Series 1D arrays .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b05d51c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series([1,2,3,4,5])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5773650d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series(['a','b','c','d'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1269138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series((1,2,3,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75684eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series(range(1,5))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b86b1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Shahzaneer Ahmed taking on Pandas\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series('Shahzaneer Ahmed taking on Pandas')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48656175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Shahzaneer\n",
       "1         Ahmed\n",
       "2        taking\n",
       "3            on\n",
       "4        pandas\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a= pd.Series([\"Shahzaneer\",\"Ahmed\",'taking','on','pandas'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a538138a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "arr = np.array([1,2,3,4])\n",
    "a = pd.Series(arr)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66da5d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GCU    Shahzaneer\n",
       "MAO        Rameez\n",
       "GCU          Zain\n",
       "PGC         Ahmad\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "arr = pd.Series(['Shahzaneer','Rameez','Zain','Ahmad'],index=['GCU','MAO','GCU','PGC'])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4f5060d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "qeemat = [1,2,3,4,5]\n",
    "jageh = ['a','b','c','d','e']\n",
    "\n",
    "obj = pd.Series(qeemat,index=jageh)\n",
    "obj\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "953c70e5",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (Temp/ipykernel_9076/230434006.py, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\H\\AppData\\Local\\Temp/ipykernel_9076/230434006.py\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    obj = pd.Series(index=jageh,qeemat)\u001b[0m\n\u001b[1;37m                                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "qeemat = [1,2,3,4,5]\n",
    "jageh = ['a','b','c','d','e']\n",
    "\n",
    "obj = pd.Series(index=jageh,qeemat)\n",
    "# obj\n",
    "#positional argument follows keyword argument\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6de9cde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    50\n",
       "B    55\n",
       "C    70\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "arr = pd.Series({'A':50,'B':55,'C':70})\n",
    "#dictionary main keys index smjhi jayengi aur  value pair jo hoga wo unki value\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5fdcc2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    Shahzaneer Ahmed\n",
       "2    Shahzaneer Ahmed\n",
       "3    Shahzaneer Ahmed\n",
       "4    Shahzaneer Ahmed\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "arr = pd.Series('Shahzaneer Ahmed',index=(range(1,5)))\n",
    "# aik value do aur index zyada dedo tou har index same value ko point karega\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5bc76a60",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values (2) does not match length of index (4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9076/703851080.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Shahzaneer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Ahmed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    428\u001b[0m                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mibase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m                 \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# create/copy the manager\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m    529\u001b[0m     \"\"\"\n\u001b[0;32m    530\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m    532\u001b[0m             \u001b[1;34m\"Length of values \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m             \u001b[1;34mf\"({len(data)}) \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Length of values (2) does not match length of index (4)"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "arr = pd.Series(['Shahzaneer','Ahmed'],index=(range(1,5)))\n",
    "#do values aur 5 index wala scene off hai error ayega \n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b963406b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "5    4\n",
       "3    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "arr = [1,2,3,4,5]\n",
    "ind = [0,1,2,5,3]\n",
    "a = pd.Series(data=arr,index=ind) #we can use the keyword data for values\n",
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7eb35ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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