Tutorial_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Load libraries and api key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Load API key from .env file in the root directory\n",
    "load_dotenv()\n",
    "API_KEY = os.environ.get('API_SIMFIN')\n",
    "\n",
    "# 8) continue with the tutorial https://github.com/SimFin/simfin-tutorials/blob/master/01_Basics.ipynb\n",
    "\n",
    "# matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "\n",
    "# Import the main functionality from the SimFin Python API.\n",
    "import simfin as sf\n",
    "\n",
    "# Import names used for easy access to SimFin's data-columns.\n",
    "from simfin.names import *\n",
    "\n",
    "# Set the directory where the data is downloaded\n",
    "sf.set_data_dir('~/simfin_data/')\n",
    "\n",
    "# Replace YOUR_API_KEY with your actual API-key.\n",
    "sf.set_api_key(api_key=API_KEY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-ttm\" on disk (1 days old).\n",
      "- Loading from disk ... Done!\n",
      "  Ticker  SimFinId Currency  Fiscal Year Fiscal Period Report Date  \\\n",
      "0      A     45846      USD         2017            Q4  2017-10-31   \n",
      "1      A     45846      USD         2018            Q1  2018-01-31   \n",
      "2      A     45846      USD         2018            Q2  2018-04-30   \n",
      "3      A     45846      USD         2018            Q3  2018-07-31   \n",
      "4      A     45846      USD         2018            Q4  2018-10-31   \n",
      "\n",
      "  Publish Date Restated Date  Shares (Basic)  Shares (Diluted)  ...  \\\n",
      "0   2017-12-21    2018-12-20     322000000.0       326000000.0  ...   \n",
      "1   2018-03-06    2019-03-05     322250000.0       325250000.0  ...   \n",
      "2   2018-05-31    2019-05-30     322500000.0       325500000.0  ...   \n",
      "3   2018-08-30    2018-08-30     322250000.0       325000000.0  ...   \n",
      "4   2018-12-20    2020-12-18     321000000.0       325000000.0  ...   \n",
      "\n",
      "   Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "0                  -38000000.0            -57000000.0   \n",
      "1                  -21000000.0            -52000000.0   \n",
      "2                    6000000.0            -46000000.0   \n",
      "3                   31000000.0            -42000000.0   \n",
      "4                   57000000.0            -37000000.0   \n",
      "\n",
      "   Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  Pretax Income (Loss)  \\\n",
      "0                   803000000                      NaN             803000000   \n",
      "1                   843000000                      NaN             843000000   \n",
      "2                   879000000                      NaN             879000000   \n",
      "3                   928000000                      NaN             928000000   \n",
      "4                   946000000                      NaN             946000000   \n",
      "\n",
      "   Income Tax (Expense) Benefit, Net  \\\n",
      "0                       -119000000.0   \n",
      "1                       -647000000.0   \n",
      "2                       -642000000.0   \n",
      "3                       -630000000.0   \n",
      "4                       -630000000.0   \n",
      "\n",
      "   Income (Loss) from Continuing Operations  Net Extraordinary Gains (Losses)  \\\n",
      "0                                 684000000                               NaN   \n",
      "1                                 196000000                               NaN   \n",
      "2                                 237000000                               NaN   \n",
      "3                                 298000000                               NaN   \n",
      "4                                 316000000                               NaN   \n",
      "\n",
      "   Net Income  Net Income (Common)  \n",
      "0   684000000            684000000  \n",
      "1   196000000            196000000  \n",
      "2   237000000            237000000  \n",
      "3   298000000            298000000  \n",
      "4   316000000            316000000  \n",
      "\n",
      "[5 rows x 28 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    }
   ],
   "source": [
    "df1 = sf.load(dataset='income', variant='ttm', market='us', refresh_days = 3)\n",
    "print(df1.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of the subset of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 58876\n",
      "Number of Columns: 28\n",
      "DataFrame Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 58876 entries, 0 to 58875\n",
      "Data columns (total 28 columns):\n",
      " #   Column                                    Non-Null Count  Dtype  \n",
      "---  ------                                    --------------  -----  \n",
      " 0   Ticker                                    58876 non-null  object \n",
      " 1   SimFinId                                  58876 non-null  int64  \n",
      " 2   Currency                                  58876 non-null  object \n",
      " 3   Fiscal Year                               58876 non-null  int64  \n",
      " 4   Fiscal Period                             58876 non-null  object \n",
      " 5   Report Date                               58876 non-null  object \n",
      " 6   Publish Date                              58876 non-null  object \n",
      " 7   Restated Date                             58876 non-null  object \n",
      " 8   Shares (Basic)                            58596 non-null  float64\n",
      " 9   Shares (Diluted)                          58593 non-null  float64\n",
      " 10  Revenue                                   54404 non-null  float64\n",
      " 11  Cost of Revenue                           47716 non-null  float64\n",
      " 12  Gross Profit                              47647 non-null  float64\n",
      " 13  Operating Expenses                        58797 non-null  float64\n",
      " 14  Selling, General & Administrative         56143 non-null  float64\n",
      " 15  Research & Development                    26634 non-null  float64\n",
      " 16  Depreciation & Amortization               25659 non-null  float64\n",
      " 17  Operating Income (Loss)                   58876 non-null  int64  \n",
      " 18  Non-Operating Income (Loss)               58574 non-null  float64\n",
      " 19  Interest Expense, Net                     53301 non-null  float64\n",
      " 20  Pretax Income (Loss), Adj.                58876 non-null  int64  \n",
      " 21  Abnormal Gains (Losses)                   40433 non-null  float64\n",
      " 22  Pretax Income (Loss)                      58876 non-null  int64  \n",
      " 23  Income Tax (Expense) Benefit, Net         50355 non-null  float64\n",
      " 24  Income (Loss) from Continuing Operations  58876 non-null  int64  \n",
      " 25  Net Extraordinary Gains (Losses)          8194 non-null   float64\n",
      " 26  Net Income                                58876 non-null  int64  \n",
      " 27  Net Income (Common)                       58876 non-null  int64  \n",
      "dtypes: float64(14), int64(8), object(6)\n",
      "memory usage: 12.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "num_rows, num_columns = df1.shape\n",
    "print(f\"Number of Rows: {num_rows}\")\n",
    "print(f\"Number of Columns: {num_columns}\")\n",
    "\n",
    "# Display basic information about the DataFrame\n",
    "print(\"DataFrame Info:\")\n",
    "print(df1.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame Head:\n",
      "  Ticker  SimFinId Currency  Fiscal Year Fiscal Period Report Date  \\\n",
      "0      A     45846      USD         2017            Q4  2017-10-31   \n",
      "1      A     45846      USD         2018            Q1  2018-01-31   \n",
      "2      A     45846      USD         2018            Q2  2018-04-30   \n",
      "3      A     45846      USD         2018            Q3  2018-07-31   \n",
      "4      A     45846      USD         2018            Q4  2018-10-31   \n",
      "\n",
      "  Publish Date Restated Date  Shares (Basic)  Shares (Diluted)  ...  \\\n",
      "0   2017-12-21    2018-12-20     322000000.0       326000000.0  ...   \n",
      "1   2018-03-06    2019-03-05     322250000.0       325250000.0  ...   \n",
      "2   2018-05-31    2019-05-30     322500000.0       325500000.0  ...   \n",
      "3   2018-08-30    2018-08-30     322250000.0       325000000.0  ...   \n",
      "4   2018-12-20    2020-12-18     321000000.0       325000000.0  ...   \n",
      "\n",
      "   Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "0                  -38000000.0            -57000000.0   \n",
      "1                  -21000000.0            -52000000.0   \n",
      "2                    6000000.0            -46000000.0   \n",
      "3                   31000000.0            -42000000.0   \n",
      "4                   57000000.0            -37000000.0   \n",
      "\n",
      "   Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  Pretax Income (Loss)  \\\n",
      "0                   803000000                      NaN             803000000   \n",
      "1                   843000000                      NaN             843000000   \n",
      "2                   879000000                      NaN             879000000   \n",
      "3                   928000000                      NaN             928000000   \n",
      "4                   946000000                      NaN             946000000   \n",
      "\n",
      "   Income Tax (Expense) Benefit, Net  \\\n",
      "0                       -119000000.0   \n",
      "1                       -647000000.0   \n",
      "2                       -642000000.0   \n",
      "3                       -630000000.0   \n",
      "4                       -630000000.0   \n",
      "\n",
      "   Income (Loss) from Continuing Operations  Net Extraordinary Gains (Losses)  \\\n",
