如何“选择不同的"?跨越 pandas 中的多个数据框列?
问题描述
我正在寻找一种与 SQL 等效的方法
I'm looking for a way to do the equivalent to the SQL
SELECT DISTINCT col1, col2 FROM dataframe_table
pandas sql 比较没有关于 distinct 的任何内容.
The pandas sql comparison doesn't have anything about distinct.
.unique() 仅适用于单个列，所以我想我可以连接这些列，或者将它们放在列表/元组中并以这种方式进行比较，但这似乎是熊猫应该做的以更本土的方式进行.
.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.
我是否遗漏了一些明显的东西，或者没有办法做到这一点?
Am I missing something obvious, or is there no way to do this?
解决方案
您可以使用drop_duplicates 方法来获取 DataFrame 中的唯一行:
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，您还可以提供 subset 关键字参数.请参阅文档字符串.
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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