問題描述
我正在尋找一種與 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 比較沒有關于 distinct 的任何內容.
The pandas sql comparison doesn't have anything about distinct.
.unique() 僅適用于單個列，所以我想我可以連接這些列，或者將它們放在列表/元組中并以這種方式進行比較，但這似乎是熊貓應該做的以更本土的方式進行.
.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.
我是否遺漏了一些明顯的東西，或者沒有辦法做到這一點?
Am I missing something obvious, or is there no way to do this?
推薦答案
您可以使用drop_duplicates 方法來獲取 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來確定唯一性，您還可以提供 subset 關鍵字參數.請參閱文檔字符串.
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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