{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os \n",
    "\n",
    "folders = os.listdir(os.getcwd() + os.sep + 'finnish')\n",
    "\n",
    "paths_to_folders = [os.getcwd() + os.sep + 'finnish' + os.sep + folder for folder in folders]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths_all = []\n",
    "for path in paths_to_folders:\n",
    "    filepaths = [path + os.sep + file for file in os.listdir(path)]\n",
    "    filepaths_all.extend(filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "asd = pd.read_parquet(filepaths_all[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "def add_year_month_time_of_day(row):\n",
    "    row['created_utc'] = int(row['created_utc'])\n",
    "    dt_utc_native = datetime.datetime.utcfromtimestamp(row['created_utc'])\n",
    "    row['year'] = dt_utc_native.year\n",
    "    row['day'] = dt_utc_native.day\n",
    "    row['month'] = dt_utc_native.month\n",
    "    row['time'] = dt_utc_native.strftime(\"%H:%M:%S\")\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = asd.apply(lambda row: add_year_month_time_of_day(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "  subreddit  created_utc score  \\\n",
      "0     Suomi   1546181040    57   \n",
      "1     Suomi   1546181271    15   \n",
      "2     Suomi   1546181411     9   \n",
      "3     Suomi   1546181411     0   \n",
      "4     Suomi   1546181804     1   \n",
      "\n",
      "                                                body predicted_language  \\\n",
      "0  Kylläpä Suomi törkeästi provosoi Venäjää. Onne...        __label__fi   \n",
      "1  Vittu! Mun verorahoilla taas paskaa ostettu. O...        __label__fi   \n",
      "2  Mutta ajattelitteko ollenkaan luontoa ennen ku...        __label__fi   \n",
      "3   Sekin olis kova.\\nDas Boot u-612 liian legend...        __label__fi   \n",
      "4  Voisi kieltää kaikkien henkeen vedettävien ain...        __label__fi   \n",
      "\n",
      "   probability  year  day  month      time  \n",
      "0     0.998374  2018   30     12  14:44:00  \n",
      "1     0.999526  2018   30     12  14:47:51  \n",
      "2     0.989323  2018   30     12  14:50:11  \n",
      "3     0.895473  2018   30     12  14:50:11  \n",
      "4     0.998957  2018   30     12  14:56:44  \n",
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    }
   ],
   "source": [
    "for i, filepath in enumerate(filepaths_all):\n",
    "    print(f\"{i+1}/{len(filepaths_all)}\")\n",
    "    try:\n",
    "        if i == 0:\n",
    "            df_all = pd.read_parquet(filepath)\n",
    "            df_all = df_all.apply(lambda row: add_year_month_time_of_day(row), axis=1)\n",
    "        else:\n",
    "            df_new = pd.read_parquet(filepath)\n",
    "            df_new = df_new.apply(lambda row: add_year_month_time_of_day(row), axis=1)\n",
    "            df_all = pd.concat([df_new, df_all])\n",
    "    except Exception as e:\n",
    "        print(df_new.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv('data_all_fi.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4476667"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration .-e14a2d6b4b35a498\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/. to G:/hf_cache/csv/.-e14a2d6b4b35a498/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73a198a7d22b4a95af5a5b9fab487982",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00e54b096f564f29b7e202992787777c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing checksums: 100%|##########| 1/1 [00:14<00:00, 14.46s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "796b345e120e43e6a63ac5c0f03564f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31f5d33610af4973b456c7dbb04d2854",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\tools\\Anaconda3\\envs\\redditEnv\\lib\\site-packages\\datasets\\download\\streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to G:/hf_cache/csv/.-e14a2d6b4b35a498/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "dataset = load_dataset(path= './',data_files='data_all_fi.csv', split='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1fc27d6c8ed400fbc65c5b7db5a0967",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b672db855ab948709e7f4c0624d26842",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1120 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11eaa5fb2e0e49ceb5fa1afa09208337",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a53c6c7c1f5942bfa064f26bc81b0721",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1120 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de80a629f798442f925b54b15c74a6c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "148324cc96804841bff0bc1ab8faaf8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1120 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1f067b850324ba6b01a31c81acc4213",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f21ff3f50b98420e90550d7eab50a1ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1120 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14e2a74e795043e39a3131d7394aa70a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.push_to_hub(\"Finnish-NLP/Reddit_fi_2006_2022\", private=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
  },
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   "codemirror_mode": {
    "name": "ipython",
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