Twitter Project 3. Categorization, Tokenization and Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge 1\n",
    "\n",
    "For this challenge of the project I will need to create binary variables based on text applying simple Natural Language Processing and \"recode\" variables using conditions.\n",
    "\n",
    "I will need to create two variables for the Twitter data based on the text column. They should be two **meaningful** categories for this dataset, and they can either be binary taking the value 0 (when the tweet is not of that category) or 1 (when the tweet is of that category) or categorical with multiple meaningful categories that a tweet can fall under.\n",
    "\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "* **Technical** summary:\n",
    "\n",
    "**The data collected were tweets dated during the 50th Greenpeace anniversary. It made sense to know which of the tweets were referring somehow to such event and to do so a binary variable has been created, namely \"anniversary_tag\". Another variablke has been created, namely \"fundrising_tag\" in order to know if any of the tweets were referring somehow to any fundrising or supportive content. Would be important for the ONG to have an approximate amount of possible supporters for an eventual fundrising campaign. There is a link between the two variable. Enthusiast users about the 50th anniversary may be also willing to fundrise an eventual campaign and this is why this 2 variables have been created**\n",
    "\n",
    "\n",
    "\n",
    "* **Business** summary:\n",
    "\n",
    "**The data collected showed that 10 tweets out of 110 were referring to the 50th anniversary of the company, about 9.1%. Instead, 9 tweets out of 110 were referring to a fundrising or supportive-related topic, about 8.1%. The data of the two variables have been merged in a third variable, namely \"possible_targets\", showing that out of a sample of 110 tweets 17 are referred to enthusiastic users mentioning the anniversary or either topics which can be in favour of a fundrising campaign, about the 15.5%. The results show that a possible 15.5% of users among the social medis followers of ONG would be willing to support a fundrising campaign although the sample is too small to accurately predict the final result. Considering the solid amount of social media followers of the ONG (31 millions), if a 15.5% would approximately contribute to the fundrising campaign with 1 dollar,an estimation of the result of the Fundraising Campaign can be deducted: 4.805.000,00$**\n",
    "\n",
    "* Possible **Biases**:\n",
    "\n",
    "1) The first bias it is about the tweets and their texts. Collecting data from Twitter makes us depending on the data owner which can decide eventually not to display the full text of the tweet for instance. Without the full text of the tweet the sentiment analysis would not produce reliable data (since relevant part of the text may be omitted) and thus a bias from the first layer would have been carried at the second layer (sentiment analysis). Also, a predictive reliable model starts with information about many people, potentially millions, and the limited amount of tweets we can collet from twitter does not allow us to gain much reliability. Also, the query made on twarc about which kind of tweets to collect may carry some bias depending on the keyword used for it. \n",
    "2) Secondly, when creating a sentiment score, bias from a tweet not containig the full text may produce false positive or false negative scores. Furthermore, the binary variable created to look if a specific word in the tweets may carry a bias and let us make the wrong assumpions: in my query I have looked for the word \"50th\" and \"anniversary\" to understand if the tweet was referring to such topics. Such query looks reliable. The second query made with the keywords \"fundrising\" and \"support\" has been made in order to undersant if the tweet was referring to such topics and then categorize the user as a possible supporter of the fundrising campaign may carry bias: only becasue the words \"fundrising\" or \"support\" were present in the tweets does not mean that the individual would be surely willing to support the campaign with a donation. The causality here is just a mere assumption. Let's see the other side of the medal, in a limited sample, let's suppose the result of our analysis are correct and indeed the users identified by our data elaboration would be willing to support the campaign with a donation. This does not automatically mean that the same portion of a biggest sample will be willing to do the same. The score generated by a model of a specific sample may not generate the same result across different groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the libraries I will need to perform, namely Pandas and Seaborn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete = pd.read_pickle('complete.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>full_text</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>followers_count_no_na</th>\n",
