{
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cyrus723/my-first-binder/blob/main/pandas101_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# pandas\n",
        "\n",
        "Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n",
        "\n",
        "Tree fundamental Pandas data structures: \n",
        "  ```\n",
        "  the Series, \n",
        "  DataFrame, \n",
        "  and Index.\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "c8WKgTLrD2Ny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Ku_XIFYF84Ip",
        "outputId": "2a83f829-ec02-4348-c3cb-e2a5c509df6c"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.3.5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(help(pd))"
      ],
      "metadata": {
        "id": "yxma0Hea88r8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d8b0cb6-4b61-4f46-94fe-e7a3c016f1d8"
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      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on package pandas:\n",
            "\n",
            "NAME\n",
            "    pandas\n",
            "\n",
            "DESCRIPTION\n",
            "    pandas - a powerful data analysis and manipulation library for Python\n",
            "    =====================================================================\n",
            "    \n",
            "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
            "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
            "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
            "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
            "    the broader goal of becoming **the most powerful and flexible open source data\n",
            "    analysis / manipulation tool available in any language**. It is already well on\n",
            "    its way toward this goal.\n",
            "    \n",
            "    Main Features\n",
            "    -------------\n",
            "    Here are just a few of the things that pandas does well:\n",
            "    \n",
            "      - Easy handling of missing data in floating point as well as non-floating\n",
            "        point data.\n",
            "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
            "        higher dimensional objects\n",
            "      - Automatic and explicit data alignment: objects can be explicitly aligned\n",
            "        to a set of labels, or the user can simply ignore the labels and let\n",
            "        `Series`, `DataFrame`, etc. automatically align the data for you in\n",
            "        computations.\n",
            "      - Powerful, flexible group by functionality to perform split-apply-combine\n",
            "        operations on data sets, for both aggregating and transforming data.\n",
            "      - Make it easy to convert ragged, differently-indexed data in other Python\n",
            "        and NumPy data structures into DataFrame objects.\n",
            "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n",
            "        data sets.\n",
            "      - Intuitive merging and joining data sets.\n",
            "      - Flexible reshaping and pivoting of data sets.\n",
            "      - Hierarchical labeling of axes (possible to have multiple labels per tick).\n",
            "      - Robust IO tools for loading data from flat files (CSV and delimited),\n",
            "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
            "        format.\n",
            "      - Time series-specific functionality: date range generation and frequency\n",
            "        conversion, moving window statistics, date shifting and lagging.\n",
            "\n",
            "PACKAGE CONTENTS\n",
            "    _config (package)\n",
            "    _libs (package)\n",
            "    _testing (package)\n",
            "    _typing\n",
            "    _version\n",
            "    api (package)\n",
            "    arrays (package)\n",
            "    compat (package)\n",
            "    conftest\n",
            "    core (package)\n",
            "    errors (package)\n",
            "    io (package)\n",
            "    plotting (package)\n",
            "    testing\n",
            "    tests (package)\n",
            "    tseries (package)\n",
            "    util (package)\n",
            "\n",
            "SUBMODULES\n",
            "    _hashtable\n",
            "    _lib\n",
            "    _tslib\n",
            "    offsets\n",
            "\n",
            "FUNCTIONS\n",
            "    __getattr__(name)\n",
            "        # GH 27101\n",
            "\n",
            "DATA\n",
            "    IndexSlice = <pandas.core.indexing._IndexSlice object>\n",
            "    NA = <NA>\n",
            "    NaT = NaT\n",
            "    __docformat__ = 'restructuredtext'\n",
            "    __git_version__ = '66e3805b8cabe977f40c05259cc3fcf7ead5687d'\n",
            "    describe_option = <pandas._config.config.CallableDynamicDoc object>\n",
            "    get_option = <pandas._config.config.CallableDynamicDoc object>\n",
            "    options = <pandas._config.config.DictWrapper object>\n",
            "    reset_option = <pandas._config.config.CallableDynamicDoc object>\n",
            "    set_option = <pandas._config.config.CallableDynamicDoc object>\n",
            "\n",
            "VERSION\n",
            "    1.3.5\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/pandas/__init__.py\n",
            "\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(pd)"
      ],
      "metadata": {
        "id": "3O6NyEIv9adB",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "874e205d-ac41-4fad-8d5b-49ef72072314"
      },
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BooleanDtype',\n",
              " 'Categorical',\n",
              " 'CategoricalDtype',\n",
              " 'CategoricalIndex',\n",
              " 'DataFrame',\n",
              " 'DateOffset',\n",
              " 'DatetimeIndex',\n",
              " 'DatetimeTZDtype',\n",
              " 'ExcelFile',\n",
