{"id":120844,"date":"2020-11-18T20:08:35","date_gmt":"2020-11-18T20:08:35","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=17015"},"modified":"2020-11-18T20:08:35","modified_gmt":"2020-11-18T20:08:35","slug":"np-shape","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2020\/11\/18\/np-shape\/","title":{"rendered":"np.shape()"},"content":{"rendered":"

This tutorial explains NumPy’s shape()<\/code> function. <\/p>\n

numpy.shape(a)<\/pre>\n
Return the shape of an array or array_like<\/em> object a<\/code>. <\/p>\n
\n\n\n\n\n\n
Argument<\/th>\nData Type<\/th>\nDescription<\/th>\n<\/tr>\n<\/thead>\n
a<\/code><\/td>\narray_like<\/em><\/td>\nNumPy array or Python list for which the shape should be returned. If it is a NumPy array, it returns the attribute a.shape<\/code>. If it is a Python list, it returns a tuple of integer values defining the number of elements in each dimension if you would’ve created a NumPy array from it.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
Return Value<\/strong>: shape<\/code> — a tuple of integers that are set to the lengths of the corresponding array dimensions. <\/p>\n
Examples<\/h2>\n
The straightforward example is when applied to a NumPy array:<\/p>\n
>>> import numpy as np\n>>> a = np.array([[1, 2], [3, 4]])\n>>> np.shape(a)\n(2, 2)<\/pre>\n
You import the NumPy library and create a two-dimensional array from a list of lists<\/a>. If you pass the NumPy array into the shape function, it returns a tuple with two values (=dimensions). Each dimension stores the number of elements in this dimension (=axis). As it is a 2×2 quadratic matrix, the result is (2,2). <\/p>\n
The following shape is another example of a multi-dimensional array:<\/p>\n
>>> b = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n>>> b\narray([[1, 2, 3, 4], [5, 6, 7, 8]])\n>>> b.shape\n(2, 4)\n>>> np.shape(b)\n(2, 4)<\/pre>\n
The shape is now (2, 4)<\/code> with two rows and four columns. <\/p>\n
np.shape() vs array.shape<\/h3>\n
Note that the result of np.shape(b)<\/code> and b.shape<\/code> is the same if b<\/code> is a NumPy array. If b<\/code> isn’t a NumPy array but a list, you cannot use b.shape<\/code> as lists don’t have the shape attribute. Let’s have a look at this example:<\/p>\n
>>> b = [[1, 2, 3, 4], [5, 6, 7, 8]]\n>>> np.shape(b)\n(2, 4)<\/pre>\n
The np.shape()<\/code> function returns the same shape tuple—even if you pass a nested list into the function instead of a NumPy array. <\/p>\n
But if you try to access the list.shape attribute, NumPy throws the following error:<\/p>\n
>>> b.shape\nTraceback (most recent call last): File \"<pyshell#9>\", line 1, in <module> b.shape\nAttributeError: 'list' object has no attribute 'shape'<\/pre>\n
So, the difference between np.shape()<\/code> and array.shape<\/code> is that the former can be used for all kinds of array_like<\/em> objects while the latter can only be used for NumPy arrays with the shape<\/code> attribute.<\/p>\n
\n
Do you want to become a NumPy master?<\/strong> Check out our interactive puzzle book Coffee Break NumPy<\/strong><\/a> and boost your data science skills! (Amazon link opens in new tab.)<\/em><\/p>\n
\n
\"Coffee<\/a><\/figure>\n<\/div>\n
References<\/h2>\n