Creating a variable based on the values of two other variables

Question

I have a dataframe in pandas that include two variables: DEC and TYPE

dec     type
 1        13
 2        2
 2        5
 2        7
 2        9
 3        5

From these two variables, I would like to create other, binary, variables based on the values of these two variables.

I haven't been able to find code to write exactly what I want, but in python-English, it would be something like:

df['new_variable'] = 1 if DEC == 1 & TYPE == 3 or 2 or 1

Please let me know if there is something I can include in my question to clarify what I am looking for.

Update from answers:

A problem I am running into occurs because for each variable I need to run two lines of code (below) and when I run the second line it overruns the coding in the first line. How do I run both lines together (ie without the second line overrunning the first line)?

harrington_citations['gov_winner'] =  np.where((harrington_citations['dec'] == 1) & harrington_citations['type'].isin([1,2,3,4,22]) , 1, 0)

harrington_citations['gov_winner'] = np.where((harrington_citations['dec'] == 2) & harrington_citations['type'].isin([1,5,9,13,18]), 1, 0)

Answer 1

Looks like you need .isin for the second condition and return 1/0:

df['new_variable'] = (df['dec'].eq(1) & df['type'].isin([3,2,1])).view('i1')

EDIT per comments, you should create 2 conditions with |comdition:

c1 = (harrington_citations['dec'] == 1) & harrington_citations['type'].isin([1,2,3,4,22])
c2 = (harrington_citations['dec'] == 2) & harrington_citations['type'].isin([1,5,9,13,18])
harrington_citations['gov_winner'] = (c1|c2).view('i1')

Answer 2

将np.nan替换为适合您的任何值：

df['new_variable'] = np.where((df['dec'] == 1) & df['type'].isin([1,2,3]), 1, np.nan)