# datasource_to_dataframe

## nominal.thirdparty.pandas.datasource_to_dataframe(datasource, channel_exact_match=None, channel_fuzzy_search_text=None, start=None, end=None, tags=None, enable_gzip=True, *, channels=None, num_workers=1, channel_batch_size=20, relative_to=None, relative_resolution='nanoseconds')

Download a dataset to a pandas dataframe, optionally filtering for only specific channels of the dataset.

Parameters:
:   - **datasource** ([*Dataset*](https://dev.nominal.io/python/ref/generated/nominal.core.Dataset.md#nominal.core.Dataset "nominal.core.dataset.Dataset"))

      The datasource to download data from
    - **channel_exact_match** ([*Sequence*](https://docs.python.org/3/library/typing.html#typing.Sequence "(in Python v3.15)")*[*[*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")*]* *|* *None*)

      Filter the returned channels to those whose names match all provided strings
      (case insensitive).
      For example, a channel named ‘engine_turbine_rpm’ would match against [‘engine’, ‘turbine’, ‘rpm’],
      whereas a channel named ‘engine_turbine_flowrate’ would not!
    - **channel_fuzzy_search_text** ([*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)") *|* *None*)

      Filters the returned channels to those whose names fuzzily match the provided
      string.
    - **channels** ([*Sequence*](https://docs.python.org/3/library/typing.html#typing.Sequence "(in Python v3.15)")*[*[*Channel*](https://dev.nominal.io/python/ref/generated/nominal.core.Channel.md#nominal.core.Channel "nominal.core.channel.Channel")*]* *|* *None*)

      List of channels to fetch data for. If provided, supercedes search parameters of
      `channel_exact_match` and `channel_fuzzy_search_text`.
    - **tags** ([*Mapping*](https://docs.python.org/3/library/typing.html#typing.Mapping "(in Python v3.15)")*[*[*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")*,* [*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)")*]* *|* *None*)

      Dictionary of tags to filter channels by
    - **start** ([*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)") *|* [*datetime*](https://docs.python.org/3/library/datetime.html#datetime.datetime "(in Python v3.15)") *|* [*int*](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)") *|* *None*)

      The minimum data updated time to filter channels by
    - **end** ([*str*](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)") *|* [*datetime*](https://docs.python.org/3/library/datetime.html#datetime.datetime "(in Python v3.15)") *|* [*int*](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)") *|* *None*)

      The maximum data start time to filter channels by
    - **enable_gzip** ([*bool*](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)"))

      If true, use gzip when exporting data from Nominal. This will almost always make export
      faster and use less bandwidth.
    - **num_workers** ([*int*](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)"))

      Use this many parallel processes for performing export requests against the backend. This should
      roughly be corresponding to the strength of your network connection, with 4-8 workers being more than
      sufficient to completely saturate most connections.
    - **channel_batch_size** ([*int*](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)"))

      Number of channels to request at a time per worker thread. Reducing this number may allow
      fetching a larger time duration (i.e., `end` - `start`), depending on how synchronized the timing is amongst
      the requested channels. This is a result of a limit of 10_000_000 unique timestamps returned per request,
      so reducing the number of channels will allow for a larger time window if channels come in at different
      times (e.g. channel A has timestamps 100, 200, 300… and channel B has timestamps 101, 201, 301, …).
      This is particularly useful when combined with num_workers when attempting to maximally utilize a machine.
    - **relative_to** ([*datetime*](https://docs.python.org/3/library/datetime.html#datetime.datetime "(in Python v3.15)") *|* [*int*](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)") *|* *None*)

      If provided, return timestamps relative to the given epoch time
    - **relative_resolution** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")*[**'picoseconds'**,* *'nanoseconds'**,* *'microseconds'**,* *'milliseconds'**,* *'seconds'**,* *'minutes'**,* *'hours'**,* *'days'**]*)

      If providing timestamps in relative time, the resolution to use

Returns:
:   A pandas dataframe whose index is the timestamp of the data, and column names match those of the selected
    :   channels.

Return type:
:   [*DataFrame*](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame "(in pandas v3.1)")

Example

```python
rid = "..." # Taken from the UI or via the SDK
dataset = client.get_dataset(rid)
df = datasource_to_dataframe(dataset)
print(df.head())  # Show first few rows of data
```