      "0                                 684000000                               NaN   \n",
      "1                                 196000000                               NaN   \n",
      "2                                 237000000                               NaN   \n",
      "3                                 298000000                               NaN   \n",
      "4                                 316000000                               NaN   \n",
      "\n",
      "   Net Income  Net Income (Common)  \n",
      "0   684000000            684000000  \n",
      "1   196000000            196000000  \n",
      "2   237000000            237000000  \n",
      "3   298000000            298000000  \n",
      "4   316000000            316000000  \n",
      "\n",
      "[5 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the DataFrame\n",
    "print(\"\\nDataFrame Head:\")\n",
    "print(df1.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Ticker', 'SimFinId', 'Currency', 'Fiscal Year', 'Fiscal Period',\n",
      "       'Report Date', 'Publish Date', 'Restated Date', 'Shares (Basic)',\n",
      "       'Shares (Diluted)', 'Revenue', 'Cost of Revenue', 'Gross Profit',\n",
      "       'Operating Expenses', 'Selling, General & Administrative',\n",
      "       'Research & Development', 'Depreciation & Amortization',\n",
      "       'Operating Income (Loss)', 'Non-Operating Income (Loss)',\n",
      "       'Interest Expense, Net', 'Pretax Income (Loss), Adj.',\n",
      "       'Abnormal Gains (Losses)', 'Pretax Income (Loss)',\n",
      "       'Income Tax (Expense) Benefit, Net',\n",
      "       'Income (Loss) from Continuing Operations',\n",
      "       'Net Extraordinary Gains (Losses)', 'Net Income',\n",
      "       'Net Income (Common)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df1.columns)\n",
    "selected_columns = ['Ticker','Fiscal Year','Fiscal Period','Operating Income (Loss)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Fiscal Period</th>\n",
       "      <th>Operating Income (Loss)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2017</td>\n",
       "      <td>Q4</td>\n",
       "      <td>841000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2018</td>\n",
       "      <td>Q1</td>\n",
       "      <td>864000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>2018</td>\n",
       "      <td>Q2</td>\n",
       "      <td>873000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>2018</td>\n",
       "      <td>Q3</td>\n",
       "      <td>897000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>2018</td>\n",
       "      <td>Q4</td>\n",
       "      <td>889000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58871</th>\n",
       "      <td>ZYXI</td>\n",
       "      <td>2021</td>\n",
       "      <td>Q2</td>\n",
       "      <td>6440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58872</th>\n",
       "      <td>ZYXI</td>\n",
       "      <td>2021</td>\n",
       "      <td>Q3</td>\n",
       "      <td>13077000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58873</th>\n",
       "      <td>ZYXI</td>\n",
       "      <td>2021</td>\n",
       "      <td>Q4</td>\n",
       "      <td>22366000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58874</th>\n",
       "      <td>ZYXI</td>\n",
       "      <td>2022</td>\n",
       "      <td>Q1</td>\n",
       "      <td>25353000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58875</th>\n",
       "      <td>ZYXI</td>\n",
       "      <td>2022</td>\n",
       "      <td>Q2</td>\n",
       "      <td>25902000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58876 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ticker  Fiscal Year Fiscal Period  Operating Income (Loss)\n",
       "0          A         2017            Q4                841000000\n",
       "1          A         2018            Q1                864000000\n",
       "2          A         2018            Q2                873000000\n",
       "3          A         2018            Q3                897000000\n",
       "4          A         2018            Q4                889000000\n",
       "...      ...          ...           ...                      ...\n",
       "58871   ZYXI         2021            Q2                  6440000\n",
       "58872   ZYXI         2021            Q3                 13077000\n",
       "58873   ZYXI         2021            Q4                 22366000\n",
       "58874   ZYXI         2022            Q1                 25353000\n",
       "58875   ZYXI         2022            Q2                 25902000\n",
       "\n",
       "[58876 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[selected_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-annual\" on disk (4 days old).\n",
      "- Loading from disk ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "df2 = sf.load(dataset='income', variant='annual', market='us',\n",
    "              index=[TICKER, REPORT_DATE],\n",
    "              parse_dates=[REPORT_DATE, PUBLISH_DATE, RESTATED_DATE])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2017-10-31      45846      USD         2017            FY   2017-12-21   \n",
      "       2018-10-31      45846      USD         2018            FY   2018-12-20   \n",
      "       2019-10-31      45846      USD         2019            FY   2019-12-19   \n",
      "       2020-10-31      45846      USD         2020            FY   2020-12-18   \n",
      "       2021-10-31      45846      USD         2021            FY   2021-12-17   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2017-10-31     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-10-31     2020-12-18     321000000.0       325000000.0   \n",
      "       2019-10-31     2021-12-17     314000000.0       318000000.0   \n",
      "       2020-10-31     2022-12-21     309000000.0       312000000.0   \n",
      "       2021-10-31     2022-12-21     305000000.0       307000000.0   \n",
      "\n",
      "                         Revenue  Cost of Revenue  ...  \\\n",
      "Ticker Report Date                                 ...   \n",
      "A      2017-10-31   4.472000e+09    -2.063000e+09  ...   \n",
      "       2018-10-31   4.914000e+09    -2.234000e+09  ...   \n",
      "       2019-10-31   5.163000e+09    -2.358000e+09  ...   \n",
      "       2020-10-31   5.339000e+09    -2.502000e+09  ...   \n",
      "       2021-10-31   6.319000e+09    -2.912000e+09  ...   \n",
      "\n",
      "                    Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "Ticker Report Date                                                       \n",
      "A      2017-10-31                   -38000000.0            -57000000.0   \n",
      "       2018-10-31                    42000000.0            -37000000.0   \n",
      "       2019-10-31                   -22000000.0            -38000000.0   \n",
      "       2020-10-31                    -4000000.0            -70000000.0   \n",
      "       2021-10-31                    13000000.0            -79000000.0   \n",
      "\n",
      "                    Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  \\\n",
      "Ticker Report Date                                                        \n",
      "A      2017-10-31                    803000000                      NaN   \n",
      "       2018-10-31                    946000000                      NaN   \n",
      "       2019-10-31                    919000000                      NaN   \n",
      "       2020-10-31                    842000000                      NaN   \n",
      "       2021-10-31                   1360000000                      NaN   \n",
      "\n",
      "                    Pretax Income (Loss)  Income Tax (Expense) Benefit, Net  \\\n",
      "Ticker Report Date                                                            \n",
      "A      2017-10-31              803000000                       -119000000.0   \n",
      "       2018-10-31              946000000                       -630000000.0   \n",
      "       2019-10-31              919000000                        152000000.0   \n",
      "       2020-10-31              842000000                       -123000000.0   \n",
      "       2021-10-31             1360000000                       -150000000.0   \n",
      "\n",
      "                    Income (Loss) from Continuing Operations  \\\n",
      "Ticker Report Date                                             \n",
      "A      2017-10-31                                  684000000   \n",
      "       2018-10-31                                  316000000   \n",
      "       2019-10-31                                 1071000000   \n",
      "       2020-10-31                                  719000000   \n",
      "       2021-10-31                                 1210000000   \n",
      "\n",
      "                    Net Extraordinary Gains (Losses)  Net Income  \\\n",
      "Ticker Report Date                                                 \n",
      "A      2017-10-31                                NaN   684000000   \n",
      "       2018-10-31                                NaN   316000000   \n",
      "       2019-10-31                                NaN  1071000000   \n",
      "       2020-10-31                                NaN   719000000   \n",
      "       2021-10-31                                NaN  1210000000   \n",
      "\n",
      "                    Net Income (Common)  \n",
      "Ticker Report Date                       \n",
      "A      2017-10-31             684000000  \n",
      "       2018-10-31             316000000  \n",
      "       2019-10-31            1071000000  \n",
      "       2020-10-31             719000000  \n",
      "       2021-10-31            1210000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup ticker and extract columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SimFinId</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Fiscal Period</th>\n",
       "      <th>Publish Date</th>\n",
