Time_Series_EDA.ipynb

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      "name": "Time Series EDA.ipynb",
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      "authorship_tag": "ABX9TyNEveL3S3j7VYQIlqi7rGGY",
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      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/manasik29/Time_Series/blob/main/Time_Series_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Time Series EDA**"
      ],
      "metadata": {
        "id": "3M8jSuiU2Ihj"
      }
    },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.7/dist-packages (0.9.0)\n",
            "Collecting pandas-datareader\n",
            "  Downloading pandas_datareader-0.10.0-py3-none-any.whl (109 kB)\n",
            "\u001b[K     |████████████████████████████████| 109 kB 12.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (1.3.5)\n",
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            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-datareader) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (3.0.4)\n",
            "Installing collected packages: pandas-datareader\n",
            "  Attempting uninstall: pandas-datareader\n",
            "    Found existing installation: pandas-datareader 0.9.0\n",
            "    Uninstalling pandas-datareader-0.9.0:\n",
            "      Successfully uninstalled pandas-datareader-0.9.0\n",
            "Successfully installed pandas-datareader-0.10.0\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade pandas-datareader"
      ]
    },
    {
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      "source": [
        "import pandas_datareader as pdr\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ],
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        "id": "VyVV78GQ2WLj"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "df_tesla = pdr.get_data_yahoo('TSLA')\n",
        "df_tesla.head()"
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              "                 High        Low       Open      Close      Volume  Adj Close\n",
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              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
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              "          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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.plot()   #volume data is dominating bcz of high value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rl6LN8S75GtW",
        "outputId": "966852c9-7ca4-4e1a-d229-428acc9f8fc2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed67c714d0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "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": [
        "df_tesla['High'].plot(figsize=(12,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "wM3QCIE-5LgS",
        "outputId": "f74af8b4-e1d4-4676-e53c-8056690fe710"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed67c10c90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla['High'].plot(xlim=['2020-01-01', '2021-09-01'], ylim=[50,900], figsize=(12,4), ls='dashdot', c = 'green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "G75kKRby6P0v",
        "outputId": "2bb50863-9112-4789-9c80-9ebc1efe799c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed67b73c10>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla['Low'].plot(xlim=['2020-01-01', '2021-09-01'], ylim=[50,900], figsize=(12,4), ls='dashed', c = 'red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "IsbX-pBs5Z4q",
        "outputId": "56a420ec-1366-4ab4-959f-6bcf58b6caa8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed67a3a3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2M-Dg1L8aBo",
        "outputId": "13bbb401-bdfa-4929-e366-7abbaf18137b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2017-08-17', '2017-08-18', '2017-08-21', '2017-08-22',\n",
              "               '2017-08-23', '2017-08-24', '2017-08-25', '2017-08-28',\n",
              "               '2017-08-29', '2017-08-30',\n",
              "               ...\n",
              "               '2022-08-03', '2022-08-04', '2022-08-05', '2022-08-08',\n",
              "               '2022-08-09', '2022-08-10', '2022-08-11', '2022-08-12',\n",
              "               '2022-08-15', '2022-08-16'],\n",
              "              dtype='datetime64[ns]', name='Date', length=1258, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.loc['2020-01-01':'2021-09-01'].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO3uzoe75t3y",
        "outputId": "62ca2c07-59e3-4776-d0da-d9274b320657"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07',\n",
              "               '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13',\n",
              "               '2020-01-14', '2020-01-15',\n",
              "               ...\n",
              "               '2021-08-19', '2021-08-20', '2021-08-23', '2021-08-24',\n",
              "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-30',\n",
