Clase10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clase 10 - Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/UI.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open        High         Low       Close   Adj Close  Volume\n",
      "Date                                                                          \n",
      "2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   84800\n",
      "2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766  107200\n",
      "2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621  143400\n",
      "2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   90300\n",
      "2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   89100\n",
      "...                ...         ...         ...         ...         ...     ...\n",
      "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999  214400\n",
      "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998  150900\n",
      "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994  142100\n",
      "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000  205300\n",
      "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   99600\n",
      "\n",
      "[251 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtro1 = data['Adj Close'] > 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-04-13    False\n",
       "2020-04-14    False\n",
       "2020-04-15    False\n",
       "2020-04-16    False\n",
       "2020-04-17    False\n",
       "              ...  \n",
       "2021-04-05     True\n",
       "2021-04-06     True\n",
       "2021-04-07     True\n",
       "2021-04-08     True\n",
       "2021-04-09     True\n",
       "Name: Adj Close, Length: 251, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtro1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>218.000000</td>\n",
       "      <td>245.779999</td>\n",
       "      <td>216.059998</td>\n",
       "      <td>245.220001</td>\n",
       "      <td>244.554260</td>\n",
       "      <td>314900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>252.580002</td>\n",
       "      <td>266.589996</td>\n",
       "      <td>252.580002</td>\n",
       "      <td>257.010010</td>\n",
       "      <td>256.312256</td>\n",
       "      <td>256400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>257.000000</td>\n",
       "      <td>260.350006</td>\n",
       "      <td>253.270004</td>\n",
       "      <td>255.899994</td>\n",
       "      <td>255.205246</td>\n",
       "      <td>131700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>257.220001</td>\n",
       "      <td>271.320007</td>\n",
       "      <td>255.759995</td>\n",
       "      <td>270.049988</td>\n",
       "      <td>269.316833</td>\n",
       "      <td>147200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>268.510010</td>\n",
       "      <td>269.019989</td>\n",
       "      <td>249.429993</td>\n",
       "      <td>250.809998</td>\n",
       "      <td>250.129074</td>\n",
       "      <td>151200</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  Volume\n",
       "Date                                                                          \n",
       "2020-11-06  218.000000  245.779999  216.059998  245.220001  244.554260  314900\n",
       "2020-11-09  252.580002  266.589996  252.580002  257.010010  256.312256  256400\n",
       "2020-11-10  257.000000  260.350006  253.270004  255.899994  255.205246  131700\n",
       "2020-11-11  257.220001  271.320007  255.759995  270.049988  269.316833  147200\n",
       "2020-11-12  268.510010  269.019989  249.429993  250.809998  250.129074  151200\n",
       "...                ...         ...         ...         ...         ...     ...\n",
       "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999  214400\n",
       "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998  150900\n",
       "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994  142100\n",
       "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000  205300\n",
       "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   99600\n",
       "\n",
       "[105 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[filtro1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Adj Close</th>\n",
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       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>201.429993</td>\n",
       "      <td>205.419998</td>\n",
       "      <td>195.610001</td>\n",
       "      <td>197.919998</td>\n",
       "      <td>197.382660</td>\n",
       "      <td>123800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>218.000000</td>\n",
       "      <td>245.779999</td>\n",
       "      <td>216.059998</td>\n",
       "      <td>245.220001</td>\n",
       "      <td>244.554260</td>\n",
       "      <td>314900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>252.580002</td>\n",
       "      <td>266.589996</td>\n",
       "      <td>252.580002</td>\n",
       "      <td>257.010010</td>\n",
       "      <td>256.312256</td>\n",
       "      <td>256400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>257.000000</td>\n",
       "      <td>260.350006</td>\n",
       "      <td>253.270004</td>\n",
       "      <td>255.899994</td>\n",
       "      <td>255.205246</td>\n",
       "      <td>131700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>257.220001</td>\n",
       "      <td>271.320007</td>\n",
       "      <td>255.759995</td>\n",
       "      <td>270.049988</td>\n",
       "      <td>269.316833</td>\n",
       "      <td>147200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  Volume\n",
       "Date                                                                          \n",
       "2020-10-20  201.429993  205.419998  195.610001  197.919998  197.382660  123800\n",
       "2020-11-06  218.000000  245.779999  216.059998  245.220001  244.554260  314900\n",
       "2020-11-09  252.580002  266.589996  252.580002  257.010010  256.312256  256400\n",
       "2020-11-10  257.000000  260.350006  253.270004  255.899994  255.205246  131700\n",
       "2020-11-11  257.220001  271.320007  255.759995  270.049988  269.316833  147200\n",
       "...                ...         ...         ...         ...         ...     ...\n",
       "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999  214400\n",
       "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998  150900\n",
       "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994  142100\n",
       "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000  205300\n",
       "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   99600\n",
       "\n",
       "[106 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Open>200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Low</th>\n",
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       "      <th>2020-10-20</th>\n",
       "      <td>195.610001</td>\n",
       "      <td>123800</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>216.059998</td>\n",
       "      <td>314900</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>252.580002</td>\n",
       "      <td>256400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>253.270004</td>\n",
       "      <td>131700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>255.759995</td>\n",
