pandas.DataFrame.from_dict¶
-
classmethod
DataFrame.
from_dict
(data, orient='columns', dtype=None, columns=None)[source]¶ 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 withorient='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