我会使用numpy的randn:

In [11]: df = pd.DataFrame(np.random.randn(100, 2))

In [12]: msk = np.random.rand(len(df)) < 0.8

In [13]: train = df[msk]

In [14]: test = df[~msk]

为了证明这是有效的:

In [15]: len(test)
Out[15]: 21

In [16]: len(train)
Out[16]: 79

import pandas as pd

from sklearn.model_selection import train_test_split

datafile_name = 'path_to_data_file'

data = pd.read_csv(datafile_name)

target_attribute = data['column_name']

X_train, X_test, y_train, y_test = train_test_split(data, target_attribute, test_size=0.8)

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:]

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)

我会使用numpy的randn:

In [11]: df = pd.DataFrame(np.random.randn(100, 2))

In [12]: msk = np.random.rand(len(df)) < 0.8

In [13]: train = df[msk]

In [14]: test = df[~msk]

为了证明这是有效的:

In [15]: len(test)
Out[15]: 21

In [16]: len(train)
Out[16]: 79

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)]