本文介绍了如何对 pandas 中的多个列进行分组依据的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我在Python大熊猫中有以下示例数据框:
I have the following sample dataframe in Python pandas:
+---+------+------+------+
| | col1 | col2 | col3 |
+---+------+------+------+
| 0 | a | d | b |
+---+------+------+------+
| 1 | a | c | b |
+---+------+------+------+
| 2 | c | b | c |
+---+------+------+------+
| 3 | b | b | c |
+---+------+------+------+
| 4 | a | a | d |
+---+------+------+------+
我想对第1-3列中的所有'a','b','c'和'd'值进行计数,以便最终得到一个数据框像这样:
I would like to perform a count of all the 'a,' 'b,' 'c,' and 'd' values across columns 1-3 so that I would end up with a dataframe like this:
+---+--------+-------+
| | letter | count |
+---+--------+-------+
| 0 | a | 4 |
+---+--------+-------+
| 1 | b | 5 |
+---+--------+-------+
| 2 | c | 4 |
+---+--------+-------+
| 3 | d | 2 |
+---+--------+-------+
我可以这样做的一种方法是将各列彼此堆叠,然后进行分组计数,但是我觉得必须有更好的方法。有人可以帮我吗?
One way I can do this is stack the columns on top of each other and THEN do a groupby count, but I feel like there has to be a better way. Can someone help me with this?
推荐答案
您可以 stack()
数据框将所有列放入行,然后执行 value_counts
:
You can stack()
the dataframe to put all columns into rows and then do value_counts
:
df.stack().value_counts()
b 5
c 4
a 4
d 2
dtype: int64
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