12-Data Transformation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation 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 data transformation in Pandas.\n",
    "</br>\n",
    "Happy Learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "     a  b\n",
       "0  one  1\n",
       "1  two  1\n",
       "2  one  2\n",
       "3  two  3\n",
       "4  one  2\n",
       "5  two  3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame({\"a\":[\"one\",\"two\"]*3,\n",
    "                   \"b\":[1,1,2,3,2,3]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "dtype: bool"
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "     a  b\n",
       "0  one  1\n",
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       "3  two  3"
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   "source": [
    "data.drop_duplicates()"
   ]
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "     a  b  c\n",
       "0  one  1  0\n",
       "1  two  1  1\n",
       "2  one  2  2\n",
       "3  two  3  3\n",
       "4  one  2  4\n",
       "5  two  3  5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[\"c\"]=range(6)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "0    False\n",
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       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated([\"a\",\"b\"],keep=\"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Tim</td>\n",
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       "      <td>tom</td>\n",
       "      <td>50</td>\n",
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       "      <td>Sam</td>\n",
       "      <td>70</td>\n",
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       "      <th>3</th>\n",
       "      <td>kate</td>\n",
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      "text/plain": [
       "  names  scores\n",
       "0   Tim      60\n",
       "1   tom      50\n",
       "2   Sam      70\n",
       "3  kate      80\n",
       "4   Kim      40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df=pd.DataFrame({\"names\":[\"Tim\",\"tom\",\"Sam\",\n",
    "                          \"kate\",\"Kim\"],\n",
    "                \"scores\":[60,50,70,80,40]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes={\"Tim\":\"A\",\"Tom\":\"A\",\"Sam\":\"B\",\n",
    "         \"Kate\":\"B\",\"Kim\":\"B\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=df[\"names\"].str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"branches\"]=n.map(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>Sam</td>\n",
       "      <td>70</td>\n",
       "      <td>B</td>\n",
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       "      <th>3</th>\n",
       "      <td>kate</td>\n",
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      "text/plain": [
       "  names  scores branches\n",
       "0   Tim      60        A\n",
       "1   tom      50        A\n",
       "2   Sam      70        B\n",
       "3  kate      80        B\n",
       "4   Kim      40        B"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80\n",
       "1    70\n",
       "2    90\n",
       "3    60\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([80,70,90,60])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80.0\n",
       "1     NaN\n",
       "2    90.0\n",
       "3    60.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace(70,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80.0\n",
       "1     NaN\n",
       "2    90.0\n",
       "3     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace([70,60],[np.nan,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     80\n",
       "1     70\n",
       "2    100\n",
       "3      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace({90:100,60:0})"
   ]
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   "cell_type": "code",
   "execution_count": 17,
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      "text/plain": [
       "   tim  tom  kim  sam\n",
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       "1    4    5    6    7\n",
       "2    8    9   10   11"
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   "source": [
    "df=pd.DataFrame(\n",
    "    np.arange(12).reshape(3,4),\n",
    "    index=[0,1,2],\n",
    "    columns=[\"tim\",\"tom\",\"kim\",\"sam\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.Series([\"one\",\"two\",\"three\"])\n",
    "df.index=df.index.map(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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       "       tim  tom  kim  sam\n",
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       "two      4    5    6    7\n",
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    "df"
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIM</th>\n",
       "      <th>TOM</th>\n",
       "      <th>KIM</th>\n",
       "      <th>SAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>One</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Three</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TIM  TOM  KIM  SAM\n",
       "One      0    1    2    3\n",
       "Two      4    5    6    7\n",
       "Three    8    9   10   11"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(index=str.title,columns=str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>tim</th>\n",
       "      <th>tom</th>\n",
       "      <th>kim</th>\n",
       "      <th>kate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ten</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tim  tom  kim  kate\n",
       "ten      0    1    2     3\n",
       "two      4    5    6     7\n",
       "three    8    9   10    11"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(index={\"one\":\"ten\"},\n",
    "          columns={\"sam\":\"kate\"},\n",
