Skip to content

Instantly share code, notes, and snippets.

/network.predictor.preconnect-min-confidence.ipynb

Created November 17, 2017 09:42
Show Gist options
  • Save anonymous/72dae79449b55d14e1e3a5dc67121a7f to your computer and use it in GitHub Desktop.
Save anonymous/72dae79449b55d14e1e3a5dc67121a7f to your computer and use it in GitHub Desktop.
Download ZIP
network.predictor.preconnect-min-confidence
Display the source blob
Display the rendered blob
Raw
network.predictor.preconnect-min-confidence.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
},
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os \n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from IPython.display import display, display_markdown\n",
"\n",
"#\n",
"# Hide the plotbox if case miss any one data source\n",
"#\n",
"HIDE_MISSING_CASE = False\n",
"\n",
"DATA_LIST = 'time_list'\n",
"DATA_NAME = 'run_time'\n",
"DELETE_KEY = \"video-recording-fps\"\n",
"\n",
"#\n",
"# Input data source\n",
"#\n",
"input_json_fp = os.getenv(\"input_json_fp\")\n",
"#input_json_fp = \"output/javascript.options.baselinejit.threshold.json\"\n",
"with open(input_json_fp) as read_fh:\n",
" data_dict = json.load(read_fh)\n",
" for item in data_dict:\n",
" if DELETE_KEY in data_dict[item]:\n",
" data_dict[item].pop(DELETE_KEY)\n",
"\n",
"\n",
"#\n",
"# Definde layout and figure size\n",
"#\n",
"layout_row = 1\n",
"layout_column = 1\n",
"fig_size_w = 10\n",
"fig_size_h = 5\n",
"matplotlib.rcParams.update({'figure.max_open_warning': 0})\n",
"\n",
"#\n",
"# generate merged data\n",
"#\n",
"casename_list = list(set([casename for lable, data in data_dict.items() for casename in data.keys()]))\n",
"\n",
"case_result = []\n",
"for casename in casename_list:\n",
" result = {\n",
" \"casename\": casename,\n",
" \"result\": {}\n",
" }\n",
" for lable, data in data_dict.items():\n",
" result[\"result\"][lable] = {}\n",
" \n",
" for lable, data in data_dict.items():\n",
" if data.get(casename):\n",
" result[\"result\"][lable][DATA_LIST] = data.get(casename).get(DATA_LIST)\n",
" else:\n",
" result[\"result\"][lable][DATA_LIST] = []\n",
" case_result.append(result)\n",
"\n",
"nightly_better_list = []\n",
"for case in sorted(case_result):\n",
" casename = case.get('casename')\n",
" result = case.get('result')\n",
" d = pd.DataFrame(result)\n",
" \n",
" # drop empty 'DATA_LIST'\n",
" for c in d:\n",
" if (d[c][DATA_LIST] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
"\n",
" # Retrive the value of 'DATA_NAME' from each run\n",
" value = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME] for c in d]).T\n",
" value.columns = d.columns\n",
" \n",
" # Plot input latency boxplot\n",
" value_min = min(value.min())\n",
" value_max = max(value.max())\n",
" value_median = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME].median() for c in d]).T\n",
" value_median.columns = d.columns\n",
" \n",
" # When HIDE_MISSING_CASE is enabled, skip the case if the case data less then input data amount.\n",
" if HIDE_MISSING_CASE and len(INPUT_SOURCE) > len(value.T):\n",
" # display summary\n",
" display_markdown('### {}\\nskip draw plot box'.format(casename), raw=True)\n",
" display(value.describe())\n",
" else:\n",
" \n",
" fig, ax = plt.subplots()\n",
" ax.plot(list(range(1,len(list(value_median.median()))+1)), list(value_median.median()), \"r:\")\n",
" \n",
" value.plot.box(layout=(layout_row, layout_column),\n",
" sharey=True, sharex=True, figsize=(fig_size_w, fig_size_h),\n",
" ylim=(0, value_max*1.1), ax=ax)\n",
" plt.title(casename)\n",
" if \"Nightly\" in value_median and \"ESR52\" in value_median:\n",
" if value_median['Nightly'][0] < value_median['ESR52'][0]:\n",
" nightly_better_list.append(casename)\n",
" # display summary\n",
" display(casename)\n",
" display(value.describe().T)"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_hover_related_product_thumbnail'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>442.222222</td>\n",
" <td>28.109135</td>\n",
" <td>400.0</td>\n",
" <td>425.000000</td>\n",
" <td>433.333333</td>\n",
" <td>466.666667</td>\n",
" <td>477.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>417.777778</td>\n",
" <td>13.042087</td>\n",
" <td>400.0</td>\n",
" <td>411.111111</td>\n",
" <td>416.666667</td>\n",
" <td>430.555556</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>431.111111</td>\n",
" <td>33.044012</td>\n",
" <td>400.0</td>\n",
" <td>405.555556</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 442.222222 28.109135 400.0 425.000000 433.333333 \n",
"65536 10.0 417.777778 13.042087 400.0 411.111111 416.666667 \n",
"default 10.0 431.111111 33.044012 400.0 405.555556 422.222222 \n",
"\n",
" 75% max \n",
"1 466.666667 477.777778 \n",
"65536 430.555556 433.333333 \n",
"default 433.333333 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>6.111111</td>\n",
" <td>1.756821</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>7.222222</td>\n",
