loading

README.md

@@ -0,0 +1,27 @@
+---
+dataset_info:
+  features:
+  - name: title
+    dtype: string
+  - name: author
+    dtype: string
+  - name: id
+    dtype: int32
+  - name: timestamp
+    dtype: string
+  - name: progressive_number
+    dtype: int32
+  - name: original_url
+    dtype: string
+  - name: newsgroup
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    path: "parquet/*.parquet"
+    num_bytes: 72373684017
+    num_examples: 85010057
+  download_size: 0
+  dataset_size: 72373684017
+---

UsenetArchiveIT.py

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+from datasets import DatasetBuilder, SplitGenerator, Split, Features, Value, ClassLabel, BuilderConfig, Version, DatasetInfo, DownloadManager, ArrowBasedBuilder
+import glob
+import json
+import multiprocessing as mp
+import os
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pandas as pd
+import pyarrow as pa
+import pyarrow.json
+# jsonl 
+
+pattern="*.bz2"
+
+paths=glob.glob(pattern)
+
+# exclude txt files
+
+paths=[file for file in paths if not ".txt." in file]
+
+n_files=len(paths)
+
+# labels are file names without the extension .jsonl.bz2
+
+labels=[file.replace(".jsonl.bz2","") for file in paths]
+
+
+
+## handle parquet conversion
+
+# create parquet directory
+
+dl_manager = DownloadManager()
+
+parquet_dir="parquet"
+
+
+    
+
+def convert_jsonl_to_parquet(file_list, parquet_dir, chunk_size=100000):
+    """Converts JSONL files to Parquet with memory efficiency.
+
+    Args:
+        file_list (list): List of JSONL file paths.
+        parquet_dir (str): Path to store output Parquet files.
+        chunk_size (int): Number of records to write to each Parquet file.
+    """
+
+    os.makedirs(parquet_dir, exist_ok=True)  # Create output directory
+
+    parquet_file_index = 0
+    current_records = [] 
+    file_index = 0
+    for file in file_list:
+        # try:
+            reader = pa.json.read_json(file)  # PyArrow JSON reader
+            
+            for batch in reader:
+                pandas_df = batch.to_pandas()
+                print(pandas_df.shape)
+                current_records.extend(pandas_df.to_dict('list'))
+                if len(current_records) >= chunk_size:
+                    table = pa.Table.from_pandas(pd.DataFrame(current_records))
+                    parquet_filename = f"output_{parquet_file_index}.parquet"
+                    parquet_path = os.path.join(parquet_dir, parquet_filename)
+                    pq.write_table(table, parquet_path)
+
+                    current_records = [] 
+                    parquet_file_index += 1
+        # except Exception as e:
+        #     print(f"Error in file {file} with error {e}")
+            file_index += 1
+            print(f"Finished processing file {file_index} of {len(file_list)}")
+    print(f"Writing last chunk to parquet file {parquet_file_index}")
+    # Write any remaining data in the last chunk
+    if current_records:
+        table = pa.Table.from_pandas(pd.DataFrame(current_records))
+        parquet_filename = f"output_{parquet_file_index}.parquet"
+        parquet_path = os.path.join(parquet_dir, parquet_filename)
+        pq.write_table(table, parquet_path)
+    
+    print(f"Conversion complete, wrote {parquet_file_index + 1} Parquet files.")
+    
+
+
+
+
+class UsenetConfig(BuilderConfig):
+    def __init__(self, version, **kwargs):
+        ().__init__(version, **kwargs)
+        
+        
+        
+        
+        
+
+        
+
+
+class UsenetArchiveIt(ArrowBasedBuilder):
+    VERSION = "1.0.0"  # Example version
+    
+    BUILDER_CONFIG_CLASS = UsenetConfig
+    
+    BUILDER_CONFIGS = [
+        UsenetConfig(
+            name="usenet_archive_it",
+            version=Version("1.0.0"),
+            description="Usenet Archive-It dataset",
+        ),
+    ]
+    
+    def _info(self):
+        # Specify dataset features here
+        return DatasetInfo(
+            features=Features({
+                "title": Value("string"),
+                "author": Value("string"),
+                "id":  Value("int32"),
+                "timestamp": Value("string"),
+                "progressive_number": Value("int32"),
+                "original_url": Value("string"),
+                "newsgroup": Value("string"), # this could be a label but difficult to get all possible labels
+                "text": Value("string"),
+            }),)
+
+    def _split_generators(self, dl_manager):
+        n = mp.cpu_count()//10  # Number of paths to process at a time
+        print(f"Extracting {n} files at a time")
+        if not os.path.isdir('parquet'):
+            extracted_files = []
+            for i in range(0, len(paths), n):
+                
+                files = paths[i:i+n]
+                extracted_files.extend(dl_manager.extract(files, num_proc=len(files)))
+                print(f"Extracted {files}")
+        else:
+            extracted_files = glob.glob(parquet_dir + "/*.parquet")
+            
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"filepath": extracted_files},
+            ),
+            
+        ]
+
+    def _generate_tables(self, filepath):
+        
+        # print("Filepath: ", filepath)
+        
+        # if parquet files are not present, convert jsonl to parquet
+        if not os.path.exists(parquet_dir):
+            print("Generating parquet files from jsonl files...")
+            convert_jsonl_to_parquet(filepath, parquet_dir)
+            
+        # read parquet files
+        parquet_files=glob.glob(parquet_dir+"/*.parquet")
+        
+
+        for index, file in enumerate(parquet_files):
+                table = pq.read_table(file)
+                yield index, table
+        
+        
+        # for file in parquet_files:
+        #     table = pq.read_table(file)
+        #     df = table.to_pandas()
+        #     for index, row in df.iterrows():
+        #         yield index, row.to_dict()
+        
+        
+        # Yields (key, example) tuples from the dataset
+        # id=0
+        # for file in filepath:
+        #     # Open and yield examples from the compressed JSON files
+        #     with open(file, "r") as f:
+        #         for i, line in enumerate(f):
+        #             try:
+        #                 data = json.loads(line)
+        #                 yield id, data
+        #                 id+=1
+        #             except Exception as e:
+        #                 print(f"Error in file {file} at line {i} with error {e}")
+
+
+# Finally, set the name of the dataset to match the script name
+datasets = UsenetArchiveIt

