20220915_Live_Tipos_de_Join_Trading_com_Dados.ipynb

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        "<a href=\"https://colab.research.google.com/github/Trading-com-Dados/tutoriais_publicos/blob/main/20220915_Live_Tipos_de_Join_Trading_com_Dados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://tradingcomdados.com/images/logotipo/logotipo-trading-com-dados.svg\" width=\"300\" align=\"left\"/>"
      ],
      "metadata": {
        "id": "zLZhtwHDFg8g"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Integrando diferentes bases de dados com Pandas / Python**\n",
        "## *Tipos de join na função merge*\n",
        "---"
      ],
      "metadata": {
        "id": "nf1lXINjFhxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Função <u>Join</u> muito utilizada em SQL**\n",
        "\n",
        "**Função <u>PROCV / VLOOKUP</u> no Excel**"
      ],
      "metadata": {
        "id": "fLqUzwvGCfNv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://i2.wp.com/www.datascienceexamples.com/wp-content/uploads/2019/12/join_types.png?resize=1024%2C960&ssl=1\" width=\"600\" align=\"left\"/>\n"
      ],
      "metadata": {
        "id": "9eVMuERsFh3z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://i0.wp.com/www.datascienceexamples.com/wp-content/uploads/2019/12/joins_with_pandas.png?resize=1024%2C554&ssl=1\" width=\"600\" align=\"left\"/>"
      ],
      "metadata": {
        "id": "2ijlsIcXgF3k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Estudo de caso #1: Juntar base de dados de dividendos com preço de fechamento**"
      ],
      "metadata": {
        "id": "yE0UriJbaQtU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
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      "execution_count": 24,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.74)\n",
            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.28.1)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
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    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "psAAhXn4bnbL"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dividendos_PETR4 = yf.Ticker('PETR4.SA').dividends\n",
        "dividendos_PETR4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpL2q3Cubqck",
        "outputId": "59858e22-8b9b-48cb-a0f8-05ba6fb85574"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2005-04-01    0.133288\n",
              "2005-07-01    0.250663\n",
              "2006-01-02    0.259000\n",
              "2006-04-04    0.132700\n",
              "2006-11-01    0.500750\n",
              "Name: Dividends, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
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      "source": [
        "fechamento_PETR4 = yf.download('PETR4.SA', period='max')['Close']\n",
        "fechamento_PETR4.head()"
      ],
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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          "data": {
            "text/plain": [
              "Date\n",
              "2000-01-03    5.875\n",
              "2000-01-04    5.550\n",
              "2000-01-05    5.494\n",
              "2000-01-06    5.475\n",
              "2000-01-07    5.500\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_join_dividend_close = pd.merge(dividendos_PETR4, fechamento_PETR4, how='left', left_on=dividendos_PETR4.index, right_on=fechamento_PETR4.index)\n",
        "df_join_dividend_close.head()"
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      "execution_count": 28,
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              "       key_0  Dividends     Close\n",
              "0 2005-04-01   0.133288  12.98750\n",
              "1 2005-07-01   0.250663  13.52375\n",
              "2 2006-01-02   0.259000  19.04500\n",
              "3 2006-04-04   0.132700  21.64500\n",
              "4 2006-11-01   0.500750  21.22500"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Estudo de caso #2: Comparar as cotações entre yfinance e trading view**"
      ],
      "metadata": {
        "id": "TauxvRkqNmPX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tvdatafeed"
      ],
      "metadata": {
        "id": "IBxnpfW1PTsO",
        "outputId": "c48fb0b1-1e42-4ec8-c216-3cdcefd2f135",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tvdatafeed in /usr/local/lib/python3.7/dist-packages (2.1.0)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.7/dist-packages (from tvdatafeed) (1.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tvdatafeed) (57.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from tvdatafeed) (2.28.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from tvdatafeed) (1.3.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tvdatafeed) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tvdatafeed) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->tvdatafeed) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->tvdatafeed) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests->tvdatafeed) (2.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->tvdatafeed) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->tvdatafeed) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->tvdatafeed) (2022.6.15)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tvDatafeed import TvDatafeed, Interval\n",
