Category: Programming

If you’re like me, you want to come to the heart of an issue fast. Here’s the 1-paragraph summary of the enumerate() function—that’s all you need to know to get started using it:

Python’s built-in enumerate(iterable) function allows you to loop over all elements in an iterable and their associated counters. Formally, it takes an iterable as an input argument and returns an iterable of tuples (i, x)—one per iterable element x. The first integer tuple value is the counter of the element x in the iterable, starting to count from 0. The second tuple value is a reference to the element x itself. For example, enumerate(['a', 'b', 'c']) returns an iterable (0, 'a'), (1, 'b'), (2, 'c'). You can modify the default start index of the counter by setting the optional second integer argument enumerate(iterable, start).

I’ve created a short visual guide into enumerate in the following graphic:

Usage Example

Learn by example! Here are some examples of how to use the enumerate() built-in function:

fruits = ['apple', 'banana', 'cherry']
for counter, value in enumerate(fruits): print(counter, value) # OUTPUT:
# 0 apple
# 1 banana
# 2 cherry

The enumerate(iterable, start) function takes an optional second argument that is the start value of the counter.

fruits = ['apple', 'banana', 'cherry']
for counter, value in enumerate(fruits, 42): print(counter, value) # OUTPUT:
# 42 apple
# 43 banana
# 44 cherry

You can use the enumerate function to create a list of tuples from an iterable where the first tuple value is the index of the element:

fruits = ['apple', 'banana', 'cherry']
fruits_with_indices = list(enumerate(fruits))
print(fruits_with_indices)
# [(0, 'apple'), (1, 'banana'), (2, 'cherry')]

Video enumerate()

Syntax enumerate()

Syntax: 
enumerate(iterable) -> loop over all elements in an iterable and their counters, starting from 0. 
enumerate(iterable, start) -> loop over all elements in an iterable and their counters, starting from start. 

Arguments	iterable	The iterable you want to enumerate.
	start	The start counter of the first element iterable[0].
Return Value	enumerate object	An iterable that allows you to iterate over each element associated to its counter, starting to count from start.

Interactive Shell Exercise: Understanding enumerate()

Consider the following interactive code:

Exercise: Change the start value of the enumerate function to your personal age and run the code. What’s the associated counter to the last fruit in the list?

Next, you’re going to dive deeper into the enumerate() function.

---

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If you like one-liners, you’ll LOVE the book. It’ll teach you everything there is to know about a single line of Python code. But it’s also an introduction to computer science, data science, machine learning, and algorithms. The universe in a single line of Python!

The book was released in 2020 with the world-class programming book publisher NoStarch Press (San Francisco).

Link: https://nostarch.com/pythononeliners

---

What is the Return Value of Python’s enumerate() function?

The return value of enumerate(iterable) is an object of type enumerate. The enumerate class definition implements the iterable interface—the __next__() function—which means that you can iterate over it.

fruits = ['apple', 'banana', 'cherry']
print(type(enumerate(fruits)))
# <class 'enumerate'>

How is Python’s enumerate() Function Implemented?

The default implementation of enumerate() is done in C++, assuming you use cPython as your Python engine. However, the documentation shows an equivalent implementation of enumerate() in Python code that helps you understand how it works under the hood:

def enumerate(sequence, start=0): counter = start for element in sequence: yield counter, element counter += 1

You can see that the return value of enumerate() is not a list but a generator that issues the (counter, element) tuples as they appear in the sequence. Thus, the implementation is memory efficient—it doesn’t generate all (counter, element) pairs in advance and holds them in memory, but generates them as they’re needed.

How to Use enumerate() on Strings?

The enumerate(iterable) function takes an iterable as an input argument. A string is an iterable, so you can pass the string as an input. The return value of the function enumerate(string) will be an enumerate object that associates a counter to each character in the string for a series of tuples (counter, character). Here’s an example:

>>> list(enumerate('finxter'))
[(0, 'f'), (1, 'i'), (2, 'n'), (3, 'x'), (4, 't'), (5, 'e'), (6, 'r')]

You can also set the optional second argument start:

>>> list(enumerate('finxter', 42))
[(42, 'f'), (43, 'i'), (44, 'n'), (45, 'x'), (46, 't'), (47, 'e'), (48, 'r')]

How to Make Your Loop More Pythonic With enumerate()?

