我正在将 pct_change 计算应用于 pandas 数据帧。当月份列被排序时，一切正常。如果不是，则计算结果不正确。
现在这是我的代码:
data = [
('product_a','1/31/2014',53)
,('product_b','1/31/2014',44)
,('product_c','1/31/2014',36)
,('product_a','11/30/2013',52)
,('product_b','11/30/2013',43)
,('product_c','11/30/2013',35)
,('product_a','3/31/2014',50)
,('product_b','3/31/2014',41)
,('product_c','3/31/2014',34)
,('product_a','12/31/2013',50)
,('product_b','12/31/2013',41)
,('product_c','12/31/2013',34)
,('product_a','2/28/2014',52)
,('product_b','2/28/2014',43)
,('product_c','2/28/2014',35)
]
product_df = DataFrame( data, columns=['prod_desc','activity_month','prod_count'] )
for index, row in product_df.iterrows():
row['activity_month']= datetime.strptime(row['activity_month'],'%m/%d/%Y')
product_df.loc[index, 'activity_month'] = date.strftime(row['activity_month'],'%Y-%m-%d')
product_df['pct_ch'] = product_df.groupby('prod_desc')['prod_count'].pct_change()
product_df = product_df.sort(['prod_desc','activity_month'])
我得到的返回:
prod_desc activity_month prod_count pct_ch
3 product_a 2013-11-30 52 -0.018868
9 product_a 2013-12-31 50 0.000000
0 product_a 2014-01-31 53 NaN
12 product_a 2014-02-28 52 0.040000
6 product_a 2014-03-31 50 -0.038462
4 product_b 2013-11-30 43 -0.022727
10 product_b 2013-12-31 41 0.000000
1 product_b 2014-01-31 44 NaN
13 product_b 2014-02-28 43 0.048780
7 product_b 2014-03-31 41 -0.046512
5 product_c 2013-11-30 35 -0.027778
11 product_c 2013-12-31 34 0.000000
2 product_c 2014-01-31 36 NaN
14 product_c 2014-02-28 35 0.029412
8 product_c 2014-03-31 34 -0.028571
这里的计算是无序的，因为每个产品第一个月的 pct_change 应该是 NaN。
我认为问题在于 pct_change 计算不包括 groupby 中的“activity_month”。当我尝试添加它时，我得到以下输出。
product_df['pct_ch'] = product_df.groupby(['prod_desc','activity_month'])['prod_count'].pct_change()
prod_desc activity_month prod_count pct_ch
3 product_a 2013-11-30 52 NaN
9 product_a 2013-12-31 50 NaN
0 product_a 2014-01-31 53 NaN
12 product_a 2014-02-28 52 NaN
6 product_a 2014-03-31 50 NaN
4 product_b 2013-11-30 43 NaN
10 product_b 2013-12-31 41 NaN
1 product_b 2014-01-31 44 NaN
13 product_b 2014-02-28 43 NaN
7 product_b 2014-03-31 41 NaN
5 product_c 2013-11-30 35 NaN
11 product_c 2013-12-31 34 NaN
2 product_c 2014-01-31 36 NaN
14 product_c 2014-02-28 35 NaN
8 product_c 2014-03-31 34 NaN
最佳答案
所以我认为您遇到的问题是 groupby 正在计算相同 prod_desc 的相邻行之间的百分比差异，并且当您执行操作时这不是按日期顺序排序的，因此移动排序上面的 groupby 将解决这个问题。您还可以删除 for 循环并使用 pandas 将其写为一行。
import pandas as pd
data = [
('product_a','1/31/2014',53)
,('product_b','1/31/2014',44)
,('product_c','1/31/2014',36)
,('product_a','11/30/2013',52)
,('product_b','11/30/2013',43)
,('product_c','11/30/2013',35)
,('product_a','3/31/2014',50)
,('product_b','3/31/2014',41)
,('product_c','3/31/2014',34)
,('product_a','12/31/2013',50)
,('product_b','12/31/2013',41)
,('product_c','12/31/2013',34)
,('product_a','2/28/2014',52)
,('product_b','2/28/2014',43)
,('product_c','2/28/2014',35)
]
product_df = pd.DataFrame( data, columns=['prod_desc','activity_month','prod_count'])
product_df['activity_month'] = pd.to_datetime(product_df['activity_month'],
format='%m/%d/%Y')
product_df = product_df.sort_values(['prod_desc','activity_month'])
product_df['pct_ch'] = product_df.groupby('prod_desc')['prod_count'].pct_change()
我认为这应该会产生您想要的答案。
prod_desc activity_month prod_count pct_ch
3 product_a 2013-11-30 52 NaN
9 product_a 2013-12-31 50 -0.038462
0 product_a 2014-01-31 53 0.060000
12 product_a 2014-02-28 52 -0.018868
6 product_a 2014-03-31 50 -0.038462
4 product_b 2013-11-30 43 NaN
10 product_b 2013-12-31 41 -0.046512
1 product_b 2014-01-31 44 0.073171
13 product_b 2014-02-28 43 -0.022727
7 product_b 2014-03-31 41 -0.046512
5 product_c 2013-11-30 35 NaN
11 product_c 2013-12-31 34 -0.028571
2 product_c 2014-01-31 36 0.058824
14 product_c 2014-02-28 35 -0.027778
8 product_c 2014-03-31 34 -0.028571
关于python - 如何在 pct_change 计算中对 pandas DataFrame 中的多列进行分组，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/23790415/