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October 12, 2016 22:29
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| "metadata": {}, | |
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| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib\n", | |
| "from IPython.core.display import display, HTML\n", | |
| "from bokeh.charts import Bar, Donut, Histogram\n", | |
| "from bokeh.plotting import gridplot, output_notebook, show\n", | |
| "from bokeh.mpl import to_bokeh\n", | |
| "output_notebook()\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def random_age_in_range(age_range):\n", | |
| " if age_range == 'Less than 20 years old':\n", | |
| " return np.random.randint(15, high=19)\n", | |
| " elif age_range == '20 to 30 years old':\n", | |
| " return np.random.randint(20, high=30)\n", | |
| " elif age_range == '31 to 40 years old':\n", | |
| " return np.random.randint(31, high=40)\n", | |
| " elif age_range == '41 to 50 years old':\n", | |
| " return np.random.randint(41, high=50)\n", | |
| " elif age_range == '51 to 60 years old':\n", | |
| " return np.random.randint(51, high=60)\n", | |
| "\n", | |
| "def attendee_of_label(meetup_rate, pyday_rate):\n", | |
| " if meetup_rate < 0.0:\n", | |
| " return 'PyDay'\n", | |
| " elif pyday_rate < 0.0:\n", | |
| " return 'Meetup'\n", | |
| " return 'Both'\n", | |
| "\n", | |
| "def get_ratings(columns):\n", | |
| " values = []\n", | |
| " sources = []\n", | |
| " for column in columns:\n", | |
| " new_values = list(attendees_data[column])\n", | |
| " for v in new_values:\n", | |
| " sources.append(column)\n", | |
| " values.extend(new_values)\n", | |
| " return pd.DataFrame({'values': values, 'source': sources})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "attendees_data = pd.read_csv('data/PyDay BCN 2016 attendee-report.csv')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "attendees_data.columns = [\n", | |
| " '#',\n", | |
| " 'age_range',\n", | |
| " 'gender',\n", | |
| " 'food',\n", | |
| " 'Other',\n", | |
| " 'language',\n", | |
| " 'occupation',\n", | |
| " 'meetup_rate',\n", | |
| " 'meetup_length',\n", | |
| " 'meetup_depth',\n", | |
| " 'tensorflow_talk',\n", | |
| " 'accessibility_talk',\n", | |
| " 'citybikes_talk',\n", | |
| " 'meetup_venue',\n", | |
| " 'meetup_refreshment',\n", | |
| " 'pyday_rate',\n", | |
| " 'pyday_length',\n", | |
| " 'pyday_depth',\n", | |
| " 'intro_python',\n", | |
| " 'intro_jupyter',\n", | |
| " 'intro_django',\n", | |
| " 'testing',\n", | |
| " 'zappa',\n", | |
| " 'asyncio',\n", | |
| " 'mongodb',\n", | |
| " 'pyday_venue',\n", | |
| " 'pyday_lunch',\n", | |
| " 'web',\n", | |
| " 'subjects',\n", | |
| " 'speakers',\n", | |
| " 'heared',\n", | |
| " 'Other.1',\n", | |
| " 'comment',\n", | |
| " 'start',\n", | |
| " 'submit',\n", | |
| " 'network',\n", | |
| "]\n", | |
| "attendees_data = attendees_data.drop(attendees_data[lambda df: df.meetup_rate.isnull()][lambda df: df.pyday_rate.isnull()].index)\n", | |
| "attendees_data.heared = attendees_data[['heared', 'Other.1']].fillna('').sum(axis=1)\n", | |
| "attendees_data.food = attendees_data[['food', 'Other']].fillna('').sum(axis=1)\n", | |
| "meetup_rating = round(attendees_data.meetup_rate.mean(), 2)\n", | |
| "pyday_rating = round(attendees_data.pyday_rate.mean(), 2)\n", | |
| "attendees_data['meetup_rate_f'] = attendees_data.meetup_rate\n", | |
| "attendees_data.meetup_rate_f = attendees_data.meetup_rate_f.fillna(-1)\n", | |
| "attendees_data['meetup_length_f'] = attendees_data.meetup_length\n", | |
| "attendees_data.meetup_length_f = attendees_data.meetup_length.fillna(-1)\n", | |
| "attendees_data['meetup_depth_f'] = attendees_data.meetup_depth\n", | |
| "attendees_data.meetup_depth_f = attendees_data.meetup_depth.fillna(-1)\n", | |
| "attendees_data['pyday_rate_f'] = attendees_data.pyday_rate\n", | |
| "attendees_data.pyday_rate_f = attendees_data.pyday_rate.fillna(-1)\n", | |
| "attendees_data['pyday_length_f'] = attendees_data.pyday_length\n", | |
| "attendees_data.pyday_length_f = attendees_data.pyday_length.fillna(-1)\n", | |
| "attendees_data['pyday_depth_f'] = attendees_data.pyday_depth\n", | |
| "attendees_data.pyday_depth_f = attendees_data.pyday_depth.fillna(-1)\n", | |
| "del attendees_data['#']\n", | |
| "del attendees_data['start']\n", | |
| "del attendees_data['submit']\n", | |
