crypto_preparation_srsen.ipynb

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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Use CSV instead"
      ],
      "metadata": {
        "id": "vSxZ4HGgyKVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "82hvgSNZNwDX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "ChPf6FYZx4K3",
        "outputId": "ff97e51e-4cb6-433f-b3dd-05d6f2094fc2"
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      "execution_count": 3,
      "outputs": [
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              "\n",
              "     <input type=\"file\" id=\"files-59c2e00d-b5da-455c-9369-3d470b765123\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-59c2e00d-b5da-455c-9369-3d470b765123\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
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              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving eth_timeseries_hourly.csv to eth_timeseries_hourly.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read csv to pandas\n",
        "# Load the uploaded CSV file as a DataFrame\n",
        "from io import StringIO\n",
        "uploaded_file = uploaded[\"eth_timeseries_hourly.csv\"]  # Access the file from the uploaded dictionary\n",
        "df = pd.read_csv(StringIO(uploaded_file.decode(\"utf-8\")), delimiter=';')  # Decode bytes to string and read into DataFram\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "tASunpFFyBi4",
        "outputId": "1b94839b-00a5-44b8-9fce-752bee62e1f9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   asset_id   timestamp            time    open   close    high     low  \\\n",
              "0         2  1567036800  29.8.2019 0:00  173.13  168.95  173.17  164.19   \n",
              "1         2  1567123200  30.8.2019 0:00  167.61  168.52  170.47  165.95   \n",
              "2         2  1567209600  31.8.2019 0:00  168.92  172.15  174.99  166.29   \n",
              "3         2  1567296000   1.9.2019 0:00  172.97  171.42  173.88  168.24   \n",
              "4         2  1567382400   2.9.2019 0:00  171.45  178.76  181.47  170.77   \n",
              "\n",
              "   volume  volume_24h    market_cap  ...  social_contributors  \\\n",
              "0     NaN         NaN  1.823020e+10  ...                  NaN   \n",
              "1     NaN         NaN  1.820780e+10  ...                  NaN   \n",
              "2     NaN         NaN  1.856412e+10  ...                  NaN   \n",
              "3     NaN         NaN  1.849572e+10  ...                  NaN   \n",
              "4     NaN         NaN  1.919514e+10  ...                  NaN   \n",
              "\n",
              "   influential_content  influential_content_score  social_volume  \\\n",
              "0                  NaN                        NaN          12708   \n",
              "1                  NaN                        NaN          11852   \n",
              "2                  NaN                        NaN           9138   \n",
              "3                  NaN                        NaN           8549   \n",
              "4                  NaN                        NaN          11087   \n",
              "\n",
              "   social_volume_global  social_dominance  market_cap_global  \\\n",
              "0                   NaN               NaN                NaN   \n",
              "1                   NaN               NaN                NaN   \n",
              "2                   NaN               NaN                NaN   \n",
              "3                   NaN               NaN                NaN   \n",
              "4                   NaN               NaN                NaN   \n",
              "\n",
              "   market_dominance  social_volume_calc_24h  social_score_calc_24h  \n",
              "0               NaN                     NaN                    NaN  \n",
              "1               NaN                     NaN                    NaN  \n",
              "2               NaN                     NaN                    NaN  \n",
              "3               NaN                     NaN                    NaN  \n",
              "4               NaN                     NaN                    NaN  \n",
              "\n",
              "[5 rows x 65 columns]"
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              "      <th>asset_id</th>\n",
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              "      <th>influential_content</th>\n",
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              "      <th>social_volume</th>\n",
              "      <th>social_volume_global</th>\n",
              "      <th>social_dominance</th>\n",
              "      <th>market_cap_global</th>\n",
              "      <th>market_dominance</th>\n",
              "      <th>social_volume_calc_24h</th>\n",
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              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1567036800</td>\n",
