Python_TIme_Series_Function_Part_2.ipynb

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      "name": "Python TIme Series Function - Part 2.ipynb",
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      "authorship_tag": "ABX9TyNNOXnaylVeKXUPggiI8AEr",
      "include_colab_link": true
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    "kernelspec": {
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/End-to-End-Time-Series/blob/master/Python_TIme_Series_Function_Part_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGpQ4RNkUY71",
        "colab_type": "text"
      },
      "source": [
        "Link to video explaining the code - https://www.youtube.com/watch?v=2bNeV-6-tWE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6i7OnJ7RmfRc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import datetime\n",
        "import pandas as pd\n",
        "import requests\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iU10jK1y2u9H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "859c470c-0642-4bb8-d3b2-42bab6817cc6"
      },
      "source": [
        "coindeskURL = 'https://api.coindesk.com/v1/bpi/historical/close.json?'\n",
        "\n",
        "start = datetime.date(2017, 1 ,1)\n",
        "end = datetime.date(2020, 7, 2)\n",
        "\n",
        "url = f'{coindeskURL}start={start:%Y-%m-%d}&end={end:%Y-%m-%d}'\n",
        "\n",
        "result = requests.get(url)\n",
        "result.content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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data was produced from the CoinDesk Bitcoin Price Index. BPI value data returned as USD.\",\"time\":{\"updated\":\"Jul 3, 2020 00:03:00 UTC\",\"updatedISO\":\"2020-07-03T00:03:00+00:00\"}}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
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      },
      "source": [
        "data = pd.read_json(result.content).iloc[:-2,:1]\n",
        "data.index.name = 'date'\n",
        "data.index = pd.to_datetime(data.index)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bpi</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>997.6888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>1018.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>1030.8175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>1129.8700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>1005.8150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-28</th>\n",
              "      <td>8991.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-29</th>\n",
              "      <td>9096.1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-30</th>\n",
              "      <td>9144.3850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>9153.9500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>9199.8750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1279 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  bpi\n",
              "date                 \n",
              "2017-01-01   997.6888\n",
              "2017-01-02  1018.0500\n",
              "2017-01-03  1030.8175\n",
              "2017-01-04  1129.8700\n",
              "2017-01-05  1005.8150\n",
              "...               ...\n",
              "2020-06-28  8991.5000\n",
              "2020-06-29  9096.1500\n",
              "2020-06-30  9144.3850\n",
              "2020-07-01  9153.9500\n",
              "2020-07-02  9199.8750\n",
              "\n",
              "[1279 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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      "source": [
        "data.plot()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f59dbf77eb8>"
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          "metadata": {
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          "execution_count": 5
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        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpKvGGZNjUcn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "425f3eb7-064c-44b1-eded-6b5052591321"
      },
      "source": [
        "data.groupby(data.index.dayofweek).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>bpi</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6612.779266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6612.821919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6615.719704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6595.242466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6621.517746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6636.877807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6588.415989</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              bpi\n",
              "date             \n",
              "0     6612.779266\n",
              "1     6612.821919\n",
              "2     6615.719704\n",
              "3     6595.242466\n",
              "4     6621.517746\n",
              "5     6636.877807\n",
              "6     6588.415989"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5WjsJkNzfrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "8a9b2456-ebeb-490f-8d8f-f6993e9cf735"
      },
      "source": [
        "data.groupby(data.index.month).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bpi</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6474.960095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5979.032653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5241.117152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5408.169478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6715.698325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7074.233533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6820.423568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7077.441969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6825.175222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>6727.884286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7165.116334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8672.883520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              bpi\n",
              "date             \n",
              "1     6474.960095\n",
              "2     5979.032653\n",
              "3     5241.117152\n",
              "4     5408.169478\n",
              "5     6715.698325\n",
              "6     7074.233533\n",
              "7     6820.423568\n",
              "8     7077.441969\n",
              "9     6825.175222\n",
              "10    6727.884286\n",
              "11    7165.116334\n",
              "12    8672.883520"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6Z2DcfezliJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "12710ced-10bd-4dc4-bb8f-0f8aa3949364"
      },
      "source": [
        "data.groupby(data.index.year).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bpi</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>3990.138147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>7529.385702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>7379.960562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>8468.953262</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              bpi\n",
              "date             \n",
              "2017  3990.138147\n",
              "2018  7529.385702\n",
              "2019  7379.960562\n",
              "2020  8468.953262"
            ]
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