Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index allowing dtype specification.
Of the form {field : array-like} or {field : dict}.
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
Data type to force after DataFrame construction, otherwise infer.
Column labels to use when orient='index'. Raises a ValueError if used with orient='columns' or orient='tight'.
See also
DataFrame.from_recordsDataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.
DataFrameDataFrame object creation using constructor.
DataFrame.to_dictConvert the DataFrame to a dictionary.
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
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https://pandas.pydata.org/pandas-docs/version/2.3.0/reference/api/pandas.DataFrame.from_dict.html