{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retain in Pandas\n",
    "## Similar question: \n",
    "https://stackoverflow.com/questions/33569265/retain-function-in-python\n",
    "\n",
    "### Any better way to do this than using a for loop or apply?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Shares</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>1000</td>\n",
       "      <td>117.2249</td>\n",
       "      <td>1000</td>\n",
       "      <td>117224.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-10</td>\n",
       "      <td>-500</td>\n",
       "      <td>130.5928</td>\n",
       "      <td>500</td>\n",
       "      <td>-65296.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-10</td>\n",
       "      <td>1500</td>\n",
       "      <td>137.5316</td>\n",
       "      <td>2000</td>\n",
       "      <td>206297.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>-2000</td>\n",
       "      <td>141.5150</td>\n",
       "      <td>0</td>\n",
       "      <td>-283030.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>500</td>\n",
       "      <td>151.4884</td>\n",
       "      <td>500</td>\n",
       "      <td>75744.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>500</td>\n",
       "      <td>147.8657</td>\n",
       "      <td>1000</td>\n",
       "      <td>73932.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-07-10</td>\n",
       "      <td>500</td>\n",
       "      <td>143.9750</td>\n",
       "      <td>1500</td>\n",
       "      <td>71987.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>-1000</td>\n",
       "      <td>154.7636</td>\n",
       "      <td>500</td>\n",
       "      <td>-154763.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>-200</td>\n",
       "      <td>160.9215</td>\n",
       "      <td>300</td>\n",
       "      <td>-32184.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>1000</td>\n",
       "      <td>155.3416</td>\n",
       "      <td>1300</td>\n",
       "      <td>155341.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Quantity     Price  Total Shares       Cost\n",
       "0 2017-01-10      1000  117.2249          1000  117224.90\n",
       "1 2017-02-10      -500  130.5928           500  -65296.40\n",
       "2 2017-03-10      1500  137.5316          2000  206297.40\n",
       "3 2017-04-10     -2000  141.5150             0 -283030.00\n",
       "4 2017-05-10       500  151.4884           500   75744.20\n",
       "5 2017-06-09       500  147.8657          1000   73932.85\n",
       "6 2017-07-10       500  143.9750          1500   71987.50\n",
       "7 2017-08-10     -1000  154.7636           500 -154763.60\n",
       "8 2017-09-11      -200  160.9215           300  -32184.30\n",
       "9 2017-10-10      1000  155.3416          1300  155341.60"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl = pd.read_csv('AAPL_exmaple.csv', parse_dates=['Date'])\n",
    "aapl['Total Shares'] = aapl['Quantity'].cumsum()\n",
    "aapl['Cost'] = aapl['Quantity']*aapl['Price']\n",
    "aapl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Shares</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Ave Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>1000</td>\n",
       "      <td>117.2249</td>\n",
       "      <td>1000</td>\n",
       "      <td>117224.90</td>\n",
       "      <td>117.224900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-10</td>\n",
       "      <td>-500</td>\n",
       "      <td>130.5928</td>\n",
       "      <td>500</td>\n",
       "      <td>-65296.40</td>\n",
       "      <td>117.224900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-10</td>\n",
       "      <td>1500</td>\n",
       "      <td>137.5316</td>\n",
       "      <td>2000</td>\n",
       "      <td>206297.40</td>\n",
       "      <td>132.454925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>-2000</td>\n",
       "      <td>141.5150</td>\n",
       "      <td>0</td>\n",
       "      <td>-283030.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>500</td>\n",
       "      <td>151.4884</td>\n",
       "      <td>500</td>\n",
       "      <td>75744.20</td>\n",
       "      <td>151.488400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>500</td>\n",
       "      <td>147.8657</td>\n",
       "      <td>1000</td>\n",
       "      <td>73932.85</td>\n",
       "      <td>149.677050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-07-10</td>\n",
       "      <td>500</td>\n",
       "      <td>143.9750</td>\n",
       "      <td>1500</td>\n",
       "      <td>71987.50</td>\n",
       "      <td>147.776367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>-1000</td>\n",
       "      <td>154.7636</td>\n",
       "      <td>500</td>\n",
       "      <td>-154763.60</td>\n",
       "      <td>147.776367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>-200</td>\n",
       "      <td>160.9215</td>\n",
       "      <td>300</td>\n",
       "      <td>-32184.30</td>\n",
       "      <td>147.776367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>1000</td>\n",
       "      <td>155.3416</td>\n",
       "      <td>1300</td>\n",
       "      <td>155341.60</td>\n",
       "      <td>153.595777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Quantity     Price  Total Shares       Cost    Ave Cost\n",
       "0 2017-01-10      1000  117.2249          1000  117224.90  117.224900\n",
       "1 2017-02-10      -500  130.5928           500  -65296.40  117.224900\n",
       "2 2017-03-10      1500  137.5316          2000  206297.40  132.454925\n",
       "3 2017-04-10     -2000  141.5150             0 -283030.00    0.000000\n",
       "4 2017-05-10       500  151.4884           500   75744.20  151.488400\n",
       "5 2017-06-09       500  147.8657          1000   73932.85  149.677050\n",
       "6 2017-07-10       500  143.9750          1500   71987.50  147.776367\n",
       "7 2017-08-10     -1000  154.7636           500 -154763.60  147.776367\n",
       "8 2017-09-11      -200  160.9215           300  -32184.30  147.776367\n",
       "9 2017-10-10      1000  155.3416          1300  155341.60  153.595777"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_ave_cost(df):\n",
    "    for index, row in df.iterrows():\n",
    "        if index == 0:\n",
    "            df.loc[index,'Ave Cost'] = row['Price']\n",
    "        elif row['Total Shares'] == 0:\n",
    "            df.loc[index,'Ave Cost'] = 0.0\n",
    "        else:\n",
    "            if row['Quantity'] > 0:\n",
    "                df.loc[index,'Ave Cost'] = ((df.loc[index - 1,'Ave Cost'] * df.loc[index - 1,'Total Shares']) \\\n",
    "                                            + row['Cost'])/row['Total Shares']\n",
    "            else:\n",
    "                df.loc[index,'Ave Cost'] =  df.loc[index - 1,'Ave Cost']\n",
    "    return df\n",
    "\n",
    "aapl_ave_cost = get_ave_cost(aapl)\n",
    "aapl_ave_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 4.81 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit get_ave_cost(aapl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The root problem is using for loops instead of a vectorized solution."
   ]
  }
 ],
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