              "               '2021-08-31', '2021-09-01'],\n",
              "              dtype='datetime64[ns]', name='Date', length=421, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "share_open = df_tesla.loc['2020-01-01':'2021-09-01']['Open']\n",
        "share_open"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jx-Qgf9p8Ux7",
        "outputId": "ff66a20b-423e-4926-dacb-2ef54265e526"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2020-01-02     84.900002\n",
              "2020-01-03     88.099998\n",
              "2020-01-06     88.094002\n",
              "2020-01-07     92.279999\n",
              "2020-01-08     94.739998\n",
              "                 ...    \n",
              "2021-08-26    708.309998\n",
              "2021-08-27    705.000000\n",
              "2021-08-30    714.719971\n",
              "2021-08-31    733.000000\n",
              "2021-09-01    734.080017\n",
              "Name: Open, Length: 421, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index = df_tesla.loc['2020-01-01':'2021-09-01'].index"
      ],
      "metadata": {
        "id": "oas354WR82W5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "fig,axis=plt.subplots()\n",
        "plt.tight_layout()\n",
        "fig.autofmt_xdate()\n",
        "axis.plot(index,share_open)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OVI_UKM_9ozo",
        "outputId": "5a6269d8-e055-4856-f758-5e8d729eb85f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fed67b714d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [
        "# Datetime index"
      ],
      "metadata": {
        "id": "Vsz94kRV97_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla = df_tesla.reset_index()                       #get index as column\n",
        "df_tesla"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "28q2v4zdADT2",
        "outputId": "0fab45dc-3250-479d-f196-2946e6ac8844"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        High         Low        Open       Close      Volume  \\\n",
              "0    2017-08-17   72.660004   70.318001   72.241997   70.384003  25138500.0   \n",
              "1    2017-08-18   70.800003   69.160004   70.582001   69.491997  27041000.0   \n",
              "2    2017-08-21   69.164001   66.370003   69.164001   67.571999  32477000.0   \n",
              "3    2017-08-22   68.447998   67.473999   68.225998   68.269997  21610000.0   \n",
              "4    2017-08-23   70.697998   67.660004   67.797997   70.554001  24772500.0   \n",
              "...         ...         ...         ...         ...         ...         ...   \n",
              "1253 2022-08-10  892.530029  850.109985  891.200012  883.070007  31639600.0   \n",
              "1254 2022-08-11  894.710022  857.500000  889.539978  859.890015  23385000.0   \n",
              "1255 2022-08-12  900.479980  855.099976  868.250000  900.090027  26443300.0   \n",
              "1256 2022-08-15  939.400024  903.690002  905.359985  927.960022  29717600.0   \n",
              "1257 2022-08-16  944.000000  908.650024  935.000000  911.869995  11199389.0   \n",
              "\n",
              "       Adj Close  \n",
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              "      <th>0</th>\n",
              "      <td>2017-08-17</td>\n",
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              "      <td>2017-08-18</td>\n",
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              "      <td>2022-08-10</td>\n",
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              "      <th>1254</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>894.710022</td>\n",
              "      <td>857.500000</td>\n",
              "      <td>889.539978</td>\n",
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              "      <th>1255</th>\n",
              "      <td>2022-08-12</td>\n",
              "      <td>900.479980</td>\n",
              "      <td>855.099976</td>\n",
              "      <td>868.250000</td>\n",
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              "      <td>26443300.0</td>\n",
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              "      <th>1256</th>\n",
              "      <td>2022-08-15</td>\n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnVKmGo7AHRW",
        "outputId": "2f6d6b79-cadb-4c20-91ff-797fcc04084b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1258 entries, 0 to 1257\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Date       1258 non-null   datetime64[ns]\n",
            " 1   High       1258 non-null   float64       \n",
            " 2   Low        1258 non-null   float64       \n",
            " 3   Open       1258 non-null   float64       \n",
            " 4   Close      1258 non-null   float64       \n",
            " 5   Volume     1258 non-null   float64       \n",
            " 6   Adj Close  1258 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(6)\n",
