Data_Purifier_Package_Example.ipynb

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   "source": [
    "# **Data-Purifier**\n",
    "\n",
    "**A Python library for Automated Exploratory Data Analysis, Automated Data Cleaning and Automated Data Preprocessing For Machine Learning and Natural Language Processing Application in Python.**\n",
    "\n",
    "\n",
    "For Documentation and installation instruction please visit.<br>\n",
    "https://pypi.org/project/data-purifier/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7J7p6ntx0wbW"
   },
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ojQVhOoalZVV"
   },
   "outputs": [],
   "source": [
    "# !pip install data-purifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GXuaRzg8l17o"
   },
   "outputs": [],
   "source": [
    "# !python -m spacy download en_core_web_sm"
   ]
  },
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
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       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
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       "        }\n",
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       "        "
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
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       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
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       "        }\n",
       "        </script>\n",
       "        "
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     "text": [
      "0.3.4\n"
     ]
    }
   ],
   "source": [
    "# importing packages\n",
    "\n",
    "import datapurifier as dp\n",
    "from datapurifier import Mleda\n",
    "from datapurifier import Nlpeda\n",
    "from datapurifier import Nlpurifier\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "print(dp.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "[NL-EDA](#nleda)<br>\n",
    "[NLP-Purifier](#nlppurifier)<br>\n",
    "[ML-EDA](#mleda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7v25y6EeyxK4"
   },
   "source": [
    "## Natural Langauge Processing Application Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ef0WlQBJyXa7"
   },
   "source": [
    "### Automated Data Cleaning for NLP Datasets <a name= \"#nlppurifier\"></a>"
   ]
  },
  {
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   "execution_count": 11,
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>examples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALL UPPERCASE LETTERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hi, i'd be happy with u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hi my email is email@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hi my website is https://google.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@special #characters with punctuations! ?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>having   multiple   spaces</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>&lt;html&gt;text under html tag&lt;/html&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Áccěntěd těxt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I have many stop words, so try and remove me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>base form example: run ran running runs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1234567890 is my number</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>paris101 is the best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>game is on 🔥🔥</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hilarious😂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hello :-)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        examples\n",
       "0                          ALL UPPERCASE LETTERS\n",
       "1                        hi, i'd be happy with u\n",
       "2                 hi my email is email@email.com\n",
       "3            hi my website is https://google.com\n",
       "4      @special #characters with punctuations! ?\n",
       "5                     having   multiple   spaces\n",
       "6               <html>text under html tag</html>\n",
       "7                                  Áccěntěd těxt\n",
       "8   I have many stop words, so try and remove me\n",
       "9        base form example: run ran running runs\n",
       "10                       1234567890 is my number\n",
       "11                          paris101 is the best\n",
       "12                                 game is on 🔥🔥\n",
       "13                                    Hilarious😂\n",
       "14                                     Hello :-)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = { \"examples\": [\"ALL UPPERCASE LETTERS\",\"hi, i'd be happy with u\", \"hi my email is email@email.com\",\n",
    "                     \"hi my website is https://google.com\", \"@special #characters with punctuations! ?\",\n",
    "                     \"having   multiple   spaces\", \"<html>text under html tag</html>\", \"Áccěntěd těxt\",\n",
    "                     \"I have many stop words, so try and remove me\", \"base form example: run ran running runs\",\n",
    "                      \"1234567890 is my number\", \"paris101 is the best\", \"game is on 🔥🔥\", \"Hilarious😂\", \"Hello :-)\" ]} \n",
    "\n",
    "df = pd.DataFrame(data, columns = [\"examples\"])\n",
    "df"
   ]
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    {
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      "text/plain": [
       "GridspecLayout(children=(Checkbox(value=False, description='Drop Null Rows', indent=False, layout=Layout(grid_…"
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     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34m\n",
      "Convert Word to its Base Form\u001b[0m\n"
     ]
    },
    {
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      },
      "text/plain": [
       "interactive(children=(RadioButtons(description='Technique:', options=('None', 'Stemming', 'Lemmatization'), va…"
      ]
     },
     "metadata": {},
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    },
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     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31mRemove Top Common Words\u001b[0m\n"
     ]
    },
    {
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       "interactive(children=(Checkbox(value=False, description='Remove Top Common Words'), Output()), _dom_classes=('…"
