.223170"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = df2.apply(lambda x: x.max() - x.min(), axis=1)\n",
    "df2['E'] = c\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.354177\n",
       "2013-01-02    4.157495\n",
       "2013-01-03    2.549855\n",
       "2013-01-04    4.358719\n",
       "2013-01-05    1.078375\n",
       "2013-01-06    5.331296\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.apply(lambda x: x.max() - x.min(), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Uniones\n",
    "\n",
    "La librería `pandas` proporciona diferentes métodos para la unión de Series o DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>-0.454767</td>\n",
       "      <td>1.183958</td>\n",
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       "      <th>5</th>\n",
       "      <td>-0.528190</td>\n",
       "      <td>-0.912389</td>\n",
       "      <td>0.786753</td>\n",
       "      <td>1.043857</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1.537252</td>\n",
       "      <td>-1.050597</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.352473</td>\n",
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       "      <td>0.186576</td>\n",
       "      <td>0.977988</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>-1.816031</td>\n",
       "      <td>0.601092</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "          0         1         2         3\n",
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       "3  1.012883 -0.369916 -0.552784  1.356238\n",
       "4 -0.167002  1.677076 -0.454767  1.183958\n",
       "5 -0.528190 -0.912389  0.786753  1.043857\n",
       "6  0.527898 -0.379471  1.537252 -1.050597\n",
       "7 -0.352473 -1.825571  0.186576  0.977988\n",
       "8  0.991172 -0.030169 -1.816031  0.601092\n",
       "9  1.522968  0.440188 -1.763289  1.840091"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0 -0.634450  0.763724  0.710228 -0.694768\n",
       " 1 -0.142616  1.630704  1.029687 -1.008484\n",
       " 2 -0.344466 -0.222917  0.294177 -0.859483,\n",
       "           0         1         2         3\n",
       " 3  1.012883 -0.369916 -0.552784  1.356238\n",
       " 4 -0.167002  1.677076 -0.454767  1.183958\n",
       " 5 -0.528190 -0.912389  0.786753  1.043857\n",
       " 6  0.527898 -0.379471  1.537252 -1.050597,\n",
       "           0         1         2         3\n",
       " 7 -0.352473 -1.825571  0.186576  0.977988\n",
       " 8  0.991172 -0.030169 -1.816031  0.601092\n",
       " 9  1.522968  0.440188 -1.763289  1.840091]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = [df[:3], df[3:7], df[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "          0         1         2         3\n",
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       "3  1.012883 -0.369916 -0.552784  1.356238\n",
       "4 -0.167002  1.677076 -0.454767  1.183958\n",
       "5 -0.528190 -0.912389  0.786753  1.043857\n",
       "6  0.527898 -0.379471  1.537252 -1.050597\n",
       "7 -0.352473 -1.825571  0.186576  0.977988\n",
       "8  0.991172 -0.030169 -1.816031  0.601092\n",
       "9  1.522968  0.440188 -1.763289  1.840091"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
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   "source": [
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
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      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
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     "execution_count": 49,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.437667</td>\n",
       "      <td>0.940580</td>\n",
       "      <td>-0.355047</td>\n",
       "      <td>-0.142454</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.490131</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.881277</td>\n",
       "      <td>0.426499</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.589829</td>\n",
       "      <td>-0.331404</td>\n",
       "      <td>0.692164</td>\n",
       "      <td>0.456827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.158751</td>\n",
       "      <td>-0.199149</td>\n",
       "      <td>-0.395195</td>\n",
       "      <td>0.882798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.021648</td>\n",
       "      <td>0.764384</td>\n",
       "      <td>0.408657</td>\n",
       "      <td>-1.262260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.113406</td>\n",
       "      <td>0.107256</td>\n",
       "      <td>0.420511</td>\n",
       "      <td>-0.968303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.362774 -0.573908  0.098044  1.992482\n",
       "1  1.437667  0.940580 -0.355047 -0.142454\n",
       "2 -1.097556 -0.593504 -1.313146 -0.490131\n",
       "3  1.028989  0.098031  0.881277  0.426499\n",
       "4 -0.589829 -0.331404  0.692164  0.456827\n",
       "5 -0.158751 -0.199149 -0.395195  0.882798\n",
       "6 -0.021648  0.764384  0.408657 -1.262260\n",
       "7 -1.113406  0.107256  0.420511 -0.968303"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.028989\n",
       "B    0.098031\n",
       "C    0.881277\n",
       "D    0.426499\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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    {
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       "      <td>0.098044</td>\n",
       "      <td>1.992482</td>\n",
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       "      <td>-0.355047</td>\n",
       "      <td>-0.142454</td>\n",
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       "      <td>-0.395195</td>\n",
       "      <td>0.882798</td>\n",
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       "      <td>0.408657</td>\n",
       "      <td>-1.262260</td>\n",
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       "      <td>0.107256</td>\n",
       "      <td>0.420511</td>\n",
       "      <td>-0.968303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.028989</td>\n",
       "      <td>0.098031</td>\n",
       "      <td>0.881277</td>\n",
       "      <td>0.426499</td>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.362774 -0.573908  0.098044  1.992482\n",
       "1  1.437667  0.940580 -0.355047 -0.142454\n",
       "2 -1.097556 -0.593504 -1.313146 -0.490131\n",
       "3  1.028989  0.098031  0.881277  0.426499\n",
       "4 -0.589829 -0.331404  0.692164  0.456827\n",
       "5 -0.158751 -0.199149 -0.395195  0.882798\n",
       "6 -0.021648  0.764384  0.408657 -1.262260\n",
       "7 -1.113406  0.107256  0.420511 -0.968303\n",
       "8  1.028989  0.098031  0.881277  0.426499"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Agrupamientos\n",
    "\n",
    "Cuando hablamos de agrupar datos en `pandas` nos referimos a un proceso que inplica uno o más de los siguientes pasos:\n",
    "\n",
    "- Separar los datos en grupos basados en algún criterio\n",
    "- Aplicar una función para cada grupo de forma independiente\n",
    "- Combinar los resultados en una estructura de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <td>-1.041576</td>\n",
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      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -1.976302 -0.708903\n",
       "1  bar    one -1.709147 -0.680945\n",
       "2  foo    two  0.229683 -0.613908\n",
       "3  bar  three  0.917311 -0.819363\n",
       "4  foo    two -1.245424 -1.041576\n",
       "5  bar    two  0.904258 -1.698605\n",
       "6  foo    one -1.215414  1.879422\n",
       "7  foo  three  1.406019 -0.603691"
      ]
     },
     "execution_count": 53,
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   ],
   "source": [
    "df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',\n",
    "                         'foo', 'bar', 'foo', 'foo'],\n",
    "                   'B': ['one', 'one', 'two', 'three',\n",
    "                         'two', 'two', 'one', 'three'],\n",
    "                   'C': np.random.randn(8),\n",
    "                   'D': np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
    {
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       "            C         D\n",
       "A                      \n",
       "bar  0.112423 -3.198913\n",
       "foo -2.801438 -1.088656"
      ]
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     "execution_count": 56,
     "metadata": {},
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   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [
    {
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      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -1.709147 -0.680945\n",
       "    three  0.917311 -0.819363\n",
       "    two    0.904258 -1.698605\n",
       "foo one   -3.191715  1.170519\n",
       "    three  1.406019 -0.603691\n",
       "    two   -1.015741 -1.655484"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  }
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