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{
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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