如何将数据框的列标题复制到每个行值？

Question

I'm trying to transform a pandas data frame, applying a function to all values, to concatenate the column header and the actual value.

My original dataframe is something like this:

|---------------------|------------------|
|         Col1        |        Col2      |
|---------------------|------------------|
|          12         |         34       |
|---------------------|------------------|
|          12         |         34       |

The output should be:

|---------------------|------------------|
|       Col1          |      Col2        |
|---------------------|------------------|
|       Col1_12       |      Col2_34     |
|---------------------|------------------|
|       Col1_12       |      Col2_34     |

What I tried is this:

mypandasdf.applymap(lambda x: 'columnname'+'_'+str(x))

But I'm struggling with the column name value. How can I put the real column name instead of a string? Or is there any other/better way to do it?

Answer 1

df = pd.DataFrame({'colA': [12,34], 'colB': [56,78]})

df = df.columns.values + '_' + df.astype(str)

print(df)

Output:

      colA     colB
0  colA_12  colB_56
1  colA_34  colB_78

Answer 2

Use DataFrame.columns to access each column and concatenate as string to each value:

for col in df.columns:
    df[col] = col + '_' + df[col].astype(str)

---

print(df)
      Col1     Col2
0  Col1_12  Col2_34
1  Col1_12  Col2_34

Answer 3

Convert each row in your dataframe as string and concat the column name:

df = pd.DataFrame({
                   "col1": [12 , 34],
                   'col2': [7,9]},)
for c in df:
    df[c] = c + '_' + df[c].astype(str)
df

Result:

    col1    col2
0   col1_12 col2_7
1   col1_34 col2_9

Answer 4

IIUC

df.apply(lambda x : x.name+'_'+x.astype(str))
Out[1323]: 
      Col1     Col2
0  Col1_12  Col2_34
1  Col1_12  Col2_34