Beginner Python coders and coders coming from other programming languages such as Java or C++, often think in indices when creating loops such as this one:

# NON_PYTHONIC
fruits = ['apple', 'banana', 'cherry']
for i in range(len(fruits)): print(i, fruits[i])

The output of this correct, but unpythonic code is:

0 apple
1 banana
2 cherry

While the code does what it needs to do, it shouts into the world that its creator is not an experienced Python coder, but a newbie in Python. Why? Because an experienced Python coder will always prefer the enumerate() function due its more idiomatic and crisp functionality:

# PYTHONIC
fruits = ['apple', 'banana', 'cherry']
for i, fruit in enumerate(fruits): print(i, fruit)

You don’t have to use a single indexing mechanism—which reduces the likelihood of a bug and improves readability of your code.

Python enumerate() step

How to set a step in the indices used by the enumerate() function? For example, you may want to use only every third counter:

0 element_0
3 element_1
6 element_2

The answer is to multiply the returned counter value from a default call of the enumerate() function with the step size like this:

lst = ['element_0', 'element_1', 'element_2']
step = 3
for i, x in enumerate(lst): print(i*step, x)

OUTPUT:
0 element_0
3 element_1
6 element_2

Summary

Python’s built-in enumerate(iterable) function allows you to loop over all elements in an iterable and their associated counters.

Formally, it takes an iterable as an input argument and returns an iterable of tuples(i, x)—one per iterable element x.

• The first integer tuple value is the counter of the element x in the iterable, starting to count from 0.
• The second tuple value is a reference to the element x itself.

For example, enumerate(['a', 'b', 'c']) returns an iterable (0, 'a'), (1, 'b'), (2, 'c').

print(*enumerate(['a', 'b', 'c']))
# (0, 'a'), (1, 'b'), (2, 'c')

You can modify the default start index of the counter by setting the optional second integer argument enumerate(iterable, start).

print(*enumerate(['a', 'b', 'c'], 10))
# (10, 'a') (11, 'b') (12, 'c')

---

I hope you enjoyed the article! To improve your Python education, you may want to join the popular free Finxter Email Academy:

Do you want to boost your Python skills in a fun and easy-to-consume way? Consider the following resources and become a master coder!

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

References:

• https://docs.python.org/3/library/functions.html#enumerate
• https://book.pythontips.com/en/latest/enumerate.html
• https://realpython.com/python-enumerate/
• https://www.geeksforgeeks.org/enumerate-in-python/
• https://www.programiz.com/python-programming/methods/built-in/enumerate
• https://www.w3schools.com/python/ref_func_enumerate.asp
• https://www.afternerd.com/blog/python-enumerate/
• https://careerkarma.com/blog/python-enumerate/

The post Python enumerate() — A Simple Illustrated Guide with Video first appeared on Finxter.

Python’s built-in divmod(a, b) function takes two integer or float numbers a and b as input arguments and returns a tuple (a // b, a % b). The first tuple value is the result of the integer division a//b. The second tuple is the result of the remainder, also called modulo operation a % b. In case of float inputs, divmod() still returns the division without remainder by rounding down to the next round number.

Usage

Learn by example! Here are some examples of how to use the divmod() built-in function with integer arguments:

# divmod() with integers
>>> divmod(10, 2)
(5, 0)
>>> divmod(10, 3)
(3, 1)
>>> divmod(10, 4)
(2, 2)
>>> divmod(10, 5)
(2, 0)
>>> divmod(10, 10)
(1, 0)

You can also use float arguments as follows:

# divmod() with floats
>>> divmod(10.0, 2.0)
(5.0, 0.0)
>>> divmod(10.0, 3.0)
(3.0, 1.0)
>>> divmod(10.0, 4.0)
(2.0, 2.0)
>>> divmod(10.0, 5.0)
(2.0, 0.0)
>>> divmod(10.0, 10.0)
(1.0, 0.0)

Video divmod()

Syntax divmod()

Syntax: 
divmod(a, b) -> returns a tuple of two numbers. The first is the result of the division without remainder a/b. The second is the remainder (modulo) a%b. 

