How to populate a column with a value if condition is met across 2+ other colums

My dataframe is similar to the table below. I have 6 columns, each with 'Yes' or 'No' if a specific antibiotic was given.

AZITH	CLIN	CFTX	METRO	CFTN	DOXY
Yes	Yes	No	No	No	No
No	Yes	No	Yes	No	No
Yes	Yes	No	No	No	No
No	No	No	No	No	No
Yes	Yes	Yes	Yes	Yes	Yes
No	Yes	Yes	Yes	No	Yes
No	No	No	No	No	Yes
No	No	No	No	No	No
Yes	Yes	Yes	No	No	No
Yes	No	Yes	Yes	No	No
Yes	No	No	No	Yes	No
No	No	Yes	Yes	No	Yes
No	No	No	No	Yes	Yes
No	No	No	Yes	No	Yes

I want to fill the column 'TREATED' with 'True' if specific combinations of antibiotic columns contain 'Yes.' If the conditions aren't met, then I would like to fill the 'TREATED' column with a 'False' value.

If ['AZITH'] & ['CLIN'] == 'Yes' |

['AZITH'] & ['CFTX'] & ['CLIN'] == 'Yes' |

['AZITH'] & ['CFTX'] & ['METRO']== 'Yes' |

['AZITH'] & ['CFTN'] == 'Yes' |

['CFTX'] & ['DOXY'] & ['METRO']== 'Yes' |

['CFTN'] & ['DOXY'] == 'Yes' |

['DOXY'] & ['METRO']== 'Yes' ,

Then return 'True' in column 'TREATED'

Else 'False'

What I had in mind was some sort of if statement or use of lambda function, however, I am having trouble.

This must not be exclusive to the above combinations but also include for example if all 6 medications were given. If that's the case, then 'True' should be returned because the condition has been met to give at least 2 of the treatment medications.

The desired output is below:

AZITH	CLIN	CFTX	METRO	CFTN	DOXY	TREATED
Yes	Yes	No	No	No	No	Yes
No	Yes	No	Yes	No	No	No
Yes	Yes	No	No	No	No	Yes
No	No	No	No	No	No	No
Yes	Yes	Yes	Yes	Yes	Yes	Yes
No	Yes	Yes	Yes	No	Yes	Yes
No	No	No	No	No	Yes	No
No	No	No	No	No	No	No
Yes	Yes	Yes	No	No	No	Yes
Yes	No	Yes	Yes	No	No	Yes
Yes	No	No	No	Yes	No	Yes
No	No	Yes	Yes	No	Yes	Yes
No	No	No	No	Yes	Yes	Yes
No	No	No	Yes	No	Yes	Yes

Solution

With the dataframe you provided:

import pandas as pd

df = pd.DataFrame(
    {
        "AZITH": [
            "Yes",
            "No",
            "Yes",
            "No",
            "Yes",
            "No",
            "No",
            "No",
            "Yes",
            "Yes",
            "Yes",
            "No",
            "No",
            "No",
        ],
        "CLIN": [
            "Yes",
            "Yes",
            "Yes",
            "No",
            "Yes",
            "Yes",
            "No",
            "No",
            "Yes",
            "No",
            "No",
            "No",
            "No",
            "No",
        ],
        "CFTX": [
            "No",
            "No",
            "No",
            "No",
            "Yes",
            "Yes",
            "No",
            "No",
            "Yes",
            "Yes",
            "No",
            "Yes",
            "No",
            "No",
        ],
        "METRO": [
            "No",
            "Yes",
            "No",
            "No",
            "Yes",
            "Yes",
            "No",
            "No",
            "No",
            "Yes",
            "No",
            "Yes",
            "No",
            "Yes",
        ],
        "CFTN": [
            "No",
            "No",
            "No",
            "No",
            "Yes",
            "No",
            "No",
            "No",
            "No",
            "No",
            "Yes",
            "No",
            "Yes",
            "No",
        ],
        "DOXY": [
            "No",
            "No",
            "No",
            "No",
            "Yes",
            "Yes",
            "Yes",
            "No",
            "No",
            "No",
            "No",
            "Yes",
            "Yes",
            "Yes",
        ],
    }
)