| "del attendees_data['network']\n", | |
| "del attendees_data['Other']\n", | |
| "del attendees_data['Other.1']\n", | |
| "attendees_data = attendees_data.assign(age = 0)\n", | |
| "attendees_data = attendees_data.assign(attendee_of = 'None')\n", | |
| "attendees_data_length = len(attendees_data['age'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "for index in attendees_data.index:\n", | |
| " attendees_data.age[index] = random_age_in_range(attendees_data.age_range[index])\n", | |
| " attendees_data.attendee_of[index] = attendee_of_label(\n", | |
| " attendees_data.meetup_rate_f[index],\n", | |
| " attendees_data.pyday_rate_f[index]\n", | |
| " )\n", | |
| " if attendees_data.attendee_of[index] == 'PyDay':\n", | |
| " attendees_data.meetup_rate_f[index] = -1.0\n", | |
| " attendees_data.meetup_length_f[index] = -1.0\n", | |
| " attendees_data.meetup_depth_f[index] = -1.0\n", | |
| " if attendees_data.pyday_length_f[index] == -1.0:\n", | |
| " attendees_data.pyday_length_f[index] = 0.0\n", | |
| " if attendees_data.pyday_depth_f[index] == -1.0:\n", | |
| " attendees_data.pyday_depth_f[index] = 0.0\n", | |
| " elif attendees_data.attendee_of[index] == 'Meetup':\n", | |
| " attendees_data.pyday_rate_f[index] = -1.0\n", | |
| " attendees_data.pyday_length_f[index] = -1.0\n", | |
| " attendees_data.pyday_depth_f[index] = -1.0\n", | |
| " if attendees_data.meetup_length_f[index] == -1.0:\n", | |
| " attendees_data.meetup_length_f[index] = 0.0\n", | |
| " if attendees_data.meetup_depth_f[index] == -1.0:\n", | |
| " attendees_data.meetup_depth_f[index] = 0.0\n", | |
| " else:\n", | |
| " if attendees_data.meetup_length_f[index] == -1.0:\n", | |
| " attendees_data.meetup_length_f[index] = 0.0\n", | |
| " if attendees_data.meetup_depth_f[index] == -1.0:\n", | |
| " attendees_data.meetup_depth_f[index] = 0.0\n", | |
| " if attendees_data.pyday_length_f[index] == -1.0:\n", | |
| " attendees_data.pyday_length_f[index] = 0.0\n", | |
| " if attendees_data.pyday_depth_f[index] == -1.0:\n", | |
| " attendees_data.pyday_depth_f[index] = 0.0\n", | |
| "import warnings\n", | |
| "warnings.filterwarnings('default')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "age_range = Donut(\n", | |
| " attendees_data,\n", | |
| " label='age_range',\n", | |
| " title='Attendees by age range',\n", | |
| ")\n", | |
| "gender = Donut(\n", | |
| " attendees_data,\n", | |
| " label='gender',\n", | |
| " title='Attendees by gender',\n", | |
| ")\n", | |
| "food = Donut(\n", | |
| " attendees_data,\n", | |
| " label='food',\n", | |
| " title='Attendees by food preference',\n", | |
| ")\n", | |
| "language = Donut(\n", | |
| " attendees_data,\n", | |
| " label='language',\n", | |
| " title='Attendees by language preference',\n", | |
| ")\n", | |
| "occupation = Donut(\n", | |
| " attendees_data,\n", | |
| " label='occupation',\n", | |
| " title='Attendees by occupation',\n", | |
| ")\n", | |
| "import warnings\n", | |
| "warnings.filterwarnings('default')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "average_attendee_age = np.ceil(attendees_data.age.mean())\n", | |
| "females = attendees_data.gender.value_counts('gender')['Female'] * 100\n", | |
| "non_binary = attendees_data.gender.value_counts('gender')['Non-binary'] * 100\n", | |
| "males = attendees_data.gender.value_counts('gender')['Male'] * 100\n", | |
| "non_male_percentage = round(\n", | |
| " females + non_binary,\n", | |
| " 2,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true, | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<h1>Demographics on polled attendees</h1>" | |
| ], | |
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| "<IPython.core.display.HTML object>" | |
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| " var NB_LOAD_WARNING = {'data': {'text/html':\n", | |
| " \"<div style='background-color: #fdd'>\\n\"+\n", | |
| " \"<p>\\n\"+\n", | |
| " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", | |
| " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", | |
| " \"</p>\\n\"+\n", | |
| " \"<ul>\\n\"+\n", | |
| " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", | |
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| " function display_loaded() {\n", | |
| " if (window.Bokeh !== undefined) {\n", | |
| " Bokeh.$(\"#d8679953-3523-4853-b08b-bc210a6b8b3a\").text(\"BokehJS successfully loaded.\");\n", | |
