Mystr python world for i in range len mystr print i mystr i end
Write a Python program to get a string from a given string where all occurrences of its first char have been changed to '$', except the first char itself. Sample Solution:- Python Code: Sample Output: Flowchart: The following tool visualize what the computer is doing step-by-step as it executes the said program: Python Code Editor: Have another way to solve this
solution? Contribute your code (and comments) through Disqus. Previous: Write a Python program to get a string made of the first 2 and the last 2 chars from a given a string. If the string length is less than 2, return instead of the empty string. What is the difficulty level of this exercise? Test your Programming skills with w3resource's quiz. Python: Tips of the DayGetting min/max from iterable (with/without specific function): # Getting maximum from iterable >>> a = [1, 2, -3] >>> max(a) 2 # Getting maximum from iterable >>> min(a) 1 # Bot min/max has key value to allow to get maximum by appliing function >>> max(a,key=abs) 3
Python Fundamentals
VariablesAny Python interpreter can be used as a calculator: This is great but not very interesting. To do anything useful with data, we need to assign its value to a variable. In Python, we can assign a value to a
variable, using the equals sign From now on, whenever we use In Python, variable names:
This means that, for example:
Types of dataPython knows various types of data. Three common ones are:
In the example above, variable To create a string, we add single or double quotes around some text. To identify and track a patient throughout our study, we can assign each person a unique identifier by storing it in a string: Using Variables in PythonOnce we have data stored with variable names, we can make use of it in calculations. We may want to store our patient’s weight in pounds as well as kilograms:
We might decide to add a prefix to our patient identifier:
Built-in Python functionsTo carry out common tasks with data and variables in Python, the language provides us with several
built-in functions. To display information to the screen, we use the
When we want to make use of a function, referred to as calling the function, we follow its name by parentheses. The parentheses are important: if you leave them off, the function doesn’t actually run! Sometimes you will include values or variables inside the parentheses for
the function to use. In the case of We can display multiple things at once using only one
We can also call a function inside of another function
call. For example, Python has a built-in function called
Moreover, we can do arithmetic with variables right inside the
The above command, however, did not change the value of To change the value of the
Analyzing Patient Data
Words are useful, but what’s more useful are the sentences and stories we build with them. Similarly, while a lot of powerful, general tools are built into Python, specialized tools built up from these basic units live in libraries that can be called upon when needed. Loading data into PythonTo begin processing the clinical trial inflammation data, we need to load it into Python. We can do that using a library called NumPy, which stands for Numerical Python. In general, you should use this library when you want to do fancy things with lots of numbers, especially if you have matrices or arrays. To tell Python that we’d like to start using NumPy, we need to import it: Importing a library is like getting a piece of lab equipment out of a storage locker and setting it up on the bench. Libraries provide additional functionality to the basic Python package, much like a new piece of equipment adds functionality to a lab space. Just like in the lab, importing too many libraries can sometimes complicate and slow down your programs - so we only import what we need for each program. Once we’ve imported the library, we can ask the library to read our data file for us:
The expression As an example, John Smith is the John that belongs to the Smith family. We could use the dot notation to write his name
Since we haven’t told it to do anything else with the function’s output, the
notebook displays it. In this case, that output is the data we just loaded. By default, only a few rows and columns are shown (with Our call to
This statement doesn’t produce any output because we’ve assigned the output to the variable
Now that the
data are in memory, we can manipulate them. First, let’s ask what type of thing The output tells us that
With the following command, we can see the array’s shape: The output tells us that the If we want to get a single number from the array, we must provide an index in square brackets after the variable name, just as we do in math when referring to an element of a matrix. Our inflammation data has two dimensions, so we will need to use two indices to refer to one specific value:
The expression
Slicing dataAn index like
The slice We don’t have to start slices at 0:
We also don’t have to include the upper and lower bound on the slice. If we don’t include the lower bound, Python uses 0 by default; if we don’t include the upper, the slice runs to the end of the axis, and if we don’t include either (i.e., if we use ‘:’ on its own), the slice includes everything:
The above example selects rows 0 through 2 and columns 36 through to the end of the array.