              "      <td>29.8.2019 0:00</td>\n",
              "      <td>173.13</td>\n",
              "      <td>168.95</td>\n",
              "      <td>173.17</td>\n",
              "      <td>164.19</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1567123200</td>\n",
              "      <td>30.8.2019 0:00</td>\n",
              "      <td>167.61</td>\n",
              "      <td>168.52</td>\n",
              "      <td>170.47</td>\n",
              "      <td>165.95</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.820780e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11852</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1567209600</td>\n",
              "      <td>31.8.2019 0:00</td>\n",
              "      <td>168.92</td>\n",
              "      <td>172.15</td>\n",
              "      <td>174.99</td>\n",
              "      <td>166.29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.856412e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9138</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>1567296000</td>\n",
              "      <td>1.9.2019 0:00</td>\n",
              "      <td>172.97</td>\n",
              "      <td>171.42</td>\n",
              "      <td>173.88</td>\n",
              "      <td>168.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.849572e+10</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8549</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1567382400</td>\n",
              "      <td>2.9.2019 0:00</td>\n",
              "      <td>171.45</td>\n",
              "      <td>178.76</td>\n",
              "      <td>181.47</td>\n",
              "      <td>170.77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.919514e+10</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11087</td>\n",
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              "      <td>NaN</td>\n",
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              "            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",
              "      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-45b89ad1-6962-4201-b61d-b94b22c1f6f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Drop all columns apart from: \"asset_id\", \"timestamp\", \"time\"\n",
        "\n",
        "columns_to_keep = [\"asset_id\", \"timestamp\", \"time\", \"open\", \"close\", \"high\", \"low\", \"volume\", \"volume_24h\", \"market_cap\", \"circulating_supply\", \"volatility\"]\n",
        "\n",
        "df = df[columns_to_keep]\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6ZK___ayOlr",
        "outputId": "f676bff0-fdec-46c2-dd2a-97c954456eae"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   asset_id   timestamp            time    open   close    high     low  \\\n",
            "0         2  1567036800  29.8.2019 0:00  173.13  168.95  173.17  164.19   \n",
            "1         2  1567123200  30.8.2019 0:00  167.61  168.52  170.47  165.95   \n",
            "2         2  1567209600  31.8.2019 0:00  168.92  172.15  174.99  166.29   \n",
            "3         2  1567296000   1.9.2019 0:00  172.97  171.42  173.88  168.24   \n",
            "4         2  1567382400   2.9.2019 0:00  171.45  178.76  181.47  170.77   \n",
            "\n",
            "   volume  volume_24h    market_cap  circulating_supply  volatility  \n",
            "0     NaN         NaN  1.823020e+10                 NaN    0.957914  \n",
            "1     NaN         NaN  1.820780e+10                 NaN    0.745629  \n",
            "2     NaN         NaN  1.856412e+10                 NaN    0.130370  \n",
            "3     NaN         NaN  1.849572e+10                 NaN    0.240138  \n",
            "4     NaN         NaN  1.919514e+10                 NaN    0.263261  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: for every column print the NaN value count\n",
        "\n",
        "print(df.shape)\n",
        "\n",
        "for col in df.columns:\n",
        "  print(f\"Column '{col}' has {df[col].isna().sum()} NaN values.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1TWx6UWTY4Q",
        "outputId": "971f24cd-3769-4eda-d376-0a4c0554c92a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(38656, 12)\n",
            "Column 'asset_id' has 0 NaN values.\n",
            "Column 'timestamp' has 0 NaN values.\n",
            "Column 'time' has 0 NaN values.\n",
            "Column 'open' has 57 NaN values.\n",
            "Column 'close' has 0 NaN values.\n",
            "Column 'high' has 57 NaN values.\n",
            "Column 'low' has 57 NaN values.\n",
            "Column 'volume' has 8689 NaN values.\n",
            "Column 'volume_24h' has 8515 NaN values.\n",
            "Column 'market_cap' has 55 NaN values.\n",
            "Column 'circulating_supply' has 8248 NaN values.\n",
            "Column 'volatility' has 98 NaN values.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{df['volume'].isna().sum()} values are NaN from {df.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wHg4U8WOf0z",
