Learn Python From Javascript

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Behind every technology hides a programmer who worked on its creation. For example, behind the payment you made to an online retailer was a programmer. Programmers use computer code to create solutions to problems. Due to the complexity of some problems, programmers often work as a team to solve them.

To create applications, programmers use programming languages. One of the most popular programming languages ‚Äã‚Äãis Python, a generic language used in a wide variety of contexts. Learning to program with languages ‚Äã‚Äãlike Python is an increasingly valuable skill.

In this guide, we provide tips on how to learn Python and what to focus on to master the basics.

Why should you learn Python ?

Python is used for many things, from building web applications to analyzing data and solving math problems. It is very popular with both experienced programmers and beginners. There are many reasons to learn Python.

Learning Python will keep you relevant. Learning to code will help you keep up with the changing workforce. In the United States, jobs exclusively dedicated to software development are expected to increase at a rate of 21% over the next decade . This speed is described as " much faster than average " by the United States Bureau of Labor Statistics. Given the number of developers who use it, learning to program in Python will give you a solid foundation for a career in tech.

Python is similar to English. Many developers describe Python as easy to learn because it resembles the English language in many ways. Python was also designed to be concise. If you are looking to learn programming, Python is a great language to start with.

Python is widely used. Organizations like Quora, YouTube, Dropbox and IBM all depend on Python for their business because it is versatile, simple, and powerful. You can use it to solve complex problems. Once you learn Python, you will have a skill relevant to any career in tech.

How long do you want to learn Python?

The time it takes to learn Python depends on your program and what you mean by ’learning.

Not many people can say enough about everything about Python. What you need to learn will depend on what you plan to accomplish with it.

If you are looking to become a Python expert or get into machine learning, you have a much longer road ahead. For now, we’ll just see how long it takes to gain a basic and full understanding of the language.

On average, it takes around 6-8 weeks to learn the basics. This will take you far enough to understand most lines of code in Python. Python developers have spent a lot more time in the field. If you are planning to go into data science or some other specialized field, it is more appropriate to count months and years.

One option is to stick to a five-month schedule. This program is much more suitable for those of you who are working full time. The plan is quite simple: you spend 2-3 hours a day on the computer. One day learn something; train the next day.

You need to practice every day to make sure you learn in a decent amount of time. However, this program is quite easy to maintain. You may have to sacrifice the time you spend watching Netflix, but it’s well worth it for a stellar new career !

However, what is Python for ?

Python is generic, which mean s it has a wide range of uses. Python is commonly used for data analysis, back-end web development, scientific computing, and system scripting.

Python has a large repository of libraries that extend the language. As a result, you can find large communities that use Python for a wide range of disciplines. Libraries like matplotlib are used for data science, while tools like Click are used for system scripts.

It will take you at least three months to learn the basics of Python. This assumes that you spend around ten hours per week learning the language.

Three months is not a difficult number. If you want to dive into Python libraries, you will quickly find that your commute time will lengthen. Learning a library like matplotlib on its own can take weeks and it is just an external library.

The more time you spend learning, the faster you will learn Python. You can master the basics, like variables, in just a few days. But, if you want to be at the stage where you can write longer, more complex programs, three months is a good amount of time to spend.

The best way to learn Python for free

Have you decided that the Was learning Python worth it ? Great! Now we are ready to talk about how to learn it fast.

Due to the large number of developers using Python, there is no shortage of resources you can use at the start of your journey. However, if you don’t know the language, you might need a guide. Below are our top tips for getting started with learning Python.

Step 1: Determine your motivation

Before you start learning to program in Python, determine your motivation. Why do you want to learn programming in Python ? It may not seem too important. Over time, having a clear idea in your mind as to why you are learning to program helps.

Although Python is an easy language to learn, like any skill, it takes time and energy to master it successfully. If you have a clear motivation to learn Python at first, it will be easier for you to stay focused later.

When you think about learning Python, ask yourself why you want to learn. Do you want to start a career in technology? Great! Do you want to tinker with new technologies? It is also a good reason.

