{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Columns as Functions of Existing Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready\n",
    "\n",
    "Import `pandas` and `numpy` and create a `DataFrame` to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.683402</td>\n",
       "      <td>-0.530933</td>\n",
       "      <td>2.211594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.492285</td>\n",
       "      <td>0.422495</td>\n",
       "      <td>0.047873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.287670</td>\n",
       "      <td>-1.712541</td>\n",
       "      <td>0.665600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.206087</td>\n",
       "      <td>-0.479746</td>\n",
       "      <td>0.112255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.384278</td>\n",
       "      <td>0.314534</td>\n",
       "      <td>-1.529952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C\n",
       "2020-01-01  0.683402 -0.530933  2.211594\n",
       "2020-01-02  0.492285  0.422495  0.047873\n",
       "2020-01-03 -0.287670 -1.712541  0.665600\n",
       "2020-01-04 -1.206087 -0.479746  0.112255\n",
       "2020-01-05  0.384278  0.314534 -1.529952"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2020', periods=8)\n",
    "data = np.random.randn(8, 3)\n",
    "df = pd.DataFrame(data,\n",
    "                  index=dates, columns=['A', 'B', 'C'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to do it\n",
    "\n",
    "### Using vectorized operations\n",
    "\n",
    "1. Create a new column as a function of an existing column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Squared_A'] = df['A']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CosA'] = np.cos(df['A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create a new column as a function of two or more existing columns using  vectorized operation such as product and sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>Squared_A</th>\n",
       "      <th>CosA</th>\n",
       "      <th>Sum_AB</th>\n",
       "      <th>Prod_AB</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.683402</td>\n",
       "      <td>-0.530933</td>\n",
       "      <td>2.211594</td>\n",
       "      <td>0.467038</td>\n",
       "      <td>0.775429</td>\n",
       "      <td>0.152469</td>\n",
       "      <td>-0.362841</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.492285</td>\n",
       "      <td>0.422495</td>\n",
       "      <td>0.047873</td>\n",
       "      <td>0.242344</td>\n",
       "      <td>0.881255</td>\n",
       "      <td>0.914779</td>\n",
       "      <td>0.207988</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.287670</td>\n",
       "      <td>-1.712541</td>\n",
       "      <td>0.665600</td>\n",
       "      <td>0.082754</td>\n",
       "      <td>0.958908</td>\n",
       "      <td>-2.000210</td>\n",
       "      <td>0.492646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.206087</td>\n",
       "      <td>-0.479746</td>\n",
       "      <td>0.112255</td>\n",
       "      <td>1.454645</td>\n",
       "      <td>0.356678</td>\n",
       "      <td>-1.685832</td>\n",
       "      <td>0.578615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.384278</td>\n",
       "      <td>0.314534</td>\n",
       "      <td>-1.529952</td>\n",
       "      <td>0.147669</td>\n",
       "      <td>0.927070</td>\n",
       "      <td>0.698811</td>\n",
       "      <td>0.120868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>1.064657</td>\n",
       "      <td>0.591428</td>\n",
       "      <td>0.250544</td>\n",
       "      <td>1.133494</td>\n",
       "      <td>0.484805</td>\n",
       "      <td>1.656085</td>\n",
       "      <td>0.629668</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.459051</td>\n",
       "      <td>-0.287355</td>\n",
       "      <td>1.971743</td>\n",
       "      <td>0.210728</td>\n",
       "      <td>0.896473</td>\n",
       "      <td>-0.746406</td>\n",
       "      <td>0.131911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.992138</td>\n",
       "      <td>-0.512499</td>\n",
       "      <td>0.711691</td>\n",
       "      <td>0.984338</td>\n",
       "      <td>0.546901</td>\n",
       "      <td>0.479639</td>\n",
       "      <td>-0.508470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  Squared_A      CosA    Sum_AB  \\\n",
       "2020-01-01  0.683402 -0.530933  2.211594   0.467038  0.775429  0.152469   \n",
       "2020-01-02  0.492285  0.422495  0.047873   0.242344  0.881255  0.914779   \n",
