python programming newbie here. I was trying to create a simple histogram with altair using the following code. The dataset is large, around 1,000 rows.

count_chart = alt.Chart(df).mark_bar().encode(
    x=alt.X('r:Q', bin=True),
    y=alt.Y('count():Q')
).properties(title='Histogram plot of r counts')
count_chart

But received the following error message:
---------------------------------------------------------------------------
`ImportError                               Traceback (most recent call last)
File ~AppDataLocalminiconda3Libsite-packagesIPythoncoreformatters.py:977, in MimeBundleFormatter.__call__(self, obj, include, exclude)
    974     method = get_real_method(obj, self.print_method)
    976     if method is not None:
--> 977         return method(include=include, exclude=exclude)
    978     return None
    979 else:

File ~AppDataLocalminiconda3Libsite-packagesaltairvegalitev5api.py:2576, in TopLevelMixin._repr_mimebundle_(self, include, exclude)
   2574     return {}
   2575 else:
-> 2576     return renderers.get()(dct)

File ~AppDataLocalminiconda3Libsite-packagesaltairutilsdisplay.py:227, in HTMLRenderer.__call__(self, spec, **metadata)
    224 kwargs.update(metadata)
    225 # To get proper return value type, would need to write complex
    226 # overload signatures for spec_to_mimebundle based on `format`
--> 227 return spec_to_mimebundle(  # type: ignore[return-value]
    228     spec, format="html", output_div=self.output_div, **kwargs
    229 )

File ~AppDataLocalminiconda3Libsite-packagesaltairutilsmimebundle.py:70, in spec_to_mimebundle(spec, format, mode, vega_version, vegaembed_version, vegalite_version, embed_options, engine, **kwargs)
     68 internal_mode: Literal["vega-lite", "vega"] = mode
     69 if using_vegafusion():
---> 70     spec = compile_with_vegafusion(spec)
     71     internal_mode = "vega"
     73 # Default to the embed options set by alt.renderers.set_embed_options

File ~AppDataLocalminiconda3Libsite-packagesaltairutils_vegafusion_data.py:226, in compile_with_vegafusion(vegalite_spec)
    224 # Pre-evaluate transforms in vega spec with vegafusion
    225 row_limit = data_transformers.options.get("max_rows", None)
--> 226 transformed_vega_spec, warnings = vf.runtime.pre_transform_spec(
    227     vega_spec,
    228     vf.get_local_tz(),
    229     inline_datasets=inline_tables,
    230     row_limit=row_limit,
    231 )
    233 # Check from row limit warning and convert to MaxRowsError
    234 handle_row_limit_exceeded(row_limit, warnings)

File ~AppDataLocalminiconda3Libsite-packagesvegafusionruntime.py:364, in VegaFusionRuntime.pre_transform_spec(self, spec, local_tz, default_input_tz, row_limit, preserve_interactivity, inline_datasets, keep_signals, keep_datasets, data_encoding_threshold, data_encoding_format)
    362 else:
    363     local_tz = local_tz or get_local_tz()
--> 364     imported_inline_dataset = self._import_or_register_inline_datasets(inline_datasets)
    366     # Parse input keep signals and datasets
    367     keep_signals = parse_variables(keep_signals)

File ~AppDataLocalminiconda3Libsite-packagesvegafusionruntime.py:217, in VegaFusionRuntime._import_or_register_inline_datasets(self, inline_datasets)
    214         except ValueError:
    215             pass
--> 217     imported_inline_datasets[name] = PandasDatasource(value)
    218 elif hasattr(value, "__dataframe__"):
    219     # Let polars convert to pyarrow since it has broader support than the raw dataframe interchange
    220     # protocol, and "This operation is mostly zero copy."
    221     try:

File ~AppDataLocalminiconda3Libsite-packagesvegafusiondatasourcepandas_datasource.py:13, in PandasDatasource.__init__(self, df, sample_size, batch_size)
     11 def __init__(self, df: "pd.DataFrame", sample_size: int = 1000, batch_size: int = 8096):
     12     import pandas as pd
---> 13     import pyarrow as pa
     15     fields = []
     16     casts = {}

File ~AppDataLocalminiconda3Libsite-packagespyarrow__init__.py:65
     63 _gc_enabled = _gc.isenabled()
     64 _gc.disable()
---> 65 import pyarrow.lib as _lib
     66 if _gc_enabled:
     67     _gc.enable()

ImportError: DLL load failed while importing lib: The specified procedure could not be found.`

Before getting this error message, I got an error message about the size of the dataset, so I used this line of code before the code for the histogram: alt.data_transformers.enable(“vegafusion”), as this is supposed to be helpful for large datasets. But I then received the error message above.

I also tested a smaller dataset with the same code for the histogram, and it worked.

Thanks for any suggestions you can provide.