Pivot table to “tidy” data frame in Pandas

问题

I have an array of numbers (I think the format makes it a pivot table) that I want to turn into a "tidy" data frame. For example, I start with variable 1 down the left, variable 2 across the top, and the value of interest in the middle, something like this:

  X Y
A 1 2
B 3 4

I want to turn that into a tidy data frame like this:

V1 V2 value
A  X    1
A  Y    2
B  X    3
B  Y    4

The row and column order don't matter to me, so the following is totally acceptable:

value V1 V2
  2    A  Y
  4    B  Y
  3    B  X
  1    A  X

For my first go at this, which was able to get me the correct final answer, I looped over the rows and columns. This was terribly slow, and I suspected that some machinery in Pandas would make it go faster.

It seems that melt is close to the magic I seek, but it doesn't get me all the way there. That first array turns into this:

   V2 value
0  X    1
1  X    2
2  Y    3
3  Y    4

It gets rid of my V1 variable!

Nothing is special about melt, so I will be happy to read answers that use other approaches, particularly if melt is not much faster than my nested loops and another solution is. Nonetheless, how can I go from that array to the kind of tidy data frame I want as the output?

Example dataframe:

df = pd.DataFrame({"X":[1,3], "Y":[2,4]},index=["A","B"])

回答1:

Use DataFrame.reset_index with DataFrame.rename_axis and then DataFrame.melt. If you want order columns we could use DataFrame.reindex.

new_df = (df.rename_axis(index = 'V1')
            .reset_index()
            .melt('V1',var_name='V2')
            .reindex(columns = ['value','V1','V2']))
print(new_df)

Another approach DataFrame.stack:

new_df = (df.stack()
            .rename_axis(index = ['V1','V2'])
            .rename('value')
            .reset_index()
            .reindex(columns = ['value','V1','V2']))
print(new_df)

---

   value V1 V2
0      1  A  X
1      3  B  X
2      2  A  Y
3      4  B  Y

to names names there is another alternative like commenting @Scott Boston in the comments

回答2:

Melt is a good approach, but it doesn't seem to play nicely with identifying the results by index. You can reset the index first to move it to its own column, then use that column as the id col.

test = pd.DataFrame([[1,2],[3,4]], columns=['X', 'Y'], index=['A', 'B'])
   X  Y
A  1  2
B  3  4

test = test.reset_index()
  index  X  Y
0     A  1  2
1     B  3  4

test.melt('index',['X', 'Y'], 'prev cols')  
  index prev cols  value
0     A         X      1
1     B         X      3
2     A         Y      2
3     B         Y      4

来源：https://stackoverflow.com/questions/60082349/pivot-table-to-tidy-data-frame-in-pandas