       "      <th>Restated Date</th>\n",
       "      <th>Shares (Basic)</th>\n",
       "      <th>Shares (Diluted)</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Cost of Revenue</th>\n",
       "      <th>...</th>\n",
       "      <th>Non-Operating Income (Loss)</th>\n",
       "      <th>Interest Expense, Net</th>\n",
       "      <th>Pretax Income (Loss), Adj.</th>\n",
       "      <th>Abnormal Gains (Losses)</th>\n",
       "      <th>Pretax Income (Loss)</th>\n",
       "      <th>Income Tax (Expense) Benefit, Net</th>\n",
       "      <th>Income (Loss) from Continuing Operations</th>\n",
       "      <th>Net Extraordinary Gains (Losses)</th>\n",
       "      <th>Net Income</th>\n",
       "      <th>Net Income (Common)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>59265</td>\n",
       "      <td>USD</td>\n",
       "      <td>2018</td>\n",
       "      <td>FY</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>7.700000e+09</td>\n",
       "      <td>7.794000e+09</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>-3.835300e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.416000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36474000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36474000000</td>\n",
       "      <td>-1.990300e+10</td>\n",
       "      <td>16571000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16571000000</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>59265</td>\n",
       "      <td>USD</td>\n",
       "      <td>2019</td>\n",
       "      <td>FY</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>7.673000e+09</td>\n",
       "      <td>7.753000e+09</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>-4.291000e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.290000e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43688000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43688000000</td>\n",
       "      <td>-4.448000e+09</td>\n",
       "      <td>39240000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39240000000</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>59265</td>\n",
       "      <td>USD</td>\n",
       "      <td>2020</td>\n",
       "      <td>FY</td>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>7.610000e+09</td>\n",
       "      <td>7.683000e+09</td>\n",
       "      <td>1.430150e+11</td>\n",
       "      <td>-4.607800e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53036000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53036000000</td>\n",
       "      <td>-8.755000e+09</td>\n",
       "      <td>44281000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44281000000</td>\n",
       "      <td>44281000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>59265</td>\n",
       "      <td>USD</td>\n",
       "      <td>2021</td>\n",
       "      <td>FY</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>2023-07-27</td>\n",
       "      <td>7.554000e+09</td>\n",
       "      <td>7.617000e+09</td>\n",
       "      <td>1.680880e+11</td>\n",
       "      <td>-5.223200e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.186000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71102000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71102000000</td>\n",
       "      <td>-9.831000e+09</td>\n",
       "      <td>61271000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61271000000</td>\n",
       "      <td>61271000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>59265</td>\n",
       "      <td>USD</td>\n",
       "      <td>2022</td>\n",
       "      <td>FY</td>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>2023-07-27</td>\n",
       "      <td>7.496000e+09</td>\n",
       "      <td>7.540000e+09</td>\n",
       "      <td>1.982700e+11</td>\n",
       "      <td>-6.265000e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>3.330000e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83716000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83716000000</td>\n",
       "      <td>-1.097800e+10</td>\n",
       "      <td>72738000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72738000000</td>\n",
       "      <td>72738000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
       "Report Date                                                              \n",
       "2018-06-30      59265      USD         2018            FY   2018-08-03   \n",
       "2019-06-30      59265      USD         2019            FY   2019-08-01   \n",
       "2020-06-30      59265      USD         2020            FY   2020-07-30   \n",
       "2021-06-30      59265      USD         2021            FY   2021-07-29   \n",
       "2022-06-30      59265      USD         2022            FY   2022-07-28   \n",
       "\n",
       "            Restated Date  Shares (Basic)  Shares (Diluted)       Revenue  \\\n",
       "Report Date                                                                 \n",
       "2018-06-30     2020-07-30    7.700000e+09      7.794000e+09  1.103600e+11   \n",
       "2019-06-30     2021-07-29    7.673000e+09      7.753000e+09  1.258430e+11   \n",
       "2020-06-30     2022-07-28    7.610000e+09      7.683000e+09  1.430150e+11   \n",
       "2021-06-30     2023-07-27    7.554000e+09      7.617000e+09  1.680880e+11   \n",
       "2022-06-30     2023-07-27    7.496000e+09      7.540000e+09  1.982700e+11   \n",
       "\n",
       "             Cost of Revenue  ...  Non-Operating Income (Loss)  \\\n",
       "Report Date                   ...                                \n",
       "2018-06-30     -3.835300e+10  ...                 1.416000e+09   \n",
       "2019-06-30     -4.291000e+10  ...                 7.290000e+08   \n",
       "2020-06-30     -4.607800e+10  ...                 7.700000e+07   \n",
       "2021-06-30     -5.223200e+10  ...                 1.186000e+09   \n",
       "2022-06-30     -6.265000e+10  ...                 3.330000e+08   \n",
       "\n",
       "             Interest Expense, Net  Pretax Income (Loss), Adj.  \\\n",
       "Report Date                                                      \n",
       "2018-06-30                     NaN                 36474000000   \n",
       "2019-06-30                     NaN                 43688000000   \n",
       "2020-06-30                     NaN                 53036000000   \n",
       "2021-06-30                     NaN                 71102000000   \n",
       "2022-06-30                     NaN                 83716000000   \n",
       "\n",
       "             Abnormal Gains (Losses)  Pretax Income (Loss)  \\\n",
       "Report Date                                                  \n",
       "2018-06-30                       NaN           36474000000   \n",
       "2019-06-30                       NaN           43688000000   \n",
       "2020-06-30                       NaN           53036000000   \n",
       "2021-06-30                       NaN           71102000000   \n",
       "2022-06-30                       NaN           83716000000   \n",
       "\n",
       "             Income Tax (Expense) Benefit, Net  \\\n",
       "Report Date                                      \n",
       "2018-06-30                       -1.990300e+10   \n",
       "2019-06-30                       -4.448000e+09   \n",
       "2020-06-30                       -8.755000e+09   \n",
       "2021-06-30                       -9.831000e+09   \n",
       "2022-06-30                       -1.097800e+10   \n",
       "\n",
       "             Income (Loss) from Continuing Operations  \\\n",
       "Report Date                                             \n",
       "2018-06-30                                16571000000   \n",
       "2019-06-30                                39240000000   \n",
       "2020-06-30                                44281000000   \n",
       "2021-06-30                                61271000000   \n",
       "2022-06-30                                72738000000   \n",
       "\n",
       "             Net Extraordinary Gains (Losses)   Net Income  \\\n",
       "Report Date                                                  \n",
       "2018-06-30                                NaN  16571000000   \n",
       "2019-06-30                                NaN  39240000000   \n",
       "2020-06-30                                NaN  44281000000   \n",
       "2021-06-30                                NaN  61271000000   \n",
       "2022-06-30                                NaN  72738000000   \n",
       "\n",
       "             Net Income (Common)  \n",
       "Report Date                       \n",
       "2018-06-30           16571000000  \n",
       "2019-06-30           39240000000  \n",
       "2020-06-30           44281000000  \n",
       "2021-06-30           61271000000  \n",
       "2022-06-30           72738000000  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc['MSFT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Fiscal Period</th>\n",
       "      <th>Operating Income (Loss)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>2018</td>\n",
       "      <td>FY</td>\n",
       "      <td>35058000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>FY</td>\n",
       "      <td>42959000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>2020</td>\n",
       "      <td>FY</td>\n",
       "      <td>52959000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>2021</td>\n",
       "      <td>FY</td>\n",
       "      <td>69916000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>2022</td>\n",
       "      <td>FY</td>\n",
       "      <td>83383000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fiscal Year Fiscal Period  Operating Income (Loss)\n",
       "Report Date                                                    \n",
       "2018-06-30          2018            FY              35058000000\n",
       "2019-06-30          2019            FY              42959000000\n",
       "2020-06-30          2020            FY              52959000000\n",