       "      <th>friendsCount_no_na</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>sent_score</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1438236436084391936</td>\n",
       "      <td>CitizenWonk</td>\n",
       "      <td>2021-09-15 20:21:02+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @PBIcanada: We congratulate @GreenpeaceCA o...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>32259.000</td>\n",
       "      <td>27188.000</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1438236402114772995</td>\n",
       "      <td>mark_trudgeon</td>\n",
       "      <td>2021-09-15 20:20:54+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @cadebe_: @GJGamble @BorisJohnson @trussliz...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>340.000</td>\n",
       "      <td>1516.000</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1438236275283214343</td>\n",
       "      <td>AspieandMe</td>\n",
       "      <td>2021-09-15 20:20:23+00:00</td>\n",
       "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
       "      <td>RT @GreenpeaceCA: I scream, you scream, WE ALL...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>387.000</td>\n",
       "      <td>672.000</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1438235849058045956</td>\n",
       "      <td>gazetka75</td>\n",
       "      <td>2021-09-15 20:18:42+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @LongTimeAmy: TODAY IN HISTORY\\n\\n1971–Envi...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1438235834415714308</td>\n",
       "      <td>arynpekik</td>\n",
       "      <td>2021-09-15 20:18:38+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @zoev213: Happy 50th @greenpeaceusa Join me...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>446.000</td>\n",
       "      <td>443.000</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
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      "text/plain": [
       "                    id    screen_name                created_at  \\\n",
       "0  1438236436084391936    CitizenWonk 2021-09-15 20:21:02+00:00   \n",
       "1  1438236402114772995  mark_trudgeon 2021-09-15 20:20:54+00:00   \n",
       "2  1438236275283214343     AspieandMe 2021-09-15 20:20:23+00:00   \n",
       "3  1438235849058045956      gazetka75 2021-09-15 20:18:42+00:00   \n",
       "4  1438235834415714308      arynpekik 2021-09-15 20:18:38+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2  <a href=\"https://mobile.twitter.com\" rel=\"nofo...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/android\" ...   \n",
       "\n",
       "                                           full_text  retweet_count  \\\n",
       "0  RT @PBIcanada: We congratulate @GreenpeaceCA o...              7   \n",
       "1  RT @cadebe_: @GJGamble @BorisJohnson @trussliz...              1   \n",
       "2  RT @GreenpeaceCA: I scream, you scream, WE ALL...              2   \n",
       "3  RT @LongTimeAmy: TODAY IN HISTORY\\n\\n1971–Envi...             16   \n",
       "4  RT @zoev213: Happy 50th @greenpeaceusa Join me...              2   \n",
       "\n",
       "   favorite_count  followers_count_no_na  friendsCount_no_na  positive  \\\n",
       "0               0              32259.000           27188.000         3   \n",
       "1               0                340.000            1516.000         1   \n",
       "2               0                387.000             672.000         2   \n",
       "3               0                  1.000              35.000         2   \n",
       "4               0                446.000             443.000         3   \n",
       "\n",
       "   negative  neutral  sent_score sentiment  \n",
       "0        -1        1           2  positive  \n",
       "1        -1        0           0   neutral  \n",
       "2        -5       -1          -3  negative  \n",
       "3        -1        1           1  positive  \n",
       "4        -1        1           2  positive  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping eventual duplicates in order not have wrong results from my analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2 = complete.drop_duplicates(subset =\"full_text\",\n",