        "\n",
        "import pandas as pd\n",
        "from datetime import date"
      ],
      "metadata": {
        "id": "dDfqVhmWPbNP"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Baixar dados do Yahoo Finance"
      ],
      "metadata": {
        "id": "4DUBg_HGVLhs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "itub4_yf = yf.download('ITUB4.SA', period='max')\n",
        "itub4_yf.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 256
        },
        "id": "BCVYjMiLP8Un",
        "outputId": "279f8008-3c99-4824-a6f1-a2ff300f6678"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close  Volume\n",
              "Date                                                                 \n",
              "2000-12-21  2.815505  2.988535  2.815174  2.960552   1.807515   74224\n",
              "2000-12-22  2.930748  2.930748  2.814843  2.832229   1.729169   23312\n",
              "2000-12-25  2.832229  2.832229  2.832229  2.832229   1.729169       0\n",
              "2000-12-26  2.816664  2.834712  2.790006  2.831401   1.728664    7851\n",
              "2000-12-27  2.881074  2.927437  2.831401  2.927437   1.787296   26996"
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              "      <th>2000-12-21</th>\n",
              "      <td>2.815505</td>\n",
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              "      <td>2.832229</td>\n",
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              "      <td>2.832229</td>\n",
              "      <td>1.729169</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-12-26</th>\n",
              "      <td>2.816664</td>\n",
              "      <td>2.834712</td>\n",
              "      <td>2.790006</td>\n",
              "      <td>2.831401</td>\n",
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              "      <td>7851</td>\n",
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              "    <tr>\n",
              "      <th>2000-12-27</th>\n",
              "      <td>2.881074</td>\n",
              "      <td>2.927437</td>\n",
              "      <td>2.831401</td>\n",
              "      <td>2.927437</td>\n",
              "      <td>1.787296</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-84a590aa-6bbc-41ba-a3a6-d6fe614392c3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estrutura de código para baixar dados Trading View. É possível inserir usuário e senha para maior lista de ativos disponíveis"
      ],
      "metadata": {
        "id": "lpqpA0i6VP-J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Limite de dados costuma ser aproximadamente 5000 \"barras\" (varia com o timeframe)"
      ],
      "metadata": {
        "id": "7lAompBvVaox"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tv = TvDatafeed()\n",
        "\n",
        "itub4_tv = tv.get_hist(symbol='ITUB4',exchange='BMFBOVESPA', interval=Interval.in_daily, n_bars=10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrPUVzu8R8hh",
        "outputId": "991b9d30-20b2-4bd3-ff8e-08cf55c4fce1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tvDatafeed.main:you are using nologin method, data you access may be limited\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "itub4_tv.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "DNSk_vAUWAD0",
        "outputId": "1c790aa5-66b2-48ba-fcc8-328c4e5ee566"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               symbol      open      high       low     close  \\\n",
              "datetime                                                                        \n",
              "2000-01-03 12:00:00  BMFBOVESPA:ITUB4  0.373557  0.386764  0.372614  0.372614   \n",
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              "2000-01-07 12:00:00  BMFBOVESPA:ITUB4  0.362237  0.391480  0.360351  0.377330   \n",
              "\n",
              "                         volume  \n",
              "datetime                         \n",
              "2000-01-03 12:00:00   9252311.0  \n",
              "2000-01-04 12:00:00   9050652.0  \n",
              "2000-01-05 12:00:00  38475633.0  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Faremos o join baseando-se nas datas dos preços OHLC. Entretanto vemos que elas não estão no mesmo formato"
      ],
      "metadata": {
        "id": "ypSEXMLHWHhE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos converter os índices para o formato ANO/MÊS/DIA sem incluir as horas"
      ],
      "metadata": {
