Markov Chain Monte Carlo in Python

Markov Chain Monte Carlo in Python

Markov Chain Monte Carlo in PythonThe past few months, I encountered one term again and again in the data science world: Markov Chain Monte Carlo. In my research lab,

A Complete Real-World Implementation

The past few months, I encountered one term again and again in the data science world: Markov Chain Monte Carlo. In my research lab, in podcasts, in articles, every time I heard the phrase I would nod and think that sounds pretty cool with only a vague idea of what anyone was talking about. Several times I tried to learn MCMC and Bayesian inference, but every time I started reading the books, I soon gave up. Exasperated, I turned to the best method to learn any new skill: apply it to a problem.

Using some of my sleep data I had been meaning to explore and a hands-on application-based book (Bayesian Methods for Hackers, available free online), I finally learned Markov Chain Monte Carlo through a real-world project. As usual, it was much easier (and more enjoyable) to understand the technical concepts when I applied them to a problem rather than reading them as abstract ideas on a page. This article walks through the introductory implementation of Markov Chain Monte Carlo in Python that finally taught me this powerful modeling and analysis tool.

The full code and data for this project is on GitHub. I encourage anyone to take a look and use it on their own data. This article focuses on applications and results, so there are a lot of topics covered at a high level, but I have tried to provide links for those wanting to learn more!


My Garmin Vivosmart watch tracks when I fall asleep and wake up based on heart rate and motion. It’s not 100% accurate, but real-world data is never perfect, and we can still extract useful knowledge from noisy data with the right model!

The objective of this project was to use the sleep data to create a model that specifies the posterior probability of sleep as a function of time. As time is a continuous variable, specifying the entire posterior distribution is intractable, and we turn to methods to approximate a distribution, such as Markov Chain Monte Carlo (MCMC).

Choosing a Probability Distribution

Before we can start with MCMC, we need to determine an appropriate function for modeling the posterior probability distribution of sleep. One simple way to do this is to visually inspect the data. The observations for when I fall asleep as a function of time are shown below.

Every data point is represented as a dot, with the intensity of the dot showing the number of observations at the specific time. My watch records only the minute at which I fall asleep, so to expand the data, I added points to every minute on both sides of the precise time. If my watch says I fell asleep at 10:05 PM, then every minute before is represented as a 0 (awake) and every minute after gets a 1 (asleep). This expanded the roughly 60 nights of observations into 11340 data points.

We can see that I tend to fall asleep a little after 10:00 PM but we want to create a model that captures the transition from awake to asleep in terms of a probability. We could use a simple step function for our model that changes from awake (0) to asleep (1) at one precise time, but this would not represent the uncertainty in the data. I do not go to sleep at the same time every night, and we need a function to that models the transition as a gradual process to show the variability. The best choice given the data is a logistic function which is smoothly transitions between the bounds of 0 and 1. Following is a logistic equation for the probability of sleep as a function of time

A logistic function fits the data because the probability of being asleep transitions gradually, capturing the variability in my sleep patterns. We want to be able to plug in a time t to the function and get out the probability of sleep, which must be between 0 and 1. Rather than a straight yes or no answer to the question am I asleep at 10:00 PM, we can get a probability. To create this model, we use the data to find the best alpha and beta parameters through one of the techniques classified as Markov Chain Monte Carlo.

Markov Chain Monte Carlo

Markov Chain Monte Carlo refers to a class of methods for sampling from a probability distribution in order to construct the most likelydistribution. We cannot directly calculate the logistic distribution, so instead we generate thousands of values — called samples — for the parameters of the function (alpha and beta) to create an approximation of the distribution. The idea behind MCMC is that as we generate more samples, our approximation gets closer and closer to the actual true distribution.

There are two parts to a Markov Chain Monte Carlo method. Monte Carlo refers to a general technique of using repeated random samples to obtain a numerical answer. Monte Carlo can be thought of as carrying out many experiments, each time changing the variables in a model and observing the response. By choosing random values, we can explore a large portion of the parameter space, the range of possible values for the variables. A parameter space for our problem using normal priors for the variables (more on this in a moment) is shown below.

Clearly we cannot try every single point in these plots, but by randomly sampling from regions of higher probability (red) we can create the most likely model for our problem.