Arguments	integer	The dividend of the division operation.
	integer	The divisor of the division operation.
Return Value	tuple	Returns a tuple of two numbers. The first is the result of the division without remainder. The second is the remainder (modulo).

Interactive Shell Exercise: Understanding divmod()

Consider the following interactive code:

Exercise: Guess the output before running the code.

---

But before we move on, I’m excited to present you my brand-new Python book Python One-Liners (Amazon Link).

If you like one-liners, you’ll LOVE the book. It’ll teach you everything there is to know about a single line of Python code. But it’s also an introduction to computer science, data science, machine learning, and algorithms. The universe in a single line of Python!

The book is released in 2020 with the world-class programming book publisher NoStarch Press (San Francisco).

Link: https://nostarch.com/pythononeliners

---

Exact Mathematical Definition divmod()

You can generally use the divmod(a, b) function with two integers, one integer and one float, or two floats.

Two integers. Say you call divmod(a, b) with two integers a and b. In this case, the exact mathematical definition of the return value is (a // b, a % b).

a = 5
b = 2
print((a // b, a % b))
print(divmod(a, b))
# OUTPUT:
# (2, 1)
# (2, 1)

One integer and one float. Say you call divmod(a, b) with an integer a and a float b. In this case, the exact mathematical definition of the return value is the return value of converting the integer to a float and calling divmod(a, float(b)).

a = 5.0
b = 2
print((a // b, a % b))
print(divmod(a, b))
# OUTPUT:
# (2.0, 1.0)
# (2.0, 1.0)

Two floats. Say you call divmod(a, b) with two floats a and b. In this case, the exact mathematical definition of the return value is (float(math.floor(a / b)), a % b).

import math a = 5.0
b = 2.0
print((float(math.floor(a / b)), a % b))
print(divmod(a, b))
# OUTPUT:
# (2.0, 1.0)
# (2.0, 1.0)

Note that because of the imprecision of floating point arithmetic, the result may have a small floating point error in one of the lower decimal positions. You can read more about the floating point trap on the Finxter blog.

Related Tutorial: Floating Point Error Explained

Python divmod() Negative Numbers

Can you use the divmod() method on negative numbers for the dividend or the divisor?

You can use divmod(a, b) for negative input arguments a, b, or both. In any case, if both arguments are integers, Python performs integer division a // b to obtain the first element and modulo division a % b to obtain the second element of the returned tuple. Both operations allow negative inputs a or b. The returned tuple (x, y) is calculated so that x * b + y = a.

Here’s an example of all three cases:

>>> divmod(-10, -3)
(3, -1)
>>> divmod(-10, 3)
(-4, 2)
>>> divmod(10, -3)
(-4, -2)

Python divmod() Performance — Is It Faster Than Integer Division // and Modulo % Operators?

There are two semantically identical ways to create a tuple where the first element is the result of the integer division and the second is the result of the modulo operation:

• Use the divmod(a, b) function.
• Use the (a // b, a % b) explicit operation with Python built-in operators.

