Warren_Buffett_US_Stock_Companies.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Warren Buffett US Stock Companies.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ClaudeLewisMbua/Data-Analysis/blob/main/Warren_Buffett_US_Stock_Companies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![maxresdefault 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)\n",
        "\n",
        "Source: [A Breakdown Of Warren Buffett’s 2022 Portfolio](https://www.youtube.com/watch?v=TE5SKG_aUqQ&ab_channel=ProactiveThinker)"
      ],
      "metadata": {
        "id": "FK3Tq-bmViKF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset**\n",
        "\n",
        "**Context:** These are the publicly traded U.S. stocks owned by [Warren Buffett’s](https://en.wikipedia.org/wiki/Warren_Buffett) holding company [Berkshire Hathaway](https://en.wikipedia.org/wiki/Berkshire_Hathaway), as reported to the Securities and Exchange Commission's ([SEC](https://www.sec.gov/Archives/edgar/data/1067983/000095012320009058/xslForm13F_X01/960.xml)). He is considered one of the most successful investors in the world and has a net worth of US$78.9 billion as of August 2020, making him the world's seventh-wealthiest person.\n",
        "\n",
        "The SEC [Form 13F](https://www.investopedia.com/terms/f/form-13f.asp) is a quarterly report that is required to be filed by all institutional investment managers with at least $100 million in assets under management. It discloses their equity holdings and can provide some insights into what the smart money is doing in the market.\n",
        "\n",
        "After obtaining the list of companies in which Berkshire Hathaway invests, we proceeded to search the historical data of each company on the National Association of Securities Dealers Automated Quotations ([NASDAQ](https://www.nasdaq.com/)).\n",
        "\n",
        "**Content:** There are 49 files in csv format, composed as follows:\n",
        "* 45 files contain the U.S. stocks owned by Berkshire Hathaway.\n",
        "* 2 files contain the historical data from Berkshire Hathaway. Class A stock (BRK-A) and Class B stock (BRK-B).\n",
        "* 1 file contain the list of all the companies with additional information.\n",
        "* 1 file contain the SEC Form 13F.\n",
        "\n",
        "**Column Description:** Every company file has the same structure with the same columns:\n",
        "* Date: It is the date on which the prices were recorded.\n",
        "* Close/Last: Is the last price at which a stock trades during a regular trading session.\n",
        "* Volume: Is the number of shares that changed hands during a given day.\n",
        "* Open: Is the price at which a stock started trading when the opening bell rang.\n",
        "* High: Is the highest price at which a stock traded during the course of the trading day.\n",
        "* Low: Is the lowest price at which a stock traded during the course of the trading day.\n",
        "* Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.\n",
        "\n",
        "The two other files have different columns names:\n",
        "\n",
        "*Company List*\n",
        "\n",
        "* Name: Name of the company.\n",
        "* Symbol: Ticker symbol of the company.\n",
        "* Holdings: Number of shares.\n",
        "* Market Price: Current price at which a stock can be purchased or sold. (10/18/20)\n",
        "* Value: (Holdings * Market Price).\n",
        "* Stake: The amount of stocks an investor owns from a company.\n",
        "\n",
        "*SEC Form 13F*\n",
        "\n",
        "Name of Issuer, Title of Class, CUSIP Number, Market Value, Amount and Type of Security, Investment Discretion (Sole, Shared-Defined, Shared-Other), Other Managers, Voting Authority. Detail information of each column can be found in the SEC General Instructions [Form 13F](https://www.sec.gov/pdf/form13f.pdf) in page 5."
      ],
      "metadata": {
        "id": "pw0G879ZX2he"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# **Inspiration:** \n",
        "Possible questions which could be answered are:\n",
        "\n",
        "* Of the companies that make up the Berkshire Hathaway portfolio, which are the most profitable companies?\n",
        "* On February 21, 2020, Berkshire Hathaway suffered a large drop in its share price, what was the reason for this large drop?\n",
        "* The presidential elections in the United States will be soon, do you think this will affect a particular company in the Warren Buffett portfolio and if so, which would affect them more?"
      ],
      "metadata": {
        "id": "PyJ9h8yhdQBz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Goal**\n",
        "\n",
        "The main goal here is to analyze the *companies* that make up Warren Buffett's portfolio; Analyze their *behaviors* over the past few years, as well as their behaviors during the COVID-19 Pandemic; Determine *new trends* that can help investors make more informed decisions.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uOW3-LFv0_mb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Table of Content**\n",
        "\n",
        "* Data preparation\n",
        "* Exploratory data analysis\n",
        "* Web scraping\n",
        "* Financial charts\n",
        "* Predictions and Conclusions"
      ],
      "metadata": {
        "id": "WZ-9zLrDgiNe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Neccessary Packages**"
      ],
      "metadata": {
        "id": "qNh5d3AcJryD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBoqhiCj0nho",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b8d2642d-36fe-453a-a37e-9ecd90dd0288"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "from pandas_datareader import data\n",
        "import numpy as np\n",
        "import random as rnd\n",
        "import datetime\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set_style('whitegrid')\n",
        "%matplotlib inline\n",
        "\n",
        "# plotly\n",
        "import plotly\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go\n",
        "import cufflinks as cf\n",
        "cf.go_offline()\n",
        "\n",
        "# web scraping\n",
        "import requests\n",
        "import bs4\n",
        "import csv\n",
        "import json \n",
        "import re\n",
        "from io import StringIO"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preparation**"
      ],
      "metadata": {
        "id": "k23rVw6WKS9g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**Load Data**\n",
        "There are two ways to load the data: \n",
        "*   By simply reading the csv files\n",
        "*   By using pandas dataerder which allows us to read stock information directly from the internet. In this case we will use Yahoo Finance.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "SiXENx3sKbNI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check Features:** We start with the company list dataset"
      ],
      "metadata": {
        "id": "WNvwMJ09iFnW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company_list= pd.read_csv(\"/content/CompanyList.csv\", sep = ';')"
      ],
      "metadata": {
        "id": "vfk6mCtWKWyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company_list.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxJgXX2oiemP",
        "outputId": "4fbe0e58-796e-4336-a3b9-76fbda94a4b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46 entries, 0 to 45\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Name          46 non-null     object\n",
            " 1   Symbol        46 non-null     object\n",
            " 2   Holdings      46 non-null     object\n",
            " 3   Market Price  46 non-null     object\n",
            " 4   Value         46 non-null     object\n",
            " 5   Stake         45 non-null     object\n",
            "dtypes: object(6)\n",
            "memory usage: 2.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "company_list.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HKDmeBZPjs1k",
