21CSA519A_Case_study2_krishnan_2107470.ipynb

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      "cell_type": "markdown",
      "source": [
        "This sheet contains the program code for performing time series analysis on housing price data for 51 quarters trend/seasonality and forecasting analysis"
      ],
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        "id": "biJ4L9Rg1AFU"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn as sk\n"
      ]
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      "cell_type": "markdown",
      "source": [
        "Solution Part 3) The data regarding hosuing price index published by NHB has been considered for analysis ."
      ],
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      }
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        "pip install statsmodels\n"
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            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
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        "hs_p = pd.read_csv(\"/content/drive/MyDrive/hpi3.csv\")\n",
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              "           ds         y\n",
              "0  01-01-2009   88.9013\n",
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              "2  28-06-2009   93.1466\n",
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              "6  19-06-2010  103.5176\n",
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              "                y\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5febfc74-7e25-4299-a07b-31384979db8f 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-5febfc74-7e25-4299-a07b-31384979db8f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot time series \n",
        "hs_p.plot(x = 'ds', y = 'y')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "E8qAOeMaKzlb",
        "outputId": "e2e2360e-da24-4d11-9ef5-4e2844415c5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f244f8e9690>"
            ]
          },
          "metadata": {},
          "execution_count": 180
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import fbprophet"
      ],
      "metadata": {
        "id": "iKzgTosRMyhF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using prophet package to fit the time series data set hs_p\n",
        "from fbprophet import Prophet"
      ],
      "metadata": {
        "id": "kOUCdUbccMXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " m = Prophet()\n",
        " hsmodel = m.fit(hs_p)\n",
        " hsfcast = m.predict(hs_p)\n",
        "hsfcast.head(10)\n",
        "\n",
        "\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "GYaXh6tFdACo",
        "outputId": "4ee6352d-65e6-4f4a-9ddc-3e5ae0a5b4d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
              "0 2009-01-01   86.478688   85.384412   91.346968    86.478688    86.478688   \n",
              "1 2009-03-31   89.358902   88.524022   94.382605    89.358902    89.358902   \n",
              "2 2009-06-28   92.239116   90.902920   96.910276    92.239116    92.239116   \n",
              "3 2009-09-25   95.119330   91.113146   97.035693    95.119330    95.119330   \n",
              "4 2009-12-23   97.999544   92.606968   98.403995    97.999544    97.999544   \n",
              "5 2010-03-22  100.879757   96.456059  102.480360   100.879757   100.879757   \n",
              "6 2010-06-19  103.759971  101.175417  107.133228   103.759971   103.759971   \n",
              "7 2010-09-16  106.640185   98.775188  104.669880   106.640185   106.640185   \n",
              "8 2010-12-14  109.520399  105.319482  111.428728   109.520399   109.520399   \n",
              "9 2011-03-13  112.400613  108.355657  114.291564   112.400613   112.400613   \n",
              "\n",
              "   additive_terms  additive_terms_lower  additive_terms_upper    weekly  \\\n",
              "0        1.974583              1.974583              1.974583 -0.803550   \n",
              "1        2.047759              2.047759              2.047759 -0.526142   \n",
              "2        1.743446              1.743446              1.743446  1.441386   \n",
              "3       -0.984997             -0.984997             -0.984997  2.114823   \n",
              "4       -2.639018             -2.639018             -2.639018 -1.456832   \n",
              "5       -1.247793             -1.247793             -1.247793 -2.051443   \n",
              "6        0.436076              0.436076              0.436076  1.281759   \n",
              "7       -5.083225             -5.083225             -5.083225 -0.803550   \n",
              "8       -1.023643             -1.023643             -1.023643 -0.526142   \n",
              "9       -1.072317             -1.072317             -1.072317  1.441386   \n",
              "\n",
              "   weekly_lower  weekly_upper    yearly  yearly_lower  yearly_upper  \\\n",
              "0     -0.803550     -0.803550  2.778133      2.778133      2.778133   \n",
              "1     -0.526142     -0.526142  2.573901      2.573901      2.573901   \n",
              "2      1.441386      1.441386  0.302061      0.302061      0.302061   \n",
              "3      2.114823      2.114823 -3.099820     -3.099820     -3.099820   \n",
              "4     -1.456832     -1.456832 -1.182187     -1.182187     -1.182187   \n",
              "5     -2.051443     -2.051443  0.803650      0.803650      0.803650   \n",
              "6      1.281759      1.281759 -0.845683     -0.845683     -0.845683   \n",
