The function returns the concatenated array.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nimport numpy as np\r\n\r\n\r\n# Creating two arrays\r\narr1 = np.array([1, 2, 3])\r\narr2 = np.array([4, 5, 6])\r\n\r\n\r\n# Joining along rows (vertical stacking)\r\nconcatenated_array = np.concatenate((arr1, arr2))\r\n\r\n\r\nprint(\"Concatenated Array:\")\r\nprint(concatenated_array)<\/pre>\nOutput:<\/strong><\/p>\nConcatenated Array: <\/strong>[1 2 3 4 5 6]<\/p>\nJoining with Axis<\/h3>\n
When joining arrays, you can specify the axis along which the concatenation should occur. Here are some essential points:<\/p>\n
If axis is 0 (the default), arrays are joined along rows (stacking vertically).
\nIf axis is 1, arrays are joined along columns (stacking horizontally).
\nIf axis is 2 or higher, it corresponds to higher-dimensional concatenation.<\/p>\n
import numpy as np\r\n\r\n\r\n# Creating two 2D arrays\r\narr1 = np.array([[1, 2], [3, 4]])\r\narr2 = np.array([[5, 6]])\r\n\r\n\r\n# Joining along rows (vertical stacking, axis=0)\r\nconcatenated_axis0 = np.concatenate((arr1, arr2), axis=0)\r\n\r\n\r\n# Joining along columns (horizontal stacking, axis=1)\r\nconcatenated_axis1 = np.concatenate((arr1, arr2.T), axis=1)\r\n\r\n\r\nprint(\"Concatenated Along Rows (Axis 0):\")\r\nprint(concatenated_axis0)\r\n\r\n\r\nprint(\"Concatenated Along Columns (Axis 1):\")\r\nprint(concatenated_axis1)<\/pre>\nOutput for Concatenation Along Rows (Axis 0):<\/strong><\/p>\nConcatenated Along Rows (Axis 0):<\/strong>
\n[[1 2]
\n[3 4]
\n[5 6]]<\/p>\nOutput for Concatenation Along Columns (Axis 1):<\/strong><\/p>\nConcatenated Along Columns (Axis 1):<\/strong>
\n[[1 2 5]
\n[3 4 6]]<\/p>\nStacking Arrays<\/h3>\n
Stacking is a common operation when joining arrays of the same dimension along a new axis. There are three types of stacking:<\/p>\n
Horizontal Stacking:<\/h4>\n
This stacks arrays along rows.<\/p>\n
import numpy as np\r\n\r\n\r\n# Creating two arrays\r\narr1 = np.array([1, 2, 3])\r\narr2 = np.array([4, 5, 6])\r\n\r\n\r\n# Horizontal stacking\r\nhorizontal_stacked = np.hstack((arr1, arr2))\r\n\r\n\r\nprint(\"Horizontal Stacking:\")\r\nprint(horizontal_stacked)<\/pre>\nOutput:<\/strong><\/p>\nHorizontal Stacking:
\n[1 2 3 4 5 6]<\/p>\n
Vertical Stacking:<\/h4>\n
This stacks arrays along columns.<\/p>\n
import numpy as np\r\n\r\n\r\n# Creating two arrays\r\narr1 = np.array([1, 2, 3])\r\narr2 = np.array([4, 5, 6])\r\n\r\n\r\n# Vertical stacking\r\nvertical_stacked = np.vstack((arr1, arr2))\r\n\r\n\r\nprint(\"Vertical Stacking:\")\r\nprint(vertical_stacked)<\/pre>\nOutput:<\/strong><\/p>\nVertical Stacking:
\n[[1 2 3]
\n[4 5 6]]<\/p>\n
Height Stacking:<\/h4>\n
This stacks arrays along the height dimension (for higher-dimensional arrays).<\/p>\n
import numpy as np\r\n\r\n\r\n# Creating two arrays\r\narr1 = np.array([1, 2, 3])\r\narr2 = np.array([4, 5, 6])\r\n\r\n\r\n# Height stacking\r\nheight_stacked = np.dstack((arr1, arr2))\r\n\r\n\r\nprint(\"Height Stacking:\")\r\nprint(height_stacked)<\/pre>\nOutput:<\/strong><\/p>\nHeight Stacking:
\n[[[1 4]
\n[2 5]
\n[3 6]]]<\/p>\n
Splitting Arrays with numpy.array_split<\/h3>\n
The opposite of joining is splitting, where one array is divided into multiple arrays. NumPy provides a useful function for this called numpy.array_split().<\/p>\n
numpy.split(ary, indices_or_sections, axis=0)<\/p>\n
\n\n\nParameter<\/strong><\/td>\nDescription<\/strong><\/td>\nExample<\/strong><\/td>\n<\/tr>\n\n| ary<\/span><\/td>\n | The input numpy array to be divided into sub-arrays.<\/span><\/td>\n | ary is the input array you want to split.<\/span><\/td>\n<\/tr>\n\n| indices_or_sections<\/span><\/td>\n | An integer or a 1-D array of sorted integers determines how the array will be split. If it’s an integer, it divides the array into N equal parts; if an array, it specifies where to split.<\/span><\/td>\n | indices_or_sections can be an integer or an array.<\/span><\/td>\n<\/tr>\n\n| axis (optional)<\/span><\/td>\n | The axis along which to split the array. The default value is 0.<\/span><\/td>\n | axis is an optional parameter (default is 0).<\/span><\/td>\n<\/tr>\n\n| Returns<\/span><\/td>\n | A list of sub-arrays as views into ary.<\/span><\/td>\n | The function returns a list of sub-arrays.<\/span><\/td>\n<\/tr>\n\n| Raises<\/span><\/td>\n | ValueError is raised if indices_or_sections is given as an integer, but the split does not result in equal division.