psifr.fr.table_from_lists#

psifr.fr.table_from_lists(subjects, study, recall, lists=None, **kwargs)#

Create table format data from list format data.

Parameters:

  • subjects (list of hashable) – Subject identifier for each list.

  • study (list of list of hashable) – List of items for each study list.

  • recall (list of list of hashable) – List of recalled items for each study list.

  • lists (list of hashable, optional) – List of list numbers. If not specified, lists for each subject will be numbered sequentially starting from one.

Returns:

data – Data in table format.

Return type:

pandas.DataFrame

See also

split_lists

Split a table into list format.

Examples

>>> from psifr import fr
>>> subjects_list = [1, 1, 2, 2]
>>> study_lists = [['a', 'b'], ['c', 'd'], ['e', 'f'], ['g', 'h']]
>>> recall_lists = [['b'], ['d', 'c'], ['f', 'e'], []]
>>> fr.table_from_lists(subjects_list, study_lists, recall_lists)
    subject  list trial_type  position item
0         1     1      study         1    a
1         1     1      study         2    b
2         1     1     recall         1    b
3         1     2      study         1    c
4         1     2      study         2    d
5         1     2     recall         1    d
6         1     2     recall         2    c
7         2     1      study         1    e
8         2     1      study         2    f
9         2     1     recall         1    f
10        2     1     recall         2    e
11        2     2      study         1    g
12        2     2      study         2    h

>>> subjects_list = [1, 1]
>>> study_lists = [['a', 'b'], ['c', 'd']]
>>> recall_lists = [['b'], ['d', 'c']]
>>> col1 = ([[1, 2], [1, 2]], [[2], [2, 1]])
>>> col2 = ([[1, 1], [2, 2]], None)
>>> fr.table_from_lists(subjects_list, study_lists, recall_lists, col1=col1, col2=col2)
   subject  list trial_type  position item  col1  col2
0        1     1      study         1    a     1   1.0
1        1     1      study         2    b     2   1.0
2        1     1     recall         1    b     2   NaN
3        1     2      study         1    c     1   2.0
4        1     2      study         2    d     2   2.0
5        1     2     recall         1    d     2   NaN
6        1     2     recall         2    c     1   NaN