Pandas提供的两种排序方法Pandas提供了两种排序方法，即按标签排序和按值排序。本文介绍了如何在Pandas中用这

Pandas提供了两种排序方法，即按标签排序和按值排序。本文介绍了如何在Pandas中用这两种方法进行排序。

1.潘达斯排序示例DataFrame数据

下面的代码将创建DataFrame对象的例子用于排序：

import numpy as np

import pandas as pd

def pandas_dataframe_sorting_example():
    
    # create a 2 dimensional array with 5 rows and 3 columns, each element value is a floating number. 
    data_array = np.random.randn(5, 3)
    
    # the index array contains unsorted index number.
    index_array = [0,2,1,6,3]
    
    # the column name array.
    columns_array = ['column-3','column-1','column-2']
    
    # create the unsorted DataFrame object with the above .
    dataframe_unsorted = pd.DataFrame(data = data_array,index = index_array,columns = columns_array)
    print('dataframe_unsorted\r')
    print(dataframe_unsorted)
    

if __name__ == '__main__':
    
    pandas_dataframe_sorting_example()

下面是上述代码的执行结果，从结果中我们可以看到，行标签和数字元素都没有被排序。让我们在下面的例子中分别用标签排序和数字排序对它们进行操作。

dataframe_unsorted
   column-3  column-1  column-2
0  0.395101 -0.051456  0.327673
2 -1.417987  0.636136 -0.068395
1  0.088765  0.672521 -0.195716
6  0.600821  0.814108 -0.086112
3  1.243266  0.558752 -0.703006

2.Pandas DataFrame按标签排序的例子

Pandas DataFrame的**sort_index(axis, ascending)**方法可以用来对DataFrame对象按标签排序。

2.1 通过行标签对DataFrame对象进行排序

当你不向该方法传递任何参数时，它将按行标签以升序对DataFrame对象进行排序。
```
dataframe_unsorted.sort_index()
```
这是因为默认轴参数的值是0，默认升序参数的值是True。
```
dataframe_unsorted.sort_index(axis = 0, ascending = True)
```

2.2 按列标签对DataFrame对象进行排序

如果你想按列标对DataFrame对象进行排序，你可以向sort_index()方法传递axis = 1。
```
dataframe_sort_by_column_label = dataframe_unsorted.sort_index(axis = 1, ascending=False)
```
如果你向该方法传递ascending = False参数，它将按降序对列标进行排序。

3.Pandas DataFrame按值排序示例

DataFrame对象的**sort_values(by, kind)**方法可以用来对DataFrame对象的值进行排序。
参数by是用来指定一列或多列。
kind参数指定了排序算法，它有3个值，它们是heapsort、mergesort和quicksort。
kind参数只在按一列排序时生效，默认值是quicksort，而mergesort算法是最稳定的选择。

4.Pandas数据框排序示例源代码

下面是这个例子的完整源代码。

import pandas as pd

import numpy as np

def pandas_dataframe_sorting_example():
    
    # create a 2 dimensional array with 5 rows and 3 columns, each element value is a floating number. 
    data_array = np.random.randn(5, 3)
    
    # the index array contains unsorted index number.
    index_array = [0,2,1,6,3]
    
    # the column name array.
    columns_array = ['column-3','column-1','column-2']
    
    # create the unsorted DataFrame object with the above .
    dataframe_unsorted = pd.DataFrame(data = data_array,index = index_array,columns = columns_array)
    print('dataframe_unsorted\r')
    print(dataframe_unsorted)
    
    # sort DataFrame by row index label in ascending order.
    dataframe_sort_by_row_index_label_ascending = dataframe_unsorted.sort_index(ascending=True)
    print('\ndataframe_sort_by_row_index_label_ascending = dataframe_unsorted.sort_index(ascending=True)\r')
    print(dataframe_sort_by_row_index_label_ascending)
    
    # sort DataFrame by row index label in descending order.
    dataframe_sort_by_row_index_label_descending = dataframe_unsorted.sort_index(ascending=False)
    print('\ndataframe_sort_by_row_index_label_descending = dataframe_unsorted.sort_index(ascending=False)\r')
    print(dataframe_sort_by_row_index_label_descending)    
    
