Get_twitter_Summary_Files.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Get_twitter_Summary_Files.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Notebook to get Twitter Summary files"
      ],
      "metadata": {
        "id": "LxhnkqCMshcr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/lopezbec/COVID19_Tweets_Dataset/blob/master/Get_twitter_Summary_Files.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "</table>"
      ],
      "metadata": {
        "id": "WLP_Ypl-vOxe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMoih2B3pVwh",
        "outputId": "91e2b6c8-d419-4793-cae2-570096b441b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current directory is : /content\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "Use_Google_Drive= False #@param {type:\"boolean\"}\n",
        "\n",
        "if Use_Google_Drive:\n",
        "    import os\n",
        "    from google.colab import drive \n",
        "\n",
        "    drive.mount('/content/gdrive',force_remount=True)\n",
        "\n",
        "    Working_Directory = 'My Drive' #@param {type:\"string\"}\n",
        "    wd=\"/content/gdrive/\"+Working_Directory\n",
        "    os.chdir(wd)\n",
        "\n",
        "\n",
        "wd = os.getcwd()\n",
        "print(\"current directory is : \" + wd)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Date fields\n",
        "import pandas as pd\n",
        "from IPython.display import clear_output\n",
        "Start_date = '2021-04-18' #@param {type:\"date\"}\n",
        "#@title Date fields\n",
        "End_date = '2021-04-19' #@param {type:\"date\"}\n",
        "\n",
        "\n",
        "\n",
        "datelist = pd.date_range(Start_date, End_date).tolist()\n",
        "number_files=0\n",
        "for date in datelist:\n",
        "  for h in ['00','01', '02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23']:\n",
        "    number_files=number_files+1\n",
        "\n",
        "print(\"You have requested to download a total of \"+ str(number_files) + ' files \\n The more files you download at once the longer it will take to upload to your Drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDVzmwy9qCCv",
        "outputId": "ed206d1a-3ff2-4bfd-cbf3-c9a86bf372b8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You have requested to download a total of 48 files \n",
            " The more files you download at once the longer it will take to upload to your Drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install wget\n"
      ],
      "metadata": {
        "id": "sMMVp_4PqLay",
        "outputId": "3f34961c-f7aa-4430-b113-3ecae2746464",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=9e210ddd3a71f578fb42c28816a0cbc57ff0e9e4d8e6c8276b971f5b38dac2d9\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from datetime import datetime\n",
        "import wget\n",
        "\n",
        "Summary_Files = \"Summary_Details\" #@param [\"Summary_Details\", \"Summary_Hashtag\", \"Summary_Mentions\", \"Summary_NER\", \"Summary_NER_ES\", \"Summary_Sentiment\", \"Summary_Sentiment_ES\"]\n",
        "\n",
        "\n",
        "\n",
        "path = os.path.join(wd, Summary_Files) \n",
        "if not os.path.exists(path):\n",
        "  try:\n",
        "    os.makedirs(path)\n",
        "    os.chdir(path)\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "datelist = pd.date_range(Start_date, End_date).tolist()\n",
        "files_list=[]\n",
        "for date in datelist:\n",
        "  day=date.strftime(\"%Y_%m_%d\")\n",
        "  y=day[0:4]\n",
        "  if str(date.today().year)==y:\n",
        "    p1=\"https://raw.githubusercontent.com/lopezbec/COVID19_Tweets_Dataset/master/\" + Summary_Files+\"/\"\n",
        "  else:\n",
        "      p1=\"https://raw.githubusercontent.com/lopezbec/COVID19_Tweets_Dataset_\"+y+\"/master/\"+ Summary_Files+\"/\"\n",
        "  m=day[0:7]\n",
        "  for h in ['00','01', '02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','20','21','22','23']:\n",
        "    file=p1+m+\"/\"+day+\"_\"+str(h)+\"_\" + Summary_Files +\".csv\"\n",
        "    print(file)\n",
