Data Analysis and Visualization .ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": "## How will we tackle this task you may ask?\n\n![image](python.png)\n\nWe will be using **Python Programming Language** and it's vast toolkit specifically made for Data science.\n\nThis is a Python **Function**\n\n```python\ndef importing_test_data():\n    df=pd.read_csv(\"/work/Good_reads_Best_2000s/test_csv.csv\")\n    return df\ndf=importing_test_data()\n```\n\nDon't be scared it's just how Python let's us ___talk___ with the data.",
      "metadata": {
        "tags": [],
        "cell_id": "00000-be65520c-044e-41a8-8e0c-d86e4ed3cd13",
        "deepnote_cell_type": "markdown"
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    {
      "cell_type": "markdown",
      "source": "![image](pandas.jpeg)\nOkay, we have the data now but what can I do with it?\n## It's time to clean the data!!\n__With a dash of Regex__\n```python\npattern = re.compile(r'(\\[|\\]|\\'|\\\")+')\npattern2 = re.compile(r'(\\\\n|...more)+')\n```\n\n__And a bit of very__ ___PYTHONIC___ __looking code__\n```python\ngenres_l = []\ngenres_freq = {}\nfor i, j in df_test['Genres'].items():\n    j = re.sub(pattern, '', j).split(',')\n    for word in j:\n        if word in genres_freq:\n            genres_freq[word] += 1\n        else:\n            genres_freq[word] = 1\n    genres_l.append(j)\n\ndf_test['Genres'] = genres_l\nprint(genre_freq)\n```\n",
      "metadata": {
        "tags": [],
        "cell_id": "00001-d2410beb-bad7-4d2c-b070-9f4b08c0b797",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Data Analysis and Visualization ",
      "metadata": {
        "cell_id": "00000-d33b8591-0026-48f3-b00b-73a0fa411e0b",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "markdown",
      "source": "   # 1. Data analysis ",
      "metadata": {
        "cell_id": "00001-d5709031-69e7-4eca-8402-87ff4796fa71",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00002-a031312d-b3ed-4f06-ac10-6d2d542f6aed",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "485d3fbf",
        "execution_millis": 425,
        "execution_start": 1618407866341,
        "deepnote_cell_type": "code"
      },
      "source": "#import liberaries \nimport numpy as np \nimport pandas as pd\nimport seaborn as sns\n#import statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport plotly.graph_objs as go\nimport plotly.offline as pyo\nimport cufflinks as cf\n%matplotlib inline \n\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\ninit_notebook_mode(connected=True)\nimport sweetviz as sv\ncf.go_offline()",
      "execution_count": null,
      "outputs": [
        {
          "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": {},
          "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": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00003-a5e8b44a-ccec-460c-b07a-b5fb3e0f9b09",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d7db3491",
        "execution_start": 1618407855284,
        "execution_millis": 5622,
        "deepnote_cell_type": "code"
      },
      "source": "!pip install cufflinks",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Collecting cufflinks\n  Downloading cufflinks-0.17.3.tar.gz (81 kB)\n\u001b[K     |████████████████████████████████| 81 kB 12.7 MB/s \n\u001b[?25hRequirement already satisfied: numpy>=1.9.2 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from cufflinks) (1.19.5)\nRequirement already satisfied: pandas>=0.19.2 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from cufflinks) (1.2.4)\nRequirement already satisfied: plotly>=4.1.1 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from cufflinks) (4.14.3)\nRequirement already satisfied: six>=1.9.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from cufflinks) (1.15.0)\nCollecting colorlover>=0.2.1\n  Downloading colorlover-0.3.0-py3-none-any.whl (8.9 kB)\nRequirement already satisfied: setuptools>=34.4.1 in /root/venv/lib/python3.7/site-packages (from cufflinks) (54.1.2)\nRequirement already satisfied: ipython>=5.3.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from cufflinks) (7.22.0)\nCollecting ipywidgets>=7.0.0\n  Downloading ipywidgets-7.6.3-py2.py3-none-any.whl (121 kB)\n\u001b[K     |████████████████████████████████| 121 kB 45.8 MB/s \n\u001b[?25hRequirement already satisfied: pexpect>4.3 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (4.8.0)\nRequirement already satisfied: decorator in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (5.0.6)\nRequirement already satisfied: traitlets>=4.2 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (5.0.5)\nRequirement already satisfied: backcall in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (0.2.0)\nRequirement already satisfied: pickleshare in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (0.7.5)\nRequirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (3.0.18)\nRequirement already satisfied: pygments in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (2.8.1)\nRequirement already satisfied: jedi>=0.16 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipython>=5.3.0->cufflinks) (0.17.2)\nCollecting widgetsnbextension~=3.5.0\n  Downloading widgetsnbextension-3.5.1-py2.py3-none-any.whl (2.2 MB)\n\u001b[K     |████████████████████████████████| 2.2 MB 43.7 MB/s \n\u001b[?25hRequirement already satisfied: nbformat>=4.2.