Quora_EDA_Akshay.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Quora_EDA_Akshay",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "84DB4AAGXXc6",
        "outputId": "2a176d18-85b3-456b-918d-f7ce8a6bb503"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from subprocess import check_output\n",
        "%matplotlib inline\n",
        "import plotly.offline as py\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import plotly.tools as tls\n",
        "\n",
        "import os\n",
        "import gc\n",
        "\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "# import distance\n",
        "from nltk.stem import PorterStemmer\n",
        "from bs4 import BeautifulSoup"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2eLs0RlX5x_",
        "outputId": "ba65ae8e-7867-4d7a-8c96-ae915ad4fe89"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyE9Qh_cYbdq"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ly-P-HQYYBHX"
      },
      "source": [
        "data=pd.read_csv(r'/gdrive/MyDrive/Colab Notebooks/Datasets/Quora_train.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9LgZ4zsjYsSE",
        "outputId": "990735d8-2944-4041-9024-dbe29f05de10"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>qid1</th>\n",
              "      <th>qid2</th>\n",
              "      <th>question1</th>\n",
              "      <th>question2</th>\n",
              "      <th>is_duplicate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>What is the step by step guide to invest in sh...</td>\n",
              "      <td>What is the step by step guide to invest in sh...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
              "      <td>What would happen if the Indian government sto...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>How can I increase the speed of my internet co...</td>\n",
              "      <td>How can Internet speed be increased by hacking...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
              "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
              "      <td>Which fish would survive in salt water?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  qid1  ...                                          question2 is_duplicate\n",
              "0   0     1  ...  What is the step by step guide to invest in sh...            0\n",
              "1   1     3  ...  What would happen if the Indian government sto...            0\n",
              "2   2     5  ...  How can Internet speed be increased by hacking...            0\n",
              "3   3     7  ...  Find the remainder when [math]23^{24}[/math] i...            0\n",
              "4   4     9  ...            Which fish would survive in salt water?            0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaot04mnYucP",
        "outputId": "01c07d6f-b696-489b-f97d-222a18b2dbd3"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 404290 entries, 0 to 404289\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count   Dtype \n",
            "---  ------        --------------   ----- \n",
            " 0   id            404290 non-null  int64 \n",
            " 1   qid1          404290 non-null  int64 \n",
            " 2   qid2          404290 non-null  int64 \n",
            " 3   question1     404289 non-null  object\n",
            " 4   question2     404288 non-null  object\n",
            " 5   is_duplicate  404290 non-null  int64 \n",
            "dtypes: int64(4), object(2)\n",
            "memory usage: 18.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhNeln85Ywv2",
        "outputId": "c34559f5-e605-4ead-93e5-02c85105b338"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404290, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BbdOjhGYzR-",
        "outputId": "3bb536ad-c086-4b74-e0fc-958e4e090148"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id              0\n",
              "qid1            0\n",
              "qid2            0\n",
              "question1       1\n",
              "question2       2\n",
              "is_duplicate    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "bu0LZFvDcQOO",
        "outputId": "f6b17668-30f0-4282-a571-6ca4d6bd99b2"
      },
      "source": [
        "#checking imbalance of data\n",
        "\n",
        "print(data.is_duplicate.value_counts())\n",
        "\n",
        "sns.countplot(data=data,x=data.is_duplicate)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    255027\n",
            "1    149263\n",
            "Name: is_duplicate, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoAnBvBamGs1",
        "outputId": "f33552ed-3d62-47c7-b891-16362eb0d34b"
      },
      "source": [
        "#Calculation ratio of each class\n",
        "\n",
        "print('{}% Question pairs are similar class is_duplicate = 0'.format(data.is_duplicate.value_counts()[0]/data.shape[0]*100))\n",
        "\n",
        "print('{}% Question pairs are not similar class is_duplicate = 1'.format(data.is_duplicate.value_counts()[1]/data.shape[0]*100))\n",
        "\n",
        "# We can se there is slight imbalance in data"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63.08021469737069% Question pairs are similar class is_duplicate = 0\n",
            "36.9197853026293% Question pairs are not similar class is_duplicate = 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QPxbWO4p0te"
      },
      "source": [
        "### Checking for unique questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_JW1CiOqCOd",
        "outputId": "05befdf7-86cf-4315-e8d1-5de7621d4484"
      },
      "source": [
        "#merging qids1 and qids2\n",
        "\n",
        "total_qids=pd.Series(data.qid1.tolist() + data.qid2.tolist())\n",
        "\n",
        "unique_qids = len(np.unique(total_qids))\n",
        "print('Number of unique qids are {}\\n'.format(unique_qids))\n",
        "\n",
        "qids_morethan_onetime=(total_qids.value_counts() > 1).sum()\n",
        "print('Number of unique questions that appear more than one time: {} ({}%)\\n'.format(qs_morethan_onetime,qids_morethan_onetime/unique_qids*100))\n",
        "\n",
        "print('Max number of times a single question is repeated: {}\\n'.format(np.max(total_qids.value_counts())))\n",
        "\n",
        "#80% of questions are unique."
