Data_structures_advanced (1).ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "7ba3d65a-a523-4260-be16-6548e90f85cd",
   "metadata": {},
   "source": [
    "### Dictionaries\n",
    "Another useful data type built into Python is the dictionary. Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys.\n",
    "\n",
    "It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.\n",
    "\n",
    "![image.png](attachment:7bcb2e31-02f8-4ac3-a82c-be022bfd21ce.png)\n",
    "\n",
    "#### Recall the dictionery operations\n",
    "- Creat dictionery\n",
    "- Access ditionery entries\n",
    "- iterating over dictioneries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0266ca00",
   "metadata": {},
   "source": [
    "Example:\n",
    "1. Create a dictionary containing account information\n",
    "    - Tyeps of the account\n",
    "    - banks\n",
    "    - years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0615d09d-4da4-4293-ab2d-45a84cc7e01a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tyep': {'Saving', 'Current'}, 'bank': {'SAM', 'COM', 'BOC'}, 'year': 2000}\n",
      "Keys dict_keys(['tyep', 'bank', 'year'])\n",
      "Items dict_items([('tyep', {'Saving', 'Current'}), ('bank', {'SAM', 'COM', 'BOC'}), ('year', 2000)])\n",
      "the key =  tyep has items {'Saving', 'Current'}\n",
      "the key =  bank has items {'SAM', 'COM', 'BOC'}\n",
      "the key =  year has items 2000\n",
      "the key =  tyep has items {'Saving', 'Current'} of the type <class 'set'>\n",
      "the key =  bank has items {'SAM', 'COM', 'BOC'} of the type <class 'set'>\n",
      "the key =  year has items 2000 of the type <class 'int'>\n",
      "tyep\n",
      "bank\n",
      "year\n"
     ]
    }
   ],
   "source": [
    "accounts = {'tyep':{'Saving', 'Current'}, 'bank': {\"BOC\",\"SAM\",\"COM\"}, 'year': 2000}\n",
    "print(accounts)\n",
    "print(\"Keys\", accounts.keys())\n",
    "print(\"Items\", accounts.items())\n",
    "for key in accounts.keys():\n",
    "    print(\"the key = \", key, \"has items\", accounts[key])\n",
    "\n",
    "for key, value in accounts.items():\n",
    "    print(\"the key = \", key, \"has items\", value, \"of the type\", type(value))\n",
    "\n",
    "for key in accounts:\n",
    "    print(key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "083e4f35-0b0b-4182-b37d-ac9e191c06e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "accounts['city'] = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "94340dda-9cbc-4bdf-932d-2d6710ebfc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "accounts['city'].add(\"Colombo\")\n",
    "accounts['city'].add(\"Kandy\")\n",
    "accounts['city'].add(\"Nigambo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e481216b-0e91-4045-a6cf-7b2d9a4c159d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tyep': {'Current', 'Saving'},\n",
       " 'bank': {'BOC', 'COM', 'SAM'},\n",
       " 'year': 2000,\n",
       " 'city': {'Colombo', 'Kandy', 'Nigambo'}}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c693b3a1-0278-43af-abdc-aebc2156fa1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_accounts(accounts, key, value):\n",
    "    if key in accounts:\n",
    "        accounts[key].add(value)\n",
    "    else:\n",
    "        accounts[key] = set()\n",
    "        accounts[key].add(value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8bd1aaeb-893a-4a73-8146-e385a4ec0b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_accounts(accounts, \"city\", \"Matara\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6130f594-13c6-408e-b2d1-719f6f839652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tyep': {'Current', 'Saving'},\n",
       " 'bank': {'BOC', 'COM', 'SAM'},\n",
       " 'year': 2000,\n",
       " 'city': {'Colombo', 'Kandy', 'Matara', 'Nigambo'}}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "71c71850-619e-45de-b957-8d2df63a3244",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_accounts(accounts, \"bank\", \"SEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d3f60f9c-986b-4847-a1b3-03c1c762e2bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tyep': {'Current', 'Saving'},\n",
       " 'bank': {'BOC', 'COM', 'SAM', 'SEY'},\n",
       " 'year': 2000,\n",
       " 'city': {'Colombo', 'Kandy', 'Matara', 'Nigambo'}}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "85ce1f56-323d-48e2-b160-2adaa737bea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_accounts(accounts, \"type\", \"COMMERCIAL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "32b15c48-4269-4638-be6d-88cb8f2b88ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tyep': {'Current', 'Saving'},\n",
       " 'bank': {'BOC', 'COM', 'SAM', 'SEY'},\n",
       " 'year': 2000,\n",
       " 'city': {'Colombo', 'Kandy', 'Matara', 'Nigambo'},\n",
       " 'type': {'COMMERCIAL'}}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb1be4c-41b5-4452-8594-11fb5c78e0d7",
   "metadata": {},
   "source": [
    "\n",
    "#### Example - Data Structures\n",
    "\n",
    "Import Bank-Data with names\n",
    "1. Create a list of names using the names in \"Name\" column. Find how many distict names are there.\n",
    "2. Create two list of first names and last names. Find how many distinct first and last names are there.\n",
    "3. What most frequent contact type used by the people who have the surname \"Williams\"?\n",
    "4. Among married people how many of them bear the surname \"Warner\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f07fb2a-bdea-42f2-9ebb-c37082beeb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db477a16-73ac-49eb-b91e-b80fc8a5d296",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"bankdata_2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1810ad3a-6dee-4a8a-8427-27fe17864bb4",
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>Mike Willy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>Robert Willy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>Jennifer Willy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>Lilly Williams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>Robert Willy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \\\n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no   \n",
       "1  cellular   11   may       220         1    339         4  failure  no   \n",
       "2  cellular   16   apr       185         1    330         1  failure  no   \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no   \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no   \n",
       "\n",
       "             Name  \n",
       "0      Mike Willy  \n",
       "1    Robert Willy  \n",
       "2  Jennifer Willy  \n",
       "3  Lilly Williams  \n",
