06-Important Methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some Useful Methods 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 some useful methods.\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": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([1,2,3,4],\n",
    "            index=[\"a\",\"b\",\"c\",\"d\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2.0\n",
       "d    4.0\n",
       "a    1.0\n",
       "c    3.0\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2=s.reindex([\"b\",\"d\",\"a\",\"c\",\"e\"])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    yellow\n",
       "4    purple\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3=pd.Series([\"blue\",\"yellow\",\"purple\"],\n",
    "             index=[0,2,4])\n",
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    yellow\n",
       "3    yellow\n",
       "4    purple\n",
       "5    purple\n",
       "dtype: object"
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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "s3.reindex(range(6),method=\"ffill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Tim</th>\n",
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       "      <th>Kate</th>\n",
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       "   Tim  Tom  Kate\n",
       "a    0    1     2\n",
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       "d    6    7     8"
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     "execution_count": 7,
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   "source": [
    "df=pd.DataFrame(np.arange(9).reshape(3,3),\n",
    "                index=[\"a\",\"c\",\"d\"],\n",
    "                columns=[\"Tim\",\"Tom\",\"Kate\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "   Tim  Tom  Kate\n",
       "d  6.0  7.0   8.0\n",
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       "b  NaN  NaN   NaN\n",
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      ]
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "df2=df.reindex([\"d\",\"c\",\"b\",\"a\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "   Kate  Tim  Tom\n",
       "a     2    0    1\n",
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   "source": [
    "names=[\"Kate\",\"Tim\",\"Tom\"]\n",
    "df.reindex(columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Tim  Tom  Kate\n",
       "c    3    4     5\n",
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       "a    0    1     2"
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     "metadata": {},
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   "source": [
    "df.loc[[\"c\",\"d\",\"a\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.arange(5.),\n",
    "            index=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "new_s=s.drop(\"b\")\n",
    "new_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "a    0.0\n",
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     "metadata": {},
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   "source": [
    "s.drop([\"c\",\"d\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       A   B   C   D\n",
       "Kate   0   1   2   3\n",
       "Tim    4   5   6   7\n",
       "Tom    8   9  10  11\n",
       "Alex  12  13  14  15"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data=pd.DataFrame(np.arange(16).reshape(4,4),\n",
    "                  index=[\"Kate\",\"Tim\",\n",
    "                         \"Tom\",\"Alex\"],\n",
    "                  columns=list(\"ABCD\"))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "       A   B   C   D\n",
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   "source": [
    "data.drop([\"Kate\",\"Tim\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Kate</th>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>9</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>Alex</th>\n",
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      "text/plain": [
       "       B   C   D\n",
       "Kate   1   2   3\n",
       "Tim    5   6   7\n",
       "Tom    9  10  11\n",
       "Alex  13  14  15"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(\"A\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Tim</th>\n",
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       "      <th>Tom</th>\n",
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       "      <th>Alex</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A   B   C   D\n",
       "Tim    4   5   6   7\n",
       "Tom    8   9  10  11\n",
       "Alex  12  13  14  15"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(\"Kate\",axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kate</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A   B   C   D\n",
       "Kate   0   1   2   3\n",
       "Tim    4   5   6   7\n",
       "Tom    8   9  10  11\n",
       "Alex  12  13  14  15"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    6.0\n",
       "B    7.0\n",
       "C    8.0\n",
       "D    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kate     1.5\n",
       "Tim      5.5\n",
       "Tom      9.5\n",
       "Alex    13.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=\"columns\")"
   ]
  },
  {
   "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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