Step 2: Master programming with Python fundamentals

You may be tempted to start with a bold idea that you want to develop and try to create an app with that idea. While this approach works for some people, most new developers should focus on learning the basics. There is always time to work on more advanced projects later.

We’ll go over the main topics you should cover on your journey to learning Python:

Syntax

  • How a Python program is created
  • Variables
  • Data types
  • Printing declarations on the console
  • Arithmetic (basic mathematics)
  • Comments

Conditionals

Conditionals help control the flow of a program. They tell a program that it should execute certain code when a specific condition is met. For example, a conditional statement can tell a program to execute a block of code when a user logs on.

The main The sub-topics related to the conditions to learn are:

Loops

While programming, you may want to run the same block of code multiple times. For example, if you are creating a guessing game, you might want to allow a user to guess five times. A loop is a Python feature that allows you to execute a block of code a certain number of times.

Functions

Functions are an essential aspect of Python. They help reduce repetitions. Using functions, developers can write code that can be easily reused.

For example, a Python developer could create a function that adds two numbers. Instead of repeating the same block of code every time he needs to add two numbers, he could just use the function he created.

The sub-arguments of the main function in Python to learn are:

Lists and dictionaries

After learning the functions, you are ready to begin exploring the data type of the list.

Lists store collections of similar information as a single variable. For example, a Python list can store a list of shoes sold in a department store. Another Python List can store a list of businesses that provide food to a restaurant. Lists make it easy to store similar information in one place. They make it easier to manipulate this information later.

Dictionaries are like lists. Dictionaries provide a way for programmers to store data with keys and values. Keys act like labels for the values ‚Äã‚Äãthey store.

The main topics to cover in these areas are:

Objects and Classes

Python is a object oriented programming language . classes are projects of objects. Classes define how an object should be structured and what data it can store. Developers use classes to reduce duplication and increase code efficiency.

Objects are individual instances of a class. For example, a class can define the structure of a player for a game. An object would be a single player. This object will store values ‚Äã‚Äãsuch as the name of that particular player and the date they signed up for the service.

Working with files

Files are used in all Python programs. Developers use files to store and retrieve information. Read our guide to Reading from Python Files for more information on using files.

Other subtopics

These are just a few components of the Python programming language. If you are proficient in the topics covered above, you will be well on your way to becoming a Python expert.

As you continue your journey towards learning Python, you will come across more advanced topics. All of the most advanced arguments are built on the principles we have mentioned above. So, once you have mastered the basics, you will have no problem tackling the new and complicated aspects of Python.

Python online course

Learn Python with Codecademy

Codecademy offers an interactive course for Python. In this course, you will learn all the basics of programming in Python. You will be given a number of snippets to work with and challenges to further your knowledge.

Done
  • Audience: Beginners
  • This Udemy course has received over 250,000 entries in its history. In this course, you will learn to program in Python 3. This course is intended for beginners who are new to Python.

    Python Fundamentals

    The fundamentals of Python help you learn the basics of Python. You will learn about the development principles behind Python. Additionally, we’ll cover the syntax needed to write applications in Python.

    Python online resources

    LearnPython.org

    This site will help you learn Python if you are new to the language or if you already have programming knowledge in Python. You will cover topics such as lists and loops.

    Towards the end of the tutorial, you will come across topics such as sets and generators which will allow you to write more powerful applications.

    Karma Python Career Guides

    If you are looking For help learning to program in Python, see the Python.Engineering Python Resource Directory. In this directory, we have dozens of articles covering all the basic Python topics you need to know to master the language. Our resources will help you get started learning Python for free, without having to sign up for a course.

    Mega-tutorial in python .

    Flask is a web framework. You can use Flask in your Python code to build websites. Flask is popular with web developers for server side development. This tutorial is a step-by-step introduction to building complete web applications using Flask.

    Python Tutorial

    The Python developer community is proud of its support of one of the most popular programming languages ‚Äã‚Äã in the world. They’ve managed to keep Python easy to use by creating walkthroughs for beginner to advanced learners. The following explainer resources represent the best Python tutorials the internet has to offer.

