Python Pandas 数据框 sort_values 不起作用
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/39590055/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Python pandas dataframe sort_values does not work
提问by jeffsia
I have the following pandas data frame which I want to sort by 'test_type'
我有以下Pandas数据框，我想按“test_type”排序
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
My code to load the dataframe and sort it is, the first print line prints the data frame above.
我的代码加载数据框并对其进行排序，第一行打印上面的数据框。
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
After doing the sort and printing the dataframe content, the data frame still looks like below.
进行排序并打印数据框内容后，数据框仍然如下所示。
Program output:
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
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?
我希望第 3 行（测试类型：sso_500 行）在排序后位于顶部。有人可以帮我弄清楚为什么它不能正常工作吗？
回答by Ami Tavory
Presumbaly, what you're trying to do is sort by the numerical value after sso_. You can do this as follows:
据推测，您要做的是按sso_. 您可以按如下方式执行此操作：
import numpy as np
df.ix[np.argsort(df.test_type.str.split('_').str[-1].astype(int).values)
This
这个
splits the strings at
_converts what's after this character to the numerical value
Finds the indices sorted according to the numerical values
Reorders the DataFrame according to these indices
将字符串拆分为
_将此字符后面的内容转换为数值
查找根据数值排序的索引
根据这些索引对 DataFrame 重新排序
Example
例子
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
回答by Nickil Maveli
Alternatively, you could also extract the numbers from test_typeand sort them. Followed by reindexing DFaccording to those indices.
或者，您也可以从中提取数字test_type并对其进行排序。然后DF根据这些索引重新索引。
df.reindex(df['test_type'].str.extract('(\d+)', expand=False) \
.astype(int).sort_values().index).reset_index(drop=True)