            "memory usage: 68.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# if we want to change ojt dtype to datetime\n",
        "# pd.to_datetime(df_tesla['Date'])"
      ],
      "metadata": {
        "id": "znzuDeozAM-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla = df_tesla.set_index('Date', drop=True)                      # set comlumn to index\n",
        "df_tesla"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "CNesc6DKAYjo",
        "outputId": "b1f1810c-51f0-4572-ba9a-fa1dc3c91c34"
      },
      "execution_count": 15,
      "outputs": [
        {
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              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2017-08-17   72.660004   70.318001   72.241997   70.384003  25138500.0   \n",
              "2017-08-18   70.800003   69.160004   70.582001   69.491997  27041000.0   \n",
              "2017-08-21   69.164001   66.370003   69.164001   67.571999  32477000.0   \n",
              "2017-08-22   68.447998   67.473999   68.225998   68.269997  21610000.0   \n",
              "2017-08-23   70.697998   67.660004   67.797997   70.554001  24772500.0   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2022-08-10  892.530029  850.109985  891.200012  883.070007  31639600.0   \n",
              "2022-08-11  894.710022  857.500000  889.539978  859.890015  23385000.0   \n",
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              "2022-08-16  944.000000  908.650024  935.000000  911.869995  11199389.0   \n",
              "\n",
              "             Adj Close  \n",
              "Date                    \n",
              "2017-08-17   70.384003  \n",
              "2017-08-18   69.491997  \n",
              "2017-08-21   67.571999  \n",
              "2017-08-22   68.269997  \n",
              "2017-08-23   70.554001  \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-711e59c5-3221-43ec-80ac-30607204f00d 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-711e59c5-3221-43ec-80ac-30607204f00d');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "datetime(2021,11,11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7p8gXJlAy2q",
        "outputId": "553c2531-9803-4128-df20-291fef36edce"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 11, 11, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datetime.now()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ET9PQ5PBwBa",
        "outputId": "65843a2d-6a68-4651-97ed-f91fdbc18199"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2022, 8, 16, 14, 41, 34, 63641)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "starttime = datetime.now()\n",
        "def add_num(num1, num2):\n",
        "  return num1+num2\n",
        "\n",
        "num1 = 10\n",
        "num2 = 20\n",
        "\n",
        "add_num(num1,num2)\n",
        "endtime = datetime.now()\n",
        "\n",
        "print(\"execution time:\", endtime-starttime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-PHhUzgB5hv",
        "outputId": "a6906072-ffa0-401b-ed56-77ad9dbc0c02"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "execution time: 0:00:00.000345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date=datetime.now()"
      ],
      "metadata": {
        "id": "YxfH2SfuMDpR"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(date)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tersXP_RfSgT",
        "outputId": "345e6e90-f3e8-4b72-d410-687c13260a97"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.date()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9GK01d7MDj2",
        "outputId": "b60777c9-8c28-473a-a184-f7a673656422"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.date(2022, 8, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.day"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFi_gUfhMDdL",
        "outputId": "63ff1421-ee6f-4bed-9a2f-03dcafb9054c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.month"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB-geereMDWS",
        "outputId": "1bbc398e-ed75-417f-d811-e5f79d75faff"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p3j98JdMDNx",
        "outputId": "844622d8-7db5-4fd8-e10c-409908b21d99"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2022"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.hour"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rgp8dIJAMDF9",
        "outputId": "a8cefbd1-e184-49f1-893c-b9872fcd2b62"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.minute"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_42B6CmMC_3",
        "outputId": "0408c39a-d122-4741-89d8-9f972dea541a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.second"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbHJwQKJMhhp",
        "outputId": "70302a7f-29a2-47fc-b7ef-01a6ed65c7ca"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date.weekday()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWHcTY4QMi6m",
        "outputId": "d7196447-86a5-421f-d53d-4e0e31fd2192"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Datetime Resampling**"