Analyzing dataNumPy has several useful functions that take an array as input to perform operations on its values. If we want to find the average inflammation for all patients on all days, for example, we can ask NumPy to compute
Let’s use three other NumPy functions to get some descriptive values about the dataset. We’ll also use multiple assignment, a convenient Python feature that will enable us to do this all in one line.
Here we’ve assigned the return value from
When analyzing data, though, we often want to look at variations in statistical values, such as the maximum inflammation per patient or the average inflammation per day. One way to do this is to create a new temporary array of the data we want, then ask it to do the calculation:
Everything in a line of code following the ‘#’ symbol is a comment that is ignored by Python. Comments allow programmers to leave explanatory notes for other programmers or their future selves. We don’t actually need to store the row in a variable of its own. Instead, we can combine the selection and the function call:
What if we need the maximum inflammation for each patient over all days (as in the next diagram on the left) or the average for each day (as in the diagram on the right)? As the diagram below shows, we want to perform the operation across an axis: To support this functionality, most array functions allow us to specify the axis we want to work on. If we ask for the average across axis 0 (rows in our 2D example), we get:
As a quick check, we can ask this array what its shape is:
The expression
which is the average inflammation per patient across all days.
Visualizing Tabular Data
Visualizing dataThe mathematician Richard Hamming once said, “The purpose of computing is insight, not numbers,” and the best way to develop insight is often to
visualize data. Visualization deserves an entire lecture of its own, but we can explore a few features of Python’s
Each row in the heat map corresponds to a patient in the clinical trial dataset, and each column corresponds to a day in the dataset. Blue pixels in this heat map represent low values, while yellow pixels represent high values. As we can see, the general number of inflammation flare-ups for the patients rises and falls over a 40-day period. So far so good as this is in line with our knowledge of the clinical trial and Dr. Maverick’s claims:
Now let’s take a look at the average inflammation over time:
Here, we have put the average inflammation per day across all patients in the variable
The maximum value rises and falls linearly, while the minimum seems to be a step function. Neither trend seems particularly likely, so either there’s a mistake in our calculations or something is wrong with our data. This insight would have been difficult to reach by examining the numbers themselves without visualization tools. Grouping plotsYou can group similar plots in a single figure using subplots. This script below uses a number of new commands. The function
The call to The call to
Storing Multiple Values in Lists
In the previous episode, we analyzed a single file of clinical trial inflammation data. However, after finding some peculiar and potentially suspicious trends in the trial data we ask Dr. Maverick if they have performed any other clinical trials. Surprisingly, they say that they have and provide us with 11 more CSV files for a further 11 clinical trials they have undertaken since the initial trial. Our goal now is to process all the inflammation data we have, which means that we still have eleven more files to go! The natural first step is to collect the names of all the files that we have to process. In Python, a list is a way to store multiple values together. In this episode, we will learn how to store multiple values in a list as well as how to work with lists. Python listsUnlike NumPy arrays, lists are built into the language so we do not have to load a library to use them. We create a list by putting values inside square brackets and separating the values with commas:
We can access elements of a list using indices – numbered positions of elements in the list. These positions are numbered starting at 0, so the first element has an index of 0.
Yes, we can use negative numbers as indices in Python. When we do so, the index There is one important difference between lists and strings: we can change the values in a list, but we cannot change individual characters in a string. For example:
works, but:
does not.
There are many ways to change the contents of lists besides assigning new values to individual elements:
While modifying in place, it is useful to remember that Python treats lists in a slightly counter-intuitive way. As we saw earlier, when we modified the
This is because Python stores a list in memory, and then can use multiple names to refer to the same list. If all we want to do is copy a (simple) list, we can again use
the
Subsets of lists and strings can be accessed by specifying ranges of values in brackets, similar to how we accessed ranges of positions in a NumPy array. This is commonly referred to as “slicing” the list/string.