        "outputId": "cb2b954e-56c2-4e5f-ce42-ace5f531d269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Name Symbol       Holdings Market Price  \\\n",
              "0            Amazon.com, Inc.   AMZN          533,3    $3,272.71   \n",
              "1    American Express Company    AXP    151,610,700      $104.91   \n",
              "2                  Apple Inc.   AAPL  1,003,466,264      $119.02   \n",
              "3  Axalta Coating Systems Ltd   AXTA     23,535,041       $25.64   \n",
              "4        Bank of America Corp    BAC  1,032,852,006       $24.24   \n",
              "\n",
              "              Value  Stake  \n",
              "0    $1,745,336,243   0.1%  \n",
              "1   $15,905,478,537  18.8%  \n",
              "2  $119,432,554,741   5.9%  \n",
              "3      $603,438,451  10.0%  \n",
              "4   $25,036,332,625  11.9%  "
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              "      <td>Amazon.com, Inc.</td>\n",
              "      <td>AMZN</td>\n",
              "      <td>533,3</td>\n",
              "      <td>$3,272.71</td>\n",
              "      <td>$1,745,336,243</td>\n",
              "      <td>0.1%</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>American Express Company</td>\n",
              "      <td>AXP</td>\n",
              "      <td>151,610,700</td>\n",
              "      <td>$104.91</td>\n",
              "      <td>$15,905,478,537</td>\n",
              "      <td>18.8%</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apple Inc.</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>1,003,466,264</td>\n",
              "      <td>$119.02</td>\n",
              "      <td>$119,432,554,741</td>\n",
              "      <td>5.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Axalta Coating Systems Ltd</td>\n",
              "      <td>AXTA</td>\n",
              "      <td>23,535,041</td>\n",
              "      <td>$25.64</td>\n",
              "      <td>$603,438,451</td>\n",
              "      <td>10.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bank of America Corp</td>\n",
              "      <td>BAC</td>\n",
              "      <td>1,032,852,006</td>\n",
              "      <td>$24.24</td>\n",
              "      <td>$25,036,332,625</td>\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us look at the SEC Form 13F"
      ],
      "metadata": {
        "id": "96_Jga6nj6Dw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEC_Form_13F = pd.read_excel(\"/content/SEC_Form_13F.xlsx\", index_col=None, header=None)"
      ],
      "metadata": {
        "id": "1cSz0CbSjsr8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SEC_Form_13F.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGhxbJvxlgwN",
        "outputId": "4ffc0901-1889-4dd5-99c8-5d592afdecf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 139 entries, 0 to 138\n",
            "Data columns (total 11 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   0       139 non-null    object\n",
            " 1   1       139 non-null    object\n",
            " 2   2       139 non-null    object\n",
            " 3   3       139 non-null    object\n",
            " 4   4       139 non-null    object\n",
            " 5   5       139 non-null    object\n",
            " 6   6       139 non-null    object\n",
            " 7   7       139 non-null    object\n",
            " 8   8       139 non-null    object\n",
            " 9   9       139 non-null    object\n",
            " 10  10      139 non-null    object\n",
            "dtypes: object(11)\n",
            "memory usage: 12.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEC_Form_13F.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "gU2dyW7ejshi",
        "outputId": "333a7fbe-510c-4497-d567-dbaff22ad163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    0               1         2               3   \\\n",
              "0       NAME OF ISSUER  TITLE OF CLASS     CUSIP  VALUE (x$1000)   \n",
              "1       AMAZON COM INC             COM  23135106       1,157,325   \n",
              "2       AMAZON COM INC             COM  23135106         313.954   \n",
              "3  AMERICAN EXPRESS CO             COM  25816109         185.844   \n",
              "4  AMERICAN EXPRESS CO             COM  25816109       1,639,858   \n",
              "\n",
              "                4         5                      6        7           8   \\\n",
              "0  SHRS OR PRN AMT  SH / PRN  INVESTMENT DISCRETION  MANAGER        SOLE   \n",
              "1            419.5        SH                   DFND        4       419.5   \n",
              "2            113.8        SH                   DFND   4,8,11       113.8   \n",
              "3        1,952,142        SH                   DFND        4   1,952,142   \n",
              "4       17,225,400        SH                   DFND      4.5  17,225,400   \n",
              "\n",
              "       9     10  \n",
              "0  SHARED  NONE  \n",
              "1       0     0  \n",
              "2       0     0  \n",
              "3       0     0  \n",
              "4       0     0  "
            ],
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              "      <th>0</th>\n",
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              "      <td>AMAZON COM INC</td>\n",
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              "      <td>313.954</td>\n",
              "      <td>113.8</td>\n",
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              "      <td>DFND</td>\n",
              "      <td>4,8,11</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>COM</td>\n",
              "      <td>25816109</td>\n",
              "      <td>185.844</td>\n",
              "      <td>1,952,142</td>\n",
              "      <td>SH</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>COM</td>\n",
              "      <td>25816109</td>\n",
              "      <td>1,639,858</td>\n",
              "      <td>17,225,400</td>\n",
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              "      <td>4.5</td>\n",
              "      <td>17,225,400</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd27dfe1-00da-4c5d-8bc3-a45acffa8360 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-bd27dfe1-00da-4c5d-8bc3-a45acffa8360');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEC_Form_13F[0][0],SEC_Form_13F[1][0],SEC_Form_13F[2][0],SEC_Form_13F[3][0],SEC_Form_13F[4][0],SEC_Form_13F[5][0],SEC_Form_13F[6][0],SEC_Form_13F[7][0],SEC_Form_13F[8][0],SEC_Form_13F[9][0],SEC_Form_13F[10][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4AIcQIAq0h_",
        "outputId": "25823f80-efce-4f5d-fa1b-1ad95178dfb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('NAME OF ISSUER',\n",
              " 'TITLE OF CLASS',\n",
              " 'CUSIP',\n",
              " 'VALUE (x$1000)',\n",
              " 'SHRS OR PRN AMT',\n",
              " 'SH / PRN',\n",
              " 'INVESTMENT DISCRETION',\n",
              " 'MANAGER',\n",
              " 'SOLE',\n",
              " 'SHARED',\n",
              " 'NONE')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_SEC_Form_13F = pd.DataFrame()\n",
        "new_SEC_Form_13F[SEC_Form_13F[0][0]] = list(SEC_Form_13F[0][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[1][0]] = list(SEC_Form_13F[1][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[2][0]] = list(SEC_Form_13F[2][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[3][0]] = list(SEC_Form_13F[3][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[4][0]] = list(SEC_Form_13F[4][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[5][0]] = list(SEC_Form_13F[5][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[6][0]] = list(SEC_Form_13F[6][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[7][0]] = list(SEC_Form_13F[7][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[8][0]] = list(SEC_Form_13F[8][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[9][0]] = list(SEC_Form_13F[9][1:])\n",
