Python Memory Management and Tips Transcripts
Chapter: Course conclusion and review
Lecture: Container types
0:00 When you think of the data structure that's gonna hold all of your data, you
0:03 have to remember they're not all created equal.
0:06 We saw that we could have a list.
0:08 We could have a typed array,
0:10 in the case of numbers, we can have Pandas data frames,
0:13 we can have series, we can have NumPy
0:15 arrays, we can have a list of classes,
0:17 all of these things have benefits and drawbacks for the way we use them in our
0:21 program, but they also have very different memory costs.
0:25 For example, our simple little typed array was something like 10 times more efficient
0:30 than storing those numbers in a list,
0:31 which is still more efficient than using things like Pandas or classes.
0:36 You may want to choose one of these more advanced data types because it does amazing
0:39 stuff for you, but just be aware that there is a memory price to be paid for it.