    The Best Python Tutorials for Beginners

    Basic Python Tutorial Series | Learn Python and Make Games

    This YouTube tutorial series is designed for beginners and covers basic concepts of Python programming, such as variables, loops, commands, functions, strings, etc. The videos provide simple, easy-to-follow instructions, making the series ideal for people with no programming experience.

    Video tutorials vary in length and focus on different aspects of Python. This particular video focuses on using Python for game development. There are exercises and challenges that help support your learning.

    Python for beginners | Programming with Mosh

    This six hour tutorial for beginners explores all the basics of Python. Part tutorial and part online course, the entire video is divided into topics, making it easy to complete on time because you can pick up where you left off. It starts with a basic introduction, covers more detailed topics, and ends with several real-world projects.

    The YouTube content creator providing this video tutorial also offersother tutorial options for beginners. There are hour-long Python tutorials, functional programming tutorials with Python, tutorials on how to use Python for loops, and more.

    Learn programming in Python | Programiz

    This Python programming tutorial does not require any prior programming experience. It includes both video and written instructions and covers everything absolute newbies need to know. The goal is to provide you with a basic understanding of Python and to prepare you for more advanced concepts.

    Some of the key topics covered are control flow statements, custom functions, Python datatypes , statements conditional and file management. This tutorial will also teach you web development, software program creation, and data science skills.

    Learn Python in 5 hours | TechWorld with Nana

    This five hour YouTube tutorial is a detailed introduction to Python for beginners. The first part of the tutorial introduces you to Python and how to install PyCharm, an integrated development environment (IDE). The video explains the advantages of using PyCharm over a simple code editor, then moves on to key aspects of Python programming.

    This online tutorial covers Python topics such as strings , variables, functions and logic, loops and sets . The last part deals with the packages that you should know about, as they will be useful for application development. Some of the Python projects you’ll be working on in this tutorial are a countdown app and a data automation exercise.

    Learn Python - Complete course for beginners [Tutorial] | freeCodeCamp

    Created by the freeCodeCamp online learning platform, this tutorial begins with instructions on how to install PyCharm on different operating systems. It continues by covering basic Python topics, such as variables, basic data types, strings, and numbers. In just four and a half hours, you’ll be ready to build a basic calculator using Python.

    The following projects include a more sophisticated calculator, a puzzle and a translator. All of these programming exercises are designed to give you a better understanding of practical Python applications. For the last project, you will test your new skills by creating a multiple choice quiz.

    The best advanced Python tutorials

    Advanced Python tutorials | Real Python

    Experienced programmers looking to improve their craft can try the advanced Real Python tutorials, which go beyond the fundamentals of programming with Python. There are several video tutorials on this online platform, each focusing on a unique subset of advanced concepts.

    Tutorials can teach you how to use Python to create speech recognition features, notebooks, and text classifications for data visualization , web development, web services, and application programming interfaces (APIs). This tutorial is ideal for programmers who want to advance their career into data science or data analysis.

    Advanced Python tutorials | NeuralNine

    NeuralNine provides a YouTube playlist of 10 advanced Python tutorials, each focusing on a different aspect of Python, including magic methods, decorators, and generators. These video tutorials are only intended for students with an advanced skill level, as there is no fundamental summary.

    Later in the tutorials, you will learn about Python design patterns, such as factory, proxy, singleton, and composite method. These design patterns are very important for experienced developers looking for better software engineering career opportunities. Design templates help professional programmers structure the programs they create.

    Advanced Python Tutorial | Python Course

    The Python Course offers a variety of online Python tutorials. You have the option to learn at your own pace or through live lessons. This didac

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    Learn Python From Javascript __del__: Questions

    __del__

    How can I make a time delay in Python?

    5 answers

    I would like to know how to put a time delay in a Python script.

    2973

    Answer #1

    import time
    time.sleep(5)   # Delays for 5 seconds. You can also use a float value.
    

    Here is another example where something is run approximately once a minute:

    import time
    while True:
        print("This prints once a minute.")
        time.sleep(60) # Delay for 1 minute (60 seconds).
    