       "2021-06-30          2021            FY              69916000000\n",
       "2022-06-30          2022            FY              83383000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc['MSFT'][OPERATING_INCOME]\n",
    "selected_columns2 = ['Fiscal Year','Fiscal Period','Operating Income (Loss)']\n",
    "df2[selected_columns2].loc['MSFT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Report Date'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.loc['MSFT'][OPERATING_INCOME].plot(grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart for the 'Operating Income' column of 'MSFT' using loc\n",
    "df2.loc['MSFT'][OPERATING_INCOME].plot.bar(grid=True)\n",
    "\n",
    "matplotlib.pyplot.xlabel('Year')  # Use the full module name here\n",
    "matplotlib.pyplot.ylabel('Operating Income')  # Use the full module name here\n",
    "matplotlib.pyplot.title('Operating Income for MSFT')  # Use the full module name here\n",
    "matplotlib.pyplot.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load (special functions) all financial statments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-ttm\" on disk (1 days old).\n",
      "- Loading from disk ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2017-10-31      45846      USD         2017            Q4   2017-12-21   \n",
      "       2018-01-31      45846      USD         2018            Q1   2018-03-06   \n",
      "       2018-04-30      45846      USD         2018            Q2   2018-05-31   \n",
      "       2018-07-31      45846      USD         2018            Q3   2018-08-30   \n",
      "       2018-10-31      45846      USD         2018            Q4   2018-12-20   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2017-10-31     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-01-31     2019-03-05     322250000.0       325250000.0   \n",
      "       2018-04-30     2019-05-30     322500000.0       325500000.0   \n",
      "       2018-07-31     2018-08-30     322250000.0       325000000.0   \n",
      "       2018-10-31     2020-12-18     321000000.0       325000000.0   \n",
      "\n",
      "                         Revenue  Cost of Revenue  ...  \\\n",
      "Ticker Report Date                                 ...   \n",
      "A      2017-10-31   4.472000e+09    -2.063000e+09  ...   \n",
      "       2018-01-31   4.616000e+09    -2.111000e+09  ...   \n",
      "       2018-04-30   4.720000e+09    -2.164000e+09  ...   \n",
      "       2018-07-31   4.809000e+09    -2.188000e+09  ...   \n",
      "       2018-10-31   4.914000e+09    -2.238000e+09  ...   \n",
      "\n",
      "                    Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "Ticker Report Date                                                       \n",
      "A      2017-10-31                   -38000000.0            -57000000.0   \n",
      "       2018-01-31                   -21000000.0            -52000000.0   \n",
      "       2018-04-30                     6000000.0            -46000000.0   \n",
      "       2018-07-31                    31000000.0            -42000000.0   \n",
      "       2018-10-31                    57000000.0            -37000000.0   \n",
      "\n",
      "                    Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  \\\n",
      "Ticker Report Date                                                        \n",
      "A      2017-10-31                    803000000                      NaN   \n",
      "       2018-01-31                    843000000                      NaN   \n",
      "       2018-04-30                    879000000                      NaN   \n",
      "       2018-07-31                    928000000                      NaN   \n",
      "       2018-10-31                    946000000                      NaN   \n",
      "\n",
      "                    Pretax Income (Loss)  Income Tax (Expense) Benefit, Net  \\\n",
      "Ticker Report Date                                                            \n",
      "A      2017-10-31              803000000                       -119000000.0   \n",
      "       2018-01-31              843000000                       -647000000.0   \n",
      "       2018-04-30              879000000                       -642000000.0   \n",
      "       2018-07-31              928000000                       -630000000.0   \n",
      "       2018-10-31              946000000                       -630000000.0   \n",
      "\n",
      "                    Income (Loss) from Continuing Operations  \\\n",
      "Ticker Report Date                                             \n",
      "A      2017-10-31                                  684000000   \n",
      "       2018-01-31                                  196000000   \n",
      "       2018-04-30                                  237000000   \n",
      "       2018-07-31                                  298000000   \n",
      "       2018-10-31                                  316000000   \n",
      "\n",
      "                    Net Extraordinary Gains (Losses)  Net Income  \\\n",
      "Ticker Report Date                                                 \n",
      "A      2017-10-31                                NaN   684000000   \n",
      "       2018-01-31                                NaN   196000000   \n",
      "       2018-04-30                                NaN   237000000   \n",
      "       2018-07-31                                NaN   298000000   \n",
      "       2018-10-31                                NaN   316000000   \n",
      "\n",
      "                    Net Income (Common)  \n",
      "Ticker Report Date                       \n",
      "A      2017-10-31             684000000  \n",
      "       2018-01-31             196000000  \n",
      "       2018-04-30             237000000  \n",
      "       2018-07-31             298000000  \n",
      "       2018-10-31             316000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "df_income = sf.load_income(variant='ttm', market='us')\n",
    "print(df_income.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-balance-ttm\" on disk (1 days old).\n",
      "- Loading from disk ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Index(['SimFinId', 'Currency', 'Fiscal Year', 'Fiscal Period', 'Publish Date',\n",
      "       'Restated Date', 'Shares (Basic)', 'Shares (Diluted)',\n",
      "       'Cash, Cash Equivalents & Short Term Investments',\n",
      "       'Accounts & Notes Receivable', 'Inventories', 'Total Current Assets',\n",
      "       'Property, Plant & Equipment, Net',\n",
      "       'Long Term Investments & Receivables', 'Other Long Term Assets',\n",
      "       'Total Noncurrent Assets', 'Total Assets', 'Payables & Accruals',\n",
      "       'Short Term Debt', 'Total Current Liabilities', 'Long Term Debt',\n",
      "       'Total Noncurrent Liabilities', 'Total Liabilities',\n",
      "       'Share Capital & Additional Paid-In Capital', 'Treasury Stock',\n",
      "       'Retained Earnings', 'Total Equity', 'Total Liabilities & Equity'],\n",
      "      dtype='object')\n",
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2017-10-31      45846      USD         2017            Q4   2017-12-21   \n",
      "       2018-01-31      45846      USD         2018            Q1   2018-03-06   \n",
      "       2018-04-30      45846      USD         2018            Q2   2018-05-31   \n",
      "       2018-07-31      45846      USD         2018            Q3   2018-08-30   \n",
      "       2018-10-31      45846      USD         2018            Q4   2018-12-20   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2017-10-31     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-01-31     2018-03-06     322250000.0       325250000.0   \n",
      "       2018-04-30     2018-05-31     322500000.0       325500000.0   \n",
      "       2018-07-31     2018-08-30     322250000.0       325000000.0   \n",
      "       2018-10-31     2018-12-20     321000000.0       325000000.0   \n",
      "\n",
      "                    Cash, Cash Equivalents & Short Term Investments  \\\n",
      "Ticker Report Date                                                    \n",
      "A      2017-10-31                                      2.678000e+09   \n",
      "       2018-01-31                                      2.887000e+09   \n",
      "       2018-04-30                                      3.011000e+09   \n",
      "       2018-07-31                                      2.131000e+09   \n",
      "       2018-10-31                                      2.247000e+09   \n",
      "\n",
      "                    Accounts & Notes Receivable  ...  Short Term Debt  \\\n",
      "Ticker Report Date                               ...                    \n",
      "A      2017-10-31                   724000000.0  ...      210000000.0   \n",
      "       2018-01-31                   751000000.0  ...      345000000.0   \n",
      "       2018-04-30                   754000000.0  ...      315000000.0   \n",
      "       2018-07-31                   733000000.0  ...              0.0   \n",
      "       2018-10-31                   776000000.0  ...              0.0   \n",
      "\n",
      "                    Total Current Liabilities  Long Term Debt  \\\n",
      "Ticker Report Date                                              \n",
      "A      2017-10-31                1.263000e+09    1.801000e+09   \n",
      "       2018-01-31                1.361000e+09    1.800000e+09   \n",
      "       2018-04-30                1.365000e+09    1.800000e+09   \n",
      "       2018-07-31                1.014000e+09    1.799000e+09   \n",
      "       2018-10-31                1.171000e+09    1.799000e+09   \n",
      "\n",
      "                    Total Noncurrent Liabilities  Total Liabilities  \\\n",
      "Ticker Report Date                                                    \n",
      "A      2017-10-31                   2.328000e+09       3.591000e+09   \n",
