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javascript.options.ion.threshold
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javascript.options.ion.threshold.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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"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'"
]
},
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"</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>564.444444</td>\n",
" <td>26.604867</td>\n",
" <td>522.222222</td>\n",
" <td>547.222222</td>\n",
" <td>566.666667</td>\n",
" <td>583.333333</td>\n",
" <td>600.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>433.333333</td>\n",
" <td>20.951312</td>\n",
" <td>400.000000</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>433.333333</td>\n",
" <td>477.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>428.888889</td>\n",
" <td>13.042087</td>\n",
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" <td>433.333333</td>\n",
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" <td>455.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 564.444444 26.604867 522.222222 547.222222 566.666667 \n",
"10000 10.0 433.333333 20.951312 400.000000 422.222222 433.333333 \n",
"default 10.0 428.888889 13.042087 411.111111 422.222222 433.333333 \n",
"\n",
" 75% max \n",
"1 583.333333 600.000000 \n",
"10000 433.333333 477.777778 \n",
"default 433.333333 455.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
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"<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>10.555556</td>\n",
" <td>4.864417</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</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>5.555556</td>\n",
" <td>0.000000</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
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" <td>5.555556</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 10.555556 4.864417 5.555556 6.944444 11.111111 11.111111 \n",
"10000 10.0 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 22.222222 \n",
"10000 11.111111 \n",
"default 5.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
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"</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>16.666667</td>\n",
" <td>7.856742</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>6.441677</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>11.666667</td>\n",
" <td>7.612891</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</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 16.666667 7.856742 11.111111 11.111111 11.111111 \n",
"10000 10.0 15.000000 6.441677 5.555556 11.111111 11.111111 \n",
"default 10.0 11.666667 7.612891 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"10000 22.222222 22.222222 \n",
"default 19.444444 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
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"html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</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>138.333333</td>\n",
" <td>66.483803</td>\n",
" <td>5.555556</td>\n",
" <td>102.777778</td>\n",
" <td>127.777778</td>\n",
" <td>194.444444</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>50.000000</td>\n",
" <td>7.856742</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</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>53.333333</td>\n",
" <td>10.210406</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>63.888889</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 10.0 138.333333 66.483803 5.555556 102.777778 127.777778 \n",
"10000 10.0 50.000000 7.856742 44.444444 44.444444 44.444444 \n",
"default 10.0 53.333333 10.210406 44.444444 44.444444 50.000000 \n",
"\n",
" 75% max \n",
"1 194.444444 233.333333 \n",
"10000 55.555556 66.666667 \n",
"default 63.888889 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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" <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>561.111111</td>\n",
" <td>83.846159</td>\n",
" <td>500.000000</td>\n",
" <td>502.777778</td>\n",
" <td>522.222222</td>\n",
" <td>594.444444</td>\n",
" <td>766.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>226.666667</td>\n",
" <td>146.209538</td>\n",
" <td>133.333333</td>\n",
" <td>155.555556</td>\n",
" <td>161.111111</td>\n",
" <td>247.222222</td>\n",
" <td>611.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>164.444444</td>\n",
" <td>57.089923</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>138.888889</td>\n",
" <td>144.444444</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 561.111111 83.846159 500.000000 502.777778 522.222222 \n",
"10000 10.0 226.666667 146.209538 133.333333 155.555556 161.111111 \n",
"default 10.0 164.444444 57.089923 133.333333 133.333333 138.888889 \n",
"\n",
" 75% max \n",
"1 594.444444 766.666667 \n",
"10000 247.222222 611.111111 \n",
"default 144.444444 277.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
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},
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" <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>205.555556</td>\n",