      ]
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31mRemove Top Rare Words\u001b[0m\n"
     ]
    },
    {
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       "interactive(children=(Checkbox(value=False, description='Remove Top Rare Words'), Output()), _dom_classes=('wi…"
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      "text/plain": [
       "Button(description='Start Purifying', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mDataframe contains 15 rows and 1 columns\n",
      "\u001b[0m\n",
      "\u001b[32mThere is no null rows present.\n",
      "\u001b[0m\n",
      "Starting execution of 'lower' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'lower' function in 0.001 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'contraction_to_expansion' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'contraction_to_expansion' function in 0.002 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'count_emails' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'count_emails' function in 0.003 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'count_urls' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'count_urls' function in 0.001 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'get_word_count' function, Please wait...\n",
      "type <obj>.word_count for getting word count series\n",
      "\u001b[1m\u001b[34mFinished execution of 'get_word_count' function in 0.001 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_numbers' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_numbers' function in 0.001 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_html_tags' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_html_tags' function in 0.006 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_emails' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_emails' function in 0.000 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_urls' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_urls' function in 0.000 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_multiple_spaces' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_multiple_spaces' function in 0.000 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_accented_chars' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_accented_chars' function in 0.342 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'convert_emoticons_to_word' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'convert_emoticons_to_word' function in 0.045 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_emojis' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_emojis' function in 0.005 secs\n",
      "\u001b[0m\n",
      "Starting execution of 'remove_special_and_punctions' function, Please wait...\n",
      "\u001b[1m\u001b[34mFinished execution of 'remove_special_and_punctions' function in 0.000 secs\n",
      "\u001b[0m\n",
      "\u001b[1m\u001b[32m\n",
      "Purifying Completed!\n",
      "\u001b[0m\n",
      "type <obj>.df to access processed and purified dataframe\n"
     ]
    }
   ],
   "source": [
    "pure = Nlpurifier(df, \"examples\")"
   ]
  },
  {
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   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "af6c7b56-6135-4f5d-d4a1-9a979f9dc7bb"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>examples</th>\n",
       "      <th>emails</th>\n",
       "      <th>emails_counts</th>\n",
       "      <th>urls_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all uppercase letters</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hi i would be happy with u</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hi my email is</td>\n",
       "      <td>[email@email.com]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hi my website is</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>special characters with punctuations</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>having multiple spaces</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>text under html tag</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>accented text</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>i have many stop words so try and remove me</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>base form example run ran running runs</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>is my number</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>paris is the best</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>game is on</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hilarious</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hello Happyfacesmiley</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       examples             emails  \\\n",
       "0                         all uppercase letters                 []   \n",
       "1                    hi i would be happy with u                 []   \n",
       "2                                hi my email is  [email@email.com]   \n",
       "3                              hi my website is                 []   \n",
       "4         special characters with punctuations                  []   \n",
       "5                        having multiple spaces                 []   \n",
       "6                           text under html tag                 []   \n",
       "7                                 accented text                 []   \n",
       "8   i have many stop words so try and remove me                 []   \n",
       "9        base form example run ran running runs                 []   \n",
       "10                                 is my number                 []   \n",
       "11                            paris is the best                 []   \n",
       "12                                  game is on                  []   \n",
       "13                                    hilarious                 []   \n",
       "14                        hello Happyfacesmiley                 []   \n",
       "\n",
       "    emails_counts  urls_counts  \n",
       "0               0            0  \n",
       "1               0            0  \n",
       "2               1            0  \n",
       "3               0            1  \n",
       "4               0            0  \n",
       "5               0            0  \n",
       "6               0            0  \n",
       "7               0            0  \n",
