Projeto_Cryptomoedas.ipynb

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        "\n",
        "# <font color = \t\t#FFD700 > PROJETO EM GRUPO Módulo 5 – Criptomoedas\n",
        "# <font color = \t\t#FFD700 >CONTEXTO\n",
        "\n",
        "### Criptomoedas são moedas digitais descentralizadas baseadas em criptografia, que operam em uma rede blockchain que permite transações seguras e transparentes sem a necessidade de intermediários, como bancos. Elas fazem parte do mundo digital, afetam a economia atual e são consideradas altamente voláteis. Por isso, você e seu squad foram escalados por uma corretora financeira para realizar uma análise exploratória relacionada à série histórica dos valores de criptomoedas.\n",
        "\n",
        "\n",
        "# <font color = \t\t#FFD700 > O QUE É PARA FAZER?\n",
        "\n",
        "### Realizar uma análise exploratória histórica dos valores de criptomoedas. As fontes de dados que serão utilizadas no projeto estão disponíveis no Kaggle.\n",
        "# <font color = \t\t#FFD700 > COMO FAZER?\n",
        "\n",
        "## <font color = \t#FFD700 > Detalhes do projeto\n",
        "### A análise deverá responder às seguintes perguntas:\n",
        " 1. Como se comportaram os valores para todas as criptomoedas? Os valores tiveram uma tendência de queda ou de aumento?\n",
        " 2. Quais os valores médios para todas as criptomoedas?\n",
        " 3. Em quais anos houve maiores quedas e valorizações?\n",
        " 4. Existe alguma tendência de aumento ou queda dos valores pelo dia da semana?\n",
        " 5. Qual moeda se mostra mais interessante em relação à valorização pela análise da série histórica?\n",
        " 6. Qual moeda se mostra menos interessante em relação à valorização pela análise da série histórica?\n",
        " 7. Existe correlação entre os valores para todas as criptomoedas?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## <font color = \t\t#FFD700 > Requisitos\n",
        "\n",
        "\n",
        "- Utilizar, VS Code, Jupyter Notebook ou Colab;\n",
        "- Realizar a limpeza dos dados;\n",
        "- Realizar análise exploratória;\n",
        "- Importar os arquivos resultantes para um banco de dados (ex.: Postgres);\n",
        "- Fazer a conexão do banco de dados com a ferramenta de visualização de dados (Tableau, Power Bi ou Looker);\n",
        "- Escolher 10 criptomoedas, do conjunto disponibilizado, que serão utilizadas por vocês nesse projeto.\n",
        "- Gerar, no mínimo, cinco (5) gráficos para a apresentação dos resultados;\n",
        "- Focar no storytelling para criar a apresentação;\n",
        "- Responder cada uma das perguntas com a visualização mais adequada;\n",
        "- O notebook utilizado na análise deve estar organizado, com descrições do passo a passo da análise em markdown, apresentação dos resultados e insights gerados;\n",
        "- Levantar mais duas (2) perguntas e respondê-las da forma que achar mais adequada;\n",
        "- O projeto desenvolvido deverá ser disponibilizado em repositório no GitHub;\n",
        "- O projeto precisa possuir uma evidência de entrega, ou seja, um pequeno parágrafo com uma explicação do que foi feito no projeto e a defesa das escolhas tomadas.\n",
        "\n",
        "# <font color = \t\t#FFD700 > Moedas selecionadas:\n",
        "- Aave\n",
        "- BinanceCripto\n",
        "- Bitcoin\n",
        "- Chainlink\n",
        "- Cosmos\n",
        "- EOS\n",
        "- Ethereum\n",
        "- Polkadot\n",
        "- USDCoin\n",
        "- XRP"
      ],
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        "# Importanto bibliotecas\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
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        "# Extrair arquivo\n",
        "aave = pd.read_csv(\"coin_Aave.csv\")\n",
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              "     SNo  Name Symbol                 Date        High         Low  \\\n",
              "0      1  Aave   AAVE  2020-10-05 23:59:59   55.112358   49.787900   \n",
              "1      2  Aave   AAVE  2020-10-06 23:59:59   53.402270   40.734578   \n",
              "2      3  Aave   AAVE  2020-10-07 23:59:59   42.408314   35.970690   \n",
              "3      4  Aave   AAVE  2020-10-08 23:59:59   44.902511   36.696057   \n",
              "4      5  Aave   AAVE  2020-10-09 23:59:59   47.569533   43.291776   \n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c9a2a6d-b121-4cbe-b142-b9175befa992 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binance = pd.read_csv(\"coin_BinanceCoin.csv\")\n",
        "binance"
      ],
      "metadata": {
        "id": "VFtyb0bq7ctE",
        "outputId": "b47f6e47-5732-4ab2-8a21-46492e8feb8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo          Name Symbol                 Date        High         Low  \\\n",
              "0        1  Binance Coin    BNB  2017-07-26 23:59:59    0.109013    0.099266   \n",
              "1        2  Binance Coin    BNB  2017-07-27 23:59:59    0.108479    0.100888   \n",
              "2        3  Binance Coin    BNB  2017-07-28 23:59:59    0.109019    0.101473   \n",
              "3        4  Binance Coin    BNB  2017-07-29 23:59:59    0.111264    0.101108   \n",
              "4        5  Binance Coin    BNB  2017-07-30 23:59:59    0.108138    0.103162   \n",
              "...    ...           ...    ...                  ...         ...         ...   \n",
              "1437  1438  Binance Coin    BNB  2021-07-02 23:59:59  290.621683  277.350302   \n",
              "1438  1439  Binance Coin    BNB  2021-07-03 23:59:59  302.605867  283.434010   \n",
              "1439  1440  Binance Coin    BNB  2021-07-04 23:59:59  314.713019  292.787370   \n",
              "1440  1441  Binance Coin    BNB  2021-07-05 23:59:59  307.684855  293.523465   \n",
              "1441  1442  Binance Coin    BNB  2021-07-06 23:59:59  321.520965  302.195584   \n",
              "\n",
              "            Open       Close        Volume     Marketcap  \n",
              "0       0.105893    0.105138  2.003950e+05  1.051380e+07  \n",
              "1       0.105108    0.107737  3.444990e+05  1.077370e+07  \n",
              "2       0.107632    0.104067  3.425680e+05  1.040670e+07  \n",
              "3       0.104782    0.107811  3.402180e+05  1.078110e+07  \n",
              "4       0.107935    0.106414  2.242610e+05  1.064140e+07  \n",
              "...          ...         ...           ...           ...  \n",
              "1437  287.754456  287.423094  1.133633e+09  4.410016e+10  \n",
              "1438  287.215592  298.237117  1.113777e+09  4.575938e+10  \n",
              "1439  298.113557  307.732096  1.387396e+09  4.721623e+10  \n",
              "1440  307.684855  302.377980  1.504870e+09  4.639473e+10  \n",
              "1441  302.195584  320.934802  2.203265e+09  4.924196e+10  \n",
              "\n",
              "[1442 rows x 10 columns]"
            ],
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNo</th>\n",
              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
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              "      <th>Marketcap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2017-07-26 23:59:59</td>\n",
              "      <td>0.109013</td>\n",
              "      <td>0.099266</td>\n",
              "      <td>0.105893</td>\n",
              "      <td>0.105138</td>\n",
              "      <td>2.003950e+05</td>\n",
              "      <td>1.051380e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2017-07-27 23:59:59</td>\n",
              "      <td>0.108479</td>\n",
              "      <td>0.100888</td>\n",
              "      <td>0.105108</td>\n",
              "      <td>0.107737</td>\n",
              "      <td>3.444990e+05</td>\n",
              "      <td>1.077370e+07</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2017-07-28 23:59:59</td>\n",
              "      <td>0.109019</td>\n",
              "      <td>0.101473</td>\n",
              "      <td>0.107632</td>\n",
              "      <td>0.104067</td>\n",
              "      <td>3.425680e+05</td>\n",
              "      <td>1.040670e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2017-07-29 23:59:59</td>\n",
              "      <td>0.111264</td>\n",
              "      <td>0.101108</td>\n",
              "      <td>0.104782</td>\n",
              "      <td>0.107811</td>\n",
              "      <td>3.402180e+05</td>\n",
              "      <td>1.078110e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2017-07-30 23:59:59</td>\n",
              "      <td>0.108138</td>\n",
              "      <td>0.103162</td>\n",
              "      <td>0.107935</td>\n",
              "      <td>0.106414</td>\n",
              "      <td>2.242610e+05</td>\n",
              "      <td>1.064140e+07</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1437</th>\n",
              "      <td>1438</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>290.621683</td>\n",
              "      <td>277.350302</td>\n",
              "      <td>287.754456</td>\n",
              "      <td>287.423094</td>\n",
              "      <td>1.133633e+09</td>\n",
              "      <td>4.410016e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1438</th>\n",
              "      <td>1439</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>302.605867</td>\n",
              "      <td>283.434010</td>\n",
              "      <td>287.215592</td>\n",
              "      <td>298.237117</td>\n",
              "      <td>1.113777e+09</td>\n",
              "      <td>4.575938e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1439</th>\n",
              "      <td>1440</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>314.713019</td>\n",
              "      <td>292.787370</td>\n",
              "      <td>298.113557</td>\n",
              "      <td>307.732096</td>\n",
              "      <td>1.387396e+09</td>\n",
