Lecture02_BasicSocialMediaDataAnalysis.ipynb

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        "# Lecture 2 - Introduction to Social Media Analytics with Python\n",
        "\n",
        "In this notebook we will learn the basics for analyzing  social media data with Python.  We will study tweets collected by keyword, tweets collected by user, and user profiles.  Some of the skills you will learn include searching and sorting dataframes and making bar and scatter plots.  For more details on the dataframe functions used in this notebook, you can look here: https://pandas.pydata.org/docs/index.html\n",
        "\n",
        "This notebook can be opened in Colab\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zlisto/social_media_analytics/blob/main/Lecture02_BasicSocialMediaDataAnalysis.ipynb)\n",
        "\n",
        "Before starting, select \"Runtime->Factory reset runtime\" to start with your directories and environment in the base state.\n",
        "\n",
        "If you want to save changes to the notebook, select \"File->Save a copy in Drive\" from the top menu in Colab.  This will save the notebook in your Google Drive.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZa4aZX9i0kG"
      },
      "source": [
        "# Clone GitHub Repository\n",
        "This will clone the repository to your machine.  This includes the code and data files.  Then change into the directory of the repository."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XWxUlXjWi0kH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "766443de-e582-4c79-c552-ab014f1dc9b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'social_media_analytics'...\n",
            "remote: Enumerating objects: 638, done.\u001b[K\n",
            "remote: Counting objects: 100% (203/203), done.\u001b[K\n",
            "remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
            "remote: Total 638 (delta 103), reused 174 (delta 75), pack-reused 435\u001b[K\n",
            "Receiving objects: 100% (638/638), 71.08 MiB | 7.55 MiB/s, done.\n",
            "Resolving deltas: 100% (335/335), done.\n",
            "Updating files: 100% (68/68), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/zlisto/social_media_analytics\n",
        "\n",
        "import os\n",
        "os.chdir(\"social_media_analytics\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvVNT6aCi0kL"
      },
      "source": [
        "## Install Requirements\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ISdCbxAii0kM",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
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      ],
      "source": [
        "pip install -q -r requirements.txt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLY42JRKi0kO"
      },
      "source": [
        "## Import packages\n",
        "\n",
        "We import the packages we are going to use.  A package contains several useful functions that make our life easier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zgoHdCXTi0kP"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#this option makes it so tweets display nicely in a dataframe\n",
        "pd.set_option(\"display.max_colwidth\", None)\n",
        "\n",
        "#this code sets the font sizes for plots\n",
        "plt.rcParams.update({'axes.labelsize': 18, 'xtick.labelsize': 14, 'ytick.labelsize': 14})\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5N1fBACi0kQ"
      },
      "source": [
        "# Keyword Tweets\n",
        "\n",
        "We begin with a set of tweets that contain a specific hashtag.  These were found using Twitter's Search API.  The tweets were saved in a csv (comma separated values) file called `\"data/lecture_02_tweets.csv\"`.  The filename is stored in the variable `fname`.   Each row\n",
        "of this file is a tweet with many columns of information.  The most important columns are \"created_at,screen_name,text\".  \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDkN1J_xi0kS"
      },
      "source": [
        "### Load Keyword Tweets\n",
        "We load the tweets from the database using the `pd.read_csv` function. The tweets are loaded into a variable called **df** which is a *dataframe*.  Dataframes store each tweet as a row and let us access the rows and columns easily.  We will use dataframes a lot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWzNafNDi0kS"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-cNAcwkJi0kT"
      },
      "outputs": [],
      "source": [
        "#filename of csv file\n",
        "fname = 'data/lecture_02_tweets.csv'\n",
        "df = pd.read_csv(fname)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuDy5tAWi0kU"
      },
      "source": [
        "### Look at the tweets using head() function\n",
        "After we load the tweets in `df`, we look at the first few tweets using the `head` function.  We can specify how many rows to show using the `n` parameter.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RbnlmRBRi0kV"
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      "outputs": [],
      "source": [
        "\n",
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              "                 created_at     screen_name  \\\n",
              "0  2022-01-07T23:58:24.000Z   DoraineGenius   \n",
              "1  2022-01-07T23:54:41.000Z  MrLegendaryAPE   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                         text  \\\n",
              "0             Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV   \n",
              "1  I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN   \n",
              "\n",
              "  lang  retweet_count  reply_count  like_count  quote_count  \\\n",
              "0   en              1            0           4            0   \n",
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              "1  1479602421311553536  1424343723563913223  1479602421311553536   \n",
              "\n",
              "   in_reply_to_user_id  geo  \\\n",
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              "0  {'urls': [{'start': 276, 'end': 299, 'url': 'https://t.co/b0maPJirBV', 'expanded_url': 'https://twitter.com/DoraineGenius/status/1479603356083658753/photo/1', 'display_url': 'pic.twitter.com/b0maPJirBV'}], 'hashtags': [{'start': 127, 'end': 137, 'tag': 'Metaverse'}, {'start': 138, 'end': 156, 'tag': 'BoredApeYachtClub'}, {'start': 157, 'end': 164, 'tag': 'NewNFT'}, {'start': 166, 'end': 175, 'tag': 'Ethereum'}, {'start': 176, 'end': 184, 'tag': 'bitcoin'}, {'start': 185, 'end': 196, 'tag': 'cryptonews'}, {'start': 197, 'end': 205, 'tag': 'NFTnews'}, {'start': 206, 'end': 211, 'tag': 'NFTs'}, {'start': 212, 'end': 223, 'tag': 'NFTartists'}, {'start': 224, 'end': 237, 'tag': 'nftcollector'}, {'start': 238, 'end': 249, 'tag': 'nftgallery'}, {'start': 250, 'end': 255, 'tag': 'NFTs'}, {'start': 256, 'end': 266, 'tag': 'cryptoart'}, {'start': 267, 'end': 275, 'tag': 'Cryptos'}], 'mentions': [{'start': 110, 'end': 126, 'username': 'luckykittensnft', 'id': '1449251281848213505'}]}  \n",
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              "      <th>0</th>\n",
              "      <td>2022-01-07T23:58:24.000Z</td>\n",
              "      <td>DoraineGenius</td>\n",
              "      <td>Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV</td>\n",
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              "      <th>1</th>\n",
              "      <td>2022-01-07T23:54:41.000Z</td>\n",
              "      <td>MrLegendaryAPE</td>\n",
              "      <td>I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.head(n=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUNpkGwRi0kW"
      },
      "source": [
        "### Select Columns of Dataframe\n",
        "\n",
        "Sometimes we just want to look at a few columns of the dataframe.  We can do this by putting the names of the columns we want into a *list*.  In Python, lists have the format `[item_1,item_2,...,item_n]`.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o2Lo0s3di0kX"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "scrolled": true,
        "id": "UuFO1NIci0kX",
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          "height": 790
        },
        "outputId": "b14bf0c4-dde7-4b2c-983a-92a13b5dbc28"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        screen_name  \\\n",
              "0     DoraineGenius   \n",
              "1    MrLegendaryAPE   \n",
              "2           ke1lerr   \n",
              "3     InfiniteDodos   \n",
              "4            __jiri   \n",
              "5       nft_tracker   \n",
              "6    kxngoftheether   \n",
              "7   metaverseunited   \n",
              "8   thehightablenft   \n",
