Longer is always possible.
The typing stuff is not required.
The more interesting part is the use of statistics (batteries included), the iterable unpacking, exception-handling, pathlib to have a high-level api for paths and the exception handling for crappy data.
You'll often receive invalid data from somewhere. Then it's good to know where which Exception could occour.
For example, if the encoding of the data is wrong, it could raise an UnicodeDecodeError.
import statistics
from io import TextIOWrapper
from pathlib import Path
from typing import Generator, Union
def reader(text_or_fd: Union[str, TextIOWrapper]) -> Generator[dict, None, None]:
"""
Generator wich returns a list with dicts inside.
Each result has the key name, which points to the first word.
After this first word integers are expected.
Each result has median, stdev, median_low, median_high
"""
if isinstance(text_or_fd, str):
iterator = text_or_fd.splitlines()
elif isinstance(text_or_fd, TextIOWrapper):
iterator = text_or_fd
else:
raise TypeError("Expecting str or TextIOWrapper as input")
for line in iterator:
if not line.strip():
# strip whitespace from line
# if len is 0, then continue
# to next iteration
# not [] -> evaluates to True
continue
# special condition, that line is empty could here not happen
name, *values = line.split() # split by whitespace
# the * in front of values unpacks the rest
# items into a list, which is assigned to the name values
# the first element is the name
if not values:
# happens if not enough values are there to unpack
# then values is an empty list
continue
try:
values = tuple(map(int, values))
except ValueError:
# happens if one of the values is not an integer
# then the function int raises a ValueError
# continue to next line if this happens (crappy data)
continue
# now using the statistics module to do statistics
median = statistics.median(values)
mean = statistics.mean(values)
stdev = statistics.stdev(values)
median_low = statistics.median_low(values)
median_high = statistics.median_high(values)
# yield each result
yield {
"name": name,
"values": values,
"median": median,
"stdev": stdev,
"median_low": median_low,
"median_high": median_high,
}
text = """
The .txt file:
joe 10 15 20 30 40
bill 23 16 19 22
sue 8 22 17 14 32 17 24 21 2 9 11 17
grace 12 28 21 45 26 10
john 14 32 25 16 89
"""
file = Path.home() / "Desktop/test.txt"
with file.open() as fd:
results_from_file = list(reader(fd))
results_from_str = list(reader(text))
print("Results from fileobject:")
print(results_from_file)
print("\n\nResults from str:")
print(results_from_str)Output:
Results from fileobject:
[{'name': 'joe', 'values': (10, 15, 20, 30, 40), 'median': 20, 'stdev': 12.041594578792296, 'median_low': 20, 'median_high': 20}, {'name': 'bill', 'values': (23, 16, 19, 22), 'median': 20.5, 'stdev': 3.1622776601683795, 'median_low': 19, 'median_high': 22}, {'name': 'sue', 'values': (8, 22, 17, 14, 32, 17, 24, 21, 2, 9, 11, 17), 'median': 17.0, 'stdev': 8.09975682388432, 'median_low': 17, 'median_high': 17}, {'name': 'grace', 'values': (12, 28, 21, 45, 26, 10), 'median': 23.5, 'stdev': 12.722683155162935, 'median_low': 21, 'median_high': 26}, {'name': 'john', 'values': (14, 32, 25, 16, 89), 'median': 25, 'stdev': 30.930567405076808, 'median_low': 25, 'median_high': 25}]
Results from str:
[{'name': 'joe', 'values': (10, 15, 20, 30, 40), 'median': 20, 'stdev': 12.041594578792296, 'median_low': 20, 'median_high': 20}, {'name': 'bill', 'values': (23, 16, 19, 22), 'median': 20.5, 'stdev': 3.1622776601683795, 'median_low': 19, 'median_high': 22}, {'name': 'sue', 'values': (8, 22, 17, 14, 32, 17, 24, 21, 2, 9, 11, 17), 'median': 17.0, 'stdev': 8.09975682388432, 'median_low': 17, 'median_high': 17}, {'name': 'grace', 'values': (12, 28, 21, 45, 26, 10), 'median': 23.5, 'stdev': 12.722683155162935, 'median_low': 21, 'median_high': 26}, {'name': 'john', 'values': (14, 32, 25, 16, 89), 'median': 25, 'stdev': 30.930567405076808, 'median_low': 25, 'median_high': 25}]
Hopefully it was not too much.
By the way, a Generator simplifies your code.