        "id": "r6wyspEjWbLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "itub4_yf.index = itub4_yf.index.date\n",
        "itub4_yf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EXBmzOUNZQSU",
        "outputId": "4947d59f-63b5-465b-ac0c-91b2f36ebaab"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close  Volume\n",
              "2000-12-21  2.815505  2.988535  2.815174  2.960552   1.807515   74224\n",
              "2000-12-22  2.930748  2.930748  2.814843  2.832229   1.729169   23312\n",
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          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "itub4_tv.index = itub4_tv.index.date\n",
        "itub4_tv.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aCmxKwU-ZA4c",
        "outputId": "23b5b85e-d50f-4b4c-ba1c-a6abd02feddc"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      symbol      open      high       low     close  \\\n",
              "2000-01-03  BMFBOVESPA:ITUB4  0.373557  0.386764  0.372614  0.372614   \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora que os índices estão num mesmo formato de data, podemos fazer o join baseado nos índices"
      ],
      "metadata": {
        "id": "cnZf0W_fZYiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usaremos o **\"INNER\"** como tipo de join, já que nos interessa apenas os dados que representam a interseção entre os conjuntos de dados"
      ],
      "metadata": {
        "id": "8scbD1cDZhnc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "É importante ressaltar que nesse caso a coluna key_0 (que serviu para unir os dois dataframes) não está no formato de data"
      ],
      "metadata": {
        "id": "F_28Gm33a8od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_join_itub4_inner = pd.merge(itub4_yf, itub4_tv, how='inner', left_on=itub4_yf.index, right_on=itub4_tv.index)\n",
        "df_join_itub4_inner"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "68SsyuIRZQuY",
        "outputId": "453fa3a7-1144-4164-c000-9af31ef69afa"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           key_0       Open       High        Low      Close  Adj Close  \\\n",
              "0     2000-12-21   2.815505   2.988535   2.815174   2.960552   1.807515   \n",
              "1     2000-12-22   2.930748   2.930748   2.814843   2.832229   1.729169   \n",
              "2     2000-12-26   2.816664   2.834712   2.790006   2.831401   1.728664   \n",
              "3     2000-12-27   2.881074   2.927437   2.831401   2.927437   1.787296   \n",
              "4     2000-12-28   2.910879   3.129443   2.864351   3.063211   1.870191   \n",
              "...          ...        ...        ...        ...        ...        ...   \n",
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              "5379  2022-09-19  26.610001  27.799999  26.580000  27.680000  27.680000   \n",
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              "5382  2022-09-22  28.590000  28.959999  28.320000  28.490000  28.490000   \n",
              "\n",
              "        Volume            symbol       open       high        low      close  \\\n",
              "0        74224  BMFBOVESPA:ITUB4   0.413176   0.438647   0.413176   0.432043   \n",
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              "      <td>3.129443</td>\n",
              "      <td>2.864351</td>\n",
              "      <td>3.063211</td>\n",
              "      <td>1.870191</td>\n",
              "      <td>77425</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>0.434873</td>\n",
              "      <td>0.467890</td>\n",
              "      <td>0.428270</td>\n",
              "      <td>0.457513</td>\n",
              "      <td>13610196.0</td>\n",
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              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5378</th>\n",
              "      <td>2022-09-16</td>\n",
              "      <td>26.600000</td>\n",
              "      <td>26.870001</td>\n",
              "      <td>26.459999</td>\n",
              "      <td>26.680000</td>\n",
              "      <td>26.680000</td>\n",
              "      <td>43782000</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>26.600000</td>\n",
              "      <td>26.870001</td>\n",
              "      <td>26.459999</td>\n",
              "      <td>26.680000</td>\n",
              "      <td>43782000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5379</th>\n",
              "      <td>2022-09-19</td>\n",
              "      <td>26.610001</td>\n",
              "      <td>27.799999</td>\n",
              "      <td>26.580000</td>\n",
              "      <td>27.680000</td>\n",
              "      <td>27.680000</td>\n",
              "      <td>41063200</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>26.610001</td>\n",
              "      <td>27.799999</td>\n",
              "      <td>26.580000</td>\n",
              "      <td>27.680000</td>\n",