        "new_SEC_Form_13F[SEC_Form_13F[10][0]] = list(SEC_Form_13F[10][1:])"
      ],
      "metadata": {
        "id": "L_1eDXJnjsW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_SEC_Form_13F.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajqBqsNcjsKt",
        "outputId": "f182693c-af68-42c0-a1b2-0067b65ed582"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 138 entries, 0 to 137\n",
            "Data columns (total 11 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   NAME OF ISSUER         138 non-null    object\n",
            " 1   TITLE OF CLASS         138 non-null    object\n",
            " 2   CUSIP                  138 non-null    object\n",
            " 3   VALUE (x$1000)         138 non-null    object\n",
            " 4   SHRS OR PRN AMT        138 non-null    object\n",
            " 5   SH / PRN               138 non-null    object\n",
            " 6   INVESTMENT DISCRETION  138 non-null    object\n",
            " 7   MANAGER                138 non-null    object\n",
            " 8   SOLE                   138 non-null    object\n",
            " 9   SHARED                 138 non-null    int64 \n",
            " 10  NONE                   138 non-null    int64 \n",
            "dtypes: int64(2), object(9)\n",
            "memory usage: 12.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_SEC_Form_13F.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "r1z8mdDIjr-O",
        "outputId": "9c49a4ee-d1cf-48eb-e656-43b02c348945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        NAME OF ISSUER TITLE OF CLASS     CUSIP VALUE (x$1000)  \\\n",
              "0       AMAZON COM INC            COM  23135106      1,157,325   \n",
              "1       AMAZON COM INC            COM  23135106        313.954   \n",
              "2  AMERICAN EXPRESS CO            COM  25816109        185.844   \n",
              "3  AMERICAN EXPRESS CO            COM  25816109      1,639,858   \n",
              "4  AMERICAN EXPRESS CO            COM  25816109         79.952   \n",
              "\n",
              "  SHRS OR PRN AMT SH / PRN INVESTMENT DISCRETION MANAGER        SOLE  SHARED  \\\n",
              "0           419.5       SH                  DFND       4       419.5       0   \n",
              "1           113.8       SH                  DFND  4,8,11       113.8       0   \n",
              "2       1,952,142       SH                  DFND       4   1,952,142       0   \n",
              "3      17,225,400       SH                  DFND     4.5  17,225,400       0   \n",
              "4         839.832       SH                  DFND     4.7     839.832       0   \n",
              "\n",
              "   NONE  \n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>AMAZON COM INC</td>\n",
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              "      <td>23135106</td>\n",
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              "      <td>419.5</td>\n",
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              "      <td>AMAZON COM INC</td>\n",
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              "      <td>23135106</td>\n",
              "      <td>313.954</td>\n",
              "      <td>113.8</td>\n",
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              "      <td>DFND</td>\n",
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              "      <td>113.8</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>COM</td>\n",
              "      <td>25816109</td>\n",
              "      <td>185.844</td>\n",
              "      <td>1,952,142</td>\n",
              "      <td>SH</td>\n",
              "      <td>DFND</td>\n",
              "      <td>4</td>\n",
              "      <td>1,952,142</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>COM</td>\n",
              "      <td>25816109</td>\n",
              "      <td>1,639,858</td>\n",
              "      <td>17,225,400</td>\n",
              "      <td>SH</td>\n",
              "      <td>DFND</td>\n",
              "      <td>4.5</td>\n",
              "      <td>17,225,400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>COM</td>\n",
              "      <td>25816109</td>\n",
              "      <td>79.952</td>\n",
              "      <td>839.832</td>\n",
              "      <td>SH</td>\n",
              "      <td>DFND</td>\n",
              "      <td>4.7</td>\n",
              "      <td>839.832</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e64fe812-69ff-4be7-9c08-b95b9c65dae1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e64fe812-69ff-4be7-9c08-b95b9c65dae1 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-e64fe812-69ff-4be7-9c08-b95b9c65dae1');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_SEC_Form_13F.to_csv(\"/content/new_SEC_Form_13F.csv\", index = False) #We save our new constructed csv file"
      ],
      "metadata": {
        "id": "AFijelH0tH6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's look at a company: Amazon for example"
      ],
      "metadata": {
        "id": "8TZRQq7DuHic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AMZN = pd.read_csv(\"/content/AMZN.csv\")"
      ],
      "metadata": {
        "id": "JArFjKn-uHIp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AMZN.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zffijc5suona",
        "outputId": "42c3fd8b-f75c-4864-ee6c-68ebf62baf16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3498 entries, 0 to 3497\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       3498 non-null   object \n",
            " 1   High       3498 non-null   float64\n",
            " 2   Low        3498 non-null   float64\n",
            " 3   Open       3498 non-null   float64\n",
            " 4   Close      3498 non-null   float64\n",
            " 5   Volume     3498 non-null   int64  \n",
            " 6   Adj Close  3498 non-null   float64\n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 191.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AMZN.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "d0eiBW03uyJX",
        "outputId": "398f61da-8549-43ae-97e6-4fc2b568729d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date       High        Low       Open      Close    Volume  Adj Close\n",
              "0  2007-01-03  39.060001  38.049999  38.680000  38.700001  12405100  38.700001\n",
              "1  2007-01-04  39.139999  38.259998  38.590000  38.900002   6318400  38.900002\n",
              "2  2007-01-05  38.790001  37.599998  38.720001  38.369999   6619700  38.369999\n",
              "3  2007-01-08  38.310001  37.169998  38.220001  37.500000   6783000  37.500000\n",
              "4  2007-01-09  38.060001  37.340000  37.599998  37.779999   5703000  37.779999"
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              "      <td>38.680000</td>\n",
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              "      <td>38.700001</td>\n",
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              "      <th>1</th>\n",
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              "      <td>39.139999</td>\n",
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              "      <td>6318400</td>\n",
              "      <td>38.900002</td>\n",
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              "      <th>2</th>\n",
              "      <td>2007-01-05</td>\n",
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              "      <th>4</th>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>38.060001</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc8e9a09-9bf8-4cce-a7c2-2c1c210597b0');\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": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cleaning the Dataframes**\n",
        "Getting back to our company list *company_list*, let us clean the DataFrame, by removing spaces, % and $ sign from it."