              "9      DavillinNFTs   \n",
              "10       Eddies_NFT   \n",
              "11        Sadmannft   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                          text  \n",
              "0              Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV  \n",
              "1   I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN  \n",
              "2                                             This was me until I met the @_cryptoapes family now I'm out here spending 7 hours straight with them chattin and crapeing on spaces! #BoredApeYachtClub and #cryptopunks communities have been successfully united already, many more to come :) #CRAPES https://t.co/O9pdAwoRZm  \n",
              "3                                                                                                                 First community AMA announcing 4 functional DAOS at mint was exciting!! \\n\\nDiscord is open 🔓\\n\\nhttps://t.co/Nf7EhfJyYj\\n\\nMore announced soon! \\n\\n#NFT #opensea #NFTGiveaway #eth #DAO #BoredApeYachtClub  \n",
              "4                                                Cryptos are down but #NFTs are up!  Are you selling for a profit or holding?\\n\\n#LazyLionsNFT #BAYC #boredapeyachtclub #CyberKongz #Cryptopunks #Fidenza #veefriends #NFTCollector #NFTNews #nftcommunity #cryptonft #nftcollector #NFTart #cryptoart https://t.co/UaZOaFx5Xv  \n",
              "5                                                                                                                                                             Sold! Bored Ape was purchased by sadsigns_vault for Ξ69.0 ($222k) $ETH #NFT #NFTs  #art #cryptoart  @BoredApeYC #BAYC #BoredApeYachtClub https://t.co/PS0eTxJUj1  \n",
              "6                                            𝕿𝖍𝖊 𝕶𝖝𝖓𝖌 𝖔𝖋 𝖙𝖍𝖊 𝕰𝖙𝖍𝖊𝖗 𝖍𝖆𝖘 𝖗𝖎𝖘𝖊𝖓!!!\\n#nft #nfts #nftart #nftcollector #cryptoart #ethereum #polygon #blockchain #Solana #boredapeyachtclub #cryptopunks #nftcommunity #rarible #opensea #nftcollectibles #digitalart #mint #minting #discord #ipfs #erc721 https://t.co/fM0c4oRbX0  \n",
              "7                   🌴NEW Rare Bored Apes Minted NOW!🦧\\n🔥SELLING FAST🔥\\n🚀Get in NOW,take advantage of the Floor Price\\n\\nOpensea: https://t.co/6QS6MbHtyU\\n\\n#NFTCommmunity  #NFTGiveaway #NFTdrop #NFTartists #nftcollectors #Metaverse #crypto #Bitcoin #Ethereum #opensea #BoredApeYachtClub #APENFT https://t.co/vtVBYdt199  \n",
              "8                   One Lucky Person That Mints A HighTable NFT Will Be Awarded 1 Pound Of Top Shelf Flowers 🔥💨 + All NFT Holders Will Be Granted 1 Quater Of Top Shelf Flowers Every Two Weeks ! #nft #NFTs #NFTCommunity #NFTGame #nftcollectors #opensea #BoredApeYachtClub #NFTdrop #NFTCollection https://t.co/ltH52FH5L7  \n",
              "9                                                                                                                                         MINTING YOUR NFTs\\n\\nhttps://t.co/T5YZLKo8rR\\n\\n#NFTs  #NFTCommunity  #NFTGame  #NFTdrop  #NFTGiveaways  #cryptopunks  #BoredApeYachtClub  #cryptocurrency  #Twitter #ETH  #Ethereum  \n",
              "10                                                                                     Hi everyone, I’m a new NFT account who is following everyone back who follows me. Let’s share the NFT love❤️#nft #FreeNFTs #Solana #NFTSPACESHIP #BoredApeYachtClub #nftart #BoredApeYC #NFTCollector #NFTCommumity #FreeNFT #NFTartist  \n",
              "11                                                                              Sadman loki 012 click the link un my bio . https://t.co/FLLJEqUjYc\\n\\n20% of the funds will be donated to an association #nftnews #apeboredyachtclub #BoredApeYachtClub #NFTCommunity #Ethereum #OpenSeaNFT #NFTs #nft https://t.co/OlBRamsp1i  "
            ],
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              "\n",
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              "    <div>\n",
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              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DoraineGenius</td>\n",
              "      <td>Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MrLegendaryAPE</td>\n",
              "      <td>I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ke1lerr</td>\n",
              "      <td>This was me until I met the @_cryptoapes family now I'm out here spending 7 hours straight with them chattin and crapeing on spaces! #BoredApeYachtClub and #cryptopunks communities have been successfully united already, many more to come :) #CRAPES https://t.co/O9pdAwoRZm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>InfiniteDodos</td>\n",
              "      <td>First community AMA announcing 4 functional DAOS at mint was exciting!! \\n\\nDiscord is open 🔓\\n\\nhttps://t.co/Nf7EhfJyYj\\n\\nMore announced soon! \\n\\n#NFT #opensea #NFTGiveaway #eth #DAO #BoredApeYachtClub</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__jiri</td>\n",
              "      <td>Cryptos are down but #NFTs are up!  Are you selling for a profit or holding?\\n\\n#LazyLionsNFT #BAYC #boredapeyachtclub #CyberKongz #Cryptopunks #Fidenza #veefriends #NFTCollector #NFTNews #nftcommunity #cryptonft #nftcollector #NFTart #cryptoart https://t.co/UaZOaFx5Xv</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>nft_tracker</td>\n",
              "      <td>Sold! Bored Ape was purchased by sadsigns_vault for Ξ69.0 ($222k) $ETH #NFT #NFTs  #art #cryptoart  @BoredApeYC #BAYC #BoredApeYachtClub https://t.co/PS0eTxJUj1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>kxngoftheether</td>\n",
              "      <td>𝕿𝖍𝖊 𝕶𝖝𝖓𝖌 𝖔𝖋 𝖙𝖍𝖊 𝕰𝖙𝖍𝖊𝖗 𝖍𝖆𝖘 𝖗𝖎𝖘𝖊𝖓!!!\\n#nft #nfts #nftart #nftcollector #cryptoart #ethereum #polygon #blockchain #Solana #boredapeyachtclub #cryptopunks #nftcommunity #rarible #opensea #nftcollectibles #digitalart #mint #minting #discord #ipfs #erc721 https://t.co/fM0c4oRbX0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>metaverseunited</td>\n",
              "      <td>🌴NEW Rare Bored Apes Minted NOW!🦧\\n🔥SELLING FAST🔥\\n🚀Get in NOW,take advantage of the Floor Price\\n\\nOpensea: https://t.co/6QS6MbHtyU\\n\\n#NFTCommmunity  #NFTGiveaway #NFTdrop #NFTartists #nftcollectors #Metaverse #crypto #Bitcoin #Ethereum #opensea #BoredApeYachtClub #APENFT https://t.co/vtVBYdt199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>thehightablenft</td>\n",
              "      <td>One Lucky Person That Mints A HighTable NFT Will Be Awarded 1 Pound Of Top Shelf Flowers 🔥💨 + All NFT Holders Will Be Granted 1 Quater Of Top Shelf Flowers Every Two Weeks ! #nft #NFTs #NFTCommunity #NFTGame #nftcollectors #opensea #BoredApeYachtClub #NFTdrop #NFTCollection https://t.co/ltH52FH5L7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>DavillinNFTs</td>\n",
              "      <td>MINTING YOUR NFTs\\n\\nhttps://t.co/T5YZLKo8rR\\n\\n#NFTs  #NFTCommunity  #NFTGame  #NFTdrop  #NFTGiveaways  #cryptopunks  #BoredApeYachtClub  #cryptocurrency  #Twitter #ETH  #Ethereum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Eddies_NFT</td>\n",
              "      <td>Hi everyone, I’m a new NFT account who is following everyone back who follows me. Let’s share the NFT love❤️#nft #FreeNFTs #Solana #NFTSPACESHIP #BoredApeYachtClub #nftart #BoredApeYC #NFTCollector #NFTCommumity #FreeNFT #NFTartist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sadmannft</td>\n",
              "      <td>Sadman loki 012 click the link un my bio . https://t.co/FLLJEqUjYc\\n\\n20% of the funds will be donated to an association #nftnews #apeboredyachtclub #BoredApeYachtClub #NFTCommunity #Ethereum #OpenSeaNFT #NFTs #nft https://t.co/OlBRamsp1i</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5dd3a283-116d-4398-a130-1c4526036651 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5dd3a283-116d-4398-a130-1c4526036651');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9c592f62-5336-43d7-a0df-9bac646182d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c592f62-5336-43d7-a0df-9bac646182d9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9c592f62-5336-43d7-a0df-9bac646182d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "cols = ['screen_name','text']\n",
        "df[cols].head(n=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebyhZZ0Ni0kY"
      },
      "source": [
        "### Sample Rows of Dataframe\n",
        "\n",
        "The `head` function will give the first few rows of the dataframe.  We can use the `sample` function to randomly sample a fixed number of rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZwpFsriXi0kY"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "0VBkppcRi0kZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "7fc0f5b2-616e-4636-ba67-15ed904811b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       screen_name  \\\n",