       "      <td>147200</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>281.070007</td>\n",
       "      <td>214400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>283.140015</td>\n",
       "      <td>150900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>275.260010</td>\n",
       "      <td>142100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>276.079987</td>\n",
       "      <td>205300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>278.279999</td>\n",
       "      <td>99600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Low  Volume\n",
       "Date                          \n",
       "2020-10-20  195.610001  123800\n",
       "2020-11-06  216.059998  314900\n",
       "2020-11-09  252.580002  256400\n",
       "2020-11-10  253.270004  131700\n",
       "2020-11-11  255.759995  147200\n",
       "...                ...     ...\n",
       "2021-04-05  281.070007  214400\n",
       "2021-04-06  283.140015  150900\n",
       "2021-04-07  275.260010  142100\n",
       "2021-04-08  276.079987  205300\n",
       "2021-04-09  278.279999   99600\n",
       "\n",
       "[106 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Open>=200][['Low','Volume']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtros Avanzados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>154.970001</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>153.559998</td>\n",
       "      <td>152.546036</td>\n",
       "      <td>84800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>157.699997</td>\n",
       "      <td>161.500000</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
       "      <td>159.509766</td>\n",
       "      <td>107200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>158.210007</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>151.259995</td>\n",
       "      <td>152.110001</td>\n",
       "      <td>151.105621</td>\n",
       "      <td>143400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>153.080002</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>151.789993</td>\n",
       "      <td>153.770004</td>\n",
       "      <td>152.754669</td>\n",
       "      <td>90300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>185.220001</td>\n",
       "      <td>187.369995</td>\n",
       "      <td>184.110001</td>\n",
       "      <td>185.610001</td>\n",
       "      <td>185.106094</td>\n",
       "      <td>92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>187.759995</td>\n",
       "      <td>188.110001</td>\n",
       "      <td>182.869995</td>\n",
       "      <td>185.360001</td>\n",
       "      <td>184.856766</td>\n",
       "      <td>130100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>187.470001</td>\n",
       "      <td>192.639999</td>\n",
       "      <td>186.309998</td>\n",
       "      <td>192.039993</td>\n",
       "      <td>191.518631</td>\n",
       "      <td>119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>194.479996</td>\n",
       "      <td>197.059998</td>\n",
       "      <td>190.889999</td>\n",
       "      <td>191.559998</td>\n",
       "      <td>191.039932</td>\n",
       "      <td>85300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>198.940002</td>\n",
       "      <td>193.820007</td>\n",
       "      <td>197.800003</td>\n",
       "      <td>197.263000</td>\n",
       "      <td>83900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  Volume\n",
       "Date                                                                          \n",
       "2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   84800\n",
       "2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766  107200\n",
       "2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621  143400\n",
       "2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   90300\n",
       "2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   89100\n",
       "...                ...         ...         ...         ...         ...     ...\n",
       "2020-10-30  185.220001  187.369995  184.110001  185.610001  185.106094   92400\n",
       "2020-11-02  187.759995  188.110001  182.869995  185.360001  184.856766  130100\n",
       "2020-11-03  187.470001  192.639999  186.309998  192.039993  191.518631  119700\n",
       "2020-11-04  194.479996  197.059998  190.889999  191.559998  191.039932   85300\n",
       "2020-11-05  194.000000  198.940002  193.820007  197.800003  197.263000   83900\n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtro1 = data.Volume > 80000\n",
    "filtro2 = data.Close <= 210\n",
    "filtro2\n",
    "\n",
    "data_filtrado = data[(filtro1)&(filtro2)]\n",
    "data_filtrado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>154.970001</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>153.559998</td>\n",
       "      <td>152.546036</td>\n",
       "      <td>84800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>157.699997</td>\n",
       "      <td>161.500000</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
       "      <td>159.509766</td>\n",
       "      <td>107200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>158.210007</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>151.259995</td>\n",
       "      <td>152.110001</td>\n",
       "      <td>151.105621</td>\n",
       "      <td>143400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>153.080002</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>151.789993</td>\n",
       "      <td>153.770004</td>\n",
       "      <td>152.754669</td>\n",
       "      <td>90300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  Volume\n",
       "Date                                                                          \n",
       "2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   84800\n",
       "2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766  107200\n",
       "2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621  143400\n",
       "2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   90300\n",
       "2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   89100\n",
       "...                ...         ...         ...         ...         ...     ...\n",
       "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999  214400\n",
       "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998  150900\n",
       "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994  142100\n",
       "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000  205300\n",
       "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   99600\n",
       "\n",
       "[213 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_filtrado = data[(data.Volume > 80000) | (data.Close <= 210)]\n",
    "data_filtrado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear Columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/UI.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['precio_medio']=(data.Open +data.Close + data.High + data.Low)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>precio_medio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>154.970001</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>153.559998</td>\n",
       "      <td>152.546036</td>\n",
       "      <td>84800</td>\n",
