I am applying a pct_change calculation to a pandas dataframe. Everything works fine when the month column is ordered. When it is not the calculation comes out incorrect.
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So i think the issue you have is that the groupby is calculating the percentage difference between adjacent rows of identical prod_desc
and this isn't ordered in date order when you perform the operation so moving the sort above the groupby will fix that issue. You can also remove the for loop and write that as one line using pandas.
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()
I think this should produce the answer you want.
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