      ],
      "metadata": {
        "id": "CeER3a37NzOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "SoLvNcP5MiuD",
        "outputId": "6456b21e-8a2c-4cbb-9601-fb77249d379f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 High        Low       Open      Close      Volume  Adj Close\n",
              "Date                                                                         \n",
              "2017-08-17  72.660004  70.318001  72.241997  70.384003  25138500.0  70.384003\n",
              "2017-08-18  70.800003  69.160004  70.582001  69.491997  27041000.0  69.491997\n",
              "2017-08-21  69.164001  66.370003  69.164001  67.571999  32477000.0  67.571999\n",
              "2017-08-22  68.447998  67.473999  68.225998  68.269997  21610000.0  68.269997\n",
              "2017-08-23  70.697998  67.660004  67.797997  70.554001  24772500.0  70.554001"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-08-17</th>\n",
              "      <td>72.660004</td>\n",
              "      <td>70.318001</td>\n",
              "      <td>72.241997</td>\n",
              "      <td>70.384003</td>\n",
              "      <td>25138500.0</td>\n",
              "      <td>70.384003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-18</th>\n",
              "      <td>70.800003</td>\n",
              "      <td>69.160004</td>\n",
              "      <td>70.582001</td>\n",
              "      <td>69.491997</td>\n",
              "      <td>27041000.0</td>\n",
              "      <td>69.491997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-21</th>\n",
              "      <td>69.164001</td>\n",
              "      <td>66.370003</td>\n",
              "      <td>69.164001</td>\n",
              "      <td>67.571999</td>\n",
              "      <td>32477000.0</td>\n",
              "      <td>67.571999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-22</th>\n",
              "      <td>68.447998</td>\n",
              "      <td>67.473999</td>\n",
              "      <td>68.225998</td>\n",
              "      <td>68.269997</td>\n",
              "      <td>21610000.0</td>\n",
              "      <td>68.269997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-23</th>\n",
              "      <td>70.697998</td>\n",
              "      <td>67.660004</td>\n",
              "      <td>67.797997</td>\n",
              "      <td>70.554001</td>\n",
              "      <td>24772500.0</td>\n",
              "      <td>70.554001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4936e32a-6e48-4179-a996-19ad62f72b6f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-4936e32a-6e48-4179-a996-19ad62f72b6f 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-4936e32a-6e48-4179-a996-19ad62f72b6f');\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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule='A').min()   #Gives us the min values of all comlumns in every year in a dateframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "LgtlzIJNMio8",
        "outputId": "a3321512-d78c-4adf-c52f-d629fbf8a549"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2017-12-31   60.891998   58.526001   59.900002   59.852001  15247500.0   \n",
              "2018-12-31   52.066002   48.917999   50.556000   50.112000  15403500.0   \n",
              "2019-12-31   37.335999   35.397999   36.220001   35.793999  12328000.0   \n",
              "2020-12-31   80.972000   70.101997   74.940002   72.244003  17357700.0   \n",
              "2021-12-31  566.210022  539.489990  552.549988  563.000000   9800600.0   \n",
              "2022-12-31  653.919983  620.570007  623.849976  628.159973  11199389.0   \n",
              "\n",
              "             Adj Close  \n",
              "Date                    \n",
              "2017-12-31   59.852001  \n",
              "2018-12-31   50.112000  \n",
              "2019-12-31   35.793999  \n",
              "2020-12-31   72.244003  \n",
              "2021-12-31  563.000000  \n",
              "2022-12-31  628.159973  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Year end frequency\n",
        "df_tesla.resample(rule='A').max()                 #Gives us the min values of all comlumns in every year in a dataframe   "
      ],
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        "colab": {
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          "height": 269
        },
        "id": "TRly5NaaOJUR",
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      "execution_count": 32,
      "outputs": [
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              "                   High          Low         Open        Close       Volume  \\\n",
              "Date                                                                          \n",
              "2017-12-31    77.921997    75.536003    76.050003    77.000000   98957000.0   \n",
              "2018-12-31    77.491997    73.424004    75.000000    75.914001  168248500.0   \n",