       "      <td>153.604999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>157.699997</td>\n",
       "      <td>161.500000</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
       "      <td>159.509766</td>\n",
       "      <td>107200</td>\n",
       "      <td>159.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>158.210007</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>151.259995</td>\n",
       "      <td>152.110001</td>\n",
       "      <td>151.105621</td>\n",
       "      <td>143400</td>\n",
       "      <td>154.947503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>153.080002</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>151.789993</td>\n",
       "      <td>153.770004</td>\n",
       "      <td>152.754669</td>\n",
       "      <td>90300</td>\n",
       "      <td>153.377499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "      <td>156.412498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "      <td>288.842506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "      <td>288.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "      <td>281.707504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "      <td>283.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "      <td>283.302498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   \n",
       "2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766   \n",
       "2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621   \n",
       "2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   \n",
       "2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999   \n",
       "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998   \n",
       "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994   \n",
       "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000   \n",
       "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   \n",
       "\n",
       "            Volume  precio_medio  \n",
       "Date                              \n",
       "2020-04-13   84800    153.604999  \n",
       "2020-04-14  107200    159.187500  \n",
       "2020-04-15  143400    154.947503  \n",
       "2020-04-16   90300    153.377499  \n",
       "2020-04-17   89100    156.412498  \n",
       "...            ...           ...  \n",
       "2021-04-05  214400    288.842506  \n",
       "2021-04-06  150900    288.550003  \n",
       "2021-04-07  142100    281.707504  \n",
       "2021-04-08  205300    283.509995  \n",
       "2021-04-09   99600    283.302498  \n",
       "\n",
       "[251 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['vol_mill_usd']=(data.Volume * data.precio_medio)/1_000_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>precio_medio</th>\n",
       "      <th>vol_mill_usd</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>154.970001</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>153.559998</td>\n",
       "      <td>152.546036</td>\n",
       "      <td>84800</td>\n",
       "      <td>153.604999</td>\n",
       "      <td>13.025704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>157.699997</td>\n",
       "      <td>161.500000</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
       "      <td>159.509766</td>\n",
       "      <td>107200</td>\n",
       "      <td>159.187500</td>\n",
       "      <td>17.064900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>158.210007</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>151.259995</td>\n",
       "      <td>152.110001</td>\n",
       "      <td>151.105621</td>\n",
       "      <td>143400</td>\n",
       "      <td>154.947503</td>\n",
       "      <td>22.219472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>153.080002</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>151.789993</td>\n",
       "      <td>153.770004</td>\n",
       "      <td>152.754669</td>\n",
       "      <td>90300</td>\n",
       "      <td>153.377499</td>\n",
       "      <td>13.849988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "      <td>156.412498</td>\n",
       "      <td>13.936354</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "      <td>288.842506</td>\n",
       "      <td>61.927833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "      <td>288.550003</td>\n",
       "      <td>43.542195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "      <td>281.707504</td>\n",
       "      <td>40.030636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "      <td>283.509995</td>\n",
       "      <td>58.204602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "      <td>283.302498</td>\n",
       "      <td>28.216929</td>\n",
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       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   \n",
       "2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766   \n",
       "2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621   \n",
       "2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   \n",
       "2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999   \n",
       "2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998   \n",
       "2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994   \n",
       "2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000   \n",
       "2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   \n",
       "\n",
       "            Volume  precio_medio  vol_mill_usd  \n",
       "Date                                            \n",
       "2020-04-13   84800    153.604999     13.025704  \n",
       "2020-04-14  107200    159.187500     17.064900  \n",
       "2020-04-15  143400    154.947503     22.219472  \n",
       "2020-04-16   90300    153.377499     13.849988  \n",
       "2020-04-17   89100    156.412498     13.936354  \n",
       "...            ...           ...           ...  \n",
       "2021-04-05  214400    288.842506     61.927833  \n",
       "2021-04-06  150900    288.550003     43.542195  \n",
       "2021-04-07  142100    281.707504     40.030636  \n",
       "2021-04-08  205300    283.509995     58.204602  \n",
       "2021-04-09   99600    283.302498     28.216929  \n",
       "\n",
       "[251 rows x 8 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>color_vela</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>154.970001</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>150.889999</td>\n",
       "      <td>153.559998</td>\n",
       "      <td>152.546036</td>\n",
       "      <td>84800</td>\n",
       "      <td>rojo</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>157.699997</td>\n",
       "      <td>161.500000</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
       "      <td>159.509766</td>\n",
       "      <td>107200</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>158.210007</td>\n",
       "      <td>151.259995</td>\n",
       "      <td>152.110001</td>\n",
       "      <td>151.105621</td>\n",
       "      <td>143400</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>153.080002</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>151.789993</td>\n",
       "      <td>153.770004</td>\n",
       "      <td>152.754669</td>\n",
       "      <td>90300</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2021-04-06</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2021-04-08</td>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "      <td>verde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