    "                     ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(complete2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     int64\n",
       "screen_name                           object\n",
       "created_at               datetime64[ns, UTC]\n",
       "source                                object\n",
       "full_text                             object\n",
       "retweet_count                          int64\n",
       "favorite_count                         int64\n",
       "followers_count_no_na                float64\n",
       "friendsCount_no_na                   float64\n",
       "positive                               int64\n",
       "negative                               int64\n",
       "neutral                                int64\n",
       "sent_score                             int64\n",
       "sentiment                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Writing a function which will detect users with more than 3000 followers: to such users a \"Token\" will be assigned which will categorize them as special users since they may be considered hot targets for the fundrising campaign**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_df(row):\n",
    "    row['special_users'] = 0\n",
    "    if (row['followers_count_no_na'] > 3000) or (row['friendsCount_no_na'] > 3000):\n",
    "        row['special_users'] = 1\n",
    "        \n",
    "    return row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2 = complete2.apply(categorise_df, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Among the 263 Users, 34 have had a Token assigned (So they have a number of Followers above 3000)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    229\n",
       "1     34\n",
       "Name: special_users, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['special_users'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**12% of the Users are Special Users**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.871\n",
       "1   0.129\n",
       "Name: special_users, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['special_users'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for bots among the Users (Users that have no Followers)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_df(row):\n",
    "    row['bots'] = 0\n",
    "    if (row['followers_count_no_na'] < 1) or (row['friendsCount_no_na'] < 1):\n",
    "        row['bots'] = 1\n",
    "        \n",
    "    return row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2 = complete2.apply(categorise_df, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apparently 151 Users are most likely bots since they have no followers. It is the 57.4%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    151\n",
       "0    112\n",
       "Name: bots, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['bots'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   0.574\n",
       "0   0.426\n",
       "Name: bots, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['bots'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0     32259.000\n",
       "1       340.000\n",
       "2       387.000\n",
       "3         1.000\n",
       "4       446.000\n",
       "         ...   \n",
       "258       0.000\n",
       "259       0.000\n",
       "260       0.000\n",
       "261       0.000\n",
       "262       0.000\n",
       "Name: followers_count_no_na, Length: 263, dtype: float64>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['followers_count_no_na'].value_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Writing a function which will allow us to make queries by words or symbols in the Tweets. Python will assign a Token for each word found in our Query**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordlist_any_present(text, query):\n",
    "    import re\n",
    "    text = str(text).lower()\n",
    "    newquery = []\n",
    "    for word in query:\n",
    "        newquery.append(str(word).lower())\n",
    "    tokens = re.findall(r\"[\\w']+|[.,!?;$@#]\", text)\n",
    "    \n",
    "    for word in newquery:\n",
    "        if word in tokens:\n",
    "            return 1\n",
    "    return 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making all the text of my Tweets Data in small case in order for the function to work properly (the function is only able to detect small case text)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      rt @pbicanada: we congratulate @greenpeaceca o...\n",
       "1      rt @cadebe_: @gjgamble @borisjohnson @trussliz...\n",
       "2      rt @greenpeaceca: i scream, you scream, we all...\n",