parquet/usenet_converted_0.parquet

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parquet/usenet_converted_1.parquet

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parquet/usenet_converted_10.parquet

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parquet/usenet_converted_11.parquet

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+size 1570997015

parquet/usenet_converted_12.parquet

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+size 2045664141

parquet/usenet_converted_13.parquet

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parquet/usenet_converted_14.parquet

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parquet/usenet_converted_15.parquet

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+size 1207458645

parquet/usenet_converted_16.parquet

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+size 1786317493

parquet/usenet_converted_2.parquet

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parquet/usenet_converted_3.parquet

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parquet/usenet_converted_4.parquet

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+size 1922036409

parquet/usenet_converted_5.parquet

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parquet/usenet_converted_6.parquet

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parquet/usenet_converted_7.parquet

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parquet/usenet_converted_8.parquet

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parquet/usenet_converted_9.parquet

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+size 2181991947

testing_conversion.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import DatasetBuilder, SplitGenerator, Split, Features, Value, ClassLabel, BuilderConfig, Version, DatasetInfo, GeneratorBasedBuilder, DownloadManager\n",
+    "import glob\n",
+    "import json\n",
+    "import multiprocessing as mp\n",
+    "import os\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import pandas as pd\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.json\n",
+    "# jsonl \n",
+    "\n",
+    "pattern=\"*.bz2\"\n",
+    "\n",
+    "paths=glob.glob(pattern)\n",
+    "\n",
+    "# exclude txt files\n",
+    "\n",
+    "paths=[file for file in paths if not \".txt.\" in file]\n",
+    "\n",
+    "n_files=len(paths)\n",
+    "\n",
+    "# labels are file names without the extension .jsonl.bz2\n",
+    "\n",
+    "labels=[file.replace(\".jsonl.bz2\",\"\") for file in paths]\n",
+    "\n",
+    "\n",
+    "\n",
+    "## handle parquet conversion\n",
+    "\n",
+    "# create parquet directory\n",
+    "\n",
+    "dl_manager = DownloadManager()\n",
+    "\n",
+    "parquet_dir=\"parquet\"\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rlazzaroni/anaconda3/lib/python3.11/site-packages/datasets/download/download_manager.py:536: FutureWarning: 'num_proc' was deprecated in version 2.6.2 and will be removed in 3.0.0. Pass `DownloadConfig(num_proc=<num_proc>)` to the initializer instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "extracted_files=[]\n",
+    "n_concurrent=25\n",
+    "\n",
+    "extracted_files = []\n",
+    "n_concurrent = 25\n",
+    "\n",
+    "for i in range(0, len(paths), n_concurrent):\n",
+    "    batch_paths = paths[i:i+n_concurrent]\n",
+    "    for file in batch_paths:\n",
+    "        extracted_files.append(dl_manager.extract(file, num_proc=n_concurrent))\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "565"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extracted_files.__len__()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "565"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def convert_jsonl_to_parquet(file_list, parquet_dir, chunk_size=5000000):\n",
+    "    \"\"\"Converts JSONL files to Parquet with memory efficiency.\n",
+    "\n",
+    "    Args:\n",
+    "        file_list (list): List of JSONL file paths.\n",
+    "        parquet_dir (str): Path to store output Parquet files.\n",
+    "        chunk_size (int): Number of records to write to each Parquet file.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    os.makedirs(parquet_dir, exist_ok=True)  # Create output directory\n",
+    "\n",
+    "    parquet_file_index = 0\n",
+    "    # current_records = [] \n",
+    "    current_batches=[]\n",
+    "    current_batches_size=0\n",
+    "\n",
+    "    file_index = 0\n",
+    "    for file in file_list:\n",
+    "        try:\n",