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| " setTimeout(display_loaded, 100)\n", | |
| " }\n", | |
| " }\n", | |
| " \n", | |
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| " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", | |
| " delete window._bokeh_onload_callbacks\n", | |
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| " <h1>Highlighted Demographic Statistics</h1>\n", | |
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| " <h2>Polled attendees</h2>\n", | |
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| " <td>\n", | |
| " <h2>66</h2>\n", | |
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| " <h2>Average age</h2>\n", | |
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| " <h2>31.0 years old</h2>\n", | |
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| " <h2>Non-binary and female</h2>\n", | |
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| " <h2>19.7 %</h2>\n", | |
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| "source": [ | |
| "display(HTML('<h1>Demographics on polled attendees</h1>'))\n", | |
| "graphs = gridplot([[age_range, gender, food,], [language, occupation, None],])\n", | |
| "show(graphs)\n", | |
| "display(\n", | |
| " HTML(\n", | |
| " \"\"\"\n", | |
| "<table>\n", | |
| " <tr>\n", | |
| " <td colspan=\"2\">\n", | |
| " <h1>Highlighted Demographic Statistics</h1>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h2>Polled attendees</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>{}</h2>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h2>Average age</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>{} years old</h2>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h2>Non-binary and female</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>{} %</h2>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table>\n", | |
| "\"\"\".format(\n", | |
| " attendees_data_length,\n", | |
| " average_attendee_age,\n", | |
| " non_male_percentage,\n", | |
| " )\n", | |
| " )\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "meetup_audience = attendees_data.attendee_of.value_counts('attendee_of')['Meetup']\n", | |
| "meetup_audience += attendees_data.attendee_of.value_counts('attendee_of')['Both']\n", | |
| "meetup_audience *= 100\n", | |
| "meetup_percentage = round(\n", | |
| " meetup_audience,\n", | |
| " 2,\n", | |
| ")\n", | |
| "pyday_audience = attendees_data.attendee_of.value_counts('attendee_of')['PyDay']\n", | |
| "pyday_audience += attendees_data.attendee_of.value_counts('attendee_of')['Both']\n", | |
| "pyday_audience *= 100\n", | |
| "pyday_percentage = round(\n", | |
| " pyday_audience,\n", | |
| " 2,\n", | |
| ")\n", | |
| "event_ratings = get_ratings(['meetup_rate', 'pyday_rate'])\n", | |
| "length_ratings = get_ratings(['meetup_length', 'pyday_length'])\n", | |
| "depth_ratings = get_ratings(['meetup_depth', 'pyday_depth'])\n", | |
| "venue_ratings = get_ratings(['meetup_venue', 'pyday_venue'])\n", | |
| "food_ratings = get_ratings(['meetup_refreshment', 'pyday_lunch'])\n", | |
| "talk_ratings = get_ratings(['tensorflow_talk','accessibility_talk','citybikes_talk',])\n", | |
| "beginners_ratings = get_ratings(['intro_python', 'intro_jupyter', 'intro_django',])\n", | |
| "initiated_ratings = get_ratings(['testing', 'zappa', 'asyncio', 'mongodb',])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "skip" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "audience_per_event = Donut(\n", | |
| " attendees_data,\n", | |
| " label='attendee_of',\n", | |
| " title='Audience by event',\n", | |
| ")\n", | |
| "event_evaluation = Bar(\n", | |
| " event_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Event Evaluation',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "length_evaluation = Bar(\n", | |
| " length_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Length Evaluation',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "length_evaluation.legend.location=\"top_right\"\n", | |
| "depth_evaluation = Bar(\n", | |
| " depth_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Depth Evaluation',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "depth_evaluation.legend.location=\"top_right\"\n", | |
| "venue_evaluation = Bar(\n", | |
