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))

像这样从df中选择range row

row_count = df.shape[0]
split_point = int(row_count*1/5)
test_data, train_data = df[:split_point], df[split_point:]

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

你也可以考虑分层划分为训练集和测试集。设定划分也随机生成训练集和测试集，但保留了原始的类比例。这使得训练集和测试集更好地反映原始数据集的属性。

import numpy as np  

def get_train_test_inds(y,train_proportion=0.7):
    '''Generates indices, making random stratified split into training set and testing sets
    with proportions train_proportion and (1-train_proportion) of initial sample.
    y is any iterable indicating classes of each observation in the sample.
    Initial proportions of classes inside training and 
    testing sets are preserved (stratified sampling).
    '''

    y=np.array(y)
    train_inds = np.zeros(len(y),dtype=bool)
    test_inds = np.zeros(len(y),dtype=bool)
    values = np.unique(y)
    for value in values:
        value_inds = np.nonzero(y==value)[0]
        np.random.shuffle(value_inds)
        n = int(train_proportion*len(value_inds))

        train_inds[value_inds[:n]]=True
        test_inds[value_inds[n:]]=True

    return train_inds,test_inds

df[train_inds]和df[test_inds]为您提供原始DataFrame df的训练和测试集。

有很多有效的答案。又多了一个。 从sklearn。交叉验证导入train_test_split

#gets a random 80% of the entire set
X_train = X.sample(frac=0.8, random_state=1)
#gets the left out portion of the dataset
X_test = X.loc[~df_model.index.isin(X_train.index)]

对我来说，更优雅一点的方法是创建一个随机列，然后按它进行分割，这样我们就可以得到一个符合我们需求的随机分割。

def split_df(df, p=[0.8, 0.2]):
import numpy as np
df["rand"]=np.random.choice(len(p), len(df), p=p)
r = [df[df["rand"]==val] for val in df["rand"].unique()]
return r