4.Selection_Techniques_on_numpy_and_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b47080d",
   "metadata": {},
   "source": [
    "# selection techniques "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fcf642",
   "metadata": {},
   "source": [
    "## -->>NUMPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "112a32c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a_array=np.arange(1,20)\n",
    "a_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e2fc44e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34,\n",
       "       36, 38])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_array+a_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6fcbdcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01,\n",
       "       1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n",
       "       8.10308393e+03, 2.20264658e+04, 5.98741417e+04, 1.62754791e+05,\n",
       "       4.42413392e+05, 1.20260428e+06, 3.26901737e+06, 8.88611052e+06,\n",
       "       2.41549528e+07, 6.56599691e+07, 1.78482301e+08])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a_array) #expotential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bf68282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,\n",
       "       2.44948974, 2.64575131, 2.82842712, 3.        , 3.16227766,\n",
       "       3.31662479, 3.46410162, 3.60555128, 3.74165739, 3.87298335,\n",
       "       4.        , 4.12310563, 4.24264069, 4.35889894])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(a_array) #square root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af18a6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
       "       1.79175947, 1.94591015, 2.07944154, 2.19722458, 2.30258509,\n",
       "       2.39789527, 2.48490665, 2.56494936, 2.63905733, 2.7080502 ,\n",
       "       2.77258872, 2.83321334, 2.89037176, 2.94443898])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(a_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23181de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(a_array) #max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfe4d6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(a_array) #min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d487373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(a_array) #index of max value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d963e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(a_array)  #index of min value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "57a9f30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144, 169,\n",
       "       196, 225, 256, 289, 324, 361])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(a_array) \n",
    "#square of every element in array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5fb180a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.477225575051661"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a_array)  #std deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bdc59a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(a_array) #variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9145b15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b3c2baad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.25065534,  2.02938031,  0.26663473,  0.00988553],\n",
       "       [ 1.18155929, -2.04645092, -1.14536042, -0.4142852 ],\n",
       "       [-0.82332001,  0.41059032,  0.74696503,  1.63813663]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array=np.random.randn(3,4)\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91498721",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "_around_dispatcher() got an unexpected keyword argument 'decimal'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mround\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdecimal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<__array_function__ internals>:179\u001b[0m, in \u001b[0;36mround_\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: _around_dispatcher() got an unexpected keyword argument 'decimal'"
     ]
    }
   ],
   "source": [
    "np.round(array,decimal=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23f967bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cricket', 'football', 'golf'], dtype='<U8')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports=np.array(['golf','cricket','cricket','football'])\n",
    "np.unique(sports) #unique prints unique value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8626086",
   "metadata": {},
   "source": [
    "## -->>PANDAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "00db7b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9da129b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cricket       1\n",
       "football      2\n",
       "basketball    3\n",
       "golf          4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports1=pd.Series([1,2,3,4],index=['cricket','football','basketball','golf'])\n",
    "sports1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7956c914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports1['football']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "57116632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cricket     11\n",
       "football     2\n",
       "baseball     3\n",
       "golf         4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports2=pd.Series([11,2,3,4],index=[\"cricket\",'football','baseball','golf'])\n",
    "sports2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d00e29ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseball       NaN\n",
       "basketball     NaN\n",
       "cricket       12.0\n",
       "football       4.0\n",
       "golf           8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports1+sports2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a6eb913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.206112</td>\n",
       "      <td>0.323352</td>\n",
       "      <td>0.565726</td>\n",
       "      <td>0.809144</td>\n",
       "      <td>0.264125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.572841</td>\n",
       "      <td>0.106684</td>\n",
       "      <td>0.164429</td>\n",
       "      <td>0.120675</td>\n",
       "      <td>0.268626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.379933</td>\n",
       "      <td>0.844859</td>\n",
       "      <td>0.437395</td>\n",
       "      <td>0.919823</td>\n",
       "      <td>0.102375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.065239</td>\n",
       "      <td>0.346067</td>\n",
       "      <td>0.197173</td>\n",
       "      <td>0.564673</td>\n",
       "      <td>0.893819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.634783</td>\n",
       "      <td>0.693006</td>\n",
       "      <td>0.240517</td>\n",
       "      <td>0.856362</td>\n",
       "      <td>0.687429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.092787</td>\n",
       "      <td>0.031803</td>\n",
       "      <td>0.508702</td>\n",
       "      <td>0.289817</td>\n",
       "      <td>0.454447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.762422</td>\n",
       "      <td>0.410660</td>\n",
       "      <td>0.692577</td>\n",
       "      <td>0.643664</td>\n",
       "      <td>0.078568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0.749529</td>\n",
       "      <td>0.946552</td>\n",
       "      <td>0.909219</td>\n",
       "      <td>0.230899</td>\n",
       "      <td>0.185097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "A  0.206112  0.323352  0.565726  0.809144  0.264125\n",