| " venue_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Venue Evaluation',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "food_evaluation = Bar(\n", | |
| " food_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Food Evaluation',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "talk_evaluation = Bar(\n", | |
| " talk_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Evaluation by talk',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "web_evaluation = Bar(\n", | |
| " attendees_data,\n", | |
| " 'web',\n", | |
| " title='Evaluation on web',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "beginners_evaluation = Bar(\n", | |
| " beginners_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Evaluation by beginners workshops',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "initiated_evaluation = Bar(\n", | |
| " initiated_ratings,\n", | |
| " 'values',\n", | |
| " group='source',\n", | |
| " title='Evaluation by initiated workshops',\n", | |
| " plot_width=400,\n", | |
| " plot_height=400,\n", | |
| ")\n", | |
| "heared_sources = Bar(\n", | |
| " attendees_data,\n", | |
| " 'heared',\n", | |
| " title='Sources for attendees',\n", | |
| " plot_width=800,\n", | |
| " plot_height=800,\n", | |
| ")\n", | |
| "heared_sources.legend.location=\"top_right\"" | |
| ] | |
| }, | |
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| } | |
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| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<h1>Audience statistics</h1>" | |
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| "data": { | |
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| "\n", | |
| "<table>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h1>Event Statistics</h1>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h1>Meetup</h1>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h1>PyDay</h1>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h2>Audience</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>63.64 % </h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>80.3 %</h2>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h2>Rating</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>4.33 out of 5</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h2>4.47 out of 5</h2>\n", | |
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| "display(HTML('<h1>Audience statistics</h1>'))\n", | |
| "graphs = gridplot([\n", | |
| " [audience_per_event, None,],\n", | |
| " ])\n", | |
| "show(graphs)\n", | |
| "display(\n", | |
| " HTML(\n", | |
| " \"\"\"\n", | |
| "<table>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <h1>Event Statistics</h1>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h1>Meetup</h1>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <h1>PyDay</h1>\n", | |
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| " <tr>\n", | |
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| " <h2>Audience</h2>\n", | |
| " </td>\n", | |
| " <td>\n", | |
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| " <td>\n", | |
| " <h2>{} %</h2>\n", | |
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| " </td>\n", | |
| " <td>\n", | |
| " <h2>{} out of 5</h2>\n", | |
| " </td>\n", | |
| "</table>\n", | |
| "\"\"\".format(\n", | |
| " meetup_percentage,\n", | |
| " pyday_percentage,\n", | |
| " meetup_rating,\n", | |
| " pyday_rating\n", | |
| " )\n", | |
| " )\n", | |
| ")" | |
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| " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", | |
| " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", | |
| " }\n", | |
| " };var element = document.getElementById(\"516ad62c-d508-470a-add8-2d2765d0ef8e\");\n", | |
| " if (element == null) {\n", | |
| " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '516ad62c-d508-470a-add8-2d2765d0ef8e' but no matching script tag was found. \")\n", | |
| " return false;\n", | |
| " }\n", | |
| " \n", | |
| " var js_urls = [];\n", | |
| " \n", | |
| " var inline_js = [\n", | |
| " function(Bokeh) {\n", | |
| " Bokeh.$(function() {\n", | |
