我正在进行具有两个数据帧的机器学习计算 – 一个用于因子,另一个用于目标值.我必须将它们分成训练和测试部分.在我看来,我找到了方法,但我正在寻找更优雅的解决方案.这是我的代码：
import pandas as pd
import numpy as np
import random
df_source = pd.DataFrame(np.random.randn(5,2),index = range(0,10,columns=list('AB'))
df_target = pd.DataFrame(np.random.randn(5,columns=list('CD'))
rows = np.asarray(random.sample(range(0,len(df_source)),2))
df_source_train = df_source.iloc[rows]
df_source_test = df_source[~df_source.index.isin(df_source_train.index)]
df_target_train = df_target.iloc[rows]
df_target_test = df_target[~df_target.index.isin(df_target_train.index)]
print('rows')
print(rows)
print('source')
print(df_source)
print('source train')
print(df_source_train)
print('source_test')
print(df_source_test)
—-编辑 – unutbu解决方案(midified)—
np.random.seed(2013) percentile = .6 rows = np.random.binomial(1,percentile,size=len(df_source)).astype(bool) df_source_train = df_source[rows] df_source_test = df_source[~rows] df_target_train = df_target[rows] df_target_test = df_target[~rows]
解决方法
如果你将行设为长度为len(df)的布尔数组,则可以使用df [rows]获取True行,并使用df [〜rows]获取False行：
import pandas as pd
import numpy as np
import random
np.random.seed(2013)
df_source = pd.DataFrame(
np.random.randn(5,index=range(0,columns=list('AB'))
rows = np.random.randint(2,size=len(df_source)).astype('bool')
df_source_train = df_source[rows]
df_source_test = df_source[~rows]
print(rows)
# [ True True False True False]
# if for some reason you need the index values of where `rows` is True
print(np.where(rows))
# (array([0,1,3]),)
print(df_source)
# A B
# 0 0.279545 0.107474
# 2 0.651458 -1.516999
# 4 -1.320541 0.679631
# 6 0.833612 0.492572
# 8 1.555721 1.741279
print(df_source_train)
# A B
# 0 0.279545 0.107474
# 2 0.651458 -1.516999
# 6 0.833612 0.492572
print(df_source_test)
# A B
# 4 -1.320541 0.679631
# 8 1.555721 1.741279