              "      <td>4.721623e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1440</th>\n",
              "      <td>1441</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>307.684855</td>\n",
              "      <td>293.523465</td>\n",
              "      <td>307.684855</td>\n",
              "      <td>302.377980</td>\n",
              "      <td>1.504870e+09</td>\n",
              "      <td>4.639473e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1441</th>\n",
              "      <td>1442</td>\n",
              "      <td>Binance Coin</td>\n",
              "      <td>BNB</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>321.520965</td>\n",
              "      <td>302.195584</td>\n",
              "      <td>302.195584</td>\n",
              "      <td>320.934802</td>\n",
              "      <td>2.203265e+09</td>\n",
              "      <td>4.924196e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1442 rows × 10 columns</p>\n",
              "</div>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-be43a997-485e-44ef-98cd-ae7ee9fc0feb');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bitcoin = pd.read_csv(\"coin_Bitcoin.csv\")\n",
        "bitcoin"
      ],
      "metadata": {
        "id": "hRKuorz5JfMd",
        "outputId": "a8bc97cd-613d-4909-998a-4f047c635dc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo     Name Symbol                 Date          High           Low  \\\n",
              "0        1  Bitcoin    BTC  2013-04-29 23:59:59    147.488007    134.000000   \n",
              "1        2  Bitcoin    BTC  2013-04-30 23:59:59    146.929993    134.050003   \n",
              "2        3  Bitcoin    BTC  2013-05-01 23:59:59    139.889999    107.720001   \n",
              "3        4  Bitcoin    BTC  2013-05-02 23:59:59    125.599998     92.281898   \n",
              "4        5  Bitcoin    BTC  2013-05-03 23:59:59    108.127998     79.099998   \n",
              "...    ...      ...    ...                  ...           ...           ...   \n",
              "2986  2987  Bitcoin    BTC  2021-07-02 23:59:59  33939.588699  32770.680780   \n",
              "2987  2988  Bitcoin    BTC  2021-07-03 23:59:59  34909.259899  33402.696536   \n",
              "2988  2989  Bitcoin    BTC  2021-07-04 23:59:59  35937.567147  34396.477458   \n",
              "2989  2990  Bitcoin    BTC  2021-07-05 23:59:59  35284.344430  33213.661034   \n",
              "2990  2991  Bitcoin    BTC  2021-07-06 23:59:59  35038.536363  33599.916169   \n",
              "\n",
              "              Open         Close        Volume     Marketcap  \n",
              "0       134.444000    144.539993  0.000000e+00  1.603769e+09  \n",
              "1       144.000000    139.000000  0.000000e+00  1.542813e+09  \n",
              "2       139.000000    116.989998  0.000000e+00  1.298955e+09  \n",
              "3       116.379997    105.209999  0.000000e+00  1.168517e+09  \n",
              "4       106.250000     97.750000  0.000000e+00  1.085995e+09  \n",
              "...            ...           ...           ...           ...  \n",
              "2986  33549.600177  33897.048590  3.872897e+10  6.354508e+11  \n",
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              "2989  35284.344430  33746.002456  2.672155e+10  6.326962e+11  \n",
              "2990  33723.509655  34235.193451  2.650126e+10  6.418992e+11  \n",
              "\n",
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              "    <tr>\n",
              "      <th>2986</th>\n",
              "      <td>2987</td>\n",
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              "      <td>BTC</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>33939.588699</td>\n",
              "      <td>32770.680780</td>\n",
              "      <td>33549.600177</td>\n",
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              "      <td>3.872897e+10</td>\n",
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              "      <td>BTC</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>35937.567147</td>\n",
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              "      <td>6.418992e+11</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chainlink = pd.read_csv(\"coin_ChainLink.csv\")\n",
        "chainlink"
      ],
      "metadata": {
        "id": "w_sfMvLvJmu0",
        "outputId": "a1393807-cd86-4ed9-a04a-341c1edbe650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo       Name Symbol                 Date       High        Low  \\\n",
              "0        1  Chainlink   LINK  2017-09-21 23:59:59   0.207892   0.155292   \n",
              "1        2  Chainlink   LINK  2017-09-22 23:59:59   0.181675   0.139869   \n",
              "2        3  Chainlink   LINK  2017-09-23 23:59:59   0.157440   0.126297   \n",
              "3        4  Chainlink   LINK  2017-09-24 23:59:59   0.190250   0.130275   \n",
              "4        5  Chainlink   LINK  2017-09-25 23:59:59   0.182637   0.158580   \n",
              "...    ...        ...    ...                  ...        ...        ...   \n",
              "1380  1381  Chainlink   LINK  2021-07-02 23:59:59  18.520256  17.305501   \n",
              "1381  1382  Chainlink   LINK  2021-07-03 23:59:59  18.886003  17.898075   \n",
              "1382  1383  Chainlink   LINK  2021-07-04 23:59:59  19.894503  18.028957   \n",
              "1383  1384  Chainlink   LINK  2021-07-05 23:59:59  19.246891  17.814752   \n",
              "1384  1385  Chainlink   LINK  2021-07-06 23:59:59  20.240997  18.351877   \n",
              "\n",
              "           Open      Close        Volume     Marketcap  \n",
              "0      0.189132   0.169680  2.126270e+06  5.938800e+07  \n",
              "1      0.169826   0.150503  1.423220e+06  5.267605e+07  \n",
              "2      0.149562   0.131129  1.186670e+06  4.589515e+07  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cosmos = pd.read_csv(\"coin_Cosmos.csv\")\n",
        "cosmos"
      ],
      "metadata": {
        "id": "siD4574HJuAF",
        "outputId": "589d8eb4-d33c-4354-bf51-e1a82f5c28b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SNo    Name Symbol                 Date       High        Low       Open  \\\n",
              "0      1  Cosmos   ATOM  2019-03-15 23:59:59   7.715249   6.432468   6.633174   \n",
              "1      2  Cosmos   ATOM  2019-03-16 23:59:59   8.305615   6.694531   7.507990   \n",
              "2      3  Cosmos   ATOM  2019-03-17 23:59:59   7.357443   4.727895   7.357443   \n",
              "3      4  Cosmos   ATOM  2019-03-18 23:59:59   5.229982   4.828242   4.828242   \n",
              "4      5  Cosmos   ATOM  2019-03-19 23:59:59   5.206172   4.794501   5.099978   \n",
              "..   ...     ...    ...                  ...        ...        ...        ...   \n",
              "840  841  Cosmos   ATOM  2021-07-02 23:59:59  11.764925  10.543935  11.048080   \n",
              "841  842  Cosmos   ATOM  2021-07-03 23:59:59  12.198276  11.540298  11.791034   \n",
              "842  843  Cosmos   ATOM  2021-07-04 23:59:59  12.433965  11.520693  11.799601   \n",
              "843  844  Cosmos   ATOM  2021-07-05 23:59:59  13.968271  11.955753  12.320437   \n",
              "844  845  Cosmos   ATOM  2021-07-06 23:59:59  14.020420  12.823983  13.558099   \n",
              "\n",
              "         Close        Volume     Marketcap  \n",
              "0     7.504351  6.057301e+06  0.000000e+00  \n",
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              "844  13.159371  2.537822e+08  2.865093e+09  \n",
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              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2019-03-15 23:59:59</td>\n",
              "      <td>7.715249</td>\n",
              "      <td>6.432468</td>\n",
              "      <td>6.633174</td>\n",
              "      <td>7.504351</td>\n",
              "      <td>6.057301e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2019-03-16 23:59:59</td>\n",
              "      <td>8.305615</td>\n",
              "      <td>6.694531</td>\n",
              "      <td>7.507990</td>\n",
              "      <td>7.383882</td>\n",
              "      <td>3.477393e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2019-03-17 23:59:59</td>\n",
              "      <td>7.357443</td>\n",
              "      <td>4.727895</td>\n",
              "      <td>7.357443</td>\n",
              "      <td>4.776164</td>\n",
              "      <td>2.653565e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2019-03-18 23:59:59</td>\n",
              "      <td>5.229982</td>\n",
              "      <td>4.828242</td>\n",
              "      <td>4.828242</td>\n",
              "      <td>5.110341</td>\n",
              "      <td>2.567201e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2019-03-19 23:59:59</td>\n",
              "      <td>5.206172</td>\n",
              "      <td>4.794501</td>\n",
              "      <td>5.099978</td>\n",
              "      <td>4.821883</td>\n",
              "      <td>3.891084e+06</td>\n",
              "      <td>0.000000e+00</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>840</th>\n",
              "      <td>841</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>11.764925</td>\n",
              "      <td>10.543935</td>\n",
              "      <td>11.048080</td>\n",
              "      <td>11.764925</td>\n",
              "      <td>1.891418e+08</td>\n",
              "      <td>2.559039e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>842</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>12.198276</td>\n",
              "      <td>11.540298</td>\n",
              "      <td>11.791034</td>\n",
              "      <td>11.817927</td>\n",
              "      <td>1.551317e+08</td>\n",
              "      <td>2.571184e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842</th>\n",
              "      <td>843</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>12.433965</td>\n",