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       "<p>251 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   \n",
       "1    2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766   \n",
       "2    2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621   \n",
       "3    2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   \n",
       "4    2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "246  2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999   \n",
       "247  2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998   \n",
       "248  2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994   \n",
       "249  2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000   \n",
       "250  2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   \n",
       "\n",
       "     Volume color_vela  \n",
       "0     84800       rojo  \n",
       "1    107200      verde  \n",
       "2    143400       rojo  \n",
       "3     90300      verde  \n",
       "4     89100      verde  \n",
       "..      ...        ...  \n",
       "246  214400      verde  \n",
       "247  150900       rojo  \n",
       "248  142100       rojo  \n",
       "249  205300      verde  \n",
       "250   99600       rojo  \n",
       "\n",
       "[251 rows x 8 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejercicio crear columnas pero iterando\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/UI.csv', index_col = 0)\n",
    "data=data.reset_index()\n",
    "\n",
    "for i in range(len(data)):\n",
    "    if data.loc[i,'Close']>= data.loc[i,'Open']:\n",
    "        data.loc[i,'color_vela']='verde'\n",
    "    else:\n",
    "        data.loc[i,'color_vela']='rojo'\n",
    "        \n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2020-04-13</td>\n",
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       "      <th>1</th>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>157.699997</td>\n",
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       "      <td>156.979996</td>\n",
       "      <td>160.570007</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>158.210007</td>\n",
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       "      <td>143400</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>153.080002</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>155.889999</td>\n",
       "      <td>158.009995</td>\n",
       "      <td>153.830002</td>\n",
       "      <td>157.919998</td>\n",
       "      <td>156.877243</td>\n",
       "      <td>89100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>289.200012</td>\n",
       "      <td>294.320007</td>\n",
       "      <td>281.070007</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>290.779999</td>\n",
       "      <td>214400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2021-04-06</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>283.140015</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>289.059998</td>\n",
       "      <td>150900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>283.600006</td>\n",
       "      <td>290.070007</td>\n",
       "      <td>275.260010</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>277.899994</td>\n",
       "      <td>142100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2021-04-08</td>\n",
       "      <td>279.989990</td>\n",
       "      <td>289.970001</td>\n",
       "      <td>276.079987</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>205300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>285.149994</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>278.279999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>99600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2020-04-13  154.970001  155.000000  150.889999  153.559998  152.546036   \n",
       "1    2020-04-14  157.699997  161.500000  156.979996  160.570007  159.509766   \n",
       "2    2020-04-15  158.210007  158.210007  151.259995  152.110001  151.105621   \n",
       "3    2020-04-16  153.080002  154.869995  151.789993  153.770004  152.754669   \n",
       "4    2020-04-17  155.889999  158.009995  153.830002  157.919998  156.877243   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "246  2021-04-05  289.200012  294.320007  281.070007  290.779999  290.779999   \n",
       "247  2021-04-06  291.000000  291.000000  283.140015  289.059998  289.059998   \n",
       "248  2021-04-07  283.600006  290.070007  275.260010  277.899994  277.899994   \n",
       "249  2021-04-08  279.989990  289.970001  276.079987  288.000000  288.000000   \n",
       "250  2021-04-09  285.149994  288.000000  278.279999  281.779999  281.779999   \n",
       "\n",
       "     Volume  \n",
       "0     84800  \n",
       "1    107200  \n",
       "2    143400  \n",
       "3     90300  \n",
       "4     89100  \n",
       "..      ...  \n",
       "246  214400  \n",
       "247  150900  \n",
       "248  142100  \n",
       "249  205300  \n",
       "250   99600  \n",
       "\n",
       "[251 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('C:/Users/DELL/Documents/UI.csv', index_col = 0)\n",
    "data.reset_index(inplace=True)\n",
    "#equivalente data = data.reset_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregar filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>color_vela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09-01-18</td>\n",
       "      <td>175</td>\n",
       "      <td>176</td>\n",
       "      <td>177</td>\n",
       "      <td>12333</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Open  Close  Adj Close  Volume color_vela\n",
       "0  09-01-18   175    176        177   12333       rojo"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', index_col = 0)\n",
    "fila = {'Date': ['09-01-18'],\n",
    "        'Open':[175],\n",
    "        'Close':[176],\n",
    "        'Adj Close':[177],\n",
    "        'Volume':[12333],\n",
    "        'color_vela':['rojo']}\n",
    "\n",
    "df2 = pd.DataFrame(fila) # creando un dataframe con los nuevos datos\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>color_vela</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>09-01-18</th>\n",
       "      <td>175</td>\n",
       "      <td>176</td>\n",
       "      <td>177</td>\n",
       "      <td>12333</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Open  Close  Adj Close  Volume color_vela\n",
       "Date                                               \n",
       "09-01-18   175    176        177   12333       rojo"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.set_index('Date', inplace=True) # cambiamos el indice a la columna Date\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>color_vela</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.300003</td>\n",
       "      <td>169.259995</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>25555900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-01-18</th>\n",
       "      <td>172.529999</td>\n",
       "      <td>174.550003</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
       "      <td>29517900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
       "      <td>173.470001</td>\n",
