pandas_tips.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open EXCEL (xlsx/xls) >> ``pd.read_excel (..\\hoja.xlsx')``\n",
    "\n",
    "- opcion 1: ``pd.read_excel ('D:\\\\Pablo\\\\Python\\\\working_files\\\\ef.xlsx')``\n",
    "- opcion 2: ``pd.read_excel (r'D:\\Pablo\\Python\\working_files\\ef.xlsx')``  Si le ponemos r antes del string, no necesitamos doble barra en el path...es mas cómodo si uno quiere copiar y pegar el path desde la barra de direcciones de la ventana de windows.                                                                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame desde hoja excel >> ``pd.read_excel()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401965</td>\n",
       "      <td>5.834831</td>\n",
       "      <td>81.157104</td>\n",
       "      <td>0.223031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.401916</td>\n",
       "      <td>6.029653</td>\n",
       "      <td>90.715759</td>\n",
       "      <td>0.256382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.410506</td>\n",
       "      <td>6.343121</td>\n",
       "      <td>93.426514</td>\n",
       "      <td>0.289780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.413583</td>\n",
       "      <td>6.300395</td>\n",
       "      <td>84.699570</td>\n",
       "      <td>0.298103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417686</td>\n",
       "      <td>6.205818</td>\n",
       "      <td>82.960640</td>\n",
       "      <td>0.297078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.418653</td>\n",
       "      <td>6.100525</td>\n",
       "      <td>82.618279</td>\n",
       "      <td>0.297879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.416392</td>\n",
       "      <td>6.425298</td>\n",
       "      <td>75.609131</td>\n",
       "      <td>0.299304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.414242</td>\n",
       "      <td>6.724474</td>\n",
       "      <td>74.705811</td>\n",
       "      <td>0.300772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.408364</td>\n",
       "      <td>7.244003</td>\n",
       "      <td>79.311035</td>\n",
       "      <td>0.302267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.406625</td>\n",
       "      <td>7.454670</td>\n",
       "      <td>80.434563</td>\n",
       "      <td>0.303763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B          C         D\n",
       "0  0.401965  5.834831  81.157104  0.223031\n",
       "1  0.401916  6.029653  90.715759  0.256382\n",
       "2  0.410506  6.343121  93.426514  0.289780\n",
       "3  0.413583  6.300395  84.699570  0.298103\n",
       "4  0.417686  6.205818  82.960640  0.297078\n",
       "5  0.418653  6.100525  82.618279  0.297879\n",
       "6  0.416392  6.425298  75.609131  0.299304\n",
       "7  0.414242  6.724474  74.705811  0.300772\n",
       "8  0.408364  7.244003  79.311035  0.302267\n",
       "9  0.406625  7.454670  80.434563  0.303763"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si cargamos una hoja excel solo con valores (sin título en cada columna, o header) deberemos especificarlos nosotros.\n",
    "# Para nombrar las columnas, primero debemos pedirle que no use la primer fila escrita como titulo con header=None\n",
    "# luego le pasamos una LIST con los nombres de las columnas con names=[LIST] (sino llena con numeros enteros los headers)\n",
    "\n",
    "abcd = pd.read_excel (r'D:\\Pablo\\Python\\working_files\\abcd.xlsx', header=None, names=['A','B','C','D'])\n",
    "abcd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.647595</td>\n",
       "      <td>33.876701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.391701</td>\n",
       "      <td>38.231804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.245206</td>\n",
       "      <td>41.272648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.236700</td>\n",
       "      <td>41.172573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.527667</td>\n",
       "      <td>43.616482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.704542</td>\n",
       "      <td>54.737694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.568918</td>\n",
       "      <td>74.151917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.436698</td>\n",
       "      <td>87.158440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.828910</td>\n",
       "      <td>88.786430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.944400</td>\n",
       "      <td>88.330299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          E          F\n",
       "0  5.647595  33.876701\n",
       "1  5.391701  38.231804\n",
       "2  5.245206  41.272648\n",
       "3  5.236700  41.172573\n",
       "4  5.527667  43.616482\n",
       "5  5.704542  54.737694\n",
       "6  5.568918  74.151917\n",
       "7  5.436698  87.158440\n",
       "8  6.828910  88.786430\n",
       "9  6.944400  88.330299"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si la hoja excel tiene titulo en cada columna, es mas sencillo\n",
    "\n",
    "ef = pd.read_excel (r'D:\\Pablo\\Python\\working_files\\ef.xlsx')\n",
