Counting the number of entries in a dataframe that satisfies multiple criteria-CodePudding

I have a dataframe with 9 columns, two of which are gender and smoker status. Every row in the dataframe is a person, and each column is their entry on a particular trait. I want to count the number of entries that satisfy the condition of being both a smoker and is male. I have tried using a sum function:

maleSmoke = sum(1 for i in data['gender'] if i is 'm' and i in data['smoker'] if i is 1 )

but this always returns 0. This method works when I only check one criteria however and I can't figure how to expand it to a second. I also tried writing a function that counted its way through every entry into the dataframe but this also returns 0 for all entries.

def countSmokeGender(df):
    maleSmoke = 0
    femaleSmoke = 0
    maleNoSmoke = 0
    femaleNoSmoke = 0
    
    for i in range(20000):
        if df['gender'][i] is 'm' and df['smoker'][i] is 1:
            maleSmoke = maleSmoke   1
        if df['gender'][i] is 'f' and df['smoker'][i] is 1:
            femaleSmoke = femaleSmoke   1
        if df['gender'][i] is 'm' and df['smoker'][i] is 0:
            maleNoSmoke = maleNoSmoke   1
        if df['gender'][i] is 'f' and df['smoker'][i] is 0:
            femaleNoSmoke = femaleNoSmoke   1
    
    return maleSmoke, femaleSmoke, maleNoSmoke, femaleNoSmoke

I've tried pulling out the data sets as numpy arrays and counting those but that wasn't working either.

CodePudding user response：

Are you using pandas?

Assuming you are, you can simply do this:

# How many male smokers
len(df[(df['gender']=='m') & (df['smoker']==1)])
# How many female smokers
len(df[(df['gender']=='f') & (df['smoker']==1)])
# How many male non-smokers
len(df[(df['gender']=='m') & (df['smoker']==0)])
# How many female non-smokers
len(df[(df['gender']=='f') & (df['smoker']==0)])

Or, you can use groupby:

df.groupby(['gender'])['smoker'].sum()