       "3      rt @longtimeamy: today in history\\n\\n1971–envi...\n",
       "4      rt @zoev213: happy 50th @greenpeaceusa join me...\n",
       "                             ...                        \n",
       "258    rt @dedubius: @gjgamble @chrisgpackham @borisj...\n",
       "259    @cowboy_satin @23brookside @people4bernie @ber...\n",
       "260    rt @jubileam8: #glyphosate spraying purposeful...\n",
       "261    rt @abukaalfred: different countries, differen...\n",
       "262      happy 50th greenpeace ! https://t.co/gtbwdyogri\n",
       "Name: full_text, Length: 263, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2[\"full_text\"].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating 2 different tags in order to apply the query. Namely the \"Anniversary Tag\" will look for the words 'happy' or 'birthday' since such enthusiast users may be targeted by the fundraising campaign. Instead, the \"Fundraising tag\" will look for the words 'fundraising 'or 'support' since we may find users which are already expressively willing to support the Organization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2['anniversary_tag'] = complete2['full_text'].apply(wordlist_any_present, args=(['happy','birthday'],))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2['fundraising_tag'] = complete2['full_text'].apply(wordlist_any_present, args=(['fundraising ', 'support'],))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The \"Anniversary Tag\" has been detected in 29 posts, the 11% of the Tweets collected in the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    234\n",
       "1     29\n",
       "Name: anniversary_tag, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['anniversary_tag'] .value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.890\n",
       "1   0.110\n",
       "Name: anniversary_tag, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['anniversary_tag'] .value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The \"Fundraising Tag\" has been detected in 15 posts, approximately the 5% of the Tweets collected in the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    250\n",
       "1     13\n",
       "Name: fundraising_tag, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['fundraising_tag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.951\n",
       "1   0.049\n",
       "Name: fundraising_tag, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['fundraising_tag'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>id</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>full_text</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>followers_count_no_na</th>\n",
       "      <th>friendsCount_no_na</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>sent_score</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>special_users</th>\n",
       "      <th>bots</th>\n",
       "      <th>anniversary_tag</th>\n",
       "      <th>fundraising_tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1438236436084391936</td>\n",
       "      <td>CitizenWonk</td>\n",
       "      <td>2021-09-15 20:21:02+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @PBIcanada: We congratulate @GreenpeaceCA o...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>32259.000</td>\n",
       "      <td>27188.000</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1438236402114772995</td>\n",
       "      <td>mark_trudgeon</td>\n",
       "      <td>2021-09-15 20:20:54+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @cadebe_: @GJGamble @BorisJohnson @trussliz...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>340.000</td>\n",
       "      <td>1516.000</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1438236275283214343</td>\n",
       "      <td>AspieandMe</td>\n",
       "      <td>2021-09-15 20:20:23+00:00</td>\n",
       "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
       "      <td>RT @GreenpeaceCA: I scream, you scream, WE ALL...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>387.000</td>\n",
       "      <td>672.000</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3</td>\n",
       "      <td>negative</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1438235849058045956</td>\n",
       "      <td>gazetka75</td>\n",
       "      <td>2021-09-15 20:18:42+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @LongTimeAmy: TODAY IN HISTORY\\n\\n1971–Envi...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1438235834415714308</td>\n",
       "      <td>arynpekik</td>\n",