" <td>5.270463</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"65536 10.0 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 11.111111 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>18.888889</td>\n",
" <td>7.499428</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>6.000686</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 18.888889 7.499428 11.111111 11.111111 22.222222 \n",
"65536 10.0 13.888889 6.000686 5.555556 11.111111 11.111111 \n",
"default 10.0 12.777778 6.953698 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 19.444444 22.222222 \n",
"default 19.444444 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>19.910637</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>63.888889</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>61.111111</td>\n",
" <td>15.044516</td>\n",
" <td>22.222222</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>16.396995</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 43.333333 19.910637 11.111111 33.333333 38.888889 \n",
"65536 10.0 61.111111 15.044516 22.222222 58.333333 66.666667 \n",
"default 10.0 64.444444 16.396995 44.444444 50.000000 66.666667 \n",
"\n",
" 75% max \n",
"1 63.888889 66.666667 \n",
"65536 66.666667 77.777778 \n",
"default 66.666667 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>142.222222</td>\n",
" <td>50.457441</td>\n",
" <td>111.111111</td>\n",
" <td>113.888889</td>\n",
" <td>127.777778</td>\n",
" <td>133.333333</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>205.555556</td>\n",
" <td>70.516418</td>\n",
" <td>100.000000</td>\n",
" <td>133.333333</td>\n",
" <td>244.444444</td>\n",
" <td>266.666667</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>177.777778</td>\n",
" <td>59.027324</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>233.333333</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 142.222222 50.457441 111.111111 113.888889 127.777778 \n",
"65536 10.0 205.555556 70.516418 100.000000 133.333333 244.444444 \n",
"default 10.0 177.777778 59.027324 111.111111 133.333333 144.444444 \n",
"\n",
" 75% max \n",
"1 133.333333 277.777778 \n",
"65536 266.666667 266.666667 \n",
"default 233.333333 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>78.888889</td>\n",
" <td>11.049210</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>77.777778</td>\n",
" <td>86.111111</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>11.475506</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>85.555556</td>\n",
" <td>12.883353</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 78.888889 11.049210 66.666667 69.444444 77.777778 \n",
"65536 10.0 64.444444 11.475506 44.444444 55.555556 66.666667 \n",
"default 10.0 85.555556 12.883353 66.666667 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"1 86.111111 100.000000 \n",
"65536 75.000000 77.777778 \n",
"default 88.888889 111.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>83.333333</td>\n",
" <td>10.798059</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>82.222222</td>\n",
" <td>11.944086</td>\n",
" <td>55.555556</td>\n",
" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>90.000000</td>\n",
" <td>9.728834</td>\n",
" <td>77.777778</td>\n",
" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 83.333333 10.798059 66.666667 77.777778 88.888889 \n",
"65536 10.0 82.222222 11.944086 55.555556 80.555556 88.888889 \n",
"default 10.0 90.000000 9.728834 77.777778 80.555556 88.888889 \n",
"\n",
" 75% max \n",
"1 88.888889 100.000000 \n",
"65536 88.888889 88.888889 \n",
"default 100.000000 100.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_scroll_home_1_txt'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>51.111111</td>\n",
" <td>9.369712</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>48.888889</td>\n",
" <td>10.734353</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>50.000000</td>\n",
" <td>7.856742</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 51.111111 9.369712 33.333333 44.444444 55.555556 \n",
"65536 10.0 48.888889 10.734353 33.333333 44.444444 50.000000 \n",
"default 10.0 50.000000 7.856742 33.333333 44.444444 55.555556 \n",
"\n",
" 75% max \n",
"1 55.555556 66.666667 \n",
"65536 55.555556 66.666667 \n",
"default 55.555556 55.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_type_comment_1_txt'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>35.555556</td>\n",
" <td>11.475506</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>9.369712</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>32.222222</td>\n",
" <td>11.049210</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 35.555556 11.475506 11.111111 33.333333 33.333333 \n",
"65536 10.0 40.000000 9.369712 33.333333 33.333333 33.333333 \n",
"default 10.0 32.222222 11.049210 11.111111 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 41.666667 55.555556 \n",
"65536 44.444444 55.555556 \n",
