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table.to_dict(columns, slice) # to_dict returns python dict with column names as keys and lists of values as values. The optional slice permits an effective retrieval of a subset of rows.
Table.from_dict()
table.to_list() returns list of column names, + list of each column
Table.from_list(column names + data) (the inverse to .to_list()
table.transpose(columns=['Monday', 'Tuesday','Wednesday', 'Thursday', 'Friday'], as='day') turns the columns into a single column under the heading of 'day'
complete the list of importable formats.
remove requirement for column name declaration
new Column methods:
Column[:] returns list of native python types. No more numpy arrays or numpy types.
v2022_11_0
. See comments for commit references.Config
"memory"
and exclusively use RAM.new
tools.py
date_range(start, stop, step)
like 2022/1/1, 2023/1/1, timedelta(days1) # returns list ofdatetime
s.new Table methods
table.remove_duplicate_rows()
table.drop_na(*arg)
removes rows with and None, np.nantable.replace(target, replacement)
which searches across all columns, e.g.t.replace(None, -1)
table.replace_missing_values(source=[...], target=column_name)
which looks up nearest neighbour in sources and substitute into target. implement replace missing values with MP support #18table.to_pandas()
Table.from_pandas(pd.DataFrame)
table.to_h5()
Table.from_h5()
table.to_dict(columns, slice)
# to_dict returns python dict with column names as keys and lists of values as values. The optionalslice
permits an effective retrieval of a subset of rows.Table.from_dict()
table.to_list()
returns list of column names, + list of each columnTable.from_list(column names + data)
(the inverse to.to_list()
table.transpose(columns=['Monday', 'Tuesday','Wednesday', 'Thursday', 'Friday'], as='day')
turns the columns into a single column under the heading of 'day'new Column methods:
Column[:]
returns list of native python types. No more numpy arrays or numpy types.Column.to_numpy(slice)
returns numpy's ndarray.Documentation
Cleaner code:
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