How to convert numbers to alphabet in Python? [duplicate]
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The Premium Edition adds important features such as complete software maintenance, security advisory, frequent minor upgrade versions, downloads, Pack exports and imports, 24×7 scheduling and more. Simply double-click the downloaded file to install it. You can choose your language settings from within the program. A Converts small binary files into Rexx scripts that will, in turn, recreate the original binary file when executed. Originally created to store icon files in Rexx installation scripts.
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Perl, batch and Rexx versions all use DEBUG. EXE to read info from BIOS. K P Perl and Rexx versions both use DEBUG. Rexx Support to perform its tasks.
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This will make it extremely hard to port them to Windows. Execute it if you want a “free coffee mug holder”. Windows versions use Windows Media Player. 2 and Windows versions due to differences in handling of standard input. 2 version will even work for RPL workstations.
NT J K P V Based on the instructions found at Simon Kershaw’s KEEPING THE FEAST. K P See also the matching True command. D NT P Rexx, Batch and Perl versions all use DEBUG. HTML specification requires lower case tags. K Rexx script uses ANSI.
Windows NT see George Roettger’s home page for help on running ANSI. NT K Tested in Windows 2000 only. A Port of Unix’ TEE command: redirect standard input to standard output and to a file simultaneously. A Display tomorrow’s date in 2 formats. SYS files before and after a software installation, this program will prove extremely useful when comparing other ASCII files as well.
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Rexx, Batch and Perl versions use DEBUG. A Display week number. D NT J K P PS V DOS batch version needs Solid Oak Software’s WEEKOFYR. A Display yesterday’s date in YYYYMMDD format. 4 Writing Structured Programs By now you will have a sense of the capabilities of the Python programming language for processing natural language.
However, if you’re new to Python or to programming, you may still be wrestling with Python and not feel like you are in full control yet. How can you write well-structured, readable programs that you and others will be able to re-use easily? How do the fundamental building blocks work, such as loops, functions and assignment? What are some of the pitfalls with Python programming and how can you avoid them? Along the way, you will consolidate your knowledge of fundamental programming constructs, learn more about using features of the Python language in a natural and concise way, and learn some useful techniques in visualizing natural language data. In the other chapters of this book, we have organized the programming concepts as dictated by the needs of NLP. Here we revert to a more conventional approach where the material is more closely tied to the structure of the programming language.
There’s not room for a complete presentation of the language, so we’ll just focus on the language constructs and idioms that are most important for NLP. However, there are some surprising subtleties here. However, assignment statements do not always involve making copies in this way. Assignment always copies the value of an expression, but a value is not always what you might expect it to be. To understand what is going on here, we need to know how lists are stored in the computer’s memory.
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Observe that changing one of the items inside our nested list of lists changed them all. This is because each of the three elements is actually just a reference to one and the same list in memory. Now modify one of the elements of the list, and observe that all the elements are changed. We began with a list containing three references to a single empty list object.
This last step modified one of the three object references inside the nested list. It is crucial to appreciate this difference between modifying an object via an object reference, and overwriting an object reference. This copies the object references inside the list. Equality Python provides two ways to check that a pair of items are the same.
We can use it to verify our earlier observations about objects. Now let’s put a new python in this nest. This reveals that the second item of the list has a distinct identifier. If you try running this code snippet yourself, expect to see different numbers in the resulting list, and also the interloper may be in a different position.
Having two kinds of equality might seem strange. However, it’s really just the type-token distinction, familiar from natural language, here showing up in a programming language. 2 Sequences So far, we have seen two kinds of sequence object: strings and lists. We’ve actually seen them in the previous chapters, and sometimes referred to them as “pairs”, since there were always two members. However, tuples can have any number of members. Tuples are constructed using the comma operator.
Parentheses are a more general feature of Python syntax, designed for grouping. Notice in this code sample that we computed multiple values on a single line, separated by commas. These comma-separated expressions are actually just tuples — Python allows us to omit the parentheses around tuples if there is no ambiguity. When we print a tuple, the parentheses are always displayed. By using tuples in this way, we are implicitly aggregating items together.
The sequence functions illustrated in 4. We can convert between these sequence types. Red lorry, yellow lorry, red lorry, yellow lorry. In the next example, we use tuples to re-arrange the contents of our list. We can omit the parentheses because the comma has higher precedence than assignment.