If you want to take a slice from the beginning of a sequence, you can omit the first index in the range:
And similarly, you can omit the ending index in the range to take a slice to the very end of the sequence:
Repeating Actions with Loops
In the episode about visualizing data, we wrote Python code that plots values of interest from our first inflammation dataset ( We have a dozen data sets right now and potentially more on the way if Dr. Maverick can keep up their surprisingly fast clinical trial rate. We want to create plots for all of our data sets with a single statement. To do that, we’ll have to teach the computer how to repeat things. An example task that we might want to repeat is accessing numbers in a list, which we will do by printing each number on a line of its own. In Python, a list is basically an ordered collection of elements, and every element has a unique number associated with it — its index. This means that we can access elements in a list using their indices. For example, we can get the first number in the list
This is a bad approach for three reasons:
Here’s a better approach: a for loop
This is shorter — certainly shorter than something that prints every number in a hundred-number list — and more robust as well:
The improved version uses a for loop to repeat an operation — in this case, printing — once for each thing in a sequence. The general form of a loop is:
Using the odds example above, the loop might look like this: where each number ( We can call the loop variable anything we like, but there must be a colon at the end of the line starting the loop, and we must indent anything we want to run inside the loop. Unlike many other languages, there is no command to signify the end of the loop body (e.g.
Here’s another loop that repeatedly updates a variable:
It’s worth tracing the execution of this little program step by step. Since there are three names in Note that a loop variable is a variable that is being used to record progress in a loop. It still exists after the loop is over, and we can re-use variables previously defined as loop variables as well:
Note also that finding the length of an object is such a common operation that Python actually has a built-in function to do it called
Analyzing Data from Multiple Files
As a final piece to processing our inflammation data, we need a way to get a list of all the files in our The
As these examples show, If we want to start by analyzing just the first three files in alphabetical order, we can use the
The plots generated for the second clinical trial file look very similar to the plots for the first file: their average plots show similar “noisy” rises and falls; their maxima plots show exactly the same linear rise and fall; and their minima plots show similar staircase structures. The third dataset shows much noisier average and maxima plots that are far less suspicious than the first two datasets, however the minima plot shows that the third dataset minima is consistently zero across every day of the trial. If we produce a heat map for the third data file we see the following: We can see that there are zero values sporadically distributed across all patients and days of the clinical trial, suggesting that there were potential issues with data collection throughout the trial. In addition, we can see that the last patient in the study didn’t have any inflammation flare-ups at all throughout the trial, suggesting that they may not even suffer from arthritis!
After spending some time investigating the heat map and statistical plots, as well as doing the above exercises to plot differences between datasets and to generate composite patient statistics, we gain some insight into the twelve clinical trial datasets. The datasets appear to fall into two categories:
In fact, it appears that all three of the “noisy” datasets ( Dr. Maverick confesses that they fabricated the clinical data after they found out that the initial trial suffered from a number of issues, including unreliable data-recording and poor participant selection. They created fake data to prove their drug worked, and when we asked for more data they tried to generate more fake datasets, as well as throwing in the original poor-quality dataset a few times to try and make all the trials seem a bit more “realistic”. Congratulations! We’ve investigated the inflammation data and proven that the datasets have been synthetically generated. But it would be a shame to throw away the synthetic datasets that have taught us so much already, so we’ll forgive the imaginary Dr. Maverick and continue to use the data to learn how to program.
Making Choices
In our last lesson, we discovered something suspicious was going on in our inflammation data by drawing some plots. How can we use Python to automatically recognize the different features we saw, and take a different action for each? In this lesson, we’ll learn how to write code that runs only when certain conditions are true. ConditionalsWe can ask Python to take different actions, depending on a condition, with an
The second line of this code uses the keyword Conditional statements don’t have to include an
We can also chain several tests together using
Note that to test for equality we use a double equals sign
We can also combine tests using
while
Checking our DataNow that we’ve seen how conditionals work, we can use them to check for the suspicious features we saw in our inflammation data. We are about to use functions provided by the From the first couple of plots, we saw that maximum daily inflammation exhibits a strange behavior and raises one unit a day. Wouldn’t it be a good idea to detect such behavior and report it as suspicious? Let’s do that! However, instead of checking every single day of the study, let’s merely check if maximum inflammation in the beginning (day 0) and in the middle (day 20) of the study are equal to the corresponding day numbers.