              "2019-12-31    87.061996    85.269997    87.000000    86.188004  150030500.0   \n",
              "2020-12-31   718.719971   691.119995   699.989990   705.669983  304694000.0   \n",
              "2021-12-31  1243.489990  1217.000000  1234.410034  1229.910034   89396500.0   \n",
              "2022-12-31  1208.000000  1136.040039  1189.550049  1199.780029   50521900.0   \n",
              "\n",
              "              Adj Close  \n",
              "Date                     \n",
              "2017-12-31    77.000000  \n",
              "2018-12-31    75.914001  \n",
              "2019-12-31    86.188004  \n",
              "2020-12-31   705.669983  \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
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          "metadata": {},
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    {
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        "df_tesla.resample(rule='A').max()['Open'].plot()"
      ],
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        "id": "LwV5FL0pOY3v",
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      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed64b794d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "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": [
        "df_tesla.resample(rule='A').min()['Open'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "u3utH7GIOYyH",
        "outputId": "003bec04-dde8-4f8f-9df7-8a4651ade1b9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed62199b90>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "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": [
        "## Quarterly frequency\n",
        "df_tesla.resample(rule='QS').max()  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "ubPT5fvKOYut",
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              "                   High          Low         Open        Close       Volume  \\\n",
              "Date                                                                          \n",
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              "              Adj Close  \n",
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          "metadata": {},
          "execution_count": 35
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    },
    {
      "cell_type": "code",
      "source": [
        "## Business year end frequency\n",
        "df_tesla.resample(rule='BA').max() "
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 269
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        "outputId": "80917085-dfcf-4ee1-ddf3-23ffd1090be7"
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      "execution_count": 36,
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              "                   High          Low         Open        Close       Volume  \\\n",
              "Date                                                                          \n",
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              "\n",
              "              Adj Close  \n",
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              "      <td>691.119995</td>\n",
              "      <td>699.989990</td>\n",
              "      <td>705.669983</td>\n",
              "      <td>304694000.0</td>\n",
              "      <td>705.669983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>1243.489990</td>\n",
              "      <td>1217.000000</td>\n",
              "      <td>1234.410034</td>\n",
              "      <td>1229.910034</td>\n",
              "      <td>89396500.0</td>\n",
              "      <td>1229.910034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-30</th>\n",
              "      <td>1208.000000</td>\n",
              "      <td>1136.040039</td>\n",
              "      <td>1189.550049</td>\n",
              "      <td>1199.780029</td>\n",
              "      <td>50521900.0</td>\n",
              "      <td>1199.780029</td>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Quarterly Business frquency\n",
        "df_tesla.resample(rule='BQS').min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "0pNh67wEQGiT",
        "outputId": "743c6318-d281-43ce-d249-c425de0f4bff"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2017-07-03   68.447998   66.370003   67.797997   67.571999  15247500.0   \n",
              "2017-10-02   60.891998   58.526001   59.900002   59.852001  16220500.0   \n",
              "2018-01-01   53.736000   49.641998   51.298000   51.556000  16098000.0   \n",
              "2018-04-02   52.066002   48.917999   50.556000   50.495998  19375500.0   \n",
              "2018-07-02   53.669998   50.450001   52.020000   52.647999  18013000.0   \n",
              "2018-10-01   52.372002   49.554001   51.049999   50.112000  15403500.0   \n",
              "2019-01-01   52.636002   50.891998   51.942001   52.084000  18383500.0   \n",
              "2019-04-01   37.335999   35.397999   36.220001   35.793999  25632500.0   \n",
