def as_dataframe(self, label=None, labels=None):
"""Return all the selected time series as a :mod:`pandas` dataframe.
.. note::
Use of this method requires that you have :mod:`pandas` installed.
Examples::
# Generate a dataframe with a multi-level column header including
# the resource type and all available resource and metric labels.
# This can be useful for seeing what labels are available.
dataframe = query.as_dataframe()
# Generate a dataframe using a particular label for the column
# names.
dataframe = query.as_dataframe(label='instance_name')
# Generate a dataframe with a multi-level column header.
dataframe = query.as_dataframe(labels=['zone', 'instance_name'])
# Generate a dataframe with a multi-level column header, assuming
# the metric is issued by more than one type of resource.
dataframe = query.as_dataframe(
labels=['resource_type', 'instance_id'])
:type label: string or None
:param label: The label name to use for the dataframe header.
This can be the name of a resource label or metric label
(e.g., ``"instance_name"``), or the string ``"resource_type"``.
:type labels: list of strings, or None
:param labels: A list or tuple of label names to use for the dataframe
header. If more than one label name is provided, the resulting
dataframe will have a multi-level column header. Providing values
for both ``label`` and ``labels`` is an error.
:rtype: :class:`pandas.DataFrame`
:returns: A dataframe where each column represents one time series.
"""
return _build_dataframe(self, label, labels) # pragma: NO COVER
def as_dataframe(self, label=None, labels=None):
"""Return all the selected time series as a :mod:`pandas` dataframe.
.. note::
Use of this method requires that you have :mod:`pandas` installed.
Examples::
# Generate a dataframe with a multi-level column header including
# the resource type and all available resource and metric labels.
# This can be useful for seeing what labels are available.
dataframe = query.as_dataframe()
# Generate a dataframe using a particular label for the column
# names.
dataframe = query.as_dataframe(label='instance_name')
# Generate a dataframe with a multi-level column header.
dataframe = query.as_dataframe(labels=['zone', 'instance_name'])
# Generate a dataframe with a multi-level column header, assuming
# the metric is issued by more than one type of resource.
dataframe = query.as_dataframe(
labels=['resource_type', 'instance_id'])
:type label: string or None
:param label: The label name to use for the dataframe header.
This can be the name of a resource label or metric label
(e.g., ``"instance_name"``), or the string ``"resource_type"``.
:type labels: list of strings, or None
:param labels: A list or tuple of label names to use for the dataframe
header. If more than one label name is provided, the resulting
dataframe will have a multi-level column header. Providing values
for both ``label`` and ``labels`` is an error.
:rtype: :class:`pandas.DataFrame`
:returns: A dataframe where each column represents one time series.
"""
return _build_dataframe(self, label, labels) # pragma: NO COVER
def _callFUT(self, *args, **kwargs):
from gcloud.monitoring._dataframe import _build_dataframe
return _build_dataframe(*args, **kwargs)
def _callFUT(self, *args, **kwargs):
from gcloud.monitoring._dataframe import _build_dataframe
return _build_dataframe(*args, **kwargs)