              " 'ExcelWriter',\n",
              " 'Flags',\n",
              " 'Float32Dtype',\n",
              " 'Float64Dtype',\n",
              " 'Float64Index',\n",
              " 'Grouper',\n",
              " 'HDFStore',\n",
              " 'Index',\n",
              " 'IndexSlice',\n",
              " 'Int16Dtype',\n",
              " 'Int32Dtype',\n",
              " 'Int64Dtype',\n",
              " 'Int64Index',\n",
              " 'Int8Dtype',\n",
              " 'Interval',\n",
              " 'IntervalDtype',\n",
              " 'IntervalIndex',\n",
              " 'MultiIndex',\n",
              " 'NA',\n",
              " 'NaT',\n",
              " 'NamedAgg',\n",
              " 'Period',\n",
              " 'PeriodDtype',\n",
              " 'PeriodIndex',\n",
              " 'RangeIndex',\n",
              " 'Series',\n",
              " 'SparseDtype',\n",
              " 'StringDtype',\n",
              " 'Timedelta',\n",
              " 'TimedeltaIndex',\n",
              " 'Timestamp',\n",
              " 'UInt16Dtype',\n",
              " 'UInt32Dtype',\n",
              " 'UInt64Dtype',\n",
              " 'UInt64Index',\n",
              " 'UInt8Dtype',\n",
              " '__builtins__',\n",
              " '__cached__',\n",
              " '__doc__',\n",
              " '__docformat__',\n",
              " '__file__',\n",
              " '__getattr__',\n",
              " '__git_version__',\n",
              " '__loader__',\n",
              " '__name__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__spec__',\n",
              " '__version__',\n",
              " '_config',\n",
              " '_hashtable',\n",
              " '_is_numpy_dev',\n",
              " '_lib',\n",
              " '_libs',\n",
              " '_np_version_under1p18',\n",
              " '_testing',\n",
              " '_tslib',\n",
              " '_typing',\n",
              " '_version',\n",
              " 'api',\n",
              " 'array',\n",
              " 'arrays',\n",
              " 'bdate_range',\n",
              " 'compat',\n",
              " 'concat',\n",
              " 'core',\n",
              " 'crosstab',\n",
              " 'cut',\n",
              " 'date_range',\n",
              " 'describe_option',\n",
              " 'errors',\n",
              " 'eval',\n",
              " 'factorize',\n",
              " 'get_dummies',\n",
              " 'get_option',\n",
              " 'infer_freq',\n",
              " 'interval_range',\n",
              " 'io',\n",
              " 'isna',\n",
              " 'isnull',\n",
              " 'json_normalize',\n",
              " 'lreshape',\n",
              " 'melt',\n",
              " 'merge',\n",
              " 'merge_asof',\n",
              " 'merge_ordered',\n",
              " 'notna',\n",
              " 'notnull',\n",
              " 'offsets',\n",
              " 'option_context',\n",
              " 'options',\n",
              " 'pandas',\n",
              " 'period_range',\n",
              " 'pivot',\n",
              " 'pivot_table',\n",
              " 'plotting',\n",
              " 'qcut',\n",
              " 'read_clipboard',\n",
              " 'read_csv',\n",
              " 'read_excel',\n",
              " 'read_feather',\n",
              " 'read_fwf',\n",
              " 'read_gbq',\n",
              " 'read_hdf',\n",
              " 'read_html',\n",
              " 'read_json',\n",
              " 'read_orc',\n",
              " 'read_parquet',\n",
              " 'read_pickle',\n",
              " 'read_sas',\n",
              " 'read_spss',\n",
              " 'read_sql',\n",
              " 'read_sql_query',\n",
              " 'read_sql_table',\n",
              " 'read_stata',\n",
              " 'read_table',\n",
              " 'read_xml',\n",
              " 'reset_option',\n",
              " 'set_eng_float_format',\n",
              " 'set_option',\n",
              " 'show_versions',\n",
              " 'test',\n",
              " 'testing',\n",
              " 'timedelta_range',\n",
              " 'to_datetime',\n",
              " 'to_numeric',\n",
              " 'to_pickle',\n",
              " 'to_timedelta',\n",
              " 'tseries',\n",
              " 'unique',\n",
              " 'util',\n",
              " 'value_counts',\n",
              " 'wide_to_long']"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRI3nzsAEjq0"
      },
      "source": [
        "### Creating a pandas Series\n",
        "A Pandas Series is a one-dimensional array of indexed data where data can be many different things:\n",
        "```\n",
        "    a Python dict\n",
        "    an ndarray\n",
        "    a scalar value (like 5)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd;   import numpy as np"
      ],
      "metadata": {
        "id": "Swf0Wgv2Ow6V"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series([1, 2, 3, 4, 5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BPgcB1gEeie",
        "outputId": "1a031356-e362-4b1f-fcf4-397486688e32"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "3    4\n",
              "4    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(np.random.randn(5), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPlP3UmxEenI",
        "outputId": "8bd23701-9a9e-4c78-b0e8-458498ddad38"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a   -0.203503\n",
              "b    1.013045\n",
              "c    2.181949\n",
              "d   -1.122588\n",
              "e    1.189388\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(np.array([1, 2, 3, 4, 5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsFizu_gEef7",
        "outputId": "3ad2f8a0-91bd-450a-bdf0-afd9ac9ecef1"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "3    4\n",
              "4    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(np.array([1, 2, 3, 4, 5])) + pd.Series(np.array([5, 4, 3, 2, 1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fbf4686-dbba-4f63-c4a3-798e2e94ef6a",
        "id": "uEi2h7BxISDE"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6\n",