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PyLadies BCN twitter account\"},\"renderers\":[{\"id\":\"014bf09d-a1a4-427d-b7a7-cff47797366f\",\"type\":\"GlyphRenderer\"}]},\"id\":\"dff2b856-fb24-4d3c-b27a-c1afb2331086\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"height\",\"fill_alpha\",\"x\",\"width\",\"label\",\"y\",\"color\",\"line_alpha\"],\"data\":{\"chart_index\":[{\"heared\":\"University mailing list\"}],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"heared\":[\"University mailing list\"],\"height\":[1.0],\"label\":[{\"heared\":\"University mailing list\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.8],\"x\":[\"University mailing list\"],\"y\":[0.5]}},\"id\":\"5bda5d27-f396-4a77-9651-00252e8c5e8e\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"e1bfa34c-b544-46dd-b40c-f7de16db59d7\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"79802b31-213d-4a57-826e-4ba48c7c22e6\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"63e41737-c97b-4a57-b46c-a1195db69332\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"0e17f4b8-4394-4f5f-b40b-50aa4d3b46cb\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"5bda5d27-f396-4a77-9651-00252e8c5e8e\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"522e11dd-6d68-4870-9812-006db4e574cf\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"0671e641-4b6f-48a9-84a9-8e76bf0a171d\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"height\",\"fill_alpha\",\"x\",\"width\",\"label\",\"y\",\"color\",\"line_alpha\"],\"data\":{\"chart_index\":[{\"heared\":\"Org member ^^\"}],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"heared\":[\"Org member ^^\"],\"height\":[1.0],\"label\":[{\"heared\":\"Org member ^^\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.8],\"x\":[\"Org member ^^\"],\"y\":[0.5]}},\"id\":\"a3b421d9-eab8-467d-8175-7061f555b1b3\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"line_color\",\"height\",\"fill_alpha\",\"x\",\"width\",\"label\",\"y\",\"color\",\"line_alpha\"],\"data\":{\"chart_index\":[{\"heared\":\"From PyLadies BCN twitter account\"}],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"heared\":[\"From PyLadies BCN twitter account\"],\"height\":[1.0],\"label\":[{\"heared\":\"From PyLadies BCN twitter account\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[0.8],\"x\":[\"From PyLadies BCN twitter account\"],\"y\":[0.5]}},\"id\":\"d1fe19a9-68e6-4286-a457-d7402f9254aa\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"f59ad8b3-21ad-4219-beac-52bb33f523cf\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.3\"}};\n", | |
| " var render_items = [{\"docid\":\"4db6fd15-2078-4805-9ed3-fb30d491b526\",\"elementid\":\"2da1b0f5-ef16-4c54-8d18-9c758cfe7076\",\"modelid\":\"f59ad8b3-21ad-4219-beac-52bb33f523cf\"}];\n", | |
| " \n", | |
| " Bokeh.embed.embed_items(docs_json, render_items);\n", | |
| " });\n", | |
| " },\n", | |
| " function(Bokeh) {\n", | |
| " }\n", | |
| " ];\n", | |
| " \n", | |
| " function run_inline_js() {\n", | |
| " \n", | |
| " if ((window.Bokeh !== undefined) || (force === \"1\")) {\n", | |
| " for (var i = 0; i < inline_js.length; i++) {\n", | |
| " inline_js[i](window.Bokeh);\n", | |
| " }if (force === \"1\") {\n", | |
| " display_loaded();\n", | |
| " }} else if (Date.now() < window._bokeh_timeout) {\n", | |
| " setTimeout(run_inline_js, 100);\n", | |
| " } else if (!window._bokeh_failed_load) {\n", | |
| " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", | |
| " window._bokeh_failed_load = true;\n", | |
| " } else if (!force) {\n", | |
| " var cell = $(\"#2da1b0f5-ef16-4c54-8d18-9c758cfe7076\").parents('.cell').data().cell;\n", | |
| " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", | |
| " }\n", | |
| " \n", | |
| " }\n", | |
| " \n", | |
| " if (window._bokeh_is_loading === 0) {\n", | |
| " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", | |
| " run_inline_js();\n", | |
| " } else {\n", | |
| " load_libs(js_urls, function() {\n", | |
| " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", | |
| " run_inline_js();\n", | |
| " });\n", | |
| " }\n", | |
| " }(this));\n", | |
| "</script>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "display(HTML('<h1>Audience Sources</h1>'))\n", | |
| "graphs = gridplot([\n", | |
| " [heared_sources, None,],\n", | |
| " ])\n", | |
| "show(graphs)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------------------------------------\n", | |
| "Biopython\n", | |
| "---------------------------------------------\n", | |
| "More events\n", | |
| "---------------------------------------------\n", | |
| "Python on embedded devices (RPi, Arduino & similar)\n", | |