              "      <td>41063200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5380</th>\n",
              "      <td>2022-09-20</td>\n",
              "      <td>27.820000</td>\n",
              "      <td>28.660000</td>\n",
              "      <td>27.770000</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>62939000</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>27.820000</td>\n",
              "      <td>28.660000</td>\n",
              "      <td>27.770000</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>62939000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5381</th>\n",
              "      <td>2022-09-21</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>28.680000</td>\n",
              "      <td>28.180000</td>\n",
              "      <td>28.299999</td>\n",
              "      <td>28.299999</td>\n",
              "      <td>48098500</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>28.680000</td>\n",
              "      <td>28.180000</td>\n",
              "      <td>28.299999</td>\n",
              "      <td>48098500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5382</th>\n",
              "      <td>2022-09-22</td>\n",
              "      <td>28.590000</td>\n",
              "      <td>28.959999</td>\n",
              "      <td>28.320000</td>\n",
              "      <td>28.490000</td>\n",
              "      <td>28.490000</td>\n",
              "      <td>15235900</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>28.590000</td>\n",
              "      <td>28.960000</td>\n",
              "      <td>28.320000</td>\n",
              "      <td>28.510000</td>\n",
              "      <td>15238400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5383 rows × 13 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "E se eu quisesse, usando o join, juntar ambos os dados, mesmo que a data esteja disponível em apenas um dos dataframes: **\"OUTER\"**"
      ],
      "metadata": {
        "id": "2-6ODCBkaQ0F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Veja nesse exemplo que em 2000-12-25 (Natal), a yf trouxe valores das cotações (mesmo que OHLC sendo igual), e a tv possui uma cobertura temporal maior (final do dataframe)"
      ],
      "metadata": {
        "id": "E8FOKu57aqms"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_join_itub4_outer = pd.merge(itub4_yf, itub4_tv, how='outer', left_on=itub4_yf.index, right_on=itub4_tv.index)\n",
        "df_join_itub4_outer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "rNPQNcrYadIN",
        "outputId": "83702512-5359-4c33-a328-8480cda42a2b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           key_0      Open      High       Low     Close  Adj Close   Volume  \\\n",
              "0     2000-12-21  2.815505  2.988535  2.815174  2.960552   1.807515  74224.0   \n",
              "1     2000-12-22  2.930748  2.930748  2.814843  2.832229   1.729169  23312.0   \n",
              "2     2000-12-25  2.832229  2.832229  2.832229  2.832229   1.729169      0.0   \n",
              "3     2000-12-26  2.816664  2.834712  2.790006  2.831401   1.728664   7851.0   \n",
              "4     2000-12-27  2.881074  2.927437  2.831401  2.927437   1.787296  26996.0   \n",
              "...          ...       ...       ...       ...       ...        ...      ...   \n",
              "5698  2000-12-18       NaN       NaN       NaN       NaN        NaN      NaN   \n",
              "5699  2000-12-19       NaN       NaN       NaN       NaN        NaN      NaN   \n",
              "5700  2000-12-20       NaN       NaN       NaN       NaN        NaN      NaN   \n",
              "5701  2012-02-22       NaN       NaN       NaN       NaN        NaN      NaN   \n",
              "5702  2020-11-20       NaN       NaN       NaN       NaN        NaN      NaN   \n",
              "\n",
              "                symbol       open       high        low      close      volume  \n",
              "0     BMFBOVESPA:ITUB4   0.413176   0.438647   0.413176   0.432043  13024374.0  \n",
              "1     BMFBOVESPA:ITUB4   0.430156   0.430156   0.413176   0.416007   4100486.0  \n",
              "2                  NaN        NaN        NaN        NaN        NaN         NaN  \n",
              "3     BMFBOVESPA:ITUB4   0.420723   0.423553   0.416950   0.422610   1377071.0  \n",
              "4     BMFBOVESPA:ITUB4   0.430156   0.436760   0.422610   0.434873   4741558.0  \n",
              "...                ...        ...        ...        ...        ...         ...  \n",
              "5698  BMFBOVESPA:ITUB4   0.422610   0.437703   0.422610   0.428270  15752054.0  \n",
              "5699  BMFBOVESPA:ITUB4   0.428270   0.431100   0.422610   0.427326  10920760.0  \n",
              "5700  BMFBOVESPA:ITUB4   0.420723   0.422610   0.408460   0.408460   6503412.0  \n",
              "5701  BMFBOVESPA:ITUB4   8.645534   8.678487   8.509013   8.551382  20490442.0  \n",