    "ef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame desde un array >> ``pd.DataFrame(ARRAY,columns=['col1'...])``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>79</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>94</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    r1  r2  r3  r4  r5\n",
       "0   88  12  22  71  28\n",
       "1   10  80  61  26  25\n",
       "2   87  32   4  77  10\n",
       "3   10   0  86  84   5\n",
       "4   37  41  67  79  68\n",
       "..  ..  ..  ..  ..  ..\n",
       "75   3  31  63  79  61\n",
       "76   8  39  94  16  56\n",
       "77  10  85  29  66  94\n",
       "78  42  22  52  77  80\n",
       "79  62  19  87  96  74\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy a Dataframe\n",
    "np.random.seed(14) # si queremos que randint nos genere siempre los mismos valores \n",
    "my_array= np.random.randint(0,100,(80,5))  # Generamos el array\n",
    "\n",
    "my_array_df = pd.DataFrame(my_array,columns=['r1','r2','r3','r4','r5'])\n",
    "my_array_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame desde una LISTA >> ``pd.DataFrame(LIST,columns=['col1'...])``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>l1</th>\n",
       "      <th>l2</th>\n",
       "      <th>l3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83</td>\n",
       "      <td>64</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   l1  l2  l3\n",
       "0  12  22  23\n",
       "1  83  64  52"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LIST a Dataframe\n",
    "mylist=[[12,22,23],[83,64,52]]\n",
    "mylist_df = pd.DataFrame(mylist,columns=['l1','l2','l3'])\n",
    "mylist_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``df.head(n)`` $\\;$$\\;$  ``df.tail(n)``\n",
    "- **``.head(n)``** Para ver los primeros n valores, si lo dejamos vacio me muestra los primeros 5.\n",
    "- **``.tail(n)``** Para ver los ultimos n valores, si lo dejamos vacio me muestra los ultimos 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   r1  r2  r3  r4  r5\n",
       "0  88  12  22  71  28\n",
       "1  10  80  61  26  25\n",
       "2  87  32   4  77  10"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df.head(3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
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       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>79</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>94</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    r1  r2  r3  r4  r5\n",
       "0   88  12  22  71  28\n",
       "1   10  80  61  26  25\n",
       "2   87  32   4  77  10\n",
       "3   10   0  86  84   5\n",
       "4   37  41  67  79  68\n",
       "..  ..  ..  ..  ..  ..\n",
       "75   3  31  63  79  61\n",
       "76   8  39  94  16  56\n",
       "77  10  85  29  66  94\n",
       "78  42  22  52  77  80\n",
       "79  62  19  87  96  74\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ver nombre y tipo de cada columna >> ``df.info()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 80 entries, 0 to 79\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   r1      80 non-null     int32\n",
      " 1   r2      80 non-null     int32\n",
      " 2   r3      80 non-null     int32\n",
      " 3   r4      80 non-null     int32\n",
      " 4   r5      80 non-null     int32\n",
      "dtypes: int32(5)\n",
      "memory usage: 1.7 KB\n"
     ]
    }
   ],
   "source": [
    "my_array_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtener estadisticas de columnas numericas >> ``df.describe()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>80.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.96250</td>\n",
       "      <td>47.175000</td>\n",
       "      <td>49.150000</td>\n",
       "      <td>53.450000</td>\n",
       "      <td>52.01250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.61992</td>\n",
       "      <td>28.127546</td>\n",
       "      <td>26.628765</td>\n",
       "      <td>29.270411</td>\n",
       "      <td>29.26937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.50000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>29.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.00000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>53.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>71.25000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>78.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             r1         r2         r3         r4        r5\n",
       "count  80.00000  80.000000  80.000000  80.000000  80.00000\n",
       "mean   48.96250  47.175000  49.150000  53.450000  52.01250\n",
       "std    28.61992  28.127546  26.628765  29.270411  29.26937\n",
       "min     0.00000   0.000000   3.000000   0.000000   0.00000\n",
       "25%    25.50000  26.000000  27.750000  28.500000  29.75000\n",
       "50%    52.00000  43.000000  51.500000  56.000000  53.50000\n",
       "75%    71.25000  72.000000  68.000000  79.000000  78.25000\n",
       "max    98.00000  96.000000  95.000000  99.000000  99.00000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nombrar o Renombrar columnas >> ``df.columns = ['col1','col2'..]``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "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>list1</th>\n",
