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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "621c09fe",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:32.071268Z",
"start_time": "2024-03-08T19:16:31.275321Z"
}
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"import pandas as pd\n",
"\n",
"c = 0\n",
"\n",
"df = 0\n",
"\n",
"# Read HTML content from the file\n",
"with open(\"index.html\", \"r\", encoding=\"utf-8\") as file:\n",
" html_content = file.read()\n",
"\n",
"# Parse the HTML content\n",
"soup = BeautifulSoup(html_content, \"html.parser\")\n",
"\n",
"# Find the table\n",
"table = soup.find(\"table\")\n",
"\n",
"# Extract table data\n",
"if table:\n",
" rows = table.find_all(\"tr\")\n",
" data = []\n",
" for row in rows:\n",
" columns = row.find_all(\"td\")\n",
" if columns: # Ensure it's not a header row or empty row\n",
" row_data = [column.text.strip() for column in columns]\n",
" data.append(row_data)\n",
" c += 1\n",
" # Create DataFrame\n",
" df = pd.DataFrame(data)\n",
" \n",
" # Extract first part and last word from the first column\n",
" df['Name'] = df[0].str.split().str[:-1].str.join(\" \")\n",
" df['Ticker'] = df[0].str.split().str[-1]\n",
" df[['Name', 'Ticker']].to_csv(\"CompanyList.csv\")\n",
"else:\n",
" print(\"Table not found in the HTML content.\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "38efd2c9",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:45.594430Z",
"start_time": "2024-03-08T19:16:45.591931Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2063\n"
]
}
],
"source": [
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6c185203",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:46.502190Z",
"start_time": "2024-03-08T19:16:46.489733Z"
}
},
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>Name</th>\n",
" <th>Ticker</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" <td>2062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>2062</td>\n",
" <td>2044</td>\n",
" <td>2060</td>\n",
" <td>2056</td>\n",
" <td>125</td>\n",
" <td>2061</td>\n",
" <td>2062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>20 Microns Ltd. 20MICRONS</td>\n",
" <td>₹1.14 -4.16%</td>\n",
" <td>₹8.8/₹3.79</td>\n",
" <td>₹128.05 Crs</td>\n",
" <td>Pharmaceuticals</td>\n",
" <td>Gallantt Ispat Ltd.</td>\n",
" <td>20MICRONS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>105</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 \\\n",
"count 2062 2062 2062 2062 \n",
"unique 2062 2044 2060 2056 \n",
"top 20 Microns Ltd. 20MICRONS ₹1.14 -4.16% ₹8.8/₹3.79 ₹128.05 Crs \n",
"freq 1 4 2 2 \n",
"\n",
" 4 Name Ticker \n",
"count 2062 2062 2062 \n",
"unique 125 2061 2062 \n",
"top Pharmaceuticals Gallantt Ispat Ltd. 20MICRONS \n",
"freq 105 2 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ebfb97f3",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:48.278807Z",
"start_time": "2024-03-08T19:16:48.273680Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2062"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "70727a81",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:49.269674Z",
"start_time": "2024-03-08T19:16:49.267266Z"
}
},
"outputs": [],
"source": [
"duplicates = df[df.duplicated(subset='Name', keep=False)]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "75f68012",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:50.191351Z",
"start_time": "2024-03-08T19:16:50.186194Z"
}
},
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>Name</th>\n",
" <th>Ticker</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>610</th>\n",
" <td>Gallantt Ispat Ltd.\\n ...</td>\n",
" <td>₹64.15 -0.77%</td>\n",
" <td>₹76/₹44.64</td>\n",
" <td>₹1807.11 Crs</td>\n",
" <td>Iron & Steel</td>\n",
" <td>Gallantt Ispat Ltd.</td>\n",
" <td>GALLISPAT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>611</th>\n",
" <td>Gallantt Ispat Ltd.\\n ...</td>\n",
" <td>₹216.94 +1.33%</td>\n",
" <td>₹236.4/₹49.54</td>\n",
" <td>₹5235.8 Crs</td>\n",
" <td>Iron & Steel</td>\n",
" <td>Gallantt Ispat Ltd.</td>\n",
" <td>GALLANTT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 \\\n",
"610 Gallantt Ispat Ltd.\\n ... ₹64.15 -0.77% \n",
"611 Gallantt Ispat Ltd.\\n ... ₹216.94 +1.33% \n",
"\n",
" 2 3 4 Name Ticker \n",
"610 ₹76/₹44.64 ₹1807.11 Crs Iron & Steel Gallantt Ispat Ltd. GALLISPAT \n",
"611 ₹236.4/₹49.54 ₹5235.8 Crs Iron & Steel Gallantt Ispat Ltd. GALLANTT "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"duplicates"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a0f8f58d",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-08T19:16:59.440812Z",
"start_time": "2024-03-08T19:16:59.430280Z"
}
},
"outputs": [],
"source": [
"df.to_csv(\"CompanyList.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9274e3c3011e6fc",
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|