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Any ,
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Callable ,
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Hashable ,
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+ Literal ,
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Sequence ,
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cast ,
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)
@@ -272,7 +273,7 @@ def to_hdf(
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min_itemsize : int | dict [str , int ] | None = None ,
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nan_rep = None ,
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dropna : bool | None = None ,
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- data_columns : bool | list [str ] | None = None ,
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+ data_columns : Literal [ True ] | list [str ] | None = None ,
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errors : str = "strict" ,
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encoding : str = "UTF-8" ,
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) -> None :
@@ -1078,7 +1079,7 @@ def put(
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complevel : int | None = None ,
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min_itemsize : int | dict [str , int ] | None = None ,
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nan_rep = None ,
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- data_columns : list [str ] | None = None ,
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+ data_columns : Literal [ True ] | list [str ] | None = None ,
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encoding = None ,
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errors : str = "strict" ,
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track_times : bool = True ,
@@ -1102,7 +1103,7 @@ def put(
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subsets of the data.
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append : bool, default False
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This will force Table format, append the input data to the existing.
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- data_columns : list, default None
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+ data_columns : list of columns or True , default None
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List of columns to create as data columns, or True to use all columns.
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See `here
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<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
@@ -1208,7 +1209,7 @@ def append(
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chunksize = None ,
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expectedrows = None ,
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dropna : bool | None = None ,
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- data_columns : list [str ] | None = None ,
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+ data_columns : Literal [ True ] | list [str ] | None = None ,
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encoding = None ,
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errors : str = "strict" ,
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):
@@ -3263,8 +3264,7 @@ class Table(Fixed):
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values_axes : a list of the columns which comprise the data of this
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table
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data_columns : a list of the columns that we are allowing indexing
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- (these become single columns in values_axes), or True to force all
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- columns
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+ (these become single columns in values_axes)
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nan_rep : the string to use for nan representations for string
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objects
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levels : the names of levels
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