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipywidgets>=7.0.0->cufflinks) (5.1.3)\nRequirement already satisfied: ipykernel>=4.5.1 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipywidgets>=7.0.0->cufflinks) (5.5.3)\nCollecting jupyterlab-widgets>=1.0.0\n  Downloading jupyterlab_widgets-1.0.0-py3-none-any.whl (243 kB)\n\u001b[K     |████████████████████████████████| 243 kB 47.9 MB/s \n\u001b[?25hRequirement already satisfied: jupyter-client in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (6.1.12)\nRequirement already satisfied: tornado>=4.2 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (6.1)\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from jedi>=0.16->ipython>=5.3.0->cufflinks) (0.7.1)\nRequirement already satisfied: ipython-genutils in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (0.2.0)\nRequirement already satisfied: jsonschema!=2.5.0,>=2.4 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (3.2.0)\nRequirement 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importlib-metadata->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (3.7.4.3)\nRequirement already satisfied: MarkupSafe>=0.23 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.1.1)\nRequirement already satisfied: jupyterlab-pygments in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.1.2)\nRequirement already satisfied: nbclient<0.6.0,>=0.5.0 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.3)\nRequirement already satisfied: pandocfilters>=1.4.1 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.4.3)\nRequirement already satisfied: defusedxml in 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already satisfied: nest-asyncio in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.5.1)\nRequirement already satisfied: async-generator in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.10)\nRequirement already satisfied: webencodings in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)\nRequirement already satisfied: packaging in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (20.9)\nRequirement already satisfied: pyparsing>=2.0.2 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.4.7)\nBuilding wheels for collected packages: cufflinks\n  Building wheel for cufflinks (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=67922 sha256=163566d51054455f90ab3f80ba133eb565bfe445c45ae34aec9ac410e70bd4e2\n  Stored in directory: /root/.cache/pip/wheels/e1/27/13/3fe67fa7ea7be444b831d117220b3b586b872c9acd4df480d0\nSuccessfully built cufflinks\nInstalling collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets, colorlover, cufflinks\nSuccessfully installed colorlover-0.3.0 cufflinks-0.17.3 ipywidgets-7.6.3 jupyterlab-widgets-1.0.0 widgetsnbextension-3.5.1\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00003-30c82925-5f24-42f6-a01a-6faec5da538c",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6d329e1a",
        "execution_start": 1618319071871,
        "execution_millis": 17,
        "deepnote_cell_type": "code"
      },
      "source": "#opening the data base\ndf = pd.read_csv('test_csv.csv')\ndf = df.drop(['URL'], axis= 1)",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "cell_id": "00004-a91fcfd4-f0eb-4567-a07b-b91953ff4dd4",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "f804c160",
        "execution_start": 1618319078843,
        "execution_millis": 45,
        "deepnote_cell_type": "code"
      },
      "source": "df",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
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                      {
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                {
                  "name": "Number of Ratings",
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                  "Author": "Zadie Smith",
                  "Number of Ratings": 130318,
                  "Number of Reviews": 7944,
                  "Average Ratings": 4,
                  "Number of Pages": "448.0",
                  "Published Year": "2001.0",
                  "Series": "0.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Orange Prize Nominee for Fiction Shortlist (2000)', 'Guardian First Book Award (2000)', 'James Tait Black Memorial Prize for Fiction (2000)', 'Whitbread Award for First Novel (2000)', 'John Llewellyn Rhys Prize Nominee (2000)', 'Puddly Award for Debut Novel (2001)', 'National Book Critics Circle Award Nominee for Fiction (2000)', \"Commonwealth Writers' Prize for Best First Book Overall (2001)\", 'Sunday Times/Peters Fraser + Dunlop Young Writer of the Year Award (2001)', 'Betty Trask Award (2001)']",
                  "Places": "[['Willesden, North London,\\n1974', '(United Kingdom)']]",