Next, we measure the performance of calculating the elapsed runtime in milliseconds when performing 10 million computations for relatively small integers. Let’s start with divmod():

import time
import random # Small Operands
operands = zip([random.randint(1, 100) for i in range(10**7)], [random.randint(1, 100) for i in range(10**7)]) start = time.time() for i, j in operands: divmod(i, j) stop = time.time()
print('divmod() elapsed time: ', (stop-start), 'milliseconds')
# divmod() elapsed time: 1.7654337882995605 milliseconds

Compare this to integer division and modulo:

import time
import random # Small Operands
operands = zip([random.randint(1, 100) for i in range(10**7)], [random.randint(1, 100) for i in range(10**7)]) start = time.time() for i, j in operands: (i // j, i % j) stop = time.time()
print('(i // j, i % j) elapsed time: ', (stop-start), 'milliseconds')
# (i // j, i % j) elapsed time: 1.9048900604248047 milliseconds

The result of this performance benchmark is that divmod() requires 1.76 milliseconds and the explicit way of using integer division and modulo requires 1.90 milliseconds for 10,000,000 operations. Thus, divmod() is 8% faster. The reason is that the explicit way performs many duplicate operations to calculate the result of the integer division and the modulo operation which internally uses integer division again. This effect becomes even more pronounced if you use larger integers.

Python divmod() Implementation

For integer input arguments, here’s a semantically equivalent divmod() implementation:

>>> def divmod_own(x, y): return (x // y, x % y) >>> divmod_own(10, 3)
(3, 1)
>>> divmod(10, 3)
(3, 1)

But note that this implementation still performs redundant computations (e.g., integer division) and, therefore, is less efficient than divmod().

Summary

Python’s built-in divmod(a, b) function takes two integer or float numbers a and b as input arguments and returns a tuple (a // b, a % b).

• The first tuple value is the result of the integer division a//b.
• The second tuple is the result of the remainder, also called modulo operation a % b.

In case of float inputs, divmod() still returns the division without remainder by rounding down to the next round number.

---

I hope you enjoyed the article! To improve your Python education, you may want to join the popular free Finxter Email Academy:

Do you want to boost your Python skills in a fun and easy-to-consume way? Consider the following resources and become a master coder!

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post Python divmod() — A Simple Guide with Video first appeared on Finxter.

This is a chapter draft for our upcoming book “Python Dash” with NoStarch—to appear in 2021. Stay tuned!

Why an IDE

Using an integrated development environment (IDE) has the potential to significantly accelerate your programming productivity. Many programmers do not unlock their full potential until they finally decide to switch from a simple code editor to an IDE—and mastering the advanced functionality provided by the IDE. Some advantages of IDEs over simple text editors are code highlighting, tooltips, syntax checker, code linters that check for style issues, version control to safeguard the history of programming edits, debugging with the help of breakpoints, visual aids such as flowcharts and block diagrams, performance optimization tools and profilers—just to name a few.

PyCharm for Dash Apps

In this book about dashboard applications, we recommend that you also take your time to switch to an IDE, if you haven’t already. In particular, we recommend that you use the PyCharm IDE to follow along with the provided code examples. Apart from the benefits of using IDEs, you’ll also develop web applications that can quickly grow by adding more and more features. As your Python dashboard applications grow, so will your need to aggregate all source code at a single spot and in a single development environment. Increasing complexity quickly demands the use of an IDE.

In the following, we’ll describe how to download and install PyCharm, and create your first simple dashboard application that you can view in your browser. After you’ve completed those steps, you’re well-prepared to duplicate the increasingly advanced applications in the upcoming chapters.

Download PyCharm

First, let’s start with downloading the latest PyCharm version. We assume you have a Windows PC, but the steps are very similar on a macOS and Linux computer. As soon as you’ve launched the PyCharm application, the similarity of usage increases even more across the different operating systems.

You can download the PyCharm app from the official website.

Click the download button of the free community version and wait for the download to complete.

Install PyCharm on Your Computer

Now, run the executable installer and follow the steps of the installation application. A sensible approach is to accept the default settings suggested by the PyCharm installer.

Congratulations, you’ve installed PyCharm on your system!

Open PyCharm

Now type “PyCharm” into the search bar of your operating system and run the IDE!

Create a New Dash Project in PyCharm

After choosing “New Project”, you should see a window similar to this one:

This user interface asks you to provide a project name, a virtual environment, and a Python interpreter. We call our project firstDashProject, use a virtual environment with the standard Python installation, and don’t create a main.py welcome script:

Create the project and you should see your first PyCharm dashboard project!

