fetch_series()
Fetches a table of series values per step, for non-numerical series attributes.
To narrow the results, define filters for experiments to search or attributes to include.
Supported types:
Parameters
Path of the Neptune project, as WorkspaceName/ProjectName
.
If not provided, the NEPTUNE_PROJECT
environment variable is used.
Filter specifying which experiments to include.
- If a string is provided, it's treated as a regex pattern that the experiment names must match.
- If a list of strings is provided, it's treated as exact experiment names to match.
- To provide a more complex condition on an arbitrary attribute value, pass a
Filter
object.
Filter specifying which attributes to include.
- If a string is provided, it's treated as a regex pattern that the attribute names must match.
- If a list of strings is provided, it's treated as exact attribute names to match.
- To provide a more complex condition, pass an
AttributeFilter
object.
Whether to include absolute timestamp. If set, each metric column has an additional sub-column with requested timestamp values.
Tuple specifying the range of steps to include.
If None
is used, it represents an open interval.
If True
, includes all points from the complete experiment history.
If False
, only includes points from the selected experiment.
From the tail end of each series, how many points to include at most.
Returns
pandas.DataFrame
– A table of series values per step for non-numerical series. The DataFrame has a MultiIndex with:
- Index:
["experiment", "step"]
for experiments or["run", "step"]
for runs - Columns:
"value"
– File objects, strings, or histograms"absolute_time"
if theinclude_time
argument is set to"absolute"
Raises
AttributeTypeInferenceError
– If the attribute type wasn't specified in a filter passed to theexperiments
argument, and the attribute has multiple types across the project's experiments.ConflictingAttributeTypes
– If there are conflicting attribute types under the same path and thetype_suffix_in_column_names
argument is set toFalse
.
Examples
The following examples show how to fetch series of different types.
File series
Fetch file series of two specific experiments from step 1 to 3 and include the absolute timestamp:
import neptune_query as nq
nq.fetch_series(
experiments=["seabird-4", "seabird-5"],
attributes=r"^predictions/",
step_range=(1.0, 3.0),
include_time="absolute",
)
predictions
absolute_time value
experiment step
seabird-4 1.0 2025-08-29 09:18:27.946000+00:00 File(size=24.89 KB, mime_type=image/png)
2.0 2025-08-29 09:18:29.949000+00:00 File(size=26.66 KB, mime_type=image/png)
3.0 2025-08-29 09:19:01.952000+00:00 File(size=6.89 KB, mime_type=image/png)
seabird-5 1.0 2025-08-29 09:21:07.946000+00:00 File(size=23.66 KB, mime_type=image/png)
2.0 2025-08-29 09:22:25.949000+00:00 File(size=25.43 KB, mime_type=image/png)
3.0 2025-08-29 09:24:27.952000+00:00 File(size=6.58 KB, mime_type=image/png)
Histogram series
Fetch histogram series of a specific experiment, including only the last 10 steps:
import neptune_query as nq
nq.fetch_series(
experiments=["seabird-4"],
attributes=["activations"],
tail_limit=10,
)
activations
experiment step
seabird-4 40.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[1.0, 9.0, 7.0, 3.0])
41.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[0.0, 4.0, 5.0, 6.0])
42.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[6.0, 7.0, 9.0, 2.0])
43.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[2.0, 1.0, 5.0, 8.0])
44.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[8.0, 7.0, 6.0, 1.0])
45.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[7.0, 4.0, 5.0, 9.0])
46.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[7.0, 6.0, 2.0, 9.0])
47.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[4.0, 3.0, 6.0, 7.0])
48.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[9.0, 7.0, 3.0, 0.0])
49.0 Histogram(type='COUNTING', edges=[0.0, 1.0, 2.0, 4.0, 8.0], values=[0.0, 3.0, 4.0, 7.0])
String series
Fetch text logs of a specific experiment:
import neptune_query as nq
nq.fetch_series(
experiments=["seabird-4"],
attributes=["journal"],
)
journal
experiment step
seabird-4 0.0 training my model, day 0
1.0 training my model, day 1
2.0 training my model, day 2
3.0 training my model, day 3
4.0 training my model, day 4
5.0 training my model, day 5
...
47.0 training my model, day 47
48.0 training my model, day 48
49.0 training my model, day 49