       "      <td>2021-09-15 20:18:38+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @zoev213: Happy 50th @greenpeaceusa Join me...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>446.000</td>\n",
       "      <td>443.000</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id    screen_name                created_at  \\\n",
       "0  1438236436084391936    CitizenWonk 2021-09-15 20:21:02+00:00   \n",
       "1  1438236402114772995  mark_trudgeon 2021-09-15 20:20:54+00:00   \n",
       "2  1438236275283214343     AspieandMe 2021-09-15 20:20:23+00:00   \n",
       "3  1438235849058045956      gazetka75 2021-09-15 20:18:42+00:00   \n",
       "4  1438235834415714308      arynpekik 2021-09-15 20:18:38+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2  <a href=\"https://mobile.twitter.com\" rel=\"nofo...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/android\" ...   \n",
       "\n",
       "                                           full_text  retweet_count  \\\n",
       "0  RT @PBIcanada: We congratulate @GreenpeaceCA o...              7   \n",
       "1  RT @cadebe_: @GJGamble @BorisJohnson @trussliz...              1   \n",
       "2  RT @GreenpeaceCA: I scream, you scream, WE ALL...              2   \n",
       "3  RT @LongTimeAmy: TODAY IN HISTORY\\n\\n1971–Envi...             16   \n",
       "4  RT @zoev213: Happy 50th @greenpeaceusa Join me...              2   \n",
       "\n",
       "   favorite_count  followers_count_no_na  friendsCount_no_na  positive  \\\n",
       "0               0              32259.000           27188.000         3   \n",
       "1               0                340.000            1516.000         1   \n",
       "2               0                387.000             672.000         2   \n",
       "3               0                  1.000              35.000         2   \n",
       "4               0                446.000             443.000         3   \n",
       "\n",
       "   negative  neutral  sent_score sentiment  special_users  bots  \\\n",
       "0        -1        1           2  positive              1     0   \n",
       "1        -1        0           0   neutral              0     0   \n",
       "2        -5       -1          -3  negative              0     0   \n",
       "3        -1        1           1  positive              0     0   \n",
       "4        -1        1           2  positive              0     0   \n",
       "\n",
       "   anniversary_tag  fundraising_tag  \n",
       "0                0                0  \n",
       "1                0                0  \n",
       "2                0                0  \n",
       "3                0                0  \n",
       "4                1                1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Writing a function which will allow me to categorize the results of the tags in one single column, namely the \"Possible Targets\". Among the Tweets, a 15.2% of possible targets have been found**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_df(row):\n",
    "    row['possible_targets'] = 0\n",
    "    if (row['anniversary_tag'] == 1) or (row['fundraising_tag'] == 1):\n",
    "        row['possible_targets'] = 1\n",
    "        \n",
    "    return row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete2 = complete2.apply(categorise_df, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    223\n",
       "1     40\n",
       "Name: possible_targets, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['possible_targets'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.848\n",
       "1   0.152\n",
       "Name: possible_targets, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2['possible_targets'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge 2\n",
    "\n",
    "In this part of the project I will need to:\n",
    "* Show descriptive statistics for the IV and the DV\n",
    "* Create one univariate visualisation for the IV\n",
    "* Create one univariate visualisation for the DV\n",
    "* Create one bivariate visualisation with the IV and DV in the same chart\n",
    "* Show the descriptives of the DV grouped by the IV and interpret them\n",
    "\n",
    "\n",
    "### Step-by-step Interpretation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'screen_name', 'created_at', 'source', 'full_text',\n",
       "       'retweet_count', 'favorite_count', 'followers_count_no_na',\n",
       "       'friendsCount_no_na', 'positive', 'negative', 'neutral', 'sent_score',\n",
       "       'sentiment', 'special_users', 'bots', 'anniversary_tag',\n",