              "1    6\n",
              "2    6\n",
              "3    6\n",
              "4    6\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\"b\": 1, \"a\": 0, \"c\": 2}; pd.Series(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPBi27RvEek2",
        "outputId": "26076598-45f1-4f14-ba38-3b70abfe4414"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b    1\n",
              "a    0\n",
              "c    2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Series object is basically interchangeable with a one-dimensional NumPy array. The essential difference is the presence of the index: while the Numpy Array has an implicitly defined integer index used to access the values, the Pandas Series has an explicitly defined index associated with the values."
      ],
      "metadata": {
        "id": "oDM7MwfvW6BN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a pandas DataFrame\n",
        "`DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object. Like Series, DataFrame accepts many different kinds of input:\n",
        "\n",
        "    Dict of 1D ndarrays, lists, dicts, or Series\n",
        "\n",
        "    2-D numpy.ndarray\n",
        "\n",
        "    Structured or record ndarray\n",
        "\n",
        "    A Series\n",
        "\n",
        "    Another DataFrame\n"
      ],
      "metadata": {
        "id": "jFmcFw6EI0WV"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm8B74lFX48n",
        "colab": {
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          "height": 206
        },
        "outputId": "6fa724bf-7b2e-4946-c0c2-4f134de9a46e"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())\n",
        "df"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          W         X         Y         Z\n",
              "A -1.055250 -0.255963  0.523574 -0.216817\n",
              "B -1.587131 -0.128045 -0.767518 -0.055505\n",
              "C -0.589701 -1.098669  1.061195 -0.407758\n",
              "D -0.692038 -0.886125  0.937507  0.980382\n",
              "E -1.218705 -0.135298  1.196934  1.009017"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "d = {\"one\": pd.Series([1.0, 2.0, 3.0], index=[\"a\", \"b\", \"c\"]),\n",
        "    \"two\": pd.Series([1.0, 2.0, 3.0, 4.0], index=[\"a\", \"b\", \"c\", \"d\"])\n",
        "    }\n",
        "df = pd.DataFrame(d); print(type(df));  print('\\n'); df.info();   print('\\n');  df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "KZSGAmbAEeVW",
        "outputId": "59ac9dae-5b80-4724-b94b-dc2cce5d79cf"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4 entries, a to d\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   one     3 non-null      float64\n",
            " 1   two     4 non-null      float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 96.0+ bytes\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   one  two\n",
              "a  1.0  1.0\n",
              "b  2.0  2.0\n",
              "c  3.0  3.0\n",
              "d  NaN  4.0"
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df=pd.DataFrame([[1, 2, 3, 4],\n",
        "                [5, 6, 7, 8],\n",
        "                [9, 20, 22, 34]],\n",
        "                index=[0,1,2],\n",
        "                columns=['A', 'B', 'C', 'D'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "40nKkQcPBNHT",
        "outputId": "75a5e767-a9b5-430c-c13b-8661e95f13ca"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   A   B   C   D\n",
              "0  1   2   3   4\n",
              "1  5   6   7   8\n",
              "2  9  20  22  34"
            ],
            "text/html": [
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              "          const element = document.querySelector('#df-092be2fc-9e0c-4e4b-ab96-db3335d82340');\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 271
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Yahoo Finance Data\n",
        "### creating dataframe"
      ],
      "metadata": {
        "id": "owCVVEuIBrAF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# source: Yahoo Finance\n",
        "stock = pd.DataFrame([['tech', 'AAPL', 157.25, 1.23, 26.11, 2539, 0.58, 184.01],\n",
        "                     ['tech', 'MSFT', 261.47, 0.93, 26.98, 1942, 0.95, 333.00],\n",
        "                     ['engr', 'XOM', 93.87, 1.08, 10.27, 391, 3.68, 103.28],\n",
        "                     ['engr', 'COP', 109.45, 1.38, 8.87, 137, 1,75],\n",
        "                     ['fin', 'BAC',33.47, 1.40, 10.46, 269, 2.62, 42.41],\n",
        "                     ['fin', 'JPM',114.51, 1.10, 9.18, 336, 3.52, 138.80],\n",
        "                     ['fin', 'WMT',335.50, 1.39, 7.53, 114, 3.00, 394.07]])\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "jPZIYblhvAQu",
        "outputId": "643aa04d-4821-4287-a35a-ece429d8dd92"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      0     1       2     3      4     5     6       7\n",
              "0  tech  AAPL  157.25  1.23  26.11  2539  0.58  184.01\n",
              "1  tech  MSFT  261.47  0.93  26.98  1942  0.95  333.00\n",
              "2  engr   XOM   93.87  1.08  10.27   391  3.68  103.28\n",
              "3  engr   COP  109.45  1.38   8.87   137  1.00   75.00\n",
              "4   fin   BAC   33.47  1.40  10.46   269  2.62   42.41\n",
              "5   fin   JPM  114.51  1.10   9.18   336  3.52  138.80\n",
              "6   fin   WMT  335.50  1.39   7.53   114  3.00  394.07"
            ],
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              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tech</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>157.25</td>\n",
              "      <td>1.23</td>\n",
              "      <td>26.11</td>\n",
              "      <td>2539</td>\n",
              "      <td>0.58</td>\n",
              "      <td>184.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>261.47</td>\n",
              "      <td>0.93</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