       "      <td>172.080002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>22434600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.369995</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>23660000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>175.610001</td>\n",
       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>20567800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09-01-18</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>12333</td>\n",
       "      <td>rojo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "09-01-18  175.000000         NaN         NaN  176.000000  177.000000   \n",
       "\n",
       "            Volume color_vela  \n",
       "Date                           \n",
       "02-01-18  25555900        NaN  \n",
       "03-01-18  29517900        NaN  \n",
       "04-01-18  22434600        NaN  \n",
       "05-01-18  23660000        NaN  \n",
       "08-01-18  20567800        NaN  \n",
       "09-01-18     12333       rojo  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.append(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>169.259995</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>25555900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-01-18</th>\n",
       "      <td>172.529999</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
       "      <td>29517900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
       "      <td>172.080002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>22434600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>23660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>20567800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open         Low       Close   Adj Close    Volume\n",
       "Date                                                              \n",
       "02-01-18  170.160004  169.259995  172.259995  168.339050  25555900\n",
       "03-01-18  172.529999  171.960007  172.229996  168.309738  29517900\n",
       "04-01-18  172.539993  172.080002  173.029999  169.091522  22434600\n",
       "05-01-18  173.440002  173.050003  175.000000  171.016678  23660000\n",
       "08-01-18  174.350006  173.929993  174.350006  170.381485  20567800"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', index_col = 0)\n",
    "data = data.drop('High', axis=1) # axis =0 es para filas, axis = 1 es para columnas\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>169.259995</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>25555900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-01-18</th>\n",
       "      <td>172.529999</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
       "      <td>29517900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
       "      <td>172.080002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>22434600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>23660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>20567800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open         Low       Close   Adj Close    Volume\n",
       "Date                                                              \n",
       "02-01-18  170.160004  169.259995  172.259995  168.339050  25555900\n",
       "03-01-18  172.529999  171.960007  172.229996  168.309738  29517900\n",
       "04-01-18  172.539993  172.080002  173.029999  169.091522  22434600\n",
       "05-01-18  173.440002  173.050003  175.000000  171.016678  23660000\n",
       "08-01-18  174.350006  173.929993  174.350006  170.381485  20567800"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', index_col = 0)\n",
    "data.drop('High', axis=1, inplace=True) # axis =0 es para filas, axis = 1 es para columnas\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eliminar Filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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",
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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>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>169.259995</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>25555900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-01-18</th>\n",
       "      <td>172.529999</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
       "      <td>29517900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
       "      <td>172.080002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>22434600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>23660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open         Low       Close   Adj Close    Volume\n",
       "Date                                                              \n",
       "02-01-18  170.160004  169.259995  172.259995  168.339050  25555900\n",
       "03-01-18  172.529999  171.960007  172.229996  168.309738  29517900\n",
       "04-01-18  172.539993  172.080002  173.029999  169.091522  22434600\n",
       "05-01-18  173.440002  173.050003  175.000000  171.016678  23660000"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('08-01-18', axis=0, inplace=True) #eje de filas axis=0\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Copiar un dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Problema de los apuntadores o referencias\n",
    "import pandas as pd\n",
    "ser1 = pd.Series([1,2,3,4], index=['USA','China', 'France', 'Germany'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        1\n",
       "China      2\n",
       "France     3\n",
       "Germany    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA        1\n",
       "China      2\n",
       "France     3\n",
       "Germany    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = ser1\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser2[2] =20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA         1\n",
       "China       2\n",
       "France     20\n",
       "Germany     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA         1\n",
       "China       2\n",
       "France     20\n",
       "Germany     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.655235</td>\n",
       "      <td>0.486012</td>\n",
       "      <td>-1.507257</td>\n",
       "      <td>0.998514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
       "      <td>0.232463</td>\n",
       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.528055</td>\n",
       "      <td>-0.929794</td>\n",
       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.075910</td>\n",
       "      <td>-0.122775</td>\n",
       "      <td>-1.832233</td>\n",
       "      <td>-2.756570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.204070</td>\n",
       "      <td>0.837138</td>\n",
       "      <td>0.935180</td>\n",
       "      <td>-1.212769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A -0.655235  0.486012 -1.507257  0.998514\n",
       "B -0.102822  0.232463  0.457890 -1.653535\n",
       "C -0.528055 -0.929794 -0.126996  0.063757\n",
       "D -0.075910 -0.122775 -1.832233 -2.756570\n",
       "E -0.204070  0.837138  0.935180 -1.212769"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "\n",
    "df = pd.DataFrame(randn(5,4), index=['A', 'B', 'C', 'D' ,'E'], columns='W X Y Z'.split())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
       "      <td>0.232463</td>\n",