        "outputId": "0d7d51ba-c104-4d79-928b-75a719191943"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8689 values are NaN from 38656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Drop rows where there is one or more NaNs\n",
        "\n",
        "df = df.dropna()\n",
        "print(df.shape)\n",
        "\n",
        "for col in df.columns:\n",
        "  print(f\"Column '{col}' has {df[col].isna().sum()} NaN values.\")"
      ],
      "metadata": {
        "id": "ZNMMJIiPzeyi",
        "outputId": "66f2f438-8626-4ea2-f78a-67d1dcba3732",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(21467, 12)\n",
            "Column 'asset_id' has 0 NaN values.\n",
            "Column 'timestamp' has 0 NaN values.\n",
            "Column 'time' has 0 NaN values.\n",
            "Column 'open' has 0 NaN values.\n",
            "Column 'close' has 0 NaN values.\n",
            "Column 'high' has 0 NaN values.\n",
            "Column 'low' has 0 NaN values.\n",
            "Column 'volume' has 0 NaN values.\n",
            "Column 'volume_24h' has 0 NaN values.\n",
            "Column 'market_cap' has 0 NaN values.\n",
            "Column 'circulating_supply' has 0 NaN values.\n",
            "Column 'volatility' has 0 NaN values.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{df['volume'].mean()} is the mean of the volume column\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25kI3OfrP-CF",
        "outputId": "d38d00bc-c64d-439b-dbb9-dbc3408514c4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "477760768182.97144 is the mean of the volume column\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{df['volume'].describe()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZbF3I-cQOft",
        "outputId": "8d571672-0300-4038-92d9-e27d3348b5d9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    2.146700e+04\n",
            "mean     4.777608e+11\n",
            "std      4.937083e+13\n",
            "min      1.580394e+08\n",
            "25%      6.219778e+08\n",
            "50%      9.757016e+08\n",
            "75%      1.457019e+09\n",
            "max      5.142100e+15\n",
            "Name: volume, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_value = df['volume'].mean()\n",
        "print(mean_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBHS5F7aRhZt",
        "outputId": "20c1e3bf-09a0-4ab1-ff11-5b48fad7c890"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "477760768182.97144\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['volume'].min())  # Minimum value\n",
        "print(df['volume'].max())  # Maximum value\n",
        "df['volume'].hist()  # Plot histogram (if numeric)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Smtb94C8QkFO",
        "outputId": "cfce85b9-5d38-49d6-8305-4b3b4bab3ec8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "158039390.46\n",
            "5142100136294968.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TMlerPgQoCw-",
        "outputId": "c3f3b066-ed8e-4411-eb50-8e01017068a7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      asset_id   timestamp             time        open       close  \\\n",
              "8247         2  1597604400  16.8.2020 19:00  430.301435  430.135654   \n",
              "8248         2  1597608000  16.8.2020 20:00  430.085287  429.801257   \n",
              "8249         2  1597611600  16.8.2020 21:00  430.713171  433.045384   \n",
              "8250         2  1597615200  16.8.2020 22:00  432.473834  432.853835   \n",
              "8251         2  1597618800  16.8.2020 23:00  433.107274  432.612346   \n",
              "\n",
              "            high         low       volume    volume_24h    market_cap  \\\n",
              "8247  430.741640  428.441629  356714082.0  1.226545e+10  4.826443e+10   \n",
              "8248  431.141743  427.776972  416574000.0  1.222196e+10  4.822714e+10   \n",
              "8249  433.292593  430.029907  468160381.0  1.216537e+10  4.859141e+10   \n",
              "8250  434.047175  430.824950  430095286.0  1.205450e+10  4.857017e+10   \n",
              "8251  434.604450  431.156375  441794452.0  1.212066e+10  4.854332e+10   \n",
              "\n",
              "      circulating_supply  volatility  \n",
              "8247         112199495.0    0.011960  \n",
              "8248         112199495.0    0.011569  \n",
              "8249         112199495.0    0.011214  \n",
              "8250         112199495.0    0.010781  \n",
              "8251         112199495.0    0.010392  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5a218a5-64b2-427a-af91-8160beb44c94\" 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>asset_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>time</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>volume</th>\n",
              "      <th>volume_24h</th>\n",
              "      <th>market_cap</th>\n",
              "      <th>circulating_supply</th>\n",
