如何遍历pandas数据框列并基于条件分解？

Question

I am trying to iterate over a Dataframe and conditionally factorize data. I have a Dataframe with information about house prices and instead of the data being represented by strings I would like them to be categories and represented by numbers (ie mansion = 0, house = 1). However, some columns are already integers or floats so I only want to categorize the columns that are strings.

I am trying to factorize the data so I can use it with a keras sequential neural net without manually going through each column and factorizing myself.

columns = list(dataframe)
for i in columns:
    if type(i)==str:
        xtrain.i = pd.Categorical(pd.factorize(dataframe.i)[0])

I thought this would factorize the data but I get the error

AttributeError: 'DataFrame' object has no attribute 'i' and pandas does not recognize that I am attempting to refer to the column selection. for reference, the following piece of code works in the code. (MSZoning is a listed column)

xtrain.MSZoning = pd.Categorical(pd.factorize(xtrain.MSZoning)[0])

Any help or advice would be much appreciated!

Answer 1

This is more like

for i in columns:
    if dataframe[i].dtypes=='object':
        xtrain[i] = pd.Categorical(pd.factorize(dataframe[i])[0])

And since you are doing MlP, so let us using LabelEncoder

from sklearn import preprocessing
le = preprocessing.LabelEncoder()

for i in columns:
    if dataframe[i].dtypes=='object':
        dataframe[i] = le.fit_transform(dataframe[i])