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sec-financials-parsing.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyOvFvvYw4TsGk/JCMdsRvml",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/wesslen/5adb4d026af8d4b824caf2eefafedafc/sec-financials-parsing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"Upload zip from [here](https://www.sec.gov/about/dera_financial-statement-data-set)"
],
"metadata": {
"id": "3dsFNo-JibkO"
}
},
{
"cell_type": "code",
"source": [
"!unzip 2024q2.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5UggOiGZimO0",
"outputId": "75750f2c-8683-4f54-d30d-f62b3d8cbf1b"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Archive: 2024q2.zip\n",
" inflating: sub.txt \n",
" inflating: pre.txt \n",
" inflating: num.txt \n",
" inflating: tag.txt \n",
" inflating: readme.htm \n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O_G6ZjR1htYA",
"outputId": "640119c9-8cd9-49ae-839b-1e4315204c86"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading data...\n",
"Data loaded successfully.\n"
]
}
],
"source": [
"# Install required libraries\n",
"\n",
"import os\n",
"import zipfile\n",
"import pandas as pd\n",
"import requests\n",
"from io import BytesIO\n",
"import datetime\n",
"\n",
"\n",
"def load_data(year, quarter):\n",
" base_path = f\".\"\n",
" sub_df = pd.read_csv(os.path.join(base_path, \"sub.txt\"), sep=\"\\t\", low_memory=False)\n",
" num_df = pd.read_csv(os.path.join(base_path, \"num.txt\"), sep=\"\\t\", low_memory=False)\n",
" pre_df = pd.read_csv(os.path.join(base_path, \"pre.txt\"), sep=\"\\t\", low_memory=False)\n",
"\n",
" return sub_df, num_df, pre_df\n",
"\n",
"def get_company_data(sub_df, num_df, pre_df, company_name):\n",
" company_info = sub_df[sub_df['name'].str.contains(company_name, case=False, na=False)]\n",
" if company_info.empty:\n",
" raise ValueError(f\"Company '{company_name}' not found in the dataset.\")\n",
"\n",
" cik = company_info['cik'].iloc[0]\n",
" adsh = company_info[company_info['fp'] == 'Q1']['adsh'].iloc[0]\n",
"\n",
" company_num_df = num_df[num_df['adsh'] == adsh]\n",
" company_pre_df = pre_df[pre_df['adsh'] == adsh]\n",
"\n",
" return company_num_df, company_pre_df, cik\n",
"\n",
"def generate_financial_statement(company_num_df, company_pre_df, statement_type):\n",
" stmt_filter = 'BS' if statement_type == 'BS' else 'CF'\n",
"\n",
" stmt_pre_df = company_pre_df[company_pre_df['stmt'] == stmt_filter]\n",
" stmt_df = pd.merge(stmt_pre_df, company_num_df, on=['tag', 'version'], how='left')\n",
" stmt_df = stmt_df.sort_values(['report', 'line'])\n",
"\n",
" statement = pd.DataFrame({\n",
" 'Line Item': stmt_df['plabel'],\n",
" 'Value': stmt_df['value']\n",
" })\n",
"\n",
" return statement\n",
"\n",
"# Cell 1: Download and load data\n",
"year = 2024 # Update with the desired year\n",
"quarter = 2 # Update with the desired quarter\n",
"\n",
"print(\"Loading data...\")\n",
"sub_df, num_df, pre_df = load_data(year, quarter)\n",
"\n",
"print(\"Data loaded successfully.\")"
]
},
{
"cell_type": "code",
"source": [
"# Cell 2: User input and data retrieval\n",
"company_name = input(\"Enter the company name: \")\n",
"\n",
"try:\n",
" print(f\"Retrieving data for {company_name}...\")\n",
" company_num_df, company_pre_df, cik = get_company_data(sub_df, num_df, pre_df, company_name)\n",
" print(f\"Data retrieved successfully for {company_name} (CIK: {cik})\")\n",
"except ValueError as e:\n",
" print(f\"Error: {e}\")\n",
"\n",
"# Cell 3: Generate and display Balance Sheet\n",
"print(\"Generating Balance Sheet...\")\n",
"balance_sheet = generate_financial_statement(company_num_df, company_pre_df, 'BS')\n",
"display(balance_sheet)\n",
"\n",
"# Cell 4: Generate and display Cash Flow Statement\n",
"print(\"Generating Cash Flow Statement...\")\n",
"cash_flow = generate_financial_statement(company_num_df, company_pre_df, 'CF')\n",
"display(cash_flow)\n",
"\n",
"# Optional Cell 5: Export to CSV\n",
"balance_sheet.to_csv(f\"{company_name}_balance_sheet.csv\", index=False)\n",
"cash_flow.to_csv(f\"{company_name}_cash_flow.csv\", index=False)\n",
"print(f\"Financial statements exported as CSV files.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 938
},
"id": "vsJHdSAXjYf-",
