Using pandas.concat along Axis 1 returns a Concatenation along Axis 0

I am trying to horizontally concatenate a pair of data frames with identical indices, but the result is always a vertical concatenation with NaN values inserted into every column.

dct_l = {'1':'a', '2':'b', '3':'c', '4':'d'}
df_l = pd.DataFrame.from_dict(dct_l, orient='index', columns=['Key'])

dummy = np.zeros((4,3))
index = np.arange(1,5)
columns = ['POW', 'KLA','CSE']
df_e = pd.DataFrame(dummy, index, columns)

print(df_l)

  Key
1   a
2   b
3   c
4   d

print(df_e)

   POW  KLA  CSE
1  0.0  0.0  0.0
2  0.0  0.0  0.0
3  0.0  0.0  0.0
4  0.0  0.0  0.0

pd.concat([df_l, df_e], axis=1)

Actual Result

   Key  POW  KLA  CSE
1    a  NaN  NaN  NaN
2    b  NaN  NaN  NaN
3    c  NaN  NaN  NaN
4    d  NaN  NaN  NaN
1  NaN  0.0  0.0  0.0
2  NaN  0.0  0.0  0.0
3  NaN  0.0  0.0  0.0
4  NaN  0.0  0.0  0.0

Expected Result

   Key  POW  KLA  CSE
1    a  0.0  0.0  0.0
2    b  0.0  0.0  0.0
3    c  0.0  0.0  0.0
4    d  0.0  0.0  0.0

What is happening here?

You have different dtypes for your two indexes:

df_e.index
Index([1, 2, 3, 4], dtype='int64')

df_l.index
Index(['1', '2', '3', '4'], dtype='object')

which will break the alignment (1 != '1').

Make sure they are identical.

For example:

pd.concat([df_l.rename(int), df_e], axis=1)

  Key  POW  KLA  CSE
1   a  0.0  0.0  0.0
2   b  0.0  0.0  0.0
3   c  0.0  0.0  0.0
4   d  0.0  0.0  0.0