Update f22/andy_lec_notes/lec21_Oct26_Copying/lec21_copying_template.ipynb

80d3e36f · Andy Kuemmel · b5909b67 · 80d3e36f
Commit 80d3e36f authored 2 years ago by Andy Kuemmel
--- a/f22/andy_lec_notes/lec21_Oct26_Copying/lec21_copying_template.ipynb
+++ b/f22/andy_lec_notes/lec21_Oct26_Copying/lec21_copying_template.ipynb
@@ -22,24 +22,13 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "college_dict = {\n",
+    "# Warmup 1:  determine the key that has the highest value in a dict\n",
-    "    \"UW\": {'name': \"University of Wisconsin\", 'type':'public', 'year': 1850, 'enrollment': 35474  },\n",
-    "    \"MI\": {'name': \"University of Michigan\", 'type':'public', 'year': 1817, 'enrollment': 44718  },\n",
-    "    \"MU\": {'name': \"Marquette University\",  'type':'private', 'year': 1881, 'enrollment': 11294  },\n",
-    "    \"EDG\": {'name': \"Edgewood College\",  'type':'private', 'year': 1927, 'enrollment': 2650  },\n",
-    "    }\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Warmup 1: What is the name of the university that is the oldest?\n",
    "\n",
-    "oldest_name = None\n",
+    "# which dog is the oldest?\n",
-    "year_founded = None\n"
+    "dog_dict = {\"Miles\": 2, \"Gertie\": 4, \"Sammy\": 11, \"Fido\": 3, \"Jordy\": 5}\n",
+    "oldest_dog = None\n",
+    "max_age = 0\n",
+    "\n"
   ]
  },
  {
@@ -48,12 +37,12 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "#Warmup 2: using one dict to make a new dict\n",
+    "#Warmup 2: Which dog is the oldest?\n",
-    "\n",
+    "dog_list = [ {'id': 34523, 'name': \"Miles\", 'age': 2},\n",
-    "# let's make a new dictionary of the total enrollment by type\n",
+    "            {'id': 22334, 'name': \"Gertie\", 'age': 4},\n",
-    "# key is the type, value is the total enrollment for that type\n",
+    "            {'id': 87654, 'name': \"Fido\", 'age': 3'} ]\n",
-    "\n",
+    "oldest_dog = None\n",
-    "enrollment_by_type_dict = {}"
+    "max_age = 0 "
   ]
  },
  {

 %% Cell type:code id: tags:
 ``` python
 import copy # we are going to learn about this module today
 ```
 %% Cell type:markdown id: tags:
 ## Warmup
 %% Cell type:code id: tags:
 ``` python
-college_dict = {
+# Warmup 1:  determine the key that has the highest value in a dict
-    "UW": {'name': "University of Wisconsin", 'type':'public', 'year': 1850, 'enrollment': 35474  },
-    "MI": {'name': "University of Michigan", 'type':'public', 'year': 1817, 'enrollment': 44718  },
-    "MU": {'name': "Marquette University",  'type':'private', 'year': 1881, 'enrollment': 11294  },
-    "EDG": {'name': "Edgewood College",  'type':'private', 'year': 1927, 'enrollment': 2650  },
-    }
-```
-%% Cell type:code id: tags:
-``` python
+# which dog is the oldest?
-#Warmup 1: What is the name of the university that is the oldest?
+dog_dict = {"Miles": 2, "Gertie": 4, "Sammy": 11, "Fido": 3, "Jordy": 5}
+oldest_dog = None
+max_age = 0
-oldest_name = None
-year_founded = None
 ```
 %% Cell type:code id: tags:
 ``` python
-#Warmup 2: using one dict to make a new dict
+#Warmup 2: Which dog is the oldest?
+dog_list = [ {'id': 34523, 'name': "Miles", 'age': 2},
-# let's make a new dictionary of the total enrollment by type
+            {'id': 22334, 'name': "Gertie", 'age': 4},
-# key is the type, value is the total enrollment for that type
+            {'id': 87654, 'name': "Fido", 'age': 3'} ]
+oldest_dog = None
-enrollment_by_type_dict = {}
+max_age = 0
 ```
 %% Cell type:markdown id: tags:
 # Lecture 21:  Reference Copy, Copy, Deep Copy
 Learning Objectives:
 - Correctly predict the output of code that uses reference variable mutation and reassignment.
 - Compare and contrast the 3 levels of copying:
 - reference copy
 - shallow copy(copy.copy)
 - deep copy (copy.deepcopy)
 - Write code that copies a data structure at the appropriate level for a given use case.
