retrieving_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving data from GitHub API v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "import networkx as nx\n",
    "\n",
    "import os\n",
    "\n",
    "from modules import config\n",
    "from modules import plotting\n",
    "\n",
    "# Extension to auto reload custom modules\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 1\n",
    "\n",
    "%aimport modules.plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establishing authentication\n",
    "username = 'danovad'\n",
    "token = config.GITHUB_TOKEN # Token coming from config.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Issuing a request to retrieve API rate limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resources': {'core': {'limit': 5000,\n",
       "   'used': 5,\n",
       "   'remaining': 4995,\n",
       "   'reset': 1599536422},\n",
       "  'search': {'limit': 30, 'used': 0, 'remaining': 30, 'reset': 1599532894},\n",
       "  'graphql': {'limit': 5000,\n",
       "   'used': 0,\n",
       "   'remaining': 5000,\n",
       "   'reset': 1599536434},\n",
       "  'integration_manifest': {'limit': 5000,\n",
       "   'used': 0,\n",
       "   'remaining': 5000,\n",
       "   'reset': 1599536434},\n",
       "  'source_import': {'limit': 100,\n",
       "   'used': 0,\n",
       "   'remaining': 100,\n",
       "   'reset': 1599532894},\n",
       "  'code_scanning_upload': {'limit': 500,\n",
       "   'used': 0,\n",
       "   'remaining': 500,\n",
       "   'reset': 1599536434}},\n",
       " 'rate': {'limit': 5000, 'used': 5, 'remaining': 4995, 'reset': 1599536422}}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = requests.get(\"https://api.github.com/rate_limit\", auth=(username,token))\n",
    "r.status_code\n",
    "\n",
    "json.loads(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`def github_request_by_url(url):`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def github_request_by_url(url):\n",
    "    # make the get request\n",
    "    r = requests.get(url, auth=(username,token))\n",
    "    \n",
    "    # unpack and return\n",
    "    return json.loads(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handful of functions to retrieve data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`def github_request(user, endpoint=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def github_request(userlist, endpoint = None):\n",
    "    ''' Function to retrieve user details.\n",
    "    If endpoint = None, then it will retrieve user info. \n",
    "    If endpoint is set to \n",
    "    \"following\", \n",
    "    \"followers\", \n",
    "    \"repos\", \n",
    "    \"subscriptions\",\n",
    "    \"events\", etc it will handle those requests as such. '''\n",
    "    if endpoint is not None:\n",
    "        url = f'https://api.github.com/users/{user}/{endpoint}'\n",
    "        #print(f\"Requesting {url}\")\n",
    "    else:\n",
    "        url = f'https://api.github.com/users/{user}'\n",
    "        print(f\"Requesting {url}\")\n",
    "    \n",
    "    # make the get request\n",
    "    r = requests.get(url, auth=(username,token))\n",
    "    if r.status_code != 200:\n",
    "        print(\"Something went wrong.\")\n",
    "    # unpack and return\n",
    "    return json.loads(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre><code>def generate_user_details_dict(user):\n",
    "    ''' \n",
    "    Function generates a dictionary for a specific user. \n",
    "    \n",
    "    This combines user details and users following.\n",
    "    '''</code></pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_user_details_dict(user):\n",
    "    ''' Function generates a dictionary for a specific user. \n",
    "    \n",
    "    This combines user details and users following.'''\n",
    "    \n",
    "    # First we need to get the users details\n",
    "    user_details_dict = github_request(user)\n",
    "\n",
    "    # Add user_following_list login names to user_details_dict as a key:value pair (login:list of logins)\n",
    "    user_details_dict['following_users'] = [user['login'] for user in github_request(user, endpoint = 'following')]\n",
    "    user_details_dict['followers_users'] = [user['login'] for user in github_request(user, endpoint = 'followers')]\n",
    "    \n",
    "    return user_details_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_userlist_details(userlist):\n",
    "    '''\n",
    "    For a list of users, return a list of dictionaries, \n",
    "    with their resulting details.\n",
    "    '''\n",
    "    users_details_list = []\n",
    "    for user in userlist:\n",
    "        users_details_list.append(generate_user_details_dict(user))\n",
    "    \n",
    "    return users_details_list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = get_userlist_details(['danovad','tomerovadia','cooperdramsey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['pjreddie',\n",
