基于切片的数据帧更新

sklearn.model_selection.train_test_split

我目前的做法如下:

import pandas as pd
import numpy as np
from sklearn import model_selection

dates = pd.date_range('20130101',periods=10)
df = pd.DataFrame(np.random.randn(10,4),index=dates,columns=list('ABCD')).reset_index()

split = [0.8, 0.2]
split_seed = 123

train_df, test_df = model_selection.train_test_split(df, train_size = split[0], test_size = split[1], random_state=split_seed)

train_df["Dataset"] = "train"
test_df["Dataset"] = "test"

final_df = train_df.append(test_df)

df 对象:

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

这其实并不重要,因为在这之后就不再使用原始的数据帧了。但我很好奇我怎么能做得不一样。我想不是编辑 train_df 和 test_df 再加上它们,我就可以编辑了 直接,但作为我不太熟悉如何 .loc 和 .iloc

说明我要查找的内容的Psuedo代码如下:

df["Dataset"] = "train" WHERE index in train_df.index.values
df["Dataset"] = "test" WHERE index in test_df.index.values