Split-apply-combine operations for tabular data¶

In [1]:

import pandas as pd

In [2]:

data = pd.DataFrame(
    data=[
        ['312', 'A1', 0.12, 'LEFT'],
        ['312', 'A2', 0.37, 'LEFT'],
        ['312', 'C2', 0.68, 'LEFT'],
        ['313', 'A1', 0.07, 'RIGHT'],
        ['313', 'B1', 0.08, 'RIGHT'],
        ['314', 'A2', 0.29, 'LEFT'],
        ['314', 'B1', 0.14, 'RIGHT'],
        ['314', 'C2', 0.73, 'RIGHT'],
        ['711', 'A1', 4.01, 'RIGHT'],
        ['712', 'A2', 3.29, 'LEFT'],
        ['713', 'B1', 5.74, 'LEFT'],
        ['714', 'B2', 3.32, 'RIGHT'],
    ],
    columns=['subject_id', 'condition_id', 'response_time', 'response'],
)
data

Out[2]:

	subject_id	condition_id	response_time	response
0	312	A1	0.12	LEFT
1	312	A2	0.37	LEFT
2	312	C2	0.68	LEFT
3	313	A1	0.07	RIGHT
4	313	B1	0.08	RIGHT
5	314	A2	0.29	LEFT
6	314	B1	0.14	RIGHT
7	314	C2	0.73	RIGHT
8	711	A1	4.01	RIGHT
9	712	A2	3.29	LEFT
10	713	B1	5.74	LEFT
11	714	B2	3.32	RIGHT

Group-by¶

We want to compute the mean response time by condition.

Let's start by doing it by hand, using for loops!

In [ ]:

This is a basic operation, and we would need to repeat his pattern a million times!

Pandas and all other tools for tabular data provide a command for performing operations on groups.

In [ ]:

Pivot tables¶

We want to look at response time biases when the subjects respond LEFT vs RIGHT. In principle, we expect them to have the same response time in both cases.

We compute a summary table with 1) condition_id on the rows; 2) response on the columns; 3) the average response time for all experiments with a that condition and response

We can do it with groupby, with some table manipulation commands.

In [ ]:

Pandas has a command called pivot_table that can be used to perform this kind of operation straightforwardly.

In [ ]:

8.4 KiB Raw Blame History

Split-apply-combine operations for tabular data¶

Group-by¶

Pivot tables¶

8.4 KiB

Raw Blame History