docstr: Display a static table.
  
  This differs from ``st.dataframe`` in that the table in this case is
  static: its entire contents are laid out directly on the page.
  
  Parameters
  ----------
  data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None
      The table data.
  
  Example
  -------
  >>> import streamlit as st
  >>> import pandas as pd
  >>> import numpy as np
  >>>
  >>> df = pd.DataFrame(np.random.randn(10, 5), columns=("col %d" % i for i in range(5)))
  >>>
  >>> st.table(df)
  
  .. output::
     https://doc-table.streamlit.app/
     height: 480px
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code: def table(self, data: Data = None) -> "DeltaGenerator":
      """Display a static table.
  
      This differs from ``st.dataframe`` in that the table in this case is
      static: its entire contents are laid out directly on the page.
  
      Parameters
      ----------
      data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None
          The table data.
  
      Example
      -------
      >>> import streamlit as st
      >>> import pandas as pd
      >>> import numpy as np
      >>>
      >>> df = pd.DataFrame(np.random.randn(10, 5), columns=("col %d" % i for i in range(5)))
      >>>
      >>> st.table(df)
  
      .. output::
         https://doc-table.streamlit.app/
         height: 480px
  
      """
  
      # Check if data is uncollected, and collect it but with 100 rows max, instead of 10k rows, which is done in all other cases.
      # Avoid this and use 100 rows in st.table, because large tables render slowly, take too much screen space, and can crush the app.
      if type_util.is_snowpark_data_object(data) or type_util.is_type(
          data, type_util._PYSPARK_DF_TYPE_STR
      ):
          data = type_util.convert_anything_to_df(
              data, max_unevaluated_rows=100
          )
  
      # If pandas.Styler uuid is not provided, a hash of the position
      # of the element will be used. This will cause a rerender of the table
      # when the position of the element is changed.
      delta_path = self.dg._get_delta_path_str()
      default_uuid = str(hash(delta_path))
  
      proto = ArrowProto()
      marshall(proto, data, default_uuid)
      return self.dg._enqueue("arrow_table", proto)
  
file_path: streamlit/lib/streamlit/elements/arrow.py
imports: from __future__ import annotations
  from collections.abc import Iterable
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union, cast
  import pyarrow
  from typing_extensions import TypeAlias
  from streamlit import type_util
  from streamlit.elements.lib.column_config_utils import INDEX_IDENTIFIER, ColumnConfigMappingInput, apply_data_specific_configs, marshall_column_config, process_config_mapping, update_column_config
  from streamlit.elements.lib.pandas_styler_utils import marshall_styler
  from streamlit.proto.Arrow_pb2 import Arrow
  from streamlit.runtime.metrics_util import gather_metrics
label_desc: Use image node_id 2 for calling the ArrowMixin obj's underlying member method code with example usage: obj.table(data) and returns: self