Pandas DataFrame 中哪些列是二进制的？
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Which columns are binary in a Pandas DataFrame?
提问by na899
I have a pandas dataframe with a large number of columns and I need to find which columns are binary (with values 0 or 1 only) without looking at the data. Which function should be used?
我有一个包含大量列的 Pandas 数据框，我需要在不查看数据的情况下找到哪些列是二进制的（只有值 0 或 1）。应该使用哪个功能？
回答by Alexander
To my knowledge, there is no direct function to test for this. Rather, you need to build something based on how the data was encoded (e.g. 1/0, T/F, True/False, etc.). In addition, if your column has a missing value, the entire column will be encoded as a float instead of an int.
据我所知，没有直接的函数来测试这个。相反，您需要根据数据的编码方式（例如 1/0、T/F、True/False 等）构建一些东西。此外，如果您的列有缺失值，整个列将被编码为浮点数而不是整数。
In the example below, I test whether all unique non null values are either '1' or '0'. It returns a list of all such columns.
在下面的示例中，我测试所有唯一的非空值是“1”还是“0”。它返回所有此类列的列表。
df = pd.DataFrame({'bool': [1, 0, 1, None],
'floats': [1.2, 3.1, 4.4, 5.5],
'ints': [1, 2, 3, 4],
'str': ['a', 'b', 'c', 'd']})
bool_cols = [col for col in df
if df[[col]].dropna().unique().isin([0, 1]).all().values]
# 2019-09-10 EDIT (per Hardik Gupta)
bool_cols = [col for col in df
if np.isin(df[col].dropna().unique(), [0, 1]).all()]
>>> bool_cols
['bool']
>>> df[bool_cols]
bool
0 1
1 0
2 1
3 NaN
回答by lucas
def is_binary(series, allow_na=False):
if allow_na:
series.dropna(inplace=True)
return sorted(series.unique()) == [0, 1]
This is the most efficient solution I found. It is quicker than the answers above. When handling large data sets, the difference in timing becomes relevant.
这是我找到的最有效的解决方案。它比上面的答案更快。在处理大型数据集时，时间差异变得重要。
回答by Aiden
To expand on the answer just above, using value_counts().index instead of unique() should do the trick:
为了扩展上面的答案，使用 value_counts().index 而不是 unique() 应该可以解决问题：
bool_cols = [col for col in df if
df[col].dropna().value_counts().index.isin([0,1]).all()]
回答by sedeh
Improving upon @Aiden to avoid returning an empty column:
改进@Aiden 以避免返回空列：
[col for col in df if (len(df[col].value_counts()) > 0) & all(df[col].value_counts().index.isin([0, 1]))]
回答by Hardik Gupta
Using Alexander's answer, with python version - 3.6.6
使用 Alexander 的回答，python 版本 - 3.6.6
[col for col in df if np.isin(df[col].unique(), [0, 1]).all()]