Create Your Dash File app.py in Your PyCharm Project

Let’s create a new file app.py in your project and copy&paste the code from the official documentation into your app.py file:

# -*- coding: utf-8 -*- # Run this app with `python app.py` and
# visit http://127.0.0.1:8050/ in your web browser. import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) # assume you have a "long-form" data frame
# see https://plotly.com/python/px-arguments/ for more options
df = pd.DataFrame({ "Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"], "Amount": [4, 1, 2, 2, 4, 5], "City": ["SF", "SF", "SF", "Montreal", "Montreal", "Montreal"]
}) fig = px.bar(df, x="Fruit", y="Amount", color="City", barmode="group") app.layout = html.Div(children=[ html.H1(children='Hello Dash'), html.Div(children=''' Dash: A web application framework for Python. '''), dcc.Graph( id='example-graph', figure=fig )
]) if __name__ == '__main__': app.run_server(debug=True)

You can get the code from the official Dash tutorial: https://dash.plotly.com/layout

Your PyCharm dashboard project should now look like this:

Debug Your Dash App Using PyCharm’s Tooltips

Now, let’s try to run our project by using the top menu and select Run > app.py. Unfortunately, it doesn’t already work—PyCharm doesn’t recognize dash!

You can easily fix this by hovering over the red underlined “dash” library import in your app and choosing the “install package dash” option.

This is one great advantage of an IDE is that installing dependencies in your Python projects is as simple as accepting the tooltips provided by your intelligent development environment.

Install Dash in Your Virtual Environment

Installing the dash library will take a few moments. Note that the library will be installed only in a virtual environment which means that it’ll install it not on your global operating system but only on a project level. For a different project, you may have to install dash again. While this may sound tedious, it’s actually the most Pythonic way because it keeps dependency management simple and decentralized. There won’t be any version issues because your first project needs version 1 and your second project needs version 2 of a given library. Instead, each project installs exactly the version it needs.

Install Pandas in Your Virtual Environment

PyCharm will tell you when it is done with installing the dash library in the virtual environment. Now repeat the same procedure for all red-underlined libraries in the project. If you used the code given above, you’ll have to install the pandas library (see Chapter 3) as well in your local environment. A few moments later, the pandas installation will also successfully complete. The red underlined error messages in your code will disappear and you’re ready to restart the project again by clicking “Run”.

Exploring Your First Dash App in Your Browser

On my machine, the output after running the app.py file in PyCharm is:

C:\Users\xcent\Desktop\Python\firstDashProject\venv\Scripts\python.exe C:/Users/xcent/Desktop/Python/firstDashProject/app.py
Dash is running on http://127.0.0.1:8050/ * Serving Flask app "app" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: on

Note the highlighted line (in bold). You can now copy the URL http://127.0.0.1:8050/ and paste it into your browser—the dashboard app runs on a local server that is hosted on your machine with IP address 127.0.0.1 and port 8050.  

When you visit this URL in your browser, you should see your first Dash application!

Congratulations, you’re now well-prepared to run all dashboard apps in this book—and beyond it as well—using similar steps. For further reading on PyCharm, feel free to check out our multi-site blog tutorial on https://academy.finxter.com/course/introduction-to-pycharm/

The post How to Get Started With Python Dash on PyCharm [Absolute Beginners] first appeared on Finxter.

The post 7 Ways to Make Money as a Coder – Without a Job first appeared on Finxter.

Python’s return keyword commands the execution flow to exit a function immediately and return a value to the caller of the function. You can specify an optional return value—or even a return expression—after the return keyword. If you don’t provide a return value, Python will return the default value None to the caller.

Python Return Keyword Video

Return Keyword Followed by Return Value

Here’s an example of the return keyword in combination with a return value:

def f(): return 4 print(f())
# OUTPUT: 4

Within function f(), Python returns the result 4 to the caller. The print() function then prints the output to the shell.

