{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea here is\n",
    "* to split a column into two new columns with one column containing the positives and the other negatives.\n",
    "* use a default of 0 when the condition is not true (i.e. all negatives in the positive column and all positives in the negative column are set to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          C\n",
      "0  0.496714\n",
      "1 -0.138264\n",
      "2  0.647689\n",
      "3  1.523030\n",
      "4 -0.234153\n",
      "5 -0.234137\n",
      "6  1.579213\n",
      "7  0.767435\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(42)\n",
    "df = pd.DataFrame({'C' : np.random.randn(8)})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          C     C_pos     C_neg\n",
      "0  0.496714  0.496714  0.000000\n",
      "1 -0.138264  0.000000 -0.138264\n",
      "2  0.647689  0.647689  0.000000\n",
      "3  1.523030  1.523030  0.000000\n",
      "4 -0.234153  0.000000 -0.234153\n",
      "5 -0.234137  0.000000 -0.234137\n",
      "6  1.579213  1.579213  0.000000\n",
      "7  0.767435  0.767435  0.000000\n"
     ]
    }
   ],
   "source": [
    "df['C_pos'] = df['C'].where(df['C']>0, other=0)\n",
    "df['C_neg'] = df['C'].where(df['C']<0, other=0)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample use case: Split a market value column into longs and shorts.\n",
    "\n",
    "Ref:- \n",
    "* https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.where.html\n",
    "* https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mask.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.0.3', '1.18.1')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.__version__, np.__version__)"
   ]
  }
 ],
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