We also saw
a different problem in the third dataset; the minima per day were all zero (looks like a healthy person snuck into our study). We can also check for this with an
And if neither of these conditions are true, we can use Let’s test that out:
In this way, we have asked Python to do something different depending on the condition of our data. Here we printed
messages in all cases, but we could also imagine not using the
Creating Functions
At this point, we’ve written code to draw some interesting features in our inflammation data, loop over all our data files to quickly draw these plots for each of them, and have
Python make decisions based on what it sees in our data. But, our code is getting pretty long and complicated; what if we had thousands of datasets, and didn’t want to generate a figure for every single one? Commenting out the figure-drawing code is a nuisance. Also, what if we want to use that code again, on a different dataset or at a different point in our program? Cutting and pasting it is going to make our code get very long and very repetitive, very quickly. We’d like a way to package our
code so that it is easier to reuse, and Python provides for this by letting us define things called ‘functions’ — a shorthand way of re-executing longer pieces of code. Let’s start by defining a function
The function definition opens with the keyword When we call the function, the values we pass to it are assigned to those variables so that we can use them inside the function. Inside the function, we use a return statement to send a result back to whoever asked for it. Let’s try running our function. This command should call our function, using “32” as the input and return the function value. In fact, calling our own function is no different from calling any other function:
We’ve successfully called the function that we defined, and we have access to the value that we returned. Composing FunctionsNow that we’ve seen how to turn Fahrenheit into Celsius, we can also write the function to turn Celsius into Kelvin:
What about converting Fahrenheit to Kelvin? We could write out the formula, but we don’t need to. Instead, we can compose the two functions we have already created:
This is our first taste of how larger programs are built: we define basic operations, then combine them in ever-larger chunks to get the effect we want. Real-life functions will usually be larger than the ones shown here — typically half a dozen to a few dozen lines — but they shouldn’t ever be much longer than that, or the next person who reads it won’t be able to understand what’s going on. Variable ScopeIn composing our temperature conversion functions, we created variables inside of those functions,
If you want to reuse the temperature in Kelvin after you have calculated it with
The variable Inside a function, one can read the value of such global variables:
Tidying upNow that we know how to wrap bits of code up in functions, we can make our inflammation analysis easier to read and easier to reuse. First, let’s make a
and
another function called
Wait! Didn’t we forget to specify what both of these functions should return? Well, we didn’t. In Python, functions are not required to include a Notice that rather than
jumbling this code together in one giant
By giving our functions human-readable names, we can more easily read and understand what is happening in the Testing and DocumentingOnce we start putting things in functions so that we can re-use them, we need to start testing that those functions are working correctly. To see how to do this, let’s write a function to offset a dataset so that it’s mean value shifts to a user-defined value:
We could test this on our actual data, but since we don’t know what the values ought to be, it will be hard to tell if the result was correct. Instead, let’s use NumPy to create a matrix of 0’s and then offset its values to have a mean value of 3:
That looks right, so let’s try
It’s hard to tell from the default output whether the result is correct, but there are a few tests that we can run to reassure us:
That seems almost right: the original mean was about 6.1, so the lower bound from zero is now about -6.1. The mean of the offset data isn’t quite zero — we’ll explore why not in the challenges — but it’s pretty close. We can even go further and check that the standard deviation hasn’t changed:
Those values look the same, but we probably wouldn’t notice if they were different in the sixth decimal place. Let’s do this instead:
Again, the difference is very small. It’s still possible that our function is wrong, but it seems unlikely enough that we should probably get back to doing our analysis. We have one more task first, though: we should write some documentation for our function to remind ourselves later what it’s for and how to use it. The usual way to put documentation in software is to add comments like this:
There’s a better way, though. If the first thing in a function is a string that isn’t assigned to a variable, that string is attached to the function as its documentation:
This is better because we can now ask Python’s built-in help system to show us the documentation for the function:
A string like this is called a docstring. We don’t need to use triple quotes when we write one, but if we do, we can break the string across multiple lines:
Defining DefaultsWe have passed parameters to functions in two ways: directly, as in
but we still need to say
To understand what’s going on, and make our own functions easier to use, let’s re-define our
The key change is that the second parameter is now written
But we can also now call