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:markdown id: tags:
 ## Correctly predict the output of code that uses reference variable mutation and reassignment.
 %% Cell type:code id: tags:
 ``` python
 # Example 1...predict the answer, uncomment the print statement, see if you were correct
 # for extra understanding, go to Python tutor
 dict1 = {}
 dict2 = {}
 #dict2 = dict1   # reference variable reassignment
 #dict1["MI"] = "Ann Arbor"
 #dict2["WI"] = "Madison"
 print(dict1, dict2)
 ```
 %% Output
    {'MI': 'Ann Arbor'} {}
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:markdown id: tags:
 ## Compare and contrast the 3 levels of copying:
 - reference copy
 - shallow copy (copy.copy)  # one level of object depth
 - deep copy (copy.deepcopy) # all levels of object depth
 %% Cell type:markdown id: tags:
 ![three%20levels%20copy.png](attachment:three%20levels%20copy.png)
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:code id: tags:
 ``` python
 # Run this in Python Tutor, and uncomment one option at a time.
 import copy
 x =[
    {"name":"A", "score":88},
    {"name":"B", "score":111},
    {"name":"C", "score":100}]
 #y = x              # reference copy
 #y[0]['name'] = "Andy"
 #y = copy.copy(x)    # shallow copy...copies the first level
 #y[1]['name'] = "Bianca"
 #y = copy.deepcopy(x) # deep copy...copies down to every level
 #y[2]['name'] = "Chris"
 # takeway:  Write a note to yourself
 #
 #
 #
 ```
 %% Cell type:markdown id: tags:
 ## Write code that copies a data structure at the appropriate level for a given use case.
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:markdown id: tags:
 ![player_score.png](attachment:player_score.png)
 %% Cell type:markdown id: tags:
 ![use%20case%201.png](attachment:use%20case%201.png)
 %% Cell type:code id: tags:
 ``` python
 # Example 4a: Given this playlist
 player_scores = [
    {"name":"Anna", "score":88},
    {"name":"Bianca", "score":111},
    {"name":"Chris", "score":100}
 ]
 # a.) write a function that finds the name of the person
 #    with the highest score score in a list of dicts with keys 'name' , 'score'
 def max_score(score_list):
    best_person = None
    high_score = None
    return best_person
 #print(player_scores)
 print(max_score(player_scores))
 #print(player_scores)
 # after you write this code, run it in Python Tutor
 ```
 %% Output
    Bianca
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:code id: tags:
 ``` python
 # Use Case 2 below
 ```
 %% Cell type:markdown id: tags:
 ![ex%202.png](attachment:ex%202.png)
 %% Cell type:code id: tags:
 ``` python
 # Example 4b.) use a shallow copy to sort each list of scores
 player_scores = [
    {"name":"Anna", "scores":[23, 18, 11, 17, 6]},
    {"name":"Bianca", "scores":[11, 21, 9, 4, 10]},
    {"name":"Chris", "scores":[12, 10, 3, 8, 7]}
 ]
 player_scores_copy = copy.copy(player_scores) # makes a shallow copy
 # iterate through the list player_scores_copy and sort each list
 print(player_scores)  # should be unchanged
 print(player_scores_copy)
 ```
 %% Output
    <class 'dict'>
    <class 'dict'>
    <class 'dict'>
    [{'name': 'Anna', 'scores': [11, 17, 18, 23]}, {'name': 'Bianca', 'scores': [4, 9, 11, 21]}, {'name': 'Carl', 'scores': [3, 8, 10, 12]}]
    [{'name': 'Anna', 'scores': [11, 17, 18, 23]}, {'name': 'Bianca', 'scores': [4, 9, 11, 21]}, {'name': 'Carl', 'scores': [3, 8, 10, 12]}]
 %% Cell type:code id: tags:
 ``` python
 ```
 %% Cell type:code id: tags:
 ``` python
 # Use Case 3 below
 ```
 %% Cell type:markdown id: tags:
 ![ex%203.png](attachment:ex%203.png)
 %% Cell type:code id: tags:
 ``` python
 #Example 4c.) make a deep copy as a backup, then change the original
 player_scores= [
    {"name":"Anna", "score":88},
    {"name":"Bianca", "score":111},
    {"name":"Chris", "score":100}
 ]
 players_before = copy.deepcopy(player_scores)
 # change someone's score here
 print(players_before)
 print('after changes:')
 print(player_scores)
 answer = input("enter y to keep changes: ")
 if answer != 'y':
    player_scores = players_before
 print(players_before)
 print('after changes:')
 print(player_scores)
 # after you write this code, run it in Python Tutor
 ```
 %% Output
    [{'name': 'Anna', 'score': 88}, {'name': 'Bianca', 'score': 111}, {'name': 'Carl', 'score': 100}]
    after changes:
    [{'name': 'Anna', 'score': 88}, {'name': 'Bianca', 'score': 120}, {'name': 'Carl', 'score': 100}]
    enter y to keep changes: n
    [{'name': 'Anna', 'score': 88}, {'name': 'Bianca', 'score': 111}, {'name': 'Carl', 'score': 100}]
    after changes:
    [{'name': 'Anna', 'score': 88}, {'name': 'Bianca', 'score': 111}, {'name': 'Carl', 'score': 100}]
 %% Cell type:code id: tags:
 ``` python
 # Note:  Deepcopy is also known as a Recursive copy.
 ```
 %% Cell type:code id: tags:
 ``` python
 # Think about it.  When you slice a list fully, using [:], what kind of copy is this?
 favorites = [ 17, ['Miles', 'Gertie'], {'a': 65, 'b': 66, 'c': 67}, True]
 sliced = favorites [:]
 # try in Python Tutor
 # your answer:
 ```
 %% Cell type:code id: tags:
 ``` python
 # Do Worksheet for today (good quiz practice)
 # Answers will be posted on later today to the same place you found this Notebook file.
 ```