This is an idiomatic and readable way to move items inside a list. For some NLP tasks it is necessary to cut up a sequence into two or more parts. Combining Different Sequence Types Let’s combine our knowledge of these three sequence types, together with list comprehensions, to perform the task of sorting the words in a string by their length. Each of the above lines of code contains a significant feature.
We began by talking about the commonalities in these sequence types, but the above code illustrates important differences in their roles. First, strings appear at the beginning and the end: this is typical in the context where our program is reading in some text and producing output for us to read. Lists and tuples are used in the middle, but for different purposes. We often use lists to hold sequences of words. Here, a lexicon is represented as a list because it is a collection of objects of a single type — lexical entries — of no predetermined length.
Note that these pronunciations are stored using a list. A good way to decide when to use tuples vs lists is to ask whether the interpretation of an item depends on its position. For example, a tagged token combines two strings having different interpretation, and we choose to interpret the first item as the token and the second item as the tag. In contrast, the elements of a text are all tokens, and position is not significant.
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The linguistic meaning of the words might be different, but the interpretation of list items as tokens is unchanged. The distinction between lists and tuples has been described in terms of usage. In other words, lists can be modified, while tuples cannot. Here are some of the operations on lists that do in-place modification of the list.
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Generator Expressions We’ve been making heavy use of list comprehensions, for compact and readable processing of texts. I choose it to mean – neither more nor less. Suppose we now want to process these words further. This is more than a notational convenience: in many language processing situations, generator expressions will be more efficient. If the text is very large, this could be slow. 3 Questions of Style Programming is as much an art as a science.
Here we pick up on some issues of programming style that have important ramifications for the readability of your code, including code layout, procedural vs declarative style, and the use of loop variables. Python Coding Style When writing programs you make many subtle choices about names, spacing, comments, and so on. When you look at code written by other people, needless differences in style make it harder to interpret the code. We briefly review some of its key recommendations here, and refer readers to the full guide for detailed discussion with examples.
Code layout should use four spaces per indentation level. You should make sure that when you write Python code in a file, you avoid tabs for indentation, since these can be misinterpreted by different text editors and the indentation can be messed up. Python is able to detect that the line continues over to the next line. Typing spaces instead of tabs soon becomes a chore. Procedural vs Declarative Style We have just seen how the same task can be performed in different ways, with implications for efficiency. Another factor influencing program development is programming style.
This is a low-level style, not far removed from machine code, the primitive operations performed by the computer’s CPU. The two variables are just like a CPU’s registers, accumulating values at many intermediate stages, values that are meaningless until the end. We say that this program is written in a procedural style, dictating the machine operations step by step. The first line uses a generator expression to sum the token lengths, while the second line computes the average as before. Implementation details are left to the Python interpreter. Another case where a loop variable seems to be necessary is for printing a counter with each line of output. It’s sometimes tempting to use loop variables to store a maximum or minimum value seen so far.
Let’s use this method to find the longest word in a text. We also need to balance our concerns about program efficiency with programmer efficiency. Some Legitimate Uses for Counters There are cases where we still want to use loop variables in a list comprehension. It is quite tricky to get the range of the loop variable right. Here’s an example of how we can use loop variables in building multidimensional structures.
Note that it would be incorrect to do this work using multiplication, for reasons concerning object copying that were discussed earlier in this section. Iteration is an important programming device. It is tempting to adopt idioms from other languages. However, Python offers some elegant and highly readable alternatives, as we have seen. 4 Functions: The Foundation of Structured Programming Functions provide an effective way to package and re-use program code, as already explained in 3. For example, suppose we find that we often want to read text from an HTML file.
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This involves several steps: opening the file, reading it in, normalizing whitespace, and stripping HTML markup. Read text from a file, normalizing whitespace and stripping HTML markup. It will return a string, and we can assign this to a variable, e. Each time we want to use this series of steps we only have to call the function. Using functions has the benefit of saving space in our program. More importantly, our choice of name for the function helps make the program readable.
This naming helps to provide some “semantic interpretation” — it helps a reader of our program to see what the program “means”. Notice that the above function definition contains a string. We have seen that functions help to make our work reusable and readable. They also help make it reliable. When we re-use code that has already been developed and tested, we can be more confident that it handles a variety of cases correctly.
We also remove the risk that we forget some important step, or introduce a bug. The program that calls our function also has increased reliability. To summarize, as its name suggests, a function captures functionality. It is a segment of code that can be given a meaningful name and which performs a well-defined task. Functions allow us to abstract away from the details, to see a bigger picture, and to program more effectively.