it with just one parameter, in which case
This is handy: if we usually want a function to work one way, but occasionally need it to do something else, we can allow people to pass a parameter when they need to but provide a default to make the normal case easier. The example below shows how Python matches values to parameters:
As this example shows, parameters are matched up from left to right, and any that haven’t been given a value explicitly get their default value. We can override this behavior by naming the value as we pass it in:
With that in hand, let’s look at the help for
There’s a lot of information here, but the most important part is the first couple of lines:
This tells us that
then the filename is assigned to Readable functionsConsider these two functions:
The functions As this example illustrates, both documentation and a programmer’s coding style combine to determine how easy it is for others to read and understand the programmer’s code. Choosing meaningful variable names and using blank spaces to break the code into logical “chunks” are helpful techniques for producing readable code. This is useful not only for sharing code with others, but also for the original programmer. If you need to revisit code that you wrote months ago and haven’t thought about since then, you will appreciate the value of readable code!
Errors and Exceptions
Every programmer encounters errors, both those who are just beginning, and those who have been programming for years. Encountering errors and exceptions can be very frustrating at times, and can make coding feel like a hopeless endeavour. However, understanding what the different types of errors are and when you are likely to encounter them can help a lot. Once you know why you get certain types of errors, they become much easier to fix. Errors in Python have a very specific form, called a traceback. Let’s examine one:
This particular traceback has two levels. You can determine the number of levels by looking for the number of arrows on the left hand side. In this case:
The last level is the actual place where the error occurred. The other level(s) show what function the program executed to get to the next level down. So, in this case, the program first performed a function call to the function
So what error did the program actually encounter? In the last line of the traceback, Python helpfully tells us the category or type of error (in this case, it is an If you encounter an error and don’t know what it means, it is still important to read the traceback closely. That way, if you fix the error, but encounter a new one, you can tell that the error changed. Additionally, sometimes knowing where the error occurred is enough to fix it, even if you don’t entirely understand the message. If you do encounter an error you don’t recognize, try looking at the official documentation on errors. However, note that you may not always be able to find the error there, as it is possible to create custom errors. In that case, hopefully the custom error message is informative enough to help you figure out what went wrong. Syntax ErrorsWhen you forget a colon at the end of a line, accidentally add one space too many when indenting under an People can typically figure out what is meant by text with no punctuation, but people are much smarter than computers. If Python doesn’t know how to read the program, it will give up and inform you with an error. For example:
Here, Python tells us that there is a Actually, the function above has two issues with syntax. If we fix the problem with the colon, we see that
there is also an
Both
Variable Name ErrorsAnother very common type of error is called a
Variable name errors come with some of the most informative error messages, which are usually of the form “name ‘the_variable_name’ is not defined”. Why does this error message occur? That’s a harder question to answer, because it depends on what your code is supposed to do. However, there are a few very common reasons why you might have an undefined variable. The first is that you meant to use a string, but forgot to put quotes around it:
The second reason is
that you might be trying to use a variable that does not yet exist. In the following example,
Finally, the third possibility is that you made a typo when you were writing your code. Let’s say we fixed the error above by adding the line
Index ErrorsNext up are errors having to do with containers (like lists and strings) and the items within them. If you try to access an item in a list or a string that does not exist, then you will get an error. This makes sense: if you asked someone what day they would like to get coffee, and they answered “caturday”, you might be a bit annoyed. Python gets similarly annoyed if you try to ask it for an item that doesn’t exist:
Here, Python is telling us that there is an File ErrorsThe last
type of error we’ll cover today are those associated with reading and writing files:
One reason for receiving this error is that you
specified an incorrect path to the file. For example, if I am currently in a folder called A related issue can occur if you use the “read” flag instead of the “write” flag. Python will not give you an error if you try to open a file for writing when the file does not exist. However, if you meant to open
a file for reading, but accidentally opened it for writing, and then try to read from it, you will get an
These are the most common errors with files, though many others exist. If you get an error that you’ve never seen before, searching the Internet for that error type often reveals common reasons why you might get that error.