      ],
      "metadata": {
        "id": "gaURmdIivRHb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company_list.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BGwcG8rJvYpz",
        "outputId": "5dfe5485-d832-4636-cd84-56e32e2e7439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Name Symbol       Holdings Market Price  \\\n",
              "0            Amazon.com, Inc.   AMZN          533,3    $3,272.71   \n",
              "1    American Express Company    AXP    151,610,700      $104.91   \n",
              "2                  Apple Inc.   AAPL  1,003,466,264      $119.02   \n",
              "3  Axalta Coating Systems Ltd   AXTA     23,535,041       $25.64   \n",
              "4        Bank of America Corp    BAC  1,032,852,006       $24.24   \n",
              "\n",
              "              Value  Stake  \n",
              "0    $1,745,336,243   0.1%  \n",
              "1   $15,905,478,537  18.8%  \n",
              "2  $119,432,554,741   5.9%  \n",
              "3      $603,438,451  10.0%  \n",
              "4   $25,036,332,625  11.9%  "
            ],
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              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
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              "      <td>Amazon.com, Inc.</td>\n",
              "      <td>AMZN</td>\n",
              "      <td>533,3</td>\n",
              "      <td>$3,272.71</td>\n",
              "      <td>$1,745,336,243</td>\n",
              "      <td>0.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>American Express Company</td>\n",
              "      <td>AXP</td>\n",
              "      <td>151,610,700</td>\n",
              "      <td>$104.91</td>\n",
              "      <td>$15,905,478,537</td>\n",
              "      <td>18.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apple Inc.</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>1,003,466,264</td>\n",
              "      <td>$119.02</td>\n",
              "      <td>$119,432,554,741</td>\n",
              "      <td>5.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Axalta Coating Systems Ltd</td>\n",
              "      <td>AXTA</td>\n",
              "      <td>23,535,041</td>\n",
              "      <td>$25.64</td>\n",
              "      <td>$603,438,451</td>\n",
              "      <td>10.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bank of America Corp</td>\n",
              "      <td>BAC</td>\n",
              "      <td>1,032,852,006</td>\n",
              "      <td>$24.24</td>\n",
              "      <td>$25,036,332,625</td>\n",
              "      <td>11.9%</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing $ sign and spaces \n",
        "cols = ['Holdings', 'Market Price', 'Value', 'Stake']\n",
        "\n",
        "# Company List (Table)\n",
        "company_list[cols] = company_list[cols].replace({'\\$': '', ' ': '', '%': '', ',': ''}, regex=True)"
      ],
      "metadata": {
        "id": "mO-vF7Bpvz2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We remove the last row of the company_list dataframe\n",
        "company_list.drop(company_list.tail(1).index,inplace=True)\n"
      ],
      "metadata": {
        "id": "yHHTS4Jqv-tv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Finally, convert the numbers, which are as strings, to floats.\n",
        "for x in cols:\n",
        "    company_list[x] = company_list[x].astype(float)"
      ],
      "metadata": {
        "id": "-cAoQrjwwLIg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company_list.head() #let is look at our new cleaned dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yeb0wlptwVUO",
        "outputId": "9d3467f5-3c30-4de1-b807-4a6960b42dac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Name Symbol      Holdings  Market Price  \\\n",
              "0            Amazon.com, Inc.   AMZN  5.333000e+03       3272.71   \n",
              "1    American Express Company    AXP  1.516107e+08        104.91   \n",
              "2                  Apple Inc.   AAPL  1.003466e+09        119.02   \n",
              "3  Axalta Coating Systems Ltd   AXTA  2.353504e+07         25.64   \n",
              "4        Bank of America Corp    BAC  1.032852e+09         24.24   \n",
              "\n",
              "          Value  Stake  \n",
              "0  1.745336e+09    0.1  \n",
              "1  1.590548e+10   18.8  \n",
              "2  1.194326e+11    5.9  \n",
              "3  6.034385e+08   10.0  \n",
              "4  2.503633e+10   11.9  "
            ],
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              "\n",
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              "      <td>1.745336e+09</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>American Express Company</td>\n",
              "      <td>AXP</td>\n",
              "      <td>1.516107e+08</td>\n",
              "      <td>104.91</td>\n",
              "      <td>1.590548e+10</td>\n",
              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apple Inc.</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>1.003466e+09</td>\n",
              "      <td>119.02</td>\n",
              "      <td>1.194326e+11</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Axalta Coating Systems Ltd</td>\n",
              "      <td>AXTA</td>\n",
              "      <td>2.353504e+07</td>\n",
              "      <td>25.64</td>\n",
              "      <td>6.034385e+08</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bank of America Corp</td>\n",
              "      <td>BAC</td>\n",
              "      <td>1.032852e+09</td>\n",
              "      <td>24.24</td>\n",
              "      <td>2.503633e+10</td>\n",
              "      <td>11.9</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **We now concatenate the Stock Dataframes**\n",
        "\n",
        "We are going to concatenate the stock dataframes together to a single data frame called *company_stocks*. This is so we can have better control over the dataframes and analyze them together.\n",
        "\n",
        "We will also create an aditional dataframe called *berkshire_hathaway* with Class A stock (BRK-A) and Class B stock (BRK-B).\n",
        "\n",
        "Let's create a list of the ticker symbols (as strings) in alphabetical order."
      ],
      "metadata": {
        "id": "a7fYiVI7wfdH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to load all the comapnies csv files"
      ],
      "metadata": {
        "id": "ksrGUzDpxxOi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AMZN = pd.read_csv(\"/content/AMZN.csv\")\n",
        "AXP = pd.read_csv(\"/content/AXP.csv\")\n",
        "AAPL = pd.read_csv(\"/content/AAPL.csv\")\n",
        "AXTA = pd.read_csv(\"/content/AXTA.csv\")\n",
        "BAC = pd.read_csv(\"/content/BAC.csv\")\n",
        "BK = pd.read_csv(\"/content/BK.csv\")\n",
        "GOLD = pd.read_csv(\"/content/GOLD.csv\")\n",
        "BIIB = pd.read_csv(\"/content/BIIB.csv\")\n",
        "CHTR = pd.read_csv(\"/content/CHTR.csv\")\n",
        "KO = pd.read_csv(\"/content/KO.csv\")\n",
        "COST = pd.read_csv(\"/content/COST.csv\")\n",
        "DVA = pd.read_csv(\"/content/DVA.csv\")\n",
        "GM = pd.read_csv(\"/content/GM.csv\")\n",
        "GL = pd.read_csv(\"/content/GL.csv\")\n",
        "JNJ = pd.read_csv(\"/content/JNJ.csv\")\n",
        "JPM = pd.read_csv(\"/content/JPM.csv\")\n",
        "KHC = pd.read_csv(\"/content/KHC.csv\")\n",
        "KR = pd.read_csv(\"/content/KR.csv\")\n",
        "LBTYA = pd.read_csv(\"/content/LBTYA.csv\")\n",
        "LBTYK = pd.read_csv(\"/content/LBTYK.csv\")\n",
        "LILA  = pd.read_csv(\"/content/LILA.csv\")\n",
        "LILAK = pd.read_csv(\"/content/LILAK.csv\")\n",
        "LSXMA = pd.read_csv(\"/content/LSXMA.csv\")\n",
        "LSXMK = pd.read_csv(\"/content/LSXMK.csv\")\n",
        "MTB = pd.read_csv(\"/content/MTB.csv\")\n",
        "MA = pd.read_csv(\"/content/MA.csv\")\n",
        "MDLZ = pd.read_csv(\"/content/MDLZ.csv\")\n",