       "4    Robert Willy  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0cde370-7e70-4135-a627-52bd1cfb6ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'y', 'Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1606196-7a62-45fe-94e5-747c11e44194",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = list(data.Name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a1d27fb6-291c-43f0-a3f4-e5e5ca5be2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_set = set(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31b2f665-f858-4906-a5a3-526b0cb1804f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Lilly Willy', 'Robert Stallon', 'Robert Willy', 'Robert Warner', 'Mike Willy', 'John Williams', 'John Willy', 'Robert Williams', 'Jennifer Williams', 'John Stallon', 'Lilly Browns', 'John Warner', 'Jennifer Willy', 'Mike Stallon', 'Mike Williams', 'John Browns', 'Jennifer Warner', 'Jennifer Stallon', 'Mike Warner', 'Robert Browns', 'Lilly Stallon', 'Lilly Warner', 'Mike Browns', 'Jennifer Browns', 'Lilly Williams'}\n",
      "Number of distinct names = 25\n"
     ]
    }
   ],
   "source": [
    "print(name_set)\n",
    "print(\"Number of distinct names =\", len(name_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a73784b0-cc3e-4a7d-99c0-5a89411599b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First names  =  {'John', 'Mike', 'Robert', 'Lilly', 'Jennifer'}\n",
      "Last names =  {'Warner', 'Stallon', 'Browns', 'Williams', 'Willy'}\n"
     ]
    }
   ],
   "source": [
    "first_name = list()\n",
    "last_name = list()\n",
    "for i in range(len(names)):\n",
    "    name_parts = names[i].split()\n",
    "    first_name.append(name_parts[0])\n",
    "    last_name.append(name_parts[1])\n",
    "\n",
    "print(\"First names  = \", set(first_name))\n",
    "print(\"Last names = \", set(last_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37ef63d8-ff46-49ef-8505-1b763538366c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First names =  {'John', 'Mike', 'Lilly', 'Robert', 'Jennifer'}\n",
      "Last names =  {'Warner', 'Stallon', 'Browns', 'Williams', 'Willy'}\n"
     ]
    }
   ],
   "source": [
    "first_name_set = set()\n",
    "last_name_set = set()\n",
    "for name in name_set:\n",
    "    name_parts = name.split()\n",
    "    first_name_set.add(name_parts[0])\n",
    "    last_name_set.add(name_parts[1])\n",
    "\n",
    "print(\"First names = \", first_name_set)\n",
    "print(\"Last names = \", last_name_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b756a089-0590-4271-86bb-78181ff3d401",
   "metadata": {},
   "outputs": [],
   "source": [
    "# What most frequent contact type used by the people who have the surname \"Williams\"?\n",
    "\n",
    "# We use a dictionery to structure the contact types. The idea is to get names associated with different types of contacts.\n",
    "# dict(contact_type:names)\n",
    "\n",
    "contacts = dict()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64911a89-a420-4ceb-ba7a-c9491beeb95c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_contacts(contacts, contact_type, name):\n",
    "    if contact_type in contacts:\n",
    "        contacts[contact_type].append(name)\n",
    "    else:\n",
    "        contacts[contact_type] = list()\n",
    "        contacts[contact_type].append(name)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d71172a4-028c-49fe-8d2b-6fd806413276",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mcontact[\u001b[38;5;241m1\u001b[39m]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data.contact[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0422d3d3-1ce9-48d8-9994-9c66fd63af76",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(data.contact)):\n",
    "    update_contacts(contacts, data.contact[i], last_name[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2a1bf043-fe05-4a20-9dfd-d787d2bb8de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['cellular', 'unknown', 'telephone'])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "63288dcc-4a9f-4c84-856b-fe1b34ec47be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_count(name, name_list):\n",
    "    i = 0\n",
    "    for i in range(len(name_list)):\n",
    "        if name == name_list[i]:\n",
    "            i = i+1\n",
    "    return i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b50082f6-bde0-43e0-a61c-0737abe0522d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2895"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_count(\"Williams\", contacts['cellular'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2f5ea3ed-c63e-469e-b82c-022e1ee6befa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1324"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_count(\"Williams\", contacts['unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "340759fa-9435-4f80-ba08-57ed76e100f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_count(\"Williams\", contacts['telephone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "dbb8307d-7c93-44e6-9367-608a06d0bca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_list_dict(list_dict, key, value):\n",
    "    if key in list_dict:\n",
    "        list_dict[key].append(value)\n",
    "    else:\n",
    "        list_dict[key] = list()\n",
    "        list_dict[key].append(value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d23108e1-d1c4-41ab-8f47-1b870c705710",
   "metadata": {},
   "outputs": [],
   "source": [
    "williams_frequency = dict()\n",
    "for contact_type in contacts.keys():\n",
    "    update_list_dict(williams_frequency, contact_type, get_count(\"Williams\", contacts[contact_type]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5332a87d-ac39-4d03-9b58-123536a2410f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cellular': [2895], 'unknown': [1324], 'telephone': [300]}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "williams_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "41345916-c1db-4b0a-a034-a35e5616c299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cellular': [2895], 'unknown': [1323], 'telephone': [300]}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stallon_frequency = dict()\n",
    "for contact_type in contacts.keys():\n",
    "    update_list_dict(stallon_frequency, contact_type, get_count(\"Stallon\", contacts[contact_type]))\n",
    "\n",
    "stallon_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2d299d3-4301-4764-b8cd-96dc7d796605",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae748696-5724-429c-b61c-d879bb82d591",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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