Defensive Programming
Our previous lessons have introduced the basic tools of programming: variables and lists, file I/O, loops, conditionals, and functions. What they haven’t done is show us how to tell whether a program is getting the right answer, and how to tell if it’s still getting the right answer as we make changes to it. To achieve that, we need to:
The good news is, doing these things will speed up our programming, not slow it down. As in real carpentry — the kind done with lumber — the time saved by measuring carefully before cutting a piece of wood is much greater than the time that measuring takes. AssertionsThe first step toward getting the right answers from our programs is to assume that mistakes will happen and to guard against them. This is called defensive programming, and the most common way to do it is to add assertions to our code so that it checks itself as it runs. An assertion is simply a statement that something must be true at a certain point in a program. When Python sees one, it evaluates the assertion’s condition. If it’s true, Python does nothing, but if it’s false, Python halts the program immediately and prints the error message if one is provided. For example, this piece of code halts as soon as the loop encounters a value that isn’t positive:
Programs like the Firefox browser are full of assertions: 10-20% of the code they contain are there to check that the other 80–90% are working correctly. Broadly speaking, assertions fall into three categories:
For example, suppose we are representing rectangles using a tuple of four coordinates
The preconditions on lines 6, 8, and 9 catch invalid inputs:
The post-conditions on lines 20 and 21 help us catch bugs by telling us when our calculations might have been incorrect. For example, if we normalize a rectangle that is taller than it is wide everything seems OK:
but if we normalize one that’s wider than it is tall, the assertion is triggered:
Re-reading our function, we realize that line 14 should divide But assertions aren’t just about catching errors: they also help people understand programs. Each assertion gives the person reading the program a chance to check (consciously or otherwise) that their understanding matches what the code is doing. Most good programmers follow two rules when adding assertions to their code. The first is, fail early, fail often. The greater the distance between when and where an error occurs and when it’s noticed, the harder the error will be to debug, so good code catches mistakes as early as possible. The second rule is, turn bugs into assertions or tests. Whenever you fix a bug, write an assertion that catches the mistake should you make it again. If you made a mistake in a piece of code, the odds are good that you have made other mistakes nearby, or will make the same mistake (or a related one) the next time you change it. Writing assertions to check that you haven’t regressed (i.e., haven’t re-introduced an old problem) can save a lot of time in the long run, and helps to warn people who are reading the code (including your future self) that this bit is tricky. Test-Driven DevelopmentAn assertion checks that something is true at a particular point in the program. The next step is to check the overall behavior of a piece of code, i.e., to make sure that it produces the right output when it’s given a particular input. For example, suppose we need to find where two or more time series overlap. The range of each time series is represented as a pair of numbers, which are the time the interval started and ended. The output is the largest range that they all include: Most novice programmers would solve this problem like this:
This clearly works — after all, thousands of scientists are doing it right now — but there’s a better way:
Writing the tests before writing the function they exercise is called test-driven development (TDD). Its advocates believe it produces better code faster because:
Here are three test functions for
The error is actually reassuring: we haven’t written And as a bonus of writing these tests, we’ve implicitly defined what our input and output look like: we expect a list of pairs as input, and produce a single pair as output. Something important is missing, though. We don’t have any tests for the case where the ranges don’t overlap at all:
What should And what about this case?