      "       2018-01-31                   2.811000e+09       4.172000e+09   \n",
      "       2018-04-30                   2.802000e+09       4.167000e+09   \n",
      "       2018-07-31                   2.767000e+09       3.781000e+09   \n",
      "       2018-10-31                   2.799000e+09       3.970000e+09   \n",
      "\n",
      "                    Share Capital & Additional Paid-In Capital  \\\n",
      "Ticker Report Date                                               \n",
      "A      2017-10-31                                 5.303000e+09   \n",
      "       2018-01-31                                 5.323000e+09   \n",
      "       2018-04-30                                 5.335000e+09   \n",
      "       2018-07-31                                 5.315000e+09   \n",
      "       2018-10-31                                 5.311000e+09   \n",
      "\n",
      "                    Treasury Stock  Retained Earnings  Total Equity  \\\n",
      "Ticker Report Date                                                    \n",
      "A      2017-10-31              NaN       -126000000.0  4.835000e+09   \n",
      "       2018-01-31       -3000000.0       -529000000.0  4.526000e+09   \n",
      "       2018-04-30              NaN       -412000000.0  4.617000e+09   \n",
      "       2018-07-31              NaN       -416000000.0  4.568000e+09   \n",
      "       2018-10-31              NaN       -336000000.0  4.571000e+09   \n",
      "\n",
      "                    Total Liabilities & Equity  \n",
      "Ticker Report Date                              \n",
      "A      2017-10-31                   8426000000  \n",
      "       2018-01-31                   8698000000  \n",
      "       2018-04-30                   8784000000  \n",
      "       2018-07-31                   8349000000  \n",
      "       2018-10-31                   8541000000  \n",
      "\n",
      "[5 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "df_balance = sf.load_balance(variant='ttm', market='us')\n",
    "print(df_balance.columns)\n",
    "print(df_balance.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-cashflow-ttm\" on disk (1 days old).\n",
      "- Loading from disk ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Index(['SimFinId', 'Currency', 'Fiscal Year', 'Fiscal Period', 'Publish Date',\n",
      "       'Restated Date', 'Shares (Basic)', 'Shares (Diluted)',\n",
      "       'Net Income/Starting Line', 'Depreciation & Amortization',\n",
      "       'Non-Cash Items', 'Change in Working Capital',\n",
      "       'Change in Accounts Receivable', 'Change in Inventories',\n",
      "       'Change in Accounts Payable', 'Change in Other',\n",
      "       'Net Cash from Operating Activities',\n",
      "       'Change in Fixed Assets & Intangibles',\n",
      "       'Net Change in Long Term Investment',\n",
      "       'Net Cash from Acquisitions & Divestitures',\n",
      "       'Net Cash from Investing Activities', 'Dividends Paid',\n",
      "       'Cash from (Repayment of) Debt', 'Cash from (Repurchase of) Equity',\n",
      "       'Net Cash from Financing Activities', 'Net Change in Cash'],\n",
      "      dtype='object')\n",
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2017-10-31      45846      USD         2017            Q4   2017-12-21   \n",
      "       2018-01-31      45846      USD         2018            Q1   2018-03-06   \n",
      "       2018-04-30      45846      USD         2018            Q2   2018-05-31   \n",
      "       2018-07-31      45846      USD         2018            Q3   2018-08-30   \n",
      "       2018-10-31      45846      USD         2018            Q4   2018-12-20   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2017-10-31     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-01-31     2019-03-05     322250000.0       325250000.0   \n",
      "       2018-04-30     2019-05-30     322500000.0       325500000.0   \n",
      "       2018-07-31     2018-08-30     322250000.0       325000000.0   \n",
      "       2018-10-31     2020-12-18     321000000.0       325000000.0   \n",
      "\n",
      "                    Net Income/Starting Line  Depreciation & Amortization  \\\n",
      "Ticker Report Date                                                          \n",
      "A      2017-10-31                684000000.0                  212000000.0   \n",
      "       2018-01-31                196000000.0                  208000000.0   \n",
      "       2018-04-30                237000000.0                  204000000.0   \n",
      "       2018-07-31                298000000.0                  206000000.0   \n",
      "       2018-10-31                316000000.0                  210000000.0   \n",
      "\n",
      "                    ...  Net Cash from Operating Activities  \\\n",
      "Ticker Report Date  ...                                       \n",
      "A      2017-10-31   ...                           889000000   \n",
      "       2018-01-31   ...                           988000000   \n",
      "       2018-04-30   ...                          1034000000   \n",
      "       2018-07-31   ...                          1003000000   \n",
      "       2018-10-31   ...                          1087000000   \n",
      "\n",
      "                    Change in Fixed Assets & Intangibles  \\\n",
      "Ticker Report Date                                         \n",
      "A      2017-10-31                           -176000000.0   \n",
      "       2018-01-31                           -204000000.0   \n",
      "       2018-04-30                           -209000000.0   \n",
      "       2018-07-31                           -199000000.0   \n",
      "       2018-10-31                           -176000000.0   \n",
      "\n",
      "                    Net Change in Long Term Investment  \\\n",
      "Ticker Report Date                                       \n",
      "A      2017-10-31                                  0.0   \n",
      "       2018-01-31                                  0.0   \n",
      "       2018-04-30                                  0.0   \n",
      "       2018-07-31                                  0.0   \n",
      "       2018-10-31                                  NaN   \n",
      "\n",
      "                    Net Cash from Acquisitions & Divestitures  \\\n",
      "Ticker Report Date                                              \n",
      "A      2017-10-31                                -126000000.0   \n",
      "       2018-01-31                                 -63000000.0   \n",
      "       2018-04-30                                 -64000000.0   \n",
      "       2018-07-31                                -437000000.0   \n",
      "       2018-10-31                                -516000000.0   \n",
      "\n",
      "                    Net Cash from Investing Activities  Dividends Paid  \\\n",
      "Ticker Report Date                                                       \n",
      "A      2017-10-31                         -305000000.0    -170000000.0   \n",
      "       2018-01-31                         -271000000.0    -176000000.0   \n",
      "       2018-04-30                         -279000000.0    -181000000.0   \n",
      "       2018-07-31                         -650000000.0    -187000000.0   \n",
      "       2018-10-31                         -705000000.0    -191000000.0   \n",
      "\n",
      "                    Cash from (Repayment of) Debt  \\\n",
      "Ticker Report Date                                  \n",
      "A      2017-10-31                     110000000.0   \n",
      "       2018-01-31                     156000000.0   \n",
      "       2018-04-30                      74000000.0   \n",
      "       2018-07-31                    -280000000.0   \n",
      "       2018-10-31                    -210000000.0   \n",
      "\n",
      "                    Cash from (Repurchase of) Equity  \\\n",
      "Ticker Report Date                                     \n",
      "A      2017-10-31                       -128000000.0   \n",
      "       2018-01-31                        -57000000.0   \n",
      "       2018-04-30                        -17000000.0   \n",
      "       2018-07-31                       -275000000.0   \n",
      "       2018-10-31                       -366000000.0   \n",
      "\n",
      "                    Net Cash from Financing Activities  Net Change in Cash  \n",
      "Ticker Report Date                                                          \n",
      "A      2017-10-31                         -202000000.0           389000000  \n",
      "       2018-01-31                         -107000000.0           647000000  \n",
      "       2018-04-30                         -154000000.0           621000000  \n",
      "       2018-07-31                         -772000000.0          -432000000  \n",
      "       2018-10-31                         -797000000.0          -432000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "df_cashflow = sf.load_cashflow(variant='ttm', market='us')\n",
    "print(df_cashflow.columns)\n",
    "print(df_cashflow.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Report Date vs. Publish Date vs. Restated Date\n",
    "\n",
    "By default the functions sf.load_income(), sf.load_balance() and sf.load_cashflow() use the Report Date as an index, but it can be switched to use the Publish Date instead, by setting the index argument. We give a list of the indices we want in the resulting DataFrame, in this case we want both the Ticker and Publish Date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-ttm\" on disk (1 days old).\n",
      "- Loading from disk ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "                     SimFinId Currency  Fiscal Year Fiscal Period Report Date  \\\n",
      "Ticker Publish Date                                                             \n",