       "B  0.572841  0.106684  0.164429  0.120675  0.268626\n",
       "C  0.379933  0.844859  0.437395  0.919823  0.102375\n",
       "D  0.065239  0.346067  0.197173  0.564673  0.893819\n",
       "E  0.634783  0.693006  0.240517  0.856362  0.687429\n",
       "F  0.092787  0.031803  0.508702  0.289817  0.454447\n",
       "G  0.762422  0.410660  0.692577  0.643664  0.078568\n",
       "H  0.749529  0.946552  0.909219  0.230899  0.185097"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(np.random.rand(8,5),index='A B C D E F G H'.split(),columns='Score1 Score2 Score3 Score4 Score5'.split())\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0784e4df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.206112\n",
       "B    0.572841\n",
       "C    0.379933\n",
       "D    0.065239\n",
       "E    0.634783\n",
       "F    0.092787\n",
       "G    0.762422\n",
       "H    0.749529\n",
       "Name: Score1, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Score1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76e1cccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.206112</td>\n",
       "      <td>0.323352</td>\n",
       "      <td>0.565726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.572841</td>\n",
       "      <td>0.106684</td>\n",
       "      <td>0.164429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.379933</td>\n",
       "      <td>0.844859</td>\n",
       "      <td>0.437395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.065239</td>\n",
       "      <td>0.346067</td>\n",
       "      <td>0.197173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.634783</td>\n",
       "      <td>0.693006</td>\n",
       "      <td>0.240517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.092787</td>\n",
       "      <td>0.031803</td>\n",
       "      <td>0.508702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.762422</td>\n",
       "      <td>0.410660</td>\n",
       "      <td>0.692577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0.749529</td>\n",
       "      <td>0.946552</td>\n",
       "      <td>0.909219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3\n",
       "A  0.206112  0.323352  0.565726\n",
       "B  0.572841  0.106684  0.164429\n",
       "C  0.379933  0.844859  0.437395\n",
       "D  0.065239  0.346067  0.197173\n",
       "E  0.634783  0.693006  0.240517\n",
       "F  0.092787  0.031803  0.508702\n",
       "G  0.762422  0.410660  0.692577\n",
       "H  0.749529  0.946552  0.909219"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[[\"Score1\",\"Score2\",\"Score3\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd645076",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>A</th>\n",
       "      <td>0.206112</td>\n",
       "      <td>0.323352</td>\n",
       "      <td>0.565726</td>\n",
       "      <td>0.809144</td>\n",
       "      <td>0.264125</td>\n",
       "      <td>0.529465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.572841</td>\n",
       "      <td>0.106684</td>\n",
       "      <td>0.164429</td>\n",
       "      <td>0.120675</td>\n",
       "      <td>0.268626</td>\n",
       "      <td>0.679526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.379933</td>\n",
       "      <td>0.844859</td>\n",
       "      <td>0.437395</td>\n",
       "      <td>0.919823</td>\n",
       "      <td>0.102375</td>\n",
       "      <td>1.224792</td>\n",
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       "      <th>D</th>\n",
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       "      <td>0.564673</td>\n",
       "      <td>0.893819</td>\n",
       "      <td>0.411306</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.634783</td>\n",
       "      <td>0.693006</td>\n",
       "      <td>0.240517</td>\n",
       "      <td>0.856362</td>\n",
       "      <td>0.687429</td>\n",
       "      <td>1.327790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
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       "      <td>0.031803</td>\n",
       "      <td>0.508702</td>\n",
       "      <td>0.289817</td>\n",
       "      <td>0.454447</td>\n",
       "      <td>0.124591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.762422</td>\n",
       "      <td>0.410660</td>\n",
       "      <td>0.692577</td>\n",
       "      <td>0.643664</td>\n",
       "      <td>0.078568</td>\n",
       "      <td>1.173082</td>\n",
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       "      <th>H</th>\n",
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       "      <td>0.946552</td>\n",
       "      <td>0.909219</td>\n",
       "      <td>0.230899</td>\n",
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      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5    Score6\n",
       "A  0.206112  0.323352  0.565726  0.809144  0.264125  0.529465\n",
       "B  0.572841  0.106684  0.164429  0.120675  0.268626  0.679526\n",
       "C  0.379933  0.844859  0.437395  0.919823  0.102375  1.224792\n",
       "D  0.065239  0.346067  0.197173  0.564673  0.893819  0.411306\n",
       "E  0.634783  0.693006  0.240517  0.856362  0.687429  1.327790\n",
       "F  0.092787  0.031803  0.508702  0.289817  0.454447  0.124591\n",
       "G  0.762422  0.410660  0.692577  0.643664  0.078568  1.173082\n",
       "H  0.749529  0.946552  0.909219  0.230899  0.185097  1.696081"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Score6']=df1['Score1']+df1['Score2']\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8a6bd768",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    ID   NAME  PROFIT\n",
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       "2  103    aka      89\n",
       "3  104  kevin      15\n",
       "4  105  lolly     321"
      ]
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    "df2={'ID':['101','102','103','104','105'],'NAME':['john','meery','aka','kevin','lolly'],'PROFIT':[20,54,89,15,321]}\n",
    "df=pd.DataFrame(df2)\n",
    "df"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4601f59e",
   "metadata": {},
   "outputs": [
    {
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       "0    101\n",
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      ]
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     "execution_count": 39,
     "metadata": {},
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   ]
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  {
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   "execution_count": 40,
   "id": "299bee7b",
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   "outputs": [
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       "    ID   NAME  PROFIT\n",
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   "cell_type": "code",
   "execution_count": 48,
   "id": "9dca9865",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(\"ID\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "93d0d408",
   "metadata": {},
   "outputs": [
    {
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       "    NAME  PROFIT\n",
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  {
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   "id": "eb318037",
   "metadata": {},
   "outputs": [
    {
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       "    NAME  PROFIT\n",
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   "execution_count": null,
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   "outputs": [],
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