.ipynb_checkpoints/Mechanical-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# install tweepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tweepy\n",
      "  Downloading tweepy-3.9.0-py2.py3-none-any.whl (30 kB)\n",
      "Requirement already satisfied: six>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from tweepy) (1.14.0)\n",
      "Requirement already satisfied: requests[socks]>=2.11.1 in /opt/conda/lib/python3.7/site-packages (from tweepy) (2.23.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from tweepy) (1.2.0)\n",
      "Requirement already satisfied: requests>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->tweepy) (2.23.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->tweepy) (3.0.1)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2.9)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (1.25.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2020.11.8)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2.9)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (1.25.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2020.11.8)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1)\n",
      "Installing collected packages: tweepy\n",
      "Successfully installed tweepy-3.9.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tweepy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# install GetOldTweets3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting GetOldTweets3\n",
      "  Downloading GetOldTweets3-0.0.11-py3-none-any.whl (13 kB)\n",
      "Requirement already satisfied: lxml>=3.5.0 in /opt/conda/lib/python3.7/site-packages (from GetOldTweets3) (4.5.0)\n",
      "Collecting pyquery>=1.2.10\n",
      "  Downloading pyquery-1.4.3-py3-none-any.whl (22 kB)\n",
      "Requirement already satisfied: lxml>=3.5.0 in /opt/conda/lib/python3.7/site-packages (from GetOldTweets3) (4.5.0)\n",
      "Collecting cssselect>0.7.9\n",
      "  Downloading cssselect-1.1.0-py2.py3-none-any.whl (16 kB)\n",
      "Installing collected packages: cssselect, pyquery, GetOldTweets3\n",
      "Successfully installed GetOldTweets3-0.0.11 cssselect-1.1.0 pyquery-1.4.3\n"
     ]
    }
   ],
   "source": [
    "!pip install GetOldTweets3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import GetOldTweets3 as got\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credentials and Authorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "consumer_key = \"I8mPBrUN2jGSXCd85Rdrvz0xq\"\n",
    "consumer_secret = \"z53SiNFlPHkPT8JT66qtzNsAT9gtXMcvpM6nAg3eYHI9GBhmY4\"\n",
    "access_token = \"1331376961801904137-kTsfzGYnys3KJG7pGqdNdYnpnwiOkK\"\n",
    "access_token_secret = \"DgTeyuM8pXd1Fg5ZKo8dmoTIfmuBPFDwFrlif9YTiUTq3\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth,wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query by Text Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Function is focused on completing the query then providing a CSV file of that query using panda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = []\n",
    "\n",
    "def text_query_to_csv(text_query,counts):\n",
    "    try:\n",
    "        # Creation of query method using parameters\n",
    "        tweets = tweepy.Cursor(api.search,q=text_query).items(count)\n",
    "\n",
    "        # Pulling information from tweets iterable object\n",
    "        tweets_list = [[tweet.user.screen_name, tweet.text,] for tweet in tweets]\n",
    "\n",
    "        # Creation of dataframe from tweets list\n",
    "        # Add or remove columns as you remove tweet information\n",
    "        tweets_df = pd.DataFrame(tweets_list,columns=['user', 'Text'])\n",
    "\n",
    "        # Converting dataframe to CSV \n",
    "        tweets_df.to_csv('{}-tweets.csv'.format(text_query), sep=',', index = False)\n",
    "\n",
    "    except BaseException as e:\n",
    "        print('failed on_status,',str(e))\n",
    "        #time.sleep(3)\n",
    "         #username tweet.created_at,'Datetime',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input search query to scrape tweets and name csv file\n",
    "# Max recent tweets pulls x amount of most recent tweets from that user\n",
    "text_query = ' Mechanical '\n",
    "count = 3000\n",
    "#screen_name = screen_name\n",
    "# Calling function to query X amount of relevant tweets and create a CSV file\n",
    "text_query_to_csv(text_query, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save file csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UyghurProject</td>\n",
       "      <td>\"Whether it be via football or fashion, the re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mustucos_12</td>\n",
       "      <td>RT @fashionpressnet: M·A·C“金魚”モチーフの限定コスメ「ムーン マ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NicoleZizi</td>\n",
       "      <td>RT @ComplexStyle: .@bibbygregory built his Twi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>denimedlife</td>\n",
       "      <td>It's time to slow down and consider the true c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JulieSwann14</td>\n",
       "      <td>@CanadianPenny1 You look like a fashion model!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>Ghostinthereal</td>\n",
       "      <td>RT @fashionpressnet: 『映画クレヨンしんちゃん 謎メキ！花の天カス学園』...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>MioSunshine</td>\n",
       "      <td>RT @fashionpressnet: レ・メルヴェイユーズ ラデュレ“花弁チーク”に限定...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>OneMinger</td>\n",
       "      <td>RT @SAINTHOODx: [ #SaintSchedule ]\\n\\n       พ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>jodohoikawa</td>\n",
       "      <td>RT @killuaszolldyck: y'all need to see this 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>LunnaJennie3000</td>\n",
       "      <td>RT @Dior: The 'luminarie' decorations that par...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 user                                               Text\n",
       "0       UyghurProject  \"Whether it be via football or fashion, the re...\n",
       "1         mustucos_12  RT @fashionpressnet: M·A·C“金魚”モチーフの限定コスメ「ムーン マ...\n",
       "2          NicoleZizi  RT @ComplexStyle: .@bibbygregory built his Twi...\n",
       "3         denimedlife  It's time to slow down and consider the true c...\n",
       "4        JulieSwann14  @CanadianPenny1 You look like a fashion model!...\n",
       "...               ...                                                ...\n",
       "2995   Ghostinthereal  RT @fashionpressnet: 『映画クレヨンしんちゃん 謎メキ！花の天カス学園』...\n",
       "2996      MioSunshine  RT @fashionpressnet: レ・メルヴェイユーズ ラデュレ“花弁チーク”に限定...\n",
       "2997        OneMinger  RT @SAINTHOODx: [ #SaintSchedule ]\\n\\n       พ...\n",
       "2998      jodohoikawa  RT @killuaszolldyck: y'all need to see this 19...\n",
       "2999  LunnaJennie3000  RT @Dior: The 'luminarie' decorations that par...\n",
       "\n",
       "[3000 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mechanical= pd.read_csv('./ Mechanical -tweets.csv' )\n",
    "Mechanical.head(3000)"
   ]
  }
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