        "MCO = pd.read_csv(\"/content/MCO.csv\")\n",
        "PNC = pd.read_csv(\"/content/PNC.csv\")\n",
        "PG = pd.read_csv(\"/content/PG.csv\")\n",
        "RH = pd.read_csv(\"/content/RH.csv\")\n",
        "SIRI = pd.read_csv(\"/content/SIRI.csv\")\n",
        "SNOW = pd.read_csv(\"/content/SNOW.csv\")\n",
        "SPY = pd.read_csv(\"/content/SPY.csv\")\n",
        "STNE = pd.read_csv(\"/content/STNE.csv\")\n",
        "STOR = pd.read_csv(\"/content/STOR.csv\")\n",
        "SU = pd.read_csv(\"/content/SU.csv\")\n",
        "SYF = pd.read_csv(\"/content/SYF.csv\")\n",
        "TEVA = pd.read_csv(\"/content/TEVA.csv\")\n",
        "USB = pd.read_csv(\"/content/USB.csv\")\n",
        "UPS = pd.read_csv(\"/content/UPS.csv\")\n",
        "VOO = pd.read_csv(\"/content/VOO.csv\")\n",
        "VRSN = pd.read_csv(\"/content/VRSN.csv\")\n",
        "V = pd.read_csv(\"/content/V.csv\")\n",
        "WFC = pd.read_csv(\"/content/WFC.csv\")\n",
        "BRKA = pd.read_csv(\"/content/BRKA.csv\")\n",
        "BRKB = pd.read_csv(\"/content/BRKB.csv\")\n",
        "GSPC = pd.read_csv(\"/content/GSPC.csv\")"
      ],
      "metadata": {
        "id": "UJODwAfDxwOS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company_list_stocks = [AMZN,AXP,AAPL,AXTA,BAC,BK,GOLD,BIIB,CHTR,KO,COST,DVA,GM,GL,JNJ,JPM,KHC,KR,LBTYA,LBTYK,LILA,LILAK,LSXMA,\n",
        "                       LSXMK,MTB,MA,MDLZ,MCO,PNC,PG,RH,SIRI,SNOW,SPY,STNE,STOR,SU,SYF,TEVA,USB,UPS,VOO,VRSN,V,WFC]\n",
        "\n",
        "tickers1 = ['AMZN','AXP','AAPL','AXTA','BAC','BK','GOLD','BIIB','CHTR','KO','COST','DVA','GM','GL','JNJ','JPM','KHC','KR',\n",
        "           'LBTYA','LBTYK','LILA','LILAK','LSXMA','LSXMK','MTB','MA','MDLZ','MCO','PNC','PG','RH','SIRI','SNOW','SPY','STNE',\n",
        "           'STOR','SU','SYF','TEVA','USB','UPS','VOO','VRSN','V','WFC']\n",
        "\n",
        "tickers2 = ['BRKA','BRKB']\n",
        "\n",
        "company_stocks = pd.concat(company_list_stocks, axis=1, keys=tickers1)\n",
        "\n",
        "berkshire_hathaway = pd.concat([BRKA, BRKB], axis=1, keys=tickers2)\n",
        "\n",
        "# Set the column name levels:\n",
        "company_stocks.columns.names = ['Stock Ticker','Stock Info']\n",
        "company_stocks.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "tf6iy9JwxN5e",
        "outputId": "447a2bd3-3211-4204-d3ed-860f8b0745a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Stock Ticker        AMZN                                              \\\n",
              "Stock Info          Date       High        Low       Open      Close   \n",
              "0             2007-01-03  39.060001  38.049999  38.680000  38.700001   \n",
              "1             2007-01-04  39.139999  38.259998  38.590000  38.900002   \n",
              "2             2007-01-05  38.790001  37.599998  38.720001  38.369999   \n",
              "3             2007-01-08  38.310001  37.169998  38.220001  37.500000   \n",
              "4             2007-01-09  38.060001  37.340000  37.599998  37.779999   \n",
              "\n",
              "Stock Ticker                              AXP                        ...  \\\n",
              "Stock Info      Volume  Adj Close        Date       High        Low  ...   \n",
              "0             12405100  38.700001  2007-01-03  61.900002  60.049999  ...   \n",
              "1              6318400  38.900002  2007-01-04  60.570000  59.790001  ...   \n",
              "2              6619700  38.369999  2007-01-05  59.869999  58.900002  ...   \n",
              "3              6783000  37.500000  2007-01-08  59.759998  58.349998  ...   \n",
              "4              5703000  37.779999  2007-01-09  59.950001  59.000000  ...   \n",
              "\n",
              "Stock Ticker        V                                 WFC             \\\n",
              "Stock Info      Close       Volume  Adj Close        Date       High   \n",
              "0             14.1250  708486000.0  12.901475  2007-01-03  36.009998   \n",
              "1             16.0875  198985200.0  14.693976  2007-01-04  36.020000   \n",
              "2             14.9325  149566400.0  13.639026  2007-01-05  35.830002   \n",
              "3             15.8125   87092000.0  14.442798  2007-01-08  35.889999   \n",
              "4             15.9900   43111600.0  14.604924  2007-01-09  35.849998   \n",
              "\n",
              "Stock Ticker                                                          \n",
              "Stock Info          Low       Open      Close      Volume  Adj Close  \n",
              "0             35.369999  35.930000  35.740002  12447600.0  23.962498  \n",
              "1             35.540001  35.740002  35.799999  11060500.0  24.002729  \n",
              "2             35.509998  35.790001  35.599998  10329200.0  23.868629  \n",
              "3             35.389999  35.610001  35.500000  11650400.0  23.801577  \n",
              "4             35.480000  35.580002  35.590000  10159800.0  23.861921  \n",
              "\n",
              "[5 rows x 315 columns]"
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              "      <th>Stock Ticker</th>\n",
              "      <th colspan=\"7\" halign=\"left\">AMZN</th>\n",
              "      <th colspan=\"3\" halign=\"left\">AXP</th>\n",
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              "    <tr>\n",
              "      <th>Stock Info</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>...</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>39.060001</td>\n",
              "      <td>38.049999</td>\n",
              "      <td>38.680000</td>\n",
              "      <td>38.700001</td>\n",
              "      <td>12405100</td>\n",
              "      <td>38.700001</td>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>61.900002</td>\n",
              "      <td>60.049999</td>\n",
              "      <td>...</td>\n",
              "      <td>14.1250</td>\n",
              "      <td>708486000.0</td>\n",
              "      <td>12.901475</td>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>36.009998</td>\n",
              "      <td>35.369999</td>\n",
              "      <td>35.930000</td>\n",
              "      <td>35.740002</td>\n",
              "      <td>12447600.0</td>\n",
              "      <td>23.962498</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>39.139999</td>\n",
              "      <td>38.259998</td>\n",
              "      <td>38.590000</td>\n",
              "      <td>38.900002</td>\n",
              "      <td>6318400</td>\n",
              "      <td>38.900002</td>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>60.570000</td>\n",
              "      <td>59.790001</td>\n",
              "      <td>...</td>\n",
              "      <td>16.0875</td>\n",
              "      <td>198985200.0</td>\n",
              "      <td>14.693976</td>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>36.020000</td>\n",
              "      <td>35.540001</td>\n",
              "      <td>35.740002</td>\n",
              "      <td>35.799999</td>\n",
              "      <td>11060500.0</td>\n",
              "      <td>24.002729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>38.790001</td>\n",
              "      <td>37.599998</td>\n",
              "      <td>38.720001</td>\n",
              "      <td>38.369999</td>\n",
              "      <td>6619700</td>\n",
              "      <td>38.369999</td>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>59.869999</td>\n",
              "      <td>58.900002</td>\n",
              "      <td>...</td>\n",
              "      <td>14.9325</td>\n",
              "      <td>149566400.0</td>\n",
              "      <td>13.639026</td>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>35.830002</td>\n",
              "      <td>35.509998</td>\n",
              "      <td>35.790001</td>\n",
              "      <td>35.599998</td>\n",
              "      <td>10329200.0</td>\n",