       "       'fundraising_tag', 'possible_targets'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here will be provided the Descriptive Statistics of our Independent Variable and Dependent Variables. As we can see, the avergare Sentiment score of the Tweets which talks about \"Greenpeace\" is positive. In average, any of these Tweets has been retweetted 13 times and liked 3 times by other users reading it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>sent_score</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>263.000</td>\n",
       "      <td>263.000</td>\n",
       "      <td>263.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.019</td>\n",
       "      <td>13.665</td>\n",
       "      <td>3.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.383</td>\n",
       "      <td>54.376</td>\n",
       "      <td>37.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000</td>\n",
       "      <td>648.000</td>\n",
       "      <td>602.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sent_score  retweet_count  favorite_count\n",
       "count     263.000        263.000         263.000\n",
       "mean        0.019         13.665           3.019\n",
       "std         1.383         54.376          37.205\n",
       "min        -4.000          0.000           0.000\n",
       "25%        -1.000          0.000           0.000\n",
       "50%         0.000          0.000           0.000\n",
       "75%         1.000          4.000           0.000\n",
       "max         3.000        648.000         602.000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2[['sent_score', 'retweet_count','favorite_count']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I have made a univariate visualisation for the Independet Variable. Task 2 completed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sent_score', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(complete2['sent_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I have made a univariate visualisation for each of the Dependent Variables. Task 3 completed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='retweet_count', ylabel='Density'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(complete2['retweet_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='favorite_count', ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(complete2['favorite_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I have made a  visualisation for the amount of \"Tokens\" assigned for the\"\"Anniversary Tag\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='anniversary_tag', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V9V7gfcAvAo9m8MX/X935jWfPt+Gq+t9uW+8B3n+IOu8Gju6G59vGN4GfS7KiG3/BIdapKbf00LNIi9ZngN9P8nXgRgaHkBbCx4ALgbcMtb0F+Fvg610w7GLw3/zBzgBem+SHwD3Ai6vq5iRfBXYANwH/dojtfwh4HvC5Q8y3CfiXJHuq6ulzbaOq/ifJHwCfSXIHcM0h1qkp5yWp0iKT5DUM9lL+fIHW96iquqcLs3cB366qdyzEunXkcU9BWkSSXA48HnjGAq7295KsBx7G4LzL3y/gunWEcU9BWuS6oFh5UPMfV9Vn+6hHRzZDQZLUePWRJKkxFCRJjaEgSWoMBUlS83+61XbAhwf8lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='anniversary_tag', data=complete2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I have made two visualisation for the amount of \"Tokens\" assigned for the \"Fundraising Tag\" and \"Anniversary Tag\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fundraising_tag', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='fundraising_tag', data=complete2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='anniversary_tag', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='anniversary_tag', data=complete2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I'm creating a bivariate visualisation with the IV and DV in the same chart. Task 4 completed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sent_score', ylabel='retweet_count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEHCAYAAABGNUbLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVXElEQVR4nO3df5xddX3n8debAIukUEACSfkVl+WBIipgBC0Vq8gWWreJW38srhKV3fjoCgtb3Yi6Ky1dK6TWR+nvzSo1FtrCijygWLXZKLgiWhOkAkalSwETMiQYooAUCHz2j3sCQ5jJ3MzMmTuT83o+HvM4c86959w3KO975nvO/d5UFZKkbtlt0AEkSVPP8pekDrL8JamDLH9J6iDLX5I6yPKXpA7afdAB+nHggQfW/PnzBx1DkmaUNWvWPFBVc0Z6bEaU//z581m9evWgY0jSjJLkntEec9hHkjrI8pekDrL8JamDLH9J6iDLX5I6yPKXpA6y/CWpg2bEff6Spp+lS5cyNDTE3LlzWbZs2aDjaCdZ/pLGZWhoiPXr1w86hsbJYR9J6qBWyz/Jfkk+m+R7SdYmeVWSA5KsTHJns9y/zQySpOdq+8z/UuCLVfVC4GXAWuACYFVVHQWsatYlSVOotfJPsi9wCvApgKp6vKq2AAuBFc3TVgCL2sogSRpZm2f+/xLYBPx5km8n+WSS2cDBVbUBoFkeNNLOSZYkWZ1k9aZNm1qMKUnd02b57w6cAPxpVR0PPMJODPFU1fKqWlBVC+bMGXE6aknSOLVZ/uuAdVX1zWb9s/TeDO5PMg+gWW5sMYMkaQStlX9VDQE/THJ0s+lU4LvAdcDiZtti4Nq2MkiSRtb2h7zOBa5IsidwF/Auem84VyU5G7gXeHPLGSRJ22m1/KvqVmDBCA+d2ubrSpJ2zE/4SlIHWf6S1EGWvyR1kOUvSR1k+UtSB1n+ktRBlr8kdZDlL0kdZPlLUgdZ/pLUQZa/JHWQ5S9JHWT5S1IHWf6S1EGWvyR1kOUvSR1k+UtSB1n+