                  "_deepnote_index_column": 89
                },
                {
                  "Title": "A Great and Terrible Beauty (Gemma Doyle, #1)",
                  "Author": "Libba Bray",
                  "Number of Ratings": 218068,
                  "Number of Reviews": 9971,
                  "Average Ratings": 4,
                  "Number of Pages": "403.0",
                  "Published Year": "2003.0",
                  "Series": "1.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Iowa High School Book Award (2007)', 'Lincoln Award Nominee (2007)']",
                  "Places": "[['United Kingdom', 'India,\\n1895', '    \\nLondon, England', '(United Kingdom)']]",
                  "_deepnote_index_column": 90
                },
                {
                  "Title": "Blink: The Power of Thinking Without Thinking",
                  "Author": "Malcolm Gladwell",
                  "Number of Ratings": 511530,
                  "Number of Reviews": 15742,
                  "Average Ratings": 4,
                  "Number of Pages": "296.0",
                  "Published Year": "2007.0",
                  "Series": "0.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "[]",
                  "Places": "nan",
                  "_deepnote_index_column": 91
                },
                {
                  "Title": "Fast Food Nation: The Dark Side of the All-American Meal",
                  "Author": "Eric Schlosser",
                  "Number of Ratings": 201981,
                  "Number of Reviews": 5436,
                  "Average Ratings": 4,
                  "Number of Pages": "399.0",
                  "Published Year": "2005.0",
                  "Series": "0.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Puddly Award for Nonfiction (2003)']",
                  "Places": "[['Colorado Springs, Colorado', '(United States)']]",
                  "_deepnote_index_column": 92
                },
                {
                  "Title": "The Last Olympian (Percy Jackson and the Olympians, #5)",
                  "Author": "Rick Riordan",
                  "Number of Ratings": 717179,
                  "Number of Reviews": 25215,
                  "Average Ratings": 4,
                  "Number of Pages": "381.0",
                  "Published Year": "2009.0",
                  "Series": "1.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Soaring Eagle Book Award (2010)', 'Goodreads Choice Award Nominee for Favorite Book & Young Adult Series (2009)', 'Carnegie Medal Nominee (2010)', 'Premio El Templo de las Mil Puertas Nominee for Mejor novela extranjera perteneciente a saga (2010)']",
                  "Places": "[['Manhattan, New York City, New York', '(United States)'], ['New York City, New York', '(United States)'], ['New York State', '(United States)']]",
                  "_deepnote_index_column": 93
                },
                {
                  "Title": "John Adams",
                  "Author": "David McCullough",
                  "Number of Ratings": 328379,
                  "Number of Reviews": 6489,
                  "Average Ratings": 4,
                  "Number of Pages": "751.0",
                  "Published Year": "2001.0",
                  "Series": "nan",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Pulitzer Prize for Biography or Autobiography (2002)', 'Ambassador Book Award for Biography (2002)', 'Audie Award for Nonfiction (2002)', 'Puddly Award for Nonfiction (2002)', 'American Revolution Round Table Award (2001)', 'Fraunces Tavern Museum Book Award (2002)']",
                  "Places": "[['Boston, Massachusetts', '(United States)'], ['Philadelphia, Pennsylvania', '(United States)']]",
                  "_deepnote_index_column": 94
                },
                {
                  "Title": "Anansi Boys",
                  "Author": "Neil Gaiman",
                  "Number of Ratings": 202125,
                  "Number of Reviews": 10199,
                  "Average Ratings": 4,
                  "Number of Pages": "387.0",
                  "Published Year": "2006.0",
                  "Series": "1.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Locus Award for Best Fantasy Novel (2006)', 'Mythopoeic Fantasy Award for Adult Literature (2006)', \"Grand Prix de l'Imaginaire Nominee for Roman étranger (2007)\", 'ALA Alex Award (2006)', 'British Fantasy Award for Best Novel (2006)']",
                  "Places": "[['London, England', '(United Kingdom)'], ['Florida', '(United States)']]",
                  "_deepnote_index_column": 95
                },
                {
                  "Title": "Daughter of Fortune",
                  "Author": "Isabel Allende",
                  "Number of Ratings": 119426,
                  "Number of Reviews": 4158,
                  "Average Ratings": 4,
                  "Number of Pages": "432.0",
                  "Published Year": "2006.0",