" <td>25.793312</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>200.000000</td>\n",
" <td>230.555556</td>\n",
" <td>244.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>66.666667</td>\n",
" <td>9.072184</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>55.555556</td>\n",
" <td>16.563466</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</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 10.0 205.555556 25.793312 177.777778 180.555556 200.000000 \n",
"10000 10.0 66.666667 9.072184 44.444444 66.666667 66.666667 \n",
"default 10.0 55.555556 16.563466 33.333333 44.444444 50.000000 \n",
"\n",
" 75% max \n",
"1 230.555556 244.444444 \n",
"10000 66.666667 77.777778 \n",
"default 66.666667 88.888889 "
]
},
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"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
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" <th>min</th>\n",
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" <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>253.333333</td>\n",
" <td>17.213259</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>88.888889</td>\n",
" <td>10.475656</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>97.222222</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>83.333333</td>\n",
" <td>7.856742</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 253.333333 17.213259 233.333333 244.444444 244.444444 \n",
"10000 10.0 88.888889 10.475656 66.666667 88.888889 88.888889 \n",
"default 10.0 83.333333 7.856742 66.666667 77.777778 88.888889 \n",
"\n",
" 75% max \n",
"1 255.555556 288.888889 \n",
"10000 97.222222 100.000000 \n",
"default 88.888889 88.888889 "
]
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" <td>5.856070</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>10.475656</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</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>47.777778</td>\n",
" <td>7.499428</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 50.000000 5.856070 44.444444 44.444444 50.000000 \n",
"10000 10.0 44.444444 10.475656 33.333333 33.333333 44.444444 \n",
"default 10.0 47.777778 7.499428 33.333333 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"1 55.555556 55.555556 \n",
"10000 55.555556 55.555556 \n",
"default 55.555556 55.555556 "
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" <td>655.555556</td>\n",
" <td>822.222222</td>\n",
" <td>900.000000</td>\n",
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" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>33.333333</td>\n",
" <td>5.237828</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>32.222222</td>\n",
" <td>8.198498</td>\n",
" <td>22.222222</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 646.666667 192.150490 388.888889 463.888889 655.555556 \n",
"10000 10.0 33.333333 5.237828 22.222222 33.333333 33.333333 \n",
"default 10.0 32.222222 8.198498 22.222222 25.000000 33.333333 \n",
"\n",
" 75% max \n",
"1 822.222222 900.000000 \n",
"10000 33.333333 44.444444 \n",
"default 33.333333 44.444444 "
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" <td>10.0</td>\n",
" <td>293.333333</td>\n",
" <td>31.946457</td>\n",
" <td>255.555556</td>\n",
" <td>269.444444</td>\n",
" <td>283.333333</td>\n",
" <td>308.333333</td>\n",
" <td>355.555556</td>\n",
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" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>17.800911</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>38.888889</td>\n",
" <td>13.094570</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 293.333333 31.946457 255.555556 269.444444 283.333333 \n",
"10000 10.0 24.444444 17.800911 5.555556 11.111111 22.222222 \n",
"default 10.0 38.888889 13.094570 22.222222 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 308.333333 355.555556 \n",
"10000 33.333333 55.555556 \n",
"default 41.666667 66.666667 "
]
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" </tr>\n",
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" <td>10.0</td>\n",
" <td>162.222222</td>\n",
" <td>42.616740</td>\n",
" <td>111.111111</td>\n",
" <td>138.888889</td>\n",
" <td>161.111111</td>\n",
" <td>166.666667</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>29.444444</td>\n",
" <td>12.843364</td>\n",
" <td>5.555556</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 162.222222 42.616740 111.111111 138.888889 161.111111 \n",
"10000 10.0 16.111111 10.940041 5.555556 6.944444 11.111111 \n",
"default 10.0 29.444444 12.843364 5.555556 25.000000 33.333333 \n",
"\n",
" 75% max \n",
"1 166.666667 266.666667 \n",
"10000 22.222222 33.333333 \n",
"default 33.333333 44.444444 "
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" <td>10.0</td>\n",
" <td>194.444444</td>\n",
" <td>133.461872</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>150.000000</td>\n",
" <td>325.000000</td>\n",
" <td>388.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>38.888889</td>\n",
" <td>9.442629</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>13.042087</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 194.444444 133.461872 66.666667 86.111111 150.000000 \n",
"10000 10.0 38.888889 9.442629 33.333333 33.333333 33.333333 \n",