ktRBlr8kdZDlL0kdZPlLUgdZ/pLUQZa/JHWQ5S9JHbR7mwdPcjfwEPAksLWqFiQ5ALgSmA/cDbylqh5sM4ck6dmm4sz/tVV1XFUtaNYvAFZV1VHAqmZdkjSFBjHssxBY0fy+Alg0gAyS1Gltl38Bf5dkTZIlzbaDq2oDQLM8qOUMkqTttDrmD5xcVfclOQhYmeR7/e7YvFksATj88MPbyidJndTqmX9V3dcsNwLXACcC9yeZB9AsN46y7/KqWlBVC+bMmdNmTEnqnNbKP8nsJPts+x3418DtwHXA4uZpi4Fr28ogSRpZm8M+BwPXJNn2On9ZVV9M8i3gqiRnA/cCb24xgyRpBK2Vf1XdBbxshO0/Ak5t63UlSWPzE76S1EGWvyR1kOUvSR1k+UtSB1n+ktRBlr8kdZDlL0kdZPlLUgdZ/pLUQZa/JHWQ5S9JHWT5S1IHWf6S1EGWvyR1kOUvSR1k+UtSB1n+ktRBlr8kdZDlL0kdZPlLUgdZ/pLUQWOWf5J/0c82SdLM0c+Z/819bpMkzRC7j/ZAkrnAIcDzkhwPpHloX2DvKcgmSWrJqOUP/BLwTuBQ4BPDtj8EfKjFTJKklo1a/lW1AliR5Neq6urxvkCSWcBqYH1VvSHJAcCVwHzgbuAtVfXgeI8vSdp5Ozrz3+b6JG+jV9ZPP7+qLurzNc4D1tIbLgK4AFhVVRcnuaBZ/0DfiSVJE9bPBd9rgYXAVuCRYT9jSnIo8CvAJ4dtXgisaH5fASzqM6skaZL0c+Z/aFWdPs7j/z6wFNhn2LaDq2oDQFVtSHLQOI8tSRqnfs78v57kJTt74CRvADZW1ZqdjwVJliRZnWT1pk2bxnMISdIo+jnz/wXgnUn+CXiM3i2fVVUvHWO/k4FfTfLLwF7AvkkuB+5PMq85658HbBxp56paDiwHWLBgQfX3jyNJ6kc/5X/GeA5cVR8EPgiQ5BeB91fV25P8LrAYuLhZXjue40uSxq+fYZ8a5We8LgZOS3IncFqzLkmaQv2c+X+eXtmH3vDNC4DvAy/u90Wq6gbghub3HwGn7mROSdIkGrP8q+pZF3uTnAC8p7VEkqTW7fSUzlV1C/CKFrJIkqbImGf+SX5j2OpuwAmA915K0gzWz5j/8A9obaV3DWDcc/1IkgavnzH/3wJIsk9vtR5uPZUkqVX9fJPXsUm+DdwO3JFkTZJj248mSWpLPxd8lwO/UVVHVNURwPuabZKkGaqf8p9dVV/ZttLcsz+7tUSSpNb1c8H3riT/HfiLZv3twD+1F0mS1LZ+zvzfDcwBPtf8HAi8q81QkqR29XO3z4PAf56CLJKkKdLP3T4rk+w3bH3/JF9qNZUkqVX9DPscWFVbtq00fwn47VuSNIP1U/5PJTl820qSI5jYlM6SpAHr526fDwNfS3Jjs34KsKS9SJKktvVzwfeLzTTOr6Q3p/9/qaoHtj2e5MVVdUeLGSVJk6yfM3+asr9+lIf/gt5Mn5KkGWKn5/MfQSbhGJKkKTQZ5e/FX0maYSaj/CVJM8xklP/jk3AMSdIU6ucTvqt2tK2qXjnZoaQ2LF26lLPOOoulS5cOOoo0cKPe7ZNkL2Bv4MAk+/PMhd19gZ+bgmzSpBoaGmL9+vWDjiFNCzu61fM9wPn0iv6WYdt/Avxxi5kkSS0btfyr6lLg0iTnVtUfTmEmSVLL+rnge1mS/5ZkOUCSo5K8YaydkuyV5O+T/EOSO5Js+yL4A5qZQu9slvtP8J9BkrST+ip/enf0/Hyzvg74H33s9xjwuqp6GXAccHqSVwIXAKuq6ihgVbMuSZpC/ZT/kVW1DHgCoKoepY9P9VbPw83qHs1PAQuBFc32FcCincwsSZqgfsr/8STPo/kkb5Ij6Z3VjynJrCS3AhuBlVX1TeDgqtoA0CxH/G6AJEuSrE6yetOmTf28nCSpT/1M7HYh8EXgsCRXACcD7+zn4FX1JHBc801g1yQ5tt9gVbUcWA6wYMECp5CQJtlH3/6mCe2/eeOPe8uhDeM+1ocv/+yEMmj8+pnSeWWSW3hmSufzhk/p3I+q2pLkBuB04P4k86pqQ5J59P4qkCRNoX4+4RvgDODlVXU9sHeSE/vYb8627/5tho1eD3wPuA5Y3DxtMXDt+KJLksarn2GfPwGeAl4HXAQ8BFwNvGKM/eYBK5LMovcmc1VVXZ/kZuCqJGcD9wJvHm94SdL49FP+J1XVCUm+Db0vcE+y51g7VdV3gONH2P4j4NSdTipJmjT93O3zRHP2vu1unzn0/hKQJM1Q/ZT/HwDXAAcl+SjwNeB3Wk0lSWpVP3f7XJFkDb2hmgCLqmpt68kkSa0Zs/yTXAT8X+DTVfVI+5EkSW3rZ9jnbuBMYHUzUdvvJVnYbixJUpvGLP+quqyq3g28Fric3q2Zl7cdTJLUnn6GfT4JHAPcT2/45008+8tdJEkzTD/DPs8HZgFbgM3AA1W1tc1QkqR29XO3zxsBkrwI+CXgK0lmVdWhbYeTJLWjn2GfNwCvBk4B9ge+TG/4R5I0Q/UzvcMZwFeBS6vqvpbzSJKmQD9j/g9X1ZXDiz/JJS1mkiS1rJ/yP22EbWdMdhBJ0tQZddgnya8D/wk4Msl3hj20D3BT28EkSe3Z0Zj/XwJfAD4GXDBs+0NVtbnVVJKkVo067FNVP66qu6vqTOAw4HVVdQ+wW5IXTFlCSdKk6+drHC8EPgB8sNm0J07vIEkzWj8XfN8I/CrwCEBz188+bYaSJLWrn/J/vKqKZ77Ja3a7kSRJbdth+ScJcH2S/wnsl+Q/Av8H+F9TEU6S1I4dfsK3qirJInpj/j8BjgY+UlUrpyCbJKkl/UzvcDOwpar+a9thJElTo5/yfy3wniT30Fz0Baiql7aWSpLUqn4ndpMk7UL6mc//nvEcOMlhwGeAucBTwPKqujTJAcCVwHx63w/8lqp6cDyvIe2Kli5dytDQEHPnzmXZsmWDjqNdVD+3eo7XVuB9VfUi4JXAe5McQ2+qiFVVdRSwimdPHSF13tDQEOvXr2doaGjQUbQLa638q2pDVd3S/P4QsBY4BFgIrGietgJY1FYGSdLI2jzzf1qS+cDxwDeBg6tqA/TeIICDpiKDJOkZrZd/kp8BrgbOr6qf7MR+S5KsTrJ606ZN7QWUpA5qtfyT7EGv+K+oqs81m+9PMq95fB6wcaR9q2p5VS2oqgVz5sxpM6YkdU5r5d9MDfEpYG1VfWLYQ9cBi5vfFwPXtpVBkjSyfu7zH6+TgXcAtyW5tdn2IeBi4KokZwP3Am9uMYOkDvO22dG1Vv5V9TUgozx8aluvK0nbbLttVs81JXf7SJKmF8tfkjrI8pekDrL8JamDLH9J6iDLX5I6yPKXpA6y/CWpgyx/Seogy1+SOsjyl6QOsvwlqYMsf0nqIMtfkjrI8pekDrL8JamD2vwmL2lS3XjKaya0/6O7z4KER9etm9CxXvPVGyeUQ5oOPPOXpA6y/CWpgyx/Seogy1+SOsjyl6QOsvwlqYMsf0nqIO/zlybZH73vbya0/5YHHnl6Od5jnfN7/2ZCGbTra/XMP8llSTYmuX3YtgOSrExyZ7Pcv80MkqTnanvY59PA6dttuwBYVVVHAauadUnSFGq1/Kvqq8Dm7TYvBFY0v68AFrWZQZL0XIO44HtwVW0AaJYHDSCDJHXatL3gm2QJsATg8MMPH3AaSYOy9qNfHve+j29+9OnlRI7zog+/btz7TleDOPO/P8k8gGa5caQnVdXyqlpQVQvmzJkzpQElaVc3iPK/Dljc/L4YuHYAGSSp09q+1fOvgJuBo5OsS3I2cDFwWpI7gdOadUnSFGp1zL+qzhzloVPbfF1J0o5N2wu+kqa3vWbt9qylZhbLX9K4HP/8fQYdQRPgW7YkdZDlL0kdZPlLUgdZ/pLUQZa/JHWQ5S9JHWT5S1IHWf6S1EGWvyR1kJ/w1YQtXbqUoaEh5s6dy7JlywYdR1IfLH9N2NDQEOvXrx90DEk7wfKXppnZe+77rKXUBstfmmZOPvLfDjqCOsALvpLUQZ75T2NeSJXUFst/GvNCqqS2OOwjSR3kmb84+Q9PntD+e27Zk93YjR9u+eGEjnXTuTdNKMdY9qt61lK7vufv9bPPWuoZlr864+1PPjXoCJpi5xz/tkFHmLYc9pGkDrL8JamDHPZp0b0XvWRC+2/dfACwO1s33zOhYx3+kdsmlEPSrsczf0nqoIGVf5LTk3w/yT8muWBQOSSpiwZS/klmAX8MnAEcA5yZ5JhBZJGkLhrUmf+JwD9W1V1V9Tjw18DCAWXRBNXexVOzn6L29v55aaZIDeADL0neBJxeVf+hWX8HcFJVnTPsOUuAJc3q0cD3JznGgcADk3zMNphzcplzcs2EnDMhI7ST84iqmjPSA4O62ycjbHvWu1BVLQeWtxYgWV1VC9o6/mQx5+Qy5+SaCTlnQkaY+pyDGvZZBxw2bP1Q4L4BZZGkzhlU+X8LOCrJC5LsCfw74LoBZZGkzhnIsE9VbU1yDvAlYBZwWVXdMcUxWhtSmmTmnFzmnFwzIedMyAhTnHMgF3wlSYPlJ3wlqYMsf0nqoE6Xf5JXJHmy+dzBtJNkYZLvJLk1yeokvzDoTNtL8u+bjN9J8vUkLxt0ppEkeWGSm5M8luT9g84zmpkw7UmSy5JsTHL7