Python Dictionary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________________________________________________________________________________________________________\n",
    "\n",
    "\n",
    "* Yuvraj Sutar \n",
    "* yuvrajsutar009@gmail.com\n",
    "_ _________________________________________________________________________________________________________________________\n",
    "_  _________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Key-Value Pair\n",
    "- Mutable-Can change or update\n",
    "- Unordered sequence-indexces dosent start from 0\n",
    "- Iterable & Hashable by key\n",
    "- keys must be immutable and unique\n",
    "- values can be any type of data\n",
    "- Declared using {} bracket\n",
    "- Key & values are seperated by colon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'apple', 2: 'OnePlus'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dictionary\n",
    "# dictionary with integer keys\n",
    "my_dict = {1: 'apple', 2: 'OnePlus'}\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': ['A', 'B', 'C', 'D'], 'AGE': [24, 25, 24, 23]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={\"name\":[\"A\",\"B\",\"C\",\"D\"],\"AGE\":[24,25,24,23]}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>name</th>\n",
       "      <th>AGE</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>24</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  AGE  marks\n",
       "0    A   24     93\n",
       "1    B   25     43\n",
       "2    C   24     94\n",
       "3    D   23     94"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "pandas.DataFrame(d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keys\n",
    "d[\"marks\"]=[93,43,94,94]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict[\"frnd\"]=[\"x\",\"y\",\"z\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': ('A', 'B', 'C'),\n",
       " 'Age': [24, 25, 26],\n",
       " 'gander': ['m', 'f', 'f'],\n",
       " 'Name1': ['x', 'y', 'x'],\n",
       " 'frnd': ['x', 'y', 'z']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([['A', 'B', 'C'], [24, 25, 26], ['m', 'f', 'f'], ['A', 'B', 'C']])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Values\n",
    "my_dict.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('name', ['A', 'B', 'C']), ('Age', [24, 25, 26]), ('gander', ['m', 'f', 'f']), ('name1', ['A', 'B', 'C'])])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Items\n",
    "my_dict.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add item\n",
    "my_dict[\"Age\"]=(20,20,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': ['A', 'B', 'C'],\n",
       " 'Age': (20, 20, 20),\n",
       " 'gander': ['m', 'f', 'f'],\n",
       " 'name1': ['A', 'B', 'C']}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': ['A', 'B', 'C'], 'gander': ['m', 'f', 'f'], 'name1': ['A', 'B', 'C']}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del my_dict[\"Age\"]\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# empty dictionary\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dictionary with mixed keys\n",
    "my_dict = {'name': 'John', 1: [2, 4, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'John', 1: [2, 4, 3]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using dict()\n",
    "my_dict = dict({1:'apple', 2:'ball'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'apple', 2: 'ball'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accessing elements from dictionary\n",
    "my_dict = {'name':'Jack', 'age': 26}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jack', 'age': 26}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jack'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.get('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'address'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_19144/1363890732.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Trying to access keys which doesn't exist throws error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmy_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'address'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 'address'"
     ]
    }
   ],
   "source": [
    "# Trying to access keys which doesn't exist throws error\n",
    "my_dict['address']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Itens to dictionary-We can add new items or change the value of existing items using assignment operator.\n",
    "#If the key is already present, value gets updated, else a new key: value pair is added to the dictionary.\n",
    "thisdict =\t{\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "thisdict[\"color\"] = \"red\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'red'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new dict\n",
    "my_dict = {'name':'Jack', 'age': 26}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jack', 'age': 26}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update value\n",
    "my_dict['age'] = 27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jack', 'age': 27}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add item\n",
    "my_dict['address'] = 'Downtown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jack', 'age': 27, 'address': 'Downtown'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del-to delete a perticular item\n",
    "# delete a particular item\n",
    "del my_dict[\"address\"]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jack', 'age': 27}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "attachments": {
    "dict%20method.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dict%20method.png](attachment:dict%20method.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1-clear()-removes all the elements from a dictionary.\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ford'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': 'ford', 'r': 'ford', 'a': 'ford', 'n': 'ford', 'd': 'ford'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2-copy()-returns a copy of the specified dictionary.\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = car.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.Missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ford'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4 get\n",
    "car.get(\"brand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964)])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5-items()-Returns a view object. \n",
    "#The view object contains the key-value pairs of the dictionary, as tuples in a list.\n",
    "car.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['brand', 'model', 'year'])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6-keys()-return the keys of the dictionary, as a list.\n",
    "car.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mustang'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#7-pop()-removes the specified item from the dictionary.\n",
    "car.pop(\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'year': 1964}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('year', 1964)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8-popitem()-removes the item that was last inserted into the dictionary. \n",
    "car.popitem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'color': 'white'}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'color': 'Blue'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10-update()-inserts the specified items to the dictionary.\n",
    "car.update({\"color\": \"Blue\"})\n",
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'color': 'Blue'}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11-values()-returns the values of the dictionary, as a list.\n",
    "car.values()\n",
    "car"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Yuvraj Sutar \n",
    "* yuvrajsutar009@gmail.com"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}