Python Slots Vs Dict
Dict Hash Table
Dictionary is one of the important data types available in Python. The data in a dictionary is stored as a key/value pair. It is separated by a colon(:), and the key/value pair is separated by comma(,). The keys in a dictionary are unique and can be a string, integer, tuple, etc. The values can be a list or list within a list, numbers, string, etc. Recommended Reading: Python dictionary and how to work with them. Share on: Was this article helpful?. Python References. Python print Python Library. Python type Python Library. Python isinstance Python Library. Python help Join our newsletter for the latest updates. Python File Handling Python Read Files Python Write/Create Files Python Delete Files Python NumPy NumPy Intro NumPy Getting Started NumPy Creating Arrays NumPy Array Indexing NumPy Array Slicing NumPy Data Types NumPy Copy vs View NumPy Array Shape NumPy Array Reshape NumPy Array Iterating NumPy Array Join NumPy Array Split NumPy Array Search. This saves the overhead of one dict for every object that uses slots The second half is wishful thinking, and off the mark: While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python's efficient key/value hash table structure is called a 'dict'. The contents of a dict can be written as a series of key:value pairs within braces { }, e.g. dict = {key1:value1, key2:value2, ... }. The 'empty dict' is just an empty pair of curly braces {}.
Python dictionary is a a collection of key-value pairs. Dictionary is mutable(can be changed), unordered and can be indexed (using the keys). Despite the fact that dictionary is mutable the keys.
Looking up or setting a value in a dict uses square brackets, e.g. dict['foo'] looks up the value under the key 'foo'. Strings, numbers, and tuples work as keys, and any type can be a value. Other types may or may not work correctly as keys (strings and tuples work cleanly since they are immutable). Looking up a value which is not in the dict throws a KeyError -- use 'in' to check if the key is in the dict, or use dict.get(key) which returns the value or None if the key is not present (or get(key, not-found) allows you to specify what value to return in the not-found case).
A for loop on a dictionary iterates over its keys by default. The keys will appear in an arbitrary order. The methods dict.keys() and dict.values() return lists of the keys or values explicitly. There's also an items() which returns a list of (key, value) tuples, which is the most efficient way to examine all the key value data in the dictionary. All of these lists can be passed to the sorted() function.
There are 'iter' variants of these methods called iterkeys(), itervalues() and iteritems() which avoid the cost of constructing the whole list -- a performance win if the data is huge. However, I generally prefer the plain keys() and values() methods with their sensible names. In Python 3000 revision, the need for the iterkeys() variants is going away.
Strategy note: from a performance point of view, the dictionary is one of your greatest tools, and you should use it where you can as an easy way to organize data. For example, you might read a log file where each line begins with an IP address, and store the data into a dict using the IP address as the key, and the list of lines where it appears as the value. Once you've read in the whole file, you can look up any IP address and instantly see its list of lines. The dictionary takes in scattered data and makes it into something coherent.
Dict Formatting
The % operator works conveniently to substitute values from a dict into a string by name:
Del
The 'del' operator does deletions. In the simplest case, it can remove the definition of a variable, as if that variable had not been defined. Del can also be used on list elements or slices to delete that part of the list and to delete entries from a dictionary.
Files
The open() function opens and returns a file handle that can be used to read or write a file in the usual way. The code f = open('name', 'r') opens the file into the variable f, ready for reading operations, and use f.close() when finished. Instead of 'r', use 'w' for writing, and 'a' for append. The special mode 'rU' is the 'Universal' option for text files where it's smart about converting different line-endings so they always come through as a simple 'n'. The standard for-loop works for text files, iterating through the lines of the file (this works only for text files, not binary files). The for-loop technique is a simple and efficient way to look at all the lines in a text file:
Reading one line at a time has the nice quality that not all the file needs to fit in memory at one time -- handy if you want to look at every line in a 10 gigabyte file without using 10 gigabytes of memory. The f.readlines() method reads the whole file into memory and returns its contents as a list of its lines. The f.read() method reads the whole file into a single string, which can be a handy way to deal with the text all at once, such as with regular expressions we'll see later.
For writing, f.write(string) method is the easiest way to write data to an open output file. Or you can use 'print' with an open file, but the syntax is nasty: 'print >> f, string'. In python 3000, the print syntax will be fixed to be a regular function call with a file= optional argument: 'print(string, file=f)'.
Files Unicode
The 'codecs' module provides support for reading a unicode file.
For writing, use f.write() since print does not fully support unicode.
Exercise Incremental Development
Building a Python program, don't write the whole thing in one step. Instead identify just a first milestone, e.g. 'well the first step is to extract the list of words.' Write the code to get to that milestone, and just print your data structures at that point, and then you can do a sys.exit(0) so the program does not run ahead into its not-done parts. Once the milestone code is working, you can work on code for the next milestone. Being able to look at the printout of your variables at one state can help you think about how you need to transform those variables to get to the next state. Python is very quick with this pattern, allowing you to make a little change and run the program to see how it works. Take advantage of that quick turnaround to build your program in little steps.
Exercise: wordcount.py
Combining all the basic Python material -- strings, lists, dicts, tuples, files -- try the summary wordcount.py exercise in the Basic Exercises.
This page describes the use of signals and slots in Qt for Python.The emphasis is on illustrating the use of so-called new-style signals and slots, although the traditional syntax is also given as a reference.
The main goal of this new-style is to provide a more Pythonic syntax to Python programmers.
- 2New syntax: Signal() and Slot()
Traditional syntax: SIGNAL () and SLOT()
QtCore.SIGNAL() and QtCore.SLOT() macros allow Python to interface with Qt signal and slot delivery mechanisms.This is the old way of using signals and slots.
The example below uses the well known clicked signal from a QPushButton.The connect method has a non python-friendly syntax.It is necessary to inform the object, its signal (via macro) and a slot to be connected to.
New syntax: Signal() and Slot()
The new-style uses a different syntax to create and to connect signals and slots.The previous example could be rewritten as:
Using QtCore.Signal()
Signals can be defined using the QtCore.Signal() class.Python types and C types can be passed as parameters to it.If you need to overload it just pass the types as tuples or lists.
In addition to that, it can receive also a named argument name that defines the signal name.If nothing is passed as name then the new signal will have the same name as the variable that it is being assigned to.
The Examples section below has a collection of examples on the use of QtCore.Signal().
Note: Signals should be defined only within classes inheriting from QObject.This way the signal information is added to the class QMetaObject structure.
Using QtCore.Slot()
Slots are assigned and overloaded using the decorator QtCore.Slot().Again, to define a signature just pass the types like the QtCore.Signal() class.Unlike the Signal() class, to overload a function, you don't pass every variation as tuple or list.Instead, you have to define a new decorator for every different signature.The examples section below will make it clearer.
Another difference is about its keywords.Slot() accepts a name and a result.The result keyword defines the type that will be returned and can be a C or Python type.name behaves the same way as in Signal().If nothing is passed as name then the new slot will have the same name as the function that is being decorated.
Examples
The examples below illustrate how to define and connect signals and slots in PySide2.Both basic connections and more complex examples are given.
- Hello World example: the basic example, showing how to connect a signal to a slot without any parameters.
- Next, some arguments are added. This is a modified Hello World version. Some arguments are added to the slot and a new signal is created.
- Add some overloads. A small modification of the previous example, now with overloaded decorators.
- An example with slot overloads and more complicated signal connections and emissions (note that when passing arguments to a signal you use '[]'):
- An example of an object method emitting a signal:
Python Dict Iterate
- An example of a signal emitted from another QThread:
Python Dict Vs Set
- Signals are runtime objects owned by instances, they are not class attributes: