{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f2fc4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed: Sudden Factors/Earthquake copper.csv\n",
      "Processed: Sudden Factors/Brexit.csv\n",
      "Processed: Sudden Factors/Copper trade war.csv\n",
      "Processed: Sudden Factors/Trade war.csv\n",
      "Processed: Sudden Factors/Mining sanctions.csv\n",
      "Processed: Sudden Factors/Volatility index.csv\n",
      "Processed: Sudden Factors/Extreme weather.csv\n",
      "Processed: Sudden Factors/Energy crisis.csv\n",
      "Processed: Sudden Factors/Hurricane copper.csv\n",
      "Processed: Sudden Factors/Banking crisis.csv\n",
      "Processed: Sudden Factors/Middle East conflicts.csv\n",
      "Processed: Sudden Factors/Mining natural disaster.csv\n",
      "Processed: Sudden Factors/Conflicts.csv\n",
      "Processed: Sudden Factors/Iran Iraq War.csv\n",
      "Processed: Sudden Factors/COVID.csv\n",
      "Processed: Sudden Factors/Resource nationalism.csv\n",
      "Processed: Sudden Factors/Pandemic copper.csv\n",
      "Processed: Sudden Factors/COVID copper.csv\n",
      "Processed: Sudden Factors/Transportation disruption.csv\n",
      "Processed: Sudden Factors/Copper tariff.csv\n",
      "Processed: Sudden Factors/2008 crisis.csv\n",
      "Processed: Sudden Factors/Mining accidents.csv\n",
      "Processed: Sudden Factors/Climate change mining.csv\n",
      "Processed: Sudden Factors/Terrorist attack.csv\n",
      "Processed: Sudden Factors/China US trade tensions.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "folder_path = \"Sudden Factors\"\n",
    "\n",
    "for filename in os.listdir(folder_path):\n",
    "    if filename.endswith('.csv'):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "        \n",
    "        # Read CSV, skip first two rows, use the third row as header\n",
    "        df = pd.read_csv(file_path, skiprows=2)\n",
    "        \n",
    "        # Convert the first column (assumed to be 'Week') to datetime\n",
    "        first_col = df.columns[0]\n",
    "        df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n",
    "        \n",
    "        # Rename columns: first column to 'date', second to 'Interest'\n",
    "        if len(df.columns) > 1:\n",
    "            df.rename(columns={first_col: 'Date', df.columns[1]: 'Interest'}, inplace=True)\n",
    "        \n",
    "        # Save back to CSV\n",
    "        df.to_csv(file_path, index=False)\n",
    "        print(f\"Processed: {file_path}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "269c0e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed: ./Copper Prices.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6b/p92_dgbd07ldpbpq29vbpd_m0000gn/T/ipykernel_13497/813422496.py:15: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "folder_path = \".\"\n",
    "\n",
    "for filename in os.listdir(folder_path):\n",
    "    if filename.endswith('.csv'):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "        \n",
    "        # Read CSV, skip first two rows, use the third row as header\n",
    "        df = pd.read_csv(file_path)\n",
    "        \n",
    "        # Convert the first column (assumed to be 'Week') to datetime\n",
    "        first_col = df.columns[0]\n",
    "        df[first_col] = pd.to_datetime(df[first_col], errors='coerce')\n",
    "        \n",
    "        # Save back to CSV\n",
    "        df.to_csv(file_path, index=False)\n",
    "        print(f\"Processed: {file_path}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f7d0600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed: Supply Factors/Copper mining.csv\n",
      "Processed: Supply Factors/Peru copper mining.csv\n",
      "Processed: Supply Factors/Copper ore production.csv\n",
      "Processed: Supply Factors/Scrap copper prices.csv\n",
      "Processed: Supply Factors/Copper project.csv\n",
      "Processed: Supply Factors/Copper mine accident.csv\n",
      "Processed: Supply Factors/Aluminum production.csv\n",
      "Processed: Supply Factors/Recycled copper.csv\n",
      "Processed: Supply Factors/Copper smelter.csv\n",
      "Processed: Supply Factors/China copper production.csv\n",
      "Processed: Supply Factors/Peru copper production.csv\n",
      "Processed: Supply Factors/Copper refinery.csv\n",
      "Processed: Supply Factors/Copper reserves by country.csv\n",
      "Processed: Supply Factors/Chile copper production.csv\n",
      "Processed: Supply Factors/Copper mine strike.csv\n",
      "Processed: Supply Factors/Global copper production.csv\n",
      "Processed: Supply Factors/Copper mining cost.csv\n",
      "Processed: Supply Factors/US copper production.csv\n",
      "Processed: Supply Factors/Congo copper.csv\n",
      "Processed: Supply Factors/Copper mine production.csv\n",
      "Processed: Supply Factors/Copper concentrate.csv\n",
      "Processed: Supply Factors/Copper ore reserves.csv\n",
      "Processed: Supply Factors/Copper production by country.csv\n",
      "Processed: Supply Factors/Steel production.csv\n",
      "Processed: Supply Factors/Copper mining companies.csv\n",
      "Processed: Supply Factors/Copper cathode production.csv\n",
      "Processed: Supply Factors/Copper resource.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "folder_path = \"Supply Factors\"\n",
    "\n",
    "for filename in os.listdir(folder_path):\n",
    "    if filename.endswith('.csv'):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "        \n",
    "        # Read CSV, skip first two rows, use the third row as header\n",
    "        df = pd.read_csv(file_path)\n",
    "        first_col = df.columns[0]\n",
    "        \n",
    "        # Rename columns: first column to 'date', second to 'Interest'\n",
    "        if len(df.columns) > 1:\n",
    "            df.rename(columns={first_col: 'Date', df.columns[1]: 'Interest'}, inplace=True)\n",
    "        \n",
    "        # Save back to CSV\n",
    "        df.to_csv(file_path, index=False)\n",
    "        print(f\"Processed: {file_path}\")\n",
    "\n"
   ]
  }
 ],
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