              "3153        n0mayd   \n",
              "1753  UbuQuichotte   \n",
              "\n",
              "                                                                                                                                                                                                text  \n",
              "3153  Brown Punk NFT\\n👇🏻👇🏻👇🏻👇🏻👇🏻👇🏻\\n\\nhttps://t.co/10YF665P6g\\n\\n#NFT #nftart #NFTCommunity #NFTartist #NFTs #nftcollector #NftCryptoArt #NFTCREATORS #brownpunk #NFT #CryptoPunk #BoredApeYachtClub  \n",
              "1753                                                       Zombie Ape #206 from our new collection. https://t.co/YD7VoSlq4A #new #nft #BoredApeYachtClub #MutantApeYachtClub https://t.co/XeJILiGdB1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4ffb21b-4c05-4311-98d1-09fcdcdd27dc\" class=\"colab-df-container\">\n",
              "    <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>screen_name</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3153</th>\n",
              "      <td>n0mayd</td>\n",
              "      <td>Brown Punk NFT\\n👇🏻👇🏻👇🏻👇🏻👇🏻👇🏻\\n\\nhttps://t.co/10YF665P6g\\n\\n#NFT #nftart #NFTCommunity #NFTartist #NFTs #nftcollector #NftCryptoArt #NFTCREATORS #brownpunk #NFT #CryptoPunk #BoredApeYachtClub</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1753</th>\n",
              "      <td>UbuQuichotte</td>\n",
              "      <td>Zombie Ape #206 from our new collection. https://t.co/YD7VoSlq4A #new #nft #BoredApeYachtClub #MutantApeYachtClub https://t.co/XeJILiGdB1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4ffb21b-4c05-4311-98d1-09fcdcdd27dc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e4ffb21b-4c05-4311-98d1-09fcdcdd27dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e4ffb21b-4c05-4311-98d1-09fcdcdd27dc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4e437ba5-474d-4e97-b119-20cb7e546289\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e437ba5-474d-4e97-b119-20cb7e546289')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e437ba5-474d-4e97-b119-20cb7e546289 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df[ ['screen_name','text']].sample(n=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eys_WsWki0kZ"
      },
      "source": [
        "### Search for Tweets Containing Keywords\n",
        "\n",
        "We can search for tweets in the dataframe which contains a specific keyword.  We do this with the `contains` function.  This function takes the keyword as input in the form of a string (this means you put the word inside quotes).  It also has a parameter `case` which is `True` if you want to match the case of the keyword.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C-RCnddsi0ka"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "saN_Bgxui0ka",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "6b714d9d-fb5d-430f-dcdb-a3281c3fa8cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        screen_name  \\\n",
              "2441  moon_patriot1   \n",
              "4684     NFTWannaBs   \n",
              "4211      TraderHac   \n",
              "4614   PrinceGersie   \n",
              "2227       cryptscp   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                   text  \n",
              "2441                                                                                                                                                                                                                   @apebayc @machinegunkelly get on #TribeQuokka for a #RivalNFTs to @Eminem and #BoredApeYachtClub  \n",
              "4684                                                                                                                                                                            @Eminem becoming a bored ape whats next @Drake becoming an NFTWannaB? Come on support a fellow Canadian 🙏😂😂😂 \\n#NFTs #BoredApeYachtClub  \n",
              "4211                                                                                                                                       @Eminem @britneyspears Please check my NFT listing. Open to negotiate for a good price 😊 https://t.co/ahCexTLAid #OpenSea #BAYC #BoredApeYachtClub #MAYC #NFT #NFTcollection  \n",
              "4614  Save over $100 if you buy this listing now! @Coingecko in partnership with @GoGalaGames \\n\\n#NFT #Opensea #NFTGiveaway #nftcollector #NFTCommunity #NFTGame #PlayToEarn @TownStarGame #OpenSeaNFT #OpenSeaNTF #BoredApeYachtClub #Crypto $Eth $BTC $Sol $BNB #Eminem $XRP $GALA #erc20gas https://t.co/vxycH4u6ZM  \n",
              "2227                                                                                                                                                       #NFTIndustryNews #BoredApeYachtClub Eminem Purchases BAYC NFT Coined ‘EminApe’ https://t.co/r70s39RmPq NFTs: https://t.co/nBUFvon3KU https://t.co/owjjxffiNW  "
            ],
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              "    <tr>\n",
              "      <th>2441</th>\n",
              "      <td>moon_patriot1</td>\n",
              "      <td>@apebayc @machinegunkelly get on #TribeQuokka for a #RivalNFTs to @Eminem and #BoredApeYachtClub</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4684</th>\n",
              "      <td>NFTWannaBs</td>\n",
              "      <td>@Eminem becoming a bored ape whats next @Drake becoming an NFTWannaB? Come on support a fellow Canadian 🙏😂😂😂 \\n#NFTs #BoredApeYachtClub</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4211</th>\n",
              "      <td>TraderHac</td>\n",
              "      <td>@Eminem @britneyspears Please check my NFT listing. Open to negotiate for a good price 😊 https://t.co/ahCexTLAid #OpenSea #BAYC #BoredApeYachtClub #MAYC #NFT #NFTcollection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4614</th>\n",
              "      <td>PrinceGersie</td>\n",
              "      <td>Save over $100 if you buy this listing now! @Coingecko in partnership with @GoGalaGames \\n\\n#NFT #Opensea #NFTGiveaway #nftcollector #NFTCommunity #NFTGame #PlayToEarn @TownStarGame #OpenSeaNFT #OpenSeaNTF #BoredApeYachtClub #Crypto $Eth $BTC $Sol $BNB #Eminem $XRP $GALA #erc20gas https://t.co/vxycH4u6ZM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2227</th>\n",
              "      <td>cryptscp</td>\n",
              "      <td>#NFTIndustryNews #BoredApeYachtClub Eminem Purchases BAYC NFT Coined ‘EminApe’ https://t.co/r70s39RmPq NFTs: https://t.co/nBUFvon3KU https://t.co/owjjxffiNW</td>\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "keyword = 'eminem'\n",
        "ind = df.text.str.contains(keyword, case = False)\n",
        "cols = ['screen_name','text']\n",
        "\n",
        "df[ind][cols].sample(n=5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edo5X7lXi0kb"
      },
      "source": [
        "### Add Column to Dataframe\n",
        "\n",
        "We can add a column to the dataframe to make data analysis easier.  Let's add a column called `\"has_keyword\"` which is `True` if the tweet has the word \"eminem\".  This can be done by doing `df[\"has_keyword\"] = column you want to add`.  In our case, the column we want to add is given by `df.text.str.contains(keyword, case = False)`.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3ICl172pFL8S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "9swwJMKIi0kb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "acf63ea2-9899-45db-e060-176081528b88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      screen_name  \\\n",
              "0   DoraineGenius   \n",
              "1  MrLegendaryAPE   \n",
              "2         ke1lerr   \n",
              "3   InfiniteDodos   \n",
              "4          __jiri   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                         text  \\\n",
              "0             Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV   \n",
              "1  I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN   \n",
              "2                                            This was me until I met the @_cryptoapes family now I'm out here spending 7 hours straight with them chattin and crapeing on spaces! #BoredApeYachtClub and #cryptopunks communities have been successfully united already, many more to come :) #CRAPES https://t.co/O9pdAwoRZm   \n",
              "3                                                                                                                First community AMA announcing 4 functional DAOS at mint was exciting!! \\n\\nDiscord is open 🔓\\n\\nhttps://t.co/Nf7EhfJyYj\\n\\nMore announced soon! \\n\\n#NFT #opensea #NFTGiveaway #eth #DAO #BoredApeYachtClub   \n",
