How to extract the index of an element that can be found on several rows

I am looking to extract column name and index of elements from a dataframe

import numpy as np
import pandas as pd
import random
lst = list(range(30))
segments = np.repeat(lst, 3)
random.shuffle(segments)
segments = segments.reshape(10, 9)
col_names = ['lz'+str(i) for i in range(95,104)]
rows_names = ['con'+str(i) for i in range(0,10)]
df = pd.DataFrame(segments, index=rows_names, columns=col_names)

      lz95  lz96  lz97  lz98  lz99  lz100  lz101  lz102  lz103
con0     6     9    11     7     9     24     18     10      1
con1    24     5    21    15    18     25     24      7     29
con2    17    27     2     0    11     11     18     23      0
con3    16    22    20    22    20     14     14      0      8
con4    10    10     3    13    25     14      9     17     16
con5     3    28    22     2    27     12     16     21      4
con6    26     1    19     7    19      6     29     15     26
con7    26    28     4    13    23     23      1     25     19
con8    28     8     3     6     5      8      4      5     29
con9     2    15    21    27    17     13     12     12     20

For value=12, I am able to extract the column name lz_val = df.columns[df.isin([12]).any()]
But not for rows as it extract all indexes

con_val = df[df==12].index
Index(['con0', 'con1', 'con2', 'con3', 'con4', 'con5', 'con6', 'con7', 'con8',
       'con9'],
      dtype='object')

>Solution :

What about using np.where?

rows, cols = np.where(df == 12)

rows = df.index[rows]
cols = df.columns[cols]

>>> rows
Index(['con5', 'con9', 'con9'], dtype='object')
>>> cols
Index(['lz100', 'lz101', 'lz102'], dtype='object')