How is my data stationary? 
I did a quick Dickey-Fuller test, to test whether my time series is stationary, and the result gives a P value of 9.6*10^-15, or in other words stationary.
How is this possible, my data clearly doesn't seem sttionary. The mean seems to be constant(to an extent), no seasonality issue but it's the variance?
 A: The ADF test has a null hypothesis of presence of a unit root. Clearly, your series does not appear to have a unit root, and the ADF correctly rejects $H_0$. However, absence of a unit root does not imply stationarity. Consequently, rejection of $H_0$ with an ADF test does not imply stationarity. How so?
Absence of a unit root is a necessary but not a sufficient condition for stationarity. Time-varying variance is one characteristic of nonstationarity, and it applies to your case. The ADF test is not designed to capture that, however, so no wonder the results are what they are.
A: I would agree you have visual evidence the series as non-stationary variance, and would suggest the unit root test that drives the ADF test does not detect this. While the ADF is great for testing trend stationarity, you can justify using your own discretion here on whether the variance is stationary.
There is also the possibility your version of the ADF test doesnt detect variance stationarity. From the ADF Wikipedia page:

The alternative hypothesis is different depending on which version of the test is used, but is usually stationarity or trend-stationarity.

There is a good Stack Overflow: Cross Validated post about the versions of the ADF test
