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

不需要转换为numpy。只要用pandas df来做拆分，它就会返回一个pandas df。

from sklearn.model_selection import train_test_split

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

如果你想把x和y分开

X_train, X_test, y_train, y_test = train_test_split(df[list_of_x_cols], df[y_col],test_size=0.2)

如果要分割整个df

X, y = df[list_of_x_cols], df[y_col]

在我的例子中，我想用特定的数字分割训练、测试和开发中的数据帧。我在这里分享我的解决方案

首先，为数据帧分配一个唯一的id(如果已经不存在的话)

import uuid
df['id'] = [uuid.uuid4() for i in range(len(df))]

以下是我的分割数字:

train = 120765
test  = 4134
dev   = 2816

分裂函数

def df_split(df, n):
    
    first  = df.sample(n)
    second = df[~df.id.isin(list(first['id']))]
    first.reset_index(drop=True, inplace = True)
    second.reset_index(drop=True, inplace = True)
    return first, second

现在分成培训，测试，开发

train, test = df_split(df, 120765)
test, dev   = df_split(test, 4134)

示例方法选择数据的一部分，您可以先通过传递种子值来打乱数据。

train = df.sample(frac=0.8, random_state=42)

对于测试集，您可以删除通过train DF索引的行，然后重置新DF的索引。

test = df.drop(train_data.index).reset_index(drop=True)

有许多方法可以创建训练/测试甚至验证样本。

案例1:没有任何选项的经典方法train_test_split:

from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.3)

案例2:非常小的数据集(<500行):为了通过这种交叉验证获得所有行的结果。最后，您将对可用训练集的每一行都有一个预测。

from sklearn.model_selection import KFold
kf = KFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
    reg = RandomForestRegressor(n_estimators=50, random_state=0)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = reg.fit(X_train, y_train)
    y_hat = clf.predict(X_test)
    y_hat_all.append(y_hat)

案例3a:用于分类的不平衡数据集。下面是情形1的等价解:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3)

案例3b:用于分类的不平衡数据集。在情形2之后，等价解如下:

from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
    reg = RandomForestRegressor(n_estimators=50, random_state=0)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = reg.fit(X_train, y_train)
    y_hat = clf.predict(X_test)
    y_hat_all.append(y_hat)

案例4:你需要在大数据上创建一个训练/测试/验证集来调优超参数(60%训练，20%测试和20% val)。

from sklearn.model_selection import train_test_split
X_train, X_test_val, y_train, y_test_val = train_test_split(X, y, test_size=0.6)
X_test, X_val, y_test, y_val = train_test_split(X_test_val, y_test_val, stratify=y, test_size=0.5)

这个怎么样? Df是我的数据框架

total_size=len(df)

train_size=math.floor(0.66*total_size) (2/3 part of my dataset)

#training dataset
train=df.head(train_size)
#test dataset
test=df.tail(len(df) -train_size)