我有这个时间序列数据,现在我想使用'modal_price'计算每个APMC和商品集群的趋势季节性类型(乘法或加法(。数据集有大约60000个这样的行,APMC和Cluster是相同的,但日期在变化。数据集如下:
APMC | Commodity | qtl _weight| min_price | max_price | modal_price | district_name | Year | Month
date
2014-12-01 Akole bajri 40 1375 1750 1563 Ahmadnagar 2014 12
2014-12-01 Akole paddy-unhusked 346 1400 1800 1625 Ahmadnagar 2014 12
2014-12-01 Akole wheat 55 1500 1900 1675 Ahmadnagar 2014 12
2014-12-01 Akole bhagar/vari 59 2000 2600 2400 Ahmadnagar 2014 12
2014-12-01 Akole gram 9 3200 3300 3235 Ahmadnagar 2014 12
2014-12-01 Jamkhed cotton 44199 3950 4033 3991 Ahmadnagar 2014 12
2014-12-01 Jamkhed bajri 846 1300 1488 1394 Ahmadnagar 2014 12
2014-12-01 Jamkhed wheat(husked) 155 1879 2231 2055 Ahmadnagar 2014 12
2014-12-01 Kopar gram 421 1983 2698 2463 Ahmadnagar 2014 12
2014-12-01 Kopar greengram 18 6734 7259 6759 Ahmadnagar 2014 12
2014-12-01 Kopar soybean 1507 2945 3247 3199 Ahmadnagar 2014 12
2016-11-01 Sanga wheat(husked) 222 1730 2173 1994 Ahmadnagar 2016 11
现在我尝试了使用(APMC,商品和日期作为索引(的透视表,但这对计算每个集群(APMC、商品(的平均值(计算趋势(没有帮助。我只需要知道如何使用"modal_price"计算每个集群(APMC、Commodity(的平均值,并将其添加为数据帧/数据透视表中的COLUMN。
也许groupby会为您提供趋势所需的东西,然后转换会使您能够将其投影回同一索引?类似于:
# group by your cluster
g = df.groupby(["Year", "APMC", "Commodity"])
# determine the trend per cluster but finalise back into original diimensions
trend = g.modal_price.transform(lambda x: x.mean())
df["trend"] = trend