       "2020-01-03 -0.287670 -1.712541  0.665600   0.082754  0.958908 -2.000210   \n",
       "2020-01-04 -1.206087 -0.479746  0.112255   1.454645  0.356678 -1.685832   \n",
       "2020-01-05  0.384278  0.314534 -1.529952   0.147669  0.927070  0.698811   \n",
       "2020-01-06  1.064657  0.591428  0.250544   1.133494  0.484805  1.656085   \n",
       "2020-01-07 -0.459051 -0.287355  1.971743   0.210728  0.896473 -0.746406   \n",
       "2020-01-08  0.992138 -0.512499  0.711691   0.984338  0.546901  0.479639   \n",
       "\n",
       "             Prod_AB  \n",
       "2020-01-01 -0.362841  \n",
       "2020-01-02  0.207988  \n",
       "2020-01-03  0.492646  \n",
       "2020-01-04  0.578615  \n",
       "2020-01-05  0.120868  \n",
       "2020-01-06  0.629668  \n",
       "2020-01-07  0.131911  \n",
       "2020-01-08 -0.508470  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sum_AB'] = df['A'] + df['B']\n",
    "df['Prod_AB'] = df['A'] * df['B']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Using `apply`\n",
    "\n",
    "\n",
    "3. Create a new column evaluating an ad-hoc function on existing columns. To do this we need to \n",
    "\n",
    "- Define the function that we want to evaluate\n",
    "- Use the method `apply` combined with a `lambda` expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
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       "      <th>2020-01-01</th>\n",
       "      <td>0.683402</td>\n",
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       "      <td>-0.362841</td>\n",
       "      <td>1.039929</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.492285</td>\n",
       "      <td>0.422495</td>\n",
       "      <td>0.047873</td>\n",
       "      <td>0.242344</td>\n",
       "      <td>0.881255</td>\n",
       "      <td>0.914779</td>\n",
       "      <td>0.207988</td>\n",
       "      <td>2.792179</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.287670</td>\n",
       "      <td>-1.712541</td>\n",
       "      <td>0.665600</td>\n",
       "      <td>0.082754</td>\n",
       "      <td>0.958908</td>\n",
       "      <td>-2.000210</td>\n",
       "      <td>0.492646</td>\n",
       "      <td>1.604184</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.206087</td>\n",
       "      <td>-0.479746</td>\n",
       "      <td>0.112255</td>\n",
       "      <td>1.454645</td>\n",
       "      <td>0.356678</td>\n",
       "      <td>-1.685832</td>\n",
       "      <td>0.578615</td>\n",
       "      <td>2.237496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.384278</td>\n",
       "      <td>0.314534</td>\n",
       "      <td>-1.529952</td>\n",
       "      <td>0.147669</td>\n",
       "      <td>0.927070</td>\n",
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       "      <td>0.120868</td>\n",
       "      <td>1.918843</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>1.064657</td>\n",
       "      <td>0.591428</td>\n",
       "      <td>0.250544</td>\n",
       "      <td>1.133494</td>\n",
       "      <td>0.484805</td>\n",
       "      <td>1.656085</td>\n",
       "      <td>0.629668</td>\n",
       "      <td>2.283728</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.459051</td>\n",
       "      <td>-0.287355</td>\n",
       "      <td>1.971743</td>\n",
       "      <td>0.210728</td>\n",
       "      <td>0.896473</td>\n",
       "      <td>-0.746406</td>\n",
       "      <td>0.131911</td>\n",
       "      <td>1.465180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.992138</td>\n",
       "      <td>-0.512499</td>\n",
       "      <td>0.711691</td>\n",
       "      <td>0.984338</td>\n",
       "      <td>0.546901</td>\n",
       "      <td>0.479639</td>\n",
       "      <td>-0.508470</td>\n",
       "      <td>2.175681</td>\n",
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      ],
      "text/plain": [
       "                   A         B         C  Squared_A      CosA    Sum_AB  \\\n",
       "2020-01-01  0.683402 -0.530933  2.211594   0.467038  0.775429  0.152469   \n",
       "2020-01-02  0.492285  0.422495  0.047873   0.242344  0.881255  0.914779   \n",
       "2020-01-03 -0.287670 -1.712541  0.665600   0.082754  0.958908 -2.000210   \n",
       "2020-01-04 -1.206087 -0.479746  0.112255   1.454645  0.356678 -1.685832   \n",
       "2020-01-05  0.384278  0.314534 -1.529952   0.147669  0.927070  0.698811   \n",
       "2020-01-06  1.064657  0.591428  0.250544   1.133494  0.484805  1.656085   \n",