Do two segments that touch at their endpoints overlap or not? Mathematicians usually say “yes”, but engineers usually say “no”. The best answer is “whatever is most useful in the rest of our program”, but again, any actual implementation of Since we’re planning to use the range this function returns as the X axis in a time series chart, we decide that:
Again, we get an error because we haven’t written our function, but we’re now ready to do so:
Take a moment to think about why we calculate the left endpoint of the overlap as the maximum of the input left endpoints, and the overlap right endpoint as the minimum of the input right endpoints. We’d now like to re-run our tests, but they’re scattered across three different cells. To make running them easier, let’s put them all in a function:
We can now test
The first test that was supposed to produce
Debugging
Once testing has uncovered problems, the next step is to fix them. Many novices do this by making more-or-less random changes to their code until it seems to produce the right answer, but that’s very inefficient (and the result is usually only correct for the one case they’re testing). The more experienced a programmer is, the more systematically they debug, and most follow some variation on the rules explained below. Know What It’s Supposed to DoThe first step in debugging something is to know what it’s supposed to do. “My program doesn’t work” isn’t good enough: in order to diagnose and fix problems, we need to be able to tell correct output from incorrect. If we can write a test case for the failing case — i.e., if we can assert that with these inputs, the function should produce that result — then we’re ready to start debugging. If we can’t, then we need to figure out how we’re going to know when we’ve fixed things. But writing test cases for scientific software is frequently harder than writing test cases for commercial applications, because if we knew what the output of the scientific code was supposed to be, we wouldn’t be running the software: we’d be writing up our results and moving on to the next program. In practice, scientists tend to do the following:
Make It Fail Every TimeWe can only debug something when it fails, so the second step is always to find a test case that makes it fail every time. The “every time” part is important because few things are more frustrating than debugging an intermittent problem: if we have to call a function a dozen times to get a single failure, the odds are good that we’ll scroll past the failure when it actually occurs. As part of this, it’s always important to check that our code is “plugged in”, i.e., that we’re actually exercising the problem that we think we are. Every programmer has spent hours chasing a bug, only to realize that they were actually calling their code on the wrong data set or with the wrong configuration parameters, or are using the wrong version of the software entirely. Mistakes like these are particularly likely to happen when we’re tired, frustrated, and up against a deadline, which is one of the reasons late-night (or overnight) coding sessions are almost never worthwhile. Make It Fail FastIf it takes 20 minutes for the bug to surface, we can only do three experiments an hour. This means that we’ll get less data in more time and that we’re more likely to be distracted by other things as we wait for our program to fail, which means the time we are spending on the problem is less focused. It’s therefore critical to make it fail fast. As well as making the program fail fast in time, we want to make it fail fast in space, i.e., we want to localize the failure to the smallest possible region of code:
Change One Thing at a Time, For a ReasonReplacing random chunks of code is unlikely to do much good. (After all, if you got it wrong the first time, you’ll probably get it wrong the second and third as well.) Good programmers therefore change one thing at a time, for a reason. They are either trying to gather more information (“is the bug still there if we change the order of the loops?”) or test a fix (“can we make the bug go away by sorting our data before processing it?”). Every time we make a change, however small, we should re-run our tests immediately, because the more things we change at once, the harder it is to know what’s responsible for what (those N! interactions again). And we should re-run all of our tests: more than half of fixes made to code introduce (or re-introduce) bugs, so re-running all of our tests tells us whether we have regressed. Keep Track of What You’ve DoneGood scientists keep track of what they’ve done so that they can reproduce their work, and so that they don’t waste time repeating the same experiments or running ones whose results won’t be interesting. Similarly, debugging works best when we keep track of what we’ve done and how well it worked. If we find ourselves asking, “Did left followed by right with an odd number of lines cause the crash? Or was it right followed by left? Or was I using an even number of lines?” then it’s time to step away from the computer, take a deep breath, and start working more systematically. Records are particularly useful when the time comes to ask for help. People are more likely to listen to us when we can explain clearly what we did, and we’re better able to give them the information they need to be useful.