    2973

    Answer #2

    You can use the sleep() function in the time module. It can take a float argument for sub-second resolution.

    from time import sleep
    sleep(0.1) # Time in seconds
    

    Learn Python From Javascript __del__: Questions

    __del__

    How to delete a file or folder in Python?

    5 answers

    How do I delete a file or folder in Python?

    2639

    Answer #1


    Path objects from the Python 3.4+ pathlib module also expose these instance methods:

    __dict__

    How do I merge two dictionaries in a single expression (taking union of dictionaries)?

    5 answers

    Carl Meyer By Carl Meyer

    I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

    >>> x = {"a": 1, "b": 2}
    >>> y = {"b": 10, "c": 11}
    >>> z = x.update(y)
    >>> print(z)
    None
    >>> x
    {"a": 1, "b": 10, "c": 11}
    

    How can I get that final merged dictionary in z, not x?

    (To be extra-clear, the last-one-wins conflict-handling of dict.update() is what I"m looking for as well.)

    5839

    Answer #1

    How can I merge two Python dictionaries in a single expression?

    For dictionaries x and y, z becomes a shallowly-merged dictionary with values from y replacing those from x.

    • In Python 3.9.0 or greater (released 17 October 2020): PEP-584, discussed here, was implemented and provides the simplest method:

      z = x | y          # NOTE: 3.9+ ONLY
      
    • In Python 3.5 or greater:

      z = {**x, **y}
      
    • In Python 2, (or 3.4 or lower) write a function:

      def merge_two_dicts(x, y):
          z = x.copy()   # start with keys and values of x
          z.update(y)    # modifies z with keys and values of y
          return z
      

      and now:

      z = merge_two_dicts(x, y)
      

    Explanation

    Say you have two dictionaries and you want to merge them into a new dictionary without altering the original dictionaries:

    x = {"a": 1, "b": 2}
    y = {"b": 3, "c": 4}
    

    The desired result is to get a new dictionary (z) with the values merged, and the second dictionary"s values overwriting those from the first.

    >>> z
    {"a": 1, "b": 3, "c": 4}
    

    A new syntax for this, proposed in PEP 448 and available as of Python 3.5, is

    z = {**x, **y}
    

    And it is indeed a single expression.

    Note that we can merge in with literal notation as well:

    z = {**x, "foo": 1, "bar": 2, **y}
    

    and now:

    >>> z
    {"a": 1, "b": 3, "foo": 1, "bar": 2, "c": 4}
    

    It is now showing as implemented in the release schedule for 3.5, PEP 478, and it has now made its way into the What"s New in Python 3.5 document.

    However, since many organizations are still on Python 2, you may wish to do this in a backward-compatible way. The classically Pythonic way, available in Python 2 and Python 3.0-3.4, is to do this as a two-step process:

    z = x.copy()
    z.update(y) # which returns None since it mutates z
    

    In both approaches, y will come second and its values will replace x"s values, thus b will point to 3 in our final result.

    Not yet on Python 3.5, but want a single expression

    If you are not yet on Python 3.5 or need to write backward-compatible code, and you want this in a single expression, the most performant while the correct approach is to put it in a function:

    def merge_two_dicts(x, y):
        """Given two dictionaries, merge them into a new dict as a shallow copy."""
        z = x.copy()
        z.update(y)
        return z
    

    and then you have a single expression:

    z = merge_two_dicts(x, y)
    

    You can also make a function to merge an arbitrary number of dictionaries, from zero to a very large number:

    def merge_dicts(*dict_args):
        """
        Given any number of dictionaries, shallow copy and merge into a new dict,
        precedence goes to key-value pairs in latter dictionaries.
        """
        result = {}
        for dictionary in dict_args:
            result.update(dictionary)
        return result
    

    This function will work in Python 2 and 3 for all dictionaries. e.g. given dictionaries a to g:

    z = merge_dicts(a, b, c, d, e, f, g) 
    

    and key-value pairs in g will take precedence over dictionaries a to f, and so on.