              "7     -0.803550     -0.803550 -4.279676     -4.279676     -4.279676   \n",
              "8     -0.526142     -0.526142 -0.497500     -0.497500     -0.497500   \n",
              "9      1.441386      1.441386 -2.513702     -2.513702     -2.513702   \n",
              "\n",
              "   multiplicative_terms  multiplicative_terms_lower  \\\n",
              "0                   0.0                         0.0   \n",
              "1                   0.0                         0.0   \n",
              "2                   0.0                         0.0   \n",
              "3                   0.0                         0.0   \n",
              "4                   0.0                         0.0   \n",
              "5                   0.0                         0.0   \n",
              "6                   0.0                         0.0   \n",
              "7                   0.0                         0.0   \n",
              "8                   0.0                         0.0   \n",
              "9                   0.0                         0.0   \n",
              "\n",
              "   multiplicative_terms_upper        yhat  \n",
              "0                         0.0   88.453272  \n",
              "1                         0.0   91.406661  \n",
              "2                         0.0   93.982562  \n",
              "3                         0.0   94.134333  \n",
              "4                         0.0   95.360525  \n",
              "5                         0.0   99.631964  \n",
              "6                         0.0  104.196047  \n",
              "7                         0.0  101.556960  \n",
              "8                         0.0  108.496756  \n",
              "9                         0.0  111.328296  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7af36683-0bc0-40b4-89fe-0c0cfea93a50\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-01</td>\n",
              "      <td>86.478688</td>\n",
              "      <td>85.384412</td>\n",
              "      <td>91.346968</td>\n",
              "      <td>86.478688</td>\n",
              "      <td>86.478688</td>\n",
              "      <td>1.974583</td>\n",
              "      <td>1.974583</td>\n",
              "      <td>1.974583</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>2.778133</td>\n",
              "      <td>2.778133</td>\n",
              "      <td>2.778133</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>88.453272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-03-31</td>\n",
              "      <td>89.358902</td>\n",
              "      <td>88.524022</td>\n",
              "      <td>94.382605</td>\n",
              "      <td>89.358902</td>\n",
              "      <td>89.358902</td>\n",
              "      <td>2.047759</td>\n",
              "      <td>2.047759</td>\n",
              "      <td>2.047759</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>2.573901</td>\n",
              "      <td>2.573901</td>\n",
              "      <td>2.573901</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.406661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-06-28</td>\n",
              "      <td>92.239116</td>\n",
              "      <td>90.902920</td>\n",
              "      <td>96.910276</td>\n",
              "      <td>92.239116</td>\n",
              "      <td>92.239116</td>\n",
              "      <td>1.743446</td>\n",
              "      <td>1.743446</td>\n",
              "      <td>1.743446</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>0.302061</td>\n",
              "      <td>0.302061</td>\n",
              "      <td>0.302061</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.982562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-09-25</td>\n",
              "      <td>95.119330</td>\n",
              "      <td>91.113146</td>\n",
              "      <td>97.035693</td>\n",
              "      <td>95.119330</td>\n",
              "      <td>95.119330</td>\n",
              "      <td>-0.984997</td>\n",
              "      <td>-0.984997</td>\n",
              "      <td>-0.984997</td>\n",
              "      <td>2.114823</td>\n",
              "      <td>2.114823</td>\n",
              "      <td>2.114823</td>\n",
              "      <td>-3.099820</td>\n",
              "      <td>-3.099820</td>\n",
              "      <td>-3.099820</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>94.134333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-12-23</td>\n",
              "      <td>97.999544</td>\n",
              "      <td>92.606968</td>\n",
              "      <td>98.403995</td>\n",
              "      <td>97.999544</td>\n",
              "      <td>97.999544</td>\n",
              "      <td>-2.639018</td>\n",
              "      <td>-2.639018</td>\n",
              "      <td>-2.639018</td>\n",
              "      <td>-1.456832</td>\n",
              "      <td>-1.456832</td>\n",
              "      <td>-1.456832</td>\n",
              "      <td>-1.182187</td>\n",
              "      <td>-1.182187</td>\n",
              "      <td>-1.182187</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>95.360525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2010-03-22</td>\n",
              "      <td>100.879757</td>\n",
              "      <td>96.456059</td>\n",
              "      <td>102.480360</td>\n",
              "      <td>100.879757</td>\n",