                  "Series": "1.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Premio de traducción literaria Valle Inclán Nominee for Margaret Sayers Peden (2000)']",
                  "Places": "nan",
                  "_deepnote_index_column": 96
                },
                {
                  "Title": "Year of Wonders",
                  "Author": "Geraldine Brooks",
                  "Number of Ratings": 146254,
                  "Number of Reviews": 10579,
                  "Average Ratings": 4,
                  "Number of Pages": "304.0",
                  "Published Year": "2002.0",
                  "Series": "0.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['ALA Alex Award (2002)']",
                  "Places": "[['Eyam, Derbyshire, England,\\n1666', '(United Kingdom)']]",
                  "_deepnote_index_column": 97
                },
                {
                  "Title": "The Sea of Monsters (Percy Jackson and the Olympians, #2)",
                  "Author": "Rick Riordan",
                  "Number of Ratings": 805198,
                  "Number of Reviews": 29067,
                  "Average Ratings": 4,
                  "Number of Pages": "279.0",
                  "Published Year": "2006.0",
                  "Series": "1.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "[]",
                  "Places": "[['Miami, Florida', '(United States)'], ['Atlantic Ocean', 'Bermuda Triangle,\\n2006', '    '], [''], ['Other Editions (189)'], [''], [''], [''], [''], [''], [''], [''], [''], [''], [''], [''], ['All Editions', ' | ', 'Add a New Edition', ' | ', 'Combine']]",
                  "_deepnote_index_column": 98
                },
                {
                  "Title": "What Is the What",
                  "Author": "Dave Eggers",
                  "Number of Ratings": 80852,
                  "Number of Reviews": 7222,
                  "Average Ratings": 4,
                  "Number of Pages": "475.0",
                  "Published Year": "2006.0",
                  "Series": "0.0",
                  "Genres": "['Fantasy', 'Young Adult', 'Fiction']",
                  "Awards": "['Independent Publisher Book Award (IPPY) for Literary Fiction (2007)', 'Prix Médicis Etranger (2009)', 'National Book Critics Circle Award Nominee for Fiction (2006)']",
                  "Places": "[['Sudan', 'Ethiopia', 'Kakuma', '(Kenya)'], ['…more\\nMarial Bai', '(South Sudan)'], ['…less']]",
                  "_deepnote_index_column": 99
                }
              ]
            },
            "text/plain": "                                                Title            Author  \\\n0   Harry Potter and the Deathly Hallows (Harry Po...      J.K. Rowling   \n1             The Hunger Games (The Hunger Games, #1)   Suzanne Collins   \n2   Harry Potter and the Half-Blood Prince (Harry ...      J.K. Rowling   \n3   Harry Potter and the Goblet of Fire (Harry Pot...      J.K. Rowling   \n4   Harry Potter and the Order of the Phoenix (Har...      J.K. Rowling   \n..                                                ...               ...   \n95                                        Anansi Boys       Neil Gaiman   \n96                                Daughter of Fortune    Isabel Allende   \n97                                    Year of Wonders  Geraldine Brooks   \n98  The Sea of Monsters (Percy Jackson and the Oly...      Rick Riordan   \n99                                   What Is the What       Dave Eggers   \n\n    Number of Ratings  Number of Reviews  Average Ratings  Number of Pages  \\\n0             2958364              68421              5.0            759.0   \n1             6683135             175476              4.0            374.0   \n2             2574373              41988              4.0            652.0   \n3             2748838              49554              4.0            734.0   \n4             2652319              45347              4.0            870.0   \n..                ...                ...              ...              ...   \n95             202125              10199              4.0            387.0   \n96             119426               4158              4.0            432.0   \n97             146254              10579              4.0            304.0   \n98             805198              29067              4.0            279.0   \n99              80852               7222              4.0            475.0   \n\n    Published Year  Series                                 Genres  \\\n0           2007.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n1           2008.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n2           2006.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n3           2002.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n4           2004.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n..             ...     ...                                    ...   \n95          2006.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n96          2006.