| "---------------------------------------------\n", | |
| "professional experiences with python\n", | |
| "---------------------------------------------\n", | |
| "We should have instructions to prepare the workshop before it happens not to lose time downloading and installing stuffs.\n", | |
| "---------------------------------------------\n", | |
| "use of odoo openERP\n", | |
| "---------------------------------------------\n", | |
| "more event like this \n", | |
| "---------------------------------------------\n", | |
| "Natural Language Processing\n", | |
| "Chatbots\n", | |
| "---------------------------------------------\n", | |
| "initiation workshop about scipy, opencv\n", | |
| "security\n", | |
| "---------------------------------------------\n", | |
| "python and graphics/gaming (pyopengl/pygame/cocos/kiwi...)\n", | |
| "---------------------------------------------\n", | |
| "Sessions between \"beginners\" and \"advanced\"\n", | |
| "---------------------------------------------\n", | |
| "python in the browser\n", | |
| "---------------------------------------------\n", | |
| "Big data\n", | |
| "Tricks for optimization \n", | |
| "---------------------------------------------\n", | |
| "Django\n", | |
| "---------------------------------------------\n", | |
| "Big data, parallel computing, videogames.\n", | |
| "---------------------------------------------\n", | |
| "I could assist to the presentation on MongoDB and I would have interested in that. In general, I like scientific use of Python and I am interested in related talks.\n", | |
| "---------------------------------------------\n", | |
| "\n", | |
| "\n", | |
| "---------------------------------------------\n", | |
| "- Workshop of Data Analysis\n", | |
| "- Python & Spark & Hadoop\n", | |
| "- Data Visualization with Seaborn, Bokeh\n", | |
| "- More about Django\n", | |
| "---------------------------------------------\n", | |
| "At beginner's talks or events it could be better to concentrate in one specific thing and work more with it because when you have to practise is when you see if you understand or not the theory.\n", | |
| "\n", | |
| "Maybe, because my level of informatics in generals was horrible, it was a little difficult to keep pace in class especially when we have to install all the things and programs and paquets using the console. It would have been easier if all this things had been done before or with more time and slowly.\n", | |
| "---------------------------------------------\n", | |
| "The past topics were pretty good, bit their treatment was pretty bad, ie the coverage was either too superficial and way too noobish, or too advanced and poorly structured, making too many assumptions about the audience's level of preparedness. The asyncio session was one example of this. Learning about frameworks and libraries is probably much more efficient on your own, following a good book or video tutorial. Being proficient in a topic is not enough - one has to also be a good presenter.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for index in attendees_data.subjects.dropna().index:\n", | |
| " print(\"---------------------------------------------\\n{}\".format(attendees_data.subjects[index]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------------------------------------\n", | |
| "Skyscanner\n", | |
| "---------------------------------------------\n", | |
| "Mathieu Leplatre\n", | |
| "---------------------------------------------\n", | |
| "odoo\n", | |
| "---------------------------------------------\n", | |
| "I missed other fields like humanities or social sciences \n", | |
| "---------------------------------------------\n", | |
| "Nuriaaaaaaaa !!\n", | |
| ":-P\n", | |
| "the guys from Skyscanner again please.\n", | |
| "---------------------------------------------\n", | |
| "Right now, not especially. But I would contact you if I have a proposition to do.\n", | |
| "---------------------------------------------\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for index in attendees_data.speakers.dropna().index:\n", | |
| " print(\"---------------------------------------------\\n{}\".format(attendees_data.speakers[index]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "collapsed": false, | |
| "slideshow": { | |