"default 33.333333 55.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_type_composerbox_1_txt'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>31.111111</td>\n",
" <td>13.658584</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>38.888889</td>\n",
" <td>15.930232</td>\n",
" <td>22.222222</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>27.777778</td>\n",
" <td>7.856742</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 31.111111 13.658584 11.111111 25.000000 33.333333 \n",
"65536 10.0 38.888889 15.930232 22.222222 25.000000 33.333333 \n",
"default 10.0 27.777778 7.856742 22.222222 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 55.555556 \n",
"65536 52.777778 66.666667 \n",
"default 33.333333 44.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_type_message_1_txt'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>28.333333</td>\n",
" <td>21.667458</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>10.000000</td>\n",
" <td>6.829292</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>9.0</td>\n",
" <td>18.518519</td>\n",
" <td>10.758287</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 28.333333 21.667458 5.555556 11.111111 27.777778 \n",
"65536 10.0 10.000000 6.829292 5.555556 5.555556 5.555556 \n",
"default 9.0 18.518519 10.758287 5.555556 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 77.777778 \n",
"65536 11.111111 22.222222 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gdoc_ail_pagedown_10_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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>45.555556</td>\n",
" <td>11.049210</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>9.147473</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>7.407407</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 45.555556 11.049210 33.333333 36.111111 44.444444 \n",
"65536 10.0 47.777778 9.147473 33.333333 44.444444 50.000000 \n",
"default 10.0 44.444444 7.407407 33.333333 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"1 52.777778 66.666667 \n",
"65536 55.555556 55.555556 \n",
"default 44.444444 55.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gdoc_ail_type_0'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>12.883353</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>63.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>60.000000</td>\n",
" <td>11.944086</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>63.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>60.000000</td>\n",
" <td>9.369712</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 58.888889 12.883353 44.444444 55.555556 55.555556 \n",
"65536 10.0 60.000000 11.944086 44.444444 55.555556 55.555556 \n",
"default 10.0 60.000000 9.369712 44.444444 55.555556 66.666667 \n",
"\n",
" 75% max \n",
"1 63.888889 88.888889 \n",
"65536 63.888889 88.888889 \n",
"default 66.666667 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_compose_new_mail_via_keyboard'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>202.222222</td>\n",
" <td>11.475506</td>\n",
" <td>188.888889</td>\n",
" <td>200.000000</td>\n",
" <td>200.0</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>193.333333</td>\n",
" <td>20.420813</td>\n",
" <td>166.666667</td>\n",
" <td>177.777778</td>\n",
" <td>200.0</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>212.222222</td>\n",
" <td>58.666012</td>\n",
" <td>166.666667</td>\n",
" <td>177.777778</td>\n",
" <td>200.0</td>\n",
" <td>222.222222</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 202.222222 11.475506 188.888889 200.000000 200.0 \n",
"65536 10.0 193.333333 20.420813 166.666667 177.777778 200.0 \n",
"default 10.0 212.222222 58.666012 166.666667 177.777778 200.0 \n",
"\n",
" 75% max \n",
"1 200.000000 222.222222 \n",
"65536 200.000000 222.222222 \n",
"default 222.222222 366.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_open_mail'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>191.111111</td>\n",
" <td>14.628458</td>\n",
" <td>177.777778</td>\n",
" <td>177.777778</td>\n",
" <td>188.888889</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>206.666667</td>\n",
" <td>34.822694</td>\n",
" <td>166.666667</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>208.888889</td>\n",
" <td>67.647111</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>188.888889</td>\n",
" <td>197.222222</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 191.111111 14.628458 177.777778 177.777778 188.888889 \n",
"65536 10.0 206.666667 34.822694 166.666667 200.000000 200.000000 \n",
"default 10.0 208.888889 67.647111 177.777778 180.555556 188.888889 \n",
"\n",
" 75% max \n",
"1 200.000000 222.222222 \n",
"65536 200.000000 300.000000 \n",
"default 197.222222 400.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_reply_mail'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>233.333333</td>\n",
" <td>14.814815</td>\n",
" <td>211.111111</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>234.444444</td>\n",
" <td>15.225781</td>\n",
