pandas.DataFrame.from_dict — pandas 2.2.3 documentation (original) (raw)

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:

datadict

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

orient{‘columns’, ‘index’, ‘tight’}, 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’. If ‘tight’, assume a dict with keys [‘index’, ‘columns’, ‘data’, ‘index_names’, ‘column_names’].

Added in version 1.4.0: ‘tight’ as an allowed value for the orient argument

dtypedtype, default None

Data type to force after DataFrame construction, otherwise infer.

columnslist, default None

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

Returns:

DataFrame

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

Specify orient='tight' to create the DataFrame using a ‘tight’ format:

data = {'index': [('a', 'b'), ('a', 'c')], ... 'columns': [('x', 1), ('y', 2)], ... 'data': [[1, 3], [2, 4]], ... 'index_names': ['n1', 'n2'], ... 'column_names': ['z1', 'z2']} pd.DataFrame.from_dict(data, orient='tight') z1 x y z2 1 2 n1 n2 a b 1 3 c 2 4