              "5702  BMFBOVESPA:ITUB4  22.426414  22.629439  22.168728  22.293667  23357573.0  \n",
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              "      <th>key_0</th>\n",
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              "      <td>2000-12-21</td>\n",
              "      <td>2.815505</td>\n",
              "      <td>2.988535</td>\n",
              "      <td>2.815174</td>\n",
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              "      <th>1</th>\n",
              "      <td>2000-12-22</td>\n",
              "      <td>2.930748</td>\n",
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              "      <td>2.814843</td>\n",
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              "      <td>4100486.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2.832229</td>\n",
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              "      <th>3</th>\n",
              "      <td>2000-12-26</td>\n",
              "      <td>2.816664</td>\n",
              "      <td>2.834712</td>\n",
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              "      <td>1.728664</td>\n",
              "      <td>7851.0</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
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              "      <td>0.416950</td>\n",
              "      <td>0.422610</td>\n",
              "      <td>1377071.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-12-27</td>\n",
              "      <td>2.881074</td>\n",
              "      <td>2.927437</td>\n",
              "      <td>2.831401</td>\n",
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              "      <td>1.787296</td>\n",
              "      <td>26996.0</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>0.430156</td>\n",
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              "      <td>2000-12-18</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2000-12-19</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
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              "      <th>5700</th>\n",
              "      <td>2000-12-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>0.420723</td>\n",
              "      <td>0.422610</td>\n",
              "      <td>0.408460</td>\n",
              "      <td>0.408460</td>\n",
              "      <td>6503412.0</td>\n",
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              "      <th>5701</th>\n",
              "      <td>2012-02-22</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>8.645534</td>\n",
              "      <td>8.678487</td>\n",
              "      <td>8.509013</td>\n",
              "      <td>8.551382</td>\n",
              "      <td>20490442.0</td>\n",
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              "    <tr>\n",
              "      <th>5702</th>\n",
              "      <td>2020-11-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BMFBOVESPA:ITUB4</td>\n",
              "      <td>22.426414</td>\n",
              "      <td>22.629439</td>\n",
              "      <td>22.168728</td>\n",
              "      <td>22.293667</td>\n",
              "      <td>23357573.0</td>\n",
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              "<p>5703 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4330991c-65e8-498a-9dad-522697191404')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4330991c-65e8-498a-9dad-522697191404 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4330991c-65e8-498a-9dad-522697191404');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Estudo de caso #3: Explorando outros parâmetros do merge**"
      ],
      "metadata": {
        "id": "6h3jmnACd-18"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vale3 = yf.download('VALE3.SA', period='max')\n",
        "azul4 = yf.download('AZUL4.SA', period='max')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3M-gJuZLeKuk",
        "outputId": "09171894-e732-4b9b-d266-7318cecc457b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vale3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "4VrfMobHensM",
        "outputId": "43ad7f76-a178-40e6-f1c3-c29f879f3c88"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close   Volume\n",
              "Date                                                                  \n",
              "2000-01-03  3.500000  3.542500  3.500000  3.500000   1.707494   585600\n",
              "2000-01-04  3.466666  3.474166  3.416666  3.416666   1.666839   782400\n",
              "2000-01-05  3.375000  3.416666  3.375000  3.416666   1.666839  1876800\n",
              "2000-01-06  3.416666  3.500000  3.416666  3.416666   1.666839   792000\n",
              "2000-01-07  3.458333  3.559166  3.458333  3.541666   1.727822  5347200"
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              "      <th>2000-01-03</th>\n",