"default 10.0 40.000000 13.042087 11.111111 33.333333 44.444444 \n",
"\n",
" 75% max \n",
"1 325.000000 388.888889 \n",
"10000 41.666667 55.555556 \n",
"default 44.444444 55.555556 "
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" <td>288.888889</td>\n",
" <td>16.563466</td>\n",
" <td>266.666667</td>\n",
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" <td>288.888889</td>\n",
" <td>297.222222</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>80.000000</td>\n",
" <td>11.475506</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
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" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>61.111111</td>\n",
" <td>7.856742</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>"
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" count mean std min 25% 50% \\\n",
"1 10.0 288.888889 16.563466 266.666667 280.555556 288.888889 \n",
"10000 10.0 80.000000 11.475506 66.666667 66.666667 88.888889 \n",
"default 10.0 61.111111 7.856742 44.444444 55.555556 66.666667 \n",
"\n",
" 75% max \n",
"1 297.222222 322.222222 \n",
"10000 88.888889 88.888889 \n",
"default 66.666667 66.666667 "
]
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"output_type": "display_data",
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" <td>10.0</td>\n",
" <td>577.777778</td>\n",
" <td>51.052032</td>\n",
" <td>500.000000</td>\n",
" <td>536.111111</td>\n",
" <td>583.333333</td>\n",
" <td>619.444444</td>\n",
" <td>644.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>193.333333</td>\n",
" <td>16.728281</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>188.888889</td>\n",
" <td>200.000000</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>196.666667</td>\n",
" <td>9.147473</td>\n",
" <td>177.777778</td>\n",
" <td>191.666667</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>211.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 577.777778 51.052032 500.000000 536.111111 583.333333 \n",
"10000 10.0 193.333333 16.728281 177.777778 180.555556 188.888889 \n",
"default 10.0 196.666667 9.147473 177.777778 191.666667 200.000000 \n",
"\n",
" 75% max \n",
"1 619.444444 644.444444 \n",
"10000 200.000000 233.333333 \n",
"default 200.000000 211.111111 "
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"\n",
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" 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>758.888889</td>\n",
" <td>12.883353</td>\n",
" <td>733.333333</td>\n",
" <td>755.555556</td>\n",
" <td>761.111111</td>\n",
" <td>766.666667</td>\n",
" <td>777.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>227.777778</td>\n",
" <td>51.386803</td>\n",
" <td>177.777778</td>\n",
" <td>202.777778</td>\n",
" <td>222.222222</td>\n",
" <td>222.222222</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>232.222222</td>\n",
" <td>41.722185</td>\n",
" <td>188.888889</td>\n",
" <td>205.555556</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 758.888889 12.883353 733.333333 755.555556 761.111111 \n",
"10000 10.0 227.777778 51.386803 177.777778 202.777778 222.222222 \n",
"default 10.0 232.222222 41.722185 188.888889 205.555556 222.222222 \n",
"\n",
" 75% max \n",
"1 766.666667 777.777778 \n",
"10000 222.222222 366.666667 \n",
"default 233.333333 333.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_reply_mail'"
]
},
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"<div>\n",
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" vertical-align: top;\n",
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"\n",
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"</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>634.444444</td>\n",
" <td>104.946260</td>\n",
" <td>533.333333</td>\n",
" <td>547.222222</td>\n",
" <td>616.666667</td>\n",
" <td>686.111111</td>\n",
" <td>844.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>220.000000</td>\n",
" <td>11.475506</td>\n",
" <td>200.000000</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>230.555556</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>234.444444</td>\n",
" <td>16.932044</td>\n",
" <td>200.000000</td>\n",
" <td>233.333333</td>\n",
" <td>238.888889</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 634.444444 104.946260 533.333333 547.222222 616.666667 \n",
"10000 10.0 220.000000 11.475506 200.000000 211.111111 222.222222 \n",
"default 10.0 234.444444 16.932044 200.000000 233.333333 238.888889 \n",
"\n",
" 75% max \n",
"1 686.111111 844.444444 \n",
"10000 230.555556 233.333333 \n",
"default 244.444444 255.555556 "
]
},
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"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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" <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>17.723683</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>6.441677</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</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>9.868824</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</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% \\\n",
"1 10.0 43.333333 17.723683 11.111111 33.333333 38.888889 \n",
