使用来自三列的分组问题制作数据框

Make a dataframe with grouped questions from three columns

我有以下数据框:

       A               B                  C
  I am motivated     Agree                4
  I am motivated     Strongly Agree       5
  I am motivated     Disagree             6
  I am open-minded   Agree                4
  I am open-minded   Disagree             4
  I am open-minded   Strongly Disagree    3

其中 A 列是问题,B 列是答案,C 列是 "Strongly Agree"、"Agree"、"Disagree" 和 "Strongly Disagree" 的频率A 列中的问题。

如何将其转换为以下数据框?

                  Strongly Agree    Agree     Disagree   Strongly Disagree
I am motivated        5               4           6             0
I am open-minded      0               4           4             3

我尝试查看 groupby() 以查找其他帖子中的列,但无法弄清楚。使用 python 3

使用DataFrame.pivot_table()方法:

In [250]: df.pivot_table(index='A', columns='B', values='C', aggfunc='sum', fill_value=0)
Out[250]:
B                 Agree  Disagree  Strongly Agree  Strongly Disagree
A
I am motivated        4         6               5                  0
I am open-minded      4         4               0                  3

因为这些已经是频率计数,我们可以假设我们有唯一的 Question / Opinion 对。因此,我们可以使用 set_indexunstack,因为不需要聚合。这应该可以为我们节省一些时间和效率。我们可以使用 pivot 实现相同的目标,但是,pivot 没有 fill_value 选项使我们能够保留 dtype

df.set_index(['A', 'B']).C.unstack(fill_value=0)

B                 Agree  Disagree  Strongly Agree  Strongly Disagree
A                                                                   
I am motivated        4         6               5                  0
I am open-minded      4         4               0                  3

额外学分
'B'变成pd.Categorical,列将被排序

df.B = pd.Categorical(
    df.B, ['Strongly Disagree', 'Disagree', 'Agree', 'Strongly Agree'], True)
df.set_index(['A', 'B']).C.unstack(fill_value=0)

B                 Strongly Disagree  Disagree  Agree  Strongly Agree
A                                                                   
I am motivated                    0         6      4               5
I am open-minded                  3         4      4               0