Sync itertools-in-python3 with the full update (pairwise, batched)

itertools-in-python3/README.md

@@ -28,7 +28,7 @@ Alternatively, you can download the entire repository as a `.zip` file from the
 
 ## Running the Code Examples
 
-To run the code examples, first make sure you have Python 3 installed on your machine. **The code examples WILL NOT work with Python 2.** If you need help installing Python 3, check out Real Python's [Python 3 Installation & Setup Guide](https://realpython.com/installing-python/).
+To run the code examples, first make sure you have Python 3.12 or later installed on your machine, since `swimmers.py` uses `itertools.batched()`. If you need help installing Python 3, check out Real Python's [Python 3 Installation & Setup Guide](https://realpython.com/installing-python/).
 
 To run the `sp500.py` file, type `python3 sp500.py` into your terminal. Here's how that looks, with output:
 
@@ -39,7 +39,7 @@ Max loss: -20.47% on 1987-10-19
 Longest growth streak: 14 days (1971-03-26 to 1971-04-15)
 ```
 
-> **Note:** Depending on your installation, you may need to type `python3.6` or `python36` to run the examples. If you follow the [Python 3 Installation & Setup Guide](https://realpython.com/installing-python/), you should have no problem.
+> **Note:** Depending on your installation, you may need to type `python3.15` or `python315` to run the examples. If you follow the [Python 3 Installation & Setup Guide](https://realpython.com/installing-python/), you should have no problem.
 
 Similarly, for `swimmers.py`:
 

itertools-in-python3/sp500.py

@@ -44,12 +44,12 @@ def read_prices(csvfile, _strptime=datetime.strptime):
 prices = tuple(read_prices("SP500.csv"))
 gains = tuple(
     DataPoint(day.date, 100 * (day.value / prev_day.value - 1.0))
-    for day, prev_day in zip(prices[1:], prices, strict=False)
+    for prev_day, day in it.pairwise(prices)
 )
 
 # Find maximum daily gain/loss.
 zdp = DataPoint(None, 0)  # zero DataPoint
-max_gain = ft.reduce(max, it.filterfalse(lambda p: p <= zdp, gains))
+max_gain = ft.reduce(max, it.filterfalse(lambda p: p <= zdp, gains), zdp)
 max_loss = ft.reduce(min, it.filterfalse(lambda p: p > zdp, gains), zdp)
 
 

itertools-in-python3/swimmers.py

@@ -16,15 +16,10 @@ def sort_and_group(iterable, key=None):
     return it.groupby(sorted(iterable, key=key), key=key)
 
 
-def grouper(iterable, n, fillvalue=None):
-    iters = [iter(iterable)] * n
-    return it.zip_longest(*iters, fillvalue=fillvalue)
-
-
 def read_events(csvfile, _strptime=datetime.datetime.strptime):
     def _median(times):
         return statistics.median(
-            (_strptime(time, "%M:%S:%f").time() for time in row["Times"])
+            (_strptime(time, "%M:%S:%f").time() for time in times)
         )
 
     fieldnames = ["Event", "Name", "Stroke"]
@@ -41,9 +36,7 @@ def _median(times):
     events_by_name = sort_and_group(evts, key=lambda evt: evt.name)
     best_times = (min(evt) for _, evt in events_by_name)
     sorted_by_time = sorted(best_times, key=lambda evt: evt.time)
-    teams = zip(
-        ("A", "B"), it.islice(grouper(sorted_by_time, 4), 2), strict=False
-    )
+    teams = zip(("A", "B"), it.batched(sorted_by_time, 4), strict=False)
     for team, swimmers in teams:
         print(
             "{stroke} {team}: {names}".format(