              "4                                               Cryptos are down but #NFTs are up!  Are you selling for a profit or holding?\\n\\n#LazyLionsNFT #BAYC #boredapeyachtclub #CyberKongz #Cryptopunks #Fidenza #veefriends #NFTCollector #NFTNews #nftcommunity #cryptonft #nftcollector #NFTart #cryptoart https://t.co/UaZOaFx5Xv   \n",
              "\n",
              "   has_keyword  \n",
              "0        False  \n",
              "1        False  \n",
              "2        False  \n",
              "3        False  \n",
              "4        False  "
            ],
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DoraineGenius</td>\n",
              "      <td>Hong Kong NFT start-ups jockey to become the next Bored Apes and CryptoPunks in an increasingly crowded field\\n@luckykittensnft #Metaverse #BoredApeYachtClub #NewNFT\\n\\n#Ethereum #bitcoin\\n#cryptonews #NFTnews #NFTs #NFTartists #nftcollector #nftgallery #NFTs #cryptoart #Cryptos https://t.co/b0maPJirBV</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MrLegendaryAPE</td>\n",
              "      <td>I think head bands in @BoredApeYC are super under valued.\\n\\nI'm not sure if anyone realizes this.\\nBut the head band says\\n (\"Godly Winds\")\\n\\nIf that's not cool I don't know what is.\\n\\nRandom? Or purposeful?\\n\\n@BoredApeYC\\n#MAYC #BAYC\\n#BoredApeYachtClub #BoredApeYC #MutantApeYachtClub https://t.co/e3NWZZAgyN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ke1lerr</td>\n",
              "      <td>This was me until I met the @_cryptoapes family now I'm out here spending 7 hours straight with them chattin and crapeing on spaces! #BoredApeYachtClub and #cryptopunks communities have been successfully united already, many more to come :) #CRAPES https://t.co/O9pdAwoRZm</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>InfiniteDodos</td>\n",
              "      <td>First community AMA announcing 4 functional DAOS at mint was exciting!! \\n\\nDiscord is open 🔓\\n\\nhttps://t.co/Nf7EhfJyYj\\n\\nMore announced soon! \\n\\n#NFT #opensea #NFTGiveaway #eth #DAO #BoredApeYachtClub</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__jiri</td>\n",
              "      <td>Cryptos are down but #NFTs are up!  Are you selling for a profit or holding?\\n\\n#LazyLionsNFT #BAYC #boredapeyachtclub #CyberKongz #Cryptopunks #Fidenza #veefriends #NFTCollector #NFTNews #nftcommunity #cryptonft #nftcollector #NFTart #cryptoart https://t.co/UaZOaFx5Xv</td>\n",
              "      <td>False</td>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "        const element = document.querySelector('#df-8e889fc6-51a5-40b6-b767-81b522227e6e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "keyword = 'eminem'\n",
        "ind = df.text.str.contains(keyword, case = False)\n",
        "df['has_keyword'] = ind\n",
        "cols = ['screen_name','text','has_keyword']\n",
        "\n",
        "df[cols].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d29lI8q0i0kb"
      },
      "source": [
        "### Count Rows in Dataframe\n",
        "\n",
        "We can use the `len` function to find out how many rows a dataframe has.  Let's find out how many tweets contain our keyword, and then print out the result. We can use the column we just created for this to make the code cleaner."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_pxwh_28i0kb"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "SyeHqwVHi0kc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e007f9a1-c374-4f0b-f976-d7b56cb6a290"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 164 tweets that contain the keyword 'eminem' \n"
          ]
        }
      ],
      "source": [
        "cols = ['screen_name','text']\n",
        "ind = df['has_keyword']==True\n",
        "df_keyword = df[ind][cols]\n",
        "\n",
        "#the len function tells you how many rows in a dataframe\n",
        "n_keyword = len(df_keyword)\n",
        "\n",
        "print(f\"There are {n_keyword} tweets that contain the keyword '{keyword}' \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WrHzhmGi0kc"
      },
      "source": [
        "### Sort Rows By Column Values\n",
        "We can sort a dataframe's rows by the values in a column with the `sort_value` function.  It takes as input a list of columns, and an optional parameter `ascending` which can be `True` or `False`.\n",
        "\n",
        "Let's sort the tweets in order of decreasing `retweet_count`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4fwH9l7Ji0kd"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "3xoTiHVji0kd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "2aa029f7-7c8c-4b40-b4bf-ad24650b0424"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      retweet_count      screen_name  \\\n",
              "960            1589       NFTmagzine   \n",
              "2738            830       NFTPirate_   \n",
              "3025            825        Eliza_Nft   \n",
              "4190            662  themonsterguild   \n",
              "3479            520       BoredApeBC   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                             text  \n",
              "960               #Sheepzy died for all of the #Crypto #BoredApe #BAYC #MAYC #MutantApeYachtClub #BoredApeYachtClub  and #CryptoPunks Sins\\n\\nThe answer to all things is love❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥 #SHEEPZYS\\n\\n“So if the Son sets you free, you will be free indeed” John 8:36✝️\\n🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑 https://t.co/IJ4poCtV7J  \n",
              "2738                                    🥳#NFTGiveaway🥳\\n\\n🎁Giving away 1 Bored Ape Crypto Paint #NFT! \\n\\n1️⃣Follow @BACryptoPaint &amp; @BoredApeBC\\n2️⃣❤️&amp; RT\\n3️⃣Tag Friends\\n\\n⏰Ends in 48 hrs \\n\\n#NFTCommunity #NFTGiveaways #NFTs #nftcollectors #nftart #OpenSeaNFT \\n#BoredApeYachtClub #ETH https://t.co/RiTboK2AKH  \n",
              "3025                                         🥳#NFTGiveaway🥳\\n\\n🎁Giving away 1 Bored Ape Crypto Paint #NFT! \\n\\n1️⃣Follow @BACryptoPaint &amp; @BoredApeBC\\n2️⃣❤️&amp; RT\\n3️⃣Tag Friends\\n\\n⏰Ends in 48 hrs \\n\\n#NFTCommunity #NFTGiveaways #NFTs #nftcollectors #nftart #OpenSeaNFT \\n#BoredApeYachtClub https://t.co/HJG4hAStEr  \n",
              "4190  🎁🎁 48 HR GIVEAWAY 🎁🎁\\n\\nReceive this FREE MAYC #28474 derivative:\\n\\nApeworks #325\\n\\n1⃣ Like\\n2⃣ Retweet\\n3⃣ Follow me\\n4⃣ Tag 3 NEW friends\\n\\nYou HAVE to follow me to win.\\n\\nEnds Jan 3 @ 8 pm PT\\n\\nSee more: https://t.co/ysml6oP8YG\\n\\n#BoredApeYachtClub #nftgiveaways #MAYC  #nftgiveaway https://t.co/RP8k0kIM9f  \n",
              "3479                                                                       🥳#NFTGiveaway🥳\\n\\nOne dope Bored Ape Crypto Paint #NFT\\n\\n1⃣Follow @BACryptoPaint and @BoredApeBC\\n2⃣Retweet\\n3⃣Tag 2 friends\\n\\n#NFTCommunity #NFTGiveaways  #NFTs #NFTCollectors #NFTArtist #OpenSeaNFT \\n#BoredApeYachtClub https://t.co/362g8GW7Vm  "
            ],
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              "      <th>960</th>\n",
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              "      <td>#Sheepzy died for all of the #Crypto #BoredApe #BAYC #MAYC #MutantApeYachtClub #BoredApeYachtClub  and #CryptoPunks Sins\\n\\nThe answer to all things is love❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥❤️‍🔥 #SHEEPZYS\\n\\n“So if the Son sets you free, you will be free indeed” John 8:36✝️\\n🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑🐑 https://t.co/IJ4poCtV7J</td>\n",
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              "      <td>🥳#NFTGiveaway🥳\\n\\n🎁Giving away 1 Bored Ape Crypto Paint #NFT! \\n\\n1️⃣Follow @BACryptoPaint &amp;amp; @BoredApeBC\\n2️⃣❤️&amp;amp; RT\\n3️⃣Tag Friends\\n\\n⏰Ends in 48 hrs \\n\\n#NFTCommunity #NFTGiveaways #NFTs #nftcollectors #nftart #OpenSeaNFT \\n#BoredApeYachtClub #ETH https://t.co/RiTboK2AKH</td>\n",
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              "      <td>🥳#NFTGiveaway🥳\\n\\n🎁Giving away 1 Bored Ape Crypto Paint #NFT! \\n\\n1️⃣Follow @BACryptoPaint &amp;amp; @BoredApeBC\\n2️⃣❤️&amp;amp; RT\\n3️⃣Tag Friends\\n\\n⏰Ends in 48 hrs \\n\\n#NFTCommunity #NFTGiveaways #NFTs #nftcollectors #nftart #OpenSeaNFT \\n#BoredApeYachtClub https://t.co/HJG4hAStEr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4190</th>\n",
              "      <td>662</td>\n",
              "      <td>themonsterguild</td>\n",
              "      <td>🎁🎁 48 HR GIVEAWAY 🎁🎁\\n\\nReceive this FREE MAYC #28474 derivative:\\n\\nApeworks #325\\n\\n1⃣ Like\\n2⃣ Retweet\\n3⃣ Follow me\\n4⃣ Tag 3 NEW friends\\n\\nYou HAVE to follow me to win.\\n\\nEnds Jan 3 @ 8 pm PT\\n\\nSee more: https://t.co/ysml6oP8YG\\n\\n#BoredApeYachtClub #nftgiveaways #MAYC  #nftgiveaway https://t.co/RP8k0kIM9f</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3479</th>\n",