              "      <td>23.868629</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>38.310001</td>\n",
              "      <td>37.169998</td>\n",
              "      <td>38.220001</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>6783000</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>59.759998</td>\n",
              "      <td>58.349998</td>\n",
              "      <td>...</td>\n",
              "      <td>15.8125</td>\n",
              "      <td>87092000.0</td>\n",
              "      <td>14.442798</td>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>35.889999</td>\n",
              "      <td>35.389999</td>\n",
              "      <td>35.610001</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>11650400.0</td>\n",
              "      <td>23.801577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>38.060001</td>\n",
              "      <td>37.340000</td>\n",
              "      <td>37.599998</td>\n",
              "      <td>37.779999</td>\n",
              "      <td>5703000</td>\n",
              "      <td>37.779999</td>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>59.950001</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>15.9900</td>\n",
              "      <td>43111600.0</td>\n",
              "      <td>14.604924</td>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>35.849998</td>\n",
              "      <td>35.480000</td>\n",
              "      <td>35.580002</td>\n",
              "      <td>35.590000</td>\n",
              "      <td>10159800.0</td>\n",
              "      <td>23.861921</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 315 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a90906d4-31c8-4c11-bb97-222d40549538')\"\n",
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          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "company_stocks.to_csv(\"/content/company_stocks.csv\", index = False) #We save our new constructed csv file"
      ],
      "metadata": {
        "id": "dumWLhByaQIw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "berkshire_hathaway.to_csv(\"/content/berkshire_hathaway_Stock.csv\", index = False) #We save our new constructed csv file"
      ],
      "metadata": {
        "id": "K1gPI4BjaX41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "berkshire_hathaway.columns.names = ['Stock Ticker','Stock Info']\n",
        "%connect_info.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "pVHKNuaK1Gcq",
        "outputId": "085cb48a-4ef1-4f37-a87c-d11d06b578b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Stock Ticker        BRKA                                                   \\\n",
              "Stock Info          Date    High     Low    Open   Close Volume Adj Close   \n",
              "0             2007-01-03  110600  109000  110400  109000  67000    109000   \n",
              "1             2007-01-04  109400  108250  109400  108850  46000    108850   \n",
              "2             2007-01-05  108800  107000  108800  107200  42000    107200   \n",
              "3             2007-01-08  107800  107300  107600  107500  34000    107500   \n",
              "4             2007-01-09  109600  107510  107510  109350  40000    109350   \n",
              "\n",
              "Stock Ticker        BRKB                                              \\\n",
              "Stock Info          Date       High        Low       Open      Close   \n",
              "0             2007-01-03  73.720001  72.599998  73.559998  72.860001   \n",
              "1             2007-01-04  72.940002  72.059998  72.860001  72.199997   \n",
              "2             2007-01-05  72.660004  71.260002  72.660004  71.559998   \n",
              "3             2007-01-08  71.900002  71.500000  71.820000  71.720001   \n",
              "4             2007-01-09  73.040001  71.660004  71.720001  73.000000   \n",
              "\n",
              "Stock Ticker                        \n",
              "Stock Info       Volume  Adj Close  \n",
              "0             1385000.0  72.860001  \n",
              "1             1070000.0  72.199997  \n",
              "2              930000.0  71.559998  \n",
              "3              760000.0  71.720001  \n",
              "4              610000.0  73.000000  "
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              "      <th>Stock Ticker</th>\n",
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              "      <th>Stock Info</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>110600</td>\n",
              "      <td>109000</td>\n",
              "      <td>110400</td>\n",
              "      <td>109000</td>\n",
              "      <td>67000</td>\n",
              "      <td>109000</td>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>73.720001</td>\n",
              "      <td>72.599998</td>\n",
              "      <td>73.559998</td>\n",
              "      <td>72.860001</td>\n",
              "      <td>1385000.0</td>\n",
              "      <td>72.860001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>109400</td>\n",
              "      <td>108250</td>\n",
              "      <td>109400</td>\n",
              "      <td>108850</td>\n",
              "      <td>46000</td>\n",
              "      <td>108850</td>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>72.940002</td>\n",
              "      <td>72.059998</td>\n",
              "      <td>72.860001</td>\n",
              "      <td>72.199997</td>\n",
              "      <td>1070000.0</td>\n",
              "      <td>72.199997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>108800</td>\n",
              "      <td>107000</td>\n",
              "      <td>108800</td>\n",
              "      <td>107200</td>\n",
              "      <td>42000</td>\n",
              "      <td>107200</td>\n",
              "      <td>2007-01-05</td>\n",
              "      <td>72.660004</td>\n",
              "      <td>71.260002</td>\n",
              "      <td>72.660004</td>\n",
              "      <td>71.559998</td>\n",
              "      <td>930000.0</td>\n",
              "      <td>71.559998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>107800</td>\n",
              "      <td>107300</td>\n",
              "      <td>107600</td>\n",
              "      <td>107500</td>\n",
              "      <td>34000</td>\n",
              "      <td>107500</td>\n",
              "      <td>2007-01-08</td>\n",
              "      <td>71.900002</td>\n",
              "      <td>71.500000</td>\n",
              "      <td>71.820000</td>\n",
              "      <td>71.720001</td>\n",
              "      <td>760000.0</td>\n",
              "      <td>71.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>109600</td>\n",
              "      <td>107510</td>\n",
              "      <td>107510</td>\n",
              "      <td>109350</td>\n",
              "      <td>40000</td>\n",
              "      <td>109350</td>\n",
              "      <td>2007-01-09</td>\n",
              "      <td>73.040001</td>\n",
              "      <td>71.660004</td>\n",
              "      <td>71.720001</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>610000.0</td>\n",
              "      <td>73.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis**\n",
        "\n",
        "[Exploratory data analysis](https://en.wikipedia.org/wiki/Exploratory_data_analysis) is an approach to analyze data sets to summarize their main characteristics, often with visual methods. In this case we are going to visualize and analyze the historical data of these stocks and try to find relevant information.\n",
        "\n",
        "We will also analyze the stock portfolio to see which are the most relevant companies and which are the most profitable.\n",
        "\n",
        "***To do:*** check out the documentation on [Multi-Level Indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) and [Using .xs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.xs.html), since we will be using it a lot."