       "8               0            0  \n",
       "9               0            0  \n",
       "10              0            0  \n",
       "11              0            0  \n",
       "12              0            0  \n",
       "13              0            0  \n",
       "14              0            0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "VAxheacWqOF6",
    "outputId": "0b2b8c13-cb89-4dc1-892d-8de917613628"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_examples</th>\n",
       "      <th>examples</th>\n",
       "      <th>emails</th>\n",
       "      <th>emails_counts</th>\n",
       "      <th>urls_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALL UPPERCASE LETTERS</td>\n",
       "      <td>all uppercase letters</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hi, i'd be happy with u</td>\n",
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       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>hi my email is email@email.com</td>\n",
       "      <td>hi my email is</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hi my website is https://google.com</td>\n",
       "      <td>hi my website is</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@special #characters with punctuations! ?</td>\n",
       "      <td>special characters with punctuations</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>having   multiple   spaces</td>\n",
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       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>&lt;html&gt;text under html tag&lt;/html&gt;</td>\n",
       "      <td>text under html tag</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Áccěntěd těxt</td>\n",
       "      <td>accented text</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I have many stop words, so try and remove me</td>\n",
       "      <td>i have many stop words so try and remove me</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>base form example: run ran running runs</td>\n",
       "      <td>base form example run ran running runs</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1234567890 is my number</td>\n",
       "      <td>is my number</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>paris101 is the best</td>\n",
       "      <td>paris is the best</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>game is on 🔥🔥</td>\n",
       "      <td>game is on</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hilarious😂</td>\n",
       "      <td>hilarious</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hello :-)</td>\n",
       "      <td>hello Happyfacesmiley</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               original_examples  \\\n",
       "0                          ALL UPPERCASE LETTERS   \n",
       "1                        hi, i'd be happy with u   \n",
       "2                 hi my email is email@email.com   \n",
       "3            hi my website is https://google.com   \n",
       "4      @special #characters with punctuations! ?   \n",
       "5                     having   multiple   spaces   \n",
       "6               <html>text under html tag</html>   \n",
       "7                                  Áccěntěd těxt   \n",
       "8   I have many stop words, so try and remove me   \n",
       "9        base form example: run ran running runs   \n",
       "10                       1234567890 is my number   \n",
       "11                          paris101 is the best   \n",
       "12                                 game is on 🔥🔥   \n",
       "13                                    Hilarious😂   \n",
       "14                                     Hello :-)   \n",
       "\n",
       "                                       examples             emails  \\\n",
       "0                         all uppercase letters                 []   \n",
       "1                    hi i would be happy with u                 []   \n",
       "2                                hi my email is  [email@email.com]   \n",
       "3                              hi my website is                 []   \n",
       "4         special characters with punctuations                  []   \n",
       "5                        having multiple spaces                 []   \n",
       "6                           text under html tag                 []   \n",
       "7                                 accented text                 []   \n",
       "8   i have many stop words so try and remove me                 []   \n",
       "9        base form example run ran running runs                 []   \n",
       "10                                 is my number                 []   \n",
       "11                            paris is the best                 []   \n",
       "12                                  game is on                  []   \n",
       "13                                    hilarious                 []   \n",
       "14                        hello Happyfacesmiley                 []   \n",
       "\n",
       "    emails_counts  urls_counts  \n",
       "0               0            0  \n",
       "1               0            0  \n",
       "2               1            0  \n",
       "3               0            1  \n",
       "4               0            0  \n",
       "5               0            0  \n",
       "6               0            0  \n",
       "7               0            0  \n",
       "8               0            0  \n",
       "9               0            0  \n",
       "10              0            0  \n",
       "11              0            0  \n",
       "12              0            0  \n",
       "13              0            0  \n",
       "14              0            0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_df = pure.df\n",
    "pure_df[\"original_examples\"] = df[\"examples\"]\n",
    "cols = pure_df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pure_df = pure_df[cols]\n",
    "pure_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Jx_WTK_yjLY"
   },
   "source": [
    "### Automated EDA for NLP Dataset <a name= \"#nlpeda\"></a>\n",
    "\n",
    "Currently `Google Colab` doesn't fully support `Automated EDA for NLP` feature so some of function might not be available, but it is fully supported by `Kaggle Kernels`, `Binder` and `Jupyter Notebook`, feel free to check it out 😃."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0uPqcE5c3qoc"