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              "      <td>103.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
              "      <td>1.00</td>\n",
              "      <td>75.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
              "      <td>9.18</td>\n",
              "      <td>336</td>\n",
              "      <td>3.52</td>\n",
              "      <td>138.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7418b96e-0fe0-43fd-8633-c794d45cb182')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "    .colab-df-convert:hover {\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "          document.querySelector('#df-7418b96e-0fe0-43fd-8633-c794d45cb182 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-7418b96e-0fe0-43fd-8633-c794d45cb182');\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": 272
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### assign column names and index values"
      ],
      "metadata": {
        "id": "hlIbn8EMBzU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock.columns=['sector', 'ticker', 'price', 'beta', 'pe', 'mktcap', 'dy', 'target']\n",
        "stock.index = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\"]\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Haz0piap32Av",
        "outputId": "af636e0a-5fb9-4ab3-ca9c-b35db578cb7e"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "a   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "b   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "c   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "d   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "e    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "f    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "g    fin    WMT  335.50  1.39   7.53     114  3.00  394.07"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>sector</th>\n",
              "      <th>ticker</th>\n",
              "      <th>price</th>\n",
              "      <th>beta</th>\n",
              "      <th>pe</th>\n",
              "      <th>mktcap</th>\n",
              "      <th>dy</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>tech</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>157.25</td>\n",
              "      <td>1.23</td>\n",
              "      <td>26.11</td>\n",
              "      <td>2539</td>\n",
              "      <td>0.58</td>\n",
              "      <td>184.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>261.47</td>\n",
              "      <td>0.93</td>\n",
              "      <td>26.98</td>\n",
              "      <td>1942</td>\n",
              "      <td>0.95</td>\n",
              "      <td>333.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
              "      <td>391</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
              "      <td>1.00</td>\n",
              "      <td>75.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
              "      <td>9.18</td>\n",
              "      <td>336</td>\n",
              "      <td>3.52</td>\n",
              "      <td>138.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>fin</td>\n",
              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd4cdb19-e019-4fa0-a0cd-d844be992d38 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd4cdb19-e019-4fa0-a0cd-d844be992d38');\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": 273
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### chekcking data contents"
      ],
      "metadata": {
        "id": "rkpxOiSvB4R1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvBIWnex4wP5",
        "outputId": "589f2cf7-8a40-4bdf-9b8b-b23c1fad794d"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 7 entries, a to g\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   sector  7 non-null      object \n",
            " 1   ticker  7 non-null      object \n",
            " 2   price   7 non-null      float64\n",
            " 3   beta    7 non-null      float64\n",
            " 4   pe      7 non-null      float64\n",
            " 5   mktcap  7 non-null      int64  \n",
            " 6   dy      7 non-null      float64\n",
            " 7   target  7 non-null      float64\n",
            "dtypes: float64(5), int64(1), object(2)\n",
            "memory usage: 504.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.index = [0, 1, 2, 3, 4, 5, 6]\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "iBS9ehMS9p9p",
        "outputId": "70b0a7ba-2281-4d22-9c4e-32df756e809c"
      },
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin    WMT  335.50  1.39   7.53     114  3.00  394.07"
            ],
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              "      <td>184.01</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>261.47</td>\n",
              "      <td>0.93</td>\n",
              "      <td>26.98</td>\n",
              "      <td>1942</td>\n",
              "      <td>0.95</td>\n",
              "      <td>333.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
              "      <td>391</td>\n",
              "      <td>3.68</td>\n",
              "      <td>103.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
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              "      <td>75.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
              "      <td>9.18</td>\n",
              "      <td>336</td>\n",
              "      <td>3.52</td>\n",
              "      <td>138.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0cf67a36-f15c-47de-9adf-7aa69d886cfc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### dropping column(s)"
      ],
      "metadata": {
        "id": "wpSFI6zCCE_u"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "b85ce900-afb8-4f42-843a-5f3fa0b2f26e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "IKTMBme4BgHY"
      },
      "source": [
        "stock.drop('target',axis=1)"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52\n",
              "6    fin    WMT  335.50  1.39   7.53     114  3.00"
            ],
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              "      <th></th>\n",
