# pandas.DataFrame.from_dict

classmethod `DataFrame.``from_dict`(data, orient='columns', dtype=None, columns=None)

  Construct DataFrame from dict of array-like or dicts.

  Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

  Parameters:

  data : dict

  > Of the form {field : array-like} or {field : dict}.

  orient : {‘columns’, ‘index’}, default ‘columns’

  > The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’.

  dtype : dtype, default None

  > Data type to force, otherwise infer.

  columns : list, default None

  > Column labels to use when `orient='index'`. Raises a ValueError if used with `orient='columns'`.

  > New in version 0.23.0.

  Returns: | DataFrame

  See also

  `DataFrame.from_records`
    DataFrame from ndarray (structured dtype), list of tuples, dict, or DataFrame.
  `DataFrame`
    DataFrame object creation using constructor.

  Examples

  By default the keys of the dict become the DataFrame columns:

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

  Specify `orient='index'` to create the DataFrame using dictionary keys as rows:

      >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
      >>> pd.DataFrame.from_dict(data, orient='index')
             0  1  2  3
      row_1  3  2  1  0
      row_2  a  b  c  d

  When using the ‘index’ orientation, the column names can be specified manually:

      >>> pd.DataFrame.from_dict(data, orient='index',
      ...                        columns=['A', 'B', 'C', 'D'])
             A  B  C  D
      row_1  3  2  1  0
      row_2  a  b  c  d

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