              "      <td>520</td>\n",
              "      <td>BoredApeBC</td>\n",
              "      <td>🥳#NFTGiveaway🥳\\n\\nOne dope Bored Ape Crypto Paint #NFT\\n\\n1⃣Follow @BACryptoPaint and @BoredApeBC\\n2⃣Retweet\\n3⃣Tag 2 friends\\n\\n#NFTCommunity #NFTGiveaways  #NFTs #NFTCollectors #NFTArtist #OpenSeaNFT \\n#BoredApeYachtClub https://t.co/362g8GW7Vm</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-741c8825-d938-4627-b9c6-22c17ce71009 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "cols  =['retweet_count','screen_name','text']\n",
        "df.sort_values(by = ['retweet_count'], ascending = False)[cols].head(n=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxNa7e7_i0kd"
      },
      "source": [
        "### Statistics of Columns\n",
        "\n",
        "There are built-in functions in a dataframe to calculate many different statistis, such as `mean`, `median`, `variance`, `std`, and `quantile`.  For `quantile` we need to set the quantile we want in the variable `q`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lo--9Uuki0ke"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "O1urXolXi0ke",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d9f0feb-4f2e-4857-dc9f-94710311db8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retweet count\n",
            "\tmean = 2.87\n",
            "\tmedian = 0.00\n",
            "\tst. dev. = 34.737457027884005\n",
            "\t0.90 quantile = 2.0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "mean = df['retweet_count'].mean()\n",
        "med = df['retweet_count'].median()\n",
        "std = df['retweet_count'].std()\n",
        "q = 0.9\n",
        "quant = df['retweet_count'].quantile(q)\n",
        "\n",
        "print(f\"Retweet count\\n\\tmean = {mean:.2f}\\n\\tmedian = {med:.2f}\\n\\tst. dev. = {std}\\n\\t{q:.2f} quantile = {quant}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5O-mmCdYi0kf"
      },
      "source": [
        "# User Profiles\n",
        "\n",
        "We next look at a table of user profiles.   These were saved in a csv (comma separated values) file called `\"data/lecture_02_profiles.csv\"`.  The filename is stored in the variable `fname`.   Each row\n",
        "of this file is a user profile."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fd8oBn9oi0kf"
      },
      "source": [
        "### Load User Profiles\n",
        "We load the user profiles from the database using the `DB.fetch` function. The profiles are loaded into a dataframe called **df_u**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yN0Tqj49i0kf"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BHJzI_qEi0kg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "a23e6cec-b0cf-41f0-856c-ab0e0119ebb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 5 user profiles\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 created_at                  name      screen_name  \\\n",
              "0  2021-04-12T20:02:42.000Z  Bored Ape Yacht Club       BoredApeYC   \n",
              "1  2017-12-28T02:57:47.000Z               OpenSea          opensea   \n",
              "2  2019-08-10T16:13:42.000Z               Rarible          rarible   \n",
              "3  2021-12-24T15:17:44.000Z      TrillionaireNFTS  TrillonaireNFTs   \n",
              "4  2009-03-05T16:33:48.000Z      Marshall Mathers           Eminem   \n",
              "\n",
              "                                                                                                                                                                description  \\\n",
              "0                           To become a member, buy a Bored Ape or Mutant Ape on OpenSea. https://t.co/tGx2Lgsf4W\\n\\nDiscord: https://t.co/2Z7jqjX0z1\\nCreated by @yugalabs   \n",
              "1  The largest NFT marketplace. Buy, sell, & discover the internet of goods. Get help at https://t.co/f3KCJaxqBz, or join the community on Discord: https://t.co/HZM49J1NT0   \n",
              "2             Multi-chain community-centric NFT marketplace. Create, sell and collect digital items. Anything is possible. All are welcome 💛 Links: https://t.co/pwgEU3QMBw   \n",
              "3                                                         Turning thousands into trillions through NFTs. A collection of jaw droping artwork that will make you look trice.   \n",
              "4                                                                                                                                                                       NaN   \n",
              "\n",
              "             id      location  followers_count  following_count  listed_count  \\\n",
              "0  1.381700e+18     The Swamp           478884             5077          2919   \n",
              "1  9.462140e+17  New York, NY           942331             1885          6758   \n",
              "2  1.160220e+18             🌈           334084             3404          3457   \n",
              "3  1.474400e+18     metaverse             2551                5             4   \n",
              "4  2.294022e+07       Detroit         22534616                0         53740   \n",
              "\n",
              "   tweet_count  \n",
              "0         3629  \n",
              "1         6009  \n",
              "2         4471  \n",
              "3           14  \n",
              "4         1115  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d6711af-f6ff-4f6e-9535-a7cc3b019436\" class=\"colab-df-container\">\n",
              "    <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>created_at</th>\n",
              "      <th>name</th>\n",
              "      <th>screen_name</th>\n",
              "      <th>description</th>\n",
              "      <th>id</th>\n",
              "      <th>location</th>\n",
              "      <th>followers_count</th>\n",
              "      <th>following_count</th>\n",
              "      <th>listed_count</th>\n",
              "      <th>tweet_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-04-12T20:02:42.000Z</td>\n",
              "      <td>Bored Ape Yacht Club</td>\n",
              "      <td>BoredApeYC</td>\n",
              "      <td>To become a member, buy a Bored Ape or Mutant Ape on OpenSea. https://t.co/tGx2Lgsf4W\\n\\nDiscord: https://t.co/2Z7jqjX0z1\\nCreated by @yugalabs</td>\n",
              "      <td>1.381700e+18</td>\n",
              "      <td>The Swamp</td>\n",
              "      <td>478884</td>\n",
              "      <td>5077</td>\n",
              "      <td>2919</td>\n",
              "      <td>3629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-12-28T02:57:47.000Z</td>\n",
              "      <td>OpenSea</td>\n",
              "      <td>opensea</td>\n",
              "      <td>The largest NFT marketplace. Buy, sell, &amp; discover the internet of goods. Get help at https://t.co/f3KCJaxqBz, or join the community on Discord: https://t.co/HZM49J1NT0</td>\n",
              "      <td>9.462140e+17</td>\n",
              "      <td>New York, NY</td>\n",
              "      <td>942331</td>\n",
              "      <td>1885</td>\n",
              "      <td>6758</td>\n",
              "      <td>6009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-08-10T16:13:42.000Z</td>\n",
              "      <td>Rarible</td>\n",
              "      <td>rarible</td>\n",
              "      <td>Multi-chain community-centric NFT marketplace. Create, sell and collect digital items. Anything is possible. All are welcome 💛 Links: https://t.co/pwgEU3QMBw</td>\n",
              "      <td>1.160220e+18</td>\n",
              "      <td>🌈</td>\n",
              "      <td>334084</td>\n",
              "      <td>3404</td>\n",
              "      <td>3457</td>\n",
              "      <td>4471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-12-24T15:17:44.000Z</td>\n",
              "      <td>TrillionaireNFTS</td>\n",
              "      <td>TrillonaireNFTs</td>\n",
              "      <td>Turning thousands into trillions through NFTs. A collection of jaw droping artwork that will make you look trice.</td>\n",
              "      <td>1.474400e+18</td>\n",
              "      <td>metaverse</td>\n",
              "      <td>2551</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-03-05T16:33:48.000Z</td>\n",
              "      <td>Marshall Mathers</td>\n",
              "      <td>Eminem</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.294022e+07</td>\n",
              "      <td>Detroit</td>\n",
              "      <td>22534616</td>\n",
