06: Python_Functions_for_Time_Series_Analysis_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Python_Functions_for_Time_Series_Analysis_02.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypLk73Y2w5Gc"
      },
      "source": [
        "This notebook is continuation of \"Python_Functions_for_Time_Series_Analysis_01\", some import python function will  be discussed that will be helpful in many time series cases...."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6i7OnJ7RmfRc"
      },
      "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'] = (20, 20)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iU10jK1y2u9H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f597e2a-1833-46ce-a0f2-bf80dea61b8a"
      },
      "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",
        "end = datetime.datetime.now().date()\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": 2,
      "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\":\"Dec 26, 2020 15:57:57 UTC\",\"updatedISO\":\"2020-12-26T15:57:57+00:00\"}}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6T8tWXBimgvi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "1cac0369-1303-4456-e6b5-21dcdb0611fe"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>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>2020-12-21</th>\n",
              "      <td>22718.2250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-22</th>\n",
              "      <td>23817.3067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>23229.0133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>23729.6483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-25</th>\n",
              "      <td>24707.8017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1455 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",
              "2020-12-21  22718.2250\n",
              "2020-12-22  23817.3067\n",
              "2020-12-23  23229.0133\n",
              "2020-12-24  23729.6483\n",
              "2020-12-25  24707.8017\n",
              "\n",
              "[1455 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjroYrCSolpZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "dc9e99f4-2751-400e-9fe6-579103d78472"
      },
      "source": [
        "data.plot()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a5334df60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4rPkYPqxrly",
        "outputId": "0c10ac71-5dce-4f40-f079-cd2593b4f2b5"
      },
      "source": [
        "#data.index.dayofweek"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
              "            ...\n",
              "            2, 3, 4, 5, 6, 0, 1, 2, 3, 4],\n",
              "           dtype='int64', name='date', length=1455)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpKvGGZNjUcn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ff6e25b8-7906-4aa8-c55e-8060279ac2b5"
      },
      "source": [
        "data.groupby(data.index.dayofweek).mean()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>7407.830024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7423.902703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7433.701299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7434.563758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7470.140080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7415.470575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7372.716215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              bpi\n",
              "date             \n",
              "0     7407.830024\n",
              "1     7423.902703\n",
              "2     7433.701299\n",
              "3     7434.563758\n",
              "4     7470.140080\n",
              "5     7415.470575\n",
              "6     7372.716215"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rW-kD5scyPwZ",
        "outputId": "bda9ed75-f20c-44f1-b4d4-d6013b963ddd"
      },
      "source": [
        "#data.index.month"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "            ...\n",
              "            12, 12, 12, 12, 12, 12, 12, 12, 12, 12],\n",
              "           dtype='int64', name='date', length=1455)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5WjsJkNzfrJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "7c38a2e8-2c8b-4d9c-b724-d0c3248a9764"
      },
      "source": [
        "data.groupby(data.index.month).mean()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>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>7457.743574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8215.418101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7792.112536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8018.248220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>9541.400778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11212.715347</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      7457.743574\n",
              "8      8215.418101\n",
              "9      7792.112536\n",
              "10     8018.248220\n",
              "11     9541.400778\n",
              "12    11212.715347"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxCRdQZEyUcZ",
        "outputId": "26f7defa-64fa-41e0-83db-1b9b52a47b92"
      },
      "source": [
        "#data.index.year"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,\n",
              "            ...\n",
              "            2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020],\n",
              "           dtype='int64', name='date', length=1455)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6Z2DcfezliJ",
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "216f6169-8261-4952-f5fb-6b2750fa1117"
      },
      "source": [
        "data.groupby(data.index.year).mean()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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>10837.789576</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  10837.789576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaFTnwXWjBFj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "a5875b71-6814-4b0e-a917-7703e7e13a43"
      },
      "source": [
        "data[:5]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "  </tbody>\n",
              "</table>\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xg1xufS-yhGj"
      },
      "source": [
        "**cumsum()** function gives cummulative sum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJRV9qdhjUrX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "6c08a3cc-918f-4d9d-dfba-b66dadb45453"
      },
      "source": [
        "data[:5].cumsum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>bpi</th>\n",
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              "      <th>date</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>997.6888</td>\n",
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              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>2015.7388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>3046.5563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>4176.4263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>5182.2413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  bpi\n",
              "date                 \n",
              "2017-01-01   997.6888\n",
              "2017-01-02  2015.7388\n",
              "2017-01-03  3046.5563\n",
              "2017-01-04  4176.4263\n",
              "2017-01-05  5182.2413"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-_e3IPSjUpP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "65a7dab8-139d-4628-d6fb-61d65d727d9f"
      },
      "source": [
        "data[:5]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>bpi</th>\n",
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              "    <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",
              "  </tbody>\n",
              "</table>\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmGQHyh_yxvW"
      },
      "source": [
        "**pct_change(2)** given you the percentage change of two lags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnJnkPsLjUl8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "f67f5ba4-ac9e-45ae-96a7-a47dc0ee5f23"
      },
      "source": [
        "data[:5].pct_change(2)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>0.033205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>0.109837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>-0.024255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 bpi\n",
              "date                \n",
              "2017-01-01       NaN\n",
              "2017-01-02       NaN\n",
              "2017-01-03  0.033205\n",
              "2017-01-04  0.109837\n",
              "2017-01-05 -0.024255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsYU1bjgzvuC"
      },
      "source": [
        "The expanding() function is used to provide expanding transformations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9KzMXvljBA5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
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        "outputId": "a1643044-2892-4c76-f078-65e3223251f4"
      },
      "source": [
        "data[:5].expanding().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    <tr>\n",
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              "      <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>2015.7388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>3046.5563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>4176.4263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>5182.2413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  bpi\n",
              "date                 \n",
              "2017-01-01   997.6888\n",
              "2017-01-02  2015.7388\n",
              "2017-01-03  3046.5563\n",
              "2017-01-04  4176.4263\n",
              "2017-01-05  5182.2413"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2Uwggn1jA9U",
        "colab": {
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          "height": 235
        },
        "outputId": "d5f5c42a-9e96-4fd2-f65f-ee289c34aa40"
      },
      "source": [
        "data[:5].expanding().mean()"
      ],
      "execution_count": 16,
      "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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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>997.688800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>1007.869400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>1015.518767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>1044.106575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>1036.448260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    bpi\n",
              "date                   \n",
              "2017-01-01   997.688800\n",
              "2017-01-02  1007.869400\n",
              "2017-01-03  1015.518767\n",
              "2017-01-04  1044.106575\n",
              "2017-01-05  1036.448260"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MImY4JAnwtoR"
      },
      "source": [
        "-------------------"
      ]
    }
  ]
}