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# Choosing between loc and iloc

When choosing or transitioning between `loc` and `iloc`, there is one "gotcha" worth keeping in mind, which is that the two methods use slightly different indexing schemes.

`iloc` uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. So 0:10 will select entries 0,...,9. `loc`, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10.

Why the change? Remember that `loc` can index any stdlib type: strings, for example. If we have a DataFrame with index values Apples, ..., Potatoes, ..., and we want to select "all the alphabetical fruit choices between Apples and Potatoes", then it's a heck of a lot more convenient to index `df.loc['Apples':'Potatoes']` than it is to index something like `df.loc['Apples', 'Potatoet']` (`t` coming after `s` in the alphabet).

This is particularly confusing when the DataFrame index is a simple numerical list, e.g. 0,...,1000. In this case `df.iloc[0:1000]` will return 1000 entries, while `df.loc[0:1000]` return 1001 of them! To get 1000 elements using `iloc`, you will need to go one higher and ask for `df.iloc[0:1001]`.