• Урок 1. 00:01:04
    Welcome
  • Урок 2. 00:00:58
    Why care about memory?
  • Урок 3. 00:00:46
    Smaller and faster
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    Topics covered
  • Урок 5. 00:00:42
    Student expectations
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    Meet your instructor
  • Урок 7. 00:02:52
    You'll need Python 3
  • Урок 8. 00:02:06
    Our code editor
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    Git the source
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    Python roughly equals CPython
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    Let's talk pointers
  • Урок 12. 00:01:33
    Passing values in C
  • Урок 13. 00:03:33
    Does Python have pointers?
  • Урок 14. 00:01:19
    Pass by value
  • Урок 15. 00:04:06
    Red pill / blue pill
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    CPython long source
  • Урок 17. 00:01:30
    The id() function
  • Урок 18. 00:03:15
    Loading the sample code
  • Урок 19. 00:07:48
    The size of objects
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    The *real* size of objects
  • Урок 21. 00:01:08
    Concept: Flyweight design pattern
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    Flyweight numbers in CPython
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    CPython source book
  • Урок 24. 00:01:02
    Allocation introduction
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    Allocation in action
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    Big objects may actually be many small ones
  • Урок 27. 00:02:14
    Small object allocation introduction
  • Урок 28. 00:02:18
    Allocation blocks
  • Урок 29. 00:01:06
    Allocation pools
  • Урок 30. 00:01:19
    Pools in the CPython source
  • Урок 31. 00:00:54
    Allocation arenas
  • Урок 32. 00:05:12
    Allocator stats
  • Урок 33. 00:03:00
    Ref counting in source
  • Урок 34. 00:09:03
    Reference counting
  • Урок 35. 00:03:37
    When reference counting breaks
  • Урок 36. 00:05:21
    GC in action
  • Урок 37. 00:03:56
    GC without containers?
  • Урок 38. 00:02:43
    Pythons generational garbage collector
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    When does the GC run?
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    Do you need the GC?
  • Урок 41. 00:03:40
    Ref-counting and the GIL
  • Урок 42. 00:01:41
    Data struct chapter intro
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    What we arent covering
  • Урок 44. 00:01:13
    Disabling the gc revisited
  • Урок 45. 00:06:07
    Data with cycles
  • Урок 46. 00:02:17
    Cycle busting part 1
  • Урок 47. 00:06:09
    Cycle busting with friend map
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    checking friends in the friend map
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    Different container types
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    Container sizes, starter data
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    Container sizes, lists
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    Demo: Container sizes, classes
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    Container sizes, arrays
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    Container sizes, Pandas
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    Container sizes, NumPy
  • Урок 56. 00:02:01
    Monitoring memory usages for current process
  • Урок 57. 00:00:38
    Function and memory
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    Functions clinging to memory
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    Implementing the pipeline functions
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    Tracking memory usage
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    Dropping intermediate data
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    Concept: Dropping intermediate data
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    Converting the pipeline to generators
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    Concept: Generators
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    Useful closures
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    Counting with closures
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    Concept: Closure state
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    Classes and memory introduction
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    Plain ol' fields
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    Is it a crowd?
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    Testing crowd sizes
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    Delayed fields with properties
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    Concept: Properties, a memory-oriented perspective
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    Where do classes store memory?
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    Concept: Class dictionaries
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    People with slots
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    Concept: Slots
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    Slots are faster, not just smaller
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    Profiling introduction
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    Profiling in PyCharm
  • Урок 81. 00:01:33
    A memory profiler
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    Line level memory profiling
  • Урок 83. 00:00:35
    Concept: Line by line memory with memory_profiler
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    Graphing memory usage over time
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    Concept: Graphing memory usage over time
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    A data science-focused profiler
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    Profiling with Fil
  • Урок 88. 00:00:54
    You crossed the finish line
  • Урок 89. 00:01:06
    Take the red pill (AKA pointers)
  • Урок 90. 00:01:32
    Allocating memory
  • Урок 91. 00:01:17
    Reference counting
  • Урок 92. 00:01:19
    Garbage collection
  • Урок 93. 00:00:45
    Container types
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    Memory and functions
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    Classes
  • Урок 96. 00:01:13
    Profiling
  • Урок 97. 00:00:27
    Bye and thanks
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