Here is one way to do it:

mask = (
    ((df["AZITH"] == "Yes") & (df["CLIN"] == "Yes"))
    | ((df["AZITH"] == "Yes") & (df["CLIN"] == "Yes") & (df["CFTX"] == "Yes"))
    | ((df["AZITH"] == "Yes") & (df["CFTX"] == "Yes") & (df["METRO"] == "Yes"))
    | ((df["AZITH"] == "Yes") & (df["CFTN"] == "Yes"))
    | ((df["CFTX"] == "Yes") & (df["DOXY"] == "Yes") & (df["METRO"] == "Yes"))
    | ((df["CFTN"] == "Yes") & (df["DOXY"] == "Yes"))
    | ((df["DOXY"] == "Yes") & (df["METRO"] == "Yes"))
)
df.loc[mask, "TREATED"] = "Yes"
df = df.fillna("No")

Then:

print(df)
# Output
   AZITH CLIN CFTX METRO CFTN DOXY TREATED
0    Yes  Yes   No    No   No   No     Yes
1     No  Yes   No   Yes   No   No      No
2    Yes  Yes   No    No   No   No     Yes
3     No   No   No    No   No   No      No
4    Yes  Yes  Yes   Yes  Yes  Yes     Yes
5     No  Yes  Yes   Yes   No  Yes     Yes
6     No   No   No    No   No  Yes      No
7     No   No   No    No   No   No      No
8    Yes  Yes  Yes    No   No   No     Yes
9    Yes   No  Yes   Yes   No   No     Yes
10   Yes   No   No    No  Yes   No     Yes
11    No   No  Yes   Yes   No  Yes     Yes
12    No   No   No    No  Yes  Yes     Yes
13    No   No   No   Yes   No  Yes     Yes

AZITH	CLIN	CFTX	METRO	CFTN	DOXY
Yes	Yes	No	No	No	No
No	Yes	No	Yes	No	No
Yes	Yes	No	No	No	No
No	No	No	No	No	No
Yes	Yes	Yes	Yes	Yes	Yes
No	Yes	Yes	Yes	No	Yes
No	No	No	No	No	Yes
No	No	No	No	No	No
Yes	Yes	Yes	No	No	No
Yes	No	Yes	Yes	No	No
Yes	No	No	No	Yes	No
No	No	Yes	Yes	No	Yes
No	No	No	No	Yes	Yes
No	No	No	Yes	No	Yes

AZITH	CLIN	CFTX	METRO	CFTN	DOXY
Yes	Yes	No	No	No	No
No	Yes	No	Yes	No	No
Yes	Yes	No	No	No	No
No	No	No	No	No	No
Yes	Yes	Yes	Yes	Yes	Yes
No	Yes	Yes	Yes	No	Yes
No	No	No	No	No	Yes
No	No	No	No	No	No
Yes	Yes	Yes	No	No	No
Yes	No	Yes	Yes	No	No
Yes	No	No	No	Yes	No
No	No	Yes	Yes	No	Yes
No	No	No	No	Yes	Yes
No	No	No	Yes	No	Yes

AZITH	CLIN	CFTX	METRO	CFTN	DOXY
Yes	Yes	No	No	No	No
No	Yes	No	Yes	No	No
Yes	Yes	No	No	No	No
No	No	No	No	No	No
Yes	Yes	Yes	Yes	Yes	Yes
No	Yes	Yes	Yes	No	Yes
No	No	No	No	No	Yes
No	No	No	No	No	No
Yes	Yes	Yes	No	No	No
Yes	No	Yes	Yes	No	No
Yes	No	No	No	Yes	No
No	No	Yes	Yes	No	Yes
No	No	No	No	Yes	Yes
No	No	No	Yes	No	Yes