    Critiques of Other Answers

    Don"t use what you see in the formerly accepted answer:

    z = dict(x.items() + y.items())
    

    In Python 2, you create two lists in memory for each dict, create a third list in memory with length equal to the length of the first two put together, and then discard all three lists to create the dict. In Python 3, this will fail because you"re adding two dict_items objects together, not two lists -

    >>> c = dict(a.items() + b.items())
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: unsupported operand type(s) for +: "dict_items" and "dict_items"
    

    and you would have to explicitly create them as lists, e.g. z = dict(list(x.items()) + list(y.items())). This is a waste of resources and computation power.

    Similarly, taking the union of items() in Python 3 (viewitems() in Python 2.7) will also fail when values are unhashable objects (like lists, for example). Even if your values are hashable, since sets are semantically unordered, the behavior is undefined in regards to precedence. So don"t do this:

    >>> c = dict(a.items() | b.items())
    

    This example demonstrates what happens when values are unhashable:

    >>> x = {"a": []}
    >>> y = {"b": []}
    >>> dict(x.items() | y.items())
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: unhashable type: "list"
    

    Here"s an example where y should have precedence, but instead the value from x is retained due to the arbitrary order of sets:

    >>> x = {"a": 2}
    >>> y = {"a": 1}
    >>> dict(x.items() | y.items())
    {"a": 2}
    

    Another hack you should not use:

    z = dict(x, **y)
    

    This uses the dict constructor and is very fast and memory-efficient (even slightly more so than our two-step process) but unless you know precisely what is happening here (that is, the second dict is being passed as keyword arguments to the dict constructor), it"s difficult to read, it"s not the intended usage, and so it is not Pythonic.

    Here"s an example of the usage being remediated in django.

    Dictionaries are intended to take hashable keys (e.g. frozensets or tuples), but this method fails in Python 3 when keys are not strings.

    >>> c = dict(a, **b)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: keyword arguments must be strings
    

    From the mailing list, Guido van Rossum, the creator of the language, wrote:

    I am fine with declaring dict({}, **{1:3}) illegal, since after all it is abuse of the ** mechanism.

    and

    Apparently dict(x, **y) is going around as "cool hack" for "call x.update(y) and return x". Personally, I find it more despicable than cool.

    It is my understanding (as well as the understanding of the creator of the language) that the intended usage for dict(**y) is for creating dictionaries for readability purposes, e.g.:

    dict(a=1, b=10, c=11)
    

    instead of

    {"a": 1, "b": 10, "c": 11}
    

    Response to comments

    Despite what Guido says, dict(x, **y) is in line with the dict specification, which btw. works for both Python 2 and 3. The fact that this only works for string keys is a direct consequence of how keyword parameters work and not a short-coming of dict. Nor is using the ** operator in this place an abuse of the mechanism, in fact, ** was designed precisely to pass dictionaries as keywords.

    Again, it doesn"t work for 3 when keys are not strings. The implicit calling contract is that namespaces take ordinary dictionaries, while users must only pass keyword arguments that are strings. All other callables enforced it. dict broke this consistency in Python 2:

    >>> foo(**{("a", "b"): None})
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: foo() keywords must be strings
    >>> dict(**{("a", "b"): None})
    {("a", "b"): None}
    

    This inconsistency was bad given other implementations of Python (PyPy, Jython, IronPython). Thus it was fixed in Python 3, as this usage could be a breaking change.

    I submit to you that it is malicious incompetence to intentionally write code that only works in one version of a language or that only works given certain arbitrary constraints.

    More comments:

    dict(x.items() + y.items()) is still the most readable solution for Python 2. Readability counts.

    My response: merge_two_dicts(x, y) actually seems much clearer to me, if we"re actually concerned about readability. And it is not forward compatible, as Python 2 is increasingly deprecated.

    {**x, **y} does not seem to handle nested dictionaries. the contents of nested keys are simply overwritten, not merged [...] I ended up being burnt by these answers that do not merge recursively and I was surprised no one mentioned it. In my interpretation of the word "merging" these answers describe "updating one dict with another", and not merging.

    Yes. I must refer you back to the question, which is asking for a shallow merge of two dictionaries, with the first"s values being overwritten by the second"s - in a single expression.