              "2019-07-01   43.004002   42.200001   42.720001   42.279999  16127500.0   \n",
              "2019-10-01   46.896000   44.855999   45.959999   46.285999  12328000.0   \n",
              "2020-01-01   80.972000   70.101997   74.940002   72.244003  47660500.0   \n",
              "2020-04-01   98.851997   89.279999   96.206001   90.893997  31812000.0   \n",
              "2020-07-01  227.065994  216.100006  216.600006  223.925995  24739000.0   \n",
              "2020-10-01  406.980011  379.109985  394.000000  388.040009  17357700.0   \n",
              "2021-01-01  616.479980  539.489990  600.549988  563.000000  15157700.0   \n",
              "2021-04-01  566.210022  546.979980  552.549988  563.460022  16205300.0   \n",
              "2021-07-01  647.200012  620.460022  628.369995  643.380005   9800600.0   \n",
              "2021-10-01  780.780029  763.590027  776.200012  775.219971  11680900.0   \n",
              "2022-01-03  800.700012  700.000000  700.390015  764.039978  16330900.0   \n",
              "2022-04-01  653.919983  620.570007  623.849976  628.159973  16615900.0   \n",
              "2022-07-01  690.690002  648.500000  669.000000  681.789978  11199389.0   \n",
              "\n",
              "             Adj Close  \n",
              "Date                    \n",
              "2017-07-03   67.571999  \n",
              "2017-10-02   59.852001  \n",
              "2018-01-01   51.556000  \n",
              "2018-04-02   50.495998  \n",
              "2018-07-02   52.647999  \n",
              "2018-10-01   50.112000  \n",
              "2019-01-01   52.084000  \n",
              "2019-04-01   35.793999  \n",
              "2019-07-01   42.279999  \n",
              "2019-10-01   46.285999  \n",
              "2020-01-01   72.244003  \n",
              "2020-04-01   90.893997  \n",
              "2020-07-01  223.925995  \n",
              "2020-10-01  388.040009  \n",
              "2021-01-01  563.000000  \n",
              "2021-04-01  563.460022  \n",
              "2021-07-01  643.380005  \n",
              "2021-10-01  775.219971  \n",
              "2022-01-03  764.039978  \n",
              "2022-04-01  628.159973  \n",
              "2022-07-01  681.789978  "
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              "      <th>High</th>\n",
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              "      <th>Close</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-07-03</th>\n",
              "      <td>68.447998</td>\n",
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              "      <td>67.797997</td>\n",
              "      <td>67.571999</td>\n",
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              "      <td>51.556000</td>\n",
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              "      <td>52.647999</td>\n",
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              "      <th>2018-10-01</th>\n",
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              "      <th>2019-04-01</th>\n",
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              "      <td>35.397999</td>\n",
              "      <td>36.220001</td>\n",
              "      <td>35.793999</td>\n",
              "      <td>25632500.0</td>\n",
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              "      <td>42.279999</td>\n",
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              "      <th>2020-04-01</th>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-945cfa59-8ae0-461b-ac77-96661859880f 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-945cfa59-8ae0-461b-ac77-96661859880f');\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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla['Open'].resample(rule='A').mean().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "mpDAvsRPQjpR",
        "outputId": "963675c9-0286-466e-903a-bccff73668b7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed610975d0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "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": [
        "#Max or Min increase in data\n",
        "df_tesla['Open'].resample(rule='M').max().plot(kind='bar', figsize=(17,7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "n2FMf2euQ-_v",
        "outputId": "7a82c21e-95f1-49cc-ca33-8dffaec3e267"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed60e9afd0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gives rolling mean of 5 rows and prints on the 5th row\n",
        "df_tesla['High'].rolling(5).mean().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uf-jgSRRiuy",
        "outputId": "af8c8fbf-3819-492e-b061-04a058d2a698"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-08-17          NaN\n",
              "2017-08-18          NaN\n",
              "2017-08-21          NaN\n",
              "2017-08-22          NaN\n",
              "2017-08-23    70.354001\n",
              "2017-08-24    70.088400\n",
              "2017-08-25    70.156000\n",
              "2017-08-28    70.217200\n",