              "      <td>0</td>\n",
              "      <td>53740</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d6711af-f6ff-4f6e-9535-a7cc3b019436')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d6711af-f6ff-4f6e-9535-a7cc3b019436 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d6711af-f6ff-4f6e-9535-a7cc3b019436');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-831e58e5-f37b-446e-b66e-9399d3129d67')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-831e58e5-f37b-446e-b66e-9399d3129d67 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#filename of csv file\n",
        "fname = \"data/lecture_02_profiles.csv\"\n",
        "\n",
        "df_u = pd.read_csv(fname)\n",
        "\n",
        "print(f\"We have {len(df_u)} user profiles\")\n",
        "df_u.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5pyfcNm9i0kg"
      },
      "source": [
        "### Bar Graph of Follower Count\n",
        "\n",
        "We can make a bar graph of the follower count of the users.  To make the plot, we use the `barplot` function in the *seaborn* package.  Details on the seaborn package can be found here: https://seaborn.pydata.org/#\n",
        "\n",
        "To use `barplot`, we need to input the `data`, which is the dataframe, `x`, which is the name of the column for the x-axis, and `y`, which is the name of the column for the y-axis.  There are many other functions that let us edit the plot to make it look nice.  These are from the *matplotlib* package.  One parameter is the `color` parameter.  A complete list of colors is found here: https://matplotlib.org/stable/gallery/color/named_colors.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uSPjrp3gi0ku"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "b4Qfrdfti0kv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "7c0eec18-e562-4792-9d35-b97cf4e86ad2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (8,6))\n",
        "sns.barplot(data = df_u, x = 'screen_name', y = 'followers_count',\n",
        "           color = 'crimson')\n",
        "plt.xlabel('User name', fontsize = 16)\n",
        "plt.ylabel('Followers count', fontsize = 16)\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.title(\"Twitter Users\", fontsize = 20)\n",
        "plt.grid()\n",
        "plt.yscale(\"log\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxSXmJrgi0kv"
      },
      "source": [
        "# User Tweets\n",
        "\n",
        "The tweets here were collected from the Twitter timelines of a set of users. They are stored in the csv file `\"data/lecture_02_usertweets.csv\"`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-fvKPyii0kv"
      },
      "source": [
        "### Load User Tweets\n",
        "\n",
        "We can load the user tweets with the `pd.read_csv` function into a dataframe called `df_ut` (ut for user tweets)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jACsu-Uni0kw"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hKpu9Yhfi0kw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "7edd1893-affc-415e-cb96-958b7320a74a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 3402 user tweets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    created_at screen_name  \\\n",
              "1791  2021-11-20T16:22:06.000Z     opensea   \n",
              "2593  2021-11-04T19:00:02.000Z     rarible   \n",
              "238   2021-12-19T18:45:00.000Z  BoredApeYC   \n",
              "2416  2021-12-02T10:20:36.000Z     rarible   \n",
              "3346  2020-05-31T01:55:37.000Z      Eminem   \n",
              "\n",
              "                                                                                                                                                                             text  \\\n",
              "1791                                                                                                                                      Twitter spaces strikes again - rugged 👀   \n",
              "2593  We're live on Twitter Spaces with @milalolli @VectorMeldrew @missalsimpson @dustmonkey_ and @HollyWoodNFTs talking about @NFT_LDN! ✨ \\n\\nTune in: \\nhttps://t.co/cQghKcGGZZ   \n",
              "238                                                                                                                     RT @LindseyByrnes: #NewProfilePic https://t.co/NPpylQFPCO   \n",
              "2416                                                                                       RT @nonfungiblelux: BULLISH\\n\\n#raribleontezos @tezos @rarible https://t.co/UFbJrmDkPA   \n",
              "3346                                                                                             I’m with @killermike on this one. Incredibly well done.  https://t.co/zeTQYqyk9H   \n",
              "\n",
              "     lang  retweet_count  reply_count  like_count  quote_count  \\\n",
              "1791   en             27          238         636            7   \n",
              "2593   en              6           14          65            0   \n",
              "238   und             79            0           0            0   \n",
              "2416   en             20            0           0            0   \n",
              "3346   en           9765          918       66136          540   \n",
              "\n",
              "                       id            author_id      conversation_id  \\\n",
              "1791  1462093907190243330   946213559213555712  1462093907190243330   \n",
              "2593  1456335444744998915  1160222700104757249  1456335444744998915   \n",
              "238   1472639115824881668  1381699264011771906  1472639115824881668   \n",
              "2416  1466351588763738113  1160222700104757249  1466351588763738113   \n",
              "3346  1266911177688350723             22940219  1266911177688350723   \n",
              "\n",
              "      in_reply_to_user_id   geo  \n",
              "1791                  NaN  None  \n",
              "2593                  NaN  None  \n",
              "238                   NaN  None  \n",
              "2416                  NaN  None  \n",
              "3346                  NaN  None  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efa8cdbf-6976-4d19-95e9-2e508f20acc6\" class=\"colab-df-container\">\n",
              "    <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>created_at</th>\n",
              "      <th>screen_name</th>\n",
              "      <th>text</th>\n",
              "      <th>lang</th>\n",
              "      <th>retweet_count</th>\n",
              "      <th>reply_count</th>\n",
              "      <th>like_count</th>\n",
              "      <th>quote_count</th>\n",
              "      <th>id</th>\n",
              "      <th>author_id</th>\n",
              "      <th>conversation_id</th>\n",
              "      <th>in_reply_to_user_id</th>\n",
              "      <th>geo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1791</th>\n",
              "      <td>2021-11-20T16:22:06.000Z</td>\n",
              "      <td>opensea</td>\n",
              "      <td>Twitter spaces strikes again - rugged 👀</td>\n",
              "      <td>en</td>\n",
              "      <td>27</td>\n",
              "      <td>238</td>\n",
              "      <td>636</td>\n",
              "      <td>7</td>\n",
              "      <td>1462093907190243330</td>\n",
              "      <td>946213559213555712</td>\n",
              "      <td>1462093907190243330</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2593</th>\n",
              "      <td>2021-11-04T19:00:02.000Z</td>\n",
              "      <td>rarible</td>\n",
              "      <td>We're live on Twitter Spaces with @milalolli @VectorMeldrew @missalsimpson @dustmonkey_ and @HollyWoodNFTs talking about @NFT_LDN! ✨ \\n\\nTune in: \\nhttps://t.co/cQghKcGGZZ</td>\n",
              "      <td>en</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>1456335444744998915</td>\n",
              "      <td>1160222700104757249</td>\n",
              "      <td>1456335444744998915</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>2021-12-19T18:45:00.000Z</td>\n",
              "      <td>BoredApeYC</td>\n",
              "      <td>RT @LindseyByrnes: #NewProfilePic https://t.co/NPpylQFPCO</td>\n",
              "      <td>und</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1472639115824881668</td>\n",
              "      <td>1381699264011771906</td>\n",
              "      <td>1472639115824881668</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>2021-12-02T10:20:36.000Z</td>\n",
              "      <td>rarible</td>\n",
              "      <td>RT @nonfungiblelux: BULLISH\\n\\n#raribleontezos @tezos @rarible https://t.co/UFbJrmDkPA</td>\n",
