{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:26:51.671384Z",
     "start_time": "2020-07-31T16:26:51.661645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mult(x,y):\n",
    "    return x*y\n",
    "mult(2,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:26:57.212160Z",
     "start_time": "2020-07-31T16:26:57.208925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult = lambda x, y: x*y\n",
    "mult(2,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:27:01.663377Z",
     "start_time": "2020-07-31T16:27:01.660264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x, y: x*y)(2, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sort a list of tuples based on their alphabets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:27:06.421252Z",
     "start_time": "2020-07-31T16:27:06.415929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4, 'a'), (2, 'b'), (3, 'c'), (1, 'd')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = [(1, 'd'), (2, 'b'), (4, 'a'), (3, 'c')]\n",
    "sorted(tuples, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:27:14.752650Z",
     "start_time": "2020-07-31T16:27:14.332264Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'Line2D'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-0420c724ed50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m101\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2759\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2760\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2761\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   2762\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m   2763\u001b[0m         is not None else {}), **kwargs)\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1647\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1649\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1650\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request_autoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1848\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1870\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1871\u001b[0m         \"\"\"\n\u001b[0;32m-> 1872\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1874\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \"\"\"\n\u001b[1;32m   1026\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1315\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1317\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/scraping/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Line2D'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "nums = plt.plot(sorted(range(-100, 101), key=lambda x: x * x))\n",
    "plt.plot(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nested Function and Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:28:04.832157Z",
     "start_time": "2020-07-31T16:28:04.828451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 27, 64, 125]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def power(n):\n",
    "    return lambda x: x**n\n",
    "power_3 = power(3)\n",
    "list(map(power_3,[2,3,4,5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manipulate Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:28:10.198344Z",
     "start_time": "2020-07-31T16:28:10.045345Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([[1,2,3],[4,5,6]], columns=['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:29:49.855502Z",
     "start_time": "2020-07-31T16:29:49.846672Z"
    }
   },
   "outputs": [],
   "source": [
    "#Create the 4th column that is the sum of the other 3 columns\n",
    "df['d'] = df.apply(lambda row: row['a']+row['b']+row['c'],axis=1)"
   ]
  }
 ],
 "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": "3.8.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}