              "2017-08-29    70.489600\n",
              "2017-08-30    70.488800\n",
              "Name: High, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gives max of values b/w first 5 rows and prints on the 5th row\n",
        "df_tesla['High'].rolling(5).max().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4NweACGS7r9",
        "outputId": "f388537e-15e6-45ca-f0b5-9768d7010cf2"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-08-17          NaN\n",
              "2017-08-18          NaN\n",
              "2017-08-21          NaN\n",
              "2017-08-22          NaN\n",
              "2017-08-23    72.660004\n",
              "2017-08-24    71.332001\n",
              "2017-08-25    71.332001\n",
              "2017-08-28    71.332001\n",
              "2017-08-29    71.332001\n",
              "2017-08-30    71.332001\n",
              "Name: High, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"Open:30 days rolling\"] = df_tesla['Open'].rolling(30).mean()\n",
        "df_tesla"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "SSovVd-yTNqF",
        "outputId": "f7a3fe3f-6f84-40c8-ca85-632c6471e910"
      },
      "execution_count": 46,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2017-08-17   72.660004   70.318001   72.241997   70.384003  25138500.0   \n",
              "2017-08-18   70.800003   69.160004   70.582001   69.491997  27041000.0   \n",
              "2017-08-21   69.164001   66.370003   69.164001   67.571999  32477000.0   \n",
              "2017-08-22   68.447998   67.473999   68.225998   68.269997  21610000.0   \n",
              "2017-08-23   70.697998   67.660004   67.797997   70.554001  24772500.0   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2022-08-10  892.530029  850.109985  891.200012  883.070007  31639600.0   \n",
              "2022-08-11  894.710022  857.500000  889.539978  859.890015  23385000.0   \n",
              "2022-08-12  900.479980  855.099976  868.250000  900.090027  26443300.0   \n",
              "2022-08-15  939.400024  903.690002  905.359985  927.960022  29717600.0   \n",
              "2022-08-16  944.000000  908.650024  935.000000  911.869995  11199389.0   \n",
              "\n",
              "             Adj Close  Open:30 days rolling  \n",
              "Date                                          \n",
              "2017-08-17   70.384003                   NaN  \n",
              "2017-08-18   69.491997                   NaN  \n",
              "2017-08-21   67.571999                   NaN  \n",
              "2017-08-22   68.269997                   NaN  \n",
              "2017-08-23   70.554001                   NaN  \n",
              "...                ...                   ...  \n",
              "2022-08-10  883.070007            782.905334  \n",
              "2022-08-11  859.890015            789.506667  \n",
              "2022-08-12  900.090027            795.997333  \n",
              "2022-08-15  927.960022            803.475999  \n",
              "2022-08-16  911.869995            812.342666  \n",
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              "      <th>2017-08-17</th>\n",
              "      <td>72.660004</td>\n",
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              "      <th>2017-08-23</th>\n",
              "      <td>70.697998</td>\n",
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              "      <td>67.797997</td>\n",
              "      <td>70.554001</td>\n",
              "      <td>24772500.0</td>\n",
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              "      <th>2022-08-10</th>\n",
              "      <td>892.530029</td>\n",
              "      <td>850.109985</td>\n",
              "      <td>891.200012</td>\n",
              "      <td>883.070007</td>\n",
              "      <td>31639600.0</td>\n",
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              "      <th>2022-08-11</th>\n",
              "      <td>894.710022</td>\n",
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              "      <td>889.539978</td>\n",
              "      <td>859.890015</td>\n",
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              "      <th>2022-08-12</th>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[['Open','Open:30 days rolling']].plot(figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
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          "height": 384
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        "id": "bJF0ia7iUOvQ",
        "outputId": "2eaf0e4c-a09c-4bcf-b8ac-1da58e08e420"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed61aa5e90>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Assignment**\n",
        "\n",
        "1. Read Microsoft data from pandas data reader.\n",
        "2. Get the highest price of the share from 2017 to 22.\n",
        "3. Which is the date with the highest share price?\n",
        "4. Which is the date with the lowest stock price?"
