Python 如何删除 Pandas 中仅包含零的列？
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How do I delete a column that contains only zeros in Pandas?
提问by user2587593
I currently have a dataframe consisting of columns with 1's and 0's as values, I would like to iterate through the columns and delete the ones that are made up of only 0's. Here's what I have tried so far:
我目前有一个由 1 和 0 作为值的列组成的数据框，我想遍历这些列并删除仅由 0 组成的列。这是我迄今为止尝试过的：
ones = []
zeros = []
for year in years:
for i in range(0,599):
if year[str(i)].values.any() == 1:
ones.append(i)
if year[str(i)].values.all() == 0:
zeros.append(i)
for j in ones:
if j in zeros:
zeros.remove(j)
for q in zeros:
del year[str(q)]
In which years is a list of dataframes for the various years I am analyzing, ones consists of columns with a one in them and zeros is a list of columns containing all zeros. Is there a better way to delete a column based on a condition? For some reason I have to check whether the ones columns are in the zeros list as well and remove them from the zeros list to obtain a list of all the zero columns.
其中年份是我正在分析的各个年份的数据框列表，一个由其中包含一个 1 的列组成，而 zeros 是一个包含全零的列列表。有没有更好的方法根据条件删除列？出于某种原因，我必须检查一列是否也在零列表中，并将它们从零列表中删除以获得所有零列的列表。
采纳答案by unutbu
df.loc[:, (df != 0).any(axis=0)]
Here is a break-down of how it works:
以下是其工作原理的细分：
In [74]: import pandas as pd
In [75]: df = pd.DataFrame([[1,0,0,0], [0,0,1,0]])
In [76]: df
Out[76]:
0 1 2 3
0 1 0 0 0
1 0 0 1 0
[2 rows x 4 columns]
df != 0creates a boolean DataFrame which is True where dfis nonzero:
df != 0创建一个布尔数据帧，它是 True ，其中df非零：
In [77]: df != 0
Out[77]:
0 1 2 3
0 True False False False
1 False False True False
[2 rows x 4 columns]
(df != 0).any(axis=0)returns a boolean Series indicating which columns have nonzero entries. (The anyoperation aggregates values along the 0-axis -- i.e. along the rows -- into a single boolean value. Hence the result is one boolean value for each column.)
(df != 0).any(axis=0)返回一个布尔系列，指示哪些列具有非零条目。（该any操作将沿 0 轴（即沿行）的值聚合为单个布尔值。因此，结果是每列一个布尔值。）
In [78]: (df != 0).any(axis=0)
Out[78]:
0 True
1 False
2 True
3 False
dtype: bool
And df.loccan be used to select those columns:
并且df.loc可用于选择这些列：
In [79]: df.loc[:, (df != 0).any(axis=0)]
Out[79]:
0 2
0 1 0
1 0 1
[2 rows x 2 columns]
To "delete" the zero-columns, reassign df:
要“删除”零列，请重新分配df：
df = df.loc[:, (df != 0).any(axis=0)]
回答by Jeremy Z
Here is an alternative way to use is
这是另一种使用方法是
df.replace(0,np.nan).dropna(axis=1,how="all")
df.replace(0,np.nan).dropna(axis=1,how="all")
Compared with the solution of unutbu, this way is obviously slower:
与unutbu的解决方案相比，这种方式显然更慢：
%timeit df.loc[:, (df != 0).any(axis=0)]
652 μs ± 5.7 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df.replace(0,np.nan).dropna(axis=1,how="all")
1.75 ms ± 9.49 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
回答by mork
In case you'd like a more expressiveway of getting the zero-column names so you can print / log them, and drop them, in-place, by their names:
如果您想要一种更具表现力的方式来获取零列名称，以便您可以打印/记录它们，并按其名称就地放置它们：
zero_cols = [ col for col, is_zero in ((df == 0).sum() == df.shape[0]).items() if is_zero ]
df.drop(zero_cols, axis=1, inplace=True)
Some break down:
一些分解：
# a pandas Series with {col: is_zero} items
# is_zero is True when the number of zero items in that column == num_all_rows
(df == 0).sum() == df.shape[0])
# a list comprehension of zero_col_names is built from the_series
[ col for col, is_zero in the_series.items() if is_zero ]