       "      <th>list2</th>\n",
       "      <th>list3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83</td>\n",
       "      <td>64</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   list1  list2  list3\n",
       "0     12     22     23\n",
       "1     83     64     52"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist_df.columns = ['list1','list2','list3']\n",
    "mylist_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame eligiendo columnas de otro df >> ``df[['col1',...]]``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "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>list1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   list1\n",
       "0     12\n",
       "1     83"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist_df_list1 = mylist_df[['list1']]\n",
    "mylist_df_list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INDEXING en pandas >> ``df[n:n]`` - ``df.col[n:n]`` \n",
    "Indexing es un tema extenso, aquí solamente veremos indexing de una fila y de un columna en particular.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r1  r2  r3  r4  r5\n",
       "1  10  80  61  26  25\n",
       "2  87  32   4  77  10"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing de filas \n",
    "my_array_df[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r1  r2\n",
       "1  10  80\n",
       "2  87  32"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing de columnas\n",
    "#my_array_df.r1[3:5]\n",
    "my_array_df[['r1','r2']][1:3] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIN $\\;$$\\;$ MEAN $\\;$$\\;$ MAX >> ``df.[col].min()`` $\\;$$\\;$ ``mean()`` $\\;$$\\;$ ``median()`` $\\;$$\\;$ ``max()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 48.9625, 52.0, 98)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#my_array_df.r1.min(),my_array_df.r1.mean(),my_array_df.r1.median(),my_array_df.r1.max()\n",
    "#my_array_df[['r1','r2']].min(),my_array_df[['r1','r2']].mean(),my_array_df[['r1','r2']].median(),my_array_df[['r1','r2']].max()\n",
    "\n",
    "my_array_df['r1'].min(),my_array_df['r1'].mean(),my_array_df['r1'].median(),my_array_df['r1'].max()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filtro simple ``df[df['col']<5]``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>57</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>79</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    r1  r2  r3  r4  r5\n",
       "7    1  71  63  10  45\n",
       "23   0  90  48  27  43\n",
       "38   1  93  87  57  90\n",
       "52   1   4  41  39  11\n",
       "57   0  81  64  64  88\n",
       "75   3  31  63  79  61"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df[my_array_df['r1']<5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## doble filtro ``df[df(['col1']<5) & (df['col2']<80)]``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>79</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    r1  r2  r3  r4  r5\n",
       "7    1  71  63  10  45\n",
       "52   1   4  41  39  11\n",
       "75   3  31  63  79  61"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df[(my_array_df['r1']<5) & (my_array_df['r2']<80)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## triple filtro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>79</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    r1  r2  r3  r4  r5\n",
       "7    1  71  63  10  45\n",
       "75   3  31  63  79  61"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df[(my_array_df['r1']<5) & (my_array_df['r2']<80) & (my_array_df['r3']==63)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCATENAR Dataframes >> ``pd.concat([df1,df2,..,dfn],axis=1)``\n",
    "- **``axis=1``** es importante porque va a poner una columna al lado de la otra\n",
    "\n",
    "\n",
    "- **``pd.concat``** solamente copia todas las columnas de un DataFrame y de otro sin chequear si tienen el mismo nombre, por ejemplo.**NO reconoce columnas con el mismo nombre**. Si queremos mergear 2 DataFrames con algunas columnas que se llamen igual y que esto  lo reconozca debemos utilizar **``pd.merge``**.\n",
    "\n",
    "\n",
    "- Si los DataFrames no tienen la **misma cantidad de filas**, rellena con NaN las vacías."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401965</td>\n",
       "      <td>5.834831</td>\n",
       "      <td>81.157104</td>\n",
       "      <td>0.223031</td>\n",
       "      <td>5.647595</td>\n",
       "      <td>33.876701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.401916</td>\n",
       "      <td>6.029653</td>\n",
       "      <td>90.715759</td>\n",
       "      <td>0.256382</td>\n",
       "      <td>5.391701</td>\n",
       "      <td>38.231804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.410506</td>\n",
       "      <td>6.343121</td>\n",
       "      <td>93.426514</td>\n",
       "      <td>0.289780</td>\n",
       "      <td>5.245206</td>\n",
       "      <td>41.272648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.413583</td>\n",