   },
   "source": [
    "#### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['boston_housing', 'breast_cancer', 'diabetes', 'iris', 'pima_indians_diabetes', 'sample_superstore', 'tips', 'titanic', 'us_arrests', 'womens_clothing_e_commerce_reviews', 'zoo']\n"
     ]
    }
   ],
   "source": [
    "print(dp.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y16R_nG426xS",
    "outputId": "8ec99133-7ed6-4719-aa41-5d2d8e9c68f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['womens_clothing_e-commerce_reviews']\n"
     ]
    }
   ],
   "source": [
    "print(dp.get_text_dataset_names())  # to get all text dataset names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 246
    },
    "id": "qPdITt2M24p2",
    "outputId": "b2436b7e-e761-4231-a7e7-28cceb2988bc"
   },
   "outputs": [
    {
     "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>review</th>\n",
       "      <th>rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  rating\n",
       "0  Absolutely wonderful - silky and sexy and comf...       0\n",
       "1  Love this dress!  it's sooo pretty.  i happene...       4\n",
       "2  I had such high hopes for this dress and reall...       0\n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       0\n",
       "4  This shirt is very flattering to all due to th...       6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dp.load_dataset(\"womens_clothing_e_commerce_reviews\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "XU0YiMavkeZ3",
    "outputId": "cc009591-fc6c-43bd-b718-87753a2d0858"
   },
   "outputs": [],
   "source": [
    "# df = df[[\"Review Text\", \"Positive Feedback Count\"]]\n",
    "# df.columns = [\"review\", \"rating\"]\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    "id": "7Pfeq_XklBdJ",
    "outputId": "67f7bd2d-9f42-4404-aa04-d6c4a6dd0442",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mDataframe contains 23486 rows and 2 columns\n",
      "\u001b[0m\n",
      "\n",
      "Null Information of Dataframe: \n"
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       "    .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>column_name</th>\n",
       "      <th>null_rows</th>\n",
       "      <th>null_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>review</td>\n",
       "      <td>845</td>\n",
       "      <td>3.597888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  column_name  null_rows  null_percentage\n",
       "0      review        845         3.597888"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please select to 'drop all null rows', to continue analysis of data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "443e582ee6c44e55a267e3aeb7bc2141",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Checkbox(value=False, description='Drop all null rows'), Output()), _dom_classes=('widge…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Basic Analysis of data\n",
    "\n",
    "eda = Nlpeda(df, \"review\", analyse=\"basic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "lulgBWj1lGfs",
    "outputId": "fe418d4c-7adf-4e14-df46-55ba93879d60"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>review</th>\n",
       "      <th>rating</th>\n",
       "      <th>word_counts</th>\n",
       "      <th>char_counts</th>\n",
       "      <th>average_word_lengths</th>\n",
       "      <th>stop_words_counts</th>\n",
       "      <th>upper_case_word_counts</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>53</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>303</td>\n",
       "      <td>3.822581</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.339583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>500</td>\n",
       "      <td>4.112245</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>124</td>\n",
       "      <td>4.681818</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>192</td>\n",
       "      <td>4.361111</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0.512891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22636</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>131</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0.552667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22637</th>\n",
       "      <td>It reminds me of maternity clothes. soft, stre...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>223</td>\n",
       "      <td>4.894737</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.091667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22638</th>\n",
       "      <td>This fit well, but the top was very see throug...</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>208</td>\n",
       "      <td>3.976190</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22639</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>427</td>\n",
       "      <td>3.976744</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22640</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>4.842105</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.413889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22641 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  rating  word_counts  \\\n",
       "0      Absolutely wonderful - silky and sexy and comf...       0            8   \n",
       "1      Love this dress!  it's sooo pretty.  i happene...       4           62   \n",
       "2      I had such high hopes for this dress and reall...       0           98   \n",
       "3      I love, love, love this jumpsuit. it's fun, fl...       0           22   \n",
       "4      This shirt is very flattering to all due to th...       6           36   \n",
       "...                                                  ...     ...          ...   \n",
       "22636  I was very happy to snag this dress at such a ...       0           28   \n",
       "22637  It reminds me of maternity clothes. soft, stre...       0           38   \n",
       "22638  This fit well, but the top was very see throug...       1           42   \n",
       "22639  I bought this dress for a wedding i have this ...       2           86   \n",
       "22640  This dress in a lovely platinum is feminine an...      22           19   \n",
       "\n",
       "       char_counts  average_word_lengths  stop_words_counts  \\\n",
       "0               53              5.750000                  2   \n",