"10000 10.0 18.333333 6.441677 5.555556 13.888889 22.222222 \n",
"default 10.0 14.444444 9.868824 5.555556 5.555556 11.111111 \n",
"\n",
" 75% max \n",
"1 55.555556 66.666667 \n",
"10000 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
},
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"html": [
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"</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>235.555556</td>\n",
" <td>82.118720</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>205.555556</td>\n",
" <td>222.222222</td>\n",
" <td>466.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>111.111111</td>\n",
" <td>78.392631</td>\n",
" <td>77.777778</td>\n",
" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>106.666667</td>\n",
" <td>83.837978</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>344.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 235.555556 82.118720 200.000000 200.000000 205.555556 \n",
"10000 10.0 111.111111 78.392631 77.777778 80.555556 88.888889 \n",
"default 10.0 106.666667 83.837978 66.666667 77.777778 77.777778 \n",
"\n",
" 75% max \n",
"1 222.222222 466.666667 \n",
"10000 88.888889 333.333333 \n",
"default 88.888889 344.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
},
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"html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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" <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>15.555556</td>\n",
" <td>8.996265</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>28.888889</td>\n",
" <td>7.768954</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>30.000000</td>\n",
" <td>7.499428</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</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 15.555556 8.996265 5.555556 11.111111 11.111111 \n",
"10000 10.0 28.888889 7.768954 11.111111 25.000000 33.333333 \n",
"default 10.0 30.000000 7.499428 11.111111 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"10000 33.333333 33.333333 \n",
"default 33.333333 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",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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" <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>6.666667</td>\n",
" <td>2.342428</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>10000</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>5.856070</td>\n",
" <td>11.111111</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>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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 6.666667 2.342428 5.555556 5.555556 5.555556 \n",
"10000 10.0 16.666667 5.856070 11.111111 11.111111 16.666667 \n",
"default 10.0 18.888889 7.499428 11.111111 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 5.555556 11.111111 \n",
"10000 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
]
},
{
"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",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</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>1312.222222</td>\n",
" <td>473.913457</td>\n",
" <td>444.444444</td>\n",
" <td>1058.333333</td>\n",
" <td>1516.666667</td>\n",
" <td>1658.333333</td>\n",
" <td>1744.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>948.888889</td>\n",
" <td>72.803925</td>\n",
" <td>833.333333</td>\n",
" <td>911.111111</td>\n",
" <td>922.222222</td>\n",
" <td>997.222222</td>\n",
" <td>1088.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>887.777778</td>\n",
" <td>302.592819</td>\n",
" <td>33.333333</td>\n",
" <td>950.000000</td>\n",
" <td>966.666667</td>\n",
" <td>994.444444</td>\n",
" <td>1055.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 1312.222222 473.913457 444.444444 1058.333333 1516.666667 \n",
"10000 10.0 948.888889 72.803925 833.333333 911.111111 922.222222 \n",
"default 10.0 887.777778 302.592819 33.333333 950.000000 966.666667 \n",
"\n",
" 75% max \n",
"1 1658.333333 1744.444444 \n",
"10000 997.222222 1088.888889 \n",
"default 994.444444 1055.555556 "
]
},
{
"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",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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" <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>418.888889</td>\n",
" <td>302.755974</td>\n",
" <td>22.222222</td>\n",
" <td>94.444444</td>\n",
" <td>538.888889</td>\n",
" <td>666.666667</td>\n",
" <td>766.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>11.770555</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</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>43.333333</td>\n",
" <td>14.296488</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</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 10.0 418.888889 302.755974 22.222222 94.444444 538.888889 \n",
"10000 10.0 47.777778 11.770555 33.333333 36.111111 50.000000 \n",
"default 10.0 43.333333 14.296488 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 666.666667 766.666667 \n",
"10000 55.555556 66.666667 \n",
"default 52.777778 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
