ping_ingestion_fall.ipynb

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   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "import pandas as pd\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "\n",
    "\n",
    "bucket = \"telemetry-parquet\"\n",
    "prefix = \"main_summary/v2\"\n",
    "dataset = sqlContext.read.load(\"s3://{}/{}\".format(bucket, prefix), \"parquet\")"
   ]
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    "by_day = dataset.groupBy('submission_date')\n",
    "tot_usage = by_day.sum('subsession_length').toPandas()\n",
    "num_pings = by_day.count().toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of pings per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
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    "collapsed": false
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   "outputs": [
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       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mozilla/852.embed\" height=\"525\" width=\"100%\"></iframe>"
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   "source": [
    "num_pings = num_pings.sort_values(by='submission_date')\n",
    "ping_data = Data([Scatter(x=num_pings['submission_date'].apply(pd.to_datetime), y=num_pings['count'])])\n",
    "py.iplot(ping_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of usage hours per day"
   ]
  },
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   "cell_type": "code",
   "execution_count": 138,
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    "tot_usage = tot_usage.sort_values(by='submission_date')\n",
    "usage_data = Data([Scatter(x=tot_usage['submission_date'].apply(pd.to_datetime), y=tot_usage['sum(subsession_length)'] / 3600)])\n",
    "py.iplot(usage_data)"
   ]
  },
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    "collapsed": true
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