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique qids are 537933\n",
            "\n",
            "Number of unique questions that appear more than one time: 111780 (20.77953945937505%)\n",
            "\n",
            "Max number of times a single question is repeated: 157\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Cp2Ah5p-rSjc",
        "outputId": "3e2ff388-cad5-4d0c-e7d9-ea34ceba66cc"
      },
      "source": [
        "#Plot representing unique and repeated questions \n",
        "plt.title (\"Plot representing unique and repeated questions  \")\n",
        "sns.barplot(x=['unique_qids','qids_morethan_onetime'],y=[unique_qids,qids_morethan_onetime])\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kmyV1SbpI0jqZvm7Od2X+sFodJ2b6vZL2raWPzrQuSSfU0pu2YWZmfaM3ZxLHAXfX5r8BfDcidgQWAUdl+lHAokz/bpZD0s7AIcAbgNHADzPw9AN+AOwH7AwcmmVbtWFmZn2grSAhaQjwPuCcnBfwbuDSLDIROCCnx+Q8mb93lh8DTIqI5yPifqAL2D1fXRExJyL+DkwCxvTQhpmZ9YF2zyS+B3wBeCnntwAWR8QLOT8PGJzTg4G5AJn/ZJZ/Ob1hmVJ6qzaWIGm8pBmSZixYsKDNVTIzs570GCQkvR94LCJu7YP+LJOIODsiRkTEiIEDB3a6O2Zmrxr92yizJ/BBSfsD6wEbA/8FbCqpf37SHwLMz/LzgaHAPEn9gU2AhbX0bvVlmqUvbNGGmZn1gR7PJCLixIgYEhHDqG48XxsRhwHXAQdmsXHA5Tk9OefJ/GsjIjL9kHz6aXtgODAduAUYnk8yrZNtTM5lSm2YmVkfWJ7vSRwPfEZSF9X9g3Mz/Vxgi0z/DHACQETMBi4G7gJ+AxwbES/mWcLHgSlUT09dnGVbtWFmZn2gnctNL4uIacC0nJ5D9WRSY5nngIMKy58OnN4k/UrgyibpTdswM7O+4W9cm5lZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWVGPQULSepKmS7pD0mxJp2T69pJultQl6SJJ62T6ujnflfnDanWdmOn3Stq3lj4607oknVBLb9qGmZn1jXbOJJ4H3h0RbwJ2BUZLGgl8A/huROwILAKOyvJHAYsy/btZDkk7A4cAbwBGAz+U1E9SP+AHwH7AzsChWZYWbZiZWR/oMUhE5ZmcXTtfAbwbuDTTJwIH5PSYnCfz95akTJ8UEc9HxP1AF7B7vroiYk5E/B2YBIzJZUptmJlZH2jrnkR+4r8deAyYCvwFWBwRL2SRecDgnB4MzAXI/CeBLerpDcuU0rdo0UZj/8ZLmiFpxoIFC9pZJTMza0NbQSIiXoyIXYEhVJ/8d1qpveqliDg7IkZExIiBAwd2ujtmZq8avXq6KSIWA9cBbwM2ldQ/s4YA83N6PjAUIPM3ARbW0xuWKaUvbNGGmZn1gXaebhooadOcHgC8F7ibKlgcmMXGAZfn9OScJ/OvjYjI9EPy6aftgeHAdOAWYHg+ybQO1c3tyblMqQ0zM+sD/XsuwjbAxHwKaS3g4oi4QtJdwCRJXwP+BJyb5c8FfiqpC3iC6qBPRMyWdDFwF/ACcGxEvAgg6ePAFKAfcF5EzM66ji+0YWZmfaDHIBERM4E3N0mfQ3V/ojH9OeCgQl2nA6c3Sb8SuLLdNszMrG/4G9dmZlbkIGFmZkUOEmZmVuQgYWZmRQ4SZmZW5CBhZmZFDhJmZlbkIGFmZkUOEmZmVuQgYWZmRQ4SZmZW5CBhZmZFDhJmZlbkIGFmZkUOEmZmVuQgYWZmRQ4SZmZW5CBhZmZFDhJmZlbkIGFmZkUOEmZmVuQgYWZmRQ4SZmZW5CBhZmZFDhJmZlbkIGFmZkUOEmZmVuQgYWZmRQ4SZmZW5CBhZmZFPQYJSUMlXSfpLkmzJR2X6ZtLmirpvvy7WaZL0pmSuiTNlLRbra5xWf4+SeNq6W+RNCuXOVOSWrVhZmZ9o50ziReAz0bEzsBI4FhJOwMnANdExHDgmpwH2A8Ynq/xwFlQHfCBk4A9gN2Bk2oH/bOAo2vLjc70UhtmZtYHegwSEfFwRNyW008DdwODgTHAxCw2ETggp8cA50flJmBTSdsA+wJTI+KJiFgETAVGZ97GEXFTRARwfkNdzdowM7M+0Kt7EpKGAW8Gbga2joiHM+sRYOucHgzMrS02L9Napc9rkk6LNhr7NV7SDEkzFixY0JtVMjOzFtoOEpI2BH4BfCoinqrn5RlArOC+LaFVGxFxdkSMiIgRAwcOXJndMDNbo7QVJCStTRUgfhYRv8zkR/NSEfn3sUyfDwytLT4k01qlD2mS3qoNMzPrA+083STgXODuiPhOLWsy0P2E0jjg8lr62HzKaSTwZF4ymgLsI2mzvGG9DzAl856SNDLbGttQV7M2zMysD/Rvo8yewOHALEm3Z9oXgTOAiyUdBTwIHJx5VwL7A13As8CRABHxhKTTgFuy3KkR8UROHwNMAAYAV+WLFm2YmVkf6DFIRMTvARWy925SPoBjC3WdB5zXJH0GsEuT9IXN2jAzs77hb1ybmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFTlImJlZkYOEmZkVOUiYmVmRg4SZmRU5SJiZWZGDhJmZFfXvdAfMrH0PnfrGTnfBVkHbfnXWSqu7xzMJSedJekzSnbW0zSVNlXRf/t0s0yXpTEldkmZK2q22zLgsf5+kcbX0t0ialcucKUmt2jAzs77TzuWmCcDohrQTgGsiYjhwTc4D7AcMz9d44CyoDvjAScAewO7ASbWD/lnA0bXlRvfQhpmZ9ZEeg0RE3AA80ZA8BpiY0xOBA2rp50flJmBTSdsA+wJTI+KJiFgETAVGZ97GEXFTRARwfkNdzdowM7M+sqw3rreOiIdz+hFg65weDMytlZuXaa3S5zVJb9XGUiSNlzRD0owFCxYsw+qYmVkzy/10U54BxAroyzK3ERFnR8SIiBgxcODAldkVM7M1yrIGiUfzUhH597FMnw8MrZUbkmmt0oc0SW/VhpmZ9ZFlDRKTge4nlMYBl9fSx+ZTTiOBJ/OS0RRgH0mb5Q3rfYApmfeUpJH5VNPYhrqatWFmZn2kx+9JSLoQGAVsKWke1VNKZwAXSzoKeBA4OItfCewPdAHPAkcCRMQTkk4Dbslyp0ZE983wY6ieoBoAXJUvWrRhZmZ9pMcgERGHFrL2blI2gGML9ZwHnNckfQawS5P0hc3aMDOzvuOf5TAzsyIHCTMzK3KQMDOzIgcJMzMrcpAwM7MiBwkzMytykDAzsyIHCTMzK3KQMDOzIgcJMzMrcpAwM7MiBwkzMytykDAzsyIHCTMzK3KQMDOzIgcJMzMrcpAwM7MiBwkzMytykDAzsyIHCTMzK3KQMDOzIgcJMzMrcpAwM7MiBwkzMytykDAzsyIHCTMzK3KQMDOzIgcJMzMrcpAwM7MiBwkzMyta5YOEpNGS7pXUJemETvfHzGxNskoHCUn9gB8A+wE7A4dK2rmzvTIzW3Os0kEC2B3oiog5EfF3YBIwpsN9MjNbY/TvdAd6MBiYW5ufB+zRWEjSeGB8zj4j6d4+6NuaYkvg8U53otP0rXGd7oItzWOz20laEbVs1yxxVQ8SbYmIs4GzO92PVyNJMyJiRKf7YdbIY7NvrOqXm+YDQ2vzQzLNzMz6wKoeJG4BhkvaXtI6wCHA5A73ycxsjbFKX26KiBckfRyYAvQDzouI2R3u1prGl/FsVeWx2QcUEZ3ug5mZraJW9ctNZmbWQQ4SZmZW5CBhZmZFDhKvUpJGSDqzw30YJOnSQt40SX7GvQ9IOlXSe5qkj5J0RSf61BNJB9R/gmd1GS+SdpW0f23+g6v7b86t0k832bKLiBnAjA734a/AgZ3sg0FEfLXTfWhGUv+IeKGQfQBwBXBXH3ZpRdgVGAFcCRARk1nNH9v3mcRqQtIwSXfW5j8n6eT8hPUNSdMl/VnSOzL/5U+JkraQdLWk2ZLOkfSgpC1Ldeb0DpJ+I+lWSb+TtFOLvm0v6Y+SZkn6mqRnGvssaYCkSZLulnQZMCDT+0maIOnOXP7TK37rrTkkfSnHwe8lXZjv6QRJB2b+aEn3SLoN+Jfacu+SdHu+/iRpo0L9oyRdL+lySXMknSHpsBx/syTtkOWGSbpW0kxJ10jaNtMnSPqRpJuB/2w2ziS9Hfgg8M3szw7Z/EFNxvmwXO62fL291s9pki7N9f2ZpOJvV0jaO9d7lqTzJK2b6Q9IOiXrntW9H0jaIMtNz+XGqPou16nAh7LfH5J0hKTv19b9LEk35bYblXXcLWlCrS/75P50m6RLJG3Y+5GwAkWEX6vBCxgG3Fmb/xxwMjAN+Ham7Q/8NqdHAVfk9JnAV3P6fUBQ/e5N0zpz+hpgeE7vAVzbom+TgbE5fSzwTGOfgc9Qfc8F4J+BF6g+cb0FmFqra9NOb+vV9ZXbchawPrAx0JXv6QSqM7r1qH4LbTgg4OLaGPkVsGdObwj0L7QxClgMbAOsS/ULCKdk3nHA92r1jcvpjwD/k9MTqM4Q+rUaZ919rrVbGufrA+vl9HBgRq2fT1L9SsNawB+BvQrr1L1dXpvz5wOfyukHgE/k9DHAOTn9deDD3WMW+DOwAXAE8P1a3