将df分成训练，验证，测试。给定增广数据的df，只选择相关列和独立列。将最近的10%的行(使用'dates'列)分配给test_df。随机将剩余行的10%分配给validate_df，其余的分配给train_df。不要重新索引。检查所有行是否都是唯一分配的。只使用本地蟒和熊猫库。

方法1:将行分割为训练、验证、测试数据框架。

train_df = augmented_df[dependent_and_independent_columns]
test_df = train_df.sort_values('dates').tail(int(len(augmented_df)*0.1)) # select latest 10% of dates for test data
train_df = train_df.drop(test_df.index) # drop rows assigned to test_df
validate_df = train_df.sample(frac=0.1) # randomly assign 10%
train_df = train_df.drop(validate_df.index) # drop rows assigned to validate_df
assert len(augmented_df) == len(set(train_df.index).union(validate_df.index).union(test_df.index)) # every row must be uniquely assigned to a df

方法2:当validate必须是train的子集时拆分行(fastai)

train_validate_test_df = augmented_df[dependent_and_independent_columns]
test_df = train_validate_test_df.loc[augmented_df.sort_values('dates').tail(int(len(augmented_df)*0.1)).index] # select latest 10% of dates for test data
train_validate_df = train_validate_test_df.drop(test_df.index) # drop rows assigned to test_df
validate_df = train_validate_df.sample(frac=validate_ratio) # assign 10% to validate_df
train_df = train_validate_df.drop(validate_df.index) # drop rows assigned to validate_df
assert len(augmented_df) == len(set(train_df.index).union(validate_df.index).union(test_df.index)) # every row must be uniquely assigned to a df
# fastai example usage
dls = fastai.tabular.all.TabularDataLoaders.from_df(
train_validate_df, valid_idx=train_validate_df.index.get_indexer_for(validate_df.index))

我认为你还需要一个副本，而不是一个切片的数据框架，如果你想以后添加列。

msk = np.random.rand(len(df)) < 0.8
train, test = df[msk].copy(deep = True), df[~msk].copy(deep = True)

如果你希望有一个数据帧和两个数据帧(不是numpy数组)，这应该可以做到:

def split_data(df, train_perc = 0.8):

   df['train'] = np.random.rand(len(df)) < train_perc

   train = df[df.train == 1]

   test = df[df.train == 0]

   split_data ={'train': train, 'test': test}

   return split_data

可以使用~(波浪符)排除使用df.sample()采样的行，让pandas单独处理索引的采样和过滤，以获得两个集。

train_df = df.sample(frac=0.8, random_state=100)
test_df = df[~df.index.isin(train_df.index)]

Scikit Learn的train_test_split就是一个很好的例子。它将拆分numpy数组和数据框架。

from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)

我将使用scikit-learn自己的training_test_split，并从索引生成它

from sklearn.model_selection import train_test_split


y = df.pop('output')
X = df

X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe train