Kaggle机器学习比赛的数据集中会有一些缺失值和一些离散值，分享一下使用pandas快速处理的方法。
首先将训练集和测试集合并（在后续步骤中训练集与测试集一起算均值、方差，属于比赛中的小技巧）
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| train = pandas.read_csv("train.csv")
test = pandas.read_csv("test.csv")
all_features = pandas.concat((train.iloc[:, 1:-1], test.iloc[:, 1:]))
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将数值型特征重新缩放到零均值和单位方差来标准化数据，缺失值替换为0，即均值
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| numeric_features = all_features.dtypes[all_features.dtypes != "object"].index
all_features[numeric_features] = all_features[numeric_features].apply(
lambda x: (x - x.mean()) / x.std()
)
all_features[numeric_features] = all_features[numeric_features].fillna(0)
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对离散数值采用one-hot编码处理
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| all_features = pandas.get_dummies(all_features, dummy_na=True)
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处理完后，再重新分为训练集和测试集
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| n_train = train.shape[0]
train_features = torch.tensor(all_features[:n_train].values.astype(numpy.float32), dtype=torch.float32)
test_features = torch.tensor(all_features[n_train:].values.astype(numpy.float32), dtype=torch.float32)
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