              "      <th>sector</th>\n",
              "      <th>ticker</th>\n",
              "      <th>price</th>\n",
              "      <th>beta</th>\n",
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              "      <th>0</th>\n",
              "      <td>tech</td>\n",
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              "      <td>157.25</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>261.47</td>\n",
              "      <td>0.93</td>\n",
              "      <td>26.98</td>\n",
              "      <td>1942</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
              "      <td>391</td>\n",
              "      <td>3.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>WMT</td>\n",
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              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
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              "                                                     [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",
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# but this wont affect the orginal data\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "BZftp4evCRps",
        "outputId": "62dfbae7-9e3c-4b54-b6c5-2300f7d448ec"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin    WMT  335.50  1.39   7.53     114  3.00  394.07"
            ],
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              "      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-4749af96-60f9-4b39-9160-fa1f6906f3d4 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-4749af96-60f9-4b39-9160-fa1f6906f3d4');\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": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# must put down inplace option\n",
        "stock.drop('target', axis=1, inplace=True)\n",
        "stock"
      ],
      "metadata": {
        "id": "nyXqYMo8RwLg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "837ae5ee-89d2-499d-e26c-9c8e1e9fe4ad"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52\n",
              "6    fin    WMT  335.50  1.39   7.53     114  3.00"
            ],
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              "      <th></th>\n",
              "      <th>sector</th>\n",
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              "      <th>0</th>\n",
              "      <td>tech</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>157.25</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
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              "      <td>0.93</td>\n",
              "      <td>26.98</td>\n",
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              "      <td>0.95</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
              "      <td>391</td>\n",
              "      <td>3.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
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              "      <td>3.52</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
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              "\n",
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              "\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-ed1d2177-c524-45f0-9609-32ab3cef5ef7 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-ed1d2177-c524-45f0-9609-32ab3cef5ef7');\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": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock['target'] = [184.01, 333.00, 103.28, 75.00, 42.41, 138.80, 394.07]\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "xxgCpl0-RwH9",
        "outputId": "e79b83d8-7611-45e6-e2bb-01f710947d7e"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin    WMT  335.50  1.39   7.53     114  3.00  394.07"
            ],
            "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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sector</th>\n",
              "      <th>ticker</th>\n",
              "      <th>price</th>\n",
              "      <th>beta</th>\n",
              "      <th>pe</th>\n",
              "      <th>mktcap</th>\n",
              "      <th>dy</th>\n",
              "      <th>target</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
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              "      <td>tech</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
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              "      <td>391</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
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              "      <td>75.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42b7cf88-1f94-4ab7-b4f1-80ec0925e1ae 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-42b7cf88-1f94-4ab7-b4f1-80ec0925e1ae');\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": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock2 = stock.drop(6, axis=0)\n",
        "stock2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "UgLiTDfvRwEN",
        "outputId": "610a6f2f-5963-4773-fac0-312bc365b5ad"
      },
      "execution_count": 280,
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              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "newstock = pd.DataFrame([['ind', 184.71, 1.00, 14.61, 98, 2.63, 214.00]], \n",
        "                        index=['CAT'],\n",
        "                        columns=['sector', 'price', 'beta', 'pe', 'mktcap', 'dy', 'target'])\n",
        "                        \n",
        "newstock"
      ],
      "metadata": {
        "colab": {
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          "height": 81
        },
        "id": "wDUx_fPbF8LX",
        "outputId": "35d6b4da-6e60-42cf-df4c-9019961cccb5"
      },
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sector   price  beta     pe  mktcap    dy  target\n",
              "CAT    ind  184.71   1.0  14.61      98  2.63   214.0"
            ],
            "text/html": [