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" <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>10000</th>\n",
" <td>10.0</td>\n",
" <td>255.000000</td>\n",
" <td>122.066399</td>\n",
" <td>5.555556</td>\n",
" <td>205.555556</td>\n",
" <td>244.444444</td>\n",
" <td>288.888889</td>\n",
" <td>455.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>311.111111</td>\n",
" <td>11.712139</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>311.111111</td>\n",
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" <td>333.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" count mean std min 25% 50% \\\n",
"10000 10.0 255.000000 122.066399 5.555556 205.555556 244.444444 \n",
"default 10.0 311.111111 11.712139 300.000000 300.000000 311.111111 \n",
"\n",
" 75% max \n",
"10000 288.888889 455.555556 \n",
"default 319.444444 333.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_type_0'"
]
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
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" <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>445.555556</td>\n",
" <td>27.442420</td>\n",
" <td>411.111111</td>\n",
" <td>433.333333</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>78.888889</td>\n",
" <td>8.198498</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>86.111111</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>102.222222</td>\n",
" <td>8.764563</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 445.555556 27.442420 411.111111 433.333333 433.333333 \n",
"10000 10.0 78.888889 8.198498 66.666667 77.777778 77.777778 \n",
"default 10.0 102.222222 8.764563 88.888889 100.000000 100.000000 \n",
"\n",
" 75% max \n",
"1 444.444444 500.000000 \n",
"10000 86.111111 88.888889 \n",
"default 100.000000 122.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_composemail_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <thead>\n",
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" <th></th>\n",
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" <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>313.333333</td>\n",
" <td>213.392482</td>\n",
" <td>55.555556</td>\n",
" <td>150.000000</td>\n",
" <td>300.000000</td>\n",
" <td>402.777778</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>381.111111</td>\n",
" <td>32.731187</td>\n",
" <td>333.333333</td>\n",
" <td>366.666667</td>\n",
" <td>372.222222</td>\n",
" <td>405.555556</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>440.000000</td>\n",
" <td>82.018433</td>\n",
" <td>377.777778</td>\n",
" <td>411.111111</td>\n",
" <td>411.111111</td>\n",
" <td>438.888889</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 313.333333 213.392482 55.555556 150.000000 300.000000 \n",
"10000 10.0 381.111111 32.731187 333.333333 366.666667 372.222222 \n",
"default 10.0 440.000000 82.018433 377.777778 411.111111 411.111111 \n",
"\n",
" 75% max \n",
"1 402.777778 666.666667 \n",
"10000 405.555556 433.333333 \n",
"default 438.888889 666.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_type_composemail_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
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" <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>74.444444</td>\n",
" <td>48.304589</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>55.555556</td>\n",
" <td>108.333333</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>22.222222</td>\n",
" <td>35.717225</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>22.777778</td>\n",
" <td>10.621948</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</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 74.444444 48.304589 33.333333 36.111111 55.555556 \n",
"10000 10.0 22.222222 35.717225 5.555556 5.555556 11.111111 \n",
"default 10.0 22.777778 10.621948 5.555556 13.888889 22.222222 \n",
"\n",
" 75% max \n",
"1 108.333333 166.666667 \n",
"10000 19.444444 122.222222 \n",
"default 33.333333 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",
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"\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>997.777778</td>\n",
" <td>122.703877</td>\n",
" <td>811.111111</td>\n",
" <td>905.555556</td>\n",
" <td>994.444444</td>\n",
" <td>1086.111111</td>\n",
" <td>1166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>292.222222</td>\n",
" <td>18.921540</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>294.444444</td>\n",
" <td>300.000000</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>347.777778</td>\n",
" <td>88.277216</td>\n",
" <td>288.888889</td>\n",
" <td>300.000000</td>\n",
" <td>322.222222</td>\n",
" <td>333.333333</td>\n",
" <td>577.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 997.777778 122.703877 811.111111 905.555556 994.444444 \n",
"10000 10.0 292.222222 18.921540 266.666667 277.777778 294.444444 \n",
"default 10.0 347.777778 88.277216 288.888889 300.000000 322.222222 \n",
"\n",
" 75% max \n",
"1 1086.111111 1166.666667 \n",
"10000 300.000000 333.333333 \n",
"default 333.333333 577.777778 "
]
},
{
"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>65.555556</td>\n",
" <td>106.888915</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>20.555556</td>\n",
