Hướng dẫn dùng pd.dataframe.from_dict python

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’].

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

Data type to force, otherwise infer.

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

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