names = ['Marcus', 'Christine', 'Dan', 'Joe', 'Jenny']
for name in names:
print(name)
The for-in structure above loops through each item in a collection and
returns the individual item in the name variable given between for and in.
Substituting any collection or iterable given in place of names
achieves the same result.
This can include the range function call which instead gives a number range.
for num in range(1, 10):
print(num)
As you can see any function call that returns a collection or
iterable (iterable in the case of range) can be used.
Here range which returns an iterable describing the numbers,
spaced by 1 whole integer by default when no third positional argument
is given from 1 to one step below the second number, in this case 9, 10 is excluded.
If you're going to read a file with lines representing an entry,
open is the primary function to use.
In this example from Advent of Code there are lines of input given.
Each line is a pair of number ranges in need of processing.
2-4,6-8
2-3,4-5
5-7,7-9
2-8,3-7
6-6,4-6
2-6,4-8
To read each line in the range, do something like this:
try:
with open(self.file_path) as file:
for line in file:
# Remove any whitespace
pair_str = line.split()[0]
pair = ShipAssignmentPair(pair_str)
assignments.append(pair)
except IOError as e:
print(f"ShipAssignment file open error:\n{e}")
except:
print("Unknown Error during file ShipAssignment opening!")
finally:
self.assignments = assignments
file.close()
The open function takes a file path.
The with-as structure creates a closure to safely open a file then
perform some actions on the file then
close it when done.
The try-except-finally syntax handles any errors associated with reading or
processing the file contents.
The line, except IOError as e,
will raise an IOError exception if the file can't be opened or read.
The empty except: line will handle any other exceptions.
The finally line opens a block of code to be performed after
the file is read and processed in assignments.append(pair).
The for-in loop involving the file object opened
will iterate every line of the file,
including any newline characters indicating the end of the line.
It will probably be necessary to split() any whitespace,
including any newline characters out, otherwise they end up in the results.
To extract the numbers from a string, in order, this list comprehension should suffice.
txt = "h3110 23 cat 444.44 rabbit 11 2 dog"
print([int(s) for s in txt.split() if s.isdigit()])
# output: [23, 11, 2]
The for s in txt.split() sets up a loop to
get every whitespace separated substring.
Any other split delimiter could be used instead as its first argument.
Then, the if s.isdigit() will determine if the substring has a number.
Finally, the int(s) turns it into a number,
float could be used as well.
When the list comprehension is done,
the list [23, 11, 2] should be returned.
TODO
TODO
TODO
TODO
For more information check out this handy guide
One of the best things about Python is not just it's large and practical Standard Library, but also the fact that it has one of the largest ecosystems of libraries around. Quickly, a library aka a module is a reusable collection of code made for specific tasks. It's possible to download such libraries using pip and incorporating this code for your purposes.
Generally, libraries get imported at the beginning of a piece of code. This helps avoid mistakes that make programs less time efficient. For example, by avoiding importing a module twice.
import numpy
Another standard practice with importing libraries is aliasing it by
using a shorter name.
This makes code more readable and keeps the width of code files narrower.
Note that the Python community has evolved some standard abbreviations for
these aliases so pay attention to the ones they use in your own work.
To alias a library, simply follow up the import statement with
an as statement and then the alias for that library.
import numpy as np
NumPy, short for Numerical Python, is a library that adds support for multi-dimensional arrays, matrices and tensors. NumPy also offers a large collection of high-level mathematical functions, particularly in the fields of linear algebra, statistics and scientific computing.
Python over the years has become one of the preferred ways that we analyze data. Statistics is of course one of the primary ways that we look at data, and python is full of modules that can make this easier on us. In the notes on statistics using python