              "      <td>100.879757</td>\n",
              "      <td>-1.247793</td>\n",
              "      <td>-1.247793</td>\n",
              "      <td>-1.247793</td>\n",
              "      <td>-2.051443</td>\n",
              "      <td>-2.051443</td>\n",
              "      <td>-2.051443</td>\n",
              "      <td>0.803650</td>\n",
              "      <td>0.803650</td>\n",
              "      <td>0.803650</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99.631964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2010-06-19</td>\n",
              "      <td>103.759971</td>\n",
              "      <td>101.175417</td>\n",
              "      <td>107.133228</td>\n",
              "      <td>103.759971</td>\n",
              "      <td>103.759971</td>\n",
              "      <td>0.436076</td>\n",
              "      <td>0.436076</td>\n",
              "      <td>0.436076</td>\n",
              "      <td>1.281759</td>\n",
              "      <td>1.281759</td>\n",
              "      <td>1.281759</td>\n",
              "      <td>-0.845683</td>\n",
              "      <td>-0.845683</td>\n",
              "      <td>-0.845683</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104.196047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2010-09-16</td>\n",
              "      <td>106.640185</td>\n",
              "      <td>98.775188</td>\n",
              "      <td>104.669880</td>\n",
              "      <td>106.640185</td>\n",
              "      <td>106.640185</td>\n",
              "      <td>-5.083225</td>\n",
              "      <td>-5.083225</td>\n",
              "      <td>-5.083225</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>-0.803550</td>\n",
              "      <td>-4.279676</td>\n",
              "      <td>-4.279676</td>\n",
              "      <td>-4.279676</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.556960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2010-12-14</td>\n",
              "      <td>109.520399</td>\n",
              "      <td>105.319482</td>\n",
              "      <td>111.428728</td>\n",
              "      <td>109.520399</td>\n",
              "      <td>109.520399</td>\n",
              "      <td>-1.023643</td>\n",
              "      <td>-1.023643</td>\n",
              "      <td>-1.023643</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>-0.526142</td>\n",
              "      <td>-0.497500</td>\n",
              "      <td>-0.497500</td>\n",
              "      <td>-0.497500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>108.496756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2011-03-13</td>\n",
              "      <td>112.400613</td>\n",
              "      <td>108.355657</td>\n",
              "      <td>114.291564</td>\n",
              "      <td>112.400613</td>\n",
              "      <td>112.400613</td>\n",
              "      <td>-1.072317</td>\n",
              "      <td>-1.072317</td>\n",
              "      <td>-1.072317</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>1.441386</td>\n",
              "      <td>-2.513702</td>\n",
              "      <td>-2.513702</td>\n",
              "      <td>-2.513702</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>111.328296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
          "execution_count": 208
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    {
      "cell_type": "code",
      "source": [
        "hsfig = m.plot_components(hsfcast)"
      ],
      "metadata": {
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          "height": 657
        },
        "id": "FhlEKcR7Z3Wt",
        "outputId": "19f9df4e-9697-440a-af38-4fb2de2ff97f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install statsmodels\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-CtJ7wic7dB",
        "outputId": "3e1778ac-b3d1-48b9-c2c5-7aa389dbd790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (0.5.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.3.5)\n",
            "Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from statsmodels) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->statsmodels) (2022.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.4.0->statsmodels) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sms\n",
        "import statsmodels.formula.api as smf\n"
      ],
      "metadata": {
        "id": "6uAJLXtZehfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hs_p.head(5)"
      ],
      "metadata": {
        "colab": {
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              "           ds        y\n",
              "0  01-01-2009  88.9013\n",
              "1  31-03-2009  91.8104\n",
              "2  28-06-2009  93.1466\n",
              "3  25-09-2009  94.6526\n",
              "4  23-12-2009  96.2271"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "hs_p.info()\n"
      ],
      "metadata": {
        "colab": {
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        "id": "humYiXfggaau",
        "outputId": "c7662750-00b8-4b62-9e94-928d54272d3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   ds      51 non-null     object \n",
            " 1   y       51 non-null     float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 944.0+ bytes\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "hs_p.set_index('y',inplace = True)"
      ],
      "metadata": {
        "id": "yOY8CnMEjIbI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}