Validating values

I have a list of values stored, I need to compare each value with each column cell in a data frame, if its found then place a value of "YES" in a Key column:

dep_list = df_epl[['Values']].copy()
dep_list = dep_list.dropna()
dep_list = dep_list.drop_duplicates()
dlist = dep_list['Values'].tolist()

df_existing['Key'] = ""
for l in dlist:
    for index, row in df_existing.iterrows():
        if row['Code'] == l:
            row['Key'] = "YES"
        else:
            pass

Everything is working except when writing "YES" value into the Key column. I have tried a lot of ways and it just don't work.

In your code you are comparing row['Key'] to l, where the former is always "" as its defined immediately before the for loop.

I am guessing you actually meant to write if row['Values'] == l?

And with pandas you can actually use the following to check if your column value is inside a list:

import pandas as pd

df1 = pd.DataFrame({'value': range(10)})

list_to_check = [1,2,3]

# create Key
df1['Key'] = 'No'
df1.loc[df1['value'].isin(list_to_check), 'Key'] = 'YES'

df1

Returns:

  value	Key
0 	0 	No
1 	1 	YES
2 	2 	YES
3 	3 	YES
4 	4 	No
5 	5 	No
6 	6 	No
7 	7 	No
8 	8 	No
9 	9 	No

pandas.apply should work fine

import pandas as pd

# assuming dlist is a list of strings
dlist = ["aa", "bb"]

df = pd.DataFrame(columns=["X","Y","Z"], data=[["a","c","b"],["c","bb","aa"]])

print(df)

   X   Y   Z
0  a   c   b
1  c  bb  aa

df["key"] = ""


df["key"] = df.apply(lambda x: "Yes" if any([y in dlist for y in x.values]) else "", axis=1)

print(df)

   X   Y   Z  key
0  a   c   b     
1  c  bb  aa  Yes