{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T18:53:04.906061Z", "start_time": "2018-12-31T18:53:04.807031Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] }, { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Data science imports\n", "import pandas as pd\n", "import numpy as np\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import sys\n", "sys.path.append('../..')\n", "\n", "# Options for pandas\n", "pd.options.display.max_columns = 20\n", "\n", "# Display all cell outputs\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = 'all'\n", "\n", "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "from plotly.offline import iplot\n", "import cufflinks\n", "cufflinks.go_offline()\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T19:15:50.868591Z", "start_time": "2018-12-31T19:15:45.110580Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 121 entries in table.\n", "Processed 121 articles in 4.56 seconds.\n" ] } ], "source": [ "from retrieval import get_data\n", "df = get_data(fname='stats.html', processes=50)\n", "df = df.drop(columns='text')" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T19:16:12.589713Z", "start_time": "2018-12-31T19:16:12.529026Z" } }, "outputs": [], "source": [ "df_display = df.drop(columns = [c for c in df if '' in c])\n", "df_display = df_display.drop(columns = ['days_since_publication', \n", " 'claps_per_word', 'editing_days',\n", " 'title_word_count']).sort_values('published_date', ascending=False).rename(columns={'num_responses': 'responses',\n", " 'published_date': 'date'})\n", "df_display = df_display[df_display['type'] == 'published']" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T19:16:18.805968Z", "start_time": "2018-12-31T19:16:18.753976Z" } }, "outputs": [], "source": [ "pd.options.display.max_colwidth = 25" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T19:16:19.126133Z", "start_time": "2018-12-31T19:16:19.055341Z" } }, "outputs": [ { "data": { "text/html": [ "
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clapsfansresponsespublicationdateread_ratioread_timereadsstarted_datetagstitletypeviewsword_count
10225572Towards Data Science2018-12-29 11:36:0028.7482502018-12-28 11:41:00[Science, Towards Dat...The Copernican Princi...published8701898
14353693None2018-12-27 12:31:0019.53271902018-12-25 15:29:00[Books, Reading, Educ...Books of 2018published9737125
5336433None2018-12-25 15:28:0039.0682662018-12-23 14:45:00[Education, Books, Le...What I learned in 2018published6812185
174821422Towards Data Science2018-12-17 20:04:0029.58514022018-12-17 08:43:00[Docker, Data Science...Docker for Data Scien...published47401075
348022064Towards Data Science2018-12-15 12:39:0029.37925182018-12-15 07:52:00[Data Visualization, ...Introduction to Inter...published85731806
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" ], "text/plain": [ " claps fans responses publication date \\\n", "10 225 57 2 Towards Data Science 2018-12-29 11:36:00 \n", "14 353 69 3 None 2018-12-27 12:31:00 \n", "5 336 43 3 None 2018-12-25 15:28:00 \n", "17 482 142 2 Towards Data Science 2018-12-17 20:04:00 \n", "34 802 206 4 Towards Data Science 2018-12-15 12:39:00 \n", "\n", " read_ratio read_time reads started_date \\\n", "10 28.74 8 250 2018-12-28 11:41:00 \n", "14 19.53 27 190 2018-12-25 15:29:00 \n", "5 39.06 8 266 2018-12-23 14:45:00 \n", "17 29.58 5 1402 2018-12-17 08:43:00 \n", "34 29.37 9 2518 2018-12-15 07:52:00 \n", "\n", " tags title type views \\\n", "10 [Science, Towards Dat... The Copernican Princi... published 870 \n", "14 [Books, Reading, Educ... Books of 2018 published 973 \n", "5 [Education, Books, Le... What I learned in 2018 published 681 \n", "17 [Docker, Data Science... Docker for Data Scien... published 4740 \n", "34 [Data Visualization, ... Introduction to Inter... published 8573 \n", "\n", " word_count \n", "10 1898 \n", "14 7125 \n", "5 2185 \n", "17 1075 \n", "34 1806 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_display.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T18:59:09.289898Z", "start_time": "2018-12-31T18:59:09.021582Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "cells": { "values": [ [ 225, 353, 0, 6, 0, 5, 24, 0, 26, 336, 120, 482, 205, 802, 705, 420, 3500, 2000, 2600, 619, 1300, 2200, 806, 581, 743, 806, 2300, 649, 3200, 980, 2000, 924, 2500, 305, 3300, 222, 787, 3100, 2800, 1100, 657, 651, 3700, 825, 822, 4000, 2600, 632, 707, 2100, 2700, 2300, 5400, 1300, 8200, 6200, 3800, 2200, 4100, 13500, 364, 7800, 1000, 1300, 5300, 2800, 2600, 2900, 1100, 2700, 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Trying asking for it.", "Machine Learning Kaggle Competition: Part Three Optimization", "Automated Machine Learning Hyperparameter Tuning in Python", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "A Feature Selection Tool for Machine Learning in Python", "Machine Learning Kaggle Competition Part Two: Improving", "Automated Feature Engineering in Python", "Machine Learning Kaggle Competition Part One: Getting Started", "Automated Machine Learning on the Cloud in Python", "A Complete Machine Learning Walk-Through in Python: Part Three", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Complete Machine Learning Project Walk-Through in Python: Part One", "If your files are saved only on your laptop they might as well not exist!", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Introduction to Bayesian Linear Regression", "Visualizing Data with Pairs Plots in Python", "Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard", "Histograms and Density Plots in Python", "Data Visualization with Bokeh in Python, Part II: Interactions", "Data Visualization with Bokeh in Python, Part I: Getting Started", "Controlling the Web with Python", "Beyond Accuracy: Precision and Recall", "Unintended Consequences and Goodhart’s Law", "Data Visualization Hackathon Style", "Bayes’ Rule Applied", "Slow Tech: Take Back Your Mind", "Markov Chain Monte Carlo in Python", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "Python is the Perfect Tool for any Problem", "Statistical Significance Explained", "How to Master New Skills", "Overfitting vs. Underfitting: A Complete Example", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trace = go.Table(columnwidth=0.2,\n", "header=dict(values=list(df_display.columns),\n", " fill = dict(color='#C2D4FF'),\n", " align = ['left'] * 5),\n", "cells=dict(height = 10, \n", " values=[df_display[c] for c in df_display]))\n", "\n", "data = [trace] \n", "iplot(data)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T20:32:01.937522Z", "start_time": "2018-12-31T20:32:01.658571Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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