如何在DataFrame上应用Series.value_counts（）？ [英] How to apply Series.value_counts() on DataFrame?

问题描述

在DataFrame中，我想计算每一列的值并将这些值用作索引。

In a DataFrame i would like to count the values of each column and use the values as index.

我想转一下：

I would like to turn this:

Q1                   Q2                  Q3
Strongly agree       Agree               Undecided
Undecided            Agree               More or less disagree
Strongly agree       Agree               Undecided
Strongly agree       Strongly Disagree   Disagree
More or less agree   Undecided           Strongly disagree

此内容：

                        Q1  Q2  Q3
Strongly agree          3   0   0
Agree                   0   3   0
More or less agree      1   0   0
Undecided               1   1   2
More or less disagree   0   0   1
Disagree                0   0   1
Strongly disagree       0   1   1

大熊猫怎么可能？

推荐答案

如果您坚持使用 value_counts ，则可以使用 stack 和 groupby 事先，然后在取消堆叠 value_counts >：

If you insist on value_counts, you can use stack and groupby beforehand and then call value_counts before unstacking:

df.stack().groupby(level=[1]).value_counts().unstack(0, fill_value=0)

                       Q1  Q2  Q3
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

---

另一种选择是使用融化和数据透视表：

(df.melt()
   .pivot_table(columns='variable', index='value', aggfunc='size', fill_value=0))

variable               Q1  Q2  Q3
value                            
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

< hr>

使用交叉表的解决方案：

v = df.melt()
pd.crosstab(v['value'], v['variable'])

variable               Q1  Q2  Q3
value                            
Agree                   0   3   0
Disagree                0   0   1
More or less agree      1   0   0
More or less disagree   0   0   1
Strongly Disagree       0   1   0
Strongly agree          3   0   0
Strongly disagree       0   0   1
Undecided               1   1   2

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