用于计算pandas列中每个值的百分比的函数

Question

I am taking part in the Titanic Tutorials over at Kaggle to learn pandas/machine learning.

Here is my kernel: https://www.kaggle.com/trenzalore888/titanic/titanic-learning

I want to create a function which takes two arguments, dataframe and column name. I want this function to calculate the percentage each class is (assuming it's binary, ie 0 or 1).

I can do this hard coded ie to work specifically for the Titanic set, but I want to create a function so I can use it in the future.

Here is my failed attempt:

traintotal=(len(train.index))
testtotal=(len(test.index))

def Is_data_imbalanced (df,objectivecolumn) :
    objectivecount= df.objectivecolumn[df.objectivecolumn > 0].sum()
    objectivecountpercentage=(objectivecount/traintotal)*100
    objectivecountrounded= np.ceil(objectivecountpercentage)
    return objectivecountrounded

Is_data_imbalanced(train,"Survived")

Unfortunately I get an attribute error:

AttributeError: 'DataFrame' object has no attribute 'objectivecolumn'

Below is the hardcoded version that works:

traintotal=(len(train.index))
print("there are", traintotal,"rows in the train data")

testtotal=(len(test.index))
print("there are {} rows in the test data".format(testtotal))

Survialcount= train.Survived[train.Survived > 0].sum()
Survialcountpercentage=(Survialcount/traintotal)*100
print(Survialcountpercentage)

survivalcountrounded= np.ceil(Survialcountpercentage)

print(" ",survivalcountrounded,"percent survived")

Does anyone know how I can get this to work? It seems like it takes df for train fine, but the 2nd argument columnname for .Survived is not working.

Answer 1

Assuming it really is binary then all you need is

def Is_data_imbalanced(df, objectivecolumn):
    return int(df[objectivecolumn].mean() * 100)