+    "            reader = pa.json.read_json(file)  # PyArrow JSON reader\n",
+    "            file_index += 1\n",
+    "            # iterate over the batches of records \n",
+    "            for batch in reader.to_batches():\n",
+    "                batch_chunk_size = batch.num_rows\n",
+    "                # pandas_df = batch.to_pandas()\n",
+    "                # current_records.extend(pandas_df.to_dict('list'))\n",
+    "                current_batches.append(batch)\n",
+    "                current_batches_size+=batch_chunk_size\n",
+    "                if current_batches_size >= chunk_size:\n",
+    "                    # table = pa.Table.from_pandas(pd.DataFrame(current_records))\n",
+    "                    table = pa.Table.from_batches(current_batches)\n",
+    "                    parquet_filename = f\"usenet_converted_{parquet_file_index}.parquet\"\n",
+    "                    parquet_path = os.path.join(parquet_dir, parquet_filename)\n",
+    "                    pq.write_table(table, parquet_path)\n",
+    "                    print(f\"Wrote {parquet_filename} with {current_batches_size} records\")\n",
+    "\n",
+    "                    current_records = [] \n",
+    "                    parquet_file_index += 1\n",
+    "                    \n",
+    "                    current_batches_size=0\n",
+    "                    current_batches=[]\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error in file {file} with error {e}\")\n",
+    "            file_index += 1\n",
+    "        # every 50 files print the progress\n",
+    "        if file_index % 50 == 0:\n",
+    "            print(f\"Finished processing file {file_index} of {len(file_list)}\")\n",
+    "    print(f\"Writing last chunk to parquet file {parquet_file_index}\")\n",
+    "    # Write any remaining data in the last chunk\n",
+    "    if current_records:\n",
+    "        table = pa.Table.from_pandas(pd.DataFrame(current_records))\n",
+    "        parquet_filename = f\"output_{parquet_file_index}.parquet\"\n",
+    "        parquet_path = os.path.join(parquet_dir, parquet_filename)\n",
+    "        pq.write_table(table, parquet_path)\n",
+    "    \n",
+    "    print(f\"Conversion complete, wrote {parquet_file_index} Parquet files.\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "if os.path.exists(parquet_dir):\n",
+    "    pass\n",
+    "else:\n",
+    "    convert_jsonl_to_parquet(extracted_files, parquet_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['parquet/usenet_converted_6.parquet',\n",
+       " 'parquet/usenet_converted_9.parquet',\n",
+       " 'parquet/usenet_converted_15.parquet',\n",
+       " 'parquet/usenet_converted_10.parquet',\n",
+       " 'parquet/usenet_converted_11.parquet',\n",
+       " 'parquet/usenet_converted_12.parquet',\n",
+       " 'parquet/usenet_converted_16.parquet',\n",
+       " 'parquet/usenet_converted_13.parquet',\n",
+       " 'parquet/usenet_converted_14.parquet',\n",
+       " 'parquet/usenet_converted_4.parquet',\n",
+       " 'parquet/usenet_converted_5.parquet',\n",
+       " 'parquet/usenet_converted_0.parquet',\n",
+       " 'parquet/usenet_converted_8.parquet',\n",
+       " 'parquet/usenet_converted_7.parquet',\n",
+       " 'parquet/usenet_converted_1.parquet',\n",
+       " 'parquet/usenet_converted_3.parquet',\n",
+       " 'parquet/usenet_converted_2.parquet']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## test parquet conversion\n",
+    "\n",
+    "parquet_files=glob.glob(parquet_dir+\"/*.parquet\")\n",
+    "\n",
+    "parquet_files\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a df\n",
+    "df = pq.read_table(parquet_files[0]).to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "notebookRunGroups": {
+     "groupValue": "2"
+    }
+   },
+   "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>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>id</th>\n",
+       "      <th>progressive_number</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>newsgroup</th>\n",
+       "      <th>original_url</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q.p.g.a. dopo i primissimi ascolti...</td>\n",
+       "      <td>Alessia</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2009-11-29 15:16:26</td>\n",
+       "      <td>it.fan.musica.baglioni</td>\n",
+       "      <td>https://groups.google.com/g/it.fan.musica.bagl...</td>\n",
+       "      <td>&gt; mia moglie esce pazza per 'sto baglioni.\\nMe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q.p.g.a. dopo i primissimi ascolti...</td>\n",
