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Update from_pandas and from_csv docs for index_dims kwarg (#1208)
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tiledb/dataframe_.py

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@@ -414,7 +414,7 @@ def from_pandas(uri: str, dataframe: "pd.DataFrame", **kwargs):
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* **sparse** - (default True) Create sparse schema
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* **chunksize** - (default None) Maximum number of rows to read at a time. Note that this is also a `pandas.read_csv` argument
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which `tiledb.read_csv` checks for in order to correctly read a file batchwise.
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* **index_dims** - Set the df index using a list of existing column names
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* **index_dims** (``List[str]``) -- List of column name(s) to use as dimension(s) in TileDB array schema. This is the recommended way to create dimensions.
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* **allows_duplicates** - Generated schema should allow duplicates
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* **mode** - (default ``ingest``), Ingestion mode: ``ingest``, ``schema_only``, ``append``
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* **attr_filters** - FilterList to apply to Attributes: FilterList or Dict[str -> FilterList] for any attribute(s). Unspecified attributes will use default.
@@ -694,7 +694,7 @@ def from_csv(uri: str, csv_file: Union[str, List[str]], **kwargs):
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* Any `pandas.read_csv <https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html>`_ supported keyword argument
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* **ctx** - A TileDB context
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* **sparse** - (default True) Create sparse schema
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* **index_dims** - Set the df index using a list of existing column names
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* **index_dims** (``List[str]``) -- List of column name(s) to use as dimension(s) in TileDB array schema. This is the recommended way to create dimensions. (note: the Pandas ``read_csv`` argument ``index_col`` will be passed through if provided, which results in indexes that will be converted to dimnesions by default; however ``index_dims`` is preferred).
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* **allows_duplicates** - Generated schema should allow duplicates
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* **mode** - (default ``ingest``), Ingestion mode: ``ingest``, ``schema_only``, ``append``
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* **attr_filters** - FilterList to apply to Attributes: FilterList or Dict[str -> FilterList] for any attribute(s). Unspecified attributes will use default.

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