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pythonpandasoffsetmoving-averagedatetimeoffset

How do I calculate week over week changes in Pandas?

I have the following df of values for various slices across time:

    date        A   B   C
0   2016-01-01  5   7   2
1   2016-01-02  6   12  15
...
2   2016-01-08  9   5   16
...
3   2016-12-24  5   11  13
4   2016-12-31  3   52  22

I would like to create a new dataframe that calculates the w-w change in each slice, by date. For example, I want the new table to be blank for all slices from jan 1 - jan 7. I want the value of jan 8 to be the jan 8 value for the given slice minus the value of the jan 1 value of that slice. I then want the value of jan 9 to be the jan 9 value for the given slice minus the value of the jan 2 slice. So and so forth, all the way down.

The example table would look like this:

    date        A   B   C
0   2016-01-01  0   0   0
1   2016-01-02  0   0   0
...
2   2016-01-08  4   -2  14
...
3   2016-12-24  4   12  2
4   2016-12-31  -2  41  9

You may assume the offset is ALWAYS 7. In other words, there are no missing dates.

Solution

  • If we know offset is always 7 then use shift(), here is a quick example showing how it works :

    df = pandas.DataFrame({'x': range(30)})
df.shift(7)
       x
0    NaN
1    NaN
2    NaN
3    NaN
4    NaN
5    NaN
6    NaN
7    0.0
8    1.0
9    2.0
10   3.0
11   4.0
12   5.0
...

    So with this you can do :

    df - df.shift(7)
      x
0   NaN
1   NaN
2   NaN
3   NaN
4   NaN
5   NaN
6   NaN
7   7.0
8   7.0  
...

    In your case, don't forget to set_index('date') before.