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n97          2002.0     0.0  ['Fantasy', 'Young Adult', 'Fiction']   \n98          2006.0     1.0  ['Fantasy', 'Young Adult', 'Fiction']   \n99          2006.0     0.0  ['Fantasy', 'Young Adult', 'Fiction']   \n\n                                               Awards  \\\n0   ['Locus Award Nominee for Best Young Adult Nov...   \n1   ['Locus Award Nominee for Best Young Adult Boo...   \n2   ['Locus Award Nominee for Best Young Adult Nov...   \n3   ['Hugo Award for Best Novel (2001)', \"Mythopoe...   \n4   ['Bram Stoker Award for Works for Young Reader...   \n..                                                ...   \n95  ['Locus Award for Best Fantasy Novel (2006)', ...   \n96  ['Premio de traducción literaria Valle Inclán ...   \n97                          ['ALA Alex Award (2002)']   \n98                                                 []   \n99  ['Independent Publisher Book Award (IPPY) for ...   \n\n                                               Places  \n0   [['London, England', '(United Kingdom)'], ['Ho...  \n1   [['District 12, Panem', 'Capitol, Panem', 'Pan...  \n2   [['Hogwarts School of Witchcraft and Wizardry'...  \n3   [['Hogwarts School of Witchcraft and Wizardry,...  \n4   [['Hogwarts School of Witchcraft and Wizardry'...  \n..                                                ...  \n95  [['London, England', '(United Kingdom)'], ['Fl...  \n96                                                NaN  \n97  [['Eyam, Derbyshire, England,\\n1666', '(United...  \n98  [['Miami, Florida', '(United States)'], ['Atla...  \n99  [['Sudan', 'Ethiopia', 'Kakuma', '(Kenya)'], [...  \n\n[100 rows x 11 columns]",
            "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>Title</th>\n      <th>Author</th>\n      <th>Number of Ratings</th>\n      <th>Number of Reviews</th>\n      <th>Average Ratings</th>\n      <th>Number of Pages</th>\n      <th>Published Year</th>\n      <th>Series</th>\n      <th>Genres</th>\n      <th>Awards</th>\n      <th>Places</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Harry Potter and the Deathly Hallows (Harry Po...</td>\n      <td>J.K. Rowling</td>\n      <td>2958364</td>\n      <td>68421</td>\n      <td>5.0</td>\n      <td>759.0</td>\n      <td>2007.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Locus Award Nominee for Best Young Adult Nov...</td>\n      <td>[['London, England', '(United Kingdom)'], ['Ho...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>The Hunger Games (The Hunger Games, #1)</td>\n      <td>Suzanne Collins</td>\n      <td>6683135</td>\n      <td>175476</td>\n      <td>4.0</td>\n      <td>374.0</td>\n      <td>2008.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Locus Award Nominee for Best Young Adult Boo...</td>\n      <td>[['District 12, Panem', 'Capitol, Panem', 'Pan...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n      <td>J.K. Rowling</td>\n      <td>2574373</td>\n      <td>41988</td>\n      <td>4.0</td>\n      <td>652.0</td>\n      <td>2006.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Locus Award Nominee for Best Young Adult Nov...</td>\n      <td>[['Hogwarts School of Witchcraft and Wizardry'...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Harry Potter and the Goblet of Fire (Harry Pot...</td>\n      <td>J.K. Rowling</td>\n      <td>2748838</td>\n      <td>49554</td>\n      <td>4.0</td>\n      <td>734.0</td>\n      <td>2002.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Hugo Award for Best Novel (2001)', \"Mythopoe...</td>\n      <td>[['Hogwarts School of Witchcraft and Wizardry,...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n      <td>J.K. Rowling</td>\n      <td>2652319</td>\n      <td>45347</td>\n      <td>4.0</td>\n      <td>870.0</td>\n      <td>2004.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Bram Stoker Award for Works for Young Reader...</td>\n      <td>[['Hogwarts School of Witchcraft and Wizardry'...</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>Anansi Boys</td>\n      <td>Neil Gaiman</td>\n      <td>202125</td>\n      <td>10199</td>\n      <td>4.0</td>\n      <td>387.0</td>\n      <td>2006.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Locus Award for Best Fantasy Novel (2006)', ...</td>\n      <td>[['London, England', '(United Kingdom)'], ['Fl...</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>Daughter of Fortune</td>\n      <td>Isabel Allende</td>\n      <td>119426</td>\n      <td>4158</td>\n      <td>4.0</td>\n      <td>432.0</td>\n      <td>2006.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Premio de traducción literaria Valle Inclán ...</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>Year of Wonders</td>\n      <td>Geraldine Brooks</td>\n      <td>146254</td>\n      <td>10579</td>\n      <td>4.0</td>\n      <td>304.0</td>\n      <td>2002.0</td>\n      <td>0.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['ALA Alex Award (2002)']</td>\n      <td>[['Eyam, Derbyshire, England,\\n1666', '(United...