[SOLVED] Part specification along different axes of numpy array

Table of Contents

Issue

Why is arr[0:5][0:10] the same as arr[0:10][0:5] and what should I write if I want to get the array with shape (10,5)?

In the process of trying to crop a 2D numpy array I end up with the wrong dimensions. Ok, I figure, I just got my axes switched up, so I switch the order of the part specification.. and still get the same problem! I wrote this sanity check to make sure the problem wasn’t somewhere else in my code. For me, using Python 3.7 with numpy it finds the arrays have the same shape and prints ":(". Here’s the function:

def sanitycheck():
    testarray=np.zeros((10,10))
    a=testarray[0:5][0:10]
    b=testarray[0:10][0:5]
    if np.shape(a)==np.shape(b):
        print(":(")

Solution

When you do arr[0:5], you take the first 5 items of the first dimension of arr (rows), then adding [0:10] you get the first 10 items, again on the first dimension (so only 5). The same is true with the reverse operation (arr[0:10][0:5]), you get the first 10 rows, then the first 5 rows of those 10 rows. In both cases, you never affect the second dimension!

What you want, to have shape (10, 5), is to slice both dimensions at once:

arr[0:10, 0:5]

Example

input:

[[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14]
 [ 15  16  17  18  19  20  21  22  23  24  25  26  27  28  29]
 [ 30  31  32  33  34  35  36  37  38  39  40  41  42  43  44]
 [ 45  46  47  48  49  50  51  52  53  54  55  56  57  58  59]
 [ 60  61  62  63  64  65  66  67  68  69  70  71  72  73  74]
 [ 75  76  77  78  79  80  81  82  83  84  85  86  87  88  89]
 [ 90  91  92  93  94  95  96  97  98  99 100 101 102 103 104]
 [105 106 107 108 109 110 111 112 113 114 115 116 117 118 119]
 [120 121 122 123 124 125 126 127 128 129 130 131 132 133 134]
 [135 136 137 138 139 140 141 142 143 144 145 146 147 148 149]
 [150 151 152 153 154 155 156 157 158 159 160 161 162 163 164]
 [165 166 167 168 169 170 171 172 173 174 175 176 177 178 179]
 [180 181 182 183 184 185 186 187 188 189 190 191 192 193 194]
 [195 196 197 198 199 200 201 202 203 204 205 206 207 208 209]
 [210 211 212 213 214 215 216 217 218 219 220 221 222 223 224]]

arr[0:5][0:10] or arr[0:10][0:5]:

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44],
       [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]])

arr[0:10, 0:5]:

array([[  0,   1,   2,   3,   4],
       [ 15,  16,  17,  18,  19],
       [ 30,  31,  32,  33,  34],
       [ 45,  46,  47,  48,  49],
       [ 60,  61,  62,  63,  64],
       [ 75,  76,  77,  78,  79],
       [ 90,  91,  92,  93,  94],
       [105, 106, 107, 108, 109],
       [120, 121, 122, 123, 124],
       [135, 136, 137, 138, 139]])

Answered By – mozway

Answer Checked By – Willingham (BugsFixing Volunteer)

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