Markov Chain

A Markov Chain is a process where the next state depends only on the current state. (A state in this context refers to the assignment of values to the parameters). A Markov Chain is memoryless because only the current state matters and not how it arrived in that state. If that’s a little difficult to understand, consider an everyday phenomenon, the weather. If we want to predict the weather tomorrow we can get a reasonable estimate using only the weather today. If it snowed today, we look at historical data showing the distribution of weather on the day after it snows to estimate probabilities of the weather tomorrow. The concept of a Markov Chain is that we do not need to know the entire history of a process to predict the next output, an approximation that works well in many real-world situations.

Putting together the ideas of Markov Chain and Monte Carlo, MCMC is a method that repeatedly draws random values for the parameters of a distribution based on the current values. Each sample of values is random, but the choices for the values are limited by the current state and the assumed prior distribution of the parameters. MCMC can be considered as a random walk that gradually converges to the true distribution.

In order to draw random values of alpha and beta, we need to assume a prior distribution for these values. As we have no assumptions about the parameters ahead of time, we can use a normal distribution. The normal, or Gaussian distribution, is defined by the mean, showing the location of the data, and the variance, showing the spread. Several normal distributions with different means and spreads are below:

The specific MCMC algorithm we are using is called Metropolis Hastings. In order to connect our observed data to the model, every time a set of random values are drawn, the algorithm evaluates them against the data. If they do not agree with the data (I’m simplifying a little here), the values are rejected and the model remains in the current state. If the random values are in agreement with the data, the values are assigned to the parameters and become the current state. This process continues for a specified number of steps, with the accuracy of the model improving with the number of steps.

Putting it all together, the basic procedure for Markov Chain Monte Carlo in our problem is as follows:

  1. Select an initial set of values for alpha and beta, the parameters of the logistic function.
  2. Randomly assign new values to alpha and beta based on the current state.
  3. Check if the new random values agree with the observations. If they do not, reject the values and return to the previous state. If they do, accept the values as the new current state.
  4. Repeat steps 2 and 3 for the specified number of iterations.

The algorithm returns all of the values it generates for alpha and beta. We can then use the average of these values as the most likely final values for alpha and beta in the logistic function. MCMC cannot return the “True” value but rather an approximation for the distribution. The final model for the probability of sleep given the data will be the logistic function with the average values of alpha and beta.

Python Implementation

The above details went over my head many times until I applied them in Python! Seeing the results first-hand is a lot more helpful than reading someone else describe. To implement MCMC in Python, we will use the PyMC3 Bayesian inference library. It abstracts away most of the details, allowing us to create models without getting lost in the theory.

The following code creates the full model with the parameters, alpha and beta, the probability, p, and the observations, observed The step variable refers to the specific algorithm, and the sleep_trace holds all of the values of the parameters generated by the model.

with pm.Model() as sleep_model:
    # Create the alpha and beta parameters
    # Assume a normal distribution
    alpha = pm.Normal('alpha', mu=0.0, tau=0.05, testval=0.0)
    beta = pm.Normal('beta', mu=0.0, tau=0.05, testval=0.0)
    # The sleep probability is modeled as a logistic function
    p = pm.Deterministic('p', 1. / (1. + tt.exp(beta * time + alpha)))
    # Create the bernoulli parameter which uses observed data to inform the algorithm
    observed = pm.Bernoulli('obs', p, observed=sleep_obs)
    # Using Metropolis Hastings Sampling
    step = pm.Metropolis()
    # Draw the specified number of samples
    sleep_trace = pm.sample(N_SAMPLES, step=step);

(Check out the notebook for the full code)

To get a sense of what occurs when we run this code, we can look at all the value of alpha and beta generated during the model run.

These are called trace plots. We can see that each state is correlated to the previous — the Markov Chain — but the values oscillate significantly — the Monte Carlo sampling.

In MCMC, it is common practice to discard up to 90% of the trace. The algorithm does not immediately converge to the true distribution and the initial values are often inaccurate. The later values for the parameters are generally better which means they are what we should use for building our model. We used 10000 samples and discarded the first 50%, but an industry application would likely use hundreds of thousands or millions of samples.

