如何“选择不同"?跨越 pandas 中的多个数据框列? [英] How to "select distinct" across multiple data frame columns in pandas?

问题描述

我正在寻找一种等效于sql的方法

I'm looking for a way to do the equivalent to the sql

从dataframe_table选择SELECT col1，col2"

"SELECT DISTINCT col1, col2 FROM dataframe_table"

pandas sql比较中没有关于"distinct"的任何信息

The pandas sql comparison doesn't have anything about "distinct"

.unique()仅适用于单个列，因此我想可以合并这些列，或将它们放在列表/元组中并进行比较，但这似乎是熊猫应该以更原生的方式进行的操作.

.unique() only works for a single column, so I suppose I could concat the columns, or put them in a list/tuple and compare that way, but this seems like something pandas should do in a more native way.

我是否缺少明显的东西，还是没有办法做到?

Am I missing something obvious, or is there no way to do this?

推荐答案

您可以使用

You can use the drop_duplicates method to get the unique rows in a DataFrame:

In [29]: df = pd.DataFrame({'a':[1,2,1,2], 'b':[3,4,3,5]})

In [30]: df
Out[30]:
   a  b
0  1  3
1  2  4
2  1  3
3  2  5

In [32]: df.drop_duplicates()
Out[32]:
   a  b
0  1  3
1  2  4
3  2  5

如果您只想使用某些列来确定唯一性，则还可以提供subset关键字参数.参见文档字符串.

You can also provide the subset keyword argument if you only want to use certain columns to determine uniqueness. See the docstring.

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