       "2020-01-07 -0.459051 -0.287355  1.971743   0.210728  0.896473 -0.746406   \n",
       "2020-01-08  0.992138 -0.512499  0.711691   0.984338  0.546901  0.479639   \n",
       "\n",
       "             Prod_AB  function_ABC  \n",
       "2020-01-01 -0.362841      1.039929  \n",
       "2020-01-02  0.207988      2.792179  \n",
       "2020-01-03  0.492646      1.604184  \n",
       "2020-01-04  0.578615      2.237496  \n",
       "2020-01-05  0.120868      1.918843  \n",
       "2020-01-06  0.629668      2.283728  \n",
       "2020-01-07  0.131911      1.465180  \n",
       "2020-01-08 -0.508470      2.175681  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sin, cos\n",
    "\n",
    "def my_function(x, y, z):\n",
    "    if x + y >3:\n",
    "        return sin(x) + sin(y) + sin(z)\n",
    "    else:\n",
    "        return cos(x) + cos(y) + cos(z)\n",
    "\n",
    "\n",
    "df['function_ABC'] = df.apply(lambda x: my_function(x['A'], x['B'], x['C']), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Create a column which maps the values of a column to a set of colors depending on some boundaries. \n",
    "For example, we are going to map column `C` as follows:\n",
    "\n",
    "- 'black' for values < -3\n",
    "- 'yellow' for values in [-3, 3]\n",
    "- 'green' for values >3\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>Squared_A</th>\n",
       "      <th>Sum_AB</th>\n",
       "      <th>Prod_AB</th>\n",
       "      <th>function_ABC</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.708974</td>\n",
       "      <td>1.653839</td>\n",
       "      <td>0.460311</td>\n",
       "      <td>0.502644</td>\n",
       "      <td>2.362813</td>\n",
       "      <td>1.172529</td>\n",
       "      <td>2.091836</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.244630</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.309403</td>\n",
       "      <td>0.059844</td>\n",
       "      <td>-0.181016</td>\n",
       "      <td>-0.015562</td>\n",
       "      <td>0.125863</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.323868</td>\n",
       "      <td>0.438768</td>\n",
       "      <td>0.570030</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.762637</td>\n",
       "      <td>0.142103</td>\n",
       "      <td>1.282718</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.440328</td>\n",
       "      <td>-0.276695</td>\n",
       "      <td>1.078906</td>\n",
       "      <td>0.193889</td>\n",
       "      <td>-0.717024</td>\n",
       "      <td>0.121837</td>\n",
       "      <td>0.182027</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.504205</td>\n",
       "      <td>0.600484</td>\n",
       "      <td>1.312837</td>\n",
       "      <td>0.254223</td>\n",
       "      <td>1.104689</td>\n",
       "      <td>0.302767</td>\n",
       "      <td>2.015066</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-1.148653</td>\n",
       "      <td>1.119476</td>\n",
       "      <td>1.374591</td>\n",
       "      <td>1.319403</td>\n",
       "      <td>-0.029177</td>\n",
       "      <td>-1.285889</td>\n",
       "      <td>0.968473</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-1.795673</td>\n",
       "      <td>-1.071452</td>\n",
       "      <td>0.695752</td>\n",
       "      <td>3.224442</td>\n",
       "      <td>-2.867125</td>\n",
       "      <td>1.923978</td>\n",
       "      <td>-1.211755</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-1.455186</td>\n",
       "      <td>-0.705272</td>\n",
       "      <td>-0.082883</td>\n",
       "      <td>2.117567</td>\n",
       "      <td>-2.160459</td>\n",
       "      <td>1.026303</td>\n",
       "      <td>-1.724354</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  Squared_A    Sum_AB   Prod_AB  \\\n",
       "2020-01-01  0.708974  1.653839  0.460311   0.502644  2.362813  1.172529   \n",
       "2020-01-02 -0.244630  0.063613  0.309403   0.059844 -0.181016 -0.015562   \n",
       "2020-01-03  0.323868  0.438768  0.570030   0.104891  0.762637  0.142103   \n",
       "2020-01-04 -0.440328 -0.276695  1.078906   0.193889 -0.717024  0.121837   \n",
       "2020-01-05  0.504205  0.600484  1.312837   0.254223  1.104689  0.302767   \n",
       "2020-01-06 -1.148653  1.119476  1.374591   1.319403 -0.029177 -1.285889   \n",
       "2020-01-07 -1.795673 -1.071452  0.695752   3.224442 -2.867125  1.923978   \n",
       "2020-01-08 -1.455186 -0.705272 -0.082883   2.117567 -2.160459  1.026303   \n",
       "\n",
       "            function_ABC   color  \n",
       "2020-01-01      2.091836  yellow  \n",