              "      <td>en</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1466351588763738113</td>\n",
              "      <td>1160222700104757249</td>\n",
              "      <td>1466351588763738113</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3346</th>\n",
              "      <td>2020-05-31T01:55:37.000Z</td>\n",
              "      <td>Eminem</td>\n",
              "      <td>I’m with @killermike on this one. Incredibly well done.  https://t.co/zeTQYqyk9H</td>\n",
              "      <td>en</td>\n",
              "      <td>9765</td>\n",
              "      <td>918</td>\n",
              "      <td>66136</td>\n",
              "      <td>540</td>\n",
              "      <td>1266911177688350723</td>\n",
              "      <td>22940219</td>\n",
              "      <td>1266911177688350723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#filename of csv file\n",
        "fname = 'data/lecture_02_usertweets.csv'\n",
        "df_ut = pd.read_csv(fname)\n",
        "print(f\"We have {len(df_ut)} user tweets\")\n",
        "df_ut.sample(n=5)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3f3okdTi0kx"
      },
      "source": [
        "### Group Tweets\n",
        "\n",
        "We can group the tweets using the `groupby` function.  Once we group the tweets, we can calculate apply other functions to tweets in the group, such as `mean`.  We do this for the `retweet_count` column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ks0410IGi0kx"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "9GKKp_e9i0kx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2fb59e15-a532-468b-99e3-f49d62245120"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-96f5af2282a4>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_ut.groupby('screen_name').mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 retweet_count  reply_count    like_count  quote_count  \\\n",
              "screen_name                                                              \n",
              "BoredApeYC          124.068244     9.302093    108.936306     3.618744   \n",
              "Eminem             5518.810526  1053.678947  37084.584211   766.400000   \n",
              "TrillonaireNFTs     418.285714   416.642857    454.928571   116.642857   \n",
              "opensea             115.584167   134.103731    179.563239     5.386715   \n",
              "rarible              29.650000    29.016000     74.770000     2.435000   \n",
              "\n",
              "                           id     author_id  conversation_id  \\\n",
              "screen_name                                                    \n",
              "BoredApeYC       1.464450e+18  1.381699e+18     1.464450e+18   \n",
              "Eminem           1.333806e+18  2.294022e+07     1.333805e+18   \n",
              "TrillonaireNFTs  1.476982e+18  1.474399e+18     1.476979e+18   \n",
              "opensea          1.465770e+18  9.462136e+17     1.465763e+18   \n",
              "rarible          1.448659e+18  1.160223e+18     1.448329e+18   \n",
              "\n",
              "                 in_reply_to_user_id  \n",
              "screen_name                           \n",
              "BoredApeYC              1.194489e+18  \n",
              "Eminem                  3.361061e+08  \n",
              "TrillonaireNFTs         1.377484e+18  \n",
              "opensea                 9.212699e+17  \n",
              "rarible                 8.630221e+17  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>retweet_count</th>\n",
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              "      <th>opensea</th>\n",
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              "      <td>134.103731</td>\n",
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              "      <td>5.386715</td>\n",
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              "      <td>9.462136e+17</td>\n",
              "      <td>1.465763e+18</td>\n",
              "      <td>9.212699e+17</td>\n",
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              "      <th>rarible</th>\n",
              "      <td>29.650000</td>\n",
              "      <td>29.016000</td>\n",
              "      <td>74.770000</td>\n",
              "      <td>2.435000</td>\n",
              "      <td>1.448659e+18</td>\n",
              "      <td>1.160223e+18</td>\n",
              "      <td>1.448329e+18</td>\n",
              "      <td>8.630221e+17</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-640cda00-fea9-481a-a300-70e19c26881a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-640cda00-fea9-481a-a300-70e19c26881a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-72193ff3-020c-4929-a537-985fc21361eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-72193ff3-020c-4929-a537-985fc21361eb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-72193ff3-020c-4929-a537-985fc21361eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df_ut.groupby('screen_name').mean()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zzJ-Yoli0ky"
      },
      "source": [
        "### Barplot Retweet Count of Groups\n",
        "\n",
        "We can make a barplot of a column value on the y-axis, and the group on the x-axis.  Seaborn knows to group together tweets in the same group, and plot the mean value along with error bars. In this case, we will plot `retweet_count` on the y-axis, and the groups are the `screen_name` column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "36LwJuZRi0ky"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "f4WVbZbLi0ky",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "76910d9c-54ab-4ae8-bdcf-6c5f76a06a93"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (8,6))\n",
        "sns.barplot(data = df_ut, x = 'screen_name', y = 'retweet_count', color = \"blue\")\n",
        "plt.xlabel('Screen name', fontsize = 16)\n",
        "plt.ylabel('Retweet count', fontsize = 16)\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.title(\"Twitter Users\", fontsize = 20)\n",
        "plt.grid()\n",
        "plt.yscale(\"log\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97YMDLMvi0kz"
      },
      "source": [
        "### Subplots\n",
        "\n",
        "We can plot two figures side by side using the `subplot` function.  You need to specify the number of rows and columns in your subplot grid, and specify which grid box the plot goes in.  It is something like this: `subplot(rows, columns, box_number)`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UNoWsLH6i0kz"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Re1Gjmn3i0k0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "90919745-2ed6-4bbb-e647-1093f875c963"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (16,6))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "sns.barplot(data = df_u, x = 'screen_name', y = 'followers_count',\n",
        "           color = 'crimson')\n",
        "plt.xlabel('Screen name', fontsize = 16)\n",
        "plt.ylabel('Followers count', fontsize = 16)\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.title(\"Twitter Users\", fontsize = 20)\n",
        "plt.grid()\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.barplot(data = df_ut, x = 'screen_name', y = 'retweet_count', color = \"blue\")\n",
        "plt.xlabel('Screen name', fontsize = 16)\n",
        "plt.ylabel('Retweet count', fontsize = 16)\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.title(\"Twitter Users\", fontsize = 20)\n",
        "plt.grid()\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-GL1ieYi0k1"
      },
      "source": [
        "### Compare Retweet Count of Tweets Containing Different Keywords\n",
        "\n",
        "We can compare the retweet count of tweets that contain a keyword versus those that do not.  We do this by defining the variable `keyword` which is the keyword we care about, and then adding a column to the dataframe called `has_keyword_{keyword}` that is `True` if the tweet has the word.  The `{keyword}` means the column name will have the actual keyword, which makes things easier to analyze.  We can then plot the retweet count grouped by screen name, and separate within the group those where `has_keyword_{keyword}` is `True` and `False`.  We use the `hue` parameter for this in-group separation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o80ehT-6i0k1"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "VIKeMz--i0k2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "outputId": "55f502f2-621f-4898-835e-cd6164633f2f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "keyword = 'drops'\n",
        "ind = df_ut.text.str.contains(keyword, case = False)\n",
        "df_ut[f'has_keyword_{keyword}'] = ind\n",