      "A      2017-12-21       45846      USD         2017            Q4  2017-10-31   \n",
      "       2018-03-06       45846      USD         2018            Q1  2018-01-31   \n",
      "       2018-05-31       45846      USD         2018            Q2  2018-04-30   \n",
      "       2018-08-30       45846      USD         2018            Q3  2018-07-31   \n",
      "       2018-12-20       45846      USD         2018            Q4  2018-10-31   \n",
      "\n",
      "                    Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Publish Date                                                   \n",
      "A      2017-12-21      2018-12-20     322000000.0       326000000.0   \n",
      "       2018-03-06      2019-03-05     322250000.0       325250000.0   \n",
      "       2018-05-31      2019-05-30     322500000.0       325500000.0   \n",
      "       2018-08-30      2018-08-30     322250000.0       325000000.0   \n",
      "       2018-12-20      2020-12-18     321000000.0       325000000.0   \n",
      "\n",
      "                          Revenue  Cost of Revenue  ...  \\\n",
      "Ticker Publish Date                                 ...   \n",
      "A      2017-12-21    4.472000e+09    -2.063000e+09  ...   \n",
      "       2018-03-06    4.616000e+09    -2.111000e+09  ...   \n",
      "       2018-05-31    4.720000e+09    -2.164000e+09  ...   \n",
      "       2018-08-30    4.809000e+09    -2.188000e+09  ...   \n",
      "       2018-12-20    4.914000e+09    -2.238000e+09  ...   \n",
      "\n",
      "                     Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "Ticker Publish Date                                                       \n",
      "A      2017-12-21                    -38000000.0            -57000000.0   \n",
      "       2018-03-06                    -21000000.0            -52000000.0   \n",
      "       2018-05-31                      6000000.0            -46000000.0   \n",
      "       2018-08-30                     31000000.0            -42000000.0   \n",
      "       2018-12-20                     57000000.0            -37000000.0   \n",
      "\n",
      "                     Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  \\\n",
      "Ticker Publish Date                                                        \n",
      "A      2017-12-21                     803000000                      NaN   \n",
      "       2018-03-06                     843000000                      NaN   \n",
      "       2018-05-31                     879000000                      NaN   \n",
      "       2018-08-30                     928000000                      NaN   \n",
      "       2018-12-20                     946000000                      NaN   \n",
      "\n",
      "                     Pretax Income (Loss)  Income Tax (Expense) Benefit, Net  \\\n",
      "Ticker Publish Date                                                            \n",
      "A      2017-12-21               803000000                       -119000000.0   \n",
      "       2018-03-06               843000000                       -647000000.0   \n",
      "       2018-05-31               879000000                       -642000000.0   \n",
      "       2018-08-30               928000000                       -630000000.0   \n",
      "       2018-12-20               946000000                       -630000000.0   \n",
      "\n",
      "                     Income (Loss) from Continuing Operations  \\\n",
      "Ticker Publish Date                                             \n",
      "A      2017-12-21                                   684000000   \n",
      "       2018-03-06                                   196000000   \n",
      "       2018-05-31                                   237000000   \n",
      "       2018-08-30                                   298000000   \n",
      "       2018-12-20                                   316000000   \n",
      "\n",
      "                     Net Extraordinary Gains (Losses)  Net Income  \\\n",
      "Ticker Publish Date                                                 \n",
      "A      2017-12-21                                 NaN   684000000   \n",
      "       2018-03-06                                 NaN   196000000   \n",
      "       2018-05-31                                 NaN   237000000   \n",
      "       2018-08-30                                 NaN   298000000   \n",
      "       2018-12-20                                 NaN   316000000   \n",
      "\n",
      "                     Net Income (Common)  \n",
      "Ticker Publish Date                       \n",
      "A      2017-12-21              684000000  \n",
      "       2018-03-06              196000000  \n",
      "       2018-05-31              237000000  \n",
      "       2018-08-30              298000000  \n",
      "       2018-12-20              316000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "df_income2 = sf.load_income(variant='ttm', market='us',\n",
    "                            index=[TICKER, PUBLISH_DATE])\n",
    "print(df_income2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the kind of analysis you want to do, an alternative to using the Publish Date as the DataFrame index, is to add an offset to all the Report Dates in a DataFrame using a small helper-function. For example, we can add 90 days (about 3 months) to all the Report Dates, and use that as an estimate for when the data was available publicly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2018-01-29      45846      USD         2017            Q4   2017-12-21   \n",
      "       2018-05-01      45846      USD         2018            Q1   2018-03-06   \n",
      "       2018-07-29      45846      USD         2018            Q2   2018-05-31   \n",
      "       2018-10-29      45846      USD         2018            Q3   2018-08-30   \n",
      "       2019-01-29      45846      USD         2018            Q4   2018-12-20   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2018-01-29     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-05-01     2019-03-05     322250000.0       325250000.0   \n",
      "       2018-07-29     2019-05-30     322500000.0       325500000.0   \n",
      "       2018-10-29     2018-08-30     322250000.0       325000000.0   \n",
      "       2019-01-29     2020-12-18     321000000.0       325000000.0   \n",
      "\n",
      "                         Revenue  Cost of Revenue  ...  \\\n",
      "Ticker Report Date                                 ...   \n",
      "A      2018-01-29   4.472000e+09    -2.063000e+09  ...   \n",
      "       2018-05-01   4.616000e+09    -2.111000e+09  ...   \n",
      "       2018-07-29   4.720000e+09    -2.164000e+09  ...   \n",
      "       2018-10-29   4.809000e+09    -2.188000e+09  ...   \n",
      "       2019-01-29   4.914000e+09    -2.238000e+09  ...   \n",
      "\n",
      "                    Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "Ticker Report Date                                                       \n",
      "A      2018-01-29                   -38000000.0            -57000000.0   \n",
      "       2018-05-01                   -21000000.0            -52000000.0   \n",
      "       2018-07-29                     6000000.0            -46000000.0   \n",
      "       2018-10-29                    31000000.0            -42000000.0   \n",
      "       2019-01-29                    57000000.0            -37000000.0   \n",
      "\n",
      "                    Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  \\\n",
      "Ticker Report Date                                                        \n",
      "A      2018-01-29                    803000000                      NaN   \n",
      "       2018-05-01                    843000000                      NaN   \n",
      "       2018-07-29                    879000000                      NaN   \n",
      "       2018-10-29                    928000000                      NaN   \n",
      "       2019-01-29                    946000000                      NaN   \n",
      "\n",
      "                    Pretax Income (Loss)  Income Tax (Expense) Benefit, Net  \\\n",
      "Ticker Report Date                                                            \n",
      "A      2018-01-29              803000000                       -119000000.0   \n",
      "       2018-05-01              843000000                       -647000000.0   \n",
      "       2018-07-29              879000000                       -642000000.0   \n",
      "       2018-10-29              928000000                       -630000000.0   \n",
      "       2019-01-29              946000000                       -630000000.0   \n",
      "\n",
      "                    Income (Loss) from Continuing Operations  \\\n",
      "Ticker Report Date                                             \n",
      "A      2018-01-29                                  684000000   \n",
      "       2018-05-01                                  196000000   \n",
      "       2018-07-29                                  237000000   \n",
      "       2018-10-29                                  298000000   \n",
      "       2019-01-29                                  316000000   \n",
      "\n",
      "                    Net Extraordinary Gains (Losses)  Net Income  \\\n",
      "Ticker Report Date                                                 \n",
      "A      2018-01-29                                NaN   684000000   \n",
      "       2018-05-01                                NaN   196000000   \n",
      "       2018-07-29                                NaN   237000000   \n",
      "       2018-10-29                                NaN   298000000   \n",
      "       2019-01-29                                NaN   316000000   \n",
      "\n",
      "                    Net Income (Common)  \n",
      "Ticker Report Date                       \n",
      "A      2018-01-29             684000000  \n",