        "    wget.download(file)\n",
        "\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "oKbKVE9nqFlM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "o8NGZ19frduR",
        "outputId": "fded507c-dd6c-4cfe-f15f-8429cc46ce27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021_04_18_00_Summary_Details.csv  2021_04_19_00_Summary_Details.csv\n",
            "2021_04_18_01_Summary_Details.csv  2021_04_19_01_Summary_Details.csv\n",
            "2021_04_18_02_Summary_Details.csv  2021_04_19_02_Summary_Details.csv\n",
            "2021_04_18_03_Summary_Details.csv  2021_04_19_03_Summary_Details.csv\n",
            "2021_04_18_04_Summary_Details.csv  2021_04_19_04_Summary_Details.csv\n",
            "2021_04_18_05_Summary_Details.csv  2021_04_19_05_Summary_Details.csv\n",
            "2021_04_18_06_Summary_Details.csv  2021_04_19_06_Summary_Details.csv\n",
            "2021_04_18_07_Summary_Details.csv  2021_04_19_07_Summary_Details.csv\n",
            "2021_04_18_08_Summary_Details.csv  2021_04_19_08_Summary_Details.csv\n",
            "2021_04_18_09_Summary_Details.csv  2021_04_19_09_Summary_Details.csv\n",
            "2021_04_18_10_Summary_Details.csv  2021_04_19_10_Summary_Details.csv\n",
            "2021_04_18_11_Summary_Details.csv  2021_04_19_11_Summary_Details.csv\n",
            "2021_04_18_12_Summary_Details.csv  2021_04_19_12_Summary_Details.csv\n",
            "2021_04_18_13_Summary_Details.csv  2021_04_19_13_Summary_Details.csv\n",
            "2021_04_18_14_Summary_Details.csv  2021_04_19_14_Summary_Details.csv\n",
            "2021_04_18_15_Summary_Details.csv  2021_04_19_15_Summary_Details.csv\n",
            "2021_04_18_16_Summary_Details.csv  2021_04_19_16_Summary_Details.csv\n",
            "2021_04_18_17_Summary_Details.csv  2021_04_19_17_Summary_Details.csv\n",
            "2021_04_18_18_Summary_Details.csv  2021_04_19_18_Summary_Details.csv\n",
            "2021_04_18_19_Summary_Details.csv  2021_04_19_19_Summary_Details.csv\n",
            "2021_04_18_20_Summary_Details.csv  2021_04_19_20_Summary_Details.csv\n",
            "2021_04_18_21_Summary_Details.csv  2021_04_19_21_Summary_Details.csv\n",
            "2021_04_18_22_Summary_Details.csv  2021_04_19_22_Summary_Details.csv\n",
            "2021_04_18_23_Summary_Details.csv  2021_04_19_23_Summary_Details.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# If you would like to merge the file into a single larger file"
      ],
      "metadata": {
        "id": "l8Eb0HL_51fy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import os\n",
        "import pandas as pd \n",
        "\n",
        "path = os.path.join(wd, Summary_Files)  #This is the absolute path where files are located\n",
        "\n",
        "Merged_files=pd.DataFrame()     #Create a empty data frame to store all the data\n",
        "for root,dirs,files in os.walk(path):\n",
        "  for file in files:\n",
        "    if file.endswith(\".csv\"):      #only read .csv files\n",
        "      data = pd.read_csv(file) \n",
        "      Merged_files = pd.concat([Merged_files, data])    #Merge by row the files  \"data\" into the \"Merged_files\"\n",
        "Merged_files\n",
        "\n"
      ],
      "metadata": {
        "id": "YidIMkG6rg2v",
        "outputId": "ceca0868-4c9e-42b0-a061-7ab2b4910dbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Tweet_ID Language Geolocation_coordinate   RT  Likes  \\\n",
              "0       1383800103937265674      und                     NO   NO      2   \n",
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              "\n",
              "        Retweets Country                    Date Created  \n",
              "0              0     NaN  Sun Apr 18 15:10:29 +0000 2021  \n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# You can filter the  tweets using Pandas"
      ],
      "metadata": {
        "id": "jomi1rhR6WYL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "Just_English_Tweets= True #@param {type:\"boolean\"}\n",
        "Just_with_Geolocation_information = False #@param {type:\"boolean\"}\n",
        "Just_Original_tweets = False #@param {type:\"boolean\"}\n",
