2_processing_and_merging_2014_2019_data.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "N_sGBUBi2YSL"
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the csv with data from 2014 to 2019"
      ],
      "metadata": {
        "id": "fhtLy-z7QlcR"
      }
    },
    {
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      "execution_count": null,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "imported\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(r'/content/drive/MyDrive/Colab Notebooks/DATA_1/A16_19_export.csv', error_bad_lines=False,engine='python')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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              "                       timestamp  \\\n",
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              "20165010  Register now for the early access of the Codug...  \n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the NLP (polarity score) function with TextBlob lib"
      ],
      "metadata": {
        "id": "a6gdwzXmQv_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob as tb\n",
        "#defining the function that will calculate the polarity score of a given text item - tweet\n",
        "def calculate_polarity(tweet):\n",
        "    analysis = tb(tweet)\n",
        "    out=analysis.sentiment.polarity\n",
        "    \n",
        "    return out\n",
        "#defining the list for the function output items\n",
        "sent=[]"
      ],
      "metadata": {
        "id": "lQtBJ18CInSv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iterating over the text of tweets in dataframe, calculating the sentiment and appending the output.\n",
        "Dropping the text column as it is not needed anymore.\n",
        "Replacing it with sentiment score data.\n"
      ],
      "metadata": {
        "id": "dDADZOcyQ2TG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for item in df[\"text\"]:\n",
        "  str_fr=str(item)\n",
        "  sent.append(calculate_polarity(str_fr))\n",
        "\n",
        "df=df.drop([\"text\"], axis=1)\n",
        "print(len(sent))\n",
        "df[\"sentiment\"]=sent\n",
        "\n",
        "df.to_csv(r\"/content/drive/MyDrive/Colab Notebooks/DATA_1/A_16_19_sent.csv\",index=False)\n",
        "print(\"saved\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcN0QfxBIpGR",
        "outputId": "a39bc455-53bc-455e-b84c-f78224759f82"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "20165013\n",
            "saved\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Re-reading the files for the purpose of my not so fast laptop which was crashing constantly..."
      ],
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        "id": "1Hj9mk6GR2Lk"
      }
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    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_csv(r'/content/drive/MyDrive/Colab Notebooks/DATA_1/A_16_19_sent.csv',engine='python')"
      ],
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        "id": "cWl2cOpqb1FV"
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "Splitting the timestamp to extract date."
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      "metadata": {
        "id": "8r3scSGkSM1m"
      }
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    {
      "cell_type": "code",
      "source": [
        "df2[['date', 'time']] = df2['timestamp'].str.split(' ', 1, expand=True)"
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      "metadata": {
        "id": "d-pPgxIY4Zke"
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      "execution_count": 5,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "Reading Stock data:"
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      "metadata": {
        "id": "iLGAG6QbSRTP"
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    {
      "cell_type": "code",
      "source": [
        "prices=pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/DATA_1/BTC-USD.csv\", engine='python')\n",
        "prices = prices[['Date', 'Close']]"
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      "metadata": {
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      "execution_count": 6,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Mapping stock data to the dates in previous df:"
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        "id": "uk0yYYCmSUwS"
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    {
      "cell_type": "code",
      "source": [
        "df_merged=df2.merge(prices, left_on='date', right_on=\"Date\", how='left')"
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      "metadata": {
        "id": "-LQIMGWq4rxa"
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      "execution_count": 7,
      "outputs": []
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      "execution_count": 18,
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      "execution_count": 19,
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              "          dates        prices  sentiment\n",
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              "..          ...           ...        ...\n",
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              "212  2022-11-20  16291.832031   0.073256\n",
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    },
    {
      "cell_type": "markdown",
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        "Appending the dataframes together"
      ],
      "metadata": {
        "id": "y8glwCJJTT9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "csv3=csv1.append(csv2, ignore_index=True)"
      ],
      "metadata": {
        "id": "2RpxRI0yPcED"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "csv3"
      ],
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      },
      "execution_count": 24,
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              "           dates        prices  sentiment\n",
              "0     2014-09-17    457.334015   0.011888\n",
              "1     2014-09-18    424.440002   0.010501\n",
              "2     2014-09-19    394.795990   0.004012\n",
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              "4     2014-09-21    398.821014   0.003570\n",
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              "2106  2022-11-20  16291.832031   0.073256\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",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating correlation matrix between the price of cryptocurrency and twitter sentiments:"
      ],
      "metadata": {
        "id": "G31LSNogTYBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "corr_matrix = csv3.corr()\n",
        "sn.heatmap(corr_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "UdYjCHZDP0SM",
        "outputId": "2f2ce374-fd3f-4478-ca6d-e13c20de33d5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe940680400>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation seems to be high. Stay tuned for the updates B)"
      ],
      "metadata": {
        "id": "GuRK3-WZTeOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "csv3.to_csv(\"/content/drive/MyDrive/Colab Notebooks/DATA_1/final_data_master.csv\")"
      ],
      "metadata": {
        "id": "R8kxhpmhWWKk"
      },
      "execution_count": 26,
      "outputs": []
    }
  ]
}