MCMC converges to the true value given enough steps, but assessing convergence can be difficult. I will leave that topic out of this post (one way is by measuring the auto-correlation of the traces) but it is an important consideration if we want the most accurate results. PyMC3 has built in functions for assessing the quality of models, including trace and autocorrelation plots.

pm.traceplot(sleep_trace, ['alpha', 'beta'])

Sleep Model

After finally building and running the model, it’s time to use the results. We will the the average of the last 5000 alpha and beta samples as the most likely values for the parameters which allows us to create a single curve modeling the posterior sleep probability:


The model represents the data well. Moreover, it captures the inherent variability in my sleep patterns. Rather than a single yes or no answer, the model gives us a probability. For example, we can query the model to find out the probability I am asleep at a given time and find the time at which the probability of being asleep passes 50%:

9:30  PM probability of being asleep: 4.80%.
10:00 PM probability of being asleep: 27.44%.
10:30 PM probability of being asleep: 73.91%.
The probability of sleep increases to above 50% at 10:14 PM.

Although I try to go to bed at 10:00 PM, that clearly does not happen most nights! We can see that the average time I go to bed is around 10:14 PM.

These values are the most likely estimates given the data. However, there is uncertainty associated with these probabilities because the model is approximate. To represent this uncertainty, we can make predictions of the sleep probability at a given time using all of the alpha and beta samples instead of the average and then plot a histogram of the results.

These results give a better indicator of what an MCMC model really does. The method does not find a single answer, but rather a sample of possible values. Bayesian Inference is useful in the real-world because it expresses predictions in terms of probabilities. We can say there is one most likely answer, but the more accurate response is that there are a range of values for any prediction.

Wake Model

I can use the waking data to find a similar model for when I wake up in the morning. I try to always be up at 6:00 AM with my alarm, but we can see that does not always happen! The following image shows the final model for the transition from sleeping to waking along with the observations.

We can query the model to find the probability I’m asleep at a given time and the most likely time for me to wake up.

**Probability of being awake at 5:30 AM: 14.10%. 
Probability of being awake at 6:00 AM: 37.94%. 
Probability of being awake at 6:30 AM: 69.49%.**
**The probability of being awake passes 50% at 6:11 AM.**

Looks like I have some work to do with that alarm!

Duration of Sleep

A final model I wanted to create — both out of curiosity and for the practice — was my duration of sleep. First, we need to find a function to model the distribution of the data. Ahead of time, I think it would be normal, but we can only find out by examining the data!

A normal distribution would work, but it would not capture the outlying points on the right side (times when I severely slept in). We could use two separate normal distributions to represent the two modes, but instead, I will use a skewed normal. The skewed normal has three parameters, the mean, the variance, and alpha, the skew. All three of these must be learned from the MCMC algorithm. The following code creates the model and implements the Metropolis Hastings sampling.

with pm.Model() as duration_model:
    # Three parameters to sample
    alpha_skew = pm.Normal('alpha_skew', mu=0, tau=0.5, testval=3.0)
    mu_ = pm.Normal('mu', mu=0, tau=0.5, testval=7.4)
    tau_ = pm.Normal('tau', mu=0, tau=0.5, testval=1.0)
    # Duration is a deterministic variable
    duration_ = pm.SkewNormal('duration', alpha = alpha_skew, mu = mu_, 
                              sd = 1/tau_, observed = duration)
    # Metropolis Hastings for sampling
    step = pm.Metropolis()
    duration_trace = pm.sample(N_SAMPLES, step=step)

Now, we can use the average values of the three parameters to construct the most likely distribution. Following is the final skewed normal distribution on top of the data.

It looks like a nice fit! We can query the model to find the likelihood I get at least a certain amount of sleep and the most likely duration of sleep:

Probability of at least 6.5 hours of sleep = 99.16%.
Probability of at least 8.0 hours of sleep = 44.53%.
Probability of at least 9.0 hours of sleep = 10.94%.
The most likely duration of sleep is 7.67 hours.

I’m not entirely pleased with those results, but what can you expect as a graduate student?


Once again, completing this project showed me the importance of solving problems, preferably ones with real world applications! Along the way to building an end-to-end implementation of Bayesian Inference using Markov Chain Monte Carlo, I picked up many of the fundamentals and enjoyed myself in the process. Not only did I learn a little bit about my habits (and what I need to improve), but now I can finally understand what everyone is talking about when they say MCMC and Bayesian Inference. Data science is about constantly adding tools to your repertoire and the most effective way to do that is to find a problem and get started!

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What's Python IDLE? How to use Python IDLE to interact with Python?

What's Python IDLE? How to use Python IDLE to interact with Python?

In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.

If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!

In this tutorial, you’ll learn:

  • What Python IDLE is
  • How to interact with Python directly using IDLE
  • How to edit, execute, and debug Python files with IDLE
  • How to customize Python IDLE to your liking

Table of Contents

What Is Python IDLE?

Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.

Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.

An Interactive Interpreter

The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.

The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.

A File Editor

Every programmer needs to be able to edit and save text files. Python programs are files with the .py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.

Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!

How to Use the Python IDLE Shell

The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:

This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:

Here, you used print() to output the string "Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.

Next, take a look at the menu bar. You’ll see a few options for using the shell:

You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:

In the image above, you first declare a variable, x = 5. When you call print(x), the shell shows the correct output, which is the number 5. However, when you restart the shell and try to call print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable x is not defined. The shell has forgotten about everything that came before it was restarted.

You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:

A KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.

How to Work With Python Files

Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.

Opening a File

To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:

From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.

If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.

The content of this window will be the same as the paths that are returned when you call sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.

Editing a File

Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:

The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:

  1. The name of the file that you’re editing
  2. The full path to the folder where you can find this file on your computer
  3. The version of Python that IDLE is using

In the image above, you’re editing the file, which is located in the Documents folder. The Python version is 3.7.1, which you can see in parentheses.

There are also two numbers in the bottom right corner of the window:

  1. Ln: shows the line number that your cursor is on.
  2. Col: shows the column number that your cursor is on.

It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.

There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:

The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the .py extension so that syntax highlighting will be enabled.

Executing a File

When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.

To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run python3 -i [filename] in your terminal.

When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.

How to Improve Your Workflow

Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.

Automatic Indentation

IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.

You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.

Code Completion and Call Tips

When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.

Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:

The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.

Code Context

The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.

To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:

As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.

How to Debug in IDLE

A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!

Interpreter DEBUG Mode

If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see [DEBUG ON] appear just before the prompt (>>>), which means the interpreter is ready and waiting.

When you execute your Python file, the debugger window will appear:

In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.

You can also click the following buttons to move through your code:

  • Go: Press this to advance execution to the next breakpoint. You’ll learn about these in the next section.
  • Step: Press this to execute the current line and go to the next one.
  • Over: If the current line of code contains a function call, then press this to step over that function. In other words, execute that function and go to the next line, but don’t pause while executing the function (unless there is a breakpoint).
  • Out: If the current line of code is in a function, then press this to step out of this function. In other words, continue the execution of this function until you return from it.

Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.

You’ll also see four checkboxes in the debug window:

  1. Globals: your program’s global information
  2. Locals: your program’s local information during execution
  3. Stack: the functions that run during execution
  4. Source: your file in the IDLE editor

When you select one of these, you’ll see the relevant information in your debug window.


A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.

To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.

Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.

Errors and Exceptions

When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.

Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.

How to Customize Python IDLE

There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.

To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.

There are 5 areas of Python IDLE that you can customize:

  1. Fonts/Tabs
  2. Highlights
  3. Keys
  4. General
  5. Extensions

Let’s take a look at each of them now.


The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:

You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.

This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.


The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.

Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:

  1. IDLE Day
  2. IDLE Night
  3. IDLE New

You can select from these pre-installed themes or create your own custom theme right in this window:

Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.

You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!


The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:

The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!

Creating Your Own Shortcuts

The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.

Select one pair from the list and press Get New Keys for Selection. A new window will pop up:

Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.


The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:

Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.


The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.

To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!


In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

You’ve learned how to:

  • Work with the Python IDLE shell
  • Use Python IDLE as a file editor
  • Improve your workflow with features to help you code faster
  • Debug your code and view errors and exceptions
  • Customize Python IDLE to your liking

Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!

Importance of Python Programming skills

Importance of Python Programming skills

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving...

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving challenges. The codes of Python can easily be deployed in Data Science and Machine Learning. Due to this ease of deployment and easier syntaxes, this platform has a lot of real world problem solving applications. According to the sources the companies are eagerly hunting for the professionals with python skills along with SQL. An average python developer in the united states makes around 1 lakh U.S Dollars per annum. In some of the top IT hubs in our country like Bangalore, the demand for professionals in the domains of Data Science and Python Programming has surpassed over the past few years. As a result of which a lot of various python certification courses are available right now.

Array in Python: An array is defined as a data structure that can hold a fixed number of elements that are of the same python data type. The following are some of the basic functions of array in python:

  1. To find the transverse
  2. For insertion of the elements
  3. For deletion of the elements
  4. For searching the elements

Along with this one can easily crack any python interview by means of python interview questions

Tkinter Python Tutorial | Python GUI Programming Using Tkinter Tutorial | Python Training

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

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