" <td>14.826384</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>51.111111</td>\n",
" <td>49.745580</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" <td>177.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 65.555556 106.888915 11.111111 22.222222 33.333333 \n",
"10000 10.0 20.555556 14.826384 5.555556 11.111111 16.666667 \n",
"default 10.0 51.111111 49.745580 11.111111 22.222222 33.333333 \n",
"\n",
" 75% max \n",
"1 41.666667 366.666667 \n",
"10000 22.222222 55.555556 \n",
"default 66.666667 177.777778 "
]
},
{
"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>17.222222</td>\n",
" <td>12.129276</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>9.816562</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>12.559870</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>30.555556</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 17.222222 12.129276 5.555556 6.944444 11.111111 \n",
"10000 10.0 12.777778 9.816562 5.555556 5.555556 8.333333 \n",
"default 10.0 16.666667 12.559870 5.555556 5.555556 11.111111 \n",
"\n",
" 75% max \n",
"1 30.555556 33.333333 \n",
"10000 19.444444 33.333333 \n",
"default 30.555556 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>85.555556</td>\n",
" <td>115.208405</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" <td>38.888889</td>\n",
" <td>63.888889</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10000</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>8.888889</td>\n",
" <td>5.367177</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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 85.555556 115.208405 5.555556 22.222222 38.888889 \n",
"10000 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 \n",
"default 10.0 8.888889 5.367177 5.555556 5.555556 5.555556 \n",
"\n",
" 75% max \n",
"1 63.888889 322.222222 \n",
"10000 9.722222 22.222222 \n",
"default 11.111111 22.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
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AAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1029c16d0>"
]
},
{
"output_type": "display_data",
"png": 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/netbbsn1Y4/l+vbbc/3QQ7munB9tzpxc33NPru++O9f335/rjt73rr8edV1e\nm6+biBLXPIuz2/a45EVs1UZbX7R1x25gdnEDOHsI3H0Y3F3UhwF/+wz8raa+9QC4taaevl91XYcB\n0/Zatb7u46vWV++6av3bnZlzxJwcol57rTqvsXrx4mq9dOmq85cu/ef5tfWbb+aLTNfWtfPfeGPV\n+W+8UQ16TdXLllXrJUtWrV9/Hfr1W3X+G2+sOr/2hwEN68WL/3n+0qWrzq+tX3utGkQb1ik1Pv/t\nt1ed31S9cmWuK8+vMr9SV+YvX57rFSvedb31Yc/C7AnVfbGj972LvkFnm3PEnE7fZncUnfmFOXbs\n2DRr1qxO254kSVJbRcTslNLYlpYr1TMVEU8Bi4EVwPLWbFCSJKknaY/DfONTSi+1w3okSZK6HQeg\nS5IklVA2TCXgzxExOyKOaY8GSZIkdSdlD/ONSynNj4j3ANMi4qGU0ozaBYqQdQzApptuWnJzkiRJ\nXUupnqmU0vzi3xeBq4AdG1nm3JTS2JTS2CFDhpTZnCRJUpfT5jAVEWtFxDqV+8CewP3t1TBJkqTu\noMxhvqHAVcXJJPsBl6WUbmiXVkmSJHUTbQ5TKaUngG3asS2SJEndjqdGkCRJKsEwJUmSVIJhSpIk\nqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJ\nhilJkqQSDFOSJEklGKYkSZJKMExJkiSVYJiSJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxT\nkiRJJRimJEmSSjBMSZIklWCYkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUYpiRJ\nkkowTEmSJJVgmJIkSSrBMCVJklSCYUqSJKkEw5QkSVIJhilJkqQSDFOSJEklGKYkSZJKMExJkiSV\nYJiSJEkqwTAlSZJUgmFKkiSpBMOUJElSCYYpSZKkEgxTkiRJJRimJEmSSigVpiJi74h4OCIei4hT\n26tRkiRJ3UWbw1RE9AV+DuwDbAVMiIit2qthkiRJ3UGZnqkdgcdSSk+klN4GfgMc0D7NkiRJ6h7K\nhKnhwLya+plimiRJUq/Rr6M3EBHHAMcU5esR8XBHb1OrGAy8VO9GSB3M/Vy9gft55xvRmoXKhKn5\nwCY19cbFtFWklM4Fzi2xHZUQEbNSSmPr3Q6pI7mfqzdwP++6yhzmuwsYFRGbRcRqwKHAH9unWZIk\nSd1Dm3umUkrLI+LLwI1AX+CClNID7dYySZKkbqDUmKmU0nXAde3UFnUMD7GqN3A/V2/gft5FRUqp\n3m2QJEnqtrycjCRJUgmGqR7cmhwmAAADTElEQVQqIi6IiBcj4v56t0Vqjcb22YgYFBHTIuLR4t/1\ni+kRET8pLmX194jYvuYxRxTLPxoRR9RM/1BEzCke85OIiM59hlJVRJwZEV9vZv6QiPhbRNwTEbu2\nYf1HRsTPivsHeoWSjmWY6rkuBPaudyOkd+FC/nmfPRW4KaU0CripqCFfxmpUcTsG+AXk8AV8E9iJ\nfJWGb1YCWLHMF2se5+dDXdnuwJyU0nYppVtLrutA8mXf1EEMUz1USmkGsLDe7ZBaq4l99gDgouL+\nReQvhcr0i1N2B7BeRAwD9gKmpZQWppQWAdOAvYt566aU7kh5oOjFNeuSOkVETIqIRyJiJrBlMW3z\niLghImZHxK0R8f6I2Bb4HnBARNwbEWtExC8iYlZEPBAR36pZ51MRMbi4PzYibm6wzV2ATwHfL9a1\neWc9396kw8+ALkklDE0pPVfcfx4YWtxv6nJWzU1/ppHpUqeIiA+Rz8e4Lfm7925gNvkXesemlB6N\niJ2A/0kpfTwizgDGppS+XDx+UkppYUT0BW6KiA+mlP7e0nZTSrdHxB+Ba1NKv+2gp9frGaYkdQsp\npRQR/vxY3dWuwFUppTcAioAzANgFuLJmCN/qTTz+4OLybP2AYeTDdi2GKXUOw5SkruyFiBiWUnqu\nOFT3YjG9qctZzQd2azD95mL6xo0sL9VTH+CVlNK2zS0UEZsBXwd2SCktiogLyUEMYDnVITsDGnm4\nOoFjpiR1ZX8EKr/IOwK4umb654tf9e0MvFocDrwR2DMi1i8Gnu8J3FjMey0idi5+xff5mnVJnWEG\ncGAx/mkdYH/gDeDJiPgMvPMr1W0aeey6wBLg1YgYSv4BRsVTwIeK+59uYtuLgXXKPwU1xTDVQ0XE\nVOCvwJYR8UxETKx3m6TmNLHPngV8IiIeBfYoashXXngCeAw4DzgOIKW0EPgv8rVD7wL+XzGNYpnz\ni8c8DlzfGc9LAkgp3Q1cDtxH3vfuKmYdDkyMiPuAB8g/rmj42PuAe4CHgMuA22pmfws4JyJmASua\n2PxvgJOL0yw4AL0DeAZ0SZKkEuyZkiRJKsEwJUmSVIJhSpIkqQTDlCRJUgmGKUmSpBIMU5IkSSUY\npiRJkkowTEmSJJXw/wHYAwKfPF63fwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106684110>"
]
},
{
"output_type": "display_data",
"png": 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7bvl6LUl68sl1w9QZZ0i9e0sjRqT63/4tnT27/PJUP/GEtOee5W5KoAYIUwCA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\nAIAMhCkAAIAMhCkAAIAMhCkAAIAM/weTO4qXL9qIUQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106afa150>"
]
},
{
"output_type": "display_data",