Be HumbleAnd speaking of help: if we can’t find a bug in 10 minutes, we should be humble and ask for help. Explaining the problem to someone else is often useful, since hearing what we’re thinking helps us spot inconsistencies and hidden assumptions. If you don’t have someone nearby to share your problem description with, get a rubber duck! Asking for help also helps alleviate confirmation bias. If we have just spent an hour writing a complicated program, we want it to work, so we’re likely to keep telling ourselves why it should, rather than searching for the reason it doesn’t. People who aren’t emotionally invested in the code can be more objective, which is why they’re often able to spot the simple mistakes we have overlooked. Part of being humble is learning from our mistakes. Programmers tend to get the same things wrong over and over: either they don’t understand the language and libraries they’re working with, or their model of how things work is wrong. In either case, taking note of why the error occurred and checking for it next time quickly turns into not making the mistake at all. And that is what makes us most productive in the long run. As the saying goes, A week of hard work can sometimes save you an hour of thought. If we train ourselves to avoid making some kinds of mistakes, to break our code into modular, testable chunks, and to turn every assumption (or mistake) into an assertion, it will actually take us less time to produce working programs, not more.
Command-Line Programs
The Jupyter Notebook and other interactive tools are great for prototyping code and exploring data, but sooner or later we will want to use our program in a pipeline or run it in a shell script to process thousands of data files. In order to do that, we need to make our programs work like other Unix command-line tools. For example, we may want a program that reads a dataset and prints the average inflammation per patient.
This program does exactly what we want - it prints the average inflammation per patient for a given file.
We might also want to look at the minimum of the first four lines
or the maximum inflammations in several files one after another:
Our scripts should do the following:
To make this work, we need to know how to handle command-line arguments in a program, and understand how to handle standard input. We’ll tackle these questions in turn below. Command-Line ArgumentsUsing the text editor of your choice, save the following in a text file called
The first line imports a library called
Create another file called
The strange name
the only thing in the list is the full path to our script, which is always
then Python adds each of those arguments to that magic list. With this in hand, let’s build a version of
This function gets the name of the
script from
There is no output because we have defined a function, but haven’t actually called it. Let’s add a call to
and run that:
Handling Multiple FilesThe next step is to teach our program how to handle multiple files. Since 60 lines of output per file is a lot to page through, we’ll start by using three smaller files, each of which has three days of data for two patients:
Using small data files as input also allows us to check our results more easily: here, for example, we can see that our program is calculating the mean correctly for each line, whereas we were really taking it on faith before. This is yet another rule of programming: test the simple things first. We want our program to process
each file separately, so we need a loop that executes once for each filename. If we specify the files on the command line, the filenames will be in The solution to both problems is to loop over the contents of
and here it is in action:
Handling Command-Line FlagsThe next step is to teach our program to pay attention to the
This works:
but there are several things wrong with it:
This version pulls the processing of each file out of the loop into a function of its own. It also checks that
This is four lines longer than its predecessor, but broken into more digestible chunks of 8 and 12 lines. Handling Standard InputThe next thing our program has to do is read data from standard input if no filenames are given so that we can put it in a pipeline, redirect input to it, and so on. Let’s experiment in another script called
This little
program reads lines from a special “file” called
A common mistake is to try to run something that reads from standard input like this:
i.e., to forget the We now need to rewrite the program so that it loads data from
Let’s try it out:
That’s better. In fact, that’s done: the program now does everything we set out to do.
How do you print the range of letters in Python?Python: Print letters from the English alphabet from a-z and A-Z. Sample Solution:. Python Code: import string print("Alphabet from a-z:") for letter in string.ascii_lowercase: print(letter, end =" ") print("\nAlphabet from A-Z:") for letter in string.ascii_uppercase: print(letter, end =" ") ... . Pictorial Presentation:. How do you print range A to Z in Python?Using String module. import string for i in string.ascii_lowercase: print(i, end=" "). a b c d e f g h i j k l m n o p q r s t u v w x y z.. import string for i in string.ascii_uppercase: print(i, end=" "). A B C D E F G H I J K L M N O P Q R S T U V W X Y Z.. for i in range(97,123): print(chr(i), end=" "). How do you print a length in Python?To get the length of a string, use the len() function.
How do you fix string problems in Python?Python String Exercise.. Python program to check whether the string is Symmetrical or Palindrome.. Program to print all palindromes in a given range.. Check if characters of a given string can be rearranged to form a palindrome.. Rearrange characters to form palindrome if possible.. |