I'd like to do something like this. Pick one or n column ranges and another column
df_premises = df.iloc[:, 0:8 and 11] equivalent to df_premises = df.iloc[:, [0,1,2,3,,8,11]]
I've tried;
df_premises = df.iloc[:, 0:8, 11] - causes errordf_premises = df.iloc[:, 0:8 + 11] - returns 0:18 You can use: df.iloc[:, lambda x: x.index < 9 or x.index == 11]
Simpler solution will to define a list before that, and use the list inside iloc.
For example:
my_range = range(9)
my_range.append(11)
df_premises = df.iloc[:, my_range]
as mentioned in pandas documentation, input must be one of the following:
An integer, e.g. 5.
A list or array of integers, e.g. [4, 3, 0].
A slice object with ints, e.g. 1:7.
A boolean array.
A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing
You can use simple slicing like df.iloc[:3] or a function like df.iloc[lambda x: x.index % 2 == 0].
So specific for what you asked about, following will work:
df.iloc[:, lambda x: x.index < 9 or x.index == 11]
Exaple: When row number 300 achieves a condition, the code outputs [270:300] number of rows:
Makes windows from the condition
MINUTES_BEFORE_FOR_WINDOW_TIME = 30
rows_valid = df_s[df_s['valid'] ==1 ].index #condition
rows_valid_bef = [ np.r_[ i - MINUTES_BEFORE_FOR_WINDOW_TIME: i +1 ] for i in rows_valid ]
df_s = df_s.iloc[np.concatenate(rows_valid_bef) ]#one dimension
