{"id":4271,"date":"2022-08-09T00:18:22","date_gmt":"2022-08-09T00:18:22","guid":{"rendered":"https:\/\/www.pythontutorial.net\/?page_id=4271"},"modified":"2022-08-16T03:47:34","modified_gmt":"2022-08-16T03:47:34","slug":"numpy-array-slicing","status":"publish","type":"page","link":"https:\/\/www.pythontutorial.net\/python-numpy\/numpy-array-slicing\/","title":{"rendered":"Numpy Array Slicing"},"content":{"rendered":"\n

Summary<\/strong>: in this tutorial, you’ll learn about the numpy array slicing that extracts one or more elements from a numpy array.<\/p>\n\n\n\n

Numpy array slicing on on-dimensional arrays #<\/a><\/h2>\n\n\n\n

NumPy arrays use brackets []<\/code> and :<\/code> notations for slicing like lists<\/a>. By using slices, you can select a range of elements in an array with the following syntax:<\/p>\n\n\n

[m:n]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
This slice selects elements starting with m<\/code> and ending with n-1<\/code>. Note that the nth element is not included. In fact, the slice m:n<\/code> can be explicitly defined as:<\/p>\n\n\n
[m:n:1<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
The number 1 specifies that the slice selects every element between m<\/code> and n<\/code>. <\/p>\n\n\n\n
To select every two elements, you can use the following slice:<\/p>\n\n\n
[m:n:2<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
In general, the following expression selects every k<\/code> element between m<\/code> and n<\/code>:<\/p>\n\n\n
[m:n:k]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
If k<\/code> is negative, the slice returns elements in reversed order starting from m<\/code> to n+1<\/code>. The following table illustrates the slicing expressions:<\/p>\n\n\n\n
Slicing Expression<\/th>Meaning<\/th><\/tr><\/thead>
a[m:n]<\/code><\/td>Select elements with an index starting at m and ending at n-1.<\/td><\/tr>
a[:]<\/code> or a[0:-1]<\/code><\/td>Select all elements in a given axis<\/td><\/tr>
a[:n]<\/code><\/td>Select elements starting with index 0 and up to element with index n-1<\/td><\/tr>
a[m:]<\/code><\/td>Select elements starting with index m and up to the last element<\/td><\/tr>
a[m:-1]<\/code><\/td>Select elements starting with index m and up to the last element<\/td><\/tr>
a[m:n:k]<\/code><\/td>Select elements with index m through n (exclusive), with an increment k<\/td><\/tr>
a[::-1]<\/code><\/td>Select all elements in reverse order<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
See the following example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.arange(0<\/span>, 10<\/span>)\n\nprint('a='<\/span>, a)\nprint('a[2:5]='<\/span>, a[2<\/span>:5<\/span>])\nprint('a[:]='<\/span>, a[:])\nprint('a[0:-1]='<\/span>, a[0<\/span>:-1<\/span>])\nprint('a[0:6]='<\/span>, a[0<\/span>:6<\/span>])\nprint('a[7:]='<\/span>, a[7<\/span>:])\nprint('a[5:-1]='<\/span>, a[5<\/span>:-1<\/span>])\nprint('a[0:5:2]='<\/span>, a[0<\/span>:5<\/span>:2<\/span>])\nprint('a[::-1]='<\/span>, a[::-1<\/span>])<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
a= [0<\/span> 1<\/span> 2<\/span> 3<\/span> 4<\/span> 5<\/span> 6<\/span> 7<\/span> 8<\/span> 9<\/span>]\na[2<\/span>:5<\/span>]= [2<\/span> 3<\/span> 4<\/span>]\na[:]= [0<\/span> 1<\/span> 2<\/span> 3<\/span> 4<\/span> 5<\/span> 6<\/span> 7<\/span> 8<\/span> 9<\/span>] \na[0<\/span>:-1<\/span>]= [0<\/span> 1<\/span> 2<\/span> 3<\/span> 4<\/span> 5<\/span> 6<\/span> 7<\/span> 8<\/span>]\na[0<\/span>:6<\/span>]= [0<\/span> 1<\/span> 2<\/span> 3<\/span> 4<\/span> 5<\/span>]\na[7<\/span>:]= [7<\/span> 8<\/span> 9<\/span>]\na[5<\/span>:-1<\/span>]= [5<\/span> 6<\/span> 7<\/span> 8<\/span>]\na[0<\/span>:5<\/span>:2<\/span>]= [0<\/span> 2<\/span> 4<\/span>]\na[::-1<\/span>]= [9<\/span> 8<\/span> 7<\/span> 6<\/span> 5<\/span> 4<\/span> 3<\/span> 2<\/span> 1<\/span> 0<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Numpy array slicing on multidimensional arrays #<\/a><\/h2>\n\n\n\n
To slice a multidimensional array, you apply the square brackets []<\/code> and the :<\/code> notation to each dimension (or axis). The slice returns a reduced array where each element matches the selection rules. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [1<\/span>, 2<\/span>, 3<\/span>],\n    [4<\/span>, 5<\/span>, 6<\/span>],\n    [7<\/span>, 8<\/span>, 9<\/span>]\n])\n\n\nprint(a[0<\/span>:2<\/span>, :])<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[[1<\/span> 2<\/span> 3<\/span>]\n [4<\/span> 5<\/span> 6<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"numpy<\/figure>\n\n\n\n
In this example, array a is a 2-D array. In the expression a[0:2, :]<\/code>:<\/p>\n\n\n\n
First, the 0:2<\/code> selects the element at index 0 and 1, not 2 that returns:<\/p>\n\n\n
[[1<\/span> 2<\/span> 3<\/span>]\n[4<\/span> 5<\/span> 6<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Then, the :<\/code> select all elements. Therefore the whole expression returns:<\/p>\n\n\n
[[1<\/span> 2<\/span> 3<\/span>]\n [4<\/span> 5<\/span> 6<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Consider another example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [1<\/span>, 2<\/span>, 3<\/span>],\n    [4<\/span>, 5<\/span>, 6<\/span>],\n    [7<\/span>, 8<\/span>, 9<\/span>]\n])\n\nprint(a[1<\/span>:, 1<\/span>:])<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[[5<\/span> 6<\/span>]\n [8<\/span> 9<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"numpy<\/figure>\n\n\n\n
In the expression a[1:, 1:]<\/code>:<\/p>\n\n\n\n
First, 1:<\/code> selects the elements starting at index 1 to the last element of the first axis (or row), which returns:<\/p>\n\n\n
[[4<\/span> 5<\/span> 6<\/span>]\n[7<\/span> 8<\/span> 9<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Second, 1:<\/code> selects the elements starting at index 1 to the last elements of the second axis (or column), which returns:<\/p>\n\n\n
[[5<\/span> 6<\/span>]\n [8<\/span> 9<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Summary #<\/a><\/h2>\n\n\n\n
  • Use slicing to extract elements from a numpy array<\/li>
  • Use a[m:n:p]<\/code> to slice one-dimensional arrays.<\/li>
  • Use a[m:n:p, i:j:k, ...]<\/code> to slice multidimensional arrays<\/li><\/ul>\n
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