Pandas, mark a cell in dataframe, when there exists the same number in the frame

I have 2 pandas dataframes. One of these dataframes has column labels that are specific numbers. The second dataframe has those column header values scattered throughout. They have a matching column between the both of them. I need to mark the cell in the first dataframe with a string "x" where those numbers appear in the second dataframe.

I've tried making functions, and using the following, here is an example:

DF1: 

SN      12  13  14  15  16
1121
1122
1123
1143
1156

DF2:
SN      ID1  ID2 ID3 ID4 ID5
1121    12   15  14   16 NAN 
1122    12   16  14  NAN NAN 
1123
1143
1156   NAN   NAN  NAN  14  NAN


Result (hopefully):

DF:
SN      12  13  14  15  16
1121     x           x    x
1122
1123
1143
1156

It seems like you're trying to mark the cells in DF1 with "x" based on the values that match the column names of DF1 and are found in the corresponding rows of DF2.

import pandas as pd
import numpy as np

# Sample Data
data1 = {'SN': [1121, 1122, 1123, 1143, 1156], 12: [None]*5, 13: [None]*5, 14: [None]*5, 15: [None]*5, 16: [None]*5}
data2 = {'SN': [1121, 1122, 1123, 1143, 1156], 'ID1': [12, 12, np.nan, np.nan, np.nan], 'ID2': [15, 16, np.nan, np.nan, np.nan], 'ID3': [14, 14, np.nan, np.nan, np.nan], 'ID4': [16, np.nan, np.nan, np.nan, 14], 'ID5': [np.nan, np.nan, np.nan, np.nan, np.nan]}

DF1 = pd.DataFrame(data1)
DF2 = pd.DataFrame(data2)

# Set 'SN' as index for both dataframes
DF1.set_index('SN', inplace=True)
DF2.set_index('SN', inplace=True)

# Loop through the columns of DF1 (excluding SN) and mark corresponding values in DF2
for col in DF1.columns:
    condition = DF2.isin([col]).any(axis=1)
    DF1.loc[condition, col] = 'x'

# Fill NaN with empty strings
DF1.fillna("", inplace=True)

print(DF1)

Here is an option:

df1.fillna(1).mul(df1.columns).T.isin(df2.stack().groupby(level=0).agg(list).to_dict()).T.applymap({True:'X'}.get)

Output:

        12    13    14    15    16
SN                                
1121     X  None     X     X     X
1122     X  None     X  None     X
1123  None  None  None  None  None
1143  None  None  None  None  None
1156  None  None     X  None  None

Assuming SN the index of both DataFrames and the same dtype for the values and the column headers:

s = DF2.stack()
out = DF1.mask(pd.crosstab(s.index.get_level_values(0), s).ge(1)
                 .reindex_like(DF1).fillna(False), 'X').fillna('')

Output:

     12 13 14 15 16
SN                 
1121  X     X  X  X
1122  X     X     X
1123               
1143               
1156        X      

Reproducible inputs:

DF1 = pd.DataFrame.from_dict({'index': [1121, 1122, 1123, 1143, 1156],
                              'columns': [12, 13, 14, 15, 16],
                              'data': [[None, None, None, None, None],
                                       [None, None, None, None, None],
                                       [None, None, None, None, None],
                                       [None, None, None, None, None],
                                       [None, None, None, None, None]],
                              'index_names': ['SN'],
                              'column_names': [None]}, orient='tight')

nan = float('nan')
DF2 = pd.DataFrame.from_dict({'index': [1121, 1122, 1123, 1143, 1156],
                              'columns': ['ID1', 'ID2', 'ID3', 'ID4', 'ID5'],
                              'data': [[12.0, 15.0, 14.0, 16.0, nan],
                                       [12.0, 16.0, 14.0, nan, nan],
                                       [nan, nan, nan, nan, nan],
                                       [nan, nan, nan, nan, nan],
                                       [nan, nan, nan, 14.0, nan]],
                              'index_names': ['SN'],
                              'column_names': [None]}, orient='tight')