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Classe « DataFrame »

Méthode pandas.DataFrame.from_records

Signature de la méthode from_records

def from_records(data, index=None, exclude=None, columns=None, coerce_float: 'bool' = False, nrows: 'int | None' = None) -> 'DataFrame' 

Description

help(DataFrame.from_records)

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
    Structured input data.

    .. deprecated:: 2.1.0
        Passing a DataFrame is deprecated.
index : str, list of fields, array-like
    Field of array to use as the index, alternately a specific set of
    input labels to use.
exclude : sequence, default None
    Columns or fields to exclude.
columns : sequence, default None
    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
    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
    Number of rows to read if data is an iterator.

Returns
-------
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

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