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-0a4d104b-d1da-4450-b5f7-3300c0031965 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-0a4d104b-d1da-4450-b5f7-3300c0031965');\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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "stock3 = stock.append(newstock)\n",
        "stock3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dsloyDS8F8IK",
        "outputId": "022e73a3-c325-4e2e-acfa-64a9265a0845"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0     tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1     tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2     engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3     engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4      fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5      fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6      fin    WMT  335.50  1.39   7.53     114  3.00  394.07\n",
              "CAT    ind    NaN  184.71  1.00  14.61      98  2.63  214.00"
            ],
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              "      <th>3</th>\n",
              "      <td>engr</td>\n",
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              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
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              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
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              "      <th>5</th>\n",
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              "      <td>1.10</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>WMT</td>\n",
              "      <td>335.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAT</th>\n",
              "      <td>ind</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 282
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock3 = pd.concat([stock, newstock])\n",
        "stock3"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "KrkBCYUuEaGo",
        "outputId": "14493cf9-d298-4735-9a81-891e6f9894db"
      },
      "execution_count": 283,
      "outputs": [
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              "    sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0     tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1     tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
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              "5      fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6      fin    WMT  335.50  1.39   7.53     114  3.00  394.07\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### replacing value"
      ],
      "metadata": {
        "id": "AdDOQ-9DCOgu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[6, 'ticker'] = 'GS'\n",
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "gTvd4WAP_XG_",
        "outputId": "3f13ee59-417e-4cb8-d634-d89a9b495b6b"
      },
      "execution_count": 284,
      "outputs": [
        {
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              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin     GS  335.50  1.39   7.53     114  3.00  394.07"
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              "    .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8abf8ee9-bd9a-46b8-8e4e-407906c40f1f 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-8abf8ee9-bd9a-46b8-8e4e-407906c40f1f');\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": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock['price'].replace(335.50, 435.50, inplace=True)"
      ],
      "metadata": {
        "id": "wAJJCTVCSglz"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "hlMd_zl5Sgeb",
        "outputId": "1e9d10dc-238a-4268-efea-40f75bde6205"
      },
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin     GS  435.50  1.39   7.53     114  3.00  394.07"
            ],
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              "      <td>tech</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
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              "      <td>75.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>GS</td>\n",
              "      <td>435.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### selecting row or column or both"
      ],
      "metadata": {
        "id": "wNnApZD-SZcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock['ticker']); print('\\n');\n",
        "print(stock[['ticker']]); print('\\n');\n",
        "print(stock.ticker); print('\\n');\n",
        "print(stock.loc[:, 'ticker']); print('\\n');\n",
        "print(stock.iloc[:, 1]); print('\\n');\n",
        "print(stock.iloc[:, -1]); print('\\n');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsJpZEu79p6T",
        "outputId": "3c053e28-e246-4545-cb3f-adc9f619dd0c"
      },
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    AAPL\n",
            "1    MSFT\n",
            "2     XOM\n",
            "3     COP\n",
            "4     BAC\n",
            "5     JPM\n",
            "6      GS\n",
            "Name: ticker, dtype: object\n",
            "\n",
            "\n",
            "  ticker\n",
            "0   AAPL\n",
            "1   MSFT\n",
            "2    XOM\n",
            "3    COP\n",
            "4    BAC\n",
            "5    JPM\n",
            "6     GS\n",
            "\n",
            "\n",
            "0    AAPL\n",
            "1    MSFT\n",
            "2     XOM\n",
            "3     COP\n",
            "4     BAC\n",
            "5     JPM\n",
            "6      GS\n",
            "Name: ticker, dtype: object\n",
            "\n",
            "\n",
            "0    AAPL\n",
            "1    MSFT\n",
            "2     XOM\n",
            "3     COP\n",
            "4     BAC\n",
            "5     JPM\n",
            "6      GS\n",
            "Name: ticker, dtype: object\n",
            "\n",
            "\n",
            "0    AAPL\n",
            "1    MSFT\n",
            "2     XOM\n",
            "3     COP\n",
            "4     BAC\n",
            "5     JPM\n",
            "6      GS\n",
            "Name: ticker, dtype: object\n",
            "\n",
            "\n",
