Python 比较熊猫数据框的行（行有一些重叠的值）
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Comparing rows of pandas dataframe (rows have some overlapping values)
提问by mlimb
I have a pandas dataframe with 21 columns. I am focusing on a subset of rows that have exactly same column data values except for 6 that are unique to each row. I don't know which column headings these 6 values correspond to a priori.
我有一个包含 21 列的 Pandas 数据框。我关注的是具有完全相同列数据值的行的子集，除了每行唯一的 6 个行。我不知道这 6 个值对应于哪个列标题。
I tried converting each row to Index objects, and performed set operation on two rows. Ex.
我尝试将每一行转换为 Index 对象，并对两行执行设置操作。前任。
row1 = pd.Index(sample_data[0])
row2 = pd.Index(sample_data[1])
row1 - row2
which returns an Index object containing values unique to row1. Then I can manually deduce which columns have unique values.
它返回一个 Index 对象，其中包含对 row1 唯一的值。然后我可以手动推断哪些列具有唯一值。
How can I programmatically grab the column headings that these values correspond to in the initial dataframe? Or, is there a way to compare two or multiple dataframe rows and extract the 6 different column values of each row, as well as the corresponding headings? Ideally, it would be nice to generate a new dataframe with the unique columns.
如何以编程方式获取这些值在初始数据框中对应的列标题？或者，有没有办法比较两个或多个数据框行并提取每行的 6 个不同列值以及相应的标题？理想情况下，生成具有唯一列的新数据框会很好。
In particular, is there a way to do this using set operations?
特别是，有没有办法使用集合操作来做到这一点？
Thank you.
谢谢你。
回答by Garrett
Here's a quick solution to return only the columns in which the first two rows differ.
这是仅返回前两行不同的列的快速解决方案。
In [13]: df = pd.DataFrame(zip(*[range(5), list('abcde'), list('aaaaa'),
... list('bbbbb')]), columns=list('ABCD'))
In [14]: df
Out[14]:
A B C D
0 0 a a b
1 1 b a b
2 2 c a b
3 3 d a b
4 4 e a b
In [15]: df[df.columns[df.iloc[0] != df.iloc[1]]]
Out[15]:
A B
0 0 a
1 1 b
2 2 c
3 3 d
4 4 e
And a solution to find all columns with more than one unique value throughout the entire frame.
以及在整个框架中查找具有多个唯一值的所有列的解决方案。
In [33]: df[df.columns[df.apply(lambda s: len(s.unique()) > 1)]]
Out[33]:
A B
0 0 a
1 1 b
2 2 c
3 3 d
4 4 e
回答by Jeff Tratner
You don't really need the index, you could just compare two rows and use that to filter the columns with a list comprehension.
您实际上并不需要索引，您可以只比较两行并使用它来过滤具有列表理解的列。
df = pd.DataFrame({"col1": np.ones(10), "col2": np.ones(10), "col3": range(2,12)})
row1 = df.irow(0)
row2 = df.irow(1)
unique_columns = row1 != row2
cols = [colname for colname, unique_column in zip(df.columns, bools) if unique_column]
print cols # ['col3']
If you know the standard value for each column, you can convert all the rows to a list of booleans, i.e.:
如果您知道每列的标准值，则可以将所有行转换为布尔值列表，即：
standard_row = np.ones(3)
columns = df.columns
unique_columns = df.apply(lambda x: x != standard_row, axis=1)
unique_columns.apply(lambda x: [col for col, unique_column in zip(columns, x) if unique_column], axis=1)