      ],
      "metadata": {
        "id": "60bAbSi41Z92"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Berkshire Hathaway Portfolio**"
      ],
      "metadata": {
        "id": "s4GWSAuE2QWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company_list.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9eX4SC3xOosx",
        "outputId": "743f23b2-8169-4d7c-9e68-bc997398f2d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Name Symbol      Holdings  Market Price  \\\n",
              "0            Amazon.com, Inc.   AMZN  5.333000e+03       3272.71   \n",
              "1    American Express Company    AXP  1.516107e+08        104.91   \n",
              "2                  Apple Inc.   AAPL  1.003466e+09        119.02   \n",
              "3  Axalta Coating Systems Ltd   AXTA  2.353504e+07         25.64   \n",
              "4        Bank of America Corp    BAC  1.032852e+09         24.24   \n",
              "\n",
              "          Value  Stake  \n",
              "0  1.745336e+09    0.1  \n",
              "1  1.590548e+10   18.8  \n",
              "2  1.194326e+11    5.9  \n",
              "3  6.034385e+08   10.0  \n",
              "4  2.503633e+10   11.9  "
            ],
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              "      <th>0</th>\n",
              "      <td>Amazon.com, Inc.</td>\n",
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              "      <td>5.333000e+03</td>\n",
              "      <td>3272.71</td>\n",
              "      <td>1.745336e+09</td>\n",
              "      <td>0.1</td>\n",
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              "      <th>1</th>\n",
              "      <td>American Express Company</td>\n",
              "      <td>AXP</td>\n",
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              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apple Inc.</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>1.003466e+09</td>\n",
              "      <td>119.02</td>\n",
              "      <td>1.194326e+11</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Axalta Coating Systems Ltd</td>\n",
              "      <td>AXTA</td>\n",
              "      <td>2.353504e+07</td>\n",
              "      <td>25.64</td>\n",
              "      <td>6.034385e+08</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bank of America Corp</td>\n",
              "      <td>BAC</td>\n",
              "      <td>1.032852e+09</td>\n",
              "      <td>24.24</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "#df = px.data.gapminder().query(\"year == 2007\").query(\"continent == 'Americas'\")\n",
        "fig = px.pie(company_list, values='Holdings', names='Symbol',\n",
        "             title='Holdings per Company of Warren Buffett Portfolio',\n",
        "             hover_data=['Name'], labels={'Name':'Company Name'})\n",
        "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "fig.show(renderer=\"colab\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "H2YABzNPUAJA",
        "outputId": "1950e798-05e6-4c98-a83a-291c0fca01d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(company_list, x=\"Value\", y=\"Symbol\", orientation='h', title='Value per Company of Warren Buffett Portfolio',\n",
        "             hover_data=['Name'], labels={'Name':'Company Name'})\n",
        "fig.update_layout(barmode='stack', yaxis={'categoryorder':'total ascending'})\n",
        "\n",
        "fig.show(renderer=\"colab\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "gjYUWq9xW7vQ",
        "outputId": "62843086-3070-4a2a-f9fa-e51f34651e93"
      },
      "execution_count": null,
      "outputs": [
        {
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        "percentage_value=pd.DataFrame()\n",
        "percentage_value[\"Symbol\"] = company_list.Symbol\n",
        "percentage_value[\"percent\"] = round((company_list['Value'] / company_list['Value'].sum()) * 100,3)\n",
        "percentage_value=percentage_value.sort_values(by=['percent'],ascending=False)\n",
        "percentage_value.head()"
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              "   Symbol  percent\n",
              "2    AAPL   49.290\n",
              "4     BAC   10.333\n",
              "9      KO    8.259\n",
              "1     AXP    6.564\n",
              "16    KHC    4.322"
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      "cell_type": "code",
      "source": [
        "fig = px.bar(company_list, x=\"Stake\", y=\"Symbol\", orientation='h', title='Stakes per Company of Warren Buffett Portfolio',\n",
        "             hover_data=['Name'], labels={'Name':'Company Name'})\n",
        "fig.update_layout(barmode='stack', yaxis={'categoryorder':'total ascending'})\n",
        "\n",
        "fig.show(renderer=\"colab\")"
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ascending\"},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Stakes per Company of Warren Buffett Portfolio\"},\"barmode\":\"stack\"},                        {\"responsive\": true}                    ).then(function(){\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top 5**"
      ],
      "metadata": {
        "id": "mGH4WsJxeoyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stake=pd.DataFrame()\n",
        "stake[\"Symbol\"] = company_list.Symbol\n",
        "stake[\"percent\"] = round(company_list['Stake'],3)\n",
        "stake=stake.sort_values(by=['percent'],ascending=False)\n",
        "stake.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k7k7nu4wecFU",
        "outputId": "8bab9d4d-5ef3-4b1c-ca47-f2231c95a723"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Symbol  percent\n",
              "11    DVA     28.6\n",
              "16    KHC     26.6\n",
              "1     AXP     18.8\n",
              "23  LSXMK     18.6\n",
              "22  LSXMA     14.7"
            ],
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              "      <th>Symbol</th>\n",
              "      <th>percent</th>\n",
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              "      <th>11</th>\n",
              "      <td>DVA</td>\n",
              "      <td>28.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KHC</td>\n",
              "      <td>26.6</td>\n",
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              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>LSXMK</td>\n",
              "      <td>18.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>LSXMA</td>\n",
              "      <td>14.7</td>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-2028defb-00f6-4567-afe1-2c8f7a574439 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-2028defb-00f6-4567-afe1-2c8f7a574439');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **New Return Dataframes**"
      ],
      "metadata": {
        "id": "sK4BGaPCfcbk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Daily Returns DataFrame:**\n",
        "\n",
        "Here, we are going to create a new empty DataFrame called returns. This dataframe will contain the returns for each stock.\n"
      ],
      "metadata": {
        "id": "N2YH2suOfe4i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Returns are typically defined by:\n",
        "\n",
        " ![return.PNG](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "GF59K5zTgVQ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use pandas *pct_change()* method on the *Close column* to create a column representing this *return value*. Then we can create a for loop and for each **Stock Ticker** creates a returns column and set's it as a column in the returns DataFrame.\n",
        "\n",
        "Our first value has [NaN](https://en.wikipedia.org/wiki/NaN) because you can not get a percent return on the very first day because there is nothing in the past to compare it to."