      "       2018-05-01             196000000  \n",
      "       2018-07-29             237000000  \n",
      "       2018-10-29             298000000  \n",
      "       2019-01-29             316000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "df_income3 = sf.add_date_offset(df=df_income,\n",
    "                                date_index=REPORT_DATE,\n",
    "                                offset=pd.DateOffset(days=90))\n",
    "print(df_income3.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing with the original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    SimFinId Currency  Fiscal Year Fiscal Period Publish Date  \\\n",
      "Ticker Report Date                                                              \n",
      "A      2017-10-31      45846      USD         2017            Q4   2017-12-21   \n",
      "       2018-01-31      45846      USD         2018            Q1   2018-03-06   \n",
      "       2018-04-30      45846      USD         2018            Q2   2018-05-31   \n",
      "       2018-07-31      45846      USD         2018            Q3   2018-08-30   \n",
      "       2018-10-31      45846      USD         2018            Q4   2018-12-20   \n",
      "\n",
      "                   Restated Date  Shares (Basic)  Shares (Diluted)  \\\n",
      "Ticker Report Date                                                   \n",
      "A      2017-10-31     2018-12-20     322000000.0       326000000.0   \n",
      "       2018-01-31     2019-03-05     322250000.0       325250000.0   \n",
      "       2018-04-30     2019-05-30     322500000.0       325500000.0   \n",
      "       2018-07-31     2018-08-30     322250000.0       325000000.0   \n",
      "       2018-10-31     2020-12-18     321000000.0       325000000.0   \n",
      "\n",
      "                         Revenue  Cost of Revenue  ...  \\\n",
      "Ticker Report Date                                 ...   \n",
      "A      2017-10-31   4.472000e+09    -2.063000e+09  ...   \n",
      "       2018-01-31   4.616000e+09    -2.111000e+09  ...   \n",
      "       2018-04-30   4.720000e+09    -2.164000e+09  ...   \n",
      "       2018-07-31   4.809000e+09    -2.188000e+09  ...   \n",
      "       2018-10-31   4.914000e+09    -2.238000e+09  ...   \n",
      "\n",
      "                    Non-Operating Income (Loss)  Interest Expense, Net  \\\n",
      "Ticker Report Date                                                       \n",
      "A      2017-10-31                   -38000000.0            -57000000.0   \n",
      "       2018-01-31                   -21000000.0            -52000000.0   \n",
      "       2018-04-30                     6000000.0            -46000000.0   \n",
      "       2018-07-31                    31000000.0            -42000000.0   \n",
      "       2018-10-31                    57000000.0            -37000000.0   \n",
      "\n",
      "                    Pretax Income (Loss), Adj.  Abnormal Gains (Losses)  \\\n",
      "Ticker Report Date                                                        \n",
      "A      2017-10-31                    803000000                      NaN   \n",
      "       2018-01-31                    843000000                      NaN   \n",
      "       2018-04-30                    879000000                      NaN   \n",
      "       2018-07-31                    928000000                      NaN   \n",
      "       2018-10-31                    946000000                      NaN   \n",
      "\n",
      "                    Pretax Income (Loss)  Income Tax (Expense) Benefit, Net  \\\n",
      "Ticker Report Date                                                            \n",
      "A      2017-10-31              803000000                       -119000000.0   \n",
      "       2018-01-31              843000000                       -647000000.0   \n",
      "       2018-04-30              879000000                       -642000000.0   \n",
      "       2018-07-31              928000000                       -630000000.0   \n",
      "       2018-10-31              946000000                       -630000000.0   \n",
      "\n",
      "                    Income (Loss) from Continuing Operations  \\\n",
      "Ticker Report Date                                             \n",
      "A      2017-10-31                                  684000000   \n",
      "       2018-01-31                                  196000000   \n",
      "       2018-04-30                                  237000000   \n",
      "       2018-07-31                                  298000000   \n",
      "       2018-10-31                                  316000000   \n",
      "\n",
      "                    Net Extraordinary Gains (Losses)  Net Income  \\\n",
      "Ticker Report Date                                                 \n",
      "A      2017-10-31                                NaN   684000000   \n",
      "       2018-01-31                                NaN   196000000   \n",
      "       2018-04-30                                NaN   237000000   \n",
      "       2018-07-31                                NaN   298000000   \n",
      "       2018-10-31                                NaN   316000000   \n",
      "\n",
      "                    Net Income (Common)  \n",
      "Ticker Report Date                       \n",
      "A      2017-10-31             684000000  \n",
      "       2018-01-31             196000000  \n",
      "       2018-04-30             237000000  \n",
      "       2018-07-31             298000000  \n",
      "       2018-10-31             316000000  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_income.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load share price \"latest\" or daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-shareprices-latest\" not on disk.\n",
      "- Downloading ... 100.0%\n",
      "- Extracting zip-file ... Done!\n",
      "- Loading from disk ... Done!\n",
      "CPU times: user 65.8 ms, sys: 12.8 ms, total: 78.6 ms\n",
      "Wall time: 2.94 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SimFinId</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>High</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Dividend</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Shares Outstanding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>45846</td>\n",
       "      <td>126.31</td>\n",
       "      <td>125.25</td>\n",
       "      <td>127.18</td>\n",
       "      <td>125.65</td>\n",
       "      <td>125.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2186509</td>\n",
       "      <td>2.960000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>367153</td>\n",
       "      <td>31.48</td>\n",
       "      <td>30.30</td>\n",
       "      <td>31.48</td>\n",
       "      <td>30.34</td>\n",
       "      <td>30.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5743863</td>\n",
       "      <td>1.784043e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAC</th>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>7962652</td>\n",
       "      <td>10.64</td>\n",
       "      <td>10.63</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58591</td>\n",
       "      <td>9.849854e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACI</th>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>11820349</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.57</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5755</td>\n",
       "      <td>5.709500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAGH</th>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>12444287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450000</td>\n",
       "      <td>2.109094e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SimFinId    Open     Low    High   Close  Adj. Close  \\\n",
       "Ticker Date                                                               \n",
       "A      2023-08-15     45846  126.31  125.25  127.18  125.65      125.65   \n",
       "AA     2023-08-15    367153   31.48   30.30   31.48   30.34       30.34   \n",
       "AAC    2023-08-15   7962652   10.64   10.63   10.65   10.65       10.65   \n",
       "AACI   2023-08-15  11820349   10.59   10.57   10.70   10.65       10.65   \n",
       "AAGH   2023-08-15  12444287     NaN     NaN     NaN     NaN         NaN   \n",
       "\n",
       "                   Dividend   Volume  Shares Outstanding  \n",
       "Ticker Date                                               \n",
       "A      2023-08-15       NaN  2186509        2.960000e+08  \n",
       "AA     2023-08-15       NaN  5743863        1.784043e+08  \n",
       "AAC    2023-08-15       NaN    58591        9.849854e+07  \n",
       "AACI   2023-08-15       NaN     5755        5.709500e+06  \n",
       "AAGH   2023-08-15       NaN   450000        2.109094e+10  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_prices_latest = sf.load_shareprices(variant='latest', market='us')\n",
    "df_prices_latest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "daily price covering several years. (large download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# df_prices = sf.load_shareprices(variant='daily', market='us')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load companies info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-companies\" on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "        SimFinId                  Company Name  IndustryId\n",
      "Ticker                                                    \n",
      "A          45846      AGILENT TECHNOLOGIES INC    106001.0\n",
      "A18      1253413            Trip.com Group Ltd         NaN\n",
      "A21      1333027                  Li Auto Inc.         NaN\n",
      "AA        367153                    Alcoa Corp    110004.0\n",
      "AAC      7962652  Ares Acquisition Corporation    104002.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    }
   ],
   "source": [
