Numpy babban ɗakin karatu na Python mai ƙarfi ne kuma ana amfani da shi sosai wanda ya ƙware wajen sarrafa tsararraki da matrices, yana baiwa masu haɓaka damar sauƙaƙe ayyukan lissafi masu rikitarwa. Ƙimar ɗakin karatu da aikinta sun sa ya zama kyakkyawan zaɓi don aiwatar da hanyoyin lissafin lissafi a wurare daban-daban. Ɗaya daga cikin irin wannan yanayin amfani ya ƙunshi ginshiƙai masu motsi a cikin tsari mai girma biyu, kuma wannan labarin zai mayar da hankali ga samar da ingantacciyar hanya don cimma wannan aikin.
Don farawa da, bari mu ayyana matsala: idan aka ba da Tsari mai girma biyu, muna buƙatar matsar da takamaiman shafi daga matsayinsa na yanzu zuwa wani. Ana iya magance wannan matsalar ta amfani da manyan abubuwan fihirisar Numpy. Za mu nuna mafita tare da bayanin mataki-mataki na lambar.
import numpy as np
def move_columns(arr, source_column_index, target_column_index):
rearranged_columns = np.insert(arr, target_column_index, arr[:, source_column_index], axis=1)
rearranged_columns = np.delete(rearranged_columns, source_column_index + (source_column_index < target_column_index), axis=1)
return rearranged_columns
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The function <b>move_columns()</b> takes three parameters: <b>arr</b> is the Numpy two-dimensional array, <b>source_column_index</b> represents the index of the column to move, and <b>target_column_index</b> specifies the index where the column should be moved to.
The first step in our solution is to insert the desired column at the target position using the <b>np.insert()</b> function. This process will duplicate the source column, so we'll have an extra column in the temporary array.
Next, we need to remove the original column, which we achieve using the <b>np.delete()</b> function. Notice that the index of the original column can change depending on whether the source index is less than or greater than the target index. If the source index is less than the target index, we need to increase the index by 1 to account for the insertion made in the previous step.
Finally, the rearranged array is returned by the function.
<h2>Understanding Numpy Indexing</h2>
Numpy provides <b>advanced indexing</b> capabilities, which help developers perform complex array manipulations more effectively. In our solution, we utilized Numpy's slicing operations to extract a specific column from the array. The following code snippet demonstrates the basic idea of using advanced indexing with Numpy:
[code lang="Python"]
import numpy as np
arr = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
column = arr[:, 1]
A cikin misalin da ke sama, irin[:, 1] yana wakiltar duk layuka na shafi na biyu. Wannan haɗin gwiwar yana kama da slicing jerin Python, kuma yana sauƙaƙa cirewa da sarrafa sassa daban-daban na tsararru.
Aiki tare da numpy.insert() da numpy.delete()
Numpy's saka () da kuma goge () Ayyuka sune mahimman tubalan ginin da aka yi amfani da su a cikin maganin mu. Waɗannan ayyuka suna ƙyale masu haɓakawa su sarrafa tsararraki ta ƙara da cire abubuwa. Musamman, da nupy.insert() Aiki yana saka tsararraki ko ƙima a cikin tsararrun da ke akwai tare da ƙayyadaddun axis. A daya bangaren kuma, da nupy.delete() Aiki yana cire abubuwa daga tsararru tare da ƙayyadadden axis.
Kamar yadda muka gani a cikin maganinmu, waɗannan ayyukan sun ba mu damar canza ginshiƙai da share ainihin ginshiƙi daga tsararrun, yadda ya kamata a sake tsara ginshiƙan yadda ake so.
A ƙarshe, wannan labarin ya ba da bayyani na yanayin amfani na yau da kullun don Numpy: ginshiƙai masu motsi a cikin tsararru mai girma biyu. Ta hanyar yin amfani da abubuwan ci-gaba na Numpy, da yin amfani da ikon ayyukan numpy.insert() da numpy.delete(), mun gabatar da ingantaccen bayani ga wannan matsalar. Ƙarfin Numpy ya wuce wannan misalin, don haka jin daɗin bincika ɗimbin ayyuka da yake bayarwa don magance ƙalubalen ilimin lissafi na musamman a Python.