"png": 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PfN8lpTQzItYB7omI52snppRSRKTGFixC2ViA9dZbr51lSFqhfe1rsMUWsM46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\n",
"text": [
"<matplotlib.figure.Figure at 0x106c48fd0>"
]
},
{
"output_type": "display_data",
"png": 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SejOlNBIYCuwMbL2yB46IoyNiakRMnTt37sruTuo9ttgC3vnOPH/GGbDzzjBr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\n9xloAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106c55050>"
]
},
{
"output_type": "display_data",
"png": 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i1mRJ6lwTJ8IjjzTWbKXkcxAl9UrWZEnqXDvtBIcdlqdvuSWPr/Xss5WGJElV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\n",
"text": [
"<matplotlib.figure.Figure at 0x106bcd690>"
]
},
{
"output_type": "display_data",
"png": 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RHWx/SPUzdSOALSnNf50mWeobJlmSBqqHImLLzFxcNfktqeZ39LNe\ni4D91ph/dTV/fDvrS80yDFiWmVPWtlJEvAz4LLB7Zj4WEWdREjSAlbR1CVq/nc3VB+yTJWmgugBo\nvUPwCOD8hvmHV3cZ7gk8XjUrXgq8JSI2qzq8vwW4tFr2RETsWd1VeHjDvqS6/Ql4V9W/ajTwDmA5\ncE9EvBf+ecfsru1s+yLgH8DjETGWctNHq3uBV1fT7+ngtZ8ERvf8ENQRk6whJiLmAP8XeEVE3B8R\n05sdk9SZDq7brwFvjog7gTdVZSi/NHE3sAA4Hfg4QGY+CnyJ8tuq1wFfrOZRrfMf1TZ3Ab/vi+OS\nMvMG4JfATZTr7rpq0fuB6RFxE3Ab5YaONbe9Cfgb8HfgP4H/blh8MvC9iJgHrOrg5X8B/O9qOAg7\nvtfAEd8lSZJqYE2WJElSDUyyJEmSamCSJUmSVAOTLEmSpBqYZEmSJNXAJEuSJKkGJlmSJEk1MMmS\nJEmqwf8Ha0stbLXuccwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x106cd9750>"
]
},
{
"output_type": "display_data",
"png": 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4G9WHed4GfAS4PSJuaV47AjgWOC8iJgMzgA8PThMlSZKGr17DVGZeDUQPk3ca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\nA8SPKgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106e5db10>"
]
},
{
"output_type": "display_data",
"png": 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gypQ9CAyKiKGdnrmkzvORj+Sft92WXy791lvV5iNJ3cAq9cmKiBHAbsBDwJCU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\nLEmSpBJYZEmSJJXg/wGU0HVl3AUvgwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106e2d5d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x106e3cb90>"
]
},
{
"output_type": "display_data",
"png": 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HIa2PFj63Y4DLi+nLyV8YTfOvSNldwGYRsTVwMHBTSmlhSulF4Cbg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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107076d50>"
]
},
{
"output_type": "display_data",
"png": 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ej3jXXfDBD8IFF3h1oiStQgxZ3ciwk25odRVqs94a/Vpdhe6n8XzE7baDm2+G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\n",
"text": [
"<matplotlib.figure.Figure at 0x107090050>"
]
},
{
"output_type": "display_data",
"png": 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6I5KkhjDJktS7IkqCBfCjH8H48TBjRr0xSVIDOPBdUn0+8AF44AF485vrjkSS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\nBzZUEYelo/h3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107125550>"
]
},
{
"output_type": "display_data",
"png": 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DkqT2rilXF/aLiD7FdE/gI8AccrL1qWKxM4Hbi+k7ijLF/PtSSqmaQUsqUffu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\nVUaSNRBYWFFeVNRJkiR1Gl1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"text": [
"<matplotlib.figure.Figure at 0x107208b50>"
]
},
{
"output_type": "display_data",
"png": 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VoakFl3CQtOlWr4bNNivuQFy2DLbZpts9hkfqjnx8VLV8rI6k2hswoDnB2ndf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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1072aca90>"
]
},
{
"output_type": "display_data",
"png": 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CK9r0CH3pS/Ca15TliNIztcMOpT1wIFxySesQ8pAhZXhuxIjSHjq09Fa1hKRN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\n",
"text": [
"<matplotlib.figure.Figure at 0x107341f50>"
]
},
{
"output_type": "display_data",
"png": 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YsXDhwlrFSlK/ssoqjWkXjjiidP2NqO7rWXfdcmdgS/szn4Edd2zsO3u2AUsa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\n8CwIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10734d110>"
]
},
{
"output_type": "display_data",
"png": 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3RMQj5fsaZX1ExOkRMTUi7ouID1ZZvKROaI01YNVV8/LOO8P558NOO+X2aafl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\nBQxZkiRJFTBkSZIkVcCQJUmSVIH/D0IZcbKhWoV2AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107428f50>"
]
},
{
"output_type": "display_data",
"png": 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h1BOamuDUU8HeUklSjar5d3w34BPAIxHxYDHuFHKI+lVETAT+BhzSMyVK3ejG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\nwDAlSZJUg/8HKlfrqT08b9gAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1074c81d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x107566410>"
]
},
{
"output_type": "display_data",
"png": 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wSZKkmhnAJEmSamYAkyRJqpkBTJIkqWYGMEmSpJr9fxxNH7TgncvH\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1076926d0>"
]
},
{
"output_type": "display_data",