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              "      <td>3.500000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.707494</td>\n",
              "      <td>585600</td>\n",
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              "      <th>2000-01-04</th>\n",
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              "      <th>2000-01-05</th>\n",
              "      <td>3.375000</td>\n",
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              "      <th>2000-01-07</th>\n",
              "      <td>3.458333</td>\n",
              "      <td>3.559166</td>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8bcf5375-736e-4e0f-9497-c2afbad048cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8bcf5375-736e-4e0f-9497-c2afbad048cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "azul4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Vtb8CKoKepAd",
        "outputId": "09ab7036-7feb-42b7-ba76-360dc1669d38"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2017-04-11  21.809999  22.980000  21.770000  22.400000  22.400000  13060500\n",
              "2017-04-12  22.450001  22.990000  22.299999  22.959999  22.959999   1745900\n",
              "2017-04-13  22.969999  23.010000  22.549999  22.990000  22.990000    549600\n",
              "2017-04-17  23.000000  23.000000  22.620001  22.850000  22.850000    249300\n",
              "2017-04-18  22.820000  24.790001  22.600000  23.910000  23.910000   1125100"
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              "      </button>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transformar o índice em uma nova coluna chamada \"Data\""
      ],
      "metadata": {
        "id": "29dQFc94e_bl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vale3['Data'] = vale3.index\n",
        "azul4['Data'] = azul4.index\n",
        "azul4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "d36sx0pPfFf1",
        "outputId": "5a1f2368-befe-4b0f-9c22-8ea7425d6134"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
              "Date                                                                          \n",
              "2017-04-11  21.809999  22.980000  21.770000  22.400000  22.400000  13060500   \n",
              "2017-04-12  22.450001  22.990000  22.299999  22.959999  22.959999   1745900   \n",
              "2017-04-13  22.969999  23.010000  22.549999  22.990000  22.990000    549600   \n",
              "2017-04-17  23.000000  23.000000  22.620001  22.850000  22.850000    249300   \n",
              "2017-04-18  22.820000  24.790001  22.600000  23.910000  23.910000   1125100   \n",
              "\n",
              "                 Data  \n",
              "Date                   \n",
              "2017-04-11 2017-04-11  \n",
              "2017-04-12 2017-04-12  \n",
              "2017-04-13 2017-04-13  \n",
              "2017-04-17 2017-04-17  \n",
              "2017-04-18 2017-04-18  "
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              "      <th>2017-04-11</th>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste exemplo as datas não se sobrepõe, mas o nome das colunas é o mesmo. Vamos juntar apenas a coluna 'Adj Close' de cada um, baseado na coluna 'Data' e definir um sufixo para sabermos diferenciá-las"
      ],
      "metadata": {
        "id": "EViqcsYuerSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_vale3_azul4 = pd.merge(vale3[['Adj Close', 'Data']], azul4[['Adj Close', 'Data']], how='inner', on='Data', suffixes=['_vale3', '_azul3'])\n",
        "df_vale3_azul4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "IsoOX9s5erMd",
        "outputId": "67735ebc-d0cd-4c0b-aa4d-168d47b5e3a6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Adj Close_vale3       Data  Adj Close_azul3\n",
              "0           19.192825 2017-04-11        22.400000\n",
              "1           18.362431 2017-04-12        22.959999\n",
              "2           18.362431 2017-04-13        22.990000\n",
              "3           18.369127 2017-04-17        22.850000\n",
              "4           17.819994 2017-04-18        23.910000\n",
              "...               ...        ...              ...\n",
              "1353        68.250000 2022-09-16        16.799999\n",
              "1354        70.459999 2022-09-19        17.750000\n",
              "1355        69.449997 2022-09-20        17.870001\n",
              "1356        68.449997 2022-09-21        17.490000\n",
              "1357        69.669998 2022-09-22        17.059999\n",
              "\n",
              "[1358 rows x 3 columns]"
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              "      <th>4</th>\n",
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              "<p>1358 rows × 3 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 43
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MlmkgH5t0qLi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}