    # sort DataFrame by column index label.
    dataframe_sort_by_column_label = dataframe_unsorted.sort_index(axis = 1, ascending=False)
    print('\ndataframe_sort_by_column_label = dataframe_unsorted.sort_index(axis = 1, ascending=False)\r')
    print(dataframe_sort_by_column_label)
    
    # sort DataFrame by column value.
    dataframe_sort_by_column_value = dataframe_unsorted.sort_values(by='column-1')
    print('\ndataframe_sort_by_column_value = dataframe_unsorted.sort_values(by=\'column-1\')\r')
    print(dataframe_sort_by_column_value)
    
    # when 2 rows has same colimn-1 value then order by the column-2 value.
    dataframe_sort_by_multiple_columns_value = dataframe_unsorted.sort_values(by=['column-1','column-2'], ascending=False)
    print('\ndataframe_sort_by_multiple_columns_value = dataframe_unsorted.sort_values(by=[\'column-1\',\'column-2\'], ascending=False)\r')
    print(dataframe_sort_by_multiple_columns_value)
    
    dataframe_sorting_algorithm = dataframe_unsorted.sort_values(by='column-1' ,kind='heapsort')
    print('\ndataframe_sorting_algorithm = dataframe_unsorted.sort_values(by=\'column-1\' ,kind=\'heapsort\')\r')
    print (dataframe_sorting_algorithm)
    
    

if __name__ == '__main__':
    
    pandas_dataframe_sorting_example()

下面是上述例子的源代码执行输出：

dataframe_unsorted
   column-3  column-1  column-2
0  0.395101 -0.051456  0.327673
2 -1.417987  0.636136 -0.068395
1  0.088765  0.672521 -0.195716
6  0.600821  0.814108 -0.086112
3  1.243266  0.558752 -0.703006

dataframe_sort_by_row_index_label_ascending = dataframe_unsorted.sort_index(ascending=True)
   column-3  column-1  column-2
0  0.395101 -0.051456  0.327673
1  0.088765  0.672521 -0.195716
2 -1.417987  0.636136 -0.068395
3  1.243266  0.558752 -0.703006
6  0.600821  0.814108 -0.086112

dataframe_sort_by_row_index_label_descending = dataframe_unsorted.sort_index(ascending=False)
   column-3  column-1  column-2
6  0.600821  0.814108 -0.086112
3  1.243266  0.558752 -0.703006
2 -1.417987  0.636136 -0.068395
1  0.088765  0.672521 -0.195716
0  0.395101 -0.051456  0.327673

dataframe_sort_by_column_label = dataframe_unsorted.sort_index(axis = 1, ascending=False)
   column-3  column-2  column-1
0  0.395101  0.327673 -0.051456
2 -1.417987 -0.068395  0.636136
1  0.088765 -0.195716  0.672521
6  0.600821 -0.086112  0.814108
3  1.243266 -0.703006  0.558752

dataframe_sort_by_column_value = dataframe_unsorted.sort_values(by='column-1')
   column-3  column-1  column-2
0  0.395101 -0.051456  0.327673
3  1.243266  0.558752 -0.703006
2 -1.417987  0.636136 -0.068395
1  0.088765  0.672521 -0.195716
6  0.600821  0.814108 -0.086112

dataframe_sort_by_multiple_columns_value = dataframe_unsorted.sort_values(by=['column-1','column-2'], ascending=False)
   column-3  column-1  column-2
6  0.600821  0.814108 -0.086112
1  0.088765  0.672521 -0.195716
2 -1.417987  0.636136 -0.068395
3  1.243266  0.558752 -0.703006
0  0.395101 -0.051456  0.327673

dataframe_sorting_algorithm = dataframe_unsorted.sort_values(by='column-1' ,kind='heapsort')
   column-3  column-1  column-2
0  0.395101 -0.051456  0.327673
3  1.243266  0.558752 -0.703006
2 -1.417987  0.636136 -0.068395
1  0.088765  0.672521 -0.195716
6  0.600821  0.814108 -0.086112