Why Doesn't 2._Add_(3) Work in Python

Why doesn't 2.__add__(3) work in Python?

2. is parsed as a float, so 2.__add__ is a SyntaxError.

You can evaluate

(2).__add__(3) instead.

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In [254]: (2).__add__(3)
Out[254]: 5

Python 3.x rounding behavior

Python 3's way (called "round half to even" or "banker's rounding") is considered the standard rounding method these days, though some language implementations aren't on the bus yet.

The simple "always round 0.5 up" technique results in a slight bias toward the higher number. With large numbers of calculations, this can be significant. The Python 3.0 approach eliminates this issue.

There is more than one method of rounding in common use. IEEE 754, the international standard for floating-point math, defines five different rounding methods (the one used by Python 3.0 is the default). And there are others.

This behavior is not as widely known as it ought to be. AppleScript was, if I remember correctly, an early adopter of this rounding method. The round command in AppleScript offers several options, but round-toward-even is the default as it is in IEEE 754. Apparently the engineer who implemented the round command got so fed up with all the requests to "make it work like I learned in school" that he implemented just that: round 2.5 rounding as taught in school is a valid AppleScript command. :-)

Python: can't assign to literal

The left hand side of the = operator needs to be a variable. What you're doing here is telling python: "You know the number one? Set it to the inputted string.". 1 is a literal number, not a variable. 1 is always 1, you can't "set" it to something else.

A variable is like a box in which you can store a value. 1 is a value that can be stored in the variable. The input call returns a string, another value that can be stored in a variable.

Instead, use lists:

import random

namelist = []
namelist.append(input("Please enter name 1:"))  #Stored in namelist[0]
namelist.append(input('Please enter name 2:'))  #Stored in namelist[1]
namelist.append(input('Please enter name 3:'))  #Stored in namelist[2]
namelist.append(input('Please enter name 4:'))  #Stored in namelist[3]
namelist.append(input('Please enter name 5:'))  #Stored in namelist[4]

nameindex = random.randint(0, 5)
print('Well done {}. You are the winner!'.format(namelist[nameindex]))

Using a for loop, you can cut down even more:

import random

namecount = 5
namelist=[]
for i in range(0, namecount):
  namelist.append(input("Please enter name %s:" % (i+1))) #Stored in namelist[i]

nameindex = random.randint(0, namecount)
print('Well done {}. You are the winner!'.format(namelist[nameindex]))

What is the difference between i = i + 1 and i += 1 in a 'for' loop?

The difference is that one modifies the data-structure itself (in-place operation) b += 1 while the other just reassigns the variable a = a + 1.

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Just for completeness:

x += y is not always doing an in-place operation, there are (at least) three exceptions:

• If x doesn't implement an __iadd__ method then the x += y statement is just a shorthand for x = x + y. This would be the case if x was something like an int.

• If __iadd__ returns NotImplemented, Python falls back to x = x + y.

• The __iadd__ method could theoretically be implemented to not work in place. It'd be really weird to do that, though.

As it happens your bs are numpy.ndarrays which implements __iadd__ and return itself so your second loop modifies the original array in-place.

You can read more on this in the Python documentation of "Emulating Numeric Types".

These [__i*__] methods are called to implement the augmented arithmetic assignments (+=, -=, *=, @=, /=, //=, %=, **=, <<=, >>=, &=, ^=, |=). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an __iadd__() method, x += y is equivalent to x = x.__iadd__(y) . Otherwise, x.__add__(y) and y.__radd__(x) are considered, as with the evaluation of x + y. In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += ["item"] raise an exception when the addition works?), but this behavior is in fact part of the data model.

I'm getting Key error in python

A KeyError generally means the key doesn't exist. So, are you sure the path key exists?

From the official python docs:

exception KeyError

Raised when a mapping (dictionary) key is not found in the set of
existing keys.

For example:

>>> mydict = {'a':'1','b':'2'}
>>> mydict['a']
'1'
>>> mydict['c']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'c'
>>>

So, try to print the content of meta_entry and check whether path exists or not.

>>> mydict = {'a':'1','b':'2'}
>>> print mydict
{'a': '1', 'b': '2'}

Or, you can do:

>>> 'a' in mydict
True
>>> 'c' in mydict
False

What is the purpose of the single underscore _ variable in Python?

_ has 3 main conventional uses in Python:

1. To hold the result of the last executed expression in an interactive
   interpreter session (see docs). This precedent was set by the standard CPython
   interpreter, and other interpreters have followed suit

2. For translation lookup in i18n (see the
   gettext
   documentation for example), as in code like

   raise forms.ValidationError(_("Please enter a correct username"))
   

3. As a general purpose "throwaway" variable name:

   1. To indicate that part
      of a function result is being deliberately ignored (Conceptually, it is being discarded.), as in code like:

      label, has_label, _ = text.partition(':')
      

   2. As part of a function definition (using either def or lambda), where
      the signature is fixed (e.g. by a callback or parent class API), but
      this particular function implementation doesn't need all of the
      parameters, as in code like:

      def callback(_):
          return True
      

      [For a long time this answer didn't list this use case, but it came up often enough, as noted here, to be worth listing explicitly.]

   This use case can conflict with the translation lookup use case, so it is necessary to avoid using _ as a throwaway variable in any code block that also uses it for i18n translation (many folks prefer a double-underscore, __, as their throwaway variable for exactly this reason).

   Linters often recognize this use case. For example year, month, day = date() will raise a lint warning if day is not used later in the code. The fix, if day is truly not needed, is to write year, month, _ = date(). Same with lambda functions, lambda arg: 1.0 creates a function requiring one argument but not using it, which will be caught by lint. The fix is to write lambda _: 1.0. An unused variable is often hiding a bug/typo (e.g. set day but use dya in the next line).

   The pattern matching feature added in Python 3.10 elevated this usage from "convention" to "language syntax" where match statements are concerned: in match cases, _ is a wildcard pattern, and the runtime doesn't even bind a value to the symbol in that case.

   For other use cases, remember that _ is still a valid variable name, and hence will still keep objects alive. In cases where this is undesirable (e.g. to release memory or external resources) an explicit del name call will both satisfy linters that the name is being used, and promptly clear the reference to the object.

Asking the user for input until they give a valid response

The simplest way to accomplish this is to put the input method in a while loop. Use continue when you get bad input, and break out of the loop when you're satisfied.

When Your Input Might Raise an Exception

Use try and except to detect when the user enters data that can't be parsed.

while True:
    try:
        # Note: Python 2.x users should use raw_input, the equivalent of 3.x's input
        age = int(input("Please enter your age: "))
    except ValueError:
        print("Sorry, I didn't understand that.")
        #better try again... Return to the start of the loop
        continue
    else:
        #age was successfully parsed!
        #we're ready to exit the loop.
        break
if age >= 18: 
    print("You are able to vote in the United States!")
else:
    print("You are not able to vote in the United States.")

Implementing Your Own Validation Rules

If you want to reject values that Python can successfully parse, you can add your own validation logic.

while True:
    data = input("Please enter a loud message (must be all caps): ")
    if not data.isupper():
        print("Sorry, your response was not loud enough.")
        continue
    else:
        #we're happy with the value given.
        #we're ready to exit the loop.
        break

while True:
    data = input("Pick an answer from A to D:")
    if data.lower() not in ('a', 'b', 'c', 'd'):
        print("Not an appropriate choice.")
    else:
        break

Combining Exception Handling and Custom Validation

Both of the above techniques can be combined into one loop.

while True:
    try:
        age = int(input("Please enter your age: "))
    except ValueError:
        print("Sorry, I didn't understand that.")
        continue

    if age < 0:
        print("Sorry, your response must not be negative.")
        continue
    else:
        #age was successfully parsed, and we're happy with its value.
        #we're ready to exit the loop.
        break
if age >= 18: 
    print("You are able to vote in the United States!")
else:
    print("You are not able to vote in the United States.")

Encapsulating it All in a Function

If you need to ask your user for a lot of different values, it might be useful to put this code in a function, so you don't have to retype it every time.

def get_non_negative_int(prompt):
    while True:
        try:
            value = int(input(prompt))
        except ValueError:
            print("Sorry, I didn't understand that.")
            continue

        if value < 0:
            print("Sorry, your response must not be negative.")
            continue
        else:
            break
    return value

age = get_non_negative_int("Please enter your age: ")
kids = get_non_negative_int("Please enter the number of children you have: ")
salary = get_non_negative_int("Please enter your yearly earnings, in dollars: ")

Putting It All Together

You can extend this idea to make a very generic input function:

def sanitised_input(prompt, type_=None, min_=None, max_=None, range_=None):
    if min_ is not None and max_ is not None and max_ < min_:
        raise ValueError("min_ must be less than or equal to max_.")
    while True:
        ui = input(prompt)
        if type_ is not None:
            try:
                ui = type_(ui)
            except ValueError:
                print("Input type must be {0}.".format(type_.__name__))
                continue
        if max_ is not None and ui > max_:
            print("Input must be less than or equal to {0}.".format(max_))
        elif min_ is not None and ui < min_:
            print("Input must be greater than or equal to {0}.".format(min_))
        elif range_ is not None and ui not in range_:
            if isinstance(range_, range):
                template = "Input must be between {0.start} and {0.stop}."
                print(template.format(range_))
            else:
                template = "Input must be {0}."
                if len(range_) == 1:
                    print(template.format(*range_))
                else:
                    expected = " or ".join((
                        ", ".join(str(x) for x in range_[:-1]),
                        str(range_[-1])
                    ))
                    print(template.format(expected))
        else:
            return ui

With usage such as:

age = sanitised_input("Enter your age: ", int, 1, 101)
answer = sanitised_input("Enter your answer: ", str.lower, range_=('a', 'b', 'c', 'd'))

Common Pitfalls, and Why you Should Avoid Them

The Redundant Use of Redundant input Statements

This method works but is generally considered poor style:

data = input("Please enter a loud message (must be all caps): ")
while not data.isupper():
    print("Sorry, your response was not loud enough.")
    data = input("Please enter a loud message (must be all caps): ")

It might look attractive initially because it's shorter than the while True method, but it violates the Don't Repeat Yourself principle of software development. This increases the likelihood of bugs in your system. What if you want to backport to 2.7 by changing input to raw_input, but accidentally change only the first input above? It's a SyntaxError just waiting to happen.

Recursion Will Blow Your Stack

If you've just learned about recursion, you might be tempted to use it in get_non_negative_int so you can dispose of the while loop.

def get_non_negative_int(prompt):
    try:
        value = int(input(prompt))
    except ValueError:
        print("Sorry, I didn't understand that.")
        return get_non_negative_int(prompt)

    if value < 0:
        print("Sorry, your response must not be negative.")
        return get_non_negative_int(prompt)
    else:
        return value

This appears to work fine most of the time, but if the user enters invalid data enough times, the script will terminate with a RuntimeError: maximum recursion depth exceeded. You may think "no fool would make 1000 mistakes in a row", but you're underestimating the ingenuity of fools!

What is the maximum recursion depth in Python, and how to increase it?

It is a guard against a stack overflow, yes. Python (or rather, the CPython implementation) doesn't optimize tail recursion, and unbridled recursion causes stack overflows. You can check the recursion limit with sys.getrecursionlimit:

import sys
print(sys.getrecursionlimit())

and change the recursion limit with sys.setrecursionlimit:

sys.setrecursionlimit(1500)

but doing so is dangerous -- the standard limit is a little conservative, but Python stackframes can be quite big.

Python isn't a functional language and tail recursion is not a particularly efficient technique. Rewriting the algorithm iteratively, if possible, is generally a better idea.

Complex numbers in python

In python, you can put ‘j’ or ‘J’ after a number to make it imaginary, so you can write complex literals easily:

>>> 1j
1j
>>> 1J
1j
>>> 1j * 1j
(-1+0j)

The ‘j’ suffix comes from electrical engineering, where the variable ‘i’ is usually used for current. (Reasoning found here.)

The type of a complex number is complex, and you can use the type as a constructor if you prefer:

>>> complex(2,3)
(2+3j)

A complex number has some built-in accessors:

>>> z = 2+3j
>>> z.real
2.0
>>> z.imag
3.0
>>> z.conjugate()
(2-3j)

Several built-in functions support complex numbers:

>>> abs(3 + 4j)
5.0
>>> pow(3 + 4j, 2)
(-7+24j)

The standard module cmath has more functions that handle complex numbers:

>>> import cmath
>>> cmath.sin(2 + 3j)
(9.15449914691143-4.168906959966565j)

Understanding Python super() with __init__() methods

super() lets you avoid referring to the base class explicitly, which can be nice. But the main advantage comes with multiple inheritance, where all sorts of fun stuff can happen. See the standard docs on super if you haven't already.

Note that the syntax changed in Python 3.0: you can just say super().__init__() instead of super(ChildB, self).__init__() which IMO is quite a bit nicer. The standard docs also refer to a guide to using super() which is quite explanatory.

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