Return Keyword Followed by Return Expression

Here’s an example of the return keyword in combination with a return expression:

def f(): return 2+2 print(f())
# OUTPUT: 4

Within function f(), Python evaluates the expression 2+2=4 and returns the result 4 to the caller. The print() function then prints the output to the shell.

Return Keyword Followed by No Value

Here’s an example of the return keyword without defining a return value:

def f(): return print(f())
# OUTPUT: None

Within function f(), Python returns the default value None to the caller. The print() function then prints the output to the shell.

Interactive Code Shell

Run the following code in your browser:

Exercise: Change the three return values to 42, 42, and ‘Alice’ in the interactive code shell!

The post Return Keyword in Python – A Simple Illustrated Guide first appeared on Finxter.

If used without argument, Python’s built-in dir() function returns the function and variable names defined in the local scope—the namespace of your current module. If used with an object argument, dir(object) returns a list of attribute and method names defined in the object’s scope. Thus, dir() returns all names in a given scope.

Usage

Learn by example! Here are some examples of how to use the dir() built-in function.

Here’s the use without an argument:

alice = 22
bob = 42
print(dir())

It prints the implicitly and explicitly defined names in your module where you run this code:

['__annotations__', '__builtins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'alice', 'bob']

The last two values in the list are the names 'alice' and 'bob'.

The following code exemplifies the use of dir() with an object argument of class Car.

class Car: speed = 100 color = 'black' porsche = Car()
print(dir(porsche))

The class Car has two attributes. If you print the names of the porsche instance of the Car class, you obtain the following output:

['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'color', 'speed']

The final two attributes are 'color' and 'speed', the ones you defined. There are many other names in the list with the double underscore (called dunder). These are the attribute and method names already defined implicitly by the Python environment for any object. For example, __str__ gives the default string representation of a given object.

Video dir()

Syntax dir()

Syntax: 
dir() -> names defined in the local scope/namespace. dir(object) -> names defined for the object. 

Arguments	object	The object for which the names should be returned.
Return Value	list	Returns all names defined in the namespace of the specified object. If no object argument is given, it returns the names defined in the local namespace of the module in which you run the code.

Interactive Shell Exercise: Understanding dir()

Consider the following interactive code:

Exercise: Guess the output before running the code. Do both cars, porsche and tesla, generate the same output?

---

But before we move on, I’m excited to present you my brand-new Python book Python One-Liners (Amazon Link).

If you like one-liners, you’ll LOVE the book. It’ll teach you everything there is to know about a single line of Python code. But it’s also an introduction to computer science, data science, machine learning, and algorithms. The universe in a single line of Python!

The book is released in 2020 with the world-class programming book publisher NoStarch Press (San Francisco).

Link: https://nostarch.com/pythononeliners

---

Using dir() on Modules

You can also use Python’s built-in dir() method on modules. For example, after importing the random module, you can pass it into the dir(random) function. This gives you all the names and functions defined in the module.

import random
print("The random module contains the following names: ")
print(dir(random))

The output is the following:

The random module contains the following names: ['BPF', 'LOG4', 'NV_MAGICCONST', 'RECIP_BPF', 'Random', 'SG_MAGICCONST', 'SystemRandom', 'TWOPI', '_BuiltinMethodType', '_MethodType', '_Sequence', '_Set', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_acos', '_bisect', '_ceil', '_cos', '_e', '_exp', '_inst', '_itertools', '_log', '_os', '_pi', '_random', '_sha512', '_sin', '_sqrt', '_test', '_test_generator', '_urandom', '_warn', 'betavariate', 'choice', 'choices', 'expovariate', 'gammavariate', 'gauss', 'getrandbits', 'getstate', 'lognormvariate', 'normalvariate', 'paretovariate', 'randint', 'random', 'randrange', 'sample', 'seed', 'setstate', 'shuffle', 'triangular', 'uniform', 'vonmisesvariate', 'weibullvariate']

This way, you can quickly explore the contents of a module and which functions you may want to use in your own code!

Overwriting dir() with __dir__()

To customize the return value of the dir() function on a custom class, you can overwrite the __dir__() method and return the values to be returned. This way, you can hide names from the user or filter out only relevant names of your object.

class Car: speed = 100 color = 'gold' def __dir__(self): return ['porsche', 'tesla', 'bmw'] tesla = Car()
print(dir(tesla))

The output is the nonsensical list of “names”:

['bmw', 'porsche', 'tesla']

Summary

There are two different use cases for the dir() function.

• If used without argument, Python’s built-in dir() function returns the function and variable names defined in the local scope—the namespace of your current module.
• If used with an object argument, dir(object) returns a list of attribute and method names defined in the object’s scope.

Thus, dir() returns all names in a given scope.

Source: Documentation

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I hope you enjoyed the article! To improve your Python education, you may want to join the popular free Finxter Email Academy:

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Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post Python dir() — A Simple Guide with Video first appeared on Finxter.

Python’s built-in dict() function creates and returns a new dictionary object from the comma-separated argument list of key = value mappings. For example, dict(name = 'Alice', age = 22, profession = 'programmer') creates a dictionary with three mappings: {'name': 'Alice', 'age': 22, 'profession': 'programmer'}. A dictionary is an unordered and mutable data structure, so it can be changed after creation.

Read more about dictionaries in our full tutorial about Python Dictionaries.

Usage

Learn by example! Here are some examples of how to use the dict() built-in function:

>>> dict(name = 'Alice')
{'name': 'Alice'}
>>> dict(name = 'Alice', age = 22)
{'name': 'Alice', 'age': 22}
>>> dict(name = 'Alice', age = 22, profession = 'programmer')
{'name': 'Alice', 'age': 22, 'profession': 'programmer'}

You can pass an arbitrary number of those comma-separated key = value pairs into the dict() constructor.

Video dict()

Syntax dict()

You can use the dict() method with an arbitrary number of key=value arguments, comma-separated.

Syntax: There are four ways of using the constructor:
dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs
dict(iterable) -> new dictionary initialized from an iterable of (key, value) tuples
dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list.

Interactive Shell Exercise: Understanding dict()

Consider the following interactive code:

Exercise: Guess the output before running the code.

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The dict() function has many different options to be called with different types of arguments. You’ll learn different ways to use the dict() function next.

How to Create an Empty Dictionary?

You can create an empty dictionary by using Python’s built-in dict() function without any argument. This returns an empty dictionary. As the dictionary is a mutable data structure, you can add more mappings later by using the d[key] = value syntax.

>>> d = dict()
>>> d['Alice'] = 22
>>> d
{'Alice': 22}

How to Create a Dictionary Using Only Keyword Arguments?

You can create a dictionary with initial key: value mappings by using a list of comma-separated arguments such as in dict(name = 'Alice', age = 22) to create the dictionary {'name': 'Alice', 'age': 22}. These are called keyword arguments because each argument value has its associated keyword.

>>> dict(name = 'Alice', age = 22)
{'name': 'Alice', 'age': 22}

How to Create a Dictionary Using an Iterable?

You can initialize your new dictionary by using an iterable as an input for the dict(iterable) function. Python expects that the iterable contains (key, value) pairs. An example iterable is a list of tuples or a list of lists. The first values of the inner collection types are the keys and the second values of the inner collection types are the values of the new dictionary.

>>> dict([(1, 'one'), (2, 'two')])
{1: 'one', 2: 'two'}
>>> dict([[1, 'one'], [2, 'two']])
{1: 'one', 2: 'two'}
>>> dict(((1, 'one'), (2, 'two')))
{1: 'one', 2: 'two'}

Note that you can use inner tuples, inner lists, outer tuples or outer lists—as long as each inner collection contains exactly two values. If it contains more, Python raises an ValueError: dictionary update sequence element.

>>> dict([(1, 'one', 1.0), (2, 'two', 2.0)])
Traceback (most recent call last): File "<pyshell#22>", line 1, in <module> dict([(1, 'one', 1.0), (2, 'two', 2.0)])
ValueError: dictionary update sequence element #0 has length 3; 2 is required

You can fix this ValueError by passing only two values in the inner collections. For example use a list of tuples with only two but not three tuple elements.

How to Create a Dictionary Using an Existing Mapping Object?

If you already have a mapping object such as a dictionary mapping keys to values, you can pass this object as an argument into the dict() function. Python will then create a new dictionary based on the existing key: value mappings in the argument. The resulting dictionary will be a new object so if you change it, the changes are not reflected in the original mapping object.

>>> d = {'Alice': 22, 'Bob': 23, 'Carl': 55}
>>> d2 = dict(d)
>>> d
{'Alice': 22, 'Bob': 23, 'Carl': 55}

If you now change the original dictionary, the change is not reflected in the new dictionary d2.

>>> d['David'] = 66
>>> d
{'Alice': 22, 'Bob': 23, 'Carl': 55, 'David': 66}
>>> d2
{'Alice': 22, 'Bob': 23, 'Carl': 55}

How to Create a Dictionary Using a Mapping Object and Keyword Arguments?

Interestingly, you can also pass a mapping object into the dict() function and add some more key: value mappings using keyword arguments after the first mapping argument. For example, dict({'Alice': 22}, Bob = 23) creates a new dictionary with both key:value mappings {'Alice': 22, 'Bob': 23}.

>>> dict({'Alice': 22}, Bob = 23)
{'Alice': 22, 'Bob': 23}
>>> dict({'Alice': 22}, Bob = 23, Carl = 55)
{'Alice': 22, 'Bob': 23, 'Carl': 55}

How to Create a Dictionary Using an Iterable and Keyword Arguments?

Similarly, you can also pass an iterable of (key, value) tuples into the dict() function and add some more key: value mappings using keyword arguments after the first mapping argument. For example, dict([('Alice', 22)], Bob = 23) creates a new dictionary with both key:value mappings {'Alice': 22, 'Bob': 23}.

>>> dict([('Alice', 22)], Bob = 23)
{'Alice': 22, 'Bob': 23}
>>> dict([('Alice', 22), ('Carl', 55)], Bob = 23)
{'Alice': 22, 'Carl': 55, 'Bob': 23}

Summary

Python’s built-in dict() function creates and returns a new dictionary object from the comma-separated argument list of key = value mappings.

For example, dict(name = 'Alice', age = 22, profession = 'programmer') creates a dictionary with three mappings: {'name': 'Alice', 'age': 22, 'profession': 'programmer'}.

>>> dict(name = 'Alice', age = 22, profession = 'programmer')
{'name': 'Alice', 'age': 22, 'profession': 'programmer'}

A dictionary is an unordered and mutable data structure, so it can be changed after creation.

I hope you enjoyed the article! To improve your Python education, you may want to join the popular free Finxter Email Academy:

---

Do you want to boost your Python skills in a fun and easy-to-consume way? Consider the following resources and become a master coder!

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

The post Python dict() — A Simple Guide with Video first appeared on Finxter.

In this article, I’m going to compile the top ten Python freelancer resources on the Finxter website.

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[Article] What Are the Best Freelancing Sites for Coders?

Freelancing is the new way to organize the world’s talents. The appearance of big freelancing platforms made it possible to exchange talent efficiently—across borders, currencies, and niches.

This article is for you if:

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There are four major freelancing platforms for coders: Upwork, Fiverr, Toptal, and Freelancer.com. If you’re busy and you want to learn about the best freelancing sites right away, check out the following “Above-The-Fold” sites.

But there are dozens of big and small freelancing sites for coders. You’ll find a detailed ranking (by Alexa traffic rank 2020) below.

[Article] Top 5 Python Freelancer Jobs to Earn $51 per Hour on Upwork or Fiverr

Python freelancers earn $51 per hour on average. But how do they do it? In the following video I show you the top five trending gigs for Python freelancers:

[Article] Top 14 Places to Find Remote Freelance Developer Gigs and Work From Home

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The post Top 10 Python Freelancer Resources on Finxter first appeared on Finxter.