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# pandas.DataFrame.from_records

classmethod DataFrame.from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)

  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 since version 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