       "1              303              3.822581                 32   \n",
       "2              500              4.112245                 50   \n",
       "3              124              4.681818                  8   \n",
       "4              192              4.361111                 22   \n",
       "...            ...                   ...                ...   \n",
       "22636          131              3.714286                 15   \n",
       "22637          223              4.894737                 14   \n",
       "22638          208              3.976190                 25   \n",
       "22639          427              3.976744                 49   \n",
       "22640          110              4.842105                  6   \n",
       "\n",
       "       upper_case_word_counts  polarity  \n",
       "0                           1  0.633333  \n",
       "1                           1  0.339583  \n",
       "2                           1  0.073675  \n",
       "3                           1  0.550000  \n",
       "4                           1  0.512891  \n",
       "...                       ...       ...  \n",
       "22636                       1  0.552667  \n",
       "22637                       1  0.091667  \n",
       "22638                       1  0.414286  \n",
       "22639                       1  0.322222  \n",
       "22640                       1  0.413889  \n",
       "\n",
       "[22641 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317,
     "referenced_widgets": [
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    },
    "id": "lxSfZUf35gaw",
    "outputId": "9ff03001-47b3-45ee-fb8d-536b62f37f8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mDataframe contains 23486 rows and 2 columns\n",
      "\u001b[0m\n",
      "\n",
      "Null Information of Dataframe: \n"
     ]
    },
    {
     "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>column_name</th>\n",
       "      <th>null_rows</th>\n",
       "      <th>null_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>review</td>\n",
       "      <td>845</td>\n",
       "      <td>3.597888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  column_name  null_rows  null_percentage\n",
       "0      review        845         3.597888"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please select to 'drop all null rows', to continue analysis of data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0242d6a1f620429dad747b0d0122b799",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Checkbox(value=False, description='Drop all null rows'), Output()), _dom_classes=('widge…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Word Analysis of data\n",
    "\n",
    "eda = Nlpeda(df, \"review\", analyse=\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "8EljApX0455Q"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>bigram</th>\n",
       "      <th>frequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>true size</td>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>love dress</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>usually wear</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>looks great</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fit perfectly</td>\n",
       "      <td>609</td>\n",
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       "      <th>5</th>\n",
       "      <td>size small</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fits perfectly</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>usual size</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>look like</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>just right</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ordered size</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>love love</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>looks like</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>runs large</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>highly recommend</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>super cute</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wear size</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fabric soft</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>feel like</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fit great</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>soft comfortable</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>normally wear</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              bigram  frequence\n",
       "0          true size       1347\n",
       "1         love dress        766\n",
       "2       usually wear        691\n",
       "3        looks great        620\n",
       "4      fit perfectly        609\n",
       "5         size small        540\n",
       "6     fits perfectly        489\n",
       "7         usual size        479\n",
       "8          look like        425\n",
       "9         just right        423\n",
       "10      ordered size        375\n",
       "11         love love        374\n",
       "12        looks like        373\n",
       "13        runs large        367\n",
       "14  highly recommend        365\n",
       "15        super cute        363\n",
       "16         wear size        361\n",
       "17       fabric soft        350\n",
       "18         feel like        349\n",
       "19         fit great        348\n",
       "20  soft comfortable        342\n",
       "21     normally wear        337"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda.bigram_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gBfeua2q1A0S"
   },
   "source": [
    "## Machine Learning Application Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cFBEe6Jkyp9k"
   },
   "source": [
    "### Automated EDA for Machine Learning Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Mp3xBYsro93",
    "outputId": "37e0e88f-235c-44d3-fffe-37d62c4a8399"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['boston_housing', 'breast_cancer', 'diabetes', 'iris', 'pima_indians_diabetes', 'sample_superstore', 'tips', 'titanic', 'us_arrests', 'womens_clothing_e_commerce_reviews', 'zoo']\n"
     ]
    }
   ],
   "source": [
    "print(dp.get_dataset_names())  # to get all dataset names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UQ1oOrg06o4"
   },
   "source": [
    "#### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "G3M_nVp6rpAU",
