The steps to extract the total number of same data and maximum range of age from a single dataframe-CodePudding

I have a dataframe shown below

From this dataframe, how can i find the maximum age of an individual from 'Age' column and total number of 1 in 'Cardio' column using pandas?

The output should be 77 from 'Age' column and 5 from 'Cardio' column

I have tried this earlier

df.['Age'].max()

but its not showing anything

CodePudding user response：

Use DataFrame.agg for Series:

s = df.agg({'Age': 'max', 'Cardio': 'sum'})
print (s)
Age       77
Cardio     5
dtype: int64

If need scalars:

age, no_1 = df['Age'].max(), df['Cardio'].sum()

Or:

age, no_1 = s['Age'], s['Cardio']

CodePudding user response：

You can use agg with a dictionary mapping the column names and the aggregation functions:

df.agg({'Age': 'max', 'Cardio': 'sum'})

output:

Age       77
Cardio     5
dtype: int64

Getting the number of 0s in Cardio:

(df.assign(Cardio_0=df['Cardio'].rsub(1))
   .agg({'Age': 'max', 'Cardio': 'sum', 'Cardio_0': 'sum'})
)

output:

Age         77
Cardio       5
Cardio_0     2
dtype: int64

Second approach

def nzero(s):
    return len(s)-sum(s)
df.agg({'Age': 'max', 'Cardio': ['sum', nzero]})

output:

        Age  Cardio
max    77.0     NaN
sum     NaN     5.0
nzero   NaN     2.0

or as stack Series:

df.agg({'Age': 'max', 'Cardio': ['sum', nzero]}).stack()

output:

max    Age       77.0
sum    Cardio     5.0
nzero  Cardio     2.0
dtype: float64