       "      <td>6.300395</td>\n",
       "      <td>84.699570</td>\n",
       "      <td>0.298103</td>\n",
       "      <td>5.236700</td>\n",
       "      <td>41.172573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417686</td>\n",
       "      <td>6.205818</td>\n",
       "      <td>82.960640</td>\n",
       "      <td>0.297078</td>\n",
       "      <td>5.527667</td>\n",
       "      <td>43.616482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B          C         D         E          F\n",
       "0  0.401965  5.834831  81.157104  0.223031  5.647595  33.876701\n",
       "1  0.401916  6.029653  90.715759  0.256382  5.391701  38.231804\n",
       "2  0.410506  6.343121  93.426514  0.289780  5.245206  41.272648\n",
       "3  0.413583  6.300395  84.699570  0.298103  5.236700  41.172573\n",
       "4  0.417686  6.205818  82.960640  0.297078  5.527667  43.616482"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abcdef = pd.concat([abcd,ef],axis=1)\n",
    "abcdef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401965</td>\n",
       "      <td>5.834831</td>\n",
       "      <td>81.157104</td>\n",
       "      <td>0.223031</td>\n",
       "      <td>33.876701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.401916</td>\n",
       "      <td>6.029653</td>\n",
       "      <td>90.715759</td>\n",
       "      <td>0.256382</td>\n",
       "      <td>38.231804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.410506</td>\n",
       "      <td>6.343121</td>\n",
       "      <td>93.426514</td>\n",
       "      <td>0.289780</td>\n",
       "      <td>41.272648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.413583</td>\n",
       "      <td>6.300395</td>\n",
       "      <td>84.699570</td>\n",
       "      <td>0.298103</td>\n",
       "      <td>41.172573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417686</td>\n",
       "      <td>6.205818</td>\n",
       "      <td>82.960640</td>\n",
       "      <td>0.297078</td>\n",
       "      <td>43.616482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B          C         D          F\n",
       "0  0.401965  5.834831  81.157104  0.223031  33.876701\n",
       "1  0.401916  6.029653  90.715759  0.256382  38.231804\n",
       "2  0.410506  6.343121  93.426514  0.289780  41.272648\n",
       "3  0.413583  6.300395  84.699570  0.298103  41.172573\n",
       "4  0.417686  6.205818  82.960640  0.297078  43.616482"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Concatenar 1 DATAFRAME y 1 sola columna de otro DataFrame \n",
    "abcdf = pd.concat([abcd,ef['F']],axis=1)\n",
    "abcdf.head()"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MERGE Dataframes >> ``pd.merge(df1,df2,...,dfn,on='col')``\n",
    "- **``pd.merge``** junta DataFrames que tengan **1 ó más columnas en comun**. Va a utilizar la columna **``on='col'``** como fija, y va a copiar todo el resto de columnas una al lado de la otra, dejando la **``'col'``** intacta.\n",
    "![pandas_1_1-2.JPG](attachment:pandas_1_1-2.JPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>email</th>\n",
       "      <th>age</th>\n",
       "      <th>add_id</th>\n",
       "      <th>click</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>alice@gmail.com</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>alice@gmail.com</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>alice@gmail.com</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>bob@hotmail.com</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>bob@hotmail.com</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>bob@hotmail.com</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>carol@yahoo.com</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>carol@yahoo.com</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>carol@yahoo.com</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>carol@yahoo.com</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>carol@yahoo.com</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id            email  age  add_id  click\n",
       "0         1  alice@gmail.com   20       1      1\n",
       "1         1  alice@gmail.com   20       2      0\n",
       "2         1  alice@gmail.com   20       5      0\n",
       "3         2  bob@hotmail.com   25       3      0\n",
       "4         2  bob@hotmail.com   25       4      1\n",
       "5         2  bob@hotmail.com   25       1      0\n",
       "6         3  carol@yahoo.com   30       2      0\n",
       "7         3  carol@yahoo.com   30       1      0\n",
       "8         3  carol@yahoo.com   30       3      0\n",
       "9         3  carol@yahoo.com   30       4      0\n",
       "10        3  carol@yahoo.com   30       5      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = [[1,'alice@gmail.com',20],[2,'bob@hotmail.com',25],[3,'carol@yahoo.com',30]]\n",
    "t2=[[1,1,1],[1,2,0],[1,5,0],[2,3,0],[2,4,1],[2,1,0],[3,2,0],[3,1,0],[3,3,0],[3,4,0],[3,5,1]]\n",
    "\n",
    "t1_df = pd.DataFrame(t1,columns=['user_id','email','age'])\n",
    "t2_df = pd.DataFrame(t2,columns=['user_id','add_id','click'])\n",
    "\n",
