6_month_data.ipynb

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   "source": [
    "# General:\n",
    "import tweepy           # To consume Twitter's API\n",
    "import pandas as pd     # To handle data\n",
    "import numpy as np      # For number computing\n",
    "\n",
    "# For plotting and visualization:\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad489eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Twitter App access keys for @user\n",
    "\n",
    "# Consume:\n",
    "CONSUMER_KEY    = 'asXMHLaVCzai3GKutxfJUGiRP'\n",
    "CONSUMER_SECRET = 'PLkJ818tU7efRfn0udgScR0rUY5Mp4PKr9HcvN5oSkBFFMf6s1'\n",
    "\n",
    "# Access:\n",
    "ACCESS_TOKEN  = '936311765872152577-o7S3PfEQAQA7L8TK8vWpEWnzaH4DuWN'\n",
    "ACCESS_SECRET = '80OgMMMfLP3vfwaBTWUOui4PDJBsNC8Hzv9jJvNRguUXP'\n",
    "\n",
    "\n",
    "\n",
    "# AAAAAAAAAAAAAAAAAAAAAJOqYQEAAAAAK8cHRoVkYqZIy0yIENdg6pbLrso%3Dx2yxG5VOzltBnW9kpaqBfimTXp9aqRMJwK6wVXQ2PQkEmgbLSG"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90240119",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import our access keys:\n",
    "from credentials import *    # This will allow us to use the keys as variables\n",
    "\n",
    "# API's setup:\n",
    "def twitter_setup():\n",
    "    \"\"\"\n",
    "    Utility function to setup the Twitter's API\n",
    "    with our access keys provided.\n",
    "    \"\"\"\n",
    "    # Authentication and access using keys:\n",
    "    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n",
    "    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n",
    "\n",
    "    # Return API with authentication:\n",
    "    api = tweepy.API(auth)\n",
    "    return api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0636a976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tweets extracted: 200.\n",
      "\n",
      "5 recent tweets:\n",
      "\n",
      "@nervosevents #Web3Champs #Nervos https://t.co/sSQAgjbarP\n",
      "\n",
      "hoping to meet someone with similar music taste by 14th feb✨👀\n",
      "\n",
      "Study on exam day mornings are so underrated!🌄\n",
      "\n",
      "Heard SAF's single call changed tides? True?\n",
      "\n",
      "@Jaspritbumrah93 is true devotee, no matter bowling or batting he always hit the stumps 😂\n",
      "#IndvsEng #indiancricketteam\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We create an extractor object:\n",
    "extractor = twitter_setup()\n",
    "\n",
    "# We create a tweet list as follows:\n",
    "tweets = extractor.user_timeline(screen_name=\"tknishh\", count=200)\n",
    "print(\"Number of tweets extracted: {}.\\n\".format(len(tweets)))\n",
    "\n",
    "# We print the most recent 5 tweets:\n",
    "print(\"5 recent tweets:\\n\")\n",
    "for tweet in tweets[:5]:\n",
    "    print(tweet.text)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d96639f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a pandas dataframe as follows:\n",
    "data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "\n",
    "# We display the first 10 elements of the dataframe:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e879ea20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Internal methods of a single tweet object:\n",
    "print(dir(tweets[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e683ba15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We add relevant data:\n",
    "data['len']  = np.array([len(tweet.text) for tweet in tweets])\n",
    "data['ID']   = np.array([tweet.id for tweet in tweets])\n",
    "data['Date'] = np.array([tweet.created_at for tweet in tweets])\n",
    "data['Source'] = np.array([tweet.source for tweet in tweets])\n",
    "data['Likes']  = np.array([tweet.favorite_count for tweet in tweets])\n",
    "data['RTs']    = np.array([tweet.retweet_count for tweet in tweets])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1adf981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display of first 10 elements from dataframe:\n",
    "display(data.head(10))"
   ]
  }
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