" <td>200.000000</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>237.777778</td>\n",
" <td>22.951012</td>\n",
" <td>200.000000</td>\n",
" <td>225.000000</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 233.333333 14.814815 211.111111 233.333333 233.333333 \n",
"65536 10.0 234.444444 15.225781 200.000000 233.333333 233.333333 \n",
"default 10.0 237.777778 22.951012 200.000000 225.000000 233.333333 \n",
"\n",
" 75% max \n",
"1 233.333333 255.555556 \n",
"65536 233.333333 255.555556 \n",
"default 255.555556 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>14.444444</td>\n",
" <td>8.764563</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>7.407407</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>14.444444</td>\n",
" <td>7.027284</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 14.444444 8.764563 5.555556 11.111111 11.111111 \n",
"65536 10.0 16.666667 7.407407 5.555556 11.111111 22.222222 \n",
"default 10.0 14.444444 7.027284 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"1 19.444444 33.333333 \n",
"65536 22.222222 22.222222 \n",
"default 22.222222 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>91.111111</td>\n",
" <td>35.058251</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>188.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>71.111111</td>\n",
" <td>7.768954</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>8.198498</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 91.111111 35.058251 66.666667 77.777778 77.777778 \n",
"65536 10.0 71.111111 7.768954 66.666667 66.666667 66.666667 \n",
"default 10.0 65.555556 8.198498 44.444444 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"1 88.888889 188.888889 \n",
"65536 75.000000 88.888889 \n",
"default 66.666667 77.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>8.302412</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>30.000000</td>\n",
" <td>9.147473</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>22.222222</td>\n",
" <td>7.407407</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 18.333333 8.302412 5.555556 11.111111 22.222222 \n",
"65536 10.0 30.000000 9.147473 11.111111 25.000000 33.333333 \n",
"default 10.0 22.222222 7.407407 11.111111 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 33.333333 44.444444 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>5.555556</td>\n",
" <td>0.000000</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>15.555556</td>\n",
" <td>7.314229</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>5.399030</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 \n",
"65536 10.0 15.555556 7.314229 5.555556 11.111111 16.666667 \n",
"default 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 \n",
"\n",
" 75% max \n",
"1 5.555556 5.555556 \n",
"65536 22.222222 22.222222 \n",
"default 9.722222 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>1028.888889</td>\n",
" <td>97.738488</td>\n",
" <td>944.444444</td>\n",
" <td>969.444444</td>\n",
" <td>1005.555556</td>\n",
" <td>1027.777778</td>\n",
" <td>1277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>853.333333</td>\n",
" <td>369.064364</td>\n",
" <td>33.333333</td>\n",
" <td>966.666667</td>\n",
" <td>966.666667</td>\n",
" <td>997.222222</td>\n",
" <td>1266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>908.888889</td>\n",
" <td>380.592275</td>\n",
" <td>222.222222</td>\n",
" <td>977.777778</td>\n",
" <td>994.444444</td>\n",
" <td>1033.333333</td>\n",
" <td>1477.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 1028.888889 97.738488 944.444444 969.444444 1005.555556 \n",
"65536 10.0 853.333333 369.064364 33.333333 966.666667 966.666667 \n",
"default 10.0 908.888889 380.592275 222.222222 977.777778 994.444444 \n",
"\n",
" 75% max \n",
"1 1027.777778 1277.777778 \n",
"65536 997.222222 1266.666667 \n",
"default 1033.333333 1477.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>52.222222</td>\n",
" <td>15.757072</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>55.555556</td>\n",
" <td>63.888889</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>45.555556</td>\n",
" <td>14.296488</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>16.563466</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 52.222222 15.757072 33.333333 36.111111 55.555556 \n",
"65536 10.0 45.555556 14.296488 33.333333 33.333333 44.444444 \n",
"default 10.0 44.444444 16.563466 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 63.888889 77.777778 \n",
"65536 52.777778 77.777778 \n",
"default 52.777778 77.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_composemail_0'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>366.666667</td>\n",
" <td>28.206567</td>\n",
" <td>333.333333</td>\n",
" <td>344.444444</td>\n",
" <td>361.111111</td>\n",
" <td>377.777778</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>406.666667</td>\n",
" <td>93.579921</td>\n",
" <td>333.333333</td>\n",
" <td>377.777778</td>\n",
" <td>383.333333</td>\n",