    "tdf = pd.merge(t1_df,t2_df,on='user_id')\n",
    "tdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply DEF a DataFrame  >> ``df.apply(DEF_NAME)``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7744</td>\n",
       "      <td>144</td>\n",
       "      <td>484</td>\n",
       "      <td>5041</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>6400</td>\n",
       "      <td>3721</td>\n",
       "      <td>676</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7569</td>\n",
       "      <td>1024</td>\n",
       "      <td>16</td>\n",
       "      <td>5929</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>7396</td>\n",
       "      <td>7056</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1369</td>\n",
       "      <td>1681</td>\n",
       "      <td>4489</td>\n",
       "      <td>6241</td>\n",
       "      <td>4624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     r1    r2    r3    r4    r5\n",
       "0  7744   144   484  5041   784\n",
       "1   100  6400  3721   676   625\n",
       "2  7569  1024    16  5929   100\n",
       "3   100     0  7396  7056    25\n",
       "4  1369  1681  4489  6241  4624"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplico la DEF a todo el Dataframe\n",
    "\n",
    "def cuadrado(x):\n",
    "    return x**2\n",
    "\n",
    "cuad_my_array_df = my_array_df.apply(cuadrado)\n",
    "cuad_my_array_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplico la DEF a todo el Dataframe\n",
    "\n",
    "def cuadrado(x):\n",
    "    return x**2\n",
    "\n",
    "my_array_df.apply(cuadrado)\n",
    "\n",
    "type (my_array_df.apply(cuadrado))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply DEF a columna >> ``df['col'].apply(DEF_NAME)``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>r3</th>\n",
       "      <th>r4</th>\n",
       "      <th>r5</th>\n",
       "      <th>r1raiz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>28</td>\n",
       "      <td>9.380832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>3.162278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "      <td>9.327379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "      <td>3.162278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>6.082763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r1  r2  r3  r4  r5    r1raiz\n",
       "0  88  12  22  71  28  9.380832\n",
       "1  10  80  61  26  25  3.162278\n",
       "2  87  32   4  77  10  9.327379\n",
       "3  10   0  86  84   5  3.162278\n",
       "4  37  41  67  79  68  6.082763"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplico la DEF solamente a la columna \"r1\"\n",
    "\n",
    "def raiz(x):\n",
    "    return x**0.5\n",
    "\n",
    "my_array_df['r1raiz'] = my_array_df[['r1']].apply(raiz)\n",
    "my_array_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Switch columnas y filas >> ``df.transpose()``\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r1</th>\n",
       "      <td>88</td>\n",
       "      <td>10</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>77</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>12</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>45</td>\n",
       "      <td>59</td>\n",
       "      <td>71</td>\n",
       "      <td>19</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>95</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>85</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r3</th>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>63</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r4</th>\n",
       "      <td>71</td>\n",
       "      <td>26</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>79</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r5</th>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>75</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>97</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>61</td>\n",
       "      <td>56</td>\n",
       "      <td>94</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9   ...  70  71  72  73  74  75  76  \\\n",
       "r1  88  10  87  10  37  77   9   1  30  77  ...  29   5  62  16  70   3   8   \n",
       "r2  12  80  32   0  41  45  59  71  19  81  ...  54  95  26   7  75  31  39   \n",
       "r3  22  61   4  86  67  17  27  63  65  13  ...  43  31  39   3  40  63  94   \n",
       "r4  71  26  77  84  79   0  90  10  57  56  ...  91  97  12  86  85  79  16   \n",
       "r5  28  25  10   5  68  31  43  45  75  79  ...  24  48  97  27  46  61  56   \n",
       "\n",
       "    77  78  79  \n",
       "r1  10  42  62  \n",
       "r2  85  22  19  \n",
       "r3  29  52  87  \n",
       "r4  66  77  96  \n",
       "r5  94  80  74  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array_df_tr = my_array_df.transpose()\n",
    "my_array_df_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SAVE PANDAS Dataframe to EXCEL >> ``df.to_excel(r\"path+filename\")``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_array_df.to_excel(r'D:\\Pablo\\Python\\working_files\\my_array_df.xlsx')"
   ]
  }
 ],
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