CODEWITHSUNDEEP
pythonpandasconditional-statements
CandleWax
CandleWax

Reputation: 2219

Pandas: Flag column if value in list exists anywhere in row

Ultimately, I want to flag a new column, 'ExclusionFlag', with 0 or 1 depending on whether a value that is found in a list exists anywhere in the row. 

df = pd.DataFrame([['cat','b','c','c'],['a','b','c','c'],['a','b','c','dog'],['dog','b','c','c']],columns=['Code1','Code2','Code3','Code4'])
excluded_codes = ['cat','dog']

#Attempt
df['ExclusionFlag'] = df.apply(lambda x: 'cat' in x.values, axis=1).any()

#Desired outcome
#Side note: I have 120 rows to check. They're labeled Code1, Code2...Code120. 

    Code1   Code2   Code3   Code4   ExclusionFlag
0   cat     b       c       c       1
1   a       b       c       c       0
2   a       b       c       dog     1
3   dog     b       c       c       1

The line of code I have marks every row as True.

And when I add the excluded_codes list in for 'cat' in my lambda expression, I get an error.

I found a couple questions like this, but what I see typically is something along the lines of (see below), which calls out a specific column, but I don't think iterating through 120 columns is the best approach. Although I could be wrong. 

df['ExclusionFlag'] = df['Code1'].isin(exclusion_codes)

Upvotes: 4

Views: 6325

Answers (2)

VM47
VM47

Reputation: 99

If you want True or False values in new column, you can check them without Any and Astype.

df['ExclusionFlag'] = df.isin(excluded_codes)

And you can check specific column also:

df['ExclusionFlag'] = df['Code2'].isin(excluded_codes)

Upvotes: 2

Vaishali
Vaishali

Reputation: 38415

Something like

df['ExclusionFlag'] = df.isin(excluded_codes).any(1).astype(int)

    Code1   Code2   Code3   Code4   ExclusionFlag
0   cat     b       c       c       1
1   a       b       c       c       0
2   a       b       c       dog     1
3   dog     b       c       c       1

Upvotes: 11

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