       "  'yovadia',\n",
       "  'josephofiowa',\n",
       "  'tomerovadia',\n",
       "  'matthewbrems',\n",
       "  'cooperdramsey',\n",
       "  'aailani',\n",
       "  'Wsauder',\n",
       "  'sjhawkes',\n",
       "  'scaress21'],\n",
       " ['mbostock',\n",
       "  'tbeck',\n",
       "  'ruggeri',\n",
       "  'emilyschultz',\n",
       "  'hellochitty',\n",
       "  'plupinska',\n",
       "  'carlbaron',\n",
       "  'composerinteralia',\n",
       "  'amplesample',\n",
       "  'tbuchannan',\n",
       "  'paskhaver',\n",
       "  'DanOvad',\n",
       "  'Cirakuze',\n",
       "  'rsa2135',\n",
       "  'taekyunggg',\n",
       "  'ayellapragada',\n",
       "  'abseee',\n",
       "  'MichaelMCoates',\n",
       "  'christinewang319',\n",
       "  'WilsonMinFong',\n",
       "  'dallaswmorgan',\n",
       "  'parkjphilip',\n",
       "  'pauliewax',\n",
       "  'RoyceJH',\n",
       "  'madelineleclair',\n",
       "  'jon-lin',\n",
       "  'cmakow',\n",
       "  'mbryantramirez',\n",
       "  'vonbarown'],\n",
       " ['Sintrastes', 'samestep', 'DanOvad', 'tomerovadia', 'mbr4477']]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[user['following_users'] for user in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_following_userlist(userlist):\n",
    "    '''Get a unique list of following users from a list of users'''\n",
    "    \n",
    "    for user in userlist:\n",
    "        generate_user_details_dict(user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_following_details_list(user):\n",
    "    '''\n",
    "    Function generates a list of dictionaries. \n",
    "    '''\n",
    "    users_details_list = []\n",
    "    users_login_set = set(user)\n",
    "    users_details_list.append(generate_user_details_dict(user))\n",
    "    \n",
    "    # Loop over all users following and generate their own user details dictionaries.\n",
    "    for fol_user in users_details_list[0]['following_users']:\n",
    "        print(fol_user)\n",
    "        users_login_set.add(fol_user)\n",
    "        users_details_list.append(generate_user_details_dict(fol_user))    \n",
    "        \n",
    "    #new_following_users = set().add([user_details['following_users'] for user_details in users_details_list])\n",
    "    #unique_new_users = users_login_set.difference(new_following_users)\n",
    "    \n",
    "    \n",
    "    return users_details_list, unique_new_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pjreddie\n",
      "yovadia\n",
      "josephofiowa\n",
      "tomerovadia\n",
      "matthewbrems\n",
      "cooperdramsey\n",
      "aailani\n",
      "Wsauder\n",
      "sjhawkes\n",
      "scaress21\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-186-1bfa3c1e7d63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0musers_details_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munique_new_users\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_following_details_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'danovad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-185-9359a225288b>\u001b[0m in \u001b[0;36mreturn_following_details_list\u001b[0;34m(user)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0musers_details_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerate_user_details_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfol_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0mnew_following_users\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0muser_details\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'following_users'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0muser_details\u001b[0m \u001b[0;32min\u001b[0m \u001b[0musers_details_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0munique_new_users\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0musers_login_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_following_users\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "users_details_list, unique_new_users = return_following_details_list('danovad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'd', 'n', 'o', 'v'}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_new_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating a dictionary of user to following users for networkX graphs\n",
    "network_nodes = {user['login']:user['following_users'] for user in users_details_list}\n",
    "network_labels = {user['login']:user['login'] for user in users_details_list}\n",
    "network_following_count = {user['login']:user['following'] for user in users_details_list}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pjreddie\n",
      "yovadia\n",
      "josephofiowa\n",
      "tomerovadia\n",
      "matthewbrems\n",
      "cooperdramsey\n",
      "aailani\n",
      "Wsauder\n",
      "sjhawkes\n",
      "scaress21\n"
     ]
    }
   ],
   "source": [
    "first = return_following_details_list('danovad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = list()\n",
