There have been a lot of good answers here but they don't generally address the higher level idea from the statistical sense: You want your data to be a representative sample from the distribution (of, say, weekly spending or spend per grocery trip) that you're trying to ascertain over the time period you're interested in. You can then use that distribution to get a very good idea of your average spending and how it varies. You can even inspect the data points in the tails and see if they occur around certain times of year (e.g. the holiday season).
By adopting a statistical stance (i.e. thinking about the distribution, the mean, and the variance and/or standard deviation) you can start to bring in insights in a more structured way. For example, you might consider how life events can change this distribution. Specifically, if you were to lose your job and tighten the purse strings or get a large pay raise and decide you can finally realize your dream of being 'sliced mango rich' á la Ali Wong, it's safe to assume that the distribution will change. While that is largely common sense, what may be less obvious is that those life moments are good potential cutoffs where you can view samples before that life event as not being representative of your new reality (i.e. the new distribution).
Further, you can also consider other types of expenditures and some of their characteristics. For example, groceries will tend to have fairly high variation, some outliers, and a (potentially variable) high frequency of occurrence. In contrast, insurance and mortgage payments, rent, etc. tend to have low variation, be relatively stable, and have a regular frequency. Therefore, the number of examples you need to fully characterize the distribution of these kinds of payments is less that it would be for your groceries – in fact, you may not even need to adopt a statistical perspective. You can just look at the payment schedule for your mortgage, your rent contract, or the past few insurance payments and get a very precise idea of how much you'll be paying in the future provided you don't move, refinance your home, or have a car accident. In effect, you can use a first principles based approach rather than a statistical approach.
In general, anywhere where you are not mandated to pay on a schedule (e.g. groceries, entertainment, travel, or most anything that's not a bill) the answer you're looking for can be provided using statistical tools. If have a little gumption and have taken a statistics course before, you'll likely have everything you need to get high quality estimates while being able to identify where they might break down (although, in most cases, the breaking down will best be viewed through the lens of common sense as mentioned above).