" <td>397.222222</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>375.555556</td>\n",
" <td>26.604867</td>\n",
" <td>344.444444</td>\n",
" <td>355.555556</td>\n",
" <td>372.222222</td>\n",
" <td>397.222222</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 366.666667 28.206567 333.333333 344.444444 361.111111 \n",
"65536 10.0 406.666667 93.579921 333.333333 377.777778 383.333333 \n",
"default 10.0 375.555556 26.604867 344.444444 355.555556 372.222222 \n",
"\n",
" 75% max \n",
"1 377.777778 422.222222 \n",
"65536 397.222222 666.666667 \n",
"default 397.222222 422.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_type_composemail_0'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>13.333333</td>\n",
" <td>9.514987</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>10.940041</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 13.333333 9.514987 5.555556 5.555556 11.111111 19.444444 \n",
"65536 10.0 16.111111 10.940041 5.555556 6.944444 11.111111 22.222222 \n",
"\n",
" max \n",
"1 33.333333 \n",
"65536 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>296.666667</td>\n",
" <td>20.984023</td>\n",
" <td>255.555556</td>\n",
" <td>283.333333</td>\n",
" <td>300.000000</td>\n",
" <td>308.333333</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>328.888889</td>\n",
" <td>77.193573</td>\n",
" <td>277.777778</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>319.444444</td>\n",
" <td>544.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>290.000000</td>\n",
" <td>15.225781</td>\n",
" <td>266.666667</td>\n",
" <td>280.555556</td>\n",
" <td>288.888889</td>\n",
" <td>297.222222</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 296.666667 20.984023 255.555556 283.333333 300.000000 \n",
"65536 10.0 328.888889 77.193573 277.777778 300.000000 300.000000 \n",
"default 10.0 290.000000 15.225781 266.666667 280.555556 288.888889 \n",
"\n",
" 75% max \n",
"1 308.333333 322.222222 \n",
"65536 319.444444 544.444444 \n",
"default 297.222222 322.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_type_in_reply_field'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>13.333333</td>\n",
" <td>7.943559</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>37.777778</td>\n",
" <td>19.737648</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>37.222222</td>\n",
" <td>62.638051</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>22.222222</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 13.333333 7.943559 5.555556 5.555556 11.111111 \n",
"65536 10.0 37.777778 19.737648 11.111111 22.222222 33.333333 \n",
"default 10.0 37.222222 62.638051 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 22.222222 22.222222 \n",
"65536 52.777778 66.666667 \n",
"default 22.222222 200.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>19.444444</td>\n",
" <td>9.532991</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>12.949729</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 19.444444 9.532991 5.555556 11.111111 22.222222 \n",
"65536 10.0 16.111111 12.949729 5.555556 5.555556 8.333333 \n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 30.555556 33.333333 \n",
"default 19.444444 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
{
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>10.000000</td>\n",
" <td>6.829292</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>5.399030</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 10.000000 6.829292 5.555556 5.555556 5.555556 11.111111 \n",
"65536 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"default 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 11.111111 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 22.222222 \n",
"default 22.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBhiyJEmSGmDIkiRJaoAhS5IkqQH/H14X5ECTh3viAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x104246690>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x107fbb2d0>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1082c7490>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x108444590>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npBKYZEmSJJXAJEuSJKkEJlmSJEkl+H8FHCV+uHV7OgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1084b5610>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npBIYZEmSJJXg/wMOnQVT13rquwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10856ee10>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x10854e290>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSpIkqQbDlCRJUg2GKUmSpBr+D6hpir1eS4zRAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108536e50>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+P/prBSyzfv2YQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108792150>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHn/TiNEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1087e2e10>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx7WhEwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108853610>