              "      <td>11.520693</td>\n",
              "      <td>11.799601</td>\n",
              "      <td>12.263548</td>\n",
              "      <td>1.449177e+08</td>\n",
              "      <td>2.668775e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>843</th>\n",
              "      <td>844</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>13.968271</td>\n",
              "      <td>11.955753</td>\n",
              "      <td>12.320437</td>\n",
              "      <td>13.548475</td>\n",
              "      <td>4.489358e+08</td>\n",
              "      <td>2.949102e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>844</th>\n",
              "      <td>845</td>\n",
              "      <td>Cosmos</td>\n",
              "      <td>ATOM</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>14.020420</td>\n",
              "      <td>12.823983</td>\n",
              "      <td>13.558099</td>\n",
              "      <td>13.159371</td>\n",
              "      <td>2.537822e+08</td>\n",
              "      <td>2.865093e+09</td>\n",
              "    </tr>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eos = pd.read_csv(\"coin_EOS.csv\")\n",
        "eos"
      ],
      "metadata": {
        "id": "SKJm6qROJ09d",
        "outputId": "0850ab8a-8faa-4870-ae37-39f4be137ce0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo Name Symbol                 Date      High       Low      Open  \\\n",
              "0        1  EOS    EOS  2017-07-02 23:59:59  2.877510  0.822648  0.996521   \n",
              "1        2  EOS    EOS  2017-07-03 23:59:59  5.395970  2.632310  2.717390   \n",
              "2        3  EOS    EOS  2017-07-04 23:59:59  4.191240  2.933450  4.098010   \n",
              "3        4  EOS    EOS  2017-07-05 23:59:59  3.523500  2.730130  3.356000   \n",
              "4        5  EOS    EOS  2017-07-06 23:59:59  3.867470  3.007830  3.014260   \n",
              "...    ...  ...    ...                  ...       ...       ...       ...   \n",
              "1461  1462  EOS    EOS  2021-07-02 23:59:59  4.000508  3.751986  3.930561   \n",
              "1462  1463  EOS    EOS  2021-07-03 23:59:59  4.060837  3.859586  3.926595   \n",
              "1463  1464  EOS    EOS  2021-07-04 23:59:59  4.181861  3.952532  4.048645   \n",
              "1464  1465  EOS    EOS  2021-07-05 23:59:59  4.061888  3.773130  4.061888   \n",
              "1465  1466  EOS    EOS  2021-07-06 23:59:59  4.004299  3.797596  3.814236   \n",
              "\n",
              "         Close        Volume     Marketcap  \n",
              "0     2.710050  3.204520e+08  0.000000e+00  \n",
              "1     4.086640  4.149500e+08  6.549307e+08  \n",
              "2     3.372000  2.185590e+08  5.501552e+08  \n",
              "3     3.003230  1.243390e+08  5.006264e+08  \n",
              "4     3.361330  1.774630e+08  5.683615e+08  \n",
              "...        ...           ...           ...  \n",
              "1461  3.934325  1.014258e+09  3.756672e+09  \n",
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              "1463  4.059930  8.086025e+08  3.876834e+09  \n",
              "1464  3.819266  8.004033e+08  3.647131e+09  \n",
              "1465  3.882325  6.967875e+08  3.707457e+09  \n",
              "\n",
              "[1466 rows x 10 columns]"
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              "      <th>SNo</th>\n",
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              "      <td>1.243390e+08</td>\n",
              "      <td>5.006264e+08</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2017-07-06 23:59:59</td>\n",
              "      <td>3.867470</td>\n",
              "      <td>3.007830</td>\n",
              "      <td>3.014260</td>\n",
              "      <td>3.361330</td>\n",
              "      <td>1.774630e+08</td>\n",
              "      <td>5.683615e+08</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461</th>\n",
              "      <td>1462</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>4.000508</td>\n",
              "      <td>3.751986</td>\n",
              "      <td>3.930561</td>\n",
              "      <td>3.934325</td>\n",
              "      <td>1.014258e+09</td>\n",
              "      <td>3.756672e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1462</th>\n",
              "      <td>1463</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>4.060837</td>\n",
              "      <td>3.859586</td>\n",
              "      <td>3.926595</td>\n",
              "      <td>4.046421</td>\n",
              "      <td>7.302703e+08</td>\n",
              "      <td>3.863820e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1463</th>\n",
              "      <td>1464</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>4.181861</td>\n",
              "      <td>3.952532</td>\n",
              "      <td>4.048645</td>\n",
              "      <td>4.059930</td>\n",
              "      <td>8.086025e+08</td>\n",
              "      <td>3.876834e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1464</th>\n",
              "      <td>1465</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>4.061888</td>\n",
              "      <td>3.773130</td>\n",
              "      <td>4.061888</td>\n",
              "      <td>3.819266</td>\n",
              "      <td>8.004033e+08</td>\n",
              "      <td>3.647131e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>1466</td>\n",
              "      <td>EOS</td>\n",
              "      <td>EOS</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>4.004299</td>\n",
              "      <td>3.797596</td>\n",
              "      <td>3.814236</td>\n",
              "      <td>3.882325</td>\n",
              "      <td>6.967875e+08</td>\n",
              "      <td>3.707457e+09</td>\n",
              "    </tr>\n",
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          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ethereum = pd.read_csv(\"coin_Ethereum.csv\")\n",
        "ethereum"
      ],
      "metadata": {
        "id": "C9OGEBVxJ6_s",
        "outputId": "81881640-42d3-4e34-8465-29af3bbeba85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo      Name Symbol                 Date         High          Low  \\\n",
              "0        1  Ethereum    ETH  2015-08-08 23:59:59     2.798810     0.714725   \n",
              "1        2  Ethereum    ETH  2015-08-09 23:59:59     0.879810     0.629191   \n",
              "2        3  Ethereum    ETH  2015-08-10 23:59:59     0.729854     0.636546   \n",
              "3        4  Ethereum    ETH  2015-08-11 23:59:59     1.131410     0.663235   \n",
              "4        5  Ethereum    ETH  2015-08-12 23:59:59     1.289940     0.883608   \n",
              "...    ...       ...    ...                  ...          ...          ...   \n",
              "2155  2156  Ethereum    ETH  2021-07-02 23:59:59  2155.596496  2021.824808   \n",
              "2156  2157  Ethereum    ETH  2021-07-03 23:59:59  2237.567155  2117.590013   \n",
              "2157  2158  Ethereum    ETH  2021-07-04 23:59:59  2384.286857  2190.837703   \n",
              "2158  2159  Ethereum    ETH  2021-07-05 23:59:59  2321.922836  2163.041394   \n",
              "2159  2160  Ethereum    ETH  2021-07-06 23:59:59  2346.294874  2197.919385   \n",
              "\n",
              "             Open        Close        Volume     Marketcap  \n",
              "0        2.793760     0.753325  6.741880e+05  4.548689e+07  \n",
              "1        0.706136     0.701897  5.321700e+05  4.239957e+07  \n",
              "2        0.713989     0.708448  4.052830e+05  4.281836e+07  \n",
              "3        0.708087     1.067860  1.463100e+06  6.456929e+07  \n",
              "4        1.058750     1.217440  2.150620e+06  7.364501e+07  \n",
              "...           ...          ...           ...           ...  \n",
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              "2158  2321.922836  2198.582464  2.010379e+10  2.562978e+11  \n",
              "2159  2197.919385  2324.679449  2.089186e+10  2.710286e+11  \n",
              "\n",
              "[2160 rows x 10 columns]"
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>2015-08-12 23:59:59</td>\n",
              "      <td>1.289940</td>\n",
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              "      <td>ETH</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>2155.596496</td>\n",
              "      <td>2021.824808</td>\n",
              "      <td>2109.892677</td>\n",
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              "      <td>2117.590013</td>\n",
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              "      <td>2226.114282</td>\n",
              "      <td>1.743336e+10</td>\n",
              "      <td>2.594475e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2157</th>\n",
              "      <td>2158</td>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>2384.286857</td>\n",
              "      <td>2190.837703</td>\n",
              "      <td>2226.550382</td>\n",
              "      <td>2321.724112</td>\n",
              "      <td>1.878711e+10</td>\n",
              "      <td>2.706217e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2158</th>\n",
              "      <td>2159</td>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>2321.922836</td>\n",
              "      <td>2163.041394</td>\n",
              "      <td>2321.922836</td>\n",
              "      <td>2198.582464</td>\n",
              "      <td>2.010379e+10</td>\n",
              "      <td>2.562978e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2159</th>\n",
              "      <td>2160</td>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-07-06 23:59:59</td>\n",
              "      <td>2346.294874</td>\n",
              "      <td>2197.919385</td>\n",
              "      <td>2197.919385</td>\n",
              "      <td>2324.679449</td>\n",
