Merge branch 'main' of https://github.com/dabeaz-course/python-mastery into main

Exercises/ex2_2.md

@@ -152,7 +152,7 @@ The key feature that makes this work is that a defaultdict
 automatically initializes elements for you--allowing an insertion of a
 new element and an `append()` operation to be combined together.
 
-## (c) Data Analysis Challenge
+## (d) Data Analysis Challenge
 
 In the last exercise you just wrote some code to read CSV-data related
 to the Chicago Transit Authority.  For example, you can grab the data
@@ -167,7 +167,7 @@ as dictionaries like this:
 It would be a shame to do all of that work and then do nothing with
 the data.
 
-In this exercise, you task is this: write a program to answer the
+In this exercise, your task is this: write a program to answer the
 following three questions:
 
 1. How many bus routes exist in Chicago?

Exercises/ex2_5.md

@@ -60,7 +60,6 @@ and adding a few more values to it:
 ```python
 >>> row = { 'route': '22', 'date': '01/01/2001', 'daytype': 'U', 'rides': 7354 }
 >>> sys.getsizeof(row)
->>> sys.getsizeof(row)
 240
 >>> row['a'] = 1
 >>> sys.getsizeof(row)
@@ -206,7 +205,7 @@ into 4 separate `append()` operations.
 
 class RideData(collections.abc.Sequence):
     def __init__(self):
-        # Each value is a list with all of the values (a column)
+        # Each value is a list with all the values (a column)
         self.routes = []
         self.dates = []
         self.daytypes = []

Exercises/ex5_1.md

@@ -115,7 +115,7 @@ kinds of input sources.  For example:
 ```python
 >>> import gzip
 >>> import stock
->>> file = gzip.open('Data/portfolio.csv.gz')
+>>> file = gzip.open('Data/portfolio.csv.gz', 'rt')
 >>> port = reader.csv_as_instances(file, stock.Stock)
 >>> port
 [Stock('AA', 100, 32.2), Stock('IBM', 50, 91.1), Stock('CAT', 150, 83.44), 

Exercises/ex5_6.md

@@ -25,7 +25,7 @@ import stock
 
 class TestStock(unittest.TestCase):
     def test_create(self):
-        s = Stock('GOOG', 100, 490.1)
+        s = stock.Stock('GOOG', 100, 490.1)
         self.assertEqual(s.name, 'GOOG')
         self.assertEqual(s.shares, 100)
         self.assertEqual(s.price, 490.1)

Exercises/ex6_2.md

@@ -160,7 +160,7 @@ class Stock(Structure):
     def cost(self):
         return self.shares * self.price
 
-    def sell(self, nshares):
+    def sell(self, shares):
         self.shares -= shares
 ```
 

Exercises/soln5_3.md

@@ -48,11 +48,10 @@ def read_csv_as_instances(filename, cls, *, headers=None):
 import csv
 
 def convert_csv(lines, converter, *, headers=None):
-    records = []
     rows = csv.reader(lines)
     if headers is None:
         headers = next(rows)
-    return map(lambda row: converter(headers, row), rows)
+    return list(map(lambda row: converter(headers, row), rows))
 ```
 
 

Solutions/1_6/pcost.py

@@ -12,7 +12,7 @@ def portfolio_cost(filename):
 
             # This catches errors in int() and float() conversions above
             except ValueError as e:
-                print("Couldn't parse:", line)
+                print("Couldn't parse:", repr(line))
                 print("Reason:", e)
 
     return total_cost

Solutions/5_3/reader.py

@@ -10,11 +10,14 @@ def convert_csv(lines, converter, *, headers=None):
 
 def csv_as_dicts(lines, types, *, headers=None):
     return convert_csv(lines,
-                       lambda headers, row: { name: func(val) for name, func, val in zip(headers, types, row) })
+                       lambda headers, row: {name: func(val) for name, func, val in zip(headers, types, row)},
+                       headers=headers)
+
 
 def csv_as_instances(lines, cls, *, headers=None):
     return convert_csv(lines,
-                       lambda headers, row: cls.from_row(row))
+                       lambda headers, row: cls.from_row(row),
+                       headers=headers)
 
 def read_csv_as_dicts(filename, types, *, headers=None):
     '''

Solutions/9_4/structly/tableformat/formatter.py

@@ -1,4 +1,4 @@
-# tableformat.py
+# formatter.py
 from abc import ABC, abstractmethod
 
 def print_table(records, fields, formatter):