</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>The Sea of Monsters (Percy Jackson and the Oly...</td>\n      <td>Rick Riordan</td>\n      <td>805198</td>\n      <td>29067</td>\n      <td>4.0</td>\n      <td>279.0</td>\n      <td>2006.0</td>\n      <td>1.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>[]</td>\n      <td>[['Miami, Florida', '(United States)'], ['Atla...</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>What Is the What</td>\n      <td>Dave Eggers</td>\n      <td>80852</td>\n      <td>7222</td>\n      <td>4.0</td>\n      <td>475.0</td>\n      <td>2006.0</td>\n      <td>0.0</td>\n      <td>['Fantasy', 'Young Adult', 'Fiction']</td>\n      <td>['Independent Publisher Book Award (IPPY) for ...</td>\n      <td>[['Sudan', 'Ethiopia', 'Kakuma', '(Kenya)'], [...</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 11 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "cell_id": "00005-9cf39f5b-e53d-4281-86ee-0f3f7c95beaa",
        "deepnote_cell_type": "code"
      },
      "source": "df.describe()",
      "execution_count": null,
      "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>Number of Ratings</th>\n      <th>Number of Reviews</th>\n      <th>Average Ratings</th>\n      <th>Number of Pages</th>\n      <th>Published Year</th>\n      <th>Series</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.000000e+02</td>\n      <td>100.000000</td>\n      <td>99.000000</td>\n      <td>97.000000</td>\n      <td>99.000000</td>\n      <td>98.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>9.095087e+05</td>\n      <td>31935.410000</td>\n      <td>4.020202</td>\n      <td>503.257732</td>\n      <td>2005.656566</td>\n      <td>0.387755</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.060243e+06</td>\n      <td>27119.255395</td>\n      <td>0.141407</td>\n      <td>416.300604</td>\n      <td>2.726146</td>\n      <td>0.489743</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>8.085200e+04</td>\n      <td>4158.000000</td>\n      <td>4.000000</td>\n      <td>153.000000</td>\n      <td>2000.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.364000e+05</td>\n      <td>12992.750000</td>\n      <td>4.000000</td>\n      <td>321.000000</td>\n      <td>2004.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.144335e+05</td>\n      <td>23443.500000</td>\n      <td>4.000000</td>\n      <td>440.000000</td>\n      <td>2006.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.217003e+06</td>\n      <td>44321.000000</td>\n      <td>4.000000</td>\n      <td>563.000000</td>\n      <td>2007.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>6.683135e+06</td>\n      <td>175476.000000</td>\n      <td>5.000000</td>\n      <td>4100.000000</td>\n      <td>2018.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Number of Ratings  Number of Reviews  Average Ratings  Number of Pages  \\\ncount       1.000000e+02         100.000000        99.000000        97.000000   \nmean        9.095087e+05       31935.410000         4.020202       503.257732   \nstd         1.060243e+06       27119.255395         0.141407       416.300604   \nmin         8.085200e+04        4158.000000         4.000000       153.000000   \n25%         2.364000e+05       12992.750000         4.000000       321.000000   \n50%         5.144335e+05       23443.500000         4.000000       440.000000   \n75%         1.217003e+06       44321.000000         4.000000       563.000000   \nmax         6.683135e+06      175476.000000         5.000000      4100.000000   \n\n       Published Year     Series  \ncount       99.000000  98.000000  \nmean      2005.656566   0.387755  \nstd          2.726146   0.489743  \nmin       2000.000000   0.000000  \n25%       2004.000000   0.000000  \n50%       2006.000000   0.000000  \n75%       2007.000000   1.000000  \nmax       2018.000000   1.000000  "
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00006-7c0dca9b-239c-4ac2-aa3f-2869941c923b",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "217caf75",
        "execution_start": 1618319096091,
        "execution_millis": 1,
        "deepnote_cell_type": "code"
      },
      "source": "published_year = df['Published Year']\nAuthor = df['Author']",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00007-cd1827cc-fdd9-420b-84d6-5e4bf8ac46ca",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "366f6d8f",
        "execution_start": 1618319118123,
        "execution_millis": 5,
        "deepnote_cell_type": "code"
      },
      "source": "pd.Categorical(df['Genres'])",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "['['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', ..., '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']', '['Fantasy', 'Young Adult', 'Fiction']']\nLength: 100\nCategories (1, object): ['['Fantasy', 'Young Adult', 'Fiction']']"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "cell_id": "00008-09c921c7-83b0-4fb2-8921-2105e496fd26",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "8ff2a416",
        "execution_start": 1618319120807,
        "execution_millis": 56,
        "deepnote_cell_type": "code"
      },
      "source": "Author_counts = df['Author'].value_counts()\nAuthor_counts[:13]",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "Stephenie Meyer         5\nJ.K. Rowling            5\nNeil Gaiman             4\nRick Riordan            3\nStieg Larsson           3\nJodi Picoult            3\nMargaret Atwood         2\nKhaled Hosseini         2\nCormac McCarthy         2\nJonathan Safran Foer    2\nDan Brown               2\nDave Eggers             2\nSuzanne Collins         2\nName: Author, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "cell_id": "00009-9395b845-06a5-4dee-9932-569ca4748ba6",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "33de199a",
        "execution_start": 1618319123640,
        "execution_millis": 8,
        "deepnote_cell_type": "code"
      },
      "source": "df['Published Year'].value_counts()\n",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "2006.0    27\n2007.0    15\n2003.0    12\n2008.0     8\n2005.0     8\n2004.0     8\n2001.0     6\n2009.0     5\n2002.0     4\n2010.0     2\n2011.0     1\n2000.0     1\n2013.0     1\n2018.0     1\nName: Published Year, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00010-0239c880-ce0e-4007-87fe-8260295b76a0",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "46554c45",
        "execution_millis": 479,
        "execution_start": 1618479392267,
        "deepnote_cell_type": "code"
      },
      "source": "by_year = df.groupby('Published Year')\n",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ab68191fc906>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mby_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Published Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "cell_id": "00011-e9ddd9ee-ab88-4f5b-bec1-98fc0ad62acc",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "59e5ac7e",
        "execution_millis": 39,
        "execution_start": 1618328412897,
        "deepnote_cell_type": "code"
      },
      "source": "report = sv.analyze(df)",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-825ded5b1381>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manalyze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m~/venv/lib/python3.7/site-packages/sweetviz/sv_public.py\u001b[0m in \u001b[0;36manalyze\u001b[0;34m(source, target_feat, feat_cfg, pairwise_analysis)\u001b[0m\n\u001b[1;32m     11\u001b[0m             pairwise_analysis: str = 'auto'):\n\u001b[1;32m     12\u001b[0m     report = sweetviz.DataframeReport(source, target_feat, None,\n\u001b[0;32m---> 13\u001b[0;31m                                       pairwise_analysis, feat_cfg)\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/venv/lib/python3.7/site-packages/sweetviz/dataframe_report.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, source, target_feature_name, compare, pairwise_analysis, fc)\u001b[0m\n\u001b[1;32m    123\u001b[0m         self.progress_bar = tqdm(total=progress_chunks, bar_format= \\\n\u001b[1;32m    124\u001b[0m                 \u001b[0;34m'{desc:45}|{bar}| [{percentage:3.0f}%]   {elapsed} -> ({remaining} left)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m                 ascii=False, dynamic_ncols=True, position=0, leave= True)\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;31m# Summarize dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0munit_scale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_scale\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit_scale\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0munit_scale\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdesc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplayed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36mstatus_printer\u001b[0;34m(_, total, desc, ncols)\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mIProgress\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# #187 #451 #558 #872\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m             raise ImportError(\n\u001b[0;32m--> 113\u001b[0;31m                 \u001b[0;34m\"IProgress not found. Please update jupyter and ipywidgets.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m                 \u001b[0;34m\" See https://ipywidgets.readthedocs.io/en/stable\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m                 \"/user_install.html\")\n",
            "\u001b[0;31mImportError\u001b[0m: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "cell_id": "00012-7401a8e8-dfec-4757-965e-a99f79d72b08",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "4df25f77",
        "execution_start": 1618328434851,
        "execution_millis": 12,
        "deepnote_cell_type": "code"
      },
      "source": "report.show_html('Best Books of the Decade: 2000s.html')",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'report' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-64a28914b6b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best Books of the Decade: 2000s.html'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'report' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00013-62f04a56-4fd8-4449-aa71-359dbea275bb",
