熊猫 dataframe 有效地将列和 append 作为新行

Question

If I have a df:

df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])

and wish to take the second column "b" and append to the end of a "new" df with the columns "a" and "b" and a name column containing the name of the "b" column and then the third column "c" and append to the end of the new df together with "a" and the name "c" appened to the name column. It is timeseries data with a datetime in "a" and a variable in b and c and there is sometimes 20 variables and sometimes 1 or 2.

How do I do that in a pretty and efficient way. right now im doing it like this but have to do it a 100 times for slightly different df but with the same idea.

col_nam_list = list(df.columns.values)
df_1 = pd.DataFrame()
df_1["a"] = df["a"]
df_1["name"] = col_nam_list[1]
df_1["value"] = df["b"]

df_2 = pd.DataFrame()
df_2["a"] = df["a"]
df_2["name"] = col_nam_list[2]
df_2["value"] = df["c"]

result = pd.concat([df_1, df_2])

This should be the output

Now this is not fun to write and looks so ugly and unnecessary long. How do I improve my method?

BR

Answer 1

IIUC, you can use pd.DataFrame.melt with parameter id_vars equal to 'a',

df.melt('a')

Output:

   a variable  value
0  1        b      2
1  4        b      5
2  7        b      8
3  1        c      3
4  4        c      6
5  7        c      9