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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "76f1b610",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ef6781e",
"metadata": {},
"outputs": [],
"source": [
"# Read the CSV file into a DataFrame\n",
"df = pd.read_csv('../data-2023/in/23-138.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de6cb72a",
"metadata": {},
"outputs": [],
"source": [
"# Rename the column names\n",
"df = df.rename(columns={\n",
" 'University of Illinois System - Investment Office': 'Name',\n",
" 'Unnamed: 1': 'Coupon',\n",
" 'Unnamed: 2': 'Maturity Date',\n",
" 'Unnamed: 3': 'Quantity',\n",
" 'Unnamed: 4': 'Cost Val',\n",
" 'Unnamed: 5': 'Market Val'\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d909ab8",
"metadata": {},
"outputs": [],
"source": [
"# Reset the index and drop rows before '9-200100-Neuberger Intermediate'\n",
"index_to_reset = df.index[df['Name'] == '9-200100-Neuberger Intermediate'][0]\n",
"df.reset_index(drop=True, inplace=True)\n",
"\n",
"# Now your DataFrame should have the index reset, and rows before '9-200100-Neuberger Intermediate' dropped\n",
"# because Agency Funds are unimportant to us.\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "383efe00",
"metadata": {},
"outputs": [],
"source": [
"# Asset Types are header rows in Account or Security column \n",
"def is_asset_type(name):\n",
" investment_types = [\n",
" 'Asset-Backed Securities',\n",
" 'Commercial Mortgage Backed Securities',\n",
" 'Corporate Bonds',\n",
" 'Municipal Bonds',\n",
" 'U.S. Agency Bonds',\n",
" 'International Government Bonds',\n",
" 'U.S. Treasury Securities',\n",
" 'Cash & Cash Equivalents',\n",
" ]\n",
" return name in investment_types"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd54c34e",
"metadata": {},
"outputs": [],
"source": [
"# Determine the position where you want to insert the 'Asset_Type' column (e.g., after 'Investment_Type')\n",
"insert_position = df.columns.get_loc('Name') + 1\n",
"\n",
"# Create a new column 'Asset Type' and fill it with the corresponding row headers\n",
"df['Asset Type'] = df['Name'].where(df['Name'].apply(is_asset_type))\n",
"\n",
"# Forward fill the 'Asset Type' column to propagate the labels down to the rows\n",
"df['Asset Type'] = df['Asset Type'].ffill()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b1b000b",
"metadata": {},
"outputs": [],
"source": [
"# Sanity check: let's export what we currently have a to .csv and check that Asset Type is right \n",
"df.to_csv('../data-2023/out/asset_type_sanity_check.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f774d484",
"metadata": {},
"outputs": [],
"source": [
"# Alright! Let's drop all the rows with null values (this includes header rows for investment data + other fund data)\n",
"\n",
"# Drop any rows with empty or null values\n",
"df = df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "344a39dd",
"metadata": {},
"outputs": [],
"source": [
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5766ff23",
"metadata": {},
"outputs": [],
"source": [
"# Sanity check: let's export what we currently have a to .csv and check that we only have investment data \n",
"df.to_csv('../data-2023/out/investment_data_sanity_check.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06801e7f",
"metadata": {},
"outputs": [],
"source": [
"# Next, let's clean unruly typed data. Investments should obey the schema :\n",
"# Account or Security (string)\n",
"# Coupon (Date format mm/dd/yyyy)\n",
"# Maturity Date (double) \n",
"# Quantity (double) \n",
"# Cost Val (double) \n",
"# Market Val (double)\n",
"\n",
"cnt_old_entries = len(df)\n",
"\n",
"# for example, this bond has switched its Maturity Date and Date Coupon (this is actually one of a kind outlier)\n",
"print(df.iloc[135])\n",
"\n",
"# Convert 'Maturity Date' to datetime format\n",
"df['Maturity Date'] = pd.to_datetime(df['Maturity Date'], errors='coerce')\n",
"\n",
"\n",
"# Clean commas from numeric column values using regular expressions\n",
"numeric_columns = ['Coupon', 'Quantity', 'Cost Val', 'Market Val']\n",
"\n",
"for col in numeric_columns:\n",
" # clean commas to register values as numbers \n",
" df[col] = pd.to_numeric(df[col].astype(str).str.replace(',', '', regex=True), errors='coerce')\n",
"\n",
"# Now, the unruly typed data becomes NaN or NaT\n",
"print(df.iloc[135])\n",
"\n",
"# Drop all unruly typed data \n",
"df = df.dropna()\n",
"\n",
"cnt_new_entries = len(df)\n",
"\n",
"print(f\"\\nSuccessfully cleaned {cnt_old_entries - cnt_new_entries} unruly typed values.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79a654e0",
"metadata": {},
"outputs": [],
"source": [
"# Sanity check: let's export what we currently have a to .csv and check that all our values are the right type. \n",
"df.to_csv('../data-2023/out/df_data_typed.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bef9be05",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": 5
}
|
