Python Pandas Dataframe sort_values不起作用 [英] Python pandas dataframe sort_values does not work

问题描述

我有以下要根据"test_type"排序的熊猫数据框

I have the following pandas data frame which I want to sort by 'test_type'

  test_type         tps          mtt        mem        cpu       90th
0  sso_1000  205.263559  4139.031090  24.175933  34.817701  4897.4766
1  sso_1500  201.127133  5740.741266  24.599400  34.634209  6864.9820
2  sso_2000  203.204082  6610.437558  24.466267  34.831947  8005.9054
3   sso_500  189.566836  2431.867002  23.559557  35.787484  2869.7670

我用于加载数据框并对其进行排序的代码，第一条打印行在上面打印数据框.

My code to load the dataframe and sort it is, the first print line prints the data frame above.

        df = pd.read_csv(file) #reads from a csv file
        print df
        df = df.sort_values(by=['test_type'], ascending=True)
        print '\nAfter sort...'
        print df

排序并打印数据框内容后，数据框仍然如下所示.

After doing the sort and printing the dataframe content, the data frame still looks like below.

程序输出:

After sort...
  test_type         tps          mtt        mem        cpu       90th
0  sso_1000  205.263559  4139.031090  24.175933  34.817701  4897.4766
1  sso_1500  201.127133  5740.741266  24.599400  34.634209  6864.9820
2  sso_2000  203.204082  6610.437558  24.466267  34.831947  8005.9054
3   sso_500  189.566836  2431.867002  23.559557  35.787484  2869.7670

我希望排序后第3行(测试类型:sso_500行)位于最前面.有人可以帮我弄清楚为什么它不能正常工作吗?

I expect row 3 (test type: sso_500 row) to be on top after sorting. Can someone help me figure why it's not working as it should?

推荐答案

概括地说，您要执行的操作是按sso_之后的数值排序.您可以按照以下步骤进行操作:

Presumbaly, what you're trying to do is sort by the numerical value after sso_. You can do this as follows:

import numpy as np

df.ix[np.argsort(df.test_type.str.split('_').str[-1].astype(int).values)

此

1. 在_

将此字符后的内容转换为数值

converts what's after this character to the numerical value

查找根据数值排序的索引

Finds the indices sorted according to the numerical values

根据这些索引对DataFrame重新排序

Reorders the DataFrame according to these indices

示例

In [15]: df = pd.DataFrame({'test_type': ['sso_1000', 'sso_500']})

In [16]: df.sort_values(by=['test_type'], ascending=True)
Out[16]: 
  test_type
0  sso_1000
1   sso_500

In [17]: df.ix[np.argsort(df.test_type.str.split('_').str[-1].astype(int).values)]
Out[17]: 
  test_type
1   sso_500
0  sso_1000

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