NumPy, pandas, and Python native sorting methods __python

in the NumPy:

Argsort returns the sorted index, which can be sorted accordingly.

#---------One-dimensional case--------------

Values=np.random.permutation (a)  #产生0到9的一个排列

Values

OUT[758]: Array ([6, 7, 0, 9, 5, 2, 4, 3, 8, 1])

Indexer=np.argsort (values) #返回排序后的values的index

Indexer

OUT[761]: Array ([2, 9, 5, 7, 6, 4, 0, 1, 8, 3], Dtype=int64)

Values[indexer] #升序排列

OUT[762]: Array ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

VALUES[INDEXER][::-1] #降序排列

OUT[763]: Array ([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

#----------------Two-dimensional situation-----------

Arr=np.random.randn. Reshape (4,5)

Arr

Array ([[0.06508931,  0.56513883,  0.62546144,-1.28835261,-2.08906088],

[1.5425056,  0.69033553,  1.60385421,-1.52568607,-0.41737589],

[ -0.91894216,  2.08802511,-0.70341454,-0.08491163,-0.17903858],

[ -0.3333518, -0.49513354,  1.56342694,  0.92744459,  0.48037342]]

Arr[:,np.argsort (Arr[2])] # from the 2nd line from small to large arrangement

Array ([[0.06508931,  0.62546144, -2.08906088, -1.28835261,  0.56513883],

[1.5425056,  1.60385421, -0.41737589, -1.52568607,  0.69033553],

[ -0.91894216, -0.70341454, -0.17903858, -0.08491163,  2.08802511],

[ -0.3333518,  1.56342694,  0.48037342,  0.92744459,-0.49513354]]

Arr[np.argsort (arr[:,0]),:] # from the No. 0 column from small to large arrangement

Array ([[ -0.91894216,  2.08802511,-0.70341454,-0.08491163,-0.17903858],

[ -0.3333518, -0.49513354,  1.56342694,  0.92744459,  0.48037342],

[0.06508931,  0.56513883,  0.62546144,-1.28835261,-2.08906088],

[1.5425056,  0.69033553,  1.60385421,-1.52568607,-0.41737589]]

in the Pandas

By using the Sort_index or Sort_values method, the default sort on the Axis=0 (that is, sorted by the size of a column) can be changed to one row by Axis=1. By=index,index is the index of the column or row that you want to sort.

Frame=dataframe (arr)

Frame

Frame.sort_index (by=0,ascending=false) # is sorted according to the value in column No. 0, in descending order.