07-Arithmetic Operations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arithmetic Operations in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to show arithmetic operations in Pandas.\n",
    "</br>\n",
    "Happy Learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=pd.Series(np.arange(4),\n",
    "             index=[\"a\",\"c\",\"d\",\"e\"])\n",
    "s2=pd.Series(np.arange(5),\n",
    "             index=[\"a\",\"c\",\"e\",\"f\",\"g\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0\n",
      "c    1\n",
      "d    2\n",
      "e    3\n",
      "dtype: int32\n",
      "a    0\n",
      "c    1\n",
      "e    2\n",
      "f    3\n",
      "g    4\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(s1)\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "c    2.0\n",
       "d    NaN\n",
       "e    5.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame(\n",
    "    np.arange(6).reshape(2,3),\n",
    "    columns=list(\"ABC\"),\n",
    "    index=[\"Tim\",\"Tom\"])\n",
    "df2=pd.DataFrame(\n",
    "    np.arange(9).reshape(3,3),\n",
    "    columns=list(\"ACD\"),\n",
    "    index=[\"Tim\",\"Kate\",\"Tom\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A  B  C\n",
      "Tim  0  1  2\n",
      "Tom  3  4  5\n",
      "      A  C  D\n",
      "Tim   0  1  2\n",
      "Kate  3  4  5\n",
      "Tom   6  7  8\n"
     ]
    }
   ],
   "source": [
    "print(df1)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>3.0</td>\n",
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       "      <th>Tom</th>\n",
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       "      <td>12.0</td>\n",
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       "        A   B     C   D\n",
       "Kate  NaN NaN   NaN NaN\n",
       "Tim   0.0 NaN   3.0 NaN\n",
       "Tom   9.0 NaN  12.0 NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df1+df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Kate</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>Tom</th>\n",
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       "        A    B     C    D\n",
       "Kate  3.0  NaN   4.0  5.0\n",
       "Tim   0.0  1.0   3.0  2.0\n",
       "Tom   9.0  4.0  12.0  8.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.add(df2,fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>Tim</th>\n",
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       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
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       "      <th>Tom</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.2</td>\n",
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       "            A     B    C\n",
       "Tim       inf  1.00  0.5\n",
       "Tom  0.333333  0.25  0.2"
      ]
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "1/df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "     A   B   C\n",
       "Tim  0   3   6\n",
       "Tom  9  12  15"
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df1*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "     A   B   C\n",
       "Tim  0   3   6\n",
       "Tom  9  12  15"
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df1.mul(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kate</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>Tom</th>\n",
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      "text/plain": [
       "      A  C  D\n",
       "Tim   0  1  2\n",
       "Kate  3  4  5\n",
       "Tom   6  7  8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3\n",
       "C    4\n",
       "D    5\n",
       "Name: Kate, dtype: int32"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=df2.iloc[1]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <th>Tim</th>\n",
       "      <td>-3</td>\n",
       "      <td>-3</td>\n",
       "      <td>-3</td>\n",
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       "    <tr>\n",
       "      <th>Kate</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "      A  C  D\n",
       "Tim  -3 -3 -3\n",
       "Kate  0  0  0\n",
       "Tom   3  3  3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2-s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tim     0\n",
       "Kate    3\n",
       "Tom     6\n",
       "Name: A, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2=df2[\"A\"]\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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      "text/plain": [
       "      A  C  D\n",
       "Tim   0  1  2\n",
       "Kate  0  1  2\n",
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   "source": [
    "df2.sub(s2,axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying a Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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    {
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       "      <th>Kim</th>\n",
       "      <td>-0.173717</td>\n",
       "      <td>-1.126917</td>\n",
       "      <td>-0.595042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan</th>\n",
       "      <td>-0.641672</td>\n",
       "      <td>-0.073913</td>\n",
       "      <td>-1.828588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>-0.389124</td>\n",
       "      <td>-1.786140</td>\n",
       "      <td>0.553646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>-0.062436</td>\n",
       "      <td>-0.251933</td>\n",
       "      <td>0.872391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C\n",
       "Kim   -0.173717 -1.126917 -0.595042\n",
       "Susan -0.641672 -0.073913 -1.828588\n",
       "Tim   -0.389124 -1.786140  0.553646\n",
       "Tom   -0.062436 -0.251933  0.872391"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(\n",
    "    np.random.randn(4,3),\n",
    "    columns=list(\"ABC\"),\n",
    "    index=[\"Kim\",\"Susan\",\"Tim\",\"Tom\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kim</th>\n",
       "      <td>0.173717</td>\n",
       "      <td>1.126917</td>\n",
       "      <td>0.595042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan</th>\n",
       "      <td>0.641672</td>\n",
       "      <td>0.073913</td>\n",
       "      <td>1.828588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>0.389124</td>\n",
       "      <td>1.786140</td>\n",
       "      <td>0.553646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>0.062436</td>\n",
       "      <td>0.251933</td>\n",
       "      <td>0.872391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C\n",
       "Kim    0.173717  1.126917  0.595042\n",
       "Susan  0.641672  0.073913  1.828588\n",
       "Tim    0.389124  1.786140  0.553646\n",
       "Tom    0.062436  0.251933  0.872391"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=lambda x:x.max()-x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.579236\n",
       "B    1.712227\n",
       "C    2.700979\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kim      0.953200\n",
       "Susan    1.754676\n",
       "Tim      2.339785\n",
       "Tom      1.124324\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(f,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kim</th>\n",
       "      <td>0.030178</td>\n",
       "      <td>1.269942</td>\n",
       "      <td>0.354075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan</th>\n",
       "      <td>0.411743</td>\n",
       "      <td>0.005463</td>\n",
       "      <td>3.343735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>0.151417</td>\n",
       "      <td>3.190294</td>\n",
       "      <td>0.306524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>0.003898</td>\n",
       "      <td>0.063470</td>\n",
       "      <td>0.761066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C\n",
       "Kim    0.030178  1.269942  0.354075\n",
       "Susan  0.411743  0.005463  3.343735\n",
       "Tim    0.151417  3.190294  0.306524\n",
       "Tom    0.003898  0.063470  0.761066"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
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