    Assuming two dictionaries of dictionaries, one might recursively merge them in a single function, but you should be careful not to modify the dictionaries from either source, and the surest way to avoid that is to make a copy when assigning values. As keys must be hashable and are usually therefore immutable, it is pointless to copy them:

    from copy import deepcopy
    
    def dict_of_dicts_merge(x, y):
        z = {}
        overlapping_keys = x.keys() & y.keys()
        for key in overlapping_keys:
            z[key] = dict_of_dicts_merge(x[key], y[key])
        for key in x.keys() - overlapping_keys:
            z[key] = deepcopy(x[key])
        for key in y.keys() - overlapping_keys:
            z[key] = deepcopy(y[key])
        return z
    

    Usage:

    >>> x = {"a":{1:{}}, "b": {2:{}}}
    >>> y = {"b":{10:{}}, "c": {11:{}}}
    >>> dict_of_dicts_merge(x, y)
    {"b": {2: {}, 10: {}}, "a": {1: {}}, "c": {11: {}}}
    

    Coming up with contingencies for other value types is far beyond the scope of this question, so I will point you at my answer to the canonical question on a "Dictionaries of dictionaries merge".

    Less Performant But Correct Ad-hocs

    These approaches are less performant, but they will provide correct behavior. They will be much less performant than copy and update or the new unpacking because they iterate through each key-value pair at a higher level of abstraction, but they do respect the order of precedence (latter dictionaries have precedence)

    You can also chain the dictionaries manually inside a dict comprehension:

    {k: v for d in dicts for k, v in d.items()} # iteritems in Python 2.7
    

    or in Python 2.6 (and perhaps as early as 2.4 when generator expressions were introduced):

    dict((k, v) for d in dicts for k, v in d.items()) # iteritems in Python 2
    

    itertools.chain will chain the iterators over the key-value pairs in the correct order:

    from itertools import chain
    z = dict(chain(x.items(), y.items())) # iteritems in Python 2
    

    Performance Analysis

    I"m only going to do the performance analysis of the usages known to behave correctly. (Self-contained so you can copy and paste yourself.)

    from timeit import repeat
    from itertools import chain
    
    x = dict.fromkeys("abcdefg")
    y = dict.fromkeys("efghijk")
    
    def merge_two_dicts(x, y):
        z = x.copy()
        z.update(y)
        return z
    
    min(repeat(lambda: {**x, **y}))
    min(repeat(lambda: merge_two_dicts(x, y)))
    min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
    min(repeat(lambda: dict(chain(x.items(), y.items()))))
    min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))
    

    In Python 3.8.1, NixOS:

    >>> min(repeat(lambda: {**x, **y}))
    1.0804965235292912
    >>> min(repeat(lambda: merge_two_dicts(x, y)))
    1.636518670246005
    >>> min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
    3.1779992282390594
    >>> min(repeat(lambda: dict(chain(x.items(), y.items()))))
    2.740647904574871
    >>> min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))
    4.266070580109954
    
    $ uname -a
    Linux nixos 4.19.113 #1-NixOS SMP Wed Mar 25 07:06:15 UTC 2020 x86_64 GNU/Linux
    

    Resources on Dictionaries

    5839

    Answer #2

    In your case, what you can do is:

    z = dict(list(x.items()) + list(y.items()))
    

    This will, as you want it, put the final dict in z, and make the value for key b be properly overridden by the second (y) dict"s value:

    >>> x = {"a":1, "b": 2}
    >>> y = {"b":10, "c": 11}
    >>> z = dict(list(x.items()) + list(y.items()))
    >>> z
    {"a": 1, "c": 11, "b": 10}
    
    

    If you use Python 2, you can even remove the list() calls. To create z:

    >>> z = dict(x.items() + y.items())
    >>> z
    {"a": 1, "c": 11, "b": 10}
    

    If you use Python version 3.9.0a4 or greater, then you can directly use:

    x = {"a":1, "b": 2}
    y = {"b":10, "c": 11}
    z = x | y
    print(z)
    
    {"a": 1, "c": 11, "b": 10}
    

    5839

    Answer #3

    An alternative:

    z = x.copy()
    z.update(y)
    

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