+       "      <td>Paolo</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2009-11-29 18:11:59</td>\n",
+       "      <td>it.fan.musica.baglioni</td>\n",
+       "      <td>https://groups.google.com/g/it.fan.musica.bagl...</td>\n",
+       "      <td>\"Alessia\" &lt;\\[email protected]\\n&gt; ha scritto...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Avrai...?</td>\n",
+       "      <td>Lock</td>\n",
+       "      <td>1133</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2003-06-18 15:50:02</td>\n",
+       "      <td>it.fan.musica.baglioni</td>\n",
+       "      <td>https://groups.google.com/g/it.fan.musica.bagl...</td>\n",
+       "      <td>Ragazzi, chi di voi ce l'ha non live?\\n-- \\nLø...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Avrai...?</td>\n",
+       "      <td>Deep</td>\n",
+       "      <td>1133</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2003-06-18 16:08:54</td>\n",
+       "      <td>it.fan.musica.baglioni</td>\n",
+       "      <td>https://groups.google.com/g/it.fan.musica.bagl...</td>\n",
+       "      <td>Lock ha scritto:\\n&gt; Ragazzi, chi di voi ce l'h...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Avrai...?</td>\n",
+       "      <td>Clab</td>\n",
+       "      <td>1133</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2003-06-18 16:18:12</td>\n",
+       "      <td>it.fan.musica.baglioni</td>\n",
+       "      <td>https://groups.google.com/g/it.fan.musica.bagl...</td>\n",
+       "      <td>\"Lock\" &lt;\\[email protected]\\n&gt; ha scritto nel...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5000013</th>\n",
+       "      <td>zidane lascia il calcio</td>\n",
+       "      <td>motion musso aka: sathia</td>\n",
+       "      <td>54066</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-04-25 20:43:13</td>\n",
+       "      <td>it.sport.calcio</td>\n",
+       "      <td>https://groups.google.com/g/it.sport.calcio/c/...</td>\n",
+       "      <td>e questa č una notizia triste.\\nse ne andrą do...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5000014</th>\n",
+       "      <td>UCS</td>\n",
+       "      <td>Andrea Logiudice</td>\n",
+       "      <td>54067</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1997-12-31 09:00:00</td>\n",
+       "      <td>it.sport.calcio</td>\n",
+       "      <td>https://groups.google.com/g/it.sport.calcio/c/...</td>\n",
+       "      <td>Lo dico e lo ribadisco, FORZA SAMP!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5000015</th>\n",
+       "      <td>INTER MAFIA ANCHE PER GLI STRANIERI!!</td>\n",
+       "      <td>alessandro</td>\n",
+       "      <td>54068</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2002-11-13 12:55:42</td>\n",
+       "      <td>it.sport.calcio</td>\n",
+       "      <td>https://groups.google.com/g/it.sport.calcio/c/...</td>\n",
+       "      <td>\"Simon Schiffeleers\" &lt;\\[email protected]\\n&gt; ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5000016</th>\n",
+       "      <td>Silenzio...</td>\n",
+       "      <td>Vide</td>\n",
+       "      <td>54069</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2004-04-28 00:16:53</td>\n",
+       "      <td>it.sport.calcio</td>\n",
+       "      <td>https://groups.google.com/g/it.sport.calcio/c/...</td>\n",
+       "      <td>Capisco che il NG sia per la maggior parte pop...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5000017</th>\n",
+       "      <td>[OT] Abbonamento a +calcio gold precisazioni</td>\n",
+       "      <td>renatuzzo</td>\n",
+       "      <td>54070</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2002-09-28 11:53:02</td>\n",
+       "      <td>it.sport.calcio</td>\n",
+       "      <td>https://groups.google.com/g/it.sport.calcio/c/...</td>\n",
+       "      <td>Scusate se magari la domanda è già stata posta...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5000018 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                title  \\\n",
+       "0               Q.p.g.a. dopo i primissimi ascolti...   \n",
+       "1               Q.p.g.a. dopo i primissimi ascolti...   \n",
+       "2                                           Avrai...?   \n",
+       "3                                           Avrai...?   \n",
+       "4                                           Avrai...?   \n",
+       "...                                               ...   \n",
