Extraction with name and direct dowload in CSV.ipynb

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  "metadata": {
    "colab": {
      "name": "Extraction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hRP9kYN81UWW",
        "outputId": "10cf9b34-5eae-4dbc-8dfc-b31f0645832c"
      },
      "source": [
        "!pip install warning\n",
        "!pip install spacy\n",
        "!pip install PyPDF2\n",
        "!pip install pyresparser"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Collecting PyPDF2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b4/01/68fcc0d43daf4c6bdbc6b33cc3f77bda531c86b174cac56ef0ffdb96faab/PyPDF2-1.26.0.tar.gz (77kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: PyPDF2\n",
            "  Building wheel for PyPDF2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyPDF2: filename=PyPDF2-1.26.0-cp37-none-any.whl size=61085 sha256=0720508987b2f2d98f734c6ad805a86bd2f3d66b9a686f574833761cf653a746\n",
            "  Stored in directory: /root/.cache/pip/wheels/53/84/19/35bc977c8bf5f0c23a8a011aa958acd4da4bbd7a229315c1b7\n",
            "Successfully built PyPDF2\n",
            "Installing collected packages: PyPDF2\n",
            "Successfully installed PyPDF2-1.26.0\n",
            "Collecting pyresparser\n",
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            "Building wheels for collected packages: docx2txt\n",
            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docx2txt: filename=docx2txt-0.8-cp37-none-any.whl size=3963 sha256=4246e25e44854af815027ee7b6fac95ddc9cbc45fa491f9b53352c71c5044600\n",
            "  Stored in directory: /root/.cache/pip/wheels/b2/1f/26/a051209bbb77fc6bcfae2bb7e01fa0ff941b82292ab084d596\n",
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            "\u001b[31mERROR: requests 2.23.0 has requirement urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you'll have urllib3 1.26.4 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: nbclient 0.5.3 has requirement jupyter-client>=6.1.5, but you'll have jupyter-client 5.3.5 which is incompatible.\u001b[0m\n",
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            "Installing collected packages: jsonschema, docx2txt, cryptography, pdfminer.six, urllib3, nltk, pycryptodome, pytz, pyresparser\n",
            "  Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
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            "  Found existing installation: pytz 2018.9\n",
            "    Uninstalling pytz-2018.9:\n",
            "      Successfully uninstalled pytz-2018.9\n",
            "Successfully installed cryptography-3.4.7 docx2txt-0.8 jsonschema-3.2.0 nltk-3.6.2 pdfminer.six-20201018 pycryptodome-3.10.1 pyresparser-1.0.6 pytz-2021.1 urllib3-1.26.4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "jsonschema",
                  "pytz",
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OL14hYbq5v2S"
      },
      "source": [
        "import warnings \n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrpzmR0q53pm"
      },
      "source": [
        "import en_core_web_sm\n",
        "nlp=en_core_web_sm.load()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLLNwJU86OCd"
      },
      "source": [
        "import PyPDF2\n",
        "import os"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Llec3xXG6XDU",
        "outputId": "0be207fb-a0d6-458b-c388-8c09aaedff44"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from pyresparser import ResumeParser"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KX15lvN_6xKz"
      },
      "source": [
        "list=[]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vkw5JRsN7PWj"
      },
      "source": [
        "file ='mahesh_Java.pdf' #Change the pdf name when you upload a new pdf "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlNDI-CZ8B1n"
      },
      "source": [
        "data=ResumeParser(file).get_extracted_data()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Kpw_l_o8Qne"
      },
      "source": [
        "# data"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5_HefDj9zIr"
      },
      "source": [
        "l0=data['name']\n",
        "list0=[l0]\n",
        "l1=data['total_experience']\n",
        "list1=[l1]\n",
        "l2=data['skills']\n",
        "list2=l2 \n",
        "l3=data['mobile_number']\n",
        "list3=[l3]\n",
        "l4=data['email']\n",
        "list4=[l4]\n",
        "#print(l0)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15KG1PCjq45V"
      },
      "source": [
        "if l1 == 0:\n",
        "  list6=['Fresher']\n",
        "elif l1>=1 | l1<=6:\n",
        "  list6=[\"Intermediate\"]\n",
        "elif l1<15:\n",
        "  list6=[\"Advances\"]\n",
        "else :\n",
        "  list6=[\"Not Mentioned\"]\n",
        "# print(list6)\n",
        "# print(list1)\n",
        "# print(list2[:5])\n",
        "# print(list3)\n",
        "# print(list4)\n",
        "# print(list0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvZBqMvPAkYL"
      },
      "source": [
        "import pandas as pd\n",
        "df0=pd.DataFrame(list0,columns=['name'])\n",
        "df1=pd.DataFrame(list2[:5],columns=['skills'])\n",
        "df2=pd.DataFrame(list6,columns=['Experiences'])\n",
        "df3=pd.DataFrame(list3,columns=['mobile'])\n",
        "df4=pd.DataFrame(list4,columns=['email'])\n",
        "#print(df0)\n",
        "# print(df1)\n",
        "# print(df2)\n",
        "# print(df3)\n",
        "# print(df4)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3C6KWdnYBRAm",
        "outputId": "ad6648a8-9a78-40e9-b659-980e59fe5f57"
      },
      "source": [
        "t0 = df1.join(df2)\n",
        "t1 = t0.join(df0)\n",
        "t2 = t1.join(df3)\n",
        "t3 = t2.join(df4)\n",
        "display(t3) "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skills</th>\n",
              "      <th>Experiences</th>\n",
              "      <th>name</th>\n",
              "      <th>mobile</th>\n",
              "      <th>email</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Js</td>\n",
              "      <td>Fresher</td>\n",
              "      <td>Mahesh Sr</td>\n",
              "      <td>201-743-9432</td>\n",
              "      <td>mahe2050.mk@gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Css</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Threading</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Json</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Workflows</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      skills Experiences       name        mobile                  email\n",
              "0         Js     Fresher  Mahesh Sr  201-743-9432  mahe2050.mk@gmail.com\n",
              "1        Css         NaN        NaN           NaN                    NaN\n",
              "2  Threading         NaN        NaN           NaN                    NaN\n",
              "3       Json         NaN        NaN           NaN                    NaN\n",
              "4  Workflows         NaN        NaN           NaN                    NaN"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJEWKIlVBX__"
      },
      "source": [
        "t3.to_csv(index=False)\n",
        "t3.to_csv('open.csv',index=False)"
      ],
      "execution_count": 60,
      "outputs": []
    }
  ]
}