reading-notes

Pain and Suffering

“All growth comes with some degree of pain, as it pulls you out of your comfort zone. The greater the growth, the greater the pain. But pain in the service of growth is a good thing, as long as that pain is what’s necessary to achieve the growth that you’re aiming for. And even better than that, this pain is only temporary. It’s what will launch you forward into the next phase of your life.

Do not confuse this with suffering.

Suffering is pain without purpose. Pain with no higher goal. Pain with no dreams, no ambition, no aspiration.”

Beginners Guide to Big O

• The method of describing time or space complexity of an algorithm.

• O(1)
  • an algorithm that will always execute in the same time (or space) regardless of the size of the input data set.
• O(N)
  • an algorithm whose performance will grow linearly and in direct proportion to the size of the input data set.
• O(N²)
  • an algorithm whose performance is directly proportional to the square of the size of the input data set.
• O(2ⁿ)
  • an algorithm whose growth doubles with each addition to the input data set.
• O(log N)
  • an algorithm whose growth is linear while the data increases exponentially.

Names and Values in Python

• Names refer to Values
• Values live until no references
• Assignment never copies data
• Immutable values cannot alias
  • ints
  • floats
  • strings
  • tuples
• names have no type
• values have no scope

References

• Pain vs. Suffering
• Beginners Guide to Big O
• Code Newbie - Big O
• YouTube - Names and Values in Python
• Python Tutor - Visualize Python
• Python environment
• Python Module of the week