| "slide_type": "slide" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "---------------------------------------------\n", | |
| "For complicated setups, could be nice to provide a checklist prior to the talk, so half of it is not spent on getting the setup. Also load tests could be done by the speakers. Wifi could be better.\n", | |
| "---------------------------------------------\n", | |
| "Thanks for organizing this event!! Looking forward to the next one :)\n", | |
| "---------------------------------------------\n", | |
| "Python Programming Introduction Course was an Introduction to Programming. Teacher was fine but for people without any programming experience.\n", | |
| "---------------------------------------------\n", | |
| "Thank you very much for the organization!! Really amazing! \n", | |
| "---------------------------------------------\n", | |
| "Better wifi next time please :)\n", | |
| "---------------------------------------------\n", | |
| "Thanks a lot for preparing all meetups!\n", | |
| "---------------------------------------------\n", | |
| "a microphone would be great in some case\n", | |
| "please consider a local server with alocal repository for the workshop. That would be awesome.\n", | |
| "---------------------------------------------\n", | |
| "That veggie spread with miso and tofu was absolutely awesome, where can I buy it? :)\n", | |
| "---------------------------------------------\n", | |
| "Pensant amb vosaltres, potser us hagués estat profitós si, a l'inici de les activitats, haguéssiu dedicat uns 5' a presentar l'organització PyBCN. Era un bon dia per a animar als participants a col·laborar-hi aportant idees/temps/estones de voluntariat... o sigui, facilitant canals de participació! ;-) Però potser vaig ser jo qui no va estar al cas i ja ho vau fer...!! En qualsevol cas, tot va ser impecable, l'enhorabona!\n", | |
| "---------------------------------------------\n", | |
| "Congratulations for organizing these events. Looking forward for more. \n", | |
| "---------------------------------------------\n", | |
| "Un pyDay molt positiu :D\n", | |
| "Si us sembla que valoro baix, \"as usual meetups\" és molt alt! Sempre us ho dic, estem molt mal acostumats amb el nivell de PyBCN!\n", | |
| "I a quin idioma prefereixo, he posat català per posar algo: no hi havia l'opció de \"Qualsevol dels 3\".\n", | |
| "\n", | |
| "Al meetup XXL, al refrigeri posterior, la beguda estava molt bé, però vaig trobar a faltar menjar una mica més contundent (pizzes?) a part dels snacks.\n", | |
| "\n", | |
| "Els workshops, els vaig trobar justos de temps que a sobre es van afegir els problemes de xarxa i amb el de Serverless i MongoDB no vam poder fer res. Els de MongoDB van fer un pla B (mostrar demo i ho van salvar bastant).\n", | |
| "Estaria bé, per futurs esdeveniments, insistir als speakers que diguin quin software i quina versió de Python (sí, és trist preguntar-ho però hi ha qui encara utilitza el Python 2.7) i quins paquets. Informant als assistents uns quants dies abans per poder-s'ho preparar. Bàsicament, que el workshop comenci amb tots els assistents tenint l'entorn perfectament configurat d'entrada.\n", | |
| "Per cert, em va anar bé el litre i mig de cafè que em vaig emportar al recollir... :D:D\n", | |
| "I gràcies per l'organització :-) \n", | |
| "---------------------------------------------\n", | |
| "Give microphones to your speakers. People in the back could barely hear the presentation.\n", | |
| "---------------------------------------------\n", | |
| "\n", | |
| "\n", | |
| "---------------------------------------------\n", | |
| "Please keep doing it! It's great that you are collecting feedback and seek to make the event better. Imo there is a lot that can be improved.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for index in attendees_data.comment.dropna().index:\n", | |
| " print(\"---------------------------------------------\\n{}\".format(attendees_data.comment[index]))" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "celltoolbar": "Slideshow", | |
| "kernelspec": { | |
| "display_name": "Python [conda env:pyday-2016]", | |
| "language": "python", | |
| "name": "conda-env-pyday-2016-py" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.5.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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