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x108919fd0>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiSpgmFKkiSpwv8H0om3nSyMoBkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108972a90>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmcJWV97/HPV8AZBCIqE0LYxigq\nCBFNh6jRBJeoYCLk4gJxQTIRJ8G5GtALgq8oNzIXclWiROnAhTBuLDEYEVzigighKIMCskgYETMg\nyyCCLEIAf/ePepo5jNPTPdNd093Tn/frdV5dz1NVTz1Vp/r0t+upc06qCkmSJE2ux0x1ByRJkjZE\nhixJkqQeGLIkSZJ6YMiSJEnqgSFLkiSpB4YsSZKkHhiyNOMk2TTJ55PcleSfk7w+yb9NVnuT2df1\nLckOSe5JslErfyPJX0ywzauS7Nmm35fkk5PQVU1Dk/1ct3Pxtyaw/vwklWTjifRjNe2eluT9k9nm\nWm6/kjx1qrav9WdST1zNTkluAP6iqr46gTbe3Np4wTgWfzWwNfCkqnqo1X1qXbc9SnszUlX9F7D5\nJLf5zMlsT9PXZD/XVTWp56I003glSzPRjsB/jicQjfM/4HG3J0njNdlX4DTzGLI0IUk+AewAfL4N\nDfyvJM9NclGSO5NcPjL80JZ/c5Lrk9yd5EdtqG9nYBh4XmvjzjVs72jgb4DXtWUXtDYvHFimkhyS\n5Drgulb3jCRfSXJHkmuTvHYN7T0myXuS/DjJbUk+nuTxbfnXtX7/WivvleSWJPPGOE4va9u9K8nH\nklwwMozX+v/vSY5vx+z6JM9v9ctbHw4caOuVSb6X5Odt/vsG5q318EqSpyT5epKfJrk9yaeSbDkw\n/4YkLx1ve22dFwycA8vblUqSPL4dzxXt+L4nyWPW8TiclmS4Pa93t2O648D85ye5pB3zS5I8f2De\nr5yHA/P+PMk1SX6W5MuDba5hfyvJwiTXtb5/NEnGajPJ0UlOaNObJLk3yf9t5U2T3J/kiWvY7sjz\nfVA7Rj9r/fjdJFe0vvzDwPKT+lwn+WKSt61Sd3mS/zFwXJ7apkc9b9die/u1Pu7ayqt9rUnymiSX\nrrLuoUk+N1C11TqeOwe15/Ludg69dWDenkluTHJ4kluAf2r170pyc5KfJPnztd1vzWBV5cPHhB7A\nDcBL2/S2wE+BvelC/B+18jxgM+DnwNPbstsAz2zTbwYuHOf23gd8cqD8qHWBAr4CPBHYtG13OXAQ\n3RD5s4HbgV1Gae/PgWXAb9ENvZ0NfGJg/qeA04AnAT8B/niM/m7V9vt/tO2/HXiQbnh0pP8Ptf5t\nBLwf+C/go8Ac4GXA3cDmbfk9gd3a8f1t4FZg3zZvftv/jVv5GyPbWUP/ntqepzntefom8PejPL+P\nOlajtLdj6+8BwCbtOO3e5n0c+BywRevrfwIL1vE4nNbKf9Dmf3jkPGjP/c+AN7ZjfkArP4k1n4f7\ntOd+57bee4CLxnFOFnAusCXdPx0rgFeM1SbwYuD7bfr5wA+Bbw/Mu3yM7Y4838PA3HaM7gf+Ffh1\nut/H24A/7Om5fhPw7wPlXYA7gTkDx+WpY52349i/jdt5sWygvTW91swB7gB2Hmjre8B+Ezl32vxX\nAk8BAvwhcB/wnIF9fAg4rrW7KfCKtq+70p17nx48Lj427MeUd8DHzH+s8sJ8OAOBpNV9GTiwvcDc\nCewHbLrKMm9mckPWiwfKrwO+tUob/wi8d5T2vgb81UD56XShaCS4bEn3x//7wD+Oo79vAv5joBy6\n0DcYsq4bmL9b24etB+p+Sgsqq2n/74Hj2/R81jJkraa9fYHvjfL8PupYjbL+u4HPrqZ+I+C/aeG2\n1b0V+Ma6HAe6P5RnDMzbHHgY2J7uD+R3Vtn+f7RtrOk8/CIt9LXyY+j+iO44xj4X8IKB8lnAEWO1\nSfdH+H668HcEcCRwY9uXo4GPjLHdked721WO0esGyv8CvKOn53oL4N6R4wMcA5y6ynFZbZgYPG/H\nsX/vBK4GthuYN+prTZs+ETimTT+TLiiNhL91OndG6eO/Am9v03vSneNzB+afChw7UH7amo6Ljw3r\n4XChJtuOwGva5fs70w39vQDYpqrupQs8C4Gbk5yX5Bk99WP5Kn36vVX69HrgN0ZZ9zeBHw+Uf0z3\nH+3WAFV1J/DPdP+ZfnAcffnNwf5U90p74yrL3Dow/Yu23Kp1mwMk+b0k56cbcruL7nhuNY5+rFaS\nrZOckeSmJD8HPjmR9uj+UP1wNfVb0V3ZWvXYbjtQHvdxaAaP6z10Vy9+k199Dh/Z1hjn4Y7AhwfO\nkzvoQvG2jO2Wgen7Bvo5aptV9QtgKd0VkT8ALgAuAn6/1V0wju3Crx630c6dSX2uq+pu4Dxg/1Z1\nAKO8CWWC5+27gI9W1eDvzaivNW3+EuDP2rDtG4GzquqBgfXX+txp+7FXkovT3XpwJ92VtMH9WFFV\n9w+UH/X7v5q2tQEzZGky1MD0crr/LrcceGxWVccCVNWXq+qP6F4IfwCcvJo2+ujTBav0afOq+stR\n1v0J3Qv4iB3ohgBuBUiyO92Q4unAR8bRl5uB7UYK7UV/u9EXH9OngXOA7avq8XRDRVnzKmu0mO54\n7VZVvwa8YYLtLacbTlnV7XRXBFc9tjdNYFvbj0wk2ZxuqOcn/Opz+KhtreE8XA68dZVzZdOqumgC\nfRyrzQvohgafDVzSyi8H9qAbzptMk/1cQ/d7cECS59ENWZ4/ynITOW9fBrwnyX4DdWO91lxMd1Xp\nhcCfAZ9Ypc21PneSzKG7MvgBuiusWwJfWGU/Vn0tu3lwW60tzRKGLE2GW+nuX4LuP+M/SfLyJBsl\nmdtuBt2u/Re9T5LNgAeAe4BfDrSxXZLH9tC/c4GnJXljupuLN2k3Bu88yvKnA3+d5MntxXcxcGZV\nPZRkbtvHI+nuEdk2yV+Nsf3zgN2S7JvuhvRDGP0q2nhsAdxRVfcn2YPuD8hEbEH3XNyVZFu6qwYT\n8SngpUlem2TjJE9KsntVPUw3jHZMki3ajcaH0h3PdbV3upvsHwv8LXBxVS2n+8P3tCR/1vrwOrr7\nhc4d4zwcBt6d5JnwyI36r5lA/8bT5gV0Q8pXV9V/04Z4gR9V1YoJbntVk/1cQ3esdwT+N93vyS9H\nWW4i5+1VdPc2fTTJq1rdqK81A+t9HPgH4MGqunCVNtf63AEeS3ev1QrgoSR70QXANTkLeHOSXZI8\nDnjvWuy3ZjhDlibD/6H7L/NOumGYfehCyAq6/zbfRXeuPYbuj+pP6C7N/yEwcjXp63QvpLckuX0y\nO9eGNF5GN6TxE7phnZEbU1fnVLr/er8J/IjunplFbd7/AZZX1Ylt6OENwPuT7LSG7d8OvAb4O7r7\nZXahGyJ6YLR1xvBXwP9OcjfdOyPPWsd2RhwNPAe4iy4Qnj2Rxqr7rK69gcPonufLgGe12Yvo7uG5\nHriQ7urGqRPY3Kfp/mjdAfwO3fNBVf0U+OPWh58C/4vuDQq3s4bzsKo+S3dunNGG064E9ppA/8bT\n5kV092aNXLW6mu6cm+yrWDDJzzVA+z04G3gp3fMxmgmdt1V1Od1zenKSvVogGu21ZsQn6Ib1Vxfk\n1/rcaa8l/7P1/Wd0QfGcMfr9Rbr7z75Od+P+19dmvzWzpbs9RNL6ku4jC24EXl9Vow2taAxJTgNu\nrKr3THVfND0l2ZTu3ZXPqarrpro/mn28kiWtB21IY8t2T8eRdPdwXDzF3ZI2dH8JXGLA0lQxZGla\nSvcdaves5vH6sdde/5K8cJT+3tMWeR7dO+5uB/6E7vOBfrEe+zc8Sv+G17G914/S3lWT3ffpYBzP\nb5/bntJj3ff2+2o/3dd9vZ1u2E+aEg4XSpIk9WDcV7Lauze+l+TcVn5ykm8nWZbkzPYODZLMaeVl\nbf78frouSZI0fa3NcOHbgWsGysfRfVrvU+neZbGg1S8Aftbqj2/LSZIkzSrjGi5snzuyhO4rEw6l\nu6dkBfAb7bODnge8r6penuTLbfo/2mcC3QLMqzVsaKuttqr58+dPfG8kSZJ6dumll95eVfPGWm7j\ncbb393SfFbJFKz8JuLOqHmrlG1n5tRPb0r5CoAWwu9ryo3720fz581m6dOk4uyJJkjR1kozr65HG\nHC5M8sfAbVV16YR79eh2D06yNMnSFSsm+0ONJUmSptZ47sn6feBV7e2wZ9B9x9aHgS3bcCB038M2\n8v1jN9G+p6nNfzzdp+Y+SlWdVFVDVTU0b96YV9wkSZJmlDFDVlW9u6q2q6r5dF9L8vWqej3dl4C+\nui12IPC5Nn1OK9Pmf31N92NJkiRtiCbyYaSHA4cmWUZ3z9Uprf4U4Emt/lDgiIl1UZIkaeYZ743v\nAFTVN+i+IZ6quh7YYzXL3E/3ZbiSJEmzll+rI0mS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQD\nQ5YkSVIPDFmSJEk9MGRJkiT1wJAlSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8M\nWZIkST0wZEmSNAOdfvrp7Lrrrmy00UbsuuuunH766VPdJa1i46nugCRJWjunn346Rx11FKeccgov\neMELuPDCC1mwYAEABxxwwBT3TiNSVVPdB4aGhmrp0qVT3Q1JkmaEXXfdlRNOOIEXvehFj9Sdf/75\nLFq0iCuvvHIKezY7JLm0qobGXM6QJUnSzLLRRhtx