              "      <th>volatility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8247</th>\n",
              "      <td>2</td>\n",
              "      <td>1597604400</td>\n",
              "      <td>16.8.2020 19:00</td>\n",
              "      <td>430.301435</td>\n",
              "      <td>430.135654</td>\n",
              "      <td>430.741640</td>\n",
              "      <td>428.441629</td>\n",
              "      <td>356714082.0</td>\n",
              "      <td>1.226545e+10</td>\n",
              "      <td>4.826443e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>2</td>\n",
              "      <td>1597608000</td>\n",
              "      <td>16.8.2020 20:00</td>\n",
              "      <td>430.085287</td>\n",
              "      <td>429.801257</td>\n",
              "      <td>431.141743</td>\n",
              "      <td>427.776972</td>\n",
              "      <td>416574000.0</td>\n",
              "      <td>1.222196e+10</td>\n",
              "      <td>4.822714e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2</td>\n",
              "      <td>1597611600</td>\n",
              "      <td>16.8.2020 21:00</td>\n",
              "      <td>430.713171</td>\n",
              "      <td>433.045384</td>\n",
              "      <td>433.292593</td>\n",
              "      <td>430.029907</td>\n",
              "      <td>468160381.0</td>\n",
              "      <td>1.216537e+10</td>\n",
              "      <td>4.859141e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8250</th>\n",
              "      <td>2</td>\n",
              "      <td>1597615200</td>\n",
              "      <td>16.8.2020 22:00</td>\n",
              "      <td>432.473834</td>\n",
              "      <td>432.853835</td>\n",
              "      <td>434.047175</td>\n",
              "      <td>430.824950</td>\n",
              "      <td>430095286.0</td>\n",
              "      <td>1.205450e+10</td>\n",
              "      <td>4.857017e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.010781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8251</th>\n",
              "      <td>2</td>\n",
              "      <td>1597618800</td>\n",
              "      <td>16.8.2020 23:00</td>\n",
              "      <td>433.107274</td>\n",
              "      <td>432.612346</td>\n",
              "      <td>434.604450</td>\n",
              "      <td>431.156375</td>\n",
              "      <td>441794452.0</td>\n",
              "      <td>1.212066e+10</td>\n",
              "      <td>4.854332e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.010392</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-a5a218a5-64b2-427a-af91-8160beb44c94 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-a5a218a5-64b2-427a-af91-8160beb44c94');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </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",
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              "    width: 32px;\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\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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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-eaa952a8-52b1-46a8-9088-ea952331f75a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "df['time'] = pd.to_datetime(df['time']).dt.strftime(\"%Y-%m-%d %H:%M\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "GUOiQpfUoYUg",
        "outputId": "70798217-7d0f-4536-935f-f8c53f71653f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-ccd20b8893ee>:2: UserWarning: Parsing dates in %d.%m.%Y %H:%M format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
            "  df['time'] = pd.to_datetime(df['time']).dt.strftime(\"%Y-%m-%d %H:%M\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      asset_id   timestamp              time        open       close  \\\n",
              "8247         2  1597604400  2020-08-16 19:00  430.301435  430.135654   \n",
              "8248         2  1597608000  2020-08-16 20:00  430.085287  429.801257   \n",
              "8249         2  1597611600  2020-08-16 21:00  430.713171  433.045384   \n",
              "8250         2  1597615200  2020-08-16 22:00  432.473834  432.853835   \n",
              "8251         2  1597618800  2020-08-16 23:00  433.107274  432.612346   \n",
              "\n",
              "            high         low       volume    volume_24h    market_cap  \\\n",
              "8247  430.741640  428.441629  356714082.0  1.226545e+10  4.826443e+10   \n",
              "8248  431.141743  427.776972  416574000.0  1.222196e+10  4.822714e+10   \n",
              "8249  433.292593  430.029907  468160381.0  1.216537e+10  4.859141e+10   \n",
              "8250  434.047175  430.824950  430095286.0  1.205450e+10  4.857017e+10   \n",
              "8251  434.604450  431.156375  441794452.0  1.212066e+10  4.854332e+10   \n",
              "\n",
              "      circulating_supply  volatility  \n",
              "8247         112199495.0    0.011960  \n",
