Convert (part) numpy recarray to 2d array?

Question

We have a set of data repetitions for individual days - the first attribute is a timestamp, and the rest are values.

A few of them:

    ts             a    b    c
2010-08-06 08:00, 1.2, 3.4, 5.6
2010-08-06 08:05, 1.2, 3.4, 5.6
2010-08-06 08:10, 1.2, 3.4, 5.6
2010-08-06 08:15, 2.2, 3.3, 5.6
2010-08-06 08:20, 1.2, 3.4, 5.6

We would like to create an array of average values ​​of each of the values ​​(as if you put all the data of the day on top of each other and average all the values ​​that line up). The timestamp times are the same, so we can do this by creating a result with timestamps, and the rest of the columns are 0, then we’ll do something like:

for day in day_data:
    result.a += day.a
    result.b += day.b
    result.c += day.c

result.a /= len(day_data)
result.b /= len(day_data)
result.c /= len(day_data)

It seems that the best way would be to convert every day to a 2d array using only numbers (discarding timestamps) and then averaging them over all elements in one operation, but we cannot find a way to do this - it is always a 1d array of objects.

Does anyone know how to do this?

Answer 1

. , float, 2D-:

new_data = data[['a','b','c']].astype(np.float).reshape((data.size, 3))

- ( , ):

new_data = np.vstack([data[item] for item in ['a','b','c']]).T

, pandas , .