I have tried this below code, values matched but result stored under same column i.e, "items" :

'''

for ticker in tickers:
 asset = str(int(df['asset'].values[0]))
 ...
 match = [ticker, asset, debt, debtc]
 def checkIfValuesExists1(df, ticker):
  for ele in ticker:
   if ele in df['items'].values:
    out[ele] = match      
   return out
 out = checkIfValuesExists1(df, match)

'''

CodePudding user response：

You can compare the items column to the all other three columns in df2 based on ticker column

m = df2.set_index('ticker').eq(df1.set_index('ticker')['items'], axis=0)

print(m)

        asset   debt  debtc
ticker
AAPL     True  False  False
MSFT    False   True  False
GOOGL   False  False   True

Then use where to turn the False part to NaN

df_ = df2.set_index('ticker').where(m, np.nan).astype('Int64').reset_index()

print(df_)

  ticker  asset  debt  debtc
0   AAPL   4564  <NA>   <NA>
1   MSFT   <NA>  7778   <NA>
2  GOOGL   <NA>  <NA>   7654

CodePudding user response：

You could also do:

(df2.melt(value_name = 'items').merge(df1).
      pivot('ticker', 'variable').reset_index())
 
         ticker items            
variable        asset  debt debtc
0          AAPL  4564   NaN   NaN
1         GOOGL   NaN   NaN  7654
2          MSFT   NaN  7778   NaN