+       "5000013                       zidane lascia il calcio   \n",
+       "5000014                                           UCS   \n",
+       "5000015         INTER MAFIA ANCHE PER GLI STRANIERI!!   \n",
+       "5000016                                   Silenzio...   \n",
+       "5000017  [OT] Abbonamento a +calcio gold precisazioni   \n",
+       "\n",
+       "                           author     id  progressive_number  \\\n",
+       "0                         Alessia   1132                   4   \n",
+       "1                           Paolo   1132                   5   \n",
+       "2                            Lock   1133                   1   \n",
+       "3                            Deep   1133                   2   \n",
+       "4                            Clab   1133                   3   \n",
+       "...                           ...    ...                 ...   \n",
+       "5000013  motion musso aka: sathia  54066                   1   \n",
+       "5000014          Andrea Logiudice  54067                   1   \n",
+       "5000015                alessandro  54068                   1   \n",
+       "5000016                      Vide  54069                   1   \n",
+       "5000017                 renatuzzo  54070                   1   \n",
+       "\n",
+       "                  timestamp               newsgroup  \\\n",
+       "0       2009-11-29 15:16:26  it.fan.musica.baglioni   \n",
+       "1       2009-11-29 18:11:59  it.fan.musica.baglioni   \n",
+       "2       2003-06-18 15:50:02  it.fan.musica.baglioni   \n",
+       "3       2003-06-18 16:08:54  it.fan.musica.baglioni   \n",
+       "4       2003-06-18 16:18:12  it.fan.musica.baglioni   \n",
+       "...                     ...                     ...   \n",
+       "5000013 2006-04-25 20:43:13         it.sport.calcio   \n",
+       "5000014 1997-12-31 09:00:00         it.sport.calcio   \n",
+       "5000015 2002-11-13 12:55:42         it.sport.calcio   \n",
+       "5000016 2004-04-28 00:16:53         it.sport.calcio   \n",
+       "5000017 2002-09-28 11:53:02         it.sport.calcio   \n",
+       "\n",
+       "                                              original_url  \\\n",
+       "0        https://groups.google.com/g/it.fan.musica.bagl...   \n",
+       "1        https://groups.google.com/g/it.fan.musica.bagl...   \n",
+       "2        https://groups.google.com/g/it.fan.musica.bagl...   \n",
+       "3        https://groups.google.com/g/it.fan.musica.bagl...   \n",
+       "4        https://groups.google.com/g/it.fan.musica.bagl...   \n",
+       "...                                                    ...   \n",
+       "5000013  https://groups.google.com/g/it.sport.calcio/c/...   \n",
+       "5000014  https://groups.google.com/g/it.sport.calcio/c/...   \n",
+       "5000015  https://groups.google.com/g/it.sport.calcio/c/...   \n",
+       "5000016  https://groups.google.com/g/it.sport.calcio/c/...   \n",
+       "5000017  https://groups.google.com/g/it.sport.calcio/c/...   \n",
+       "\n",
+       "                                                      text  \n",
+       "0        > mia moglie esce pazza per 'sto baglioni.\\nMe...  \n",
+       "1        \"Alessia\" <\\[email protected]\\n> ha scritto...  \n",
+       "2        Ragazzi, chi di voi ce l'ha non live?\\n-- \\nLø...  \n",
+       "3        Lock ha scritto:\\n> Ragazzi, chi di voi ce l'h...  \n",
+       "4        \"Lock\" <\\[email protected]\\n> ha scritto nel...  \n",
+       "...                                                    ...  \n",
+       "5000013  e questa č una notizia triste.\\nse ne andrą do...  \n",
+       "5000014                Lo dico e lo ribadisco, FORZA SAMP!  \n",
+       "5000015  \"Simon Schiffeleers\" <\\[email protected]\\n> ...  \n",
+       "5000016  Capisco che il NG sia per la maggior parte pop...  \n",
+       "5000017  Scusate se magari la domanda è già stata posta...  \n",
+       "\n",
+       "[5000018 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "table=pq.ParquetDataset(parquet_files)\n",
+    "\n",
+    "table_df=table.read(use_threads=True).to_pandas()\n",
+    "def generate_examples():\n",
+    "    for index, row in table_df.iterrows():\n",
+    "            yield index, row.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for example in generate_examples():\n",
+    "    print(example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "table_df.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}