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wrighter/present_value.ipynb

Last active October 1, 2019 12:55
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Present Value
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present_value.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"556.8374181775592"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def present_value(fv, i_rate, n_periods):\n",
" return fv / (1 + i_rate) ** n_periods\n",
"\n",
"present_value(1000, 0.05, 12)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame([(1000, 0.05, 12), (1000, 0.07, 12), (1000, 0.09, 12), (500, 0.02, 24)],\n",
" columns=['fv', 'i_rate', 'n_periods'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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>fv</th>\n",
" <th>i_rate</th>\n",
" <th>n_periods</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1000</td>\n",
" <td>0.05</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1000</td>\n",
" <td>0.07</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1000</td>\n",
" <td>0.09</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>0.02</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fv i_rate n_periods\n",
"0 1000 0.05 12\n",
"1 1000 0.07 12\n",
"2 1000 0.09 12\n",
"3 500 0.02 24"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for (index, row) in df.iterrows():\n",
" df.loc[index, 'pv'] = present_value(row.fv, row.i_rate, row.n_periods)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"df['pv2'] = df.apply(lambda r: present_value(r['fv'], r['i_rate'], r['n_periods']), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df['pv3'] = df['fv']/(1 + df['i_rate']) ** df['n_periods']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"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>fv</th>\n",
" <th>i_rate</th>\n",
" <th>n_periods</th>\n",
" <th>pv</th>\n",
" <th>pv2</th>\n",
" <th>pv3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1000</td>\n",
" <td>0.05</td>\n",
" <td>12</td>\n",
" <td>556.837418</td>\n",
" <td>556.837418</td>\n",
" <td>556.837418</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1000</td>\n",
" <td>0.07</td>\n",
" <td>12</td>\n",
" <td>444.011959</td>\n",
" <td>444.011959</td>\n",
" <td>444.011959</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1000</td>\n",
" <td>0.09</td>\n",
" <td>12</td>\n",
" <td>355.534725</td>\n",
" <td>355.534725</td>\n",
" <td>355.534725</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>0.02</td>\n",
" <td>24</td>\n",
" <td>310.860744</td>\n",
" <td>310.860744</td>\n",
" <td>310.860744</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fv i_rate n_periods pv pv2 pv3\n",
"0 1000 0.05 12 556.837418 556.837418 556.837418\n",
"1 1000 0.07 12 444.011959 444.011959 444.011959\n",
"2 1000 0.09 12 355.534725 355.534725 355.534725\n",
"3 500 0.02 24 310.860744 310.860744 310.860744"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.18 ms ± 58.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%timeit df.apply(lambda r: present_value(r['fv'], r['i_rate'], r['n_periods']), axis=1) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"493 µs ± 45.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%timeit df['fv']/(1 + df['i_rate']) ** df['n_periods'] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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