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This article mainly explains "how to add and remove elements from NumPy". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Now let the editor take you to learn "how to add and remove elements from NumPy"!

Background introduction

Today we learn the NumPy functions numpy.append and numpy.hstack to add and remove elements from the NumPy array and stack arrays horizontally and vertically. Use the Jupyter Notebook interactive environment for coding.

Getting started exampl

The above is a screenshot of the code running in Jupyter Notebook The specific code is as follows: # use the Numpy function to manipulate the array to add and remove elements # In [37]: import numpy as np# In [38]: # define a 3D array my_arraymy_array = np.array (np.arange (24)). Reshape (2m 3m 4) my_array# use the np.append () method to add elements to the array # In [39]: new_array = np.append (my_array, [5Power6) 7np.append 8]) new_array# In [40]: # View shapenew_array.shape# In [41]: new_array.reshape (7mag4) # In [42]: # in defining an array cc = np.array (np.arange (24)). Reshape (2p3) * 10 + 3 c # In [43]: my_array# In [44]: # add c#axis=0 in the first "dimension" line np.append (my_array,c) Axis=0) # In [45]: # View shapenp.append (my_array,c,axis=0). Shape# In [46]: # axis=1 is in the second "dimension" column np.append (my_array,c,axis=1) # In [47]: np.append (my_array,c,axis=2) # In [48]: np.append (my_array,c,axis=2). Shape# add element # # In [49]: my_stack = np.hstack ((my_array) using np.hstack () C) my_stack# In [50]: my_stack.shape# In [51]: # change the value of [0LECHERO In 2] my_stack [0Die 2] = 99mom In [52]: my_stack# use np.insert () to insert the element # In [53]: C # In [54]: insert_arr = np.insert [55]: np.insert (cJet 1Honey888) # In [56]: np.insert Axis=2) # use np.delete () to delete the element # In [57]: d = np.empty (c.shape) np.copyto (dmaec) d# In [58] np.delete (dmae1memaxishul) # In [59]: np.delete (dmem1memaxispian 2) # # numpy.reshape (array, shape Order ='C'): # # shaping the array without changing the array # # python program description # # numpy.reshape () method # In [60]: array = np.arange (8) print ("original array:\ n", array) # # shaping array has 2 rows and 4 columns # In [61]: array = np.arange (8). Reshape (2, 4) print ("\ n shaping array has 2 rows and 4 columns:\ n" Array) # # shaping array has four rows and two columns # In [62]: array = np.arange (8) .reshape (4,2) print ("\ nshaping array has four rows and two columns s:\ n", array) # # construct a 3D array # In [63]: array = np.arange (8). Reshape (2,2,2) print ("\ nprimitive array constructs a 3D array:\ n" Array) # # numpy.axis explanation: # according to definition The axis number of the dimension is the index of the dimension in the array shape. # it is also the location used to access the dimension during the index. # for example, if the 2D array a has a shape (5pc6), then you can visit [0re0] until [4jue 5]. # therefore, axis 0 is the first dimension ("row") and axis 1 is the second dimension ("column"). # in the higher dimensions, "rows" and "columns" stop making real sense. # try to think about the axis in terms of the shape and index involved. At this point, I believe you have a deeper understanding of "how to add and remove elements from NumPy". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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