I have a pandas Series where the index are date time.

I can plot my function with the `step()` function which plots each point of the Series relatively to the time (x is the time).

I want a less precise approach of the evolution in time. So I need to reduce the number of steps, and ignore the smallests increments. 
[![enter image description here](https://i.stack.imgur.com/pETzx.png)](https://i.stack.imgur.com/pETzx.png)
The only way I found is to use the `poly1d()` function from numpy to interpolate the points as a polynomial, and then to step the function. Unfortunately I am loosing the time index during the transformation because the index of a polynomial are x. 

Is there a way to ‘simplify’ my function to only get the dates (x values) of the biggest changes on the y axis instead of having all the dates for any change ? 
As I wrote above, I'd like to have only the biggest increments and not the small changes.

Here is the exact data:

```
2016-01-02    -5.418440
2016-01-09    -9.137942
2016-01-16    -9.137942
2016-01-23    -9.137942
2016-01-30    -9.137942
2016-02-06   -11.795107
2016-02-13   -11.795107
2016-02-20   -11.795107
2016-02-27   -11.795107
2016-03-05   -11.795107
2016-03-12   -13.106988
2016-03-19   -13.106988
2016-03-26   -13.106988
2016-04-02   -13.106988
2016-04-09   -13.106988
2016-04-16   -13.106988
2016-04-23   -13.106988
2016-04-30   -11.458878
2016-05-07     0.051123
2016-05-14     2.010179
2016-05-21    -3.210870
2016-05-28    -0.726291
2016-06-04     5.841818
2016-06-11     5.067061
2016-06-18     5.789375
2016-06-25    16.455159
2016-07-02    22.518294
2016-07-09    39.834977
2016-07-16    54.685965
2016-07-23    54.685965
2016-07-30    55.169290
2016-08-06    55.169290
2016-08-13    55.169290
2016-08-20    53.366569
2016-08-27    45.758675
2016-09-03    10.976592
2016-09-10    -0.554887
2016-09-17    -8.653451
2016-09-24   -18.198305
2016-10-01   -22.218711
2016-10-08   -21.158434
2016-10-15   -11.723798
2016-10-22    -9.928957
2016-10-29   -17.498315
2016-11-05   -22.850454
2016-11-12   -25.190656
2016-11-19   -27.250960
2016-11-26   -27.250960
2016-12-03   -27.250960
2016-12-10   -27.250960

```