       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.528055</td>\n",
       "      <td>-0.929794</td>\n",
       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "B -0.102822  0.232463  0.457890 -1.653535\n",
       "C -0.528055 -0.929794 -0.126996  0.063757"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[:][1:3] #columnas, filas\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['W']['C']=25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.655235</td>\n",
       "      <td>0.486012</td>\n",
       "      <td>-1.507257</td>\n",
       "      <td>0.998514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
       "      <td>0.232463</td>\n",
       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.929794</td>\n",
       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.075910</td>\n",
       "      <td>-0.122775</td>\n",
       "      <td>-1.832233</td>\n",
       "      <td>-2.756570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.204070</td>\n",
       "      <td>0.837138</td>\n",
       "      <td>0.935180</td>\n",
       "      <td>-1.212769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           W         X         Y         Z\n",
       "A  -0.655235  0.486012 -1.507257  0.998514\n",
       "B  -0.102822  0.232463  0.457890 -1.653535\n",
       "C  25.000000 -0.929794 -0.126996  0.063757\n",
       "D  -0.075910 -0.122775 -1.832233 -2.756570\n",
       "E  -0.204070  0.837138  0.935180 -1.212769"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Copiar el dataframe\n",
    "copia = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.655235</td>\n",
       "      <td>0.486012</td>\n",
       "      <td>-1.507257</td>\n",
       "      <td>0.998514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
       "      <td>0.232463</td>\n",
       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>-0.929794</td>\n",
       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.075910</td>\n",
       "      <td>-0.122775</td>\n",
       "      <td>-1.832233</td>\n",
       "      <td>-2.756570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.204070</td>\n",
       "      <td>0.837138</td>\n",
       "      <td>0.935180</td>\n",
       "      <td>-1.212769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           W         X         Y         Z\n",
       "A  -0.655235  0.486012 -1.507257  0.998514\n",
       "B  -0.102822  0.232463  0.457890 -1.653535\n",
       "C  27.000000 -0.929794 -0.126996  0.063757\n",
       "D  -0.075910 -0.122775 -1.832233 -2.756570\n",
       "E  -0.204070  0.837138  0.935180 -1.212769"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "copia['W']['C']=35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
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       "      <th>Z</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.655235</td>\n",
       "      <td>0.486012</td>\n",
       "      <td>-1.507257</td>\n",
       "      <td>0.998514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
       "      <td>0.232463</td>\n",
       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>-0.929794</td>\n",
       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.075910</td>\n",
       "      <td>-0.122775</td>\n",
       "      <td>-1.832233</td>\n",
       "      <td>-2.756570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.204070</td>\n",
       "      <td>0.837138</td>\n",
       "      <td>0.935180</td>\n",
       "      <td>-1.212769</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           W         X         Y         Z\n",
       "A  -0.655235  0.486012 -1.507257  0.998514\n",
       "B  -0.102822  0.232463  0.457890 -1.653535\n",
       "C  27.000000 -0.929794 -0.126996  0.063757\n",
       "D  -0.075910 -0.122775 -1.832233 -2.756570\n",
       "E  -0.204070  0.837138  0.935180 -1.212769"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
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       "      <td>0.486012</td>\n",
       "      <td>-1.507257</td>\n",
       "      <td>0.998514</td>\n",
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       "      <th>B</th>\n",
       "      <td>-0.102822</td>\n",
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       "      <td>0.457890</td>\n",
       "      <td>-1.653535</td>\n",
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       "      <th>C</th>\n",
       "      <td>35.000000</td>\n",
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       "      <td>-0.126996</td>\n",
       "      <td>0.063757</td>\n",
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       "      <th>D</th>\n",
       "      <td>-0.075910</td>\n",
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       "      <td>-1.832233</td>\n",
       "      <td>-2.756570</td>\n",
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       "      <th>E</th>\n",
       "      <td>-0.204070</td>\n",
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      ],
      "text/plain": [
       "           W         X         Y         Z\n",
       "A  -0.655235  0.486012 -1.507257  0.998514\n",
       "B  -0.102822  0.232463  0.457890 -1.653535\n",
       "C  35.000000 -0.929794 -0.126996  0.063757\n",
       "D  -0.075910 -0.122775 -1.832233 -2.756570\n",
       "E  -0.204070  0.837138  0.935180 -1.212769"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones IMPORTANTES DE PANDAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.300003</td>\n",
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       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>25555900</td>\n",
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       "    <tr>\n",
       "      <th>03-01-18</th>\n",
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       "      <td>168.309738</td>\n",
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       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
       "      <td>173.470001</td>\n",
       "      <td>172.080002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>22434600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.369995</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>23660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>175.610001</td>\n",
       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>20567800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open        High         Low       Close   Adj Close    Volume\n",
       "Date                                                                          \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050  25555900\n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738  29517900\n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522  22434600\n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678  23660000\n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485  20567800"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', index_col = 0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Porcentaje de variacion entre filas de un valor respecto al siguiente, o el anterior\n",
    "#pct_change()\n",
    "data['var_diaria']= data['Adj Close'].pct_change()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>var_diaria</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>02-01-18</th>\n",
