pandas 如何根据pandas中的列名删除重复的列数据
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how to drop duplicated columns data based on column name in pandas
提问by X.Z
Assume I have a table like below
假设我有一张如下表
A B C B
0 0 1 2 3
1 4 5 6 7
I'd like to drop column B. I tried to use drop_duplicate, but it seems that it only works based on duplicated data not header. Hope anyone know how to do this
我想删除 B 列。我尝试使用 drop_duplicate，但它似乎只适用于重复数据而不是标题。希望有人知道如何做到这一点
Thanks
谢谢
回答by jezrael
Use Index.duplicatedwith locor ilocand boolean indexing:
Index.duplicated与loc或iloc和一起使用boolean indexing：
print (~df.columns.duplicated())
[ True True True False]
df = df.loc[:, ~df.columns.duplicated()]
print (df)
A B C
0 0 1 2
1 4 5 6
df = df.iloc[:, ~df.columns.duplicated()]
print (df)
A B C
0 0 1 2
1 4 5 6
Timings:
时间：
np.random.seed(123)
cols = ['A','B','C','B']
#[1000 rows x 30 columns]
df = pd.DataFrame(np.random.randint(10, size=(1000,30)),columns = np.random.choice(cols, 30))
print (df)
In [115]: %timeit (df.groupby(level=0, axis=1).first())
1000 loops, best of 3: 1.48 ms per loop
In [116]: %timeit (df.groupby(level=0, axis=1).mean())
1000 loops, best of 3: 1.58 ms per loop
In [117]: %timeit (df.iloc[:, ~df.columns.duplicated()])
1000 loops, best of 3: 338 μs per loop
In [118]: %timeit (df.loc[:, ~df.columns.duplicated()])
1000 loops, best of 3: 346 μs per loop
回答by piRSquared
You can groupby
We use the axis=1and level=0parameters to specify that we are grouping by columns. Then use the firstmethod to grab the first column within each group defined by unique column names.
您可以groupby
使用axis=1和level=0参数来指定我们按列分组。然后使用该first方法获取由唯一列名定义的每个组中的第一列。
df.groupby(level=0, axis=1).first()
A B C
0 0 1 2
1 4 5 6
We could have also used last
我们也可以使用 last
df.groupby(level=0, axis=1).last()
A B C
0 0 3 2
1 4 7 6
Or mean
或者 mean
df.groupby(level=0, axis=1).mean()
A B C
0 0 2 2
1 4 6 6