    "          inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=[30,80,40,90,60,45,95,75,55,100,65,85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[20,40,60,80,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(20, 40], (60, 80], (20, 40], (80, 100], (40, 60], ..., (60, 80], (40, 60], (80, 100], (60, 80], (80, 100]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(20, 40] < (40, 60] < (60, 80] < (80, 100]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.cut(sc,x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, 3, 1, 1, 3, 2, 1, 3, 2, 3], dtype=int8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(20, 40], (40, 60], (60, 80], (80, 100]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 100]    4\n",
       "(60, 80]     3\n",
       "(40, 60]     3\n",
       "(20, 40]     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[20, 40), [80, 100), [40, 60), [80, 100), [60, 80), ..., [60.0, 80.0), [40.0, 60.0), NaN, [60.0, 80.0), [80.0, 100.0)]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [[20, 40) < [40, 60) < [60, 80) < [80, 100)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.cut(sc,x,right=False)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['low', 'high', 'low', 'very high', 'medium', ..., 'high', 'medium', 'very high', 'high', 'very high']\n",
       "Length: 12\n",
       "Categories (4, object): ['low' < 'medium' < 'high' < 'very high']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nm=[\"low\", \"medium\", \"high\", \"very high\"]\n",
    "pd.cut(sc,x,labels=nm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(29.93, 37.0], (79.0, 86.0], (37.0, 44.0], (86.0, 93.0], (58.0, 65.0], ..., (72.0, 79.0], (51.0, 58.0], (93.0, 100.0], (58.0, 65.0], (79.0, 86.0]]\n",
       "Length: 12\n",
       "Categories (10, interval[float64]): [(29.93, 37.0] < (37.0, 44.0] < (44.0, 51.0] < (51.0, 58.0] ... (72.0, 79.0] < (79.0, 86.0] < (86.0, 93.0] < (93.0, 100.0]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(sc,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.135, 0.631], (0.631, 2.286], (-0.629, -0.135], (0.631, 2.286], (-2.186, -0.629], ..., (-0.629, -0.135], (0.631, 2.286], (0.631, 2.286], (-0.135, 0.631], (-0.629, -0.135]]\n",
       "Length: 100\n",
       "Categories (4, interval[float64]): [(-2.186, -0.629] < (-0.629, -0.135] < (-0.135, 0.631] < (0.631, 2.286]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.random.randn(100)\n",
    "c=pd.qcut(data,4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.631, 2.286]      25\n",
       "(-0.135, 0.631]     25\n",
       "(-0.629, -0.135]    25\n",
       "(-2.186, -0.629]    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.661162</td>\n",
       "      <td>-1.315550</td>\n",
       "      <td>0.138893</td>\n",
       "      <td>-2.186859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.422096</td>\n",
       "      <td>0.587658</td>\n",
       "      <td>-0.478577</td>\n",
       "      <td>-0.285737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.283092</td>\n",
       "      <td>-0.021623</td>\n",
       "      <td>1.194335</td>\n",
       "      <td>-0.197599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.545286</td>\n",
       "      <td>-0.219977</td>\n",
       "      <td>0.353704</td>\n",
       "      <td>0.424970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.196521</td>\n",
       "      <td>3.491917</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>-0.464119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.661162 -1.315550  0.138893 -2.186859\n",
       "1 -0.422096  0.587658 -0.478577 -0.285737\n",
       "2 -0.283092 -0.021623  1.194335 -0.197599\n",
       "3 -1.545286 -0.219977  0.353704  0.424970\n",
       "4 -0.196521  3.491917  0.016217 -0.464119"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame(np.random.randn(1000,4))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.048690</td>\n",
       "      <td>0.030168</td>\n",
       "      <td>-0.050958</td>\n",
       "      <td>-0.004881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.991747</td>\n",
       "      <td>0.996656</td>\n",
       "      <td>0.992911</td>\n",
       "      <td>1.021762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.999425</td>\n",
       "      <td>-3.514219</td>\n",
       "      <td>-2.864107</td>\n",
       "      <td>-3.186480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.723203</td>\n",
       "      <td>-0.622904</td>\n",
       "      <td>-0.726046</td>\n",
       "      <td>-0.752119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.058605</td>\n",
       "      <td>0.027484</td>\n",
       "      <td>-0.036939</td>\n",
       "      <td>0.023319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.560961</td>\n",
       "      <td>0.654890</td>\n",
       "      <td>0.605512</td>\n",
       "      <td>0.739495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.952527</td>\n",
       "      <td>3.491917</td>\n",
       "      <td>3.074154</td>\n",
       "      <td>3.002287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean     -0.048690     0.030168    -0.050958    -0.004881\n",
       "std       0.991747     0.996656     0.992911     1.021762\n",
       "min      -2.999425    -3.514219    -2.864107    -3.186480\n",
       "25%      -0.723203    -0.622904    -0.726046    -0.752119\n",
       "50%      -0.058605     0.027484    -0.036939     0.023319\n",
       "75%       0.560961     0.654890     0.605512     0.739495\n",
       "max       3.952527     3.491917     3.074154     3.002287"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "col=data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      3.491917\n",
       "117    3.098920\n",
       "311   -3.067095\n",
       "903   -3.514219\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col[np.abs(col)>3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.196521</td>\n",
       "      <td>3.491917</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>-0.464119</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>-1.360727</td>\n",