              "      <td>2.089186e+10</td>\n",
              "      <td>2.710286e+11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "polkadot = pd.read_csv(\"coin_Polkadot.csv\")\n",
        "polkadot"
      ],
      "metadata": {
        "id": "nWUkOB1pKDYd",
        "outputId": "911f4f82-7744-4b63-ebb3-112b7e3f37a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SNo      Name Symbol                 Date       High        Low  \\\n",
              "0      1  Polkadot    DOT  2020-08-21 23:59:59   3.090093   2.730919   \n",
              "1      2  Polkadot    DOT  2020-08-22 23:59:59   4.510613   2.816762   \n",
              "2      3  Polkadot    DOT  2020-08-23 23:59:59   4.487058   3.716271   \n",
              "3      4  Polkadot    DOT  2020-08-24 23:59:59   4.783158   3.812670   \n",
              "4      5  Polkadot    DOT  2020-08-25 23:59:59   5.760775   4.501223   \n",
              "..   ...       ...    ...                  ...        ...        ...   \n",
              "315  316  Polkadot    DOT  2021-07-02 23:59:59  15.469187  14.675115   \n",
              "316  317  Polkadot    DOT  2021-07-03 23:59:59  15.776978  15.068263   \n",
              "317  318  Polkadot    DOT  2021-07-04 23:59:59  16.579445  15.221813   \n",
              "318  319  Polkadot    DOT  2021-07-05 23:59:59  15.996540  14.788740   \n",
              "319  320  Polkadot    DOT  2021-07-06 23:59:59  16.162344  15.210810   \n",
              "\n",
              "          Open      Close        Volume     Marketcap  \n",
              "0     2.896923   2.875028  4.931314e+07  0.000000e+00  \n",
              "1     2.878570   4.484690  4.858011e+08  0.000000e+00  \n",
              "2     4.487058   3.967066  3.207495e+08  0.000000e+00  \n",
              "3     3.987413   4.602614  4.076902e+08  0.000000e+00  \n",
              "4     4.584953   5.523393  8.988475e+08  0.000000e+00  \n",
              "..         ...        ...           ...           ...  \n",
              "315  15.219061  15.340774  7.531948e+08  1.468349e+10  \n",
              "316  15.321934  15.553716  7.112351e+08  1.489035e+10  \n",
              "317  15.553584  16.014026  7.520759e+08  1.533547e+10  \n",
              "318  15.996540  15.235582  8.448446e+08  1.459323e+10  \n",
              "319  15.240785  16.143564  1.001573e+09  1.546772e+10  \n",
              "\n",
              "[320 rows x 10 columns]"
            ],
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              "      <th>SNo</th>\n",
              "      <th>Name</th>\n",
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              "      <td>3</td>\n",
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              "      <td>2020-08-24 23:59:59</td>\n",
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              "      <td>DOT</td>\n",
              "      <td>2020-08-25 23:59:59</td>\n",
              "      <td>5.760775</td>\n",
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              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>316</td>\n",
              "      <td>Polkadot</td>\n",
              "      <td>DOT</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>15.469187</td>\n",
              "      <td>14.675115</td>\n",
              "      <td>15.219061</td>\n",
              "      <td>15.340774</td>\n",
              "      <td>7.531948e+08</td>\n",
              "      <td>1.468349e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>317</td>\n",
              "      <td>Polkadot</td>\n",
              "      <td>DOT</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>15.776978</td>\n",
              "      <td>15.068263</td>\n",
              "      <td>15.321934</td>\n",
              "      <td>15.553716</td>\n",
              "      <td>7.112351e+08</td>\n",
              "      <td>1.489035e+10</td>\n",
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              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>318</td>\n",
              "      <td>Polkadot</td>\n",
              "      <td>DOT</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>16.579445</td>\n",
              "      <td>15.221813</td>\n",
              "      <td>15.553584</td>\n",
              "      <td>16.014026</td>\n",
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              "      <td>15.996540</td>\n",
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              "    <tr>\n",
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              "      <td>1.001573e+09</td>\n",
              "      <td>1.546772e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>320 rows × 10 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "usd = pd.read_csv(\"coin_USDCoin.csv\")\n",
        "usd"
      ],
      "metadata": {
        "id": "hCtv3_b57qpP",
        "outputId": "5fb64622-8347-44c7-89f5-78bc12472e6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo      Name Symbol                 Date      High       Low  \\\n",
              "0        1  USD Coin   USDC  2018-10-09 23:59:59  1.008330  1.000030   \n",
              "1        2  USD Coin   USDC  2018-10-10 23:59:59  1.019080  1.003970   \n",
              "2        3  USD Coin   USDC  2018-10-11 23:59:59  1.031230  1.001730   \n",
              "3        4  USD Coin   USDC  2018-10-12 23:59:59  1.018710  1.001140   \n",
              "4        5  USD Coin   USDC  2018-10-13 23:59:59  1.020290  1.003550   \n",
              "...    ...       ...    ...                  ...       ...       ...   \n",
              "997    998  USD Coin   USDC  2021-07-02 23:59:59  1.000916  0.999966   \n",
              "998    999  USD Coin   USDC  2021-07-03 23:59:59  1.000670  0.999925   \n",
              "999   1000  USD Coin   USDC  2021-07-04 23:59:59  1.000187  0.998901   \n",
              "1000  1001  USD Coin   USDC  2021-07-05 23:59:59  1.000839  0.999459   \n",
              "1001  1002  USD Coin   USDC  2021-07-06 23:59:59  1.000731  0.999662   \n",
              "\n",
              "          Open     Close        Volume     Marketcap  \n",
              "0     1.004270  1.006860  1.088030e+05  0.000000e+00  \n",
              "1     1.004940  1.009620  7.117830e+05  0.000000e+00  \n",
              "2     1.009940  1.009780  4.177290e+06  0.000000e+00  \n",
              "3     1.008610  1.012720  1.322240e+06  0.000000e+00  \n",
              "4     1.014450  1.007430  6.985070e+05  0.000000e+00  \n",
              "...        ...       ...           ...           ...  \n",
              "997   1.000177  1.000035  1.787896e+09  2.539775e+10  \n",
              "998   1.000048  0.999984  1.491017e+09  2.550437e+10  \n",
              "999   0.999956  0.999500  1.578667e+09  2.551172e+10  \n",
              "1000  0.999565  1.000528  1.887496e+09  2.554724e+10  \n",
              "1001  1.000501  1.000059  2.312602e+09  2.567322e+10  \n",
              "\n",
              "[1002 rows x 10 columns]"
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              "      <th>SNo</th>\n",
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              "      <td>1</td>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>2018-10-09 23:59:59</td>\n",
              "      <td>1.008330</td>\n",
              "      <td>1.000030</td>\n",
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              "      <td>USDC</td>\n",
              "      <td>2018-10-10 23:59:59</td>\n",
              "      <td>1.019080</td>\n",
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              "      <td>USD Coin</td>\n",
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              "      <th>3</th>\n",
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              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>2018-10-12 23:59:59</td>\n",
              "      <td>1.018710</td>\n",
              "      <td>1.001140</td>\n",
              "      <td>1.008610</td>\n",
              "      <td>1.012720</td>\n",
              "      <td>1.322240e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>2018-10-13 23:59:59</td>\n",
              "      <td>1.020290</td>\n",
              "      <td>1.003550</td>\n",
              "      <td>1.014450</td>\n",
              "      <td>1.007430</td>\n",
              "      <td>6.985070e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
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              "      <th>997</th>\n",
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              "      <td>USDC</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>1.000916</td>\n",
              "      <td>0.999966</td>\n",
              "      <td>1.000177</td>\n",
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              "      <td>1.787896e+09</td>\n",
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              "      <th>998</th>\n",
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              "      <td>USDC</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>1.000670</td>\n",
              "      <td>0.999925</td>\n",
              "      <td>1.000048</td>\n",
              "      <td>0.999984</td>\n",
              "      <td>1.491017e+09</td>\n",
              "      <td>2.550437e+10</td>\n",
              "    </tr>\n",
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              "      <th>999</th>\n",
              "      <td>1000</td>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>1.000187</td>\n",
              "      <td>0.998901</td>\n",
              "      <td>0.999956</td>\n",
              "      <td>0.999500</td>\n",
              "      <td>1.578667e+09</td>\n",
              "      <td>2.551172e+10</td>\n",
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              "      <td>2021-07-05 23:59:59</td>\n",
              "      <td>1.000839</td>\n",
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              "      <td>1.000528</td>\n",
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              "      <td>1.000059</td>\n",
