朴素分类器使用字典的键遍历 df 的列？
Naive Classifier using keys of dict to iterate through the columns of df?
我目前正在创建我的朴素贝叶斯算法，我正在努力避免对所有名称进行硬编码。我想知道如何使用 dict 键遍历 df 的列而不实际提及列名，因为这会使算法对不同的数据集无用。谢谢！
train = pd.read_csv("train.csv")
test = {"feature_1":1, "feature_2":1, "feature_3":0, "feature_4":1}
def naive_bayes_predict(train, test):
probs1= {}
for keys in test:
probs1[keys] = len(train[(train.target == 1) & (train.keys == test[keys])]) / len(train[train.target == 1])
要遍历数据框的列并仅使用 pandas，您可以使用以下内容：
for i in range(df.shape[1]):
# df.iloc[:, i] would have the values of the ith feature with i starting from 0
我目前正在创建我的朴素贝叶斯算法，我正在努力避免对所有名称进行硬编码。我想知道如何使用 dict 键遍历 df 的列而不实际提及列名，因为这会使算法对不同的数据集无用。谢谢！
train = pd.read_csv("train.csv")
test = {"feature_1":1, "feature_2":1, "feature_3":0, "feature_4":1}
def naive_bayes_predict(train, test):
probs1= {}
for keys in test:
probs1[keys] = len(train[(train.target == 1) & (train.keys == test[keys])]) / len(train[train.target == 1])
要遍历数据框的列并仅使用 pandas，您可以使用以下内容：
for i in range(df.shape[1]):
# df.iloc[:, i] would have the values of the ith feature with i starting from 0