      ],
      "metadata": {
        "id": "ivR-9sKEVyye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas_datareader.data as web\n",
        "import datetime as dt\n",
        "\n",
        "start = dt.datetime(2017,1,1)\n",
        "end = dt.datetime(2022,8,16)\n",
        "\n",
        "df_msft = web.DataReader('MSFT','yahoo', start, end).reset_index()\n",
        "df_msft.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZDLjXUu3UZya",
        "outputId": "6d276486-3a4e-45c0-a452-161abc21cfec"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date       High        Low       Open      Close      Volume  \\\n",
              "0 2017-01-03  62.840000  62.130001  62.790001  62.580002  20694100.0   \n",
              "1 2017-01-04  62.750000  62.119999  62.480000  62.299999  21340000.0   \n",
              "2 2017-01-05  62.660000  62.029999  62.189999  62.299999  24876000.0   \n",
              "3 2017-01-06  63.150002  62.040001  62.299999  62.840000  19922900.0   \n",
              "4 2017-01-09  63.080002  62.540001  62.759998  62.639999  20382700.0   \n",
              "\n",
              "   Adj Close  \n",
              "0  57.930534  \n",
              "1  57.671341  \n",
              "2  57.671341  \n",
              "3  58.171207  \n",
              "4  57.986076  "
            ],
            "text/html": [
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              "      <th>0</th>\n",
              "      <td>2017-01-03</td>\n",
              "      <td>62.840000</td>\n",
              "      <td>62.130001</td>\n",
              "      <td>62.790001</td>\n",
              "      <td>62.580002</td>\n",
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              "      <td>2017-01-04</td>\n",
              "      <td>62.750000</td>\n",
              "      <td>62.119999</td>\n",
              "      <td>62.480000</td>\n",
              "      <td>62.299999</td>\n",
              "      <td>21340000.0</td>\n",
              "      <td>57.671341</td>\n",
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              "      <td>2017-01-05</td>\n",
              "      <td>62.660000</td>\n",
              "      <td>62.029999</td>\n",
              "      <td>62.189999</td>\n",
              "      <td>62.299999</td>\n",
              "      <td>24876000.0</td>\n",
              "      <td>57.671341</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-01-06</td>\n",
              "      <td>63.150002</td>\n",
              "      <td>62.040001</td>\n",
              "      <td>62.299999</td>\n",
              "      <td>62.840000</td>\n",
              "      <td>19922900.0</td>\n",
              "      <td>58.171207</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-01-09</td>\n",
              "      <td>63.080002</td>\n",
              "      <td>62.540001</td>\n",
              "      <td>62.759998</td>\n",
              "      <td>62.639999</td>\n",
              "      <td>20382700.0</td>\n",
              "      <td>57.986076</td>\n",
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    {
      "cell_type": "code",
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        "df_msft1 = pdr.get_data_yahoo('MSFT')\n",
        "df_msft1.head()"
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              "                 High        Low       Open      Close      Volume  Adj Close\n",
              "Date                                                                         \n",
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    {
      "cell_type": "code",
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        "df_msft.tail()"
      ],
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      "execution_count": 10,
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              "           Date        High         Low        Open       Close      Volume  \\\n",
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      "cell_type": "code",
      "source": [
        "#highest stock in each year\n",
        "df_msft1.resample(rule='A').max()"
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              "                  High         Low        Open       Close       Volume  \\\n",
              "Date                                                                      \n",
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              "2022-12-31  338.000000  329.779999  335.350006  334.750000   90428900.0   \n",
              "\n",
              "             Adj Close  \n",
              "Date                    \n",
              "2017-12-31   82.192513  \n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "- Highest share price this stock went upto was 349.670013."
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              "                 High        Low       Open      Close      Volume  Adj Close\n",
              "Date                                                                         \n",
              "2017-08-21  72.480003  71.699997  72.470001  72.150002  17734800.0  67.939621"
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        "- Lowest share pice of the stock is 71.699997 and it was on 21-08-2017"
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              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2021-11-22  349.670013  339.549988  344.619995  339.829987  31031100.0   \n",
              "\n",
              "             Adj Close  \n",
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        "- Highest share pice of the stock is 349.670013 and it was on 22-11-2021"
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      "execution_count": null,
      "outputs": []
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}