        "deepnote_cell_type": "code"
      },
      "source": "df1 = sv.compare(df, df)\ndf1.show_html('Compare.html')",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16ae038a6496467d8977fb331ec20b29",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=12.0), HTML(value='')), l…"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\nReport Compare.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Function to find the Min_Max normalized values of rating avearge.",
      "metadata": {
        "tags": [],
        "cell_id": "00014-1bd36312-549f-4388-b8ae-5ab4258a6ce9",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00014-bef28aa6-630f-48bb-8771-51ccf28d2795",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ce89487b",
        "execution_millis": 3,
        "execution_start": 1618407526876,
        "deepnote_cell_type": "code"
      },
      "source": "def min_max(series):\n    \"\"\" Returns the Min-Max normalised values for the average ratings value.\n    Can filter elements greater than a given threshhold value i.e. the artificial minimum\"\"\"\n    \n    min = series.min()\n    max = series.max()\n    new_dseries = series.apply(lambda x: 1 + (((x-threshhold)/(max-threshhold)) * 9))\n    return new_dseries",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Funtion to find the normalised values of rating average.",
      "metadata": {
        "tags": [],
        "cell_id": "00016-f230d891-711c-434e-95f0-886e81951a4f",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00016-458df16b-fb8c-4e17-8eaa-eaf4434d06ee",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "2759cb61",
        "execution_millis": 1,
        "execution_start": 1618407531174,
        "deepnote_cell_type": "code"
      },
      "source": "def norm_series(series):\n    \"\"\" Returns the Normalised values for the Average rating values.\n    Can filter elements greater than a given threshhold value i.e. the artificial minimum\"\"\"\n    \n    pd_series_mean = series.mean(skipna=True)\n    min = series.min()\n    max = series.max()\n    # print(pd_series_mean)\n    normed = series.apply(lambda x: 1+((x-pd_series_mean)/(max-threshhold)) * 9)\n    return normed",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Function that returns the best rated book title of a given author.",
      "metadata": {
        "tags": [],
        "cell_id": "00018-42b2c176-81c2-4d30-93a3-c2a98c1b3a77",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00019-cf988e14-0f76-4c8a-8c5c-3e920daeb238",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "1106db95",
        "execution_millis": 0,
        "execution_start": 1618400785991,
        "deepnote_cell_type": "code"
      },
      "source": "def best_book(name):\n    df_test = pd.read_csv('data_set_0.csv')\n    filter = (df_test['Author'] == name)\n    books_author = df_test[filter]\n    book = books_author['Average_rating'] == books_author['Average_rating'].max()\n    book_title = books_author[book]\n    return book_title['Title']",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00019-79d85937-b394-4801-a6e8-8638089185ab",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c742692f",
        "execution_millis": 0,
        "execution_start": 1618415162935,
        "deepnote_cell_type": "code"
      },
      "source": "# Creating Dataframe\ndf = pd.read_csv('big_books_clean.csv')\n\n# Artificial minimum\nthreshhold = 3.9\n\n# Filtered Average ratings series\nfilter1 = df['Average_rating'] >= threshhold\n\n# Threshhold - Max Normalised avarage ratings values\nmin_max_norm = min_max(df['Average_rating'][filter1])\n\n# Threshhold - Mean normalised average ratings values\nnormalised = norm_series(df['Average_rating'][filter1])",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00021-cea1beb6-6933-4fab-bb35-335a48041b1a",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d80dbad6",
        "execution_millis": 62,
        "execution_start": 1618415164614,
        "deepnote_cell_type": "code"
      },
      "source": "import plotly.graph_objs as go\nimport plotly.offline as pyo\n# Sample plotly Histogram of the two distributions from the cell above.\ntrace0 = go.Histogram(x=min_max_norm,\n                      name='Average rating min max',\n                      opacity=.5)\ntrace1 = go.Histogram(x=normalised,\n                      name='Average rating normalised',\n                      opacity=.5)\n\ndata = [trace0, trace1]\nlayout = go.Layout(title='Average rating Distribution')\nfig = go.Figure(data=data, layout=layout)\npyo.plot(fig)\n",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "'temp-plot.html'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00022-d6f523c5-49c0-42c6-87f5-9ef42717f6f8",
        "deepnote_cell_type": "code"
      },
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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