       "      <td>3.098920</td>\n",
       "      <td>-0.902404</td>\n",
       "      <td>-1.874759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>-1.408380</td>\n",
       "      <td>-3.067095</td>\n",
       "      <td>-0.034621</td>\n",
       "      <td>-1.377992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>-0.508447</td>\n",
       "      <td>-0.264550</td>\n",
       "      <td>-2.246989</td>\n",
       "      <td>-3.096799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>3.952527</td>\n",
       "      <td>-0.307437</td>\n",
       "      <td>1.160524</td>\n",
       "      <td>1.022866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>-0.146700</td>\n",
       "      <td>-0.594890</td>\n",
       "      <td>3.074154</td>\n",
       "      <td>-0.198825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>3.116662</td>\n",
       "      <td>-0.466845</td>\n",
       "      <td>-0.543486</td>\n",
       "      <td>0.038652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>0.222411</td>\n",
       "      <td>-0.131135</td>\n",
       "      <td>0.618451</td>\n",
       "      <td>3.002287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>-0.917298</td>\n",
       "      <td>-3.514219</td>\n",
       "      <td>-2.500715</td>\n",
       "      <td>0.259489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0.115918</td>\n",
       "      <td>1.314808</td>\n",
       "      <td>-0.220663</td>\n",
       "      <td>-3.186480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "4   -0.196521  3.491917  0.016217 -0.464119\n",
       "117 -1.360727  3.098920 -0.902404 -1.874759\n",
       "311 -1.408380 -3.067095 -0.034621 -1.377992\n",
       "492 -0.508447 -0.264550 -2.246989 -3.096799\n",
       "510  3.952527 -0.307437  1.160524  1.022866\n",
       "547 -0.146700 -0.594890  3.074154 -0.198825\n",
       "642  3.116662 -0.466845 -0.543486  0.038652\n",
       "867  0.222411 -0.131135  0.618451  3.002287\n",
       "903 -0.917298 -3.514219 -2.500715  0.259489\n",
       "956  0.115918  1.314808 -0.220663 -3.186480"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(np.abs(data)>3).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.0</td>\n",
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       "     0    1    2    3\n",
       "0  1.0 -1.0  1.0 -1.0\n",
       "1 -1.0  1.0 -1.0 -1.0\n",
       "2 -1.0 -1.0  1.0 -1.0\n",
       "3 -1.0 -1.0  1.0  1.0\n",
       "4 -1.0  1.0  1.0 -1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   0   1   2\n",
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       "1  3   4   5\n",
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       "3  9  10  11"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data=pd.DataFrame(\n",
    "    np.arange(12).reshape(4,3))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 0, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw=np.random.permutation(4)\n",
    "rw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   0   1   2\n",
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     "metadata": {},
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    "data.take(rw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   0   1   2\n",
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     "metadata": {},
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   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "   0  1  2\n",
       "0  0  1  2\n",
       "2  6  7  8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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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       "      <th></th>\n",
       "      <th>letter</th>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  letter  number\n",
       "0      c       0\n",
       "1      b       1\n",
       "2      a       2\n",
       "3      b       3\n",
       "4      b       4\n",
       "5      a       5"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame(\n",
    "    {\"letter\":[\"c\",\"b\",\"a\",\"b\",\"b\",\"a\"],\n",
    "                   \"number\":range(6)})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  0  0  1\n",
       "1  0  1  0\n",
       "2  1  0  0\n",
       "3  0  1  0\n",
       "4  0  1  0\n",
       "5  1  0  0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data[\"letter\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.47399816, -0.4700948 , -0.10090975, -0.49105714,  0.85748633,\n",
       "       -2.17384891, -1.89062041,  1.15155524, -1.12043372,  0.82935199])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.random.randn(10)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>(-0.511, 0.32]</th>\n",
       "      <th>(0.32, 1.152]</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   (-2.177, -1.342]  (-1.342, -0.511]  (-0.511, 0.32]  (0.32, 1.152]\n",
       "0                 0                 0               1              0\n",
       "1                 0                 0               1              0\n",
       "2                 0                 0               1              0\n",
       "3                 0                 0               1              0\n",
       "4                 0                 0               0              1\n",
       "5                 1                 0               0              0\n",
       "6                 1                 0               0              0\n",
       "7                 0                 0               0              1\n",
       "8                 0                 1               0              0\n",
       "9                 0                 0               0              1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(pd.cut(data,4))"
   ]
  },
  {
   "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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