xorbits.pandas.DataFrame.from_records#

DataFrame.from_records(records, **kw)[source]#

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters

  • data (structured ndarray, sequence of tuples or dicts, or DataFrame (Not supported yet)) –

    Structured input data.

    Deprecated since version 2.1.0(pandas): Passing a DataFrame is deprecated.

  • index (str, list of fields, array-like (Not supported yet)) – Field of array to use as the index, alternately a specific set of input labels to use.

  • exclude (sequence, default None (Not supported yet)) – Columns or fields to exclude.

  • columns (sequence, default None (Not supported yet)) – Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).

  • coerce_float (bool, default False (Not supported yet)) – Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.

  • nrows (int, default None (Not supported yet)) – Number of rows to read if data is an iterator.

Return type

DataFrame

See also

DataFrame.from_dict

DataFrame from dict of array-like or dicts.

DataFrame

DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],  
...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)  
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [{'col_1': 3, 'col_2': 'a'},  
...         {'col_1': 2, 'col_2': 'b'},
...         {'col_1': 1, 'col_2': 'c'},
...         {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)  
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]  
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])  
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

This docstring was copied from pandas.core.frame.DataFrame.