我想根据时间对分类的Pyspark数据帧进行火车测试拆分。假设前300行位于训练集中，接下来的200行位于测试组中。
我可以使用-
选择前300行train = df.show(300)
但是如何从Pyspark数据框中选择最后200行？
答案 0 :(得分：0)
假设您有一个大小为500的数据框# For this solution your DataFrame needs to be sorted by date.
limit = 100
df = pd.DataFrame({
'date1': ['1/1/2014', '2/1/2014', '3/1/2014','1/1/2014',
'2/1/2014', '3/1/2014','1/1/2014', '2/1/2014', '3/1/2014'],
'Place':['A','A','A','B','B','B','C','C','C'],
'AVG': [62,14,47,25,74,60,78,27,41]})
df2 = pd.DataFrame({'date2': ['1/1/2014', '2/1/2014'], 'Place':['A','C']})
result = []
for row in df2.to_dict('records'):
# For each date, I want to select the date that comes AFTER this one.
# Then, I take the .cumsum(), because it's the agg you wish to do.
# Filter by your limit and take the first occurrence.
# Converting this to a dict, appending it to a list, makes it easy
# to rebuild a DataFrame later.
ndf = df.loc[ (df['date1'] >= row['date2']) & (df['Place'] == row['Place']) ]\
.sort_values(by='date1')
ndf['avgsum'] = ndf['AVG'].cumsum()
final_df = ndf.loc[ ndf['avgsum'] >= limit ]
# Error handling, in case there is not avgsum above the threshold.
try:
final_df = final_df.iloc[0][['date1', 'avgsum']].rename({'date1' : 'date100'})
result.append( final_df.to_dict() )
except IndexError:
continue
df3 = pd.DataFrame(result)
final_df = pd.concat([df2, df3], axis=1, sort=False)
print(final_df)
# date2 Place avgsum date100
# 0 1/1/2014 A 123.0 3/1/2014
# 1 2/1/2014 C NaN NaN
，该数据框由df列排序。
一种简单的处理方法是对训练集使用限制，并对测试集的反向数据帧执行相同的操作。
time