//33s8kmmzxS9+CDDzJ37lwefvjhKezZ7DDe\nkOU9WZIkzTA777wzF1544aPqLrzwQnbeeecp6pFWx5AlSdIMc9RRR7FgwQLOP/98HnzwQc4//3wW\nLFjAUUcdNdVd0wBvfJckaYYZubl90aJFXHPNNey8884cc8wx3vQ+zXhPliRJ0lrwnixJkqQpZMiS\nJEnqwZghK8ncJN9JcnmSq5Ic3epPS/KjJJe1x+6tPkk+kmRZkiuSPKfvnZAkSZpuxnPj+wPAi6vq\nniSbABcm+WKb966q+swqy+8F7NQevwec2H5KkiTNGmNeyarOPa24SXus6W75fYCPt/UuBrZMss3E\nuypJkjRzjOuerCQbJbkMuA34SlV9u806pg0JHp9kTqvbFlg+sPqNrU6SJGnWGFfIqqqHq2p3YDtg\njyS7Au8GngH8LvBE4PC12XCSg5MsTbJ0xYoVa9ltSZKk6W2t3l1YVXcC5wOvqKqb25DgA8A/AXu0\nxW4Cth9YbbtWt2pbJ1XVUFUNzZs3b916L0mSNE2N592F85Js2aY3Bf4I+MHIfVZJAuwLjHzt9znA\nm9q7DJ8L3FVVN/fSe0mSpGlqPO8u3AZYkmQjulB2VlWdm+TrSeYBAS4DFrblvwDsDSwD7gMOmvxu\nS5IkTW9jhqyqugJ49mrqXzzK8gUcMvGuSZIkzVx+4rskSVIPDFmSJEk9MGRJkiT1wJAlSZLUA0OW\nJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPDFmS\nJEk9MGRJkiT1wJAlSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmS\nJPXAkCVJktQDQ5YkSVIPxgxZSeYm+U6Sy5NcleToVv/kJN9OsizJmUke2+rntPKyNn9+v7sgSZI0\n/YznStYDwIur6lnA7sArkjwXOA44vqqeCvwMWNCWXwD8rNUf35aTJEmaVcYMWdW5pxU3aY8CXgx8\nptUvAfZt0/u0Mm3+S5Jk0nosSZI0A4zrnqwkGyW5DLgN+ArwQ+DOqnqoLXIjsG2b3hZYDtDm3wU8\naTI7LUmSNN2NK2RV1cNVtTuwHbAH8IyJbjjJwUmWJlm6YsWKiTYnSZI0razVuwur6k7gfOB5wJZJ\nNm6ztgNuatM3AdsDtPmPB366mrZOqqqhqhqaN2/eOnZfkiRpehrPuwvnJdmyTW8K/BFwDV3YenVb\n7EDgc236nFamzf96VdVkdlqSJGm623jsRdgGWJJkI7pQdlZVnZvkauCMJO8Hvgec0pY/BfhEkmXA\nHcD+PfRbkiRpWhszZFXVFcCzV1N/Pd39WavW3w+8ZlJ6J0mSNEP5ie+SJEk9MGRJkiT1wJAlSZLU\nA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIP\nDFmSJEk9MGRJkiT1wJAlSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0w\nZEmSJPXAkCVJktQDQ5YkSVIPDFmSJEk9GDNkJdk+yflJrk5yVZK3t/r3JbkpyWXtsffAOu9OsizJ\ntUle3ucOSJIkTUcbj2OZh4DDquq7SbYALk3ylTbv+Kr6wODCSXYB9geeCfwm8NUkT6uqhyez45Ik\nSdPZmFeyqurmqvpum74buAbYdg2r7AOcUVUPVNWPgGXAHpPRWUmSpJlire7JSjIfeDbw7Vb1tiRX\nJDk1yRNa3bbA8oHVbmQ1oSzJwUmWJlm6YsWKte64JEnSdDbukJVkc+BfgHdU1c+BE4GnALsDNwMf\nXJsNV9VJVTVUVUPz5s1bm1UlSZKmvXGFrCSb0AWsT1XV2QBVdWtVPVxVvwROZuWQ4E3A9gOrb9fq\nJEmSZo3xvLswwCnANVX1oYH6bQYW+1PgyjZ9DrB/kjlJngzsBHxn8rosSZI0/Y3n3YW/D7wR+H6S\ny1rdkcABSXYHCrgBeCtAVV2V5Czgarp3Jh7iOwslSdJsM2bIqqoLgaxm1hfWsM4xwDET6JckSdKM\n5ie+S5Ik9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPDFmSJEk9MGRJkiT1\nwJAlSZLUA0PWLLNo0SLmzp1LEubOncuiRYumukuSJG2QDFmzyKJFixgeHmbx4sXce++9LF68mOHh\nYYOWJEk9SFVNdR8YGhqqpUuXTnU3Nnhz585l8eLFHHrooY/UfehDH+LII4/k/vvvn8KeSZI0cyS5\ntKqGxlzOkDV7JOHee+/lcY973CN19913H5ttthnT4TyQRpNkvW/T3wmtq2cd/W/c9YsH13q9Hx/3\nxz30Zs12PPzctV7n8ZtuwuXvfVkPvZk5xhuyNl4fndH0MGfOHIaHhx91JWt4eJg5c+ZMYa+ksa1r\n4Jl/xHnccOwrJ7k30prd9YsH1+28O3ZmBPv5R5w31V2YMQxZs8hb3vIWDj/8cAAWLlzI8PAwhx9+\nOAsXLpzinkmStOExZM0iJ5xwAgBHHnkkhx12GHPmzGHhwoWP1EuSpMljyJplTjjhBEOVJEnrgR/h\nIEmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPxgxZSbZPcn6Sq5NcleTtrf6JSb6S\n5Lr28wmtPkk+kmRZkiuSPKfvnZAkSZpuxnMl6yHgsKraBXgucEiSXYAjgK9V1U7A11oZYC9gp/Y4\nGDhx0nstSZI0zY0Zsqrq5qr6bpu+G7gG2BbYB1jSFlsC7Num9wE+Xp2LgS2TbDPpPZckSZrG1uqe\nrCTzgWcD3wa2rqqb26xbgK3b9LbA8oHVbmx1mk723BNOO62bfvDBrvzJT3bl++7rymee2ZXvuqsr\nn312V7799q78+c935Vtu6cpf+lJXXr68K3/1q135+uu78gUXdOVrr+3KF13Ula+8sitfcklXvuyy\nrnzZZV35kku68pVXduWLLurK117blS+4oCtff31X/upXu/Lydhp+6Utd+ZZbuvLnP9+Vb7+9K599\ndle+666ufOaZXfm++7ryJz/ZlR98sCufdlpXHnHyyfDSl64sf+xjsNdeK8sf/jC86lUryx/4AOy3\n38ryscfC/vuvLP/t38Ib3rCy/Dd/AwcdtLL87nfDwQevLL/znXDIISvL73hH9xhxyCHdMiMOPrhr\nY8RBB3XbGPGGN3R9GLH//l0fR+y3X7cPI171qm4fR+y1V3cMRrz0pd0xGrE+z72R7XnudTz3NtzX\nPU1L4/5anSSbA/8CvKOqfp7kkXlVVUnW6uvDkxxMN5zIDjvssDarbpB2W7Lb+t3gQQAfhCUfXFl+\n+DhYctzK8v3vhyXvX1m++72w5L0ry3ccCUuOXFm+9V2w5F0ryzf99cprnQfB93vdIc0Euy3Zbb2e\ne1vsDLvtDNzwNriBlfOveytcN1C++s/h6oHy5W+EywfKlx4Alw6UL341XDxQ/tY+8K2u+H2etK6H\nRxuILXY+gt2+RXdurMdzb33ZYmeAV67fjc5QqRo7GyXZBDgX+HJVfajVXQvsWVU3t+HAb1TV05P8\nY5s+fdXlRmt/aGioli5dOgm7I2k6m3/Eedxw7Ib74ryh75/GZ0M/Dzb0/RuPJJdW1dBYy43n3YUB\nTgGuGQlYzTnAgW36QOBzA/Vvau8yfC5w15oCliRJ0oZoPMOFvw+8Efh+kjZYzJHAscBZSRYAPwZe\n2+Z9AdgbWAbcRxsckCRJmk3GDFlVdSGQUWa/ZDXLF3DIapaVJEmaNfzE91lm0aJFzJ07lyTMnTuX\nRYsWTXWXJEnaIBmyZpFFixYxPDzM4sWLuffee1m8eDHDw8MGLUmSemDImkVOPvlkjjvuOA499FAe\n97jHceihh3Lcccdx8uDnyEiSpElhyJpFHnjgARYuXPiouoULF/LAAw9MUY8kSdpwGbJmkTlz5jA8\nPPyouuHhYebMmTNFPZIkacM17k9818z3lre8hcMPPxzormANDw9z+OGH/8rVLUmSNHGGrFnkhBNO\nAODII4/ksMMOY86cOSxcuPCRekmSNHkMWbPMCSecYKiSJGk98J4sSZKkHhiyJEmSeuBwoaT1av4R\n5011F3rz+E03meouaJrwPBdAuq8anFpDQ0O1dOnSqe6GpA3M/CPO44ZjXznV3ZB65Xm+/iW5tKqG\nxlrO4UJJkqQeGLIkSZJ6YMiSJEnqgSFLkiSpB4YsSZKkHvgRDpKmvSTrvu5x67bedHjntWYXz/MN\njyFL0rTnHwLNBp7nGx6HCyVJknpgyJIkSeqBIUuSJKkHhixJkqQeGLIkSZJ6YMiSJEnqwZghK8mp\nSW5LcuVA3fuS3JTksvbYe2Deu5MsS3Jtkpf31XFJkqTpbDxXsk4DXrGa+uOravf2+AJAkl2A/YFn\ntnU+lmSjyeqsJEnSTDFmyKqqbwJ3jLO9fYAzquqBqvoRsAzYYwL9kyRJmpEmck/W25Jc0YYTn9Dq\ntgWWDyxzY6uTJEmaVdY1ZJ0IPAXYHbgZ+ODaNpDk4CRLkyxdsWLFOnZDkiRpelqnkFVVt1bVw1X1\nS+BkVg4J3gRsP7Dodq1udW2cVFVDVTU0b968demGJEnStLVOISvJNgPFPwVG3nl4DrB/kjlJngzs\nBHxnYl2UJEmaeTYea4EkpwN7AlsluRF4L7Bnkt2BAm4A3gpQVVclOQu4GngIOKSqHu6n65IkSdNX\nqmqq+8DQ0FAtXbp0qrshSZI0piSXVtXQWMv5ie+SJEk9MGRJkiT1wJAlSZLUA0OWJElSDwxZkiRJ\nPