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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "460d90da-b986-4c1c-8a66-eab144b0ba8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n",
"Fetched data for all the Pages.\n"
]
}
],
"source": [
"import requests\n",
"import time\n",
"\n",
"import random\n",
"pages = [\n",
" random.randint(1, 968000015)\n",
" for _ in range(10)\n",
" ]\n",
"# print(pages)\n",
"\n",
"base_url = \"https://datasets-server.huggingface.co/rows\"\n",
"params = {\n",
" \"dataset\": \"tiiuae/falcon-refinedweb\",\n",
" \"config\": \"default\",\n",
" \"split\": \"train\",\n",
" }\n",
"# response = requests.get(base_url, params=params)\n",
"# response.raise_for_status()\n",
"# for row in response.json()[\"rows\"]:\n",
"# content = row[\"row\"][\"content\"]\n",
"num_rows_per_page = 100\n",
"retry_limit = 10\n",
"retry_delay = 5\n",
"Falcon = []\n",
"\n",
"def fetch_data_for_page(page):\n",
" params[\"offset\"] = page\n",
" params[\"limit\"] = num_rows_per_page\n",
" attempt = 0\n",
" while attempt < retry_limit:\n",
" try:\n",
" response = requests.get(base_url, params=params)\n",
" response.raise_for_status() # This will raise an HTTPError if the HTTP request returned an unsuccessful status code\n",
" for row in response.json()[\"rows\"]:\n",
" content = row[\"row\"][\"content\"]\n",
" Falcon.append(content)\n",
" len(Falcon)\n",
" print(f\"Fetched data for all the Pages.\")\n",
" break\n",
" except requests.exceptions.HTTPError as e:\n",
" attempt += 1\n",
" print(\n",
" f\"Failed to fetch data, retrying. Attempt {attempt}/{retry_limit}\"\n",
" )\n",
" if attempt < retry_limit:\n",
" time.sleep(retry_delay) # Wait before the next retry\n",
" else:\n",
" print(\n",
" \"Maximum retry limit reached. Unable to fetch data.\"\n",
" )\n",
" raise\n",
"\n",
"for page in pages:\n",
" fetch_data_for_page(page)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f8f3baf1-5480-450b-a456-174a5c114d3e",
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"# Open the CSV file for writing\n",
"with open(\"FalconDataEval2.csv\", \"w\", newline=\"\") as csvfile:\n",
" # Create a CSV writer object\n",
" writer = csv.writer(csvfile)\n",
"\n",
" # Write the header row\n",
" writer.writerow([\"Text\"])\n",
"\n",
" # Write each element in the list as a row in the CSV file\n",
" for element in Falcon:\n",
" writer.writerow([element])\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ea47c936-2c2b-4414-ba57-74fb6827ec0a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicate rows: 0\n",
"Empty DataFrame\n",
"Columns: [Text]\n",
"Index: []\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Read the CSV file into a pandas DataFrame\n",
"df = pd.read_csv(\"FalconDataEval2.csv\")\n",
"\n",
"# Check for duplicate rows\n",
"duplicate_rows = df[df.duplicated()]\n",
"\n",
"# Print the number of duplicate rows\n",
"print(f\"Number of duplicate rows: {len(duplicate_rows)}\")\n",
"\n",
"# Print the duplicate rows\n",
"print(duplicate_rows)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f4178cd6-747f-4e05-a9bf-17b97f959e06",
"metadata": {},
"outputs": [
{
"data": {
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" <td>Our Annual Garden Party is a fun-filled event ...</td>\n",
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" <td>Photos by Philip Cosores\\n“There were many poi...</td>\n",
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" <td>Media Matters Also Wants To Throw Out The Firs...</td>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>[More]\\nWhile bringing in your own cup is fine...</td>\n",
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" <td>Read at : Google Alert – gardening\\nHow to Bui...</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Text\n",
"0 Our Annual Garden Party is a fun-filled event ...\n",
"1 Photos by Philip Cosores\\n“There were many poi...\n",
"2 Media Matters Also Wants To Throw Out The Firs...\n",
"3 [More]\\nWhile bringing in your own cup is fine...\n",
"4 Read at : Google Alert – gardening\\nHow to Bui..."
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "264548c1-4cf4-441f-a433-2f5d57861dc4",
"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>Text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>The Ketologic review Diaries\\nShould you have ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>A pack of hand cooked sea salted and red wine ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>この広告は、90日以上更新していないブログに表示しています。\\nsniperspy free...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>Arthur Koestler - Wikipedia.\\nEssay - Merriam-...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>Serving Software Downloads in 976 Categories, ...</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Text\n",
"995 The Ketologic review Diaries\\nShould you have ...\n",
"996 A pack of hand cooked sea salted and red wine ...\n",
"997 この広告は、90日以上更新していないブログに表示しています。\\nsniperspy free...\n",
"998 Arthur Koestler - Wikipedia.\\nEssay - Merriam-...\n",
"999 Serving Software Downloads in 976 Categories, ..."
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "be5a87a8-cfee-4f63-992e-8fa1d4a5cdbb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text To imagine delaying myself. Hard cock, selling...\n",
"Name: 48, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_row=48\n",
"specific_row = df.iloc[target_row]\n",
"specific_row"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e97d9e18-eaa0-4a1b-96ab-c89a0f4c738d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Text The old wireline Bell telephone system was bui...\n",
"Name: 19995, dtype: object\n"
]
}
],
"source": [
"print(specific_row)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "940ef35f-7517-403d-9f42-73760182dcaa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Text The old wireline Bell telephone system was bui...\n"
]
}
],
"source": [
"print(specific_row.to_string())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "915ac669-718f-47f5-b175-a5f928b407db",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"57\n"
]
}
],
"source": [
"print(len(specific_row.to_string()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab5ee254-9ba7-496b-97c7-3b6185c21971",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
|