              "      <td>2.312602e+09</td>\n",
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              "<p>1002 rows × 10 columns</p>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2423c97b-8a53-49a7-8d2a-a7025d1983f4');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xrp = pd.read_csv(\"coin_XRP.csv\")\n",
        "xrp"
      ],
      "metadata": {
        "id": "YiRfahevJYBN",
        "outputId": "2fc1e811-65cd-4ddb-82c0-288ce59f1b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SNo Name Symbol                 Date      High       Low      Open  \\\n",
              "0        1  XRP    XRP  2013-08-05 23:59:59  0.005980  0.005613  0.005875   \n",
              "1        2  XRP    XRP  2013-08-06 23:59:59  0.005661  0.004629  0.005637   \n",
              "2        3  XRP    XRP  2013-08-07 23:59:59  0.004682  0.004333  0.004669   \n",
              "3        4  XRP    XRP  2013-08-08 23:59:59  0.004424  0.004175  0.004397   \n",
              "4        5  XRP    XRP  2013-08-09 23:59:59  0.004367  0.004253  0.004257   \n",
              "...    ...  ...    ...                  ...       ...       ...       ...   \n",
              "2888  2889  XRP    XRP  2021-07-02 23:59:59  0.667287  0.634726  0.659890   \n",
              "2889  2890  XRP    XRP  2021-07-03 23:59:59  0.683677  0.644653  0.655639   \n",
              "2890  2891  XRP    XRP  2021-07-04 23:59:59  0.707783  0.665802  0.673218   \n",
              "2891  2892  XRP    XRP  2021-07-05 23:59:59  0.695653  0.648492  0.695653   \n",
              "2892  2893  XRP    XRP  2021-07-06 23:59:59  0.679923  0.652676  0.653055   \n",
              "\n",
              "         Close        Volume     Marketcap  \n",
              "0     0.005613  0.000000e+00  4.387916e+07  \n",
              "1     0.004680  0.000000e+00  3.659101e+07  \n",
              "2     0.004417  0.000000e+00  3.453412e+07  \n",
              "3     0.004254  0.000000e+00  3.325863e+07  \n",
              "4     0.004291  0.000000e+00  3.354750e+07  \n",
              "...        ...           ...           ...  \n",
              "2888  0.656763  2.061607e+09  3.030759e+10  \n",
              "2889  0.672888  1.872820e+09  3.105172e+10  \n",
              "2890  0.694945  1.885242e+09  3.206960e+10  \n",
              "2891  0.654300  2.076373e+09  3.019395e+10  \n",
              "2892  0.665402  1.938959e+09  3.072284e+10  \n",
              "\n",
              "[2893 rows x 10 columns]"
            ],
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              "      <th>SNo</th>\n",
              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
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              "      <td>1</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
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              "      <th>1</th>\n",
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              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2013-08-06 23:59:59</td>\n",
              "      <td>0.005661</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2013-08-07 23:59:59</td>\n",
              "      <td>0.004682</td>\n",
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              "      <td>0.000000e+00</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2013-08-08 23:59:59</td>\n",
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              "      <td>0.004175</td>\n",
              "      <td>0.004397</td>\n",
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              "      <td>3.325863e+07</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2013-08-09 23:59:59</td>\n",
              "      <td>0.004367</td>\n",
              "      <td>0.004253</td>\n",
              "      <td>0.004257</td>\n",
              "      <td>0.004291</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>2888</th>\n",
              "      <td>2889</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>0.667287</td>\n",
              "      <td>0.634726</td>\n",
              "      <td>0.659890</td>\n",
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              "      <td>3.030759e+10</td>\n",
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              "      <th>2889</th>\n",
              "      <td>2890</td>\n",
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              "      <td>XRP</td>\n",
              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>0.683677</td>\n",
              "      <td>0.644653</td>\n",
              "      <td>0.655639</td>\n",
              "      <td>0.672888</td>\n",
              "      <td>1.872820e+09</td>\n",
              "      <td>3.105172e+10</td>\n",
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              "    <tr>\n",
              "      <th>2890</th>\n",
              "      <td>2891</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>0.707783</td>\n",
              "      <td>0.665802</td>\n",
              "      <td>0.673218</td>\n",
              "      <td>0.694945</td>\n",
              "      <td>1.885242e+09</td>\n",
              "      <td>3.206960e+10</td>\n",
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              "      <td>3.019395e+10</td>\n",
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              "      <td>2893</td>\n",
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              "      <td>1.938959e+09</td>\n",
              "      <td>3.072284e+10</td>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-038c0d44-6777-45de-913f-0e2efe879eec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenando todas as tabelas de criptomoedas\n",
        "crypto_coins = pd.concat([aave, binance, bitcoin, chainlink, cosmos, eos, ethereum, polkadot, usd, xrp], ignore_index= True)\n",
        "crypto_coins"
      ],
      "metadata": {
        "id": "V5DmWliZKn3P",
        "outputId": "3731aa9b-6a5d-4ce1-daf5-5c449c8a0787",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SNo  Name Symbol                 Date       High        Low  \\\n",
              "0         1  Aave   AAVE  2020-10-05 23:59:59  55.112358  49.787900   \n",
              "1         2  Aave   AAVE  2020-10-06 23:59:59  53.402270  40.734578   \n",
              "2         3  Aave   AAVE  2020-10-07 23:59:59  42.408314  35.970690   \n",
              "3         4  Aave   AAVE  2020-10-08 23:59:59  44.902511  36.696057   \n",
              "4         5  Aave   AAVE  2020-10-09 23:59:59  47.569533  43.291776   \n",
              "...     ...   ...    ...                  ...        ...        ...   \n",
              "14774  2889   XRP    XRP  2021-07-02 23:59:59   0.667287   0.634726   \n",
              "14775  2890   XRP    XRP  2021-07-03 23:59:59   0.683677   0.644653   \n",
              "14776  2891   XRP    XRP  2021-07-04 23:59:59   0.707783   0.665802   \n",
              "14777  2892   XRP    XRP  2021-07-05 23:59:59   0.695653   0.648492   \n",
              "14778  2893   XRP    XRP  2021-07-06 23:59:59   0.679923   0.652676   \n",
              "\n",
              "            Open      Close        Volume     Marketcap  \n",
              "0      52.675035  53.219243  0.000000e+00  8.912813e+07  \n",
              "1      53.291969  42.401599  5.830915e+05  7.101144e+07  \n",
              "2      42.399947  40.083976  6.828342e+05  6.713004e+07  \n",
              "3      39.885262  43.764463  1.658817e+06  2.202651e+08  \n",
              "4      43.764463  46.817744  8.155377e+05  2.356322e+08  \n",
              "...          ...        ...           ...           ...  \n",
              "14774   0.659890   0.656763  2.061607e+09  3.030759e+10  \n",
              "14775   0.655639   0.672888  1.872820e+09  3.105172e+10  \n",
              "14776   0.673218   0.694945  1.885242e+09  3.206960e+10  \n",
              "14777   0.695653   0.654300  2.076373e+09  3.019395e+10  \n",
              "14778   0.653055   0.665402  1.938959e+09  3.072284e+10  \n",
              "\n",
              "[14779 rows x 10 columns]"
            ],
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              "      <th>SNo</th>\n",
              "      <th>Name</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Aave</td>\n",
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              "      <td>2</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Aave</td>\n",
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              "      <td>2020-10-07 23:59:59</td>\n",
              "      <td>42.408314</td>\n",
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              "      <td>42.399947</td>\n",
              "      <td>40.083976</td>\n",
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              "      <td>6.713004e+07</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>2020-10-08 23:59:59</td>\n",
              "      <td>44.902511</td>\n",
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              "      <td>39.885262</td>\n",
              "      <td>43.764463</td>\n",
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              "      <td>2.202651e+08</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>2020-10-09 23:59:59</td>\n",
              "      <td>47.569533</td>\n",
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              "      <th>14774</th>\n",
              "      <td>2889</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2021-07-02 23:59:59</td>\n",
              "      <td>0.667287</td>\n",
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              "      <td>2890</td>\n",
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              "      <td>2021-07-03 23:59:59</td>\n",
              "      <td>0.683677</td>\n",
              "      <td>0.644653</td>\n",
              "      <td>0.655639</td>\n",
              "      <td>0.672888</td>\n",
              "      <td>1.872820e+09</td>\n",
              "      <td>3.105172e+10</td>\n",