       "2020-01-02      0.125863  yellow  \n",
       "2020-01-03      1.282718  yellow  \n",
       "2020-01-04      0.182027  yellow  \n",
       "2020-01-05      2.015066  yellow  \n",
       "2020-01-06      0.968473  yellow  \n",
       "2020-01-07     -1.211755  yellow  \n",
       "2020-01-08     -1.724354  yellow  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def color_function(x):\n",
    "    if x < -3:\n",
    "        return 'black'\n",
    "    elif x > 3:\n",
    "        return 'green'\n",
    "    else: \n",
    "        return 'yellow'\n",
    "\n",
    "\n",
    "df['color'] = df.apply(lambda x: color_function(x['C']), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Create a column using a condition. For example, checking of the value of a column is positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>Squared_A</th>\n",
       "      <th>Sum_AB</th>\n",
       "      <th>Prod_AB</th>\n",
       "      <th>function_ABC</th>\n",
       "      <th>color</th>\n",
       "      <th>Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.708974</td>\n",
       "      <td>1.653839</td>\n",
       "      <td>0.460311</td>\n",
       "      <td>0.502644</td>\n",
       "      <td>2.362813</td>\n",
       "      <td>1.172529</td>\n",
       "      <td>2.091836</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.244630</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.309403</td>\n",
       "      <td>0.059844</td>\n",
       "      <td>-0.181016</td>\n",
       "      <td>-0.015562</td>\n",
       "      <td>0.125863</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.323868</td>\n",
       "      <td>0.438768</td>\n",
       "      <td>0.570030</td>\n",
       "      <td>0.104891</td>\n",
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       "      <td>1.282718</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.440328</td>\n",
       "      <td>-0.276695</td>\n",
       "      <td>1.078906</td>\n",
       "      <td>0.193889</td>\n",
       "      <td>-0.717024</td>\n",
       "      <td>0.121837</td>\n",
       "      <td>0.182027</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.504205</td>\n",
       "      <td>0.600484</td>\n",
       "      <td>1.312837</td>\n",
       "      <td>0.254223</td>\n",
       "      <td>1.104689</td>\n",
       "      <td>0.302767</td>\n",
       "      <td>2.015066</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-1.148653</td>\n",
       "      <td>1.119476</td>\n",
       "      <td>1.374591</td>\n",
       "      <td>1.319403</td>\n",
       "      <td>-0.029177</td>\n",
       "      <td>-1.285889</td>\n",
       "      <td>0.968473</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-1.795673</td>\n",
       "      <td>-1.071452</td>\n",
       "      <td>0.695752</td>\n",
       "      <td>3.224442</td>\n",
       "      <td>-2.867125</td>\n",
       "      <td>1.923978</td>\n",
       "      <td>-1.211755</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-1.455186</td>\n",
       "      <td>-0.705272</td>\n",
       "      <td>-0.082883</td>\n",
       "      <td>2.117567</td>\n",
       "      <td>-2.160459</td>\n",
       "      <td>1.026303</td>\n",
       "      <td>-1.724354</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  Squared_A    Sum_AB   Prod_AB  \\\n",
       "2020-01-01  0.708974  1.653839  0.460311   0.502644  2.362813  1.172529   \n",
       "2020-01-02 -0.244630  0.063613  0.309403   0.059844 -0.181016 -0.015562   \n",
       "2020-01-03  0.323868  0.438768  0.570030   0.104891  0.762637  0.142103   \n",
       "2020-01-04 -0.440328 -0.276695  1.078906   0.193889 -0.717024  0.121837   \n",
       "2020-01-05  0.504205  0.600484  1.312837   0.254223  1.104689  0.302767   \n",
       "2020-01-06 -1.148653  1.119476  1.374591   1.319403 -0.029177 -1.285889   \n",
       "2020-01-07 -1.795673 -1.071452  0.695752   3.224442 -2.867125  1.923978   \n",
       "2020-01-08 -1.455186 -0.705272 -0.082883   2.117567 -2.160459  1.026303   \n",
       "\n",
       "            function_ABC   color   Flag  \n",
       "2020-01-01      2.091836  yellow   True  \n",
       "2020-01-02      0.125863  yellow  False  \n",
       "2020-01-03      1.282718  yellow   True  \n",
       "2020-01-04      0.182027  yellow  False  \n",
       "2020-01-05      2.015066  yellow   True  \n",
       "2020-01-06      0.968473  yellow  False  \n",
       "2020-01-07     -1.211755  yellow  False  \n",
