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Just pass the arrays to be stacked as a tuple. The vertical, horizontal, and depth stacking are more specific. >>> arr = np.array(range(10)).res... Stacking Numpy arrays of different length using padding - Stack … Rebuilds arrays divided by dsplit. NumPy Stacking and joining functions in NumPy are very useful for giving new dimensions to an array. numpy stack arrays of different shape In fact c_ would work even if second is shape (3,), as long as its length matches the length of first.. This function makes most sense for arrays with up to 3 dimensions. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. The axis parameter specifies the index of the new axis in the dimensions of the result. python - how to concatenate numpy array of different shape First Input array : [0 1 2] Second Input array : [3 4 5] Vertically stacked array: [[0 1 2] [3 4 5]] Explanation: In the above example, we stacked two numpy arrays vertically (row-wise). If you’re into deep learning, you’ll be reshaping tensors or multi-dimensional arrays regularly. How does numpy add two arrays with different shapes? dstack (tup) [source] # Stack arrays in sequence depth wise (along third axis). How to Join NumPy Arrays - onlinetutorialspoint