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              "    <tr>\n",
              "      <th>14776</th>\n",
              "      <td>2891</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>2021-07-04 23:59:59</td>\n",
              "      <td>0.707783</td>\n",
              "      <td>0.665802</td>\n",
              "      <td>0.673218</td>\n",
              "      <td>0.694945</td>\n",
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              "      <td>3.072284e+10</td>\n",
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              "<p>14779 rows × 10 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a733fe5-8f73-4b8f-bc43-60db92ed2190 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convertendo formato da data\n",
        "crypto_coins['Date'] = pd.to_datetime(crypto_coins['Date']).dt.strftime('%d-%m-%Y')"
      ],
      "metadata": {
        "id": "QX8wzbzroxao"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# renomear colunas\n",
        "crypto_coins.rename(columns={'Name': 'Nome', 'Symbol': 'Símbolo', 'Date':'Data', 'High': 'Alto', 'Low': 'Baixo', 'Open': 'Aberto', 'Close': 'Fechado', 'Marketcap': 'Valor da ação'}, inplace=True)"
      ],
      "metadata": {
        "id": "iNon8Gqrpu39"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crypto_coins.drop([\"SNo\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "9WM6rsSa9nkW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Função para converter valores em notação científica para notação decimal\n",
        "def converter_notacao_cientifica(valor):\n",
        "    try:\n",
        "        return \"{:.10f}\".format(float(valor))\n",
        "    except:\n",
        "        return valor\n",
        "#Colunas a serem convertidas\n",
        "colunas_para_converter = ['Alto', 'Baixo', 'Aberto', 'Fechado']\n",
        "\n",
        "for coluna in colunas_para_converter:\n",
        "    crypto_coins[coluna] = crypto_coins[coluna].apply(lambda x: converter_notacao_cientifica(x))"
      ],
      "metadata": {
        "id": "8bnchdrP2w_g"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convertendo algumas colunas\n",
        "A função foi criada para converter a escrita de algumas linhas ao decorrer das colunas que se encontram em notação científica. Ao converter os números nos softwares de visualização (Power BI, Looker) ele retorna o valor de Error, pois não consegue executar a conversão.\n",
        "\n",
        "1. Pré tratamento\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "mBosDwGntEZR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Pós tratamento\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "yTZaOGXCtu9J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* As imagens estão convertidas para moeda para exemplificar"
      ],
      "metadata": {
        "id": "098NG7q0e0pE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inserindo título na coluna index\n",
        "crypto_coins.index.name = \"ID\""
      ],
      "metadata": {
        "id": "Z-pJ_BRmuEZO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Inserindo o nome ID na coluna de index para permitir que o arquivo seja lido corretamente nas plataformas de visualização e banco de dados."
      ],
      "metadata": {
        "id": "M2pQXQbuuH2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crypto_coins"
      ],
      "metadata": {
        "id": "A1sdqNo0oyvZ",
        "outputId": "13d6efb9-e6c7-4d09-cf38-734bead57d34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Nome Símbolo        Data           Alto          Baixo         Aberto  \\\n",
              "ID                                                                             \n",
              "0      Aave    AAVE  05-10-2020  55.1123584700  49.7878999200  52.6750349600   \n",
              "1      Aave    AAVE  06-10-2020  53.4022700200  40.7345779100  53.2919693100   \n",
              "2      Aave    AAVE  07-10-2020  42.4083136400  35.9706897500  42.3999471100   \n",
              "3      Aave    AAVE  08-10-2020  44.9025111400  36.6960567700  39.8852623400   \n",
              "4      Aave    AAVE  09-10-2020  47.5695327400  43.2917758000  43.7644630600   \n",
              "...     ...     ...         ...            ...            ...            ...   \n",
              "14774   XRP     XRP  02-07-2021   0.6672868400   0.6347259600   0.6598902400   \n",
              "14775   XRP     XRP  03-07-2021   0.6836770000   0.6446528900   0.6556387900   \n",
              "14776   XRP     XRP  04-07-2021   0.7077829900   0.6658024700   0.6732175700   \n",
              "14777   XRP     XRP  05-07-2021   0.6956534200   0.6484921400   0.6956534200   \n",
              "14778   XRP     XRP  06-07-2021   0.6799234400   0.6526760600   0.6530552000   \n",
              "\n",
              "             Fechado        Volume  Valor da ação  \n",
              "ID                                                 \n",
              "0      53.2192429600  0.000000e+00   8.912813e+07  \n",
              "1      42.4015986100  5.830915e+05   7.101144e+07  \n",
              "2      40.0839756100  6.828342e+05   6.713004e+07  \n",
              "3      43.7644630600  1.658817e+06   2.202651e+08  \n",
              "4      46.8177441500  8.155377e+05   2.356322e+08  \n",
              "...              ...           ...            ...  \n",
              "14774   0.6567628900  2.061607e+09   3.030759e+10  \n",
              "14775   0.6728881800  1.872820e+09   3.105172e+10  \n",
              "14776   0.6949454300  1.885242e+09   3.206960e+10  \n",
              "14777   0.6543003700  2.076373e+09   3.019395e+10  \n",
              "14778   0.6654024800  1.938959e+09   3.072284e+10  \n",
              "\n",
              "[14779 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89b70da2-a93a-4c90-86f8-a6eecf8abfc9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nome</th>\n",
              "      <th>Símbolo</th>\n",
              "      <th>Data</th>\n",
              "      <th>Alto</th>\n",
              "      <th>Baixo</th>\n",
              "      <th>Aberto</th>\n",
              "      <th>Fechado</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Valor da ação</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>05-10-2020</td>\n",
              "      <td>55.1123584700</td>\n",
              "      <td>49.7878999200</td>\n",
              "      <td>52.6750349600</td>\n",
              "      <td>53.2192429600</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.912813e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>06-10-2020</td>\n",
              "      <td>53.4022700200</td>\n",
              "      <td>40.7345779100</td>\n",
              "      <td>53.2919693100</td>\n",
              "      <td>42.4015986100</td>\n",
              "      <td>5.830915e+05</td>\n",
              "      <td>7.101144e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>07-10-2020</td>\n",
              "      <td>42.4083136400</td>\n",
              "      <td>35.9706897500</td>\n",
              "      <td>42.3999471100</td>\n",
              "      <td>40.0839756100</td>\n",
              "      <td>6.828342e+05</td>\n",
              "      <td>6.713004e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>08-10-2020</td>\n",
              "      <td>44.9025111400</td>\n",
              "      <td>36.6960567700</td>\n",
              "      <td>39.8852623400</td>\n",
              "      <td>43.7644630600</td>\n",
              "      <td>1.658817e+06</td>\n",
              "      <td>2.202651e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>09-10-2020</td>\n",
              "      <td>47.5695327400</td>\n",
              "      <td>43.2917758000</td>\n",
              "      <td>43.7644630600</td>\n",
              "      <td>46.8177441500</td>\n",
              "      <td>8.155377e+05</td>\n",
              "      <td>2.356322e+08</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14774</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>02-07-2021</td>\n",
              "      <td>0.6672868400</td>\n",
              "      <td>0.6347259600</td>\n",
              "      <td>0.6598902400</td>\n",
              "      <td>0.6567628900</td>\n",
              "      <td>2.061607e+09</td>\n",
              "      <td>3.030759e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14775</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>03-07-2021</td>\n",
              "      <td>0.6836770000</td>\n",
              "      <td>0.6446528900</td>\n",
              "      <td>0.6556387900</td>\n",
              "      <td>0.6728881800</td>\n",
              "      <td>1.872820e+09</td>\n",
              "      <td>3.105172e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14776</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>04-07-2021</td>\n",
              "      <td>0.7077829900</td>\n",
              "      <td>0.6658024700</td>\n",
              "      <td>0.6732175700</td>\n",
              "      <td>0.6949454300</td>\n",
              "      <td>1.885242e+09</td>\n",
              "      <td>3.206960e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14777</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>05-07-2021</td>\n",
              "      <td>0.6956534200</td>\n",
              "      <td>0.6484921400</td>\n",
              "      <td>0.6956534200</td>\n",
              "      <td>0.6543003700</td>\n",
              "      <td>2.076373e+09</td>\n",
              "      <td>3.019395e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14778</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>06-07-2021</td>\n",
              "      <td>0.6799234400</td>\n",
              "      <td>0.6526760600</td>\n",
              "      <td>0.6530552000</td>\n",
              "      <td>0.6654024800</td>\n",
              "      <td>1.938959e+09</td>\n",