       "2020-01-08     -1.724354  yellow  False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Flag'] = df['A'] >= 0 \n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `map`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Map a column with boolean values to color names in string format. For example, map `True` to color 'green' and `False` to color 'red'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>Squared_A</th>\n",
       "      <th>Sum_AB</th>\n",
       "      <th>Prod_AB</th>\n",
       "      <th>function_ABC</th>\n",
       "      <th>color</th>\n",
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       "      <th>Flag_to_Colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.708974</td>\n",
       "      <td>1.653839</td>\n",
       "      <td>0.460311</td>\n",
       "      <td>0.502644</td>\n",
       "      <td>2.362813</td>\n",
       "      <td>1.172529</td>\n",
       "      <td>2.091836</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.244630</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.309403</td>\n",
       "      <td>0.059844</td>\n",
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       "      <td>-0.015562</td>\n",
       "      <td>0.125863</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>red</td>\n",
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       "      <th>2020-01-03</th>\n",
       "      <td>0.323868</td>\n",
       "      <td>0.438768</td>\n",
       "      <td>0.570030</td>\n",
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       "      <td>0.142103</td>\n",
       "      <td>1.282718</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.440328</td>\n",
       "      <td>-0.276695</td>\n",
       "      <td>1.078906</td>\n",
       "      <td>0.193889</td>\n",
       "      <td>-0.717024</td>\n",
       "      <td>0.121837</td>\n",
       "      <td>0.182027</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.504205</td>\n",
       "      <td>0.600484</td>\n",
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       "      <td>2.015066</td>\n",
       "      <td>yellow</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>-1.148653</td>\n",
       "      <td>1.119476</td>\n",
       "      <td>1.374591</td>\n",
       "      <td>1.319403</td>\n",
       "      <td>-0.029177</td>\n",
       "      <td>-1.285889</td>\n",
       "      <td>0.968473</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-1.795673</td>\n",
       "      <td>-1.071452</td>\n",
       "      <td>0.695752</td>\n",
       "      <td>3.224442</td>\n",
       "      <td>-2.867125</td>\n",
       "      <td>1.923978</td>\n",
       "      <td>-1.211755</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>-1.455186</td>\n",
       "      <td>-0.705272</td>\n",
       "      <td>-0.082883</td>\n",
       "      <td>2.117567</td>\n",
       "      <td>-2.160459</td>\n",
       "      <td>1.026303</td>\n",
       "      <td>-1.724354</td>\n",
       "      <td>yellow</td>\n",
       "      <td>False</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  Squared_A    Sum_AB   Prod_AB  \\\n",
       "2020-01-01  0.708974  1.653839  0.460311   0.502644  2.362813  1.172529   \n",
       "2020-01-02 -0.244630  0.063613  0.309403   0.059844 -0.181016 -0.015562   \n",
       "2020-01-03  0.323868  0.438768  0.570030   0.104891  0.762637  0.142103   \n",
       "2020-01-04 -0.440328 -0.276695  1.078906   0.193889 -0.717024  0.121837   \n",
       "2020-01-05  0.504205  0.600484  1.312837   0.254223  1.104689  0.302767   \n",
       "2020-01-06 -1.148653  1.119476  1.374591   1.319403 -0.029177 -1.285889   \n",
       "2020-01-07 -1.795673 -1.071452  0.695752   3.224442 -2.867125  1.923978   \n",
       "2020-01-08 -1.455186 -0.705272 -0.082883   2.117567 -2.160459  1.026303   \n",
       "\n",
       "            function_ABC   color   Flag Flag_to_Colors  \n",
       "2020-01-01      2.091836  yellow   True          green  \n",
       "2020-01-02      0.125863  yellow  False            red  \n",
       "2020-01-03      1.282718  yellow   True          green  \n",
       "2020-01-04      0.182027  yellow  False            red  \n",
       "2020-01-05      2.015066  yellow   True          green  \n",
       "2020-01-06      0.968473  yellow  False            red  \n",
       "2020-01-07     -1.211755  yellow  False            red  \n",
       "2020-01-08     -1.724354  yellow  False            red  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Flag_to_Colors'] = df['Flag'].map({True:'green', False:'red'}) \n",
    "df"
   ]
  }
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