117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
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# Copyright 2016 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Time series as :mod:`pandas` dataframes."""
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import itertools
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TOP_RESOURCE_LABELS = (
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'project_id',
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'aws_account',
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'location',
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'region',
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'zone',
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)
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def _build_dataframe(time_series_iterable,
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label=None, labels=None): # pragma: NO COVER
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"""Build a :mod:`pandas` dataframe out of time series.
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:type time_series_iterable:
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iterable over :class:`~gcloud.monitoring.timeseries.TimeSeries`
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:param time_series_iterable:
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An iterable (e.g., a query object) yielding time series.
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:type label: string or None
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:param label:
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The label name to use for the dataframe header. This can be the name
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of a resource label or metric label (e.g., ``"instance_name"``), or
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the string ``"resource_type"``.
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:type labels: list of strings, or None
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:param labels:
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A list or tuple of label names to use for the dataframe header.
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If more than one label name is provided, the resulting dataframe
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will have a multi-level column header.
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Specifying neither ``label`` or ``labels`` results in a dataframe
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with a multi-level column header including the resource type and
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all available resource and metric labels.
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Specifying both ``label`` and ``labels`` is an error.
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:rtype: :class:`pandas.DataFrame`
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:returns: A dataframe where each column represents one time series.
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"""
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import pandas # pylint: disable=import-error
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if labels is not None:
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if label is not None:
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raise ValueError('Cannot specify both "label" and "labels".')
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elif not labels:
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raise ValueError('"labels" must be non-empty or None.')
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columns = []
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headers = []
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for time_series in time_series_iterable:
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pandas_series = pandas.Series(
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data=[point.value for point in time_series.points],
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index=[point.end_time for point in time_series.points],
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)
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columns.append(pandas_series)
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headers.append(time_series.header())
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# Implement a smart default of using all available labels.
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if label is None and labels is None:
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resource_labels = set(itertools.chain.from_iterable(
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header.resource.labels for header in headers))
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metric_labels = set(itertools.chain.from_iterable(
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header.metric.labels for header in headers))
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labels = (['resource_type'] +
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_sorted_resource_labels(resource_labels) +
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sorted(metric_labels))
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# Assemble the columns into a DataFrame.
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dataframe = pandas.DataFrame.from_records(columns).T
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# Convert the timestamp strings into a DatetimeIndex.
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dataframe.index = pandas.to_datetime(dataframe.index)
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# Build a multi-level stack of column headers. Some labels may
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# be undefined for some time series.
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levels = []
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for key in labels or [label]:
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level = [header.labels.get(key, '') for header in headers]
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levels.append(level)
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# Build a column Index or MultiIndex. Do not include level names
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# in the column header if the user requested a single-level header
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# by specifying "label".
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dataframe.columns = pandas.MultiIndex.from_arrays(
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levels,
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names=labels or None)
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# Sort the rows just in case (since the API doesn't guarantee the
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# ordering), and sort the columns lexicographically.
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return dataframe.sort_index(axis=0).sort_index(axis=1)
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def _sorted_resource_labels(labels):
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"""Sort label names, putting well-known resource labels first."""
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head = [label for label in TOP_RESOURCE_LABELS if label in labels]
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tail = sorted(label for label in labels
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if label not in TOP_RESOURCE_LABELS)
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return head + tail
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