CODEWITHSUNDEEP
pythonarraysnumpy
maximusdooku
maximusdooku

Reputation: 5512

How can I select data in a multidimensional numpy array?

I have a numpy array of the following shape

(categories, models, types, events, days) -> (2, 3, 4, 100, 14)

Now, I want to calculate the maximum of 14 days of data per event for a particular category, model, and type

I am doing this

np.max(data[0][0][0], axis=1)

I also want to calculate the max per type and per model for example.

I will be doing several of these operations by looping through [0] as [i] .

Is this the correct way of accessing the outermost array? Is there another way?

Addendum

np.max(data[0][0][0], axis=1)

array([ 3.9264417 ,  3.3029506 ,  3.0707457 ,  3.6646023 ,  1.7508441 ,
        3.1634364 ,  6.195052  ,  1.5353022 ,  1.8033538 ,  1.4508389 ,
        1.3882699 ,  2.0849068 ,  3.654939  ,  6.6364765 ,  3.92829   ,
        6.6467876 ,  1.5442419 ,  4.639682  ,  9.361191  ,  5.261462  ,
        1.7438816 ,  5.6970205 ,  2.4356377 ,  1.6073244 ,  2.6177561 ,
        6.886767  ,  3.890399  ,  2.8880894 ,  1.9826577 ,  1.0888597 ,
        4.3763924 ,  3.8597727 ,  1.790302  ,  1.0277777 ,  6.270729  ,
        9.311213  ,  2.318774  ,  2.9298437 ,  1.139397  ,  0.9598383 ,
        3.0489902 ,  1.6736581 ,  1.3983868 ,  2.0979824 ,  4.169757  ,
        1.0739225 ,  1.5311266 ,  1.4676268 ,  1.726325  ,  1.8057758 ,
        2.226462  ,  2.6197987 ,  4.49518   ,  2.3042605 ,  5.7164993 ,
        1.182242  ,  1.5107205 ,  2.2920077 ,  2.205539  ,  1.4702082 ,
        2.154468  ,  2.0641963 ,  4.9628353 ,  1.9987459 ,  2.1360166 ,
        1.7073958 ,  1.943267  ,  7.5767093 ,  1.3124634 ,  2.2648168 ,
        1.1504744 ,  3.210688  ,  2.6720855 ,  2.998225  ,  4.365262  ,
        3.5410352 , 10.765423  ,  4.6292825 ,  3.1789696 ,  0.92157686,
        1.663245  ,  1.5835482 ,  3.1070056 ,  1.6918416 ,  8.086268  ,
        3.7994847 ,  2.4314868 ,  1.6471033 ,  1.1688241 ,  1.7820593 ,
        3.3509188 ,  1.3092748 ,  3.7915008 ,  1.018912  ,  3.2404447 ,
        1.596657  ,  2.0869658 ,  2.6753283 ,  2.1096318 ,  8.786542  ],
      dtype=float32)

Also,

type(np.array(data)) = numpy.ndarray type(data) = list

I convert it for these operations.

Upvotes: 0

Views: 123

Answers (1)

onodip
onodip

Reputation: 655

What you have now is a one dimensional array. You can reshape the array to 2D, this way it is easier to access a column. To access all elements of a column use :. If each column has a specific meaning (events, days, etc.), you could also consider to store the data as a dictionary instead, as {'days': array([...]), 'events': array([])}

from numpy import array, float32
import numpy as np

x = array([ 3.9264417 ,  3.3029506 ,  3.0707457 ,  3.6646023 ,  1.7508441 ,
        3.1634364 ,  6.195052  ,  1.5353022 ,  1.8033538 ,  1.4508389 ,
        1.3882699 ,  2.0849068 ,  3.654939  ,  6.6364765 ,  3.92829   ,
        6.6467876 ,  1.5442419 ,  4.639682  ,  9.361191  ,  5.261462  ,
        1.7438816 ,  5.6970205 ,  2.4356377 ,  1.6073244 ,  2.6177561 ,
        6.886767  ,  3.890399  ,  2.8880894 ,  1.9826577 ,  1.0888597 ,
        4.3763924 ,  3.8597727 ,  1.790302  ,  1.0277777 ,  6.270729  ,
        9.311213  ,  2.318774  ,  2.9298437 ,  1.139397  ,  0.9598383 ,
        3.0489902 ,  1.6736581 ,  1.3983868 ,  2.0979824 ,  4.169757  ,
        1.0739225 ,  1.5311266 ,  1.4676268 ,  1.726325  ,  1.8057758 ,
        2.226462  ,  2.6197987 ,  4.49518   ,  2.3042605 ,  5.7164993 ,
        1.182242  ,  1.5107205 ,  2.2920077 ,  2.205539  ,  1.4702082 ,
        2.154468  ,  2.0641963 ,  4.9628353 ,  1.9987459 ,  2.1360166 ,
        1.7073958 ,  1.943267  ,  7.5767093 ,  1.3124634 ,  2.2648168 ,
        1.1504744 ,  3.210688  ,  2.6720855 ,  2.998225  ,  4.365262  ,
        3.5410352 , 10.765423  ,  4.6292825 ,  3.1789696 ,  0.92157686,
        1.663245  ,  1.5835482 ,  3.1070056 ,  1.6918416 ,  8.086268  ,
        3.7994847 ,  2.4314868 ,  1.6471033 ,  1.1688241 ,  1.7820593 ,
        3.3509188 ,  1.3092748 ,  3.7915008 ,  1.018912  ,  3.2404447 ,
        1.596657  ,  2.0869658 ,  2.6753283 ,  2.1096318 ,  8.786542  ],
      dtype=float32)

x = np.reshape(x, (20, 5))
print x[:, -1]

>> [1.7508441  1.4508389  3.92829    5.261462   2.6177561  1.0888597
6.270729   0.9598383  4.169757   1.8057758  5.7164993  1.4702082
2.1360166  2.2648168  4.365262   0.92157686 8.086268   1.7820593
3.2404447  8.786542  ]

Upvotes: 3

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