"outputId": "232ab0b3-cc46-45ef-c1e9-e67f0d46a86e"
},
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Enter the company name: ALLIANT ENERGY CORP\n",
"Retrieving data for ALLIANT ENERGY CORP...\n",
"Data retrieved successfully for ALLIANT ENERGY CORP (CIK: 352541)\n",
"Generating Balance Sheet...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" Line Item Value\n",
"3 Statement [Table] NaN\n",
"59 Legal Entity [Axis] NaN\n",
"57 Entity [Domain] NaN\n",
"240 IPL [Member] NaN\n",
"242 WPL [Member] NaN\n",
".. ... ...\n",
"49 Common stock, shares outstanding (in shares) 2.563790e+08\n",
"235 Shares in deferred compensation trust (in shares) 3.419370e+05\n",
"236 Shares in deferred compensation trust (in shares) 3.790060e+05\n",
"230 Shares in deferred compensation trust, weighte... 3.496000e+01\n",
"231 Shares in deferred compensation trust, weighte... 3.448000e+01\n",
"\n",
"[243 rows x 2 columns]"
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"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>Line Item</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Statement [Table]</td>\n",
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" <th>240</th>\n",
" <td>IPL [Member]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>WPL [Member]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Common stock, shares outstanding (in shares)</td>\n",
" <td>2.563790e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>235</th>\n",
" <td>Shares in deferred compensation trust (in shares)</td>\n",
" <td>3.419370e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td>Shares in deferred compensation trust (in shares)</td>\n",
" <td>3.790060e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>Shares in deferred compensation trust, weighte...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>Shares in deferred compensation trust, weighte...</td>\n",
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"type": "dataframe",
"variable_name": "balance_sheet",
"summary": "{\n \"name\": \"balance_sheet\",\n \"rows\": 243,\n \"fields\": [\n {\n \"column\": \"Line Item\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 54,\n \"samples\": [\n \"ATC Holdings\",\n \"Common stock, par value (in dollars per share)\",\n \"Total liabilities and equity\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3912263548.6453214,\n \"min\": 0.0,\n \"max\": 21248000000.0,\n \"num_unique_values\": 178,\n \"samples\": [\n 17000000.0,\n 554000000.0,\n 1892000000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
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"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Generating Cash Flow Statement...\n"
]
},
{
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" Line Item Value\n",
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"185 IPL [Member] NaN\n",
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".. ... ...\n",
"63 Accrued capital expenditures 152000000.0\n",
"94 Beneficial interest obtained in exchange for s... 184000000.0\n",
"95 Beneficial interest obtained in exchange for s... 153000000.0\n",
"96 Beneficial interest obtained in exchange for s... 184000000.0\n",
"97 Beneficial interest obtained in exchange for s... 153000000.0\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "cash_flow",
"summary": "{\n \"name\": \"cash_flow\",\n \"rows\": 187,\n \"fields\": [\n {\n \"column\": \"Line Item\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 42,\n \"samples\": [\n \"Net cash flows from (used for) investing activities\",\n \"Gas stored underground\",\n \"Adjustments to reconcile net income to net cash flows from operating activities:\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 143750659.50977194,\n \"min\": -532000000.0,\n \"max\": 862000000.0,\n \"num_unique_values\": 119,\n \"samples\": [\n 417000000.0,\n -318000000.0,\n 63000000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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"metadata": {}
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{
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"text": [
"Financial statements exported as CSV files.\n"
]
}
]
}
]
}
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