              "      <td>3.072284e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14779 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89b70da2-a93a-4c90-86f8-a6eecf8abfc9')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-89b70da2-a93a-4c90-86f8-a6eecf8abfc9 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-89b70da2-a93a-4c90-86f8-a6eecf8abfc9');\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",
              "\n",
              "\n",
              "<div id=\"df-009ec3ec-b259-4f80-a863-befe07372c2b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-009ec3ec-b259-4f80-a863-befe07372c2b')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-009ec3ec-b259-4f80-a863-befe07372c2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convertendo dataframe para csv\n",
        "crypto_coins.to_csv(\"crypto_coins.csv\")"
      ],
      "metadata": {
        "id": "-hgTuN4Vo1ke"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "urls_arquivos_csv = [\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Aave.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_BinanceCoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Bitcoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_ChainLink.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Cosmos.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_EOS.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Ethereum.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Polkadot.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_USDCoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_XRP.csv'\n",
        "]\n"
      ],
      "metadata": {
        "id": "40zlLVc42c6W"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_moedas = {}\n",
        "\n",
        "for url in urls_arquivos_csv:\n",
        "    # Extrai o nome da moeda do URL\n",
        "    nome_moeda = url.split('/')[-1].replace('coin_', '').replace('.csv', '')\n",
        "\n",
        "    # Carrega o arquivo CSV em um DataFrame\n",
        "    df = pd.read_csv(url)\n",
        "\n",
        "    # Armazena o DataFrame no dicionário usando o nome da moeda como chave\n",
        "    dados_moedas[nome_moeda] = df"
      ],
      "metadata": {
        "id": "GQ24xeNj2dBS"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot dos valores de todas as criptomoedas\n",
        "plt.figure(figsize=(12, 6))\n",
        "for moeda, df in dados_moedas.items():\n",
        "    plt.plot(df['Date'], df['Close'], label=moeda)\n",
        "\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Valor de Fechamento')\n",
        "plt.title('Valores de Fechamento de Criptomoedas ao Longo do Tempo')\n",
        "plt.legend(loc='upper left')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mNyQb-jN2dGo",
        "outputId": "70ce224f-4d85-4c4e-e124-ffdd087a03ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dicionário para armazenar a maior variação percentual de preço para cada moeda\n",
        "maior_variacao = {'Moeda': None, 'Data': None, 'Variação Percentual': None}\n",
        "\n",
        "# Iterar pelas moedas\n",
        "for moeda, df in dados_moedas.items():\n",
        "    # Calcular a variação percentual de preço para cada dia\n",
        "    df['Variação Percentual'] = (df['Close'] / df['Open'] - 1) * 100\n",
        "    # Encontrar o dia com a maior variação percentual\n",
        "    dia_maior_variacao = df.loc[df['Variação Percentual'].idxmax()]\n",
        "\n",
        "    # Se a variação encontrada for maior do que a armazenada, atualizar o dicionário\n",
        "    if maior_variacao['Variação Percentual'] is None or dia_maior_variacao['Variação Percentual'] > maior_variacao['Variação Percentual']:\n",
        "        maior_variacao['Moeda'] = moeda\n",
        "        maior_variacao['Data'] = dia_maior_variacao['Date']\n",
        "        maior_variacao['Variação Percentual'] = dia_maior_variacao['Variação Percentual']\n",
        "\n",
        "# Exibir a moeda com a maior variação percentual em um único dia\n",
        "print(f'A criptomoeda com a maior variação percentual de preço em um único dia durante o período analisado é {maior_variacao[\"Moeda\"]}.')\n",
        "print(f'Isso ocorreu em {maior_variacao[\"Data\"]} com uma variação de {maior_variacao[\"Variação Percentual\"]:.2f}%.')"
      ],
      "metadata": {
        "id": "ZddOCPXg2dK3",
        "outputId": "32defda7-47f2-4c18-ce76-14735398b4aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A criptomoeda com a maior variação percentual de preço em um único dia durante o período analisado é XRP.\n",
            "Isso ocorreu em 2017-04-02 23:59:59 com uma variação de 178.78%.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dicionário para armazenar as maiores variações percentuais\n",
        "maiores_variacoes = {'Moeda': [], 'Variação Percentual': []}\n",
        "\n",
        "# Iterar pelas moedas\n",
        "for moeda, df in dados_moedas.items():\n",
        "    # Encontrar o dia com a maior variação percentual\n",
        "    dia_maior_variacao = df.loc[df['Variação Percentual'].idxmax()]\n",
        "    # Adicionar a moeda e a variação ao dicionário\n",
        "    maiores_variacoes['Moeda'].append(moeda)\n",
        "    maiores_variacoes['Variação Percentual'].append(dia_maior_variacao['Variação Percentual'])\n",
        "\n",
        "# Criar o gráfico de barras\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.bar(maiores_variacoes['Moeda'], maiores_variacoes['Variação Percentual'])\n",
        "plt.xlabel('Moeda')\n",
        "plt.ylabel('Variação Percentual (%)')\n",
        "plt.title('Maior Variação Percentual de Preço em um Único Dia por Moeda')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Exibir o gráfico\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XTbVLe-62dOd",
        "outputId": "f322bf7d-fc0e-4831-c557-77faefd6f76e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lista de links das moedas\n",
        "links_moedas = [\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Aave.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_BinanceCoin.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Bitcoin.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_ChainLink.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Cosmos.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_EOS.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Ethereum.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Polkadot.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_USDCoin.csv\",\n",
        "    \"https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_XRP.csv\"\n",
        "]\n",
        "\n",
        "# Loop para verificar as colunas em cada arquivo\n",
        "for link in links_moedas:\n",
        "    moeda_nome = link.split(\"/\")[-1].replace(\"coin_\", \"\").replace(\".csv\", \"\")\n",
        "    df = pd.read_csv(link)\n",
        "    print(f\"Colunas disponíveis em {moeda_nome}:\")\n",
        "    print(df.columns)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "id": "0iXas2EA2dRh",
        "outputId": "00ea692d-40a1-4aa5-a566-3f9abdbf3c73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colunas disponíveis em Aave:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em BinanceCoin:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em Bitcoin:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em ChainLink:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em Cosmos:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em EOS:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em Ethereum:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em Polkadot:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em USDCoin:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n",
            "Colunas disponíveis em XRP:\n",
            "Index(['SNo', 'Name', 'Symbol', 'Date', 'High', 'Low', 'Open', 'Close',\n",
            "       'Volume', 'Marketcap'],\n",
            "      dtype='object')\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Crie um dicionário para armazenar os DataFrames de cada moeda\n",
        "moedas = {\n",
        "    'Aave': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Aave.csv',\n",
        "    'BinanceCoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_BinanceCoin.csv',\n",
        "    'Bitcoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Bitcoin.csv',\n",
        "    'Chainlink': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_ChainLink.csv',\n",
        "    'Cosmos': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Cosmos.csv',\n",
        "    'EOS': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_EOS.csv',\n",
        "    'Ethereum': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Ethereum.csv',\n",
        "    'Polkadot': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Polkadot.csv',\n",
        "    'USDCoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_USDCoin.csv',\n",
        "    'XRP': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_XRP.csv'\n",
        "}\n",
        "\n",
        "# Crie um DataFrame para cada moeda e calcule a valorização média\n",
        "valorizacao_media = {}\n",
        "\n",
        "for moeda, url in moedas.items():\n",
        "    df = pd.read_csv(url)\n",
        "    df['Valorizacao Diaria (%)'] = ((df['Close'] - df['Open']) / df['Open']) * 100\n",
        "    valorizacao_media[moeda] = df['Valorizacao Diaria (%)'].mean()\n",
        "\n",
        "# Identifique a moeda com a menor valorização média\n",
        "moeda_menos_interessante = min(valorizacao_media, key=valorizacao_media.get)\n",
        "\n",
        "# Imprima a moeda menos interessante\n",
        "print(f\"A moeda menos interessante em relação à valorização pela análise da série histórica é {moeda_menos_interessante}.\")"
      ],
      "metadata": {
        "id": "LtYDLHwo2dU5",
        "outputId": "8b514111-61ab-4dbf-d52d-aaaa156ece36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A moeda menos interessante em relação à valorização pela análise da série histórica é USDCoin.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crie um dicionário para armazenar os DataFrames de cada moeda\n",
