在数据帧中，我想计算每一列的值并使用这些值作为索引。
我想把这个：
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
进入这个：
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
熊猫怎么可能这样？
最佳答案
如果您坚持value_counts，您可以事先使用stack和groupby，然后在value_counts之前致电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
melt和pivot_table：(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
使用
crosstab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
关于python - 如何在DataFrame上应用Series.value_counts()？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54544084/