python - pd.melt() 字典/一系列数据帧

Question

例如我有以下 map :

  {'df1': Jan    Feb    Mar
           1      3      5
           2      4      6
   'df2': Jan    Feb    Mar
           7      9      11
           8      10     12
    ......}

我想要以下输出:

Jan  1
Jan  2
Feb  3
Feb  4
Mar  5
Mar  6
Jan  7
Jan  8
Feb  9
Feb  10
Mar  11
Mar  12

有谁知道是否可以这样做？ 我尝试过的是迭代 DataFrames 来尝试获取

  {'df1': Jan  1
          Jan  2
          Feb  3
          Feb  4
          Mar  5
          Mar  6

   'df2': Jan  7
          Jan  8
          Feb  9
          Feb  10
          Mar  11
          Mar  12

通过使用

for x in dfMap:
    df = pd.melt(list(x.values()))

然后尝试用 df1m = 连接它

pd.concat(df.values(), ignore_index=True)

这给了我错误

AttributeError: 'list' object has no attribute 'columns'

我对编程相当陌生，真的很想学习，如果有人能解释它是如何工作的，以及为什么 list 或 dict_values 对象没有属性“列”，那就太好了。

提前致谢!

Answer 1

您可以连接和堆叠:

out = pd.concat(d.values()).stack().droplevel(0)

或者:

out = pd.concat(d.values()).melt()

---

示例:

df = pd.DataFrame(np.arange(1,10).reshape(-1,3),columns=['Jan','Feb','Mar'])
d = {}
for e,i in df.iterrows():
    d[f"df{e+1}"] = i.to_frame().T
print(d,'\n')

out = pd.concat(d.values()).stack().droplevel(0)
print(out)

---

    {'df1':    Jan  Feb  Mar
0    1    2    3, 'df2':    Jan  Feb  Mar
1    4    5    6, 'df3':    Jan  Feb  Mar
2    7    8    9} 

Jan    1
Feb    2
Mar    3
Jan    4
Feb    5
Mar    6
Jan    7
Feb    8
Mar    9
dtype: int32

随着融化:

out = pd.concat(d.values()).melt()
print(out)

  variable  value
0      Jan      1
1      Jan      4
2      Jan      7
3      Feb      2
4      Feb      5
5      Feb      8
6      Mar      3
7      Mar      6
8      Mar      9

编辑，对于已编辑的问题，请尝试:

out = pd.concat(d).stack().sort_index(level=[0,-1]).droplevel([0,1])

---

示例如下:

df = pd.DataFrame(np.arange(1,13).reshape(3,-1).T,columns=['Jan','Feb','Mar'])
d = {}
for e,i in df.groupby(df.index//2):
    d[f"df{e+1}"] = i
print(d,'\n')

out = pd.concat(d).stack().sort_index(level=[0,-1]).droplevel([0,1])
print(out)

{'df1':    Jan  Feb  Mar
0    1    5    9
1    2    6   10, 'df2':    Jan  Feb  Mar
2    3    7   11
3    4    8   12} 

Jan     1
Jan     2
Feb     5
Feb     6
Mar     9
Mar    10
Jan     3
Jan     4
Feb     7
Feb     8
Mar    11
Mar    12
dtype: int32

或者您也可以将数据帧名称转换为 int 然后排序:

out = (pd.concat(d.values(),keys=[int(key[2:]) for key in d.keys()])
   .stack().sort_index(level=[0,-1]).droplevel([0,1]))