oLPsSJLDknwlydokdyQ5b9CZRpJkryR/n+Qfmpy/NSWv29Ux/2aKiZXAP9O74PzZAUd6jiQ/AzxSVZXkpcBVVfXCQecaLsnPA2ur6sEkZwC/WVUnDTrX9pIcBBwBLAIerKqPDzbRczX/n/wBcBq926G/BZxZVd8daLDtJDkFeBj4TFUdO+g8o0kyD5hXVbck2QdYAyyahv8+A8yuqoeT7AF8DTivqr7R5ut2+cz/XOBqYOOgg4ymqh6uZ96dZ7PdB+Gmg6r6elU92Kx+g95nNqadqtpYVd8Cnhh0lh2YEdOeVNVXgc2DzjGWqtpQVbc0vz8ErAUOGWyq56qeh5vVPZqf1v9b72T5JzkEeCPwZ4POMpYkb0zyPeDzwLsHnWcMZwNfGHSIGewQ4IfD1tcxDctqJkoyHzge+OaAo4woyawkt9I7GV1ZVa3n7GT5A78PfKCqnhx0kLFU1TXNUM8i4LcHHGdUSV5Lr/w/MOgsM9iY055o5zXDp1cD51fVTwadZyRV9WRVHUfvL+cTk7Q+nNaZ8k/y3ubC6a3AAuCvk9wNvAn4kySLBhjvacNzJvm5bdubP7WPTHLgAOMBz83YXI/4JLCwqn406HzbjPbvchpz2pNJ1oyhXw1cUVWfG3SesVTVFuAG4PS2X6uzF3y3SfJp4PppesH3XwH/r7ngewLwN8ChNY3+R0tyOPBl4Kyq+vqg84wlyW8CD0/TC76707vgeyqwnt4F37cN4NPvY2qGUa6f5hd8A6wANlfV+QOOM6okc4AnqmpLkucBfwdcUlXXt/m6fofv9PZrwFlJngAeBd46nYq/8RHg+fT+egLYOh1nUEwyF1gN7As8leR84JjpNAwwTaY9GVOSvwJ+ETgwyTrgwqr61GBTjehk4B3Abc1f/AAfqqq/HVykEc0DVjR3e+1G766+VosfPPOXpE7qzJi/JOkZlr8kdZDlL0kdZPlLUgdZ/tIOJDkuyS8POoc02Sx/aceOA6a0/Jv7/aVWWf7aZSWZneTzzVS5tyd5a5KXJ7kxyZokX2pmfiTJDUkuaabW/UGSVzffL30R8NbmU8JvHeV1XjPsk8TfbmaQJMnSJLc1r39xs+24JN9opsC+Jsn+w17/d5LcCJw3Wk5psniGoV3Z6cB9VfUrAEl+lt7EcwuralNT5h/lmQnzdq+qE5thngur6vVJPgIsqKpzdvA67wfeW1U3NfPI/HMzvfUi4KSq+mmSA5rnfgY4t6puTHIRcCFwfvPYflX1mmZKght3kFOaMMtfu7LbgI8nuQS4HngQOBZY2XwaeRawYdjzt839sgaYvxOvcxPwiSRXAJ+rqnVJXg/8eVX9FKCqNjdvPvtV1Y3NfiuA/z3sOFc2y6PHyClNmOWvXVZV/SDJy+mN2X+M3pf33FFVrxpll8ea5ZPsxH8bVXVxks83r/ONpvjDzs/I+UizzBg5pQlzzF+7rGYmz59W1eXAx4GTgDlJXtU8vkeSF49xmIeAfcZ4nSOr6raquoTe/EEvpDc517uT7N0854Cq+jHwYJJXN7u+g97wzva+P46c0k7xzF+7spcAv5vkKXrf4PXrwFbgD5ohmN3pfbfDjiZP+wpwQTMx2Meq6soRnnN+830GTwLfBb5QVY8lOQ5YneRx4G+BDwGLgT9r3hTuAt61/cGq6vH0vld6Z3JKO8WJ3SSpgxz2kaQOcthH6lOSdwHnbbf5pqp67yDySBPhsI8kdZDDPpLUQZa/JHWQ5S9JHWT5S1IHWf6S1EGWvyR10P8HoWEiPKQeFnkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sent_score', y='retweet_count', data=complete2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sent_score', ylabel='favorite_count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='sent_score', y='favorite_count', data=complete2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I am providing the descriptives of the Dependent Variables grouped by the Independet Variable. As we can see the average number of Retweets goes higher as the Sentiment expressed in the Tweet is more positive (an average of 9 retweets if the Tweets expressed a negative sentiment vs 11.5 retweets if the Tweets expressed a neutral sentiment vs 20 retweets if the Tweets expressed a positive sentiment). The same can be said about the number of Favorites: Tweets got an average of 0.6 or 0.7 likes for Tweets expressing a negative or neutral sentiment vs an average of 7.5 likes ofr every Tweet expressing a positive Sentiment. Task 5 completed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-c393f3ffc914>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  complete2.groupby(['sentiment'])['retweet_count','favorite_count'].describe().transpose()\n"
     ]
    },
    {
     "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>sentiment</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">retweet_count</th>\n",
       "      <th>count</th>\n",
       "      <td>84.000</td>\n",
       "      <td>90.000</td>\n",
       "      <td>89.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.131</td>\n",
       "      <td>11.500</td>\n",
       "      <td>20.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.310</td>\n",
       "      <td>39.985</td>\n",
       "      <td>75.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">favorite_count</th>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>602.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment             negative  neutral  positive\n",
       "retweet_count  count    84.000   90.000    89.000\n",
       "               mean      9.131   11.500    20.135\n",
       "               std      38.310   39.985    75.730\n",
       "               min       0.000    0.000     0.000\n",
       "               25%       0.000    0.000     0.000\n",
       "...                        ...      ...       ...\n",
       "favorite_count min       0.000    0.000     0.000\n",
       "               25%       0.000    0.000     0.000\n",
       "               50%       0.000    0.000     0.000\n",
       "               75%       1.000    0.000     0.000\n",
       "               max      20.000   38.000   602.000\n",
       "\n",
       "[16 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete2.groupby(['sentiment'])['retweet_count','favorite_count'].describe().transpose()"
   ]
  }
 ],
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