How to calculate percent change of each month, from average of previous months

Question

I have data that looks like this:

YearMonth  PageViews  Users
202001      100        10
202002      150        12
202003      100        13
202004      120        15
202005      130        10

I want to find out the percentage difference of each new month from the average of the previous month’s usage. For example,

For 202001 it will be NA as there is no previous month
For 202002 the average of the previous month
will be only 202001, so the percentage difference for pageviews for
202002 will be 50% and user would be 20% as compared to 202001
Similarly for 202003, the average of previous months would be
average of 202001 and 202002 combined, so pageview average is 125 (100+150 divided by 2), and user
is 11, so the percentage difference for 202003 will be -20% for
pageviews and +18% for users and so on.

How can I find this using python? Any help would be appreciated. Thank you.

Asked By: Kshitij Yadav

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Source

Answer 1

Try cumsum()/range for the cumulative mean:

cols = ['PageViews','Users']
cum_mean = df[cols].cumsum().div(np.arange(len(df))+1, axis=0)
df[cols].div(cum_mean.shift()).sub(1)

Output:

   PageViews     Users
0        NaN       NaN
1   0.500000  0.200000
2  -0.200000  0.181818
3   0.028571  0.285714
4   0.106383 -0.200000

Answered By: Quang Hoang

Answer 2

Alternative using df.expanding().mean()

    ((df[['PageViews','Users']]
.div(df[['PageViews','Users']].expanding().mean().shift()))
.sub(1))

Output:

   PageViews     Users
0        NaN       NaN
1   0.500000  0.200000
2  -0.200000  0.181818
3   0.028571  0.285714
4   0.106383 -0.200000

Answered By: rhug123

How to calculate percent change of each month, from average of previous months

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