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)]

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)

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)

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:]

shuffle = np.random.permutation(len(df))
test_size = int(len(df) * 0.2)
test_aux = shuffle[:test_size]
train_aux = shuffle[test_size:]
TRAIN_DF =df.iloc[train_aux]
TEST_DF = df.iloc[test_aux]

上面有很多很好的答案，所以我只想再加一个例子，在这种情况下，你想通过使用numpy库来指定火车和测试集的确切样本数量。

# set the random seed for the reproducibility
np.random.seed(17)

# e.g. number of samples for the training set is 1000
n_train = 1000

# shuffle the indexes
shuffled_indexes = np.arange(len(data_df))
np.random.shuffle(shuffled_indexes)

# use 'n_train' samples for training and the rest for testing
train_ids = shuffled_indexes[:n_train]
test_ids = shuffled_indexes[n_train:]

train_data = data_df.iloc[train_ids]
train_labels = labels_df.iloc[train_ids]

test_data = data_df.iloc[test_ids]
test_labels = data_df.iloc[test_ids]