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPDFmSJEk9MGRJkiT1\nwJAlSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQD\nQ5YkSVIPDFmSJEk9GDNkJTk1yW1Jrhyoe2KSryS5rv18QqtPko8kWZbkiiTP6bPzkiRJ09V4rmSd\nBrxilbojgK9V1U7A11oZYC9gp/Y4GDhxcropSZI0s4wZsqrqm8Adq1TvAyxp00uAfQfqP16di4Et\nk2wzWZ2VJEmaKdb1nqytq+rmNn0LsHWb3hZYPrDcja3uVyQ5OMnSJEtXrFixjt2QJEmaniZ843tV\nFVDrsN5JVTVUVUPz5s2baDckSZKmlXUNWbeODAO2n7e1+puA7QeW267VSZIkzSrrGrLOAQ5s0wcC\nnxuof1N7l+FzgbsGhhUlSZJmjY3HWiDJ6cCewFZJbgTeCxwLnJVkAfBj4LVt8S8AewPLgPuAg3ro\nsyRJ0rQ3ZsiqqgNGmfWS1SxbwCET7ZQkSdJM5ye+S5Ik9cCQJUmS1ANDliRJUg8MWZIkST0wZEmS\nJPXAkCVJktQDQ5YkSVIPDFmSJEk9MGRJkiT1wJAlSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS\n1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPDFmSJEk9MGRJkiT1wJAlSZLUA0OWJElS\nDwxZkiRJPdh4IisnuQG4G3gYeKiqhpI8ETgTmA/cALy2qn42sW5KkiTNLJNxJetFVbV7VQ218hHA\n16pqJ+BrrSxJkjSr9DFcuA+wpE0vAfbtYRuSJEnT2kRDVgH/luTSJAe3uq2r6uY2fQuw9QS3IUmS\nNONM6J4s4AVVdVOSXwe+kuQHgzOrqpLU6lZsoexggB122GGC3ZAkSZpeJnQlq6puaj9vAz4L7AHc\nmmQbgPbztlHWPamqhqpqaN68eRPphiRJ0rSzziEryWZJthiZBl4GXAmcAxzYFjsQ+NxEOylJkjTT\nTGS4cGvgs0lG2vl0VX0pySXAWUkWAD8GXjvxbkqSJM0s6xyyqup64Fmrqf8p8JKJdEqSJGmm8xPf\nJUmSemDIkiRJ6oEhS5IkqQeGLEmSpB4YsiRJknpgyJIkSeqBIUuSJKkHhixJkqQeGLIkSZJ6YMiS\nJEnqgSFLkiSpB4YsSZKkHhiyJEmSemDIkiRJ6oEhS5IkqQeGLEmSpB4YsiRJknpgyJIkSeqBIUuS\nJKkHhixJkqQeGLIkSZJ6YMiSJEnqgSFLkiSpB4YsSZKkHhiyJEmSetBbyEryiiTXJlmW5Ii+tiNJ\nkjQd9RKykmwEfBTYC9gFOCDJLn1sS5IkaTrq60rWHsCyqrq+qv4bOAPYp6dtSZIkTTt9haxtgeUD\n5RtbnSRJ0qyw8VRtOMnBwMGteE+Sa6eqL7PUVsDtU90JqWee55oNPM/Xvx3Hs1BfIesmYPuB8nat\n7hFVdRJwUk/b1xiSLK2qoansS6IwAAAEN0lEQVTuh9Qnz3PNBp7n01dfw4WXADsleXKSxwL7A+f0\ntC1JkqRpp5crWVX1UJK3AV8GNgJOraqr+tiWJEnSdNTbPVlV9QXgC321rwlzqFazgee5ZgPP82kq\nVTXVfZAkSdrg+LU6kiRJPTBkzTJJTk1yW5Irp7ov0ngl2TLJZ5L8IMk1SZ6X5H1JbkpyWXvs3Zad\nn+QXA/XDA+18KcnlSa5KMty+nWJk3qLW/lVJ/m4q9lNq5/U71zB/XpJvJ/lekheuQ/tvTvIPbXpf\nv42lX1P2OVmaMqcB/wB8fIr7Ia2NDwNfqqpXt3csPw54OXB8VX1gNcv/sKp2X039a6vq50kCfAZ4\nDXBGkhfRfSvFs6rqgSS/3tN+SBP1EuD7VfUXk9DWvsC5wNWT0JZWwytZs0xVfRO4Y6r7IY1XkscD\nfwCcAlBV/11Vd65LW1X18za5MfBYYOSm1L8Ejq2qB9pyt02o09JaSHJUkv9MciHw9Fb3lHbl9dIk\n30ryjCS7A38H7NOu0m6a5MQkS9sV2KMH2rwhyVZteijJN1bZ5vOBVwH/t7X1lPW1v7OJIUvSdPdk\nYAXwT22I5P8l2azNe1uSK9ow+BMG12nLXrDqkEqSLwO3AXfTXc0CeBrwwjYMc0GS3+15nyQAkvwO\n3WdJ7g7sDYyceycBi6rqd4B3Ah+rqsuAvwHOrKrdq+oXwFHtg0h/G/jDJL89nu1W1UV0n1/5rtbW\nDyd1xwQYsiRNfxsDzwFOrKpnA/cCRwAnAk+h++N0M/DBtvzNwA5t2UOBTyf5tZHGqurlwDbAHODF\nA9t4IvBc4F3AWW1IUerbC4HPVtV97UrrOcBc4PnAPye5DPhHunN2dV6b5LvA94BnAt5jNY0YsiRN\ndzcCN1bVt1v5M8BzqurWqnq4qn4JnAzsAVBVD1TVT9v0pcAP6a5UPaKq7gc+R3cf1sg2zq7Od4Bf\n0n0fnDQVHgPc2a4wjTx2XnWhJE+mu8r1kqr6beA8uoAG8BAr/8bPXXVdrR+GLEnTWlXdAixP8vRW\n9RLg6iSD/9n/KXAlPPLuq43a9G8BOwHXJ9l8ZJ0kGwOvBH7Q1v9X4EVt3tPo7tfyC3e1PnwT2Lfd\nX7UF8CfAfcCPkrwGIJ1nrWbdX6O7sntXkq2BvQbm3QD8Tpveb5Rt3w1sMfFd0GgMWbNMktOB/wCe\nnuTGJAumuk/SOCwCPpXkCrrhwcXA3yX5fqt7EfDXbdk/AK5owyyfARZW1R3AZsA5bfnL6O7LGvl4\nh1OB32ofbXIGcGD5Sc1aD6rqu8CZwOXAF+m++xfg9cCCJJcDV7HyquvgupfTDRP+APg08O8Ds48G\nPpxkKfDwKJs/A3hXu3/RG9974Ce+S5Ik9cArWZIkST0wZEmSJPXAkCVJktQDQ5YkSVIPDFmSJEk9\nMGRJkiT1wJAlSZLUA0OWJElSD/4/zTXO6tTz8iIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108a0f190>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108b45310>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNWDIkiRJasCQJUmS1IAhS5IkqYH/D25dOZc5ktqyAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108b3af50>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw5QkSVINhilJkqQaDFOSJEk1/H+8aM9heBbK0gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108c44390>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAE/CAYAAACeim2eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfxkddX1j+UkOAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108d26ed0>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108d70fd0>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/x8Z7Te4VWuTuwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108e5ff50>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAE/CAYAAADhW39vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x108e11790>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkmowTEmSJNVgmJIkSarh/wM3JYG6B01khgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108f35d90>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDFmSJEkNMGRJkiQ1wJAlSZLUgP8PVBdnWJ8+CjMAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108fa3f90>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x109049c10>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x108663990>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAE/CAYAAACeim2eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x1091b6d50>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109137b10>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1092ab690>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Case list which Nightly peform better than ESR52"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nightly_better_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment