Dictionary In Python Quotes

test3 defines the sin function as a keyword argument, with its default value being a reference to the sin function within the math module. While we still do need to find a reference to this function within the module, this is only necessary when the test3 function is first defined. After this, the reference to the sin function is stored within the function definition as a local variable in the form of a default keyword argument. As mentioned previously, local variables do not need a dictionary lookup to be found; they are stored in a very slim array that has very fast lookup times. Because of this, finding the function is quite fast! While these effects are an interesting result of the way namespaces in Python are managed, test3 is definitely not “Pythonic.” Luckily, these extra dictionary lookups only start to degrade performance when they are called a lot (i.e., in the innermost block of a very fast loop, such as in the Julia set example). With this in mind, a more readable solution would be to set a local variable with the global reference before the loop is started. We’ll still have to do the global lookup once whenever the function is called, but all the calls to that function in the loop will be made faster. This speaks to the fact that even minute slowdowns in code can be amplified if that code is being run millions of times. Even though a dictionary lookup may only take several hundred nanoseconds, if we are looping millions of times over this lookup it can quickly add up. In fact, looking at Example 4-10 we see a 9.4% speedup simply by making the sin function local to the tight loop that calls it.

With our program modularized into separate functions, we can now increase our performance. Instead of trying each word in the dictionary one at a time, we will utilize threads of execution

Next, it opens the dictionary and iterates through each word in the dictionary, creating an encrypted password hash from the dictionary word and the salt. If the result matches our encrypted password hash, the function prints a message indicating the found password and returns. Otherwise, it continues to test every word in the dictionary.

On the other hand, you might decide to use operator overloading if you need to pass a user-defined object to a function that was coded to expect the operators available on a built-in type like a list or a dictionary.

the attributes of a namespace object are usually implemented as dictionaries, and class inheritance trees are (generally speaking) just dictionaries with links to other dictionaries.

If you wish to use this method, you can avoid loops by coding instance attribute assignments as assignments to attribute dictionary keys. That is, use self.__dict__['name'] = x, not self.name = x; because you’re not assigning to __dict__ itself, this avoids the loop:

In fact, within Python, instance and class objects are mostly just dictionaries with links between them.

Although we can store functions in dictionaries, too, using them to process implied instances is nowhere near as natural and structured as it is in classes.

Seibel: Some people love Lisp syntax and some can't stand it. Why is that? Deutsch: Well, I can't speak for anyone else. But I can tell you why I don't want to work with Lisp syntax anymore. There are two reasons. Number one, and I alluded to this earlier, is that the older I've gotten, the more important it is to me that the density of information per square inch in front of my face is high. The density of information per square inch in infix languages is higher than in Lisp. Seibel: But almost all languages are, in fact, prefix, except for a small handful of arithmetic operators. Deutsch: That's not actually true. In Python, for example, it's not true for list, tuple, and dictionary construction. That's done with bracketing. String formatting is done infix. Seibel: As it is in Common Lisp with FORMAT. Deutsch: OK, right. But the things that aren't done infix; the common ones, being loops and conditionals, are not prefix. They're done by alternating keywords and what it is they apply to. In that respect they are actually more verbose than Lisp. But that brings me to the other half, the other reason why I like Python syntax better, which is that Lisp is lexically pretty monotonous.

Take a moment to reflect on the following dictionary expression and what it will evaluate to: >>> {True: 'yes', 1: 'no', 1.0: 'maybe'}