              "8248         112199495.0    0.011569  \n",
              "8249         112199495.0    0.011214  \n",
              "8250         112199495.0    0.010781  \n",
              "8251         112199495.0    0.010392  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>asset_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>time</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
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              "      <th>low</th>\n",
              "      <th>volume</th>\n",
              "      <th>volume_24h</th>\n",
              "      <th>market_cap</th>\n",
              "      <th>circulating_supply</th>\n",
              "      <th>volatility</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8247</th>\n",
              "      <td>2</td>\n",
              "      <td>1597604400</td>\n",
              "      <td>2020-08-16 19:00</td>\n",
              "      <td>430.301435</td>\n",
              "      <td>430.135654</td>\n",
              "      <td>430.741640</td>\n",
              "      <td>428.441629</td>\n",
              "      <td>356714082.0</td>\n",
              "      <td>1.226545e+10</td>\n",
              "      <td>4.826443e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>2</td>\n",
              "      <td>1597608000</td>\n",
              "      <td>2020-08-16 20:00</td>\n",
              "      <td>430.085287</td>\n",
              "      <td>429.801257</td>\n",
              "      <td>431.141743</td>\n",
              "      <td>427.776972</td>\n",
              "      <td>416574000.0</td>\n",
              "      <td>1.222196e+10</td>\n",
              "      <td>4.822714e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2</td>\n",
              "      <td>1597611600</td>\n",
              "      <td>2020-08-16 21:00</td>\n",
              "      <td>430.713171</td>\n",
              "      <td>433.045384</td>\n",
              "      <td>433.292593</td>\n",
              "      <td>430.029907</td>\n",
              "      <td>468160381.0</td>\n",
              "      <td>1.216537e+10</td>\n",
              "      <td>4.859141e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.011214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8250</th>\n",
              "      <td>2</td>\n",
              "      <td>1597615200</td>\n",
              "      <td>2020-08-16 22:00</td>\n",
              "      <td>432.473834</td>\n",
              "      <td>432.853835</td>\n",
              "      <td>434.047175</td>\n",
              "      <td>430.824950</td>\n",
              "      <td>430095286.0</td>\n",
              "      <td>1.205450e+10</td>\n",
              "      <td>4.857017e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.010781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8251</th>\n",
              "      <td>2</td>\n",
              "      <td>1597618800</td>\n",
              "      <td>2020-08-16 23:00</td>\n",
              "      <td>433.107274</td>\n",
              "      <td>432.612346</td>\n",
              "      <td>434.604450</td>\n",
              "      <td>431.156375</td>\n",
              "      <td>441794452.0</td>\n",
              "      <td>1.212066e+10</td>\n",
              "      <td>4.854332e+10</td>\n",
              "      <td>112199495.0</td>\n",
              "      <td>0.010392</td>\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6494ffb4-fef0-42c4-9d24-083a2e3e2354 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6494ffb4-fef0-42c4-9d24-083a2e3e2354');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </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",
              "      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-42866fbe-15a9-4061-bbee-02024f13027a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_500 = df.head(500)\n",
        "df_500.to_csv('preprocessed_12_cols_500_rows_noNaN_eth_timeseries.csv', index=False)  # index=False prevents row indices from being written\n",
        "\n",
        "# Download the CSV file\n",
        "files.download('preprocessed_12_cols_500_rows_noNaN_eth_timeseries.csv')"
      ],
      "metadata": {
        "id": "EhiseR-qpGda",
        "outputId": "ff4693fc-75a6-4d7d-b5ca-8a15a353a500",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f13032b6-fcaa-4b37-b4df-c96bec79b1f1\", \"preprocessed_12_cols_500_rows_noNaN_eth_timeseries.csv\", 70617)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('preprocessed_12_cols_eth_timeseries.csv', index=False)  # index=False prevents row indices from being written\n",
        "\n",
        "# Download the CSV file\n",
        "files.download('preprocessed_12_cols_eth_timeseries.csv')"
      ],
      "metadata": {
        "id": "dCn8ym2uv-j0",
        "outputId": "8a05f718-eb39-431c-b3bc-1d1619d7b82b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_4e108930-a7cb-462b-9a47-d69a66c5a345\", \"preprocessed_12_cols_eth_timeseries.csv\", 3306679)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}