      ],
      "metadata": {
        "id": "G_L1Nb7ygoiK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "returns = pd.DataFrame() # we declare an empty dataframe\n",
        "\n",
        "for tick in tickers1:\n",
        "    returns[tick + ' Return'] = company_stocks[tick]['Close'].pct_change()\n",
        "    \n",
        "returns['BRKA Return'] = BRKA['Close'].pct_change()\n",
        "returns['BRKB Return'] = BRKB['Close'].pct_change()\n",
        "returns['GSPC Return'] = GSPC['Close'].pct_change()"
      ],
      "metadata": {
        "id": "Sg23DQtThJMY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Monthly Returns DataFrame:**"
      ],
      "metadata": {
        "id": "fa0lGorVh8os"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company_stocks.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "mKS-8kwBr2UY",
        "outputId": "784c48e6-0328-4ea6-baf4-93c02ef327e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Stock Ticker        AMZN                                                    \\\n",
              "Stock Info          Date       High        Low   Open      Close    Volume   \n",
              "0             2007-01-03  39.060001  38.049999  38.68  38.700001  12405100   \n",
              "1             2007-01-04  39.139999  38.259998  38.59  38.900002   6318400   \n",
              "\n",
              "Stock Ticker                    AXP                        ...        V  \\\n",
              "Stock Info    Adj Close        Date       High        Low  ...    Close   \n",
              "0             38.700001  2007-01-03  61.900002  60.049999  ...  14.1250   \n",
              "1             38.900002  2007-01-04  60.570000  59.790001  ...  16.0875   \n",
              "\n",
              "Stock Ticker                                 WFC                        \\\n",
              "Stock Info         Volume  Adj Close        Date       High        Low   \n",
              "0             708486000.0  12.901475  2007-01-03  36.009998  35.369999   \n",
              "1             198985200.0  14.693976  2007-01-04  36.020000  35.540001   \n",
              "\n",
              "Stock Ticker                                               \n",
              "Stock Info         Open      Close      Volume  Adj Close  \n",
              "0             35.930000  35.740002  12447600.0  23.962498  \n",
              "1             35.740002  35.799999  11060500.0  24.002729  \n",
              "\n",
              "[2 rows x 315 columns]"
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              "      <th>Adj Close</th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>...</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Date</th>\n",
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              "      <th>Close</th>\n",
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              "      <th>Adj Close</th>\n",
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              "      <th>0</th>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>39.060001</td>\n",
              "      <td>38.049999</td>\n",
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              "      <td>38.700001</td>\n",
              "      <td>12405100</td>\n",
              "      <td>38.700001</td>\n",
              "      <td>2007-01-03</td>\n",
              "      <td>61.900002</td>\n",
              "      <td>60.049999</td>\n",
              "      <td>...</td>\n",
              "      <td>14.1250</td>\n",
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              "      <td>35.369999</td>\n",
              "      <td>35.930000</td>\n",
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              "      <td>12447600.0</td>\n",
              "      <td>23.962498</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>39.139999</td>\n",
              "      <td>38.259998</td>\n",
              "      <td>38.59</td>\n",
              "      <td>38.900002</td>\n",
              "      <td>6318400</td>\n",
              "      <td>38.900002</td>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>60.570000</td>\n",
              "      <td>59.790001</td>\n",
              "      <td>...</td>\n",
              "      <td>16.0875</td>\n",
              "      <td>198985200.0</td>\n",
              "      <td>14.693976</td>\n",
              "      <td>2007-01-04</td>\n",
              "      <td>36.020000</td>\n",
              "      <td>35.540001</td>\n",
              "      <td>35.740002</td>\n",
              "      <td>35.799999</td>\n",
              "      <td>11060500.0</td>\n",
              "      <td>24.002729</td>\n",
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              "                                                     [key], {});\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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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_returns = pd.DataFrame()\n",
        "\n",
        "for tick in tickers1:\n",
        "  company_stocks[tick]['Date'] = pd.to_datetime(company_stocks[tick]['Date'])\n",
        "  company_stocks[tick] = company_stocks[tick].set_index(\"Date\")\n",
        "  monthly_returns[tick + ' Return'] = company_stocks[tick].resample('M', on ='Date')['Close'].ffill().pct_change()\n",
        "    \n",
        "monthly_returns['BRKA Return'] = BRKA['Close'].resample('M').ffill().pct_change()\n",
        "monthly_returns['BRKB Return'] = BRKB['Close'].resample('M').ffill().pct_change()\n",
        "monthly_returns['GSPC Return'] = GSPC['Close'].resample('M').ffill().pct_change()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "sdL4x9bSwup-",
        "outputId": "53b09842-aa5f-42b1-c2e2-9e32cc46086b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-166-fef400af74eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtickers1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mmonthly_returns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' Return'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3600\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3602\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_frame_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3603\u001b[0m         elif (\n\u001b[1;32m   3604\u001b[0m             \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item_frame_value\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3727\u001b[0m             \u001b[0mlen_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3728\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3729\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Columns must be same length as key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3731\u001b[0m             \u001b[0;31m# align right-hand-side columns if self.columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Columns must be same length as key"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_returns = pd.DataFrame()\n",
        "\n",
        "for tick in tickers1:\n",
        "    monthly_returns[tick + ' Return'] = company_stocks[tick]['Close'].resample('M', on ='Date').ffill().pct_change()\n",
        "    \n",
        "monthly_returns['BRKA Return'] = BRKA['Close'].resample('M').ffill().pct_change()\n",
        "monthly_returns['BRKB Return'] = BRKB['Close'].resample('M').ffill().pct_change()\n",
        "monthly_returns['GSPC Return'] = GSPC['Close'].resample('M').ffill().pct_change()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "XxD7e5a5iDYT",
        "outputId": "36621b09-0fcf-44da-fe37-df944c997e58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-141-439f8b81f911>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtickers1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mmonthly_returns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' Return'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompany_stocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmonthly_returns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BRKA Return'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBRKA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset)\u001b[0m\n\u001b[1;32m   5316\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5317\u001b[0m             \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5318\u001b[0;31m             \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5319\u001b[0m         )\n\u001b[1;32m   5320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset)\u001b[0m\n\u001b[1;32m   8137\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8138\u001b[0m             \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8139\u001b[0;31m             \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   8140\u001b[0m         )\n\u001b[1;32m   8141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mget_resampler\u001b[0;34m(obj, kind, **kwds)\u001b[0m\n\u001b[1;32m   1380\u001b[0m     \"\"\"\n\u001b[1;32m   1381\u001b[0m     \u001b[0mtg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m   1546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1547\u001b[0m         \"\"\"\n\u001b[0;32m-> 1548\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_set_grouper\u001b[0;34m(self, obj, sort)\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"The grouper name {key} is not found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    373\u001b[0m                 \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'The grouper name Date is not found'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Acknowledgements**\n",
        "This work is inspired from the Kaggle exercise [here](https://www.kaggle.com/code/tomasmantero/eda-warren-buffett-us-stock-companies/notebook), very well presented by [TOMAS MANTERO](https://www.kaggle.com/tomasmantero)\n"
      ],
      "metadata": {
        "id": "iJVhKVZ9fVT3"
      }
    }
  ]
}