            "0    184.01\n",
            "1    333.00\n",
            "2    103.28\n",
            "3     75.00\n",
            "4     42.41\n",
            "5    138.80\n",
            "6    394.07\n",
            "Name: target, dtype: float64\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[:, 'ticker':'beta']"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "phMyk25w9FwK",
        "outputId": "dd1f4029-8a36-481d-8828-3624261c3ca0"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  ticker   price  beta\n",
              "0   AAPL  157.25  1.23\n",
              "1   MSFT  261.47  0.93\n",
              "2    XOM   93.87  1.08\n",
              "3    COP  109.45  1.38\n",
              "4    BAC   33.47  1.40\n",
              "5    JPM  114.51  1.10\n",
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              "\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": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[:, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "BXUQK-DX9UX0",
        "outputId": "4b419a14-5ee7-4372-9ace-16580f404478"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sector ticker   price  beta     pe  mktcap    dy  target\n",
              "0   tech   AAPL  157.25  1.23  26.11    2539  0.58  184.01\n",
              "1   tech   MSFT  261.47  0.93  26.98    1942  0.95  333.00\n",
              "2   engr    XOM   93.87  1.08  10.27     391  3.68  103.28\n",
              "3   engr    COP  109.45  1.38   8.87     137  1.00   75.00\n",
              "4    fin    BAC   33.47  1.40  10.46     269  2.62   42.41\n",
              "5    fin    JPM  114.51  1.10   9.18     336  3.52  138.80\n",
              "6    fin     GS  435.50  1.39   7.53     114  3.00  394.07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0eac9f36-d28a-478a-96e9-eac55efaa208\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\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>sector</th>\n",
              "      <th>ticker</th>\n",
              "      <th>price</th>\n",
              "      <th>beta</th>\n",
              "      <th>pe</th>\n",
              "      <th>mktcap</th>\n",
              "      <th>dy</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tech</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>157.25</td>\n",
              "      <td>1.23</td>\n",
              "      <td>26.11</td>\n",
              "      <td>2539</td>\n",
              "      <td>0.58</td>\n",
              "      <td>184.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tech</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>261.47</td>\n",
              "      <td>0.93</td>\n",
              "      <td>26.98</td>\n",
              "      <td>1942</td>\n",
              "      <td>0.95</td>\n",
              "      <td>333.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>engr</td>\n",
              "      <td>XOM</td>\n",
              "      <td>93.87</td>\n",
              "      <td>1.08</td>\n",
              "      <td>10.27</td>\n",
              "      <td>391</td>\n",
              "      <td>3.68</td>\n",
              "      <td>103.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>engr</td>\n",
              "      <td>COP</td>\n",
              "      <td>109.45</td>\n",
              "      <td>1.38</td>\n",
              "      <td>8.87</td>\n",
              "      <td>137</td>\n",
              "      <td>1.00</td>\n",
              "      <td>75.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fin</td>\n",
              "      <td>BAC</td>\n",
              "      <td>33.47</td>\n",
              "      <td>1.40</td>\n",
              "      <td>10.46</td>\n",
              "      <td>269</td>\n",
              "      <td>2.62</td>\n",
              "      <td>42.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fin</td>\n",
              "      <td>JPM</td>\n",
              "      <td>114.51</td>\n",
              "      <td>1.10</td>\n",
              "      <td>9.18</td>\n",
              "      <td>336</td>\n",
              "      <td>3.52</td>\n",
              "      <td>138.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>fin</td>\n",
              "      <td>GS</td>\n",
              "      <td>435.50</td>\n",
              "      <td>1.39</td>\n",
              "      <td>7.53</td>\n",
              "      <td>114</td>\n",
              "      <td>3.00</td>\n",
              "      <td>394.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0eac9f36-d28a-478a-96e9-eac55efaa208')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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-0eac9f36-d28a-478a-96e9-eac55efaa208 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-0eac9f36-d28a-478a-96e9-eac55efaa208');\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": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[0:2, 'beta']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-zwM-neKpjv",
        "outputId": "060c8ed0-19e3-44de-d412-518e90ea5c15"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.23\n",
              "1    0.93\n",
              "2    1.08\n",
              "Name: beta, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[3, \"ticker\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FCxL2WC99UU_",
        "outputId": "d17ea4e7-2f6c-4d11-cd82-010cb846e7f3"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'COP'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc[5, \"mktcap\"] > stock.loc[6, \"mktcap\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKb9Mr3U9URz",
        "outputId": "0886ca21-8cd1-4ad5-aa8c-433f0be747a6"
      },
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJ_5U42E_aF6",
        "outputId": "f9feb699-a0ab-421c-db57-22c1d23b3bf7"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### data summary"
      ],
      "metadata": {
        "id": "BkrZsts0T1Ap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "icmRehpU9p3A",
        "outputId": "5d12f111-817a-44d3-8ff8-372fbd8d9f73"
      },