    "df_companies = sf.load_companies(index=TICKER, market='us')\n",
    "print(df_companies.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load sector and industries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"industries\" on disk (0 days old).\n",
      "- Loading from disk ... Done!\n",
      "                 Sector                    Industry\n",
      "IndustryId                                         \n",
      "100001      Industrials         Industrial Products\n",
      "100002      Industrials           Business Services\n",
      "100003      Industrials  Engineering & Construction\n",
      "100004      Industrials            Waste Management\n",
      "100005      Industrials     Industrial Distribution\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielepicheo/GitHub/.venv/lib/python3.11/site-packages/simfin/load.py:154: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  df = pd.read_csv(path, sep=';', header=0,\n"
     ]
    }
   ],
   "source": [
    "df_industries = sf.load_industries()\n",
    "print(df_industries.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector                Technology\n",
       "Industry    Application Software\n",
       "Name: 101003, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_id = df_companies.loc['MSFT'][INDUSTRY_ID]\n",
    "df_industries.loc[industry_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset and columns info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"datasets\" not on disk.\n",
      "- Downloaded bytes: 230,435\n",
      "- Moving file to data-dir ... Done!\n",
      "- Loading from disk ... Done!\n",
      "All datasets: balance, balance-banks, balance-insurance, cashflow,\n",
      "              cashflow-banks, cashflow-insurance, companies, derived,\n",
      "              derived-banks, derived-insurance, derived-shareprices, income,\n",
      "              income-banks, income-insurance, industries, markets, shareprices\n"
     ]
    }
   ],
   "source": [
    "sf.info_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:  companies\n",
      "Variants: -\n",
      "Markets:  ca, cn, de, us\n",
      "Columns:  (The * marks data that requires a paid subscription)\n",
      "- \"Ticker\" TICKER\n",
      "- \"SimFinId\" SIMFIN_ID\n",
      "- \"Company Name\" COMPANY_NAME\n",
      "- \"IndustryId\" INDUSTRY_ID\n"
     ]
    }
   ],
   "source": [
    "sf.info_datasets('companies')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"columns\" not on disk.\n",
      "- Downloaded bytes: 113,537\n",
      "- Moving file to data-dir ... Done!\n",
      "- Loading from disk ... Done!\n",
      "Name:        \"Company Name\"\n",
      "Shortcuts:   COMPANY_NAME\n",
      "Premium:     False\n",
      "Datasets:    companies\n",
      "Description: Name of a company e.g. 'Microsoft'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sf.info_columns(COMPANY_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:        \"Volume\"\n",
      "Shortcuts:   SHARE_VOLUME, VOLUME\n",
      "Premium:     False\n",
      "Datasets:    shareprices\n",
      "Description: Number of shares traded during the day. Adjusted for stock-splits.\n",
      "\n",
      "Name:        \"Shares Outstanding\"\n",
      "Shortcuts:   SHARES_OUTSTANDING\n",
      "Premium:     False\n",
      "Datasets:    shareprices\n",
      "Description: Number of common shares outstanding as per the last reported date.\n",
      "\n",
      "Name:        \"Shares (Basic)\"\n",
      "Shortcuts:   SHARES_BASIC\n",
      "Premium:     False\n",
      "Datasets:    balance, balance-banks, balance-insurance, cashflow,\n",
      "             cashflow-banks, cashflow-insurance, income, income-banks,\n",
      "             income-insurance\n",
      "Description: Shares outstanding NOT adjusted for dilution from stock-options\n",
      "             etc. Weighted average for the period. Adjusted for stock-splits.\n",
      "\n",
      "Name:        \"Shares (Diluted)\"\n",
      "Shortcuts:   SHARES_DILUTED\n",
      "Premium:     False\n",
      "Datasets:    balance, balance-banks, balance-insurance, cashflow,\n",
      "             cashflow-banks, cashflow-insurance, income, income-banks,\n",
      "             income-insurance\n",
      "Description: Shares outstanding adjusted for dilution from stock-options etc.\n",
      "             Weighted average for the period. Adjusted for stock-splits.\n",
      "\n",
      "Name:        \"Earnings Per Share, Basic\"\n",
      "Shortcuts:   EARNINGS_PER_SHARE, EPS, EPS_BASIC\n",
      "Premium:     1\n",
      "Datasets:    derived, derived-banks, derived-insurance\n",
      "Description: Basic earnings per share is the amount of a company’s profit or\n",
      "             loss for a reporting period that is available to the shares of its\n",
      "             common stock that are outstanding during the reporting period. It\n",
      "             is a useful measure of performance for companies with simplified\n",
      "             capital structures.\n",
      "\n",
      "Name:        \"Earnings Per Share, Diluted\"\n",
      "Shortcuts:   EPS_DILUTED\n",
      "Premium:     1\n",
      "Datasets:    derived, derived-banks, derived-insurance\n",
      "Description: Diluted EPS is a performance metric used to gauge the quality of a\n",
      "             company's earnings per share (EPS) if all convertible securities\n",
      "             were exercised. Convertible securities are all outstanding\n",
      "             convertible preferred shares, convertible debentures, stock options\n",
      "             (primarily employee-based) and warrants. Unless the company has no\n",
      "             additional potential shares outstanding, which is a relatively rare\n",
      "             circumstance, the diluted EPS will always be lower than the simple\n",
      "             EPS.\n",
      "\n",
      "Name:        \"Sales Per Share\"\n",
      "Shortcuts:   SALES_PER_SHARE, SPS\n",
      "Premium:     1\n",
      "Datasets:    derived, derived-banks, derived-insurance\n",
      "Description: Sales per share is a ratio that computes the total revenue earned\n",
      "             per share over a 12-month period. It is calculated by dividing\n",
      "             total revenue earned in a fiscal year by the weighted average of\n",
      "             shares outstanding for that fiscal year.\n",
      "\n",
      "Name:        \"Dividends Per Share\"\n",
      "Shortcuts:   DIVIDENDS_PS, DPS\n",
      "Premium:     1\n",
      "Datasets:    derived, derived-banks, derived-insurance\n",
      "Description: Dividend per share (DPS) is the sum of declared dividends issued by\n",
      "             a company for every ordinary share outstanding. Dividend per share\n",
      "             (DPS) is the total dividends paid out by a business, including\n",
      "             interim dividends, divided by the number of outstanding ordinary\n",
      "             shares issued. A company's DPS is usually derived using the\n",
      "             dividend paid in the most recent quarter, which is also used to\n",
      "             calculate the dividend yield.\n",
      "\n",
      "Name:        \"Piotroski F-Score\"\n",
      "Shortcuts:   PIOTROSKI\n",
      "Premium:     1\n",
      "Datasets:    derived, derived-banks, derived-insurance\n",
      "Description: The score is calculated based on 9 criteria divided into 3 groups\n",
      "             (Profitability, Leverage/Liquidity and Operating Efficiency), for\n",
      "             each criteria that is passed, 1 point is added to the total score\n",
      "             (ranges from 0 to 9); Return on Assets (1 point if it is positive\n",
      "             in the current year, 0 otherwise); Operating Cash Flow (1 point if\n",
      "             it is positive in the current year, 0 otherwise); Change in Return\n",
      "             of Assets (ROA) (1 point if ROA is higher in the current year\n",
      "             compared to the previous one, 0 otherwise); Accruals (1 point if\n",
      "             Operating Cash Flow/Total Assets is higher than ROA in the current\n",
      "             year, 0 otherwise); Change in Leverage (long-term) ratio (1 point\n",
      "             if the ratio is lower this year compared to the previous one, 0\n",
      "             otherwise); Change in Current ratio (1 point if it is higher in the\n",
      "             current year compared to the previous one, 0 otherwise); Change in\n",
      "             the number of shares (1 point if no new shares were issued during\n",
      "             the last year); Change in Gross Margin (1 point if it is higher in\n",
      "             the current year compared to the previous one, 0 otherwise); Change\n",
      "             in Asset Turnover ratio (1 point if it is higher in the current\n",
      "             year compared to the previous one, 0 otherwise);\n",
      "\n",
      "Name:        \"Market-Cap\"\n",
      "Shortcuts:   MARKET_CAP, MCAP\n",
      "Premium:     1\n",
      "Datasets:    derived-shareprices\n",
      "Description: Common shares outstanding times closing price (adjusted for stock\n",
      "             splits).\n",
      "\n",
      "Name:        \"Enterprise Value\"\n",
      "Shortcuts:   ENTERPRISE_VALUE, EV\n",
      "Premium:     1\n",
      "Datasets:    derived-shareprices\n",
      "Description: Enterprise Value, or EV for short, is a measure of a company's\n",
      "             total value, often used as a more comprehensive alternative to\n",
      "             equity market capitalization. Enterprise value is calculated as the\n",
      "             market capitalization plus debt, minority interest and preferred\n",
      "             shares, minus total cash and cash equivalents.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sf.info_columns('shares')"
   ]
  }
 ],
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