python - 将 DataFrame 分类值 reshape 为行

Question

我很难重组这个数据框。我想我应该使用 pd.pivot_table 或 pd.crosstab，但我不确定如何完成工作。

这是我的数据框:

vicro = pd.read_csv(vicroURL)
vicro_subset = vicro.ix[:,['P1', 'P10', 'P30', 'P71', 'P82', 'P90']]

In [6]: vicro
vicro         vicroURL      vicro_subset  

In [6]: vicro_subset.head()
Out[6]: 
  P1 P10 P30 P71 P82 P90
0  -   I   -   -   -   M
1  -   I   -   V   T   M
2  -   I   -   V   A   M
3  -   I   -   T   -   M
4  -   -   -   -   A   -

我要做的是获取此数据框中的所有可能值并将它们放入行中。新值将是计数。看起来像的东西:

Out[6]: 
  P1 P10 P30 P71 P82 P90
I  0   4   0   0   0   0
V  0   0   0   2   0   0
A  0   0   0   0   2   0
M  0   0   0   0   0   4
T  0   0   0   1   1   0

任何帮助将不胜感激!谢谢。

编辑:使用 melt 详细说明答案，两者都帮助我更多地了解 pandas，但在“melt”答案中我有更多的未知数:

In [8]: melted_df = pd.melt(vicro_subset)

In [9]: melted_df.head()
Out[9]: 
  variable value
0       P1     -
1       P1     -
2       P1     -
3       P1     -
4       P1     -


In [13]: grouped_melt = melted_df.groupby(['variable','value'])['value'].count()
In [14]: grouped_melt.head()
Out[14]: 
variable  value
P1        -        797
          .        269
P10       -        339
          .          1
          F        132


In [15]: unstacked_group = grouped_melt.unstack()

In [16]: unstacked_group.head()
Out[16]: 
<class 'pandas.core.frame.DataFrame'>
Index: 5 entries, P1 to P82
Data columns:
-       5  non-null values
.       2  non-null values
A       1  non-null values
AITV    1  non-null values
AT      2  non-null values

In [17]: transpose_unstack = unstacked_group.T

In [18]: transpose_unstack.head()
Out[18]: 
variable   P1  P10   P30  P71  P82  P90
value                                  
-         797  339  1005  452  604  634
.         269    1   NaN  NaN  NaN  NaN
A         NaN  NaN   NaN  NaN  282  NaN
AITV      NaN  NaN   NaN  NaN    1  NaN
AT        NaN  NaN   NaN    1    2  NaN

Answer 1

或者，像这样的东西应该可以工作:

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: df = pd.DataFrame(np.random.randint(0,5,12).reshape(3,4), 
                                            columns=list('abcd'))

In [4]: print df
   a  b  c  d
0  2  2  3  1
1  0  1  0  2
2  1  3  0  4

In [5]: new = pd.concat([df[col].value_counts() for col in df.columns], axis=1)

In [6]: new.columns = df.columns

In [7]: print new
    a   b   c   d
0   1 NaN   2 NaN
1   1   1 NaN   1
2   1   1 NaN   1
3 NaN   1   1 NaN
4 NaN NaN NaN   1