Make a DataFrame with its values as concatenated index and column names-CodePudding

I'd like to create a pandas DataFrame with its values as concatenated index and column names. In the example below, I'd have the column name concatenated to the row name; however, a vice versa scenario is also possible.

   A      B      C
A  AA     AB     AC
B  BA     BB     BC
C  CA     CB     CC

CodePudding user response：

Numpy's `char` module

We want to use Numpy's broadcasting while using a string specific function.

Setup

I used an index of ['X', 'Y', 'Z'] to make more obvious that what I'm doing is accurate.

df = pd.DataFrame(index=['X', 'Y', 'Z'], columns=['A', 'B', 'C'])

Solution

idx = df.index.to_numpy().astype(str)
col = df.columns.to_numpy().astype(str)

df.loc[:, :] = np.char.add(idx[:, None], col)

df

    A   B   C
X  XA  XB  XC
Y  YA  YB  YC
Z  ZA  ZB  ZC

CodePudding user response：

Another option is to multiply the columns list by the length of df; then use radd on axis=0 to concatenate index names from the right:

df.loc[:,:] = [df.columns.astype(str).tolist()]*df.shape[0]
df = df.radd(df.index.astype(str), axis=0)

Or do the converse by multiplying the index list by the width of df; then use add on axis=1 to concatenate the column names:

df.loc[:,:] = list(zip(*[df.index.tolist()]*df.shape[1]))
df = df.add(df.columns, axis=1)

Output:

    A   B   C
A  AA  AB  AC
B  BA  BB  BC
C  CA  CB  CC

Numpy's char module

Setup

Solution

Numpy's `char` module