        "moedas = {\n",
        "    'Aave': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Aave.csv',\n",
        "    'BinanceCoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_BinanceCoin.csv',\n",
        "    'Bitcoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Bitcoin.csv',\n",
        "    'Chainlink': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_ChainLink.csv',\n",
        "    'Cosmos': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Cosmos.csv',\n",
        "    'EOS': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_EOS.csv',\n",
        "    'Ethereum': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Ethereum.csv',\n",
        "    'Polkadot': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Polkadot.csv',\n",
        "    'USDCoin': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_USDCoin.csv',\n",
        "    'XRP': 'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_XRP.csv'\n",
        "}\n",
        "\n",
        "# Crie um DataFrame para cada moeda e calcule a valorização média\n",
        "valorizacao_media = {}\n",
        "\n",
        "for moeda, url in moedas.items():\n",
        "    df = pd.read_csv(url)\n",
        "    df['Valorizacao Diaria (%)'] = ((df['Close'] - df['Open']) / df['Open']) * 100\n",
        "    valorizacao_media[moeda] = df['Valorizacao Diaria (%)'].mean()\n",
        "\n",
        "# Crie um gráfico de barras\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.bar(valorizacao_media.keys(), valorizacao_media.values())\n",
        "plt.xlabel('Moeda')\n",
        "plt.ylabel('Valorizacao Media Diaria (%)')\n",
        "plt.title('Valorizacao Media Diaria das Criptomoedas')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Mostre o gráfico\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wI69vmq72ddZ",
        "outputId": "ad253186-dd8f-49bc-9251-511511e77b69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Para analisar a correlação importamos essa bibliotecas\n",
        "# import pandas as pd\n",
        "# import matplotlib.pyplot as plt\n",
        "# import seaborn as sns\n",
        "\n",
        "# URLs dos arquivos CSV\n",
        "urls_arquivos_csv = [\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Aave.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_BinanceCoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Bitcoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_ChainLink.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Cosmos.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_EOS.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Ethereum.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_Polkadot.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_USDCoin.csv',\n",
        "    'https://raw.githubusercontent.com/mathuscm/Criptomoedas-m5/main/ArquivosCrypto/coin_XRP.csv'\n",
        "]\n",
        "\n",
        "# Lista para armazenar os DataFrames de cada moeda\n",
        "dataframes_moedas = []\n",
        "\n",
        "# Cores para as moedas\n",
        "cores = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'orange', 'purple', 'brown']\n",
        "\n",
        "# Carregar os DataFrames das moedas a partir das URLs e armazená-los na lista\n",
        "for url in urls_arquivos_csv:\n",
        "    df_moeda = pd.read_csv(url)\n",
        "    dataframes_moedas.append(df_moeda)\n",
        "\n",
        "# Criar um DataFrame único com as colunas 'Close' de todas as moedas\n",
        "df_concatenado = pd.concat([df['Close'] for df in dataframes_moedas], axis=1)\n",
        "df_concatenado.columns = [url.split('/')[-1].split('_')[1].split('.')[0] for url in urls_arquivos_csv]\n",
        "\n",
        "# Calcular a matriz de correlação\n",
        "correlation_matrix = df_concatenado.corr()\n",
        "\n",
        "# Configurar o tamanho do gráfico\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Criar um mapa de calor para visualizar a matriz de correlação\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "\n",
        "# Configurar título e rótulos dos eixos\n",
        "plt.title('Matriz de Correlação entre Criptomoedas (2014-2021)')\n",
        "plt.xlabel('Criptomoedas')\n",
        "plt.ylabel('Criptomoedas')\n",
        "\n",
        "# Mostrar o gráfico\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "hPRx1QxjaEit",
        "outputId": "38a4db6f-54cd-4794-e6bf-6921f2f93f82"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cripto = pd.read_csv('crypto_coins.csv')\n",
        "cripto.head()"
      ],
      "metadata": {
        "id": "P7eb1X7Vc_lF",
        "outputId": "6be61b7e-0724-443b-99d1-5bf8c840ce17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  Nome Simbolo        Data       Alto      Baixo     Aberto    Fechado  \\\n",
              "0   0  Aave    AAVE  05-10-2020  55.112358  49.787900  52.675035  53.219243   \n",
              "1   1  Aave    AAVE  06-10-2020  53.402270  40.734578  53.291969  42.401599   \n",
              "2   2  Aave    AAVE  07-10-2020  42.408314  35.970690  42.399947  40.083976   \n",
              "3   3  Aave    AAVE  08-10-2020  44.902511  36.696057  39.885262  43.764463   \n",
              "4   4  Aave    AAVE  09-10-2020  47.569533  43.291776  43.764463  46.817744   \n",
              "\n",
              "         Volume    Valor_acao  \n",
              "0  0.000000e+00  8.912813e+07  \n",
              "1  5.830915e+05  7.101144e+07  \n",
              "2  6.828342e+05  6.713004e+07  \n",
              "3  1.658817e+06  2.202651e+08  \n",
              "4  8.155377e+05  2.356322e+08  "
            ],
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Nome</th>\n",
              "      <th>Simbolo</th>\n",
              "      <th>Data</th>\n",
              "      <th>Alto</th>\n",
              "      <th>Baixo</th>\n",
              "      <th>Aberto</th>\n",
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              "      <th>Valor_acao</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>05-10-2020</td>\n",
              "      <td>55.112358</td>\n",
              "      <td>49.787900</td>\n",
              "      <td>52.675035</td>\n",
              "      <td>53.219243</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.912813e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>06-10-2020</td>\n",
              "      <td>53.402270</td>\n",
              "      <td>40.734578</td>\n",
              "      <td>53.291969</td>\n",
              "      <td>42.401599</td>\n",
              "      <td>5.830915e+05</td>\n",
              "      <td>7.101144e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>07-10-2020</td>\n",
              "      <td>42.408314</td>\n",
              "      <td>35.970690</td>\n",
              "      <td>42.399947</td>\n",
              "      <td>40.083976</td>\n",
              "      <td>6.828342e+05</td>\n",
              "      <td>6.713004e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>08-10-2020</td>\n",
              "      <td>44.902511</td>\n",
              "      <td>36.696057</td>\n",
              "      <td>39.885262</td>\n",
              "      <td>43.764463</td>\n",
              "      <td>1.658817e+06</td>\n",
              "      <td>2.202651e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Aave</td>\n",
              "      <td>AAVE</td>\n",
              "      <td>09-10-2020</td>\n",
              "      <td>47.569533</td>\n",
              "      <td>43.291776</td>\n",
              "      <td>43.764463</td>\n",
              "      <td>46.817744</td>\n",
              "      <td>8.155377e+05</td>\n",
              "      <td>2.356322e+08</td>\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-0ac278c1-e273-4bc9-a4ff-503aaecef1f6 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-0ac278c1-e273-4bc9-a4ff-503aaecef1f6');\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7d4beb8b-be26-4afc-9419-12238d899c47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cripto['Data'] = pd.to_datetime(cripto['Data'], format='%Y-%m-%d')\n",
        "\n",
        "print(cripto['Data'].head(10))"
      ],
      "metadata": {
        "id": "8hO9FkiUeWbr",
        "outputId": "268086ff-2df9-4e80-9ec3-4e02532b9825",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0   2020-10-05\n",
            "1   2020-10-06\n",
            "2   2020-10-07\n",
            "3   2020-10-08\n",
            "4   2020-10-09\n",
            "5   2020-10-10\n",
            "6   2020-10-11\n",
            "7   2020-10-12\n",
            "8   2020-10-13\n",
            "9   2020-10-14\n",
            "Name: Data, dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cripto['Data'] = pd.to_datetime(cripto['Data'], format='%d-%m-%Y')\n",
        "cripto['Data'] = cripto['Data'].dt.date\n",
        "\n",
        "\n",
        "def extrair_dia_da_semana(data):\n",
        "    dias_semana = [\"segunda-feira\", \"terça-feira\", \"quarta-feira\", \"quinta-feira\", \"sexta-feira\", \"sábado\", \"domingo\"]\n",
        "    return dias_semana[data.weekday()]\n",
        "\n",
        "cripto['Dia_da_Semana'] = cripto['Data'].apply(extrair_dia_da_semana)\n",
        "\n",
        "tendencia_por_dia = cripto.groupby('Dia_da_Semana')['Valor_acao'].mean()\n",
        "tendencia_por_dia\n",
        "\n",
        "ordem_dias_semana = ['segunda-feira', 'terça-feira', 'quarta-feira', 'quinta-feira', 'sexta-feira', 'sábado', 'domingo']\n",
        "\n",
        "media_por_dia_da_semana = cripto.groupby('Dia_da_Semana')['Valor_acao'].mean().reindex(ordem_dias_semana)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(media_por_dia_da_semana.index, media_por_dia_da_semana.values, marker='o')\n",
        "plt.title('Média dos Valores por Dia da Semana')\n",
        "plt.ylabel('Média dos Valores')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "p9eYSCMZcKaD",
        "outputId": "166d546e-79bd-4d54-f414-8506ede31ce5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}