"png": 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L9cOAxfOiYv0CYGSjl48o1kmqNbNn50dq0d9IkqRmaM63CwO4BJidUvppo01T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\nSZIklcCQJUmSVAJDliRJUgn+P3e1TP8Mp+YjAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1076a94d0>"
]
},
{
"output_type": "display_data",
"png": 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ZVV6SJPWWboWsiBhACVjfycwfNbsfag0DNn/Ob/bPBYa1Xb5Ds0+SJGmD0WXI\nar4tOBm4JzP/u+3QJcDYZnsscHHb/uOj2A9Y2DasKEmStEHozpysNwD/ANwZEbc3+z4OTAIuiogO\n4H7g6ObYFZTlG2ZRlnB4b4+2WJIkqQ/oMmRl5nRgZbOSD1nB+Ql8YA3bJUmS1Ke54rskSVIFhixJ\nkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJ\nFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkiow\nZEmSJFVgyJIkSarAkCVJklSBIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiS\nJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmS\nVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkiowZEmSJFXQZciKiG9GxPyIuKtt39YRcU1E\n/Lb5c6tmf0TElyNiVkT8KiL2rtl4SZKkdVV3erK+DYxebt944NrM3AW4tikDHAbs0rzGAWf1TDMl\nSZL6li5DVmb+DHh0ud1jgCnN9hTgiLb952VxMzA4IrbvqcZKkiT1Fas7J2u7zJzXbD8IbNdsDwUe\naDtvTrNPkiRpg7LGE98zM4F8sddFxLiImBERMx5++OE1bYYkSdI6ZXVD1kOtYcDmz/nN/rnAsLbz\ndmj2vUBmnpuZozJz1JAhQ1azGZIkSeum1Q1ZlwBjm+2xwMVt+49vvmW4H7CwbVhRkiRpg9G/qxMi\nYirwJmDbiJgDnAZMAi6KiA7gfuDo5vQrgMOBWcBTwHsrtFmSJGmd12XIysxjV3LokBWcm8AH1rRR\nkiRJfZ0rvkuSJFVgyJIkSarAkCVJklSBIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJ\nkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJ\nqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkiowZEmSJFVgyJIkSarAkCVJklSB\nIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOW\nJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS33G1KlTGTlyJP369WPk\nyJFMnTq1t5skSdJK9e/tBkisg+f9AAAE6ElEQVTdMXXqVCZMmMDkyZPZf//9mT59Oh0dHQAce+yx\nvdw6SZJeqEpPVkSMjojfRMSsiBhf4x7asJx++ulMnjyZgw46iAEDBnDQQQcxefJkTj/99N5umiRJ\nKxSZ2bMVRvQD7gPeDMwBbgWOzcxfr+yaUaNG5YwZM3q0HVq/9OvXj2eeeYYBAwb8Zd/ixYsZOHAg\nS5cu7cWWSZI2NBExMzNHdXVejZ6s1wGzMvP3mfkc8D1gTIX7aAMyYsQIpk+fvsy+6dOnM2LEiF5q\nkSRJq1YjZA0FHmgrz2n2SattwoQJdHR0MG3aNBYvXsy0adPo6OhgwoQJvd00SZJWqMZw4ZHA6Mz8\nx6b8D8C+mfkvy503DhjXFF8F/KZHG6L10dbA9sBA4BlgHvBor7ZI0vpkW2BBbzdCfcJOmTmkq5Nq\nfLtwLjCsrbxDs28ZmXkucG6F+2s9FxEzujMWLkkvhv9vUU+rMVx4K7BLRLwiIjYG3gVcUuE+kiRJ\n66we78nKzCUR8S/AVUA/4JuZeXdP30eSJGldVmUx0sy8AriiRt0SDjNLqsP/t6hH9fjEd0mSJPns\nQkmSpCoMWeoVEfHNiJgfEXe17ds6Iq6JiN82f27V7I+I+HLzmKZfRcTebdeMbc7/bUSMbdu/T0Tc\n2Vzz5YiItfsOJfWWiPhkRHxkFceHRMQtEfHLiDhgNeo/ISK+2mwfERG7rUl7tf4yZKm3fBsYvdy+\n8cC1mbkLcG1TBjgM2KV5jQPOghLKgNOAfSlPGjitFcyac05su275e0nacB0C3JmZe2XmjWtY1xGA\nIUsrZMhSr8jMn/HChUTHAFOa7SmU/3m19p+Xxc3A4IjYHngLcE1mPpqZjwHXAKObYy/JzJuzTDo8\nr60uSeuhiJgQEfdFxHTKAtdExCsj4icRMTMiboyIXSNiT+C/gDERcXtEDIqIsyJiRkTcHRET2+qc\nHRHbNtujIuL65e75N8A7gM81db1ybb1f9Q1Vvl0orabtMnNes/0gsF2zvbJHNa1q/5wV7Je0HoqI\nfShrMu5J+XvtNmAm5duCJ2XmbyNiX+BrmXlwRPwHMKr1JJKImJCZj0ZEP+DaiHhNZv6qq/tm5k0R\ncQlwWWb+oNLbUx9myNI6KTMzIvzqq6TuOAD4cWY+BdAEn4HA3wDfb5uSuclKrj+6edRbf8qju3YD\nugxZUlcMWVqXPBQR22fmvGbIb36zf2WPapoLvGm5/dc3+3dYwfmSNhwbAY9n5p6rOikiXgF8BHht\nZj4WEd+mBDSAJXROqxm4gsulVXJOltYllwCtbwiOBS5u23988y3D/YCFzbDiVcChEbFVM+H9UOCq\n5tgTEbFf863C49vqkrT++RlwRDO/agvg7cBTwB8i4ij4y7eU91jBtS8B/gwsjIjtKF+0aZkN7NNs\nv3Ml914EbLHmb0HrI0OWekVETAV+DrwqIuZERAcwCXhzRPwW+NumDOXpAb8HZgFfB94PkJmPAp+i\nPC/zVuA/m30053yjueZ3wJVr431JWvsy8zbgQuAOyn/rtzaHjgM6IuIO4G7Kl2iWv/YO4JfAvcB3\ngf9rOzwR+FJEzACWruT23wM+2iwH4cR3LcMV3yVJkiqwJ0uSJKkCQ5YkSVIFhixJkqQKDFmSJEkV\nGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUwf8H/kFAt6Yf/zIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10774fb10>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1071b7910>"
]
},
{
"output_type": "display_data",
"png": 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WcMopZQ4pKIHpmWdgjz1KefBg+NjHyu1kJElqYcjSqmfGDHj00bKcCR/6UGmd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\nS5IkqQaGLEmSpBr8f1YssUY7AuvKAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106e5d690>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1078c9ad0>"
]
},
{
"output_type": "display_data",
"png": 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B4sXQzc53qS1MnjyZ8ePHc/nll7N8+XIuv/xyxo8fbwhrh1qjB+xLwOwG7YuB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\n",
"text": [
"<matplotlib.figure.Figure at 0x107a01e90>"
]
},
{
"output_type": "display_data",
"png": 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9EXFbZrZ1tp4T33uJZ2acsXYnvj/+OCxZAlttVcqXXw6veQ3svXcpn3wy7LQT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\ngSFLkiSpBoYsSZKkGvx/zXqMXYjV6OkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x107a9dc90>"
]
},
{
"output_type": "display_data",
"png": 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SjrY8e3ZqFSNwodp776XW0LIF9NlnpcMOq4T4m29O48A991wqv/KKdO65lTPe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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x107a01a10>"
]
},
{
"output_type": "display_data",
"png": 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7bBlibEvavvMd2KPhYQM/+hF85jPt8aJF8Mwz9b8PSZLWwSRLfc/IkWWosW1O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\nJKkGJlmSJEk1MMmSJEmqgUmWJElSDf5/p9FpFEY2F5QAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x107ab8e10>"
]
}
],
"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": {}
}
]
}
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