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       "      <td>-0.017412</td>\n",
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       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
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       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.369995</td>\n",
       "      <td>173.050003</td>\n",
       "      <td>175.000000</td>\n",
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       "      <td>23660000</td>\n",
       "      <td>1.138529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>175.610001</td>\n",
       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
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      ],
      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  \n",
       "Date                            \n",
       "02-01-18  25555900         NaN  \n",
       "03-01-18  29517900   -0.017412  \n",
       "04-01-18  22434600    0.464491  \n",
       "05-01-18  23660000    1.138529  \n",
       "08-01-18  20567800   -0.371422  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['var_diaria']= data['Adj Close'].pct_change(2)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>02-01-18</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>04-01-18</th>\n",
       "      <td>172.539993</td>\n",
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       "    <tr>\n",
       "      <th>05-01-18</th>\n",
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       "      <td>23660000</td>\n",
       "      <td>1.608309</td>\n",
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       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>175.610001</td>\n",
       "      <td>173.929993</td>\n",
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       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  \n",
       "Date                            \n",
       "02-01-18  25555900         NaN  \n",
       "03-01-18  29517900         NaN  \n",
       "04-01-18  22434600    0.446998  \n",
       "05-01-18  23660000    1.608309  \n",
       "08-01-18  20567800    0.762879  "
      ]
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     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
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       "      <td>172.539993</td>\n",
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       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
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      ],
      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  \n",
       "Date                            \n",
       "02-01-18  25555900         NaN  \n",
       "03-01-18  29517900       -0.02  \n",
       "04-01-18  22434600        0.46  \n",
       "05-01-18  23660000        1.14  \n",
       "08-01-18  20567800       -0.37  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['var_diaria']= round(data['Adj Close'].pct_change()*100,2)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-01-18</th>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.300003</td>\n",
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       "      <td>172.259995</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>03-01-18</th>\n",
       "      <td>172.529999</td>\n",
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       "      <td>171.960007</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
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       "      <td>-0.02</td>\n",
       "      <td>-0.029999</td>\n",
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       "    <tr>\n",
       "      <th>04-01-18</th>\n",
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       "      <td>172.080002</td>\n",
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       "      <td>0.46</td>\n",
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       "    <tr>\n",
       "      <th>05-01-18</th>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.369995</td>\n",
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       "      <td>23660000</td>\n",
       "      <td>1.14</td>\n",
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       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
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       "      <td>173.929993</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
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       "      <td>-0.37</td>\n",
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      ],
      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  var_precio  \n",
       "Date                                        \n",
       "02-01-18  25555900         NaN         NaN  \n",
       "03-01-18  29517900       -0.02   -0.029999  \n",
       "04-01-18  22434600        0.46    0.800003  \n",
       "05-01-18  23660000        1.14    1.970001  \n",
       "08-01-18  20567800       -0.37   -0.649994  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular la diferencia entre dos filas \n",
    "data['var_precio']= data['Close'].diff()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>05-01-18</th>\n",
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       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
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      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  var_precio  \n",
       "Date                                        \n",
       "02-01-18  25555900         NaN         NaN  \n",
       "03-01-18  29517900       -0.02         NaN  \n",
       "04-01-18  22434600        0.46         NaN  \n",
       "05-01-18  23660000        1.14    2.740005  \n",
       "08-01-18  20567800       -0.37    2.120010  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular la diferencia entre dos filas \n",
    "data['var_precio']= data['Close'].diff(3)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "      <th>02-01-18</th>\n",
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       "    <tr>\n",
       "      <th>05-01-18</th>\n",
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       "      <td>175.369995</td>\n",
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       "    <tr>\n",
       "      <th>08-01-18</th>\n",
       "      <td>174.350006</td>\n",
       "      <td>175.610001</td>\n",
       "      <td>173.929993</td>\n",
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       "      <td>170.381485</td>\n",
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      "text/plain": [
       "                Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                   \n",
       "02-01-18  170.160004  172.300003  169.259995  172.259995  168.339050   \n",
       "03-01-18  172.529999  174.550003  171.960007  172.229996  168.309738   \n",
       "04-01-18  172.539993  173.470001  172.080002  173.029999  169.091522   \n",
       "05-01-18  173.440002  175.369995  173.050003  175.000000  171.016678   \n",
       "08-01-18  174.350006  175.610001  173.929993  174.350006  170.381485   \n",
       "\n",
       "            Volume  var_diaria  var_precio  \n",
       "Date                                        \n",
       "02-01-18  25555900         NaN    0.029999  \n",
       "03-01-18  29517900       -0.02   -0.800003  \n",