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            price      beta         pe       mktcap        dy      target\n",
              "count    7.000000  7.000000   7.000000     7.000000  7.000000    7.000000\n",
              "mean   172.217143  1.215714  14.200000   818.285714  2.192857  181.510000\n",
              "std    135.503274  0.184829   8.492224   991.686058  1.315025  133.400706\n",
              "min     33.470000  0.930000   7.530000   114.000000  0.580000   42.410000\n",
              "25%    101.660000  1.090000   9.025000   203.000000  0.975000   89.140000\n",
              "50%    114.510000  1.230000  10.270000   336.000000  2.620000  138.800000\n",
              "75%    209.360000  1.385000  18.285000  1166.500000  3.260000  258.505000\n",
              "max    435.500000  1.400000  26.980000  2539.000000  3.680000  394.070000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71990fdf-6bc5-4c6b-a47e-f3abd6d3ebf6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>beta</th>\n",
              "      <th>pe</th>\n",
              "      <th>mktcap</th>\n",
              "      <th>dy</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>172.217143</td>\n",
              "      <td>1.215714</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>818.285714</td>\n",
              "      <td>2.192857</td>\n",
              "      <td>181.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>135.503274</td>\n",
              "      <td>0.184829</td>\n",
              "      <td>8.492224</td>\n",
              "      <td>991.686058</td>\n",
              "      <td>1.315025</td>\n",
              "      <td>133.400706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>33.470000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>7.530000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>0.580000</td>\n",
              "      <td>42.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>101.660000</td>\n",
              "      <td>1.090000</td>\n",
              "      <td>9.025000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>89.140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>114.510000</td>\n",
              "      <td>1.230000</td>\n",
              "      <td>10.270000</td>\n",
              "      <td>336.000000</td>\n",
              "      <td>2.620000</td>\n",
              "      <td>138.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>209.360000</td>\n",
              "      <td>1.385000</td>\n",
              "      <td>18.285000</td>\n",
              "      <td>1166.500000</td>\n",
              "      <td>3.260000</td>\n",
              "      <td>258.505000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>435.500000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>26.980000</td>\n",
              "      <td>2539.000000</td>\n",
              "      <td>3.680000</td>\n",
              "      <td>394.070000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71990fdf-6bc5-4c6b-a47e-f3abd6d3ebf6');\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": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock['price'].mean()); \n",
        "print(stock['beta'].median()); \n",
        "print(stock['dy'].std()); \n",
        "print(stock['mktcap'].max()); "
      ],
      "metadata": {
        "id": "4a6WIB149p0K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4472f943-468c-47e5-c5ca-f72f244446f4"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "172.21714285714285\n",
            "1.23\n",
            "1.315024895654252\n",
            "2539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.groupby('sector').mean().round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "BgjHuJhbVbGQ",
        "outputId": "f92d81f2-00cd-4754-b3a6-8e583203ae78"
      },
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         price  beta     pe   mktcap    dy  target\n",
              "sector                                            \n",
              "engr    101.66  1.23   9.57   264.00  2.34   89.14\n",
              "fin     194.49  1.30   9.06   239.67  3.05  191.76\n",
              "tech    209.36  1.08  26.54  2240.50  0.76  258.50"
            ],
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              "                                                     [key], {});\n",
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              "\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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    {
      "cell_type": "code",
      "source": [
        "stock.set_index('ticker', inplace=True)\n"
      ],
      "metadata": {
        "id": "VRZPar-T-TAw"
      },
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock.loc['JPM', 'mktcap'] > stock.loc['GS', 'mktcap']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc43yQ-T-gf9",
        "outputId": "5dcfda14-0cec-4d8a-d8d3-f1db23b349c3"
      },
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
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          "execution_count": 298
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        "stock"
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          "height": 300
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        "id": "ZXYu_YY_MwNZ",
        "outputId": "dc1dfbd3-ddd6-4382-9e81-9973ea16d017"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sector   price  beta     pe  mktcap    dy  target\n",
              "ticker                                                  \n",
              "AAPL     tech  157.25  1.23  26.11    2539  0.58  184.01\n",
              "MSFT     tech  261.47  0.93  26.98    1942  0.95  333.00\n",
              "XOM      engr   93.87  1.08  10.27     391  3.68  103.28\n",
              "COP      engr  109.45  1.38   8.87     137  1.00   75.00\n",
              "BAC       fin   33.47  1.40  10.46     269  2.62   42.41\n",
              "JPM       fin  114.51  1.10   9.18     336  3.52  138.80\n",
              "GS        fin  435.50  1.39   7.53     114  3.00  394.07"
            ],
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ]
    }
  ]
}