4_Lists.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = \"omkar\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "type(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Naruto T shirt'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shopping_list = [\"kindle\", \"Naruto T shirt\", \"Camera\"]\n",
    "shopping_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shopping_list.append(\"80k shoes\") # adding new elements: adds at last "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kindle', 'Naruto T shirt', 'Camera', '80k shoes']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shopping_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['kindle', 'Naruto T shirt', 'Camera', '80k shoes', 'milk', 'tea powder', 'sugar']\n"
     ]
    }
   ],
   "source": [
    "grocery_list = [\"milk\", \"tea powder\", \"sugar\"]\n",
    "shopping_list.extend(grocery_list) # adding new list into existing one: adds at last\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['kindle', 'Naruto T shirt', 'Biscuits', 'Camera', '80k shoes', 'milk', 'tea powder', 'sugar']\n"
     ]
    }
   ],
   "source": [
    "shopping_list.insert(2, \"Biscuits\") # adds at given index\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['kindle', 'Naruto T shirt', 'Biscuits', 'Camera', 'milk', 'tea powder', 'sugar']\n"
     ]
    }
   ],
   "source": [
    "shopping_list.pop(4) # removes element at given index : 80 k shoes removed at index 4\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Naruto T shirt', 'Biscuits', 'Camera', 'milk', 'tea powder', 'sugar']\n"
     ]
    }
   ],
   "source": [
    "shopping_list.remove('kindle') # using remove u can enter value instead of its index\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Naruto T shirt', 'Biscuits', 'Camera', 'milk', 'tea powder']\n"
     ]
    }
   ],
   "source": [
    "shopping_list.pop() # removes last element: sugar got deleted\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list.remove() takes exactly one argument (0 given)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/omkar/Omkar Pawar/Data Science/Python-For-Data-Science/4_Lists.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/omkar/Omkar%20Pawar/Data%20Science/Python-For-Data-Science/4_Lists.ipynb#X14sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m shopping_list\u001b[39m.\u001b[39;49mremove()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/omkar/Omkar%20Pawar/Data%20Science/Python-For-Data-Science/4_Lists.ipynb#X14sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mprint\u001b[39m(shopping_list)\n",
      "\u001b[0;31mTypeError\u001b[0m: list.remove() takes exactly one argument (0 given)"
     ]
    }
   ],
   "source": [
    "shopping_list.remove()\n",
    "print(shopping_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Naruto T shirt', 'Biscuits', 'Camera', 'milk', 'tea powder']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_list = shopping_list.copy() # creates a another copy of list\n",
    "extra_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shopping_list.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shopping_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_list.append('milk') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Naruto T shirt', 'Biscuits', 'Camera', 'milk', 'tea powder', 'milk']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_list # copied list is not affected even we cleared original list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_list.count('milk') # gives number of occurrences"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "38519c3442d9f767135b0650f6935cb54106bd9267700919f92a37291a156600"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}