deletions | additions
diff --git a/data/Test.ipynb b/data/Test.ipynb
index 53ff8d7..f9742f7 100644
--- a/data/Test.ipynb
+++ b/data/Test.ipynb
...
{
"metadata": {
"name": "",
"signature":
"sha256:67fe8ac608a4b17cd3f100b09d8369e9358618007af770d2b58a69a61133355e" "sha256:2e62e65ba1ec2ae9917b7bcb4d2e54d3c2ae238107c905e969a69f7af2206763"
},
"nbformat": 3,
"nbformat_minor": 0,
...
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number":
75 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def dataframe(query):\n",
" return pd.DataFrame([dict(r) for r in db.query(query)])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
...
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = dataframe('select * from tapir where total_time is not null')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"Index([u'alpha', u'client', u'clientargs', u'closest', u'config', u'debug', u'duration', u'edgefactor', u'err', u'error', u'filename', u'gen', u'genargs', u'id', u'initposts', u'initusers', u'keys', u'length', u'load', u'mode', u'name', u'nclient', u'nkeys', u'nshard', u'retwis_follow_success', u'retwis_newuser_success', u'retwis_post_success', u'retwis_timeline_success', u'rtime', u'shards', u'skew', u'store', u'threads', u'tlen', u'total_time', u'txn_count', u'txn_failed', u'txn_retries', u'txn_time', u'txns', u'wper', u'writes', u'zalpha', u'txn_abort_rate', u'throughput'], dtype='object')"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def melt_columns(df, columns):\n",
" return pd.melt(df, id_vars=list(df.columns - columns), value_vars=columns)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"txn_columns = ['retwis_newuser_success', 'retwis_post_success', 'retwis_timeline_success', 'retwis_follow_success']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
...
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number":
73 83
},
{
"cell_type": "code",
...
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number":
69 87
},
{
"cell_type": "code",
...
{
"metadata": {},
"output_type": "pyout",
"prompt_number":
70, 88,
"text": [
"0x113bac790>"
0x11417c410>"
]
},
{
...
"output_type": "display_data",
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"text": [
"0x113bace90>"
0x114010990>"
]
}
],
"prompt_number":
70 88
},
{
"cell_type": "code",
...
{
"metadata": {},
"output_type": "pyout",
"prompt_number":
71, 91,
"text": [
"0x113c0b6d0>"
0x1144e4350>"
]
},
{
...
"output_type": "display_data",
"png": 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sNpumTp2qkJAQjRgxQlFRUWpubh7wD17D4EEwAD0ICwsLvLbZbHI6nWpubg68\n19DQ0O3z9/1+v4qKijR8+PDAZ0eOHKmysrKrHj9BKOBmwsln4Dr4fD6Vl5ertbVVHR0dev755wOH\nkr755T516lS9++67kqTPP/9cKSkpamtr6zYEQkJC1NHRYe0CANeAYAC60dWDD6OiopSZmalHH31U\ns2fP1pQpUzRt2rTA5y//k6SXX35ZNTU1SklJ0bJly/T6668rIiLC8JkrTZgwQc3NzXrxxRetXTDA\nBA/RAwAYsMcAADAgGAAABgQDAMCAYAAAGBAMAAADggEAYEAwAAAM/he72sHmSmN4SgAAAABJRU5E\nrkJggg==\n",
"text": [
"0x1112dd710>"
0x1141e8f10>"
]
}
],
"prompt_number":
71 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.barplot(data=df, "p = sns.barplot(data=df, x='nclient')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png":
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"text": [
"0x113b41dd0>"
0x113ff9150>"
]
}
],
"prompt_number":
72 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = melt_columns(df, txn_columns)\n",
"d['success rate'] = d['value']\n",
"d['txn type'] = d['variable']\n",
"import
dataset" matplotlib.pyplot as plt\n",
"\n",
"sns.barplot(data=d, x='nclient', y='success rate', hue='txn type')\n",
"plt.legend(loc=9, bbox_to_anchor=(1.0, 0.5))"
],
"language": "python",
"metadata": {},
"outputs":
[], [
{
"metadata": {},
"output_type": "pyout",
"prompt_number":
60 137,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 137
},
{
"cell_type": "code",
"collapsed": false,
"input":
[
"db = dataset.connect()"
], [],
"language": "python",
"metadata": {},
"outputs": []
