英文:
pandas - filter rows with same value in many columns
问题
以下是翻译好的部分：
我有一个包含许多列的pandas DataFrame（大约有100多列，但确切数量并不重要）。
大多数行在所有列中具有相同的值，有些行在所有列中有多个唯一的值。
例如，在以下表格中，第1和2行在所有列中具有相同的值，而第3行在所有列中有多个不同的值。
| 列 1 | 列 2 | 列 3 | 列 4 | ... | 列 n |
|---|---|---|---|---|---|
| A | A | A | A | ... | A |
| A | A | A | A | ... | A |
| C | A | B | A | ... | A |
我想要筛选只在其所有列中具有唯一值的行。在前面的示例中，我只会保留第3行。
我知道如何使用掩码根据特定列中的值筛选行，但在这种情况下似乎不起作用。
有什么想法吗？
英文:
I have a pandas DataFrame with many columns (around 100+ columns but the exact amount doesn't matter).
Most rows have the same value in all columns, some rows have more than one unique value. <br>
For example, in the following table, rows 1 and 2 have the same value in all columns and row 3 has more than value in all columns.
| column 1 | column 2 | column 3 | column 4 | ... | column n |
|---|---|---|---|---|---|
| A | A | A | A | ... | A |
| A | A | A | A | ... | A |
| C | A | B | A | ... | A |
I want to filter rows which have only 1 unique value in all of its columns. In the previous example I would only keep row 3.
I know how to filter rows based on values in specific columns using masks, but this doesn't seem to work in the case. <br>
Any Ideas?
答案1
得分: 2
只返回翻译好的部分：
"Looks like you want to filter based on nunique with boolean indexing":
"看起来你想基于nunique和布尔索引进行筛选："
"out = df[df.nunique(axis=1).ne(1)]":
"输出："
column 1 column 2 column 3 column 4 column n
2 C A B A A
"Intermediate":
"中间步骤："
df.nunique(axis=1).ne(1)
0 False
1 False
2 True
dtype: bool
英文:
Looks like you want to filter based on nunique with boolean indexing:
out = df[df.nunique(axis=1).ne(1)]
Output:
column 1 column 2 column 3 column 4 column n
2 C A B A A
Intermediate:
df.nunique(axis=1).ne(1)
0 False
1 False
2 True
dtype: bool
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