<\/span><\/td>\n | A ValueError exception may be raised if not<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nSplitting Arrays in NumPy<\/h4>\nnumpy.array_split() divides an array into multiple sub-arrays.<\/p>\n If the array cannot be divided evenly, it will adjust accordingly.<\/p>\n If you want strict splitting (no adjustment), you can use numpy.split(). However, it may throw errors if elements are insufficient.<\/p>\n Accessing Split Arrays<\/h4>\nAfter splitting an array, you can access the individual sub-arrays using index notation. For example, if you split an array into three parts, you can access them as split_array[0], split_array[1], and split_array[2].<\/p>\n # Creating an array\r\narr = np.array([1, 2, 3, 4, 5, 6])\r\n\r\n\r\n# Splitting into three parts\r\nsplit_array = np.array_split(arr, 3)\r\nprint(split_array)\r\n# Output: [array([1, 2]), array([3, 4]), array([5, 6])]\r\n\r\n\r\n# Accessing split arrays\r\nprint(split_array[0])\r\n# Output: [1 2]<\/pre>\nSplitting 2-D Arrays in NumPy<\/h4>\nFor 2-D arrays, you can use functions like hsplit() (horizontal split) and vsplit() (vertical split) to split arrays along rows or columns. There’s also dsplit() for arrays with three or more dimensions.<\/p>\n import numpy as np\r\n\r\n\r\n# Creating a 2-D array\r\narr = np.array([[1, 2, 3],\r\n [4, 5, 6],\r\n [7, 8, 9]])\r\n\r\n\r\n# Horizontal split into two arrays\r\nhorizontal_split = np.hsplit(arr, 2)\r\n\r\n\r\n# Vertical split into two arrays\r\nvertical_split = np.vsplit(arr, 3)\r\n\r\n\r\nprint(\"Horizontal Split:\")\r\nfor sub_arr in horizontal_split:\r\n print(sub_arr)\r\n\r\n\r\nprint(\"\\nVertical Split:\")\r\nfor sub_arr in vertical_split:\r\n print(sub_arr)<\/pre>\nOutput for Horizontal Split:<\/strong><\/p>\nHorizontal Split:<\/strong>
\n[[1 2]
\n[4 5]
\n[7 8]]
\n[[3]
\n[6]
\n[9]]<\/p>\nOutput for Vertical Split:<\/strong><\/p>\nVertical Split:<\/strong>
\n[[1 2 3]]
\n[[4 5 6]]
\n[[7 8 9]]<\/p>\nDifference between Join and Split in NumPy<\/h3>\nLet’s summarize the key differences between joining and splitting arrays:<\/strong><\/p>\n\n\n\nAspect<\/strong><\/td>\nJoin<\/strong><\/td>\nSplit<\/strong><\/td>\n<\/tr>\n\n| Operation<\/span><\/td>\n | Combines multiple arrays<\/span><\/td>\n | Divides one array into multiple arrays<\/span><\/td>\n<\/tr>\n\n| Primary Function<\/span><\/td>\n | numpy.concatenate()<\/span><\/td>\n | numpy.array_split() or numpy.split()<\/span><\/td>\n<\/tr>\n\n| Axis Specification<\/span><\/td>\n | Choose axis for joining<\/span><\/td>\n | Choose axis for splitting<\/span><\/td>\n<\/tr>\n\n| Adjustment for Uneven<\/span><\/td>\n | Adjusts for uneven data<\/span><\/td>\n | Adjusts or may throw errors for uneven data<\/span><\/td>\n<\/tr>\n\n| Accessing Split Arrays<\/span><\/td>\n | Not applicable<\/span><\/td>\n | Access using index notation<\/span><\/td>\n<\/tr>\n\n| Use for 2-D Arrays<\/span><\/td>\n | Stacking (vertical\/horizontal\/height)<\/span><\/td>\n | Splitting along rows\/columns<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nConclusion:<\/h3>\nNumPy’s merging and partitioning functions offer robust capabilities for efficiently combining and segmenting arrays. Proficiency in concatenating arrays along various axes and dividing arrays into sub-arrays is essential for effective data handling in scientific computing, data analysis, and machine learning endeavors.<\/p>\n By gaining expertise in these functions, you’ll enhance your ability to manage intricate data manipulation tasks within your Python projects. Enjoy coding!<\/p>\n","protected":false},"excerpt":{"rendered":" NumPy’s split() and join() functions are essential tools for working with arrays of data. split() allows you to divide an array into multiple subarrays, while join() allows you to combine multiple subarrays into a...<\/p>\n","protected":false},"author":6,"featured_media":447213,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[385],"tags":[5505,383,418,419,420,5642,5644,5643,384,415],"class_list":["post-89229","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy-tutorials","tag-array-join-split-in-numpy","tag-learn-numpy","tag-numpy-array-join-and-split","tag-numpy-array-tutorial","tag-numpy-join-and-split","tag-numpy-joining-and-splitting-array","tag-numpy-joining-array","tag-numpy-splitting-array","tag-numpy-tutorial","tag-numpy-tutorial-for-begginers"],"yoast_head":"\n NumPy Join and Split Array - 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