S/P5zpNym0/BngKeGP271aqs5AtgRuADXKZ48l9t1Mvn0m8Ovwy/95KdWBu9E7gAoCI+DWwqFVl+cnl7cAlkm4Hfkx1UCjZE7gwp39aKFPvw0xgZqbPAV4j6f9JGk2149iyeQdwWUQ8GxFPsfRljp2A+yPivqiOQBfU8m4EviPpk1SBunQZCOCWiHg4Ip4H/gJcnemzeGX8vQ34eU7/FNirtvwlEfHiMoyzZuN8beAnkmYBl1D9S4Fu0yNiXkS8BNxO830D4HVU2+XPOT+Rary2ancf4ITs9zSqQLNti753+1Vu+1nAoxExK/s3O+semetwY9Y9jsIP7/UV35NYfbzAkpcH16tNP59/X6R372mpzrWAxRGxay/qWqZvZUbEIklvAvYFPgYcTPXJ0/pQRJwh6ddUn9JvlLRvRNxTKP58bfql2vxLtDf+/pZ/ezvOmo3zTwOPAm/K+p4r9LO3+0ZP7Qr414hY4henJS31K9WFuurbrXu+f7YxNSIOXca+rnA+k1h9PApsper+wrrA+3ux7A3AvwFI2g/YrFWd+Sn0fkkH5TLKA3nJjVS/qwVwWBt92IXqkhOStgTWiohfAF8GduvFetmSbgAOyPs/GwEfaMi/BxhWu8b/8oFI0g75qfYbVL+ZVrwH1aY/sOSY+F1jgR7G2dNA0/siDTYBHs5P44dT/XxPb91LtV12zPnDget7WGYK8Inu+xyS3pzp7fa75CZgz+6+5L2P1y5HfcvNQWI1ERH/oLopNh2YSrXDt+sU4J2SZlPdrHyojToPA46SdAfVqXCrf/Z0HHBsnvIPLpQ5C9hQ0t3Z5q2ZPhiYlqfWFwAn9mK9rCYibgMuAu4ArqI62Nfzn6P6vyu/VnXj+rFa9qdUPTwwE/hHLr88PgEcmfUdTjVGmimNs0nA5/Om8A6FZQF+CIzL5XfilbOUtuV2OZLqstcsqk/1P+phsdOoLnXNzP3qtEy/Dti5+8b1MvRlAdV9jAtz2/2R5Q/Yy8W/3bQGkvQAMCIiVso/bJH0TER09okMQ9WTas9ExLc63RdbfflMwszMinwmYW2T9CXgoIbkSyLi9E70x1YeSW9k6SfVno+Inm7MrtJUfUdn+4bk4yNiSif6szpwkDAzsyJfbjIzsyIHCTMzK3KQMDOzIgcJMzMr+v/h2k6pawQA3wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4rUfNNtu_Kc"
      },
      "source": [
        "### Checking for Repeated pairs of duplicate questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "riaL5lrBxAmU",
        "outputId": "e67d174e-34db-477a-d583-950ab0c4d1ff"
      },
      "source": [
        "#checking whether there are any repeated pair of questions\n",
        "\n",
        "pair_duplicates = data[['qid1','qid2','is_duplicate']].groupby(['qid1','qid2']).count().reset_index()\n",
        "\n",
        "print (\"Number of duplicate questions\",(pair_duplicates).shape[0] - data.shape[0])"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate questions 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZGWqRI5xKf-"
      },
      "source": [
        "### Number of occurrences of each question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "cKjGh1eOxGwJ",
        "outputId": "8de08ca6-9813-4e5c-8528-2529be7844a4"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "sns.histplot(x=total_qids.value_counts(),bins=160)\n",
        "\n",
        "#Using LOG scale\n",
        "plt.yscale('log')\n",
        "\n",
        "plt.title('Log-Histogram of question appearance counts')\n",
        "plt.xlabel('Number of occurences of question')\n",
        "plt.ylabel('Number of questions')\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOJ2nEVSy11V"
