So I have a DataFrame called trials, and a series of logical values called random. random is a logical series the same length as the number of rows in each columns in trials.
trials.shape
Out[72]: (199, 4)
random.value_counts()
Out[74]:
False 192
True 7
dtype: int64
I used the following solution to get the rows of all the columns in trials but it is not scalable, what if I have more columns than this is feasible for or if I want to simultaneously filter for columns and rows?
trials[['Choice','LeftContrast','RightContrast','feedback']][random]
Out[67]:
Choice LeftContrast RightContrast feedback
16 [1.0] [0.25] [0.25] [1.0]
25 [1.0] [1.0] [1.0] [1.0]
79 [1.0] [1.0] [1.0] [-1.0]
80 [1.0] [1.0] [1.0] [-1.0]
93 [1.0] [1.0] [1.0] [1.0]
122 [1.0] [0.5] [0.5] [-1.0]
166 [0.0] [0.25] [0.25] [-1.0]
How could I write this so that I don't have to specify all the columns I want compared? Calling ":" creates an error:
trials[[:]][random]
File "<ipython-input-68-a82ea1b0ab14>", line 1
trials[[:]][random]
^
SyntaxError: invalid syntax
Using .iloc also produces an error.
trials.iloc[random,:]
Traceback (most recent call last):
What if I wanted to use logicals to assess both rows and columns?