    "outputId": "deaa170e-e86a-4368-e385-6a1dbfe711bf"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df = dp.load_dataset(\"iris\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "87a5c2236b614c0d8a2bafa9d0d27419"
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    },
    "id": "zkxottJJYbQx",
    "outputId": "6854b190-5128-42c5-9200-2190d0993997",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame:  (150, 5)\n",
      "\u001b[1m\u001b[31m\n",
      "Sample of Dataframe:\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
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       "    <tr>\n",
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       "      <td>4.9</td>\n",
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       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width     species\n",
       "61           5.9          3.0           4.2          1.5  versicolor\n",
       "7            5.0          3.4           1.5          0.2      setosa\n",
       "31           5.4          3.4           1.5          0.4      setosa\n",
       "9            4.9          3.1           1.5          0.1      setosa\n",
       "60           5.0          2.0           3.5          1.0  versicolor\n",
       "94           5.6          2.7           4.2          1.3  versicolor\n",
       "86           6.7          3.1           4.7          1.5  versicolor\n",
       "73           6.1          2.8           4.7          1.2  versicolor\n",
       "88           5.6          3.0           4.1          1.3  versicolor\n",
       "29           4.7          3.2           1.6          0.2      setosa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34m\n",
      "There are total 1 categorical and 4 numerical columns\n",
      "\u001b[0m\n",
      "\u001b[1m\u001b[31mDescription of Data:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31mInformation regarding data: \n",
      "\u001b[0m\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[32m\n",
      "Congrats!!, The Dataframe has NO NULL VALUES\n",
      "\u001b[0m\n",
      "\u001b[1m\u001b[31m\n",
      "Interactive Value Count Plot:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4246b50dc0ae4072a6062378ad74d144",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Dropdown(description='Columns:', options=('sepal_length', 'sepal_width', 'petal_length', 'petal…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05fd49aa7fdf44a18d953368f82a008d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31m\n",
      "Joint Plot:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75da5e6fbc5b4b77ada4df2673a84548",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Dropdown(description='X-axis:', options=('sepal_length', 'sepal_width', 'petal_length', 'petal_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a952c12b1ca242dbad8ac6b5593c58da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31m\n",
      "Correlation Heatmap Plot:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31mPair Plot:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e4a0a17d7ea4af08687f6fe5b8fe093",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Select Hue: ', options=('species',), value=None), Checkbox(value=False, d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9cc27782ecf4475a3801aed721a7b6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31m\n",
      "Pie Plot:\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40b1b8cd47884436a9d5fabbf38501be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Columns:', options=('species',), value='species'), Output()), _dom…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<datapurifier.eda.mleda.Mleda at 0x226593bddd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleda = Mleda(df)\n",
    "mleda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6msdZfqF5I_l"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Data-Purifier-Package-Example",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tensor",
   "language": "python",
   "name": "tensor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0198b00b02ad423ba0570f5bd014497a": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
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\n",
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        "text": "Dropping rows having [' ', 'NULL', numpy.nan] values\n\u001b[1m\u001b[31mTotal Null rows dropped: 845\n\u001b[0m\nStarting execution of 'basic_eda' function, Please wait...\n\u001b[1m\u001b[34mDataframe contains 22641 rows and 2 columns\n\u001b[0m\n"
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      "_view_name": "OutputView",
      "layout": "IPY_MODEL_14cbde5de671492f99c07ca304a13071",
      "msg_id": "",
      "outputs": [
       {
        "metadata": {
         "tags": []
        },
        "output_type": "display_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>column_value</th>\n      <th>count</th>\n      <th>count_percentage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>versicolor</td>\n      <td>50</td>\n      <td>33.333333</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>virginica</td>\n      <td>50</td>\n      <td>33.333333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>setosa</td>\n      <td>50</td>\n      <td>33.333333</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
        "text/plain": "  column_value  count  count_percentage\n0   versicolor     50         33.333333\n1    virginica     50         33.333333\n2       setosa     50         33.333333"
       },
       {
        "image/png": 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\n",
        "metadata": {
         "needs_background": "light",
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 720x360 with 1 Axes>"
       }
      ]
     }
    },
    "6355d64c2df14b7d98192b887201a2c0": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
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      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
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      "_view_module_version": "1.2.0",
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      "border": null,
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      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
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\n",
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