overview-of-medium-articles.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"### Medium is a place to read articles on the Internet. Medium is a blogging platform, like Wordpress or Blogger. Medium is the new project from the guys who brought you Twitter.\n\n> “**Medium is not about who you are or whom you know, but about what you have to say.”– Erik Devaney**\n> \n\n### Almost everyone will agree that Medium is a great place to share your story. As a writer, I have found that it’s simple, clean, and easy to use. Almost anyone can produce a great looking story within minutes.\n\n\n### This Notebook might be useful for somebody who is willing to start their Medium journey or have already been a part and want to explore more !! \n"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":false,"_kg_hide-input":false},"cell_type":"code","source":"# importing the required libraries\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# for data visualisation\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly.io as pio\nimport matplotlib.pyplot as plt \nimport seaborn as sns\n\npio.templates.default = \"plotly_dark\"\nsns.set_style('darkgrid')\n%matplotlib inline\nimport os\nfor dirname, _, filenames in os.walk('../input/medium-articles-dataset/medium_data.csv'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","execution_count":1,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"## reading the data\n\ndata =pd.read_csv('/kaggle/input/medium-articles-dataset/medium_data.csv')\ndata.head()","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"   id                                                url  \\\n0   1  https://towardsdatascience.com/a-beginners-gui...   \n1   2  https://towardsdatascience.com/hands-on-graph-...   \n2   3  https://towardsdatascience.com/how-to-use-ggpl...   \n3   4  https://towardsdatascience.com/databricks-how-...   \n4   5  https://towardsdatascience.com/a-step-by-step-...   \n\n                                               title  \\\n0  A Beginner’s Guide to Word Embedding with Gens...   \n1  Hands-on Graph Neural Networks with PyTorch & ...   \n2                       How to Use ggplot2 in Python   \n3  Databricks: How to Save Files in CSV on Your L...   \n4  A Step-by-Step Implementation of Gradient Desc...   \n\n                                  subtitle   image  claps responses  \\\n0                                      NaN   1.png    850         8   \n1                                      NaN   2.png   1100        11   \n2         A Grammar of Graphics for Python   3.png    767         1   \n3  When I work on Python projects dealing…  4.jpeg    354         0   \n4          One example of building neural…  5.jpeg    211         3   \n\n   reading_time           publication        date  \n0             8  Towards Data Science  2019-05-30  \n1             9  Towards Data Science  2019-05-30  \n2             5  Towards Data Science  2019-05-30  \n3             4  Towards Data Science  2019-05-30  \n4             4  Towards Data Science  2019-05-30  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>url</th>\n      <th>title</th>\n      <th>subtitle</th>\n      <th>image</th>\n      <th>claps</th>\n      <th>responses</th>\n      <th>reading_time</th>\n      <th>publication</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>https://towardsdatascience.com/a-beginners-gui...</td>\n      <td>A Beginner’s Guide to Word Embedding with Gens...</td>\n      <td>NaN</td>\n      <td>1.png</td>\n      <td>850</td>\n      <td>8</td>\n      <td>8</td>\n      <td>Towards Data Science</td>\n      <td>2019-05-30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>https://towardsdatascience.com/hands-on-graph-...</td>\n      <td>Hands-on Graph Neural Networks with PyTorch &amp; ...</td>\n      <td>NaN</td>\n      <td>2.png</td>\n      <td>1100</td>\n      <td>11</td>\n      <td>9</td>\n      <td>Towards Data Science</td>\n      <td>2019-05-30</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>https://towardsdatascience.com/how-to-use-ggpl...</td>\n      <td>How to Use ggplot2 in Python</td>\n      <td>A Grammar of Graphics for Python</td>\n      <td>3.png</td>\n      <td>767</td>\n      <td>1</td>\n      <td>5</td>\n      <td>Towards Data Science</td>\n      <td>2019-05-30</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>https://towardsdatascience.com/databricks-how-...</td>\n      <td>Databricks: How to Save Files in CSV on Your L...</td>\n      <td>When I work on Python projects dealing…</td>\n      <td>4.jpeg</td>\n      <td>354</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Towards Data Science</td>\n      <td>2019-05-30</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>https://towardsdatascience.com/a-step-by-step-...</td>\n      <td>A Step-by-Step Implementation of Gradient Desc...</td>\n      <td>One example of building neural…</td>\n      <td>5.jpeg</td>\n      <td>211</td>\n      <td>3</td>\n      <td>4</td>\n      <td>Towards Data Science</td>\n      <td>2019-05-30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Preprocessing Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"#removing an invalid input from response \n## from the 'responses'column we found data named 'READ' which is wrong since its a Numeric column\ndata = data.drop(data[data['responses']=='Read'].index)","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data['date'] = pd.to_datetime(data['date']) ## to relevant datatype\ndata['responses'] = data['responses'].astype(int) # converting to relevant datatype","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Let us start with some Interesting Visualisations to understand our Data better!"},{"metadata":{"trusted":true},"cell_type":"code","source":"claps = data.groupby('publication')['claps'].sum().sort_values(ascending = False).reset_index()\nplt.figure(figsize = (10,8))\npx.bar(claps, x= 'publication', y='claps', color = 'publication',\n       title=\"Claps for various Publishers\" )","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n   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\"zerolinecolor\": \"#283442\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Month wise Publication\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"month\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"publication\"}}},\n                        {\"responsive\": true}\n                    ).then(function(){\n                            \nvar gd = document.getElementById('3740602d-a07b-4939-afde-69d7353d0d80');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"dis = ['claps','responses','reading_time']\ndist = pd.DataFrame(data[dis])","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (16,12))\nfor index,col in enumerate(dist):\n    plt.subplot(3,2,index+1)\n    sns.boxplot(x = dist.loc[:,col])\nfig.tight_layout(pad=1.0)","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x864 with 3 Axes>","image/png":"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Conclusions:\n\n### 1. The number of claps for many articles is around 0, so you need not worry much, if you are not getting the required claps.\n\n### 2. The number of Responses is very limited too, people hardly comment on Medium articles.\n\n### 3. The average reading time of the articles is around 5-6 mins, make sure to keep your articles short and crisp, usually people like to jump to the part where they find the most valuable information."},{"metadata":{"trusted":true},"cell_type":"code","source":"all_publication = data.groupby('publication')['reading_time'].sum().sort_values(ascending = False)\nfig = px.pie(all_publication,values = all_publication.values, names=all_publication.index,\n            title='Publication with Highest Reading Time')\nfig.show()","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>\n        \n        \n            <div id=\"b238b0f4-e0ba-494f-b57c-18bd6a396664\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n            <script type=\"text/javascript\">\n                require([\"plotly\"], function(Plotly) {\n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if 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