      },
      "source": [
        "### Checking and printing null rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qo6Maotcy5vN",
        "outputId": "781fc748-2396-45fb-800d-052f8f8bff6b"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id              0\n",
              "qid1            0\n",
              "qid2            0\n",
              "question1       1\n",
              "question2       2\n",
              "is_duplicate    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "rY9v2bd2zA1F",
        "outputId": "74001ed6-4e51-4bca-9cbb-24467294c682"
      },
      "source": [
        "data[data.isnull().any(axis=1)]"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>qid1</th>\n",
              "      <th>qid2</th>\n",
              "      <th>question1</th>\n",
              "      <th>question2</th>\n",
              "      <th>is_duplicate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>105780</th>\n",
              "      <td>105780</td>\n",
              "      <td>174363</td>\n",
              "      <td>174364</td>\n",
              "      <td>How can I develop android app?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201841</th>\n",
              "      <td>201841</td>\n",
              "      <td>303951</td>\n",
              "      <td>174364</td>\n",
              "      <td>How can I create an Android app?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363362</th>\n",
              "      <td>363362</td>\n",
              "      <td>493340</td>\n",
              "      <td>493341</td>\n",
              "      <td>NaN</td>\n",
              "      <td>My Chinese name is Haichao Yu. What English na...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  ...  is_duplicate\n",
              "105780  105780  ...             0\n",
              "201841  201841  ...             0\n",
              "363362  363362  ...             0\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q95s68DM7Onn",
        "outputId": "4803d27b-2053-4bd8-e5a7-fd87de9a13c8"
      },
      "source": [
        "# Filling the null values with ' '\n",
        "data=data.fillna('')\n",
        "data.isnull().sum()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id              0\n",
              "qid1            0\n",
              "qid2            0\n",
              "question1       0\n",
              "question2       0\n",
              "is_duplicate    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    }
  ]
}