Python 将某些列保留在 Pandas DataFrame 中，删除其他所有列
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Keep certain columns in a pandas DataFrame, deleting everything else
提问by Matt
Say I have a data table
说我有一个数据表
1 2 3 4 5 6 .. n
A x x x x x x .. x
B x x x x x x .. x
C x x x x x x .. x
And I want to slim it down so that I only have, say, columns 3 and 5 deleting all other and maintaining the structure. How could I do this with pandas? I think I understand how to delete a single column, but I don't know how to save a select few and delete all others.
我想把它缩小，这样我就只有第 3 和第 5 列删除所有其他内容并保持结构。我怎么能用熊猫做到这一点？我想我知道如何删除单个列，但我不知道如何保存选定的几列并删除所有其他列。
采纳答案by Andy Hayden
If you have a list of columns you can just select those:
如果你有一个列列表，你可以选择那些：
In [11]: df
Out[11]:
1 2 3 4 5 6
A x x x x x x
B x x x x x x
C x x x x x x
In [12]: col_list = [3, 5]
In [13]: df = df[col_list]
In [14]: df
Out[14]:
3 5
A x x
B x x
C x x
回答by unutbu
You could reassign a new value to your DataFrame, df:
您可以为您的DataFrame,重新分配一个新值df：
df = df.loc[:,[3, 5]]
As long as there are no other references to the original DataFrame, the old DataFramewill get garbage collected.
只要没有其他对原始的引用DataFrame，旧的DataFrame就会被垃圾收集。
Note that when using df.loc, the index is specified by labels. Thus above 3and 5are not ordinals, they represent the label names of the columns. If you wish to specify the columns by ordinal index, use df.iloc.
请注意，使用时df.loc，索引由标签指定。因此上面3和5不是序数，它们代表列的标签名称。如果您希望按序数索引指定列，请使用df.iloc.
回答by Fjolnir Dvorak
For those who are searching an method to do this inplace:
对于那些正在寻找就地执行此操作的方法的人：
from pandas import DataFrame
from typing import Set, Any
def remove_others(df: DataFrame, columns: Set[Any]):
cols_total: Set[Any] = set(df.columns)
diff: Set[Any] = cols_total - columns
df.drop(diff, axis=1, inplace=True)
This will create the complement of all the columns in the dataframe and the columns which should be removed. Those can safely be removed. Drop works even on an empty set.
这将创建数据框中所有列和应删除的列的补充。那些可以安全地移除。Drop 甚至在空集上也能工作。
>>> df = DataFrame({"a":[1,2,3],"b":[2,3,4],"c":[3,4,5]})
>>> df
a b c
0 1 2 3
1 2 3 4
2 3 4 5
>>> remove_others(df, {"a","b","c"})
>>> df
a b c
0 1 2 3
1 2 3 4
2 3 4 5
>>> remove_others(df, {"a"})
>>> df
a
0 1
1 2
2 3
>>> remove_others(df, {"a","not","existent"})
>>> df
a
0 1
1 2
2 3
回答by cs95
How do I keep certain columns in a pandas DataFrame, deleting everything else?
如何在 Pandas DataFrame 中保留某些列，删除其他所有列？
The answer to this question is the same as the answer to "How do I delete certain columns in a pandas DataFrame?" Here are some additional options to those mentioned so far, along with timings.
这个问题的答案与“如何删除 Pandas DataFrame 中的某些列？”的答案相同。以下是迄今为止提到的一些附加选项，以及时间安排。
DataFrame.loc
DataFrame.loc
One simple option is selection, as mentioned by in other answers,
一个简单的选择是选择，正如其他答案中提到的，
# Setup.
df
1 2 3 4 5 6
A x x x x x x
B x x x x x x
C x x x x x x
cols_to_keep = [3,5]
df[cols_to_keep]
3 5
A x x
B x x
C x x
Or,
或者，
df.loc[:, cols_to_keep]
3 5
A x x
B x x
C x x
DataFrame.reindexwith axis=1or 'columns'(0.21+)
DataFrame.reindex用axis=1或'columns'（0.21+）
However, we also have reindex, in recent versions you specify axis=1to drop:
但是，我们也有reindex，在您指定axis=1要删除的最新版本中：
df.reindex(cols_to_keep, axis=1)
# df.reindex(cols_to_keep, axis='columns')
# for versions < 0.21, use
# df.reindex(columns=cols_to_keep)
3 5
A x x
B x x
C x x
On older versions, you can also use reindex_axis: df.reindex_axis(cols_to_keep, axis=1).
在旧版本上，您还可以使用reindex_axis: df.reindex_axis(cols_to_keep, axis=1)。
DataFrame.drop
DataFrame.drop
Another alternative is to use dropto select columns by pd.Index.difference:
另一种选择是使用以下drop方式选择列pd.Index.difference：
# df.drop(cols_to_drop, axis=1)
df.drop(df.columns.difference(cols_to_keep), axis=1)
3 5
A x x
B x x
C x x
Performance
表现
The methods are roughly the same in terms of performance; reindexis faster for smaller N, while dropis faster for larger N. The performance is relative as the Y-axis is logarithmic.
方法在性能上大致相同；reindex较小的 N 更快，而drop较大的 N 更快。性能是相对的，因为 Y 轴是对数。
Setup and Code
设置和代码
import pandas as pd
import perfplot
def make_sample(n):
np.random.seed(0)
df = pd.DataFrame(np.full((n, n), 'x'))
cols_to_keep = np.random.choice(df.columns, max(2, n // 4), replace=False)
return df, cols_to_keep
perfplot.show(
setup=lambda n: make_sample(n),
kernels=[
lambda inp: inp[0][inp[1]],
lambda inp: inp[0].loc[:, inp[1]],
lambda inp: inp[0].reindex(inp[1], axis=1),
lambda inp: inp[0].drop(inp[0].columns.difference(inp[1]), axis=1)
],
labels=['__getitem__', 'loc', 'reindex', 'drop'],
n_range=[2**k for k in range(2, 13)],
xlabel='N',
logy=True,
equality_check=lambda x, y: (x.reindex_like(y) == y).values.all()
)