        "Minimun_No_Likes = 3 #@param {type:\"number\"}\n",
        "Minimun_No_Retweets =  0#@param {type:\"number\"}\n",
        "\n",
        "Summary_Details2=Merged_files    # Creating a copy to avoid working on the orignal data frame\n",
        "if(Just_English_Tweets):\n",
        "  Summary_Details2=Summary_Details2[ Summary_Details2['Language']=='en']\n",
        "if(Just_with_Geolocation_information):\n",
        "  Summary_Details2=Summary_Details2[ Summary_Details2['Geolocation_coordinate']=='YES']\n",
        "if(Just_Original_tweets):\n",
        "  Summary_Details2=Summary_Details2[ Summary_Details2['RT']=='NO']\n",
        "\n",
        "\n",
        "Summary_Details2=Summary_Details2[ Summary_Details2['Likes']>=Minimun_No_Likes]\n",
        "Summary_Details2=Summary_Details2[ Summary_Details2['Retweets']>=Minimun_No_Retweets]\n",
        "\n",
        "Summary_Details2\n",
        "\n"
      ],
      "metadata": {
        "id": "SOnjYGC26Ztx",
        "outputId": "0f9331c5-0de3-4429-f4e5-ea5900cfff3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Tweet_ID Language Geolocation_coordinate  RT  Likes  \\\n",
              "17      1383797467800428544       en                     NO  NO     12   \n",
              "62      1383797472389070864       en                     NO  NO      3   \n",
              "124     1383797482505736194       en                     NO  NO      3   \n",
              "200     1383797493746438144       en                     NO  NO     81   \n",
              "204     1383797493897469962       en                     NO  NO     10   \n",
              "...                     ...      ...                    ...  ..    ...   \n",
              "165703  1383992925051179017       en                     NO  NO      3   \n",
              "165730  1383993015451078658       en                     NO  NO      7   \n",
              "165744  1383993084220907526       en                     NO  NO      6   \n",
              "165824  1383993239473049602       en                     NO  NO      5   \n",
              "166088  1383993730403684359       en                     NO  NO     15   \n",
              "\n",
              "        Retweets Country                    Date Created  \n",
              "17             2     NaN  Sun Apr 18 15:00:00 +0000 2021  \n",
              "62             1     NaN  Sun Apr 18 15:00:01 +0000 2021  \n",
              "124            2     NaN  Sun Apr 18 15:00:04 +0000 2021  \n",
              "200           19     NaN  Sun Apr 18 15:00:06 +0000 2021  \n",
              "204            3     NaN  Sun Apr 18 15:00:06 +0000 2021  \n",
              "...          ...     ...                             ...  \n",
              "165703         1     NaN  Mon Apr 19 03:56:41 +0000 2021  \n",
              "165730         2     NaN  Mon Apr 19 03:57:02 +0000 2021  \n",
              "165744         2     NaN  Mon Apr 19 03:57:19 +0000 2021  \n",
              "165824         0     NaN  Mon Apr 19 03:57:56 +0000 2021  \n",
              "166088         3     NaN  Mon Apr 19 03:59:53 +0000 2021  \n",
              "\n",
              "[163857 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9bd014b1-717d-446c-ae2a-b243600b382c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Tweet_ID</th>\n",
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              "      <td>Sun Apr 18 15:00:06 +0000 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>en</td>\n",
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              "      <td>Mon Apr 19 03:57:19 +0000 2021</td>\n",
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              "      <td>Mon Apr 19 03:59:53 +0000 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>163857 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bd014b1-717d-446c-ae2a-b243600b382c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9bd014b1-717d-446c-ae2a-b243600b382c 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-9bd014b1-717d-446c-ae2a-b243600b382c');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}