    "#test.extend([user['following_users'] for user in first])\n",
    "test\n",
    "\n",
    "for user in first:\n",
    "    test.extend(user['following_users'])\n",
    "test = set(test)\n",
    "test\n",
    "\n",
    "a = set(['DanOvad',\n",
    " 'MichaelMCoates',\n",
    " 'RoyceJH',\n",
    " 'Sintrastes',\n",
    " 'WilsonMinFong',\n",
    " 'Wsauder'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = set([1,2,3, 5, 6])\n",
    "b = set([4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{5, 6}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.intersection(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4, 7}"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.difference(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4, 7}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.difference(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DanOvad'}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.difference(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating NetworkX Graphs\n",
    "\n",
    "Now that we have functions for:\n",
    "1. Getting user details\n",
    "2. Getting a user's following list\n",
    "3. Get details on a users following list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_DiGraph(users_details_list):\n",
    "    # instantiate the graph\n",
    "    G = nx.DiGraph(network_nodes)\n",
    "    # Calculate positions\n",
    "    pos = nx.drawing.layout.kamada_kawai_layout(G)\n",
    "    \n",
    "    # Set attributes\n",
    "    nx.set_node_attributes(G, \n",
    "        name='pos',\n",
    "        values=pos\n",
    "    )\n",
    "    nx.set_node_attributes(G,\n",
    "        name='user',\n",
    "        values={user['login']:user['login'] for user in users_details_list}\n",
    "    )\n",
    "    nx.set_node_attributes(G,\n",
    "        name='following_count',\n",
    "        values={user['login']:user['following'] for user in users_details_list}\n",
    "    )\n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = generate_DiGraph(users_details_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('DanOvad',\n",
       " {'pos': array([0.3723582 , 0.26859894]),\n",
       "  'user': 'DanOvad',\n",
       "  'following_count': 10})"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(G.nodes(data=True))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting.mpl_nx_graph(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
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            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "font": {
          "size": 16
         },
         "text": "Graph of Featured Channels"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          -0.442489259698666,
          1.0865466636172663
         ],
         "showgrid": false,
         "showticklabels": false,
         "type": "linear",
         "zeroline": false
        },
        "yaxis": {
         "autorange": true,
         "range": [
          -0.33620507949648626,
          0.7364613411868324
         ],
         "showgrid": false,
         "showticklabels": false,
         "type": "linear",
         "zeroline": false
        }
       }
      },
      "image/png": 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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('2c445f75-d3a4-4c9f-a5e2-10c983a236a2');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plotly_network_graph(G, color_setting = 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Testing to create generate_user_details_dict function\n",
    "\n",
    "users_list = []\n",
    "\n",
    "user_details_dict = github_request('danovad')\n",
    "#user_following_list = github_request('danovad', endpoint = 'following')\n",
    "user_details_dict['following'] = [user['login'] for user in github_request('danovad', endpoint = 'following')]\n",
    "\n",
    "users_list.append(user_details_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['login','id','type','followers','following','public_repos','bio','updated_at','created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "    '''This is Docstring'''\n",
    "    def __init__(self, name, age):\n",
    "        ''' This is Init docstring'''\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        \n",
    "    def greet(self, time):\n",
    "        ''' method Docstring'''\n",
    "        self.time = time\n",
    "        print(f\"Hello my name is {self.name}, I am available at {time}\")        \n",
    "        \n",
    "p1 = Person(\"Al\", 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello my name isAl, I am available at 6pm\n"
     ]
    }
   ],
   "source": [
    "\n",
    "p1.greet(time = '6pm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6pm'"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hellow my name isAl, I am available at6pm\n"
     ]
    }
   ],
   "source": [
    "p1.greet('6pm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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