CODEWITHSUNDEEP
pythonpython-3.xpandasnumpyscipy
Random Cotija
Random Cotija

Reputation: 673

Reduce data in an array in numpy according to other arrays in an efficient way

I would like to give me your help on a problem about data reduction in arrays in python, I am new to python but I have some experience in programming.

The problem consists of the following: I have an array S of n elements that come from the measurements of a sensor and come next to four other arrays that indicate the year, the month, the day and the time of the measurements (y_lna, m_lna, d_lna And h_lna), I also have another array T of m equal elements accompanied by 4 arrays (y, m, d, h), I want to create a vector of the same size as S where the values ​​of T match those of S in the hours , Days, months and years.

The data are organized in such a way that they have the values ​​from year 0 to year n in a sequential way:

Data   h d m  y
d1    00 1 1 2003
d2    03 1 1 2003
...
dn    10 5 8 2009

I have created a function that allows this to be done but I am not sure if it is done in the right way, it also takes a lot of time for the amount of iterations that it performs, is there any way to do it more efficiently? and I don't know how to deal with the nan values

def reduce_data(h, d, m, y, h_lna, d_lna, m_lna, y_lna, data):
    year = np.linspace(2003, 2016, 14, True)
    month = np.linspace(1, 12, 12, True)
    new_data = []
    for a in year:
        ind1 = [i for i in range(len(y)) if y[i] == a]
        ind1_l = [i for i in range(len(y_lna)) if y_lna[i] == a]
        for b in range(len(month)):
            ind2 = [i for i in ind1 if m[i] == b + 1]
            ind2_l = [i for i in ind1_l if m_lna[i] == b + 1]
            for c in range(len(ind2)):  # days
                ind3 = [i for i in ind2 if d[i] == c]
                ind3_l = [i for i in ind2_l if d_lna[i] == c]
                for dd in range(len(ind3)):
                    for e in range(len(ind3_l)):
                        if h[ind3[dd]] == h_lna[ind3_l[e]]:
                            new_data.append(data[ind3[dd]])
    return new_data

I appreciate your collaboration

EDIT: I add the data with which I am working, the values of the sensors are not the real ones I replaced them with random data, but the time values are the real ones (just for one year). data1 has the data of the sensor S whose time variables are the reference values for the reduction, data2 has the data of the sensor T with its time variables and finally result is the one that has the expected results.

DATA 1

        S       h_lna   d_lna   m_lna   y_lna
    0   0        8       6        2     2003
    1   2        9       6        2     2003
    2   4       10       6        2     2003
    3   6       11       6        2     2003
    4   8       12       6        2     2003
    5   10      13       6        2     2003
    6   12      14       6        2     2003
    7   14      15       6        2     2003
    8   16      16       6        2     2003
    9   18      17       6        2     2003
   10   20      18       6        2     2003

DATA 2

    T   h   d   m   y
0   864 0   6   2   2003
1   865 1   6   2   2003
2   866 2   6   2   2003
3   867 3   6   2   2003
4   868 4   6   2   2003
5   869 5   6   2   2003
6   870 6   6   2   2003
7   871 7   6   2   2003
8   872 8   6   2   2003
9   873 9   6   2   2003
10  874 10  6   2   2003
11  875 11  6   2   2003
12  876 12  6   2   2003
13  877 13  6   2   2003
14  878 14  6   2   2003
15  879 15  6   2   2003
16  880 16  6   2   2003
17  881 17  6   2   2003
18  882 18  6   2   2003
19  883 19  6   2   2003
20  884 20  6   2   2003
21  885 21  6   2   2003
22  886 22  6   2   2003
23  887 23  6   2   2003
24  888 0   7   2   2003
25  889 1   7   2   2003
26  890 2   7   2   2003
27  891 3   7   2   2003
28  892 4   7   2   2003
29  893 5   7   2   2003
30  894 6   7   2   2003
31  895 7   7   2   2003
32  896 8   7   2   2003
33  897 9   7   2   2003
34  898 10  7   2   2003

RESULT

    result  h_lna   d_lna   m_lna   y_lna
0   872        8      6      2      2003
1   873        9      6      2      2003
2   874       10      6      2      2003
3   875       11      6      2      2003
4   876       12      6      2      2003
5   877       13      6      2      2003
6   878       14      6      2      2003
7   879       15      6      2      2003
8   880       16      6      2      2003
9   881       17      6      2      2003
10  882       18      6      2      2003

Upvotes: 4

Views: 373

Answers (1)

John Zwinck
John Zwinck

Reputation: 249642

Just think of this in terms of "join." First, take Data 2 and index it for efficient lookup:

d2i = d2.set_index(['y', 'm', 'd', 'h'])

Now d2i has a MultiIndex (y,m,d,h) and a single Series (T).

Then use join():

d1.join(d2i, ['y_lna', 'm_lna', 'd_lna', 'h_lna'])

You might also consider building a proper DatetimeIndex on both data sets if you're going to use them a lot. Use pd.to_datetime() for that:

year = np.datetime64(d2.y - 1970, 'Y') # Unix epoch = 1970-01-01
month = np.timedelta64(d2.m - 1, 'M') # January adds 0
day = np.timedelta64(d2.d - 1, 'D')
hour = np.timedelta64(d2.h, 'h')
index = pd.to_datetime(year + month + day + hour)
d2s = pd.Series(d2['T'], index)

Now you'll have a single Series T with the entire date and time information as its index. If you do this with both DataFrames they will suddenly be easy to work with, using join/merge/index/asof operations.

Upvotes: 1

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