       "04-01-18  22434600        0.46   -1.970001  \n",
       "05-01-18  23660000        1.14    0.649994  \n",
       "08-01-18  20567800       -0.37         NaN  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular la diferencia entre dos filas \n",
    "data['var_precio']= data['Close'].diff(-1) # calcula la diferencia hacia el final del dataframe\n",
    "# es decir la fila siguiente\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluar condiciones y obtener tru o false\n",
    "# Evaluar si el close fue un 10% mas alto que el Open\n",
    "\n",
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', usecols=['Date','Open','Close','Adj Close'])\n",
    "data['subida'] = data.eval('Close > Open * 1.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "       Date        Open       Close   Adj Close  subida\n",
       "0  02-01-18  170.160004  172.259995  168.339050    True\n",
       "1  03-01-18  172.529999  172.229996  168.309738   False\n",
       "2  04-01-18  172.539993  173.029999  169.091522   False\n",
       "3  05-01-18  173.440002  175.000000  171.016678   False\n",
       "4  08-01-18  174.350006  174.350006  170.381485   False"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para validar\n",
    "data['% Var Intra']= round((data.Close/data.Open -1)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>subida</th>\n",
       "      <th>% Var Intra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02-01-18</td>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>True</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-01-18</td>\n",
       "      <td>172.529999</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>168.309738</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04-01-18</td>\n",
       "      <td>172.539993</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>False</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-01-18</td>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>False</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08-01-18</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date        Open       Close   Adj Close  subida  % Var Intra\n",
       "0  02-01-18  170.160004  172.259995  168.339050    True         1.23\n",
       "1  03-01-18  172.529999  172.229996  168.309738   False        -0.17\n",
       "2  04-01-18  172.539993  173.029999  169.091522   False         0.28\n",
       "3  05-01-18  173.440002  175.000000  171.016678   False         0.90\n",
       "4  08-01-18  174.350006  174.350006  170.381485   False         0.00"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>168.309738</td>\n",
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       "      <th>2</th>\n",
       "      <td>04-01-18</td>\n",
       "      <td>172.539993</td>\n",
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       "      <th>3</th>\n",
       "      <td>05-01-18</td>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>173.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08-01-18</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>175.000000</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       Date        Open       Close   Adj Close  cierre_ant\n",
       "0  02-01-18  170.160004  172.259995  168.339050         NaN\n",
       "1  03-01-18  172.529999  172.229996  168.309738  172.259995\n",
       "2  04-01-18  172.539993  173.029999  169.091522  172.229996\n",
       "3  05-01-18  173.440002  175.000000  171.016678  173.029999\n",
       "4  08-01-18  174.350006  174.350006  170.381485  175.000000"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## SHIFT\n",
    "## Funcion que permite accceder a los datos de la fila anterior para cualquier\n",
    "## columna\n",
    "## shift(-1), va hacia adelante, shift(n) la n-enesima fila anterior\n",
    "\n",
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', usecols=['Date','Open','Close','Adj Close'])\n",
    "data['cierre_ant']=data['Close'].shift()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gap_positivo']=data.eval('Open > cierre_ant ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>gap_positivo</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04-01-18</td>\n",
       "      <td>172.539993</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>169.091522</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-01-18</td>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>171.016678</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08-01-18</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date        Open       Close   Adj Close  cierre_ant  gap_positivo\n",
       "0  02-01-18  170.160004  172.259995  168.339050         NaN         False\n",
       "1  03-01-18  172.529999  172.229996  168.309738  172.259995          True\n",
       "2  04-01-18  172.539993  173.029999  169.091522  172.229996          True\n",
       "3  05-01-18  173.440002  175.000000  171.016678  173.029999          True\n",
       "4  08-01-18  174.350006  174.350006  170.381485  175.000000         False"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>gap_positivo</th>\n",
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       "      <th>0</th>\n",
       "      <td>02-01-18</td>\n",
       "      <td>170.160004</td>\n",
       "      <td>172.259995</td>\n",
       "      <td>168.339050</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>03-01-18</td>\n",
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       "      <th>2</th>\n",
       "      <td>04-01-18</td>\n",
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       "      <th>3</th>\n",
       "      <td>05-01-18</td>\n",
       "      <td>173.440002</td>\n",
       "      <td>175.000000</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08-01-18</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>174.350006</td>\n",
       "      <td>170.381485</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date        Open       Close   Adj Close  gap_positivo\n",
       "0  02-01-18  170.160004  172.259995  168.339050         False\n",
       "1  03-01-18  172.529999  172.229996  168.309738          True\n",
       "2  04-01-18  172.539993  173.029999  169.091522          True\n",
       "3  05-01-18  173.440002  175.000000  171.016678          True\n",
       "4  08-01-18  174.350006  174.350006  170.381485         False"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', usecols=['Date','Open','Close','Adj Close'])\n",
    "data['gap_positivo']=data.Open > data['Close'].shift()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de gaps positivo 3\n"
     ]
    }
   ],
   "source": [
    "#contar la cantidad de gaps positivo.\n",
    "import pandas as pd\n",
    "data = pd.read_csv('C:/Users/DELL/Documents/appl.csv', usecols=['Date','Open','Close','Adj Close'])\n",
    "contador = 0\n",
    "data['gap_positivo']=data.Open > data['Close'].shift()\n",
    "for dato in data['gap_positivo']:\n",
    "     if dato == True:\n",
    "        contador+=1\n",
    "print(f\"Total de gaps positivo {contador}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador_positivos  = data.gap_positivo[data.gap_positivo==True].count()\n",
    "contador_positivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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