        "\n",
        "fig = plt.figure(figsize = (8,6))\n",
        "sns.barplot(data = df_ut, x = 'screen_name', y = 'retweet_count',\n",
        "                hue = f'has_keyword_{keyword}')\n",
        "plt.xlabel('Screen name',fontsize  = 14)\n",
        "plt.ylabel('Retweet count',fontsize  = 14)\n",
        "plt.title(f\"Keyword ={keyword}\",fontsize = 18)\n",
        "plt.yscale('log')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "348pxr2fi0k2"
      },
      "source": [
        "### Describe Groups\n",
        "\n",
        "We can group the tweets by `screen_name` and `has_keyword` using the `groupby` function.  Then we can summarize the statistics of the groups in a dataframe by using the `describe` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XRCdYpPgi0k2"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3NrWr1ZHi0k3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "8113d3ec-c3e6-42e0-a14a-b911697317f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyword is drops\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  retweet_count                            \\\n",
              "                                          count         mean          std   \n",
              "screen_name     has_keyword_drops                                           \n",
              "BoredApeYC      False                    1099.0   124.068244   415.553516   \n",
              "Eminem          False                     188.0  5537.675532  8692.083925   \n",
              "                True                        2.0  3745.500000  4024.144692   \n",
              "TrillonaireNFTs False                      14.0   418.285714   236.813925   \n",
              "opensea         False                    1085.0   113.103226  1546.156799   \n",
              "                True                       14.0   307.857143   367.119108   \n",
              "rarible         False                     985.0    29.768528    67.400176   \n",
              "                True                       15.0    21.866667    13.922575   \n",
              "\n",
              "                                                                             \n",
              "                                     min      25%     50%      75%      max  \n",
              "screen_name     has_keyword_drops                                            \n",
              "BoredApeYC      False                0.0    15.00    28.0    69.00   6213.0  \n",
              "Eminem          False              392.0  1496.75  2648.5  5184.75  68467.0  \n",
              "                True               900.0  2322.75  3745.5  5168.25   6591.0  \n",
              "TrillonaireNFTs False                2.0   232.00   441.0   572.00    731.0  \n",
              "opensea         False                0.0    15.00    33.0    63.00  50789.0  \n",
              "                True                 1.0    37.00   107.5   614.75   1063.0  \n",
              "rarible         False                0.0     5.00    13.0    29.00   1008.0  \n",
              "                True                 0.0    15.00    25.0    27.00     48.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d027b17b-4c56-491e-b5a5-39c8aad694b7\" class=\"colab-df-container\">\n",
              "    <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">retweet_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>screen_name</th>\n",
              "      <th>has_keyword_drops</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BoredApeYC</th>\n",
              "      <th>False</th>\n",
              "      <td>1099.0</td>\n",
              "      <td>124.068244</td>\n",
              "      <td>415.553516</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>28.0</td>\n",
              "      <td>69.00</td>\n",
              "      <td>6213.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Eminem</th>\n",
              "      <th>False</th>\n",
              "      <td>188.0</td>\n",
              "      <td>5537.675532</td>\n",
              "      <td>8692.083925</td>\n",
              "      <td>392.0</td>\n",
              "      <td>1496.75</td>\n",
              "      <td>2648.5</td>\n",
              "      <td>5184.75</td>\n",
              "      <td>68467.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3745.500000</td>\n",
              "      <td>4024.144692</td>\n",
              "      <td>900.0</td>\n",
              "      <td>2322.75</td>\n",
              "      <td>3745.5</td>\n",
              "      <td>5168.25</td>\n",
              "      <td>6591.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TrillonaireNFTs</th>\n",
              "      <th>False</th>\n",
              "      <td>14.0</td>\n",
              "      <td>418.285714</td>\n",
              "      <td>236.813925</td>\n",
              "      <td>2.0</td>\n",
              "      <td>232.00</td>\n",
              "      <td>441.0</td>\n",
              "      <td>572.00</td>\n",
              "      <td>731.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">opensea</th>\n",
              "      <th>False</th>\n",
              "      <td>1085.0</td>\n",
              "      <td>113.103226</td>\n",
              "      <td>1546.156799</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>33.0</td>\n",
              "      <td>63.00</td>\n",
              "      <td>50789.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>14.0</td>\n",
              "      <td>307.857143</td>\n",
              "      <td>367.119108</td>\n",
              "      <td>1.0</td>\n",
              "      <td>37.00</td>\n",
              "      <td>107.5</td>\n",
              "      <td>614.75</td>\n",
              "      <td>1063.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">rarible</th>\n",
              "      <th>False</th>\n",
              "      <td>985.0</td>\n",
              "      <td>29.768528</td>\n",
              "      <td>67.400176</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>13.0</td>\n",
              "      <td>29.00</td>\n",
              "      <td>1008.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>15.0</td>\n",
              "      <td>21.866667</td>\n",
              "      <td>13.922575</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.00</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d027b17b-4c56-491e-b5a5-39c8aad694b7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d027b17b-4c56-491e-b5a5-39c8aad694b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d027b17b-4c56-491e-b5a5-39c8aad694b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-943f909d-161d-4f7b-8b81-becc1cbd98c8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-943f909d-161d-4f7b-8b81-becc1cbd98c8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-943f909d-161d-4f7b-8b81-becc1cbd98c8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "print(f\"Keyword is {keyword}\")\n",
        "cols = ['screen_name',f'has_keyword_{keyword}']\n",
        "df_ut.groupby(cols)[['retweet_count']].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWV1qzjSi0k3"
      },
      "source": [
        "# Save Notebook to PDF\n",
        "\n",
        "Click File -> Print and save the notebook as a PDF file. You can submit your homework this way."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save Notebook to HTML and then PDF\n",
        "\n",
        "To make your file look nice, you can convert it to an HTML first, and then print to a PDF.  Here is how.\n",
        "\n",
        "1) Save the notebook and download it to your local machine.\n",
        "\n",
        "2) Drag the notebook into the `social_media_analytics` folder.\n",
        "\n",
        "3) Run the code below, making sure you put in the filename of your notebook.  You can get the filename by right-clicking on the notebook in the files directory and \"Copy path\".\n",
        "\n",
        "4) Download the html file.\n",
        "\n",
        "5) Open the html file in a web browser, then print to a PDF."
      ],
      "metadata": {
        "id": "UvQZx6H0s3_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!jupyter nbconvert --to html \"file path\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4bmWZiAtao9",
        "outputId": "5e5ce450-ebf7-4abb-ce75-217e4865224d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NbConvertApp] Converting notebook /content/social_media_analytics/Copy of Lecture02_BasicSocialMediaDataAnalysis.ipynb to html\n",
            "[NbConvertApp] Writing 890213 bytes to /content/social_media_analytics/Copy of Lecture02_BasicSocialMediaDataAnalysis.html\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.2"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}