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Example', 'year': 2017, 'runtime': 96, 'genres': ['Documentary', 'History']}, {'name': 'Painted Lives', 'director': 'C. Example', 'year': 2019, 'runtime': 88, 'genres': ['Documentary', 'Art']}]"}]}]}]},{"type":"comment","value":"-\nA quiz to review the contents of this section:\n\n\n"}]},"html":"

After this section

Part 4 introduced dictionary construction, lookup by key, traversal with items(), and lists of record dictionaries. This section applies those structures to grouping observations and maintaining small collections of records.

Grouping and counting

Let's have a look at a list of words:

word_list = [\n  \"banana\", \"milk\", \"beer\", \"cheese\", \"sourmilk\", \"juice\", \"sausage\",\n  \"tomato\", \"cucumber\", \"butter\", \"margarine\", \"cheese\", \"sausage\",\n  \"beer\", \"sourmilk\", \"sourmilk\", \"butter\", \"beer\", \"chocolate\"\n]

We would like to analyze this list of words in different ways. For instance, we would like to know how many times each word appears in the list.

A dictionary can be a useful tool in managing this kind of information. In the example below, we go through the items in the list one by one. Using the words in the list as keys in a new dictionary, the value mapped to each key is the number of times the word has appeared:

def counts(my_list):\n    words = {}\n    for word in my_list:\n        # if the word is not yet in the dictionary, initialize the value to zero\n        if word not in words:\n            words[word] = 0\n        # increment the value\n        words[word] += 1\n    return words\n\n# call the function\nprint(counts(word_list))

The program prints out the following:

{'banana': 1, 'milk': 1, 'beer': 3, 'cheese': 2, 'sourmilk': 3, 'juice': 1, 'sausage': 2, 'tomato': 1, 'cucumber': 1, 'butter': 2, 'margarine': 1, 'chocolate': 1}

What if we wanted to categorize the words based on the initial letter in each word? One way to accomplish this would be to use dictionaries:

def categorize_by_initial(my_list):\n    groups = {}\n    for word in my_list:\n        initial = word[0]\n        # initialize a new list when the letter is first encountered\n        if initial not in groups:\n            groups[initial] = []\n        # add the word to the appropriate list\n        groups[initial].append(word)\n    return groups\n\ngroups = categorize_by_initial(word_list)\n\nfor key, value in groups.items():\n    print(f\"words beginning with {key}:\")\n    for word in value:\n        print(word)

The structure of the function is very similar to the previous exercise but this time the values mapped to the keys are lists. The program prints out the following:

words beginning with b:\nbanana\nbeer\nbutter\nbeer\nbutter\nbeer\nwords beginning with m:\nmilk\nmargarine\nwords beginning with c:\ncheese\ncucumber\ncheese\nchocolate\nwords beginning with s:\nsourmilk\nsausage\nsausage\nsourmilk\nsourmilk\nwords beginning with j:\njuice\nwords beginning with t:\ntomato

Mode: Independent practice

Please write a function named histogram, which takes a string as its argument. The function should print out a histogram representing the number of times each letter occurs in the string. Each occurrence of a letter should be represented by a star on the specific line for that letter.

For example, the function call histogram(\"abba\") should print out

\na **\nb **\n

while histogram(\"statistically\") should print out

\ns **\nt ***\na **\ni **\nc *\nl **\ny *\n

Structured records in practice

A list of dictionaries is useful when each record has the same named fields. The following exercises apply the record shape introduced in Part 4: first by adding complete records to a collection, and then by filtering that collection. Focus on preserving the agreed field names and returning records in their original shape.

Mode: Code review and repair

Pipeline stage(s): Transform

The starter contains a function named add_movie(database: list, name: str, director: str, year: int, runtime: int), which adds a new movie record to a database. The function runs without raising an error, but the new record does not follow the complete data contract described below.

Before editing, call the function and compare the keys of the new record against the four required keys listed below. Then repair the function without replacing the database or discarding records which were already there.

The database is a list, and each movie object in the list is a dictionary. The dictionary should contain the following keys.

The values attached to these keys are given as arguments to the function.

An example of its use:

database = []\nadd_movie(database, \"Gone with the Python\", \"Victor Pything\", 2017, 116)\nadd_movie(database, \"Pythons on a Plane\", \"Renny Pytholin\", 2001, 94)\nprint(database)

[{\"name\": \"Gone with the Python\", \"director\": \"Victor Pything\", \"year\": 2017, \"runtime\": 116}, {\"name\": \"Pythons on a Plane\", \"director\": \"Renny Pytholin\", \"year\": 2001, \"runtime\": 94}]

Mode: Agent-assisted change

Pipeline stage(s): Validate, Transform

The starter contains a working find_movies function which searches movie names. The requirements have now changed: every movie record has a genres field containing a list of strings, and users need to filter by genre instead.

Change the function into find_movies_by_genre(database: list, genre: str). It should return every movie whose genres list contains the requested genre. Matching is case-insensitive, the source order is preserved, and the database and its records are not changed.

Use the agent-assisted workflow:

    \n
  1. Specify the new record shapes and the filtering contract.
    2. \n
  2. Ask an agent first for the smallest exact-case genre search. Inspect which nested collection its condition traverses and verify a movie with multiple genres and a genre with no matches.
    3. \n
  3. Add the changed requirement yourself: make the genre comparison case-insensitive without changing the stored strings or returned records.
    4. \n
  4. Add a mixed-case assertion which would have failed for the agent's first version, then check that the source records remain unchanged.
    5. \n
database = [\n  {\"name\": \"Archive Nights\", \"director\": \"A. Example\", \"year\": 2017,\n   \"runtime\": 96, \"genres\": [\"Documentary\", \"History\"]},\n  {\"name\": \"Signal\", \"director\": \"B. Example\", \"year\": 2021,\n   \"runtime\": 104, \"genres\": [\"Drama\"]},\n  {\"name\": \"Painted Lives\", \"director\": \"C. Example\", \"year\": 2019,\n   \"runtime\": 88, \"genres\": [\"Documentary\", \"Art\"]}\n]\n\nmy_movies = find_movies_by_genre(database, \"documentary\")\nprint(my_movies)

[{'name': 'Archive Nights', 'director': 'A. Example', 'year': 2017, 'runtime': 96, 'genres': ['Documentary', 'History']}, {'name': 'Painted Lives', 'director': 'C. Example', 'year': 2019, 'runtime': 88, 'genres': ['Documentary', 'Art']}]

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