Python 如何在熊猫的多个数据框列中“选择不同”？
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How to "select distinct" across multiple data frame columns in pandas?
提问by Jody
I'm looking for a way to do the equivalent to the SQL
我正在寻找一种与 SQL 等效的方法
SELECT DISTINCT col1, col2 FROM dataframe_table
The pandas sql comparison doesn't have anything about distinct.
pandas sql 比较没有任何关于distinct.
.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.
.unique()仅适用于单列，所以我想我可以连接列，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以更原生的方式做的事情。
Am I missing something obvious, or is there no way to do this?
我是否遗漏了一些明显的东西，或者没有办法做到这一点？
采纳答案by joris
You can use the drop_duplicatesmethod to get the unique rows in a DataFrame:
您可以使用该drop_duplicates方法获取 DataFrame 中的唯一行：
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
You can also provide the subsetkeyword argument if you only want to use certain columns to determine uniqueness. See the docstring.
subset如果您只想使用某些列来确定唯一性，也可以提供关键字参数。请参阅文档字符串。
回答by EdChum
There is no uniquemethod for a df, if the number of unique values for each column were the same then the following would work: df.apply(pd.Series.unique)but if not then you will get an error. Another approach would be to store the values in a dict which is keyed on the column name:
没有uniquedf 的方法，如果每列的唯一值的数量相同，那么以下将起作用：df.apply(pd.Series.unique)但如果不是，那么您将收到错误。另一种方法是将值存储在以列名为键的字典中：
In [111]:
df = pd.DataFrame({'a':[0,1,2,2,4], 'b':[1,1,1,2,2]})
d={}
for col in df:
d[col] = df[col].unique()
d
Out[111]:
{'a': array([0, 1, 2, 4], dtype=int64), 'b': array([1, 2], dtype=int64)}
回答by dorante
You can take the sets of the columns and just subtract the smaller set from the larger set:
您可以获取列的集合，然后从较大的集合中减去较小的集合：
distinct_values = set(df['a'])-set(df['b'])
回答by Naiara Andrade
I think use drop duplicatesometimes will not so useful depending dataframe.
我认为使用drop duplicate有时不会那么有用，具体取决于数据框。
I found this:
我找到了这个：
[in] df['col_1'].unique()
[out] array(['A', 'B', 'C'], dtype=object)
And work for me!
并为我工作！
https://riptutorial.com/pandas/example/26077/select-distinct-rows-across-dataframe
https://riptutorial.com/pandas/example/26077/select-distinct-rows-across-dataframe
回答by Yury Wallet
I've tried different solutions. First was:
我尝试了不同的解决方案。首先是：
a_df=np.unique(df[['col1','col2']], axis=0)
and it works well for not object data Another way to do this and to avoid error (for object columns type) is to apply drop_duplicates()
它适用于非对象数据 另一种方法来做到这一点并避免错误（对于对象列类型）是应用 drop_duplicates()
a_df=df.drop_duplicates(['col1','col2'])[['col1','col2']]
You can also use SQL to do this, but it worked very slow in my case:
您也可以使用 SQL 来执行此操作，但在我的情况下它的运行速度非常慢：
from pandasql import sqldf
q="""SELECT DISTINCT col1, col2 FROM df;"""
pysqldf = lambda q: sqldf(q, globals())
a_df = pysqldf(q)
回答by ncoghlan
To solve a similar problem, I'm using groupby:
为了解决类似的问题，我正在使用groupby：
print(f"Distinct entries: {len(df.groupby(['col1', 'col2']))}")
Whether that's appropriate will depend on what you want to do with the result, though (in my case, I just wanted the equivalent of COUNT DISTINCTas shown).
不过，这是否合适取决于您想对结果做什么（在我的情况下，我只想要COUNT DISTINCT如图所示的等价物）。
