Terms have old names, new names, confusing names.
We can pull them apart by indexing and slicing them, and we can join them together by concatenating them. However, we cannot join strings and lists: If we use a for loop to process the elements of this string, all we can pick out are the individual characters — we don't get to choose the granularity.
By contrast, the elements of a list can be as big or small as we like: So lists have the advantage that we can be flexible about the elements they contain, and correspondingly flexible about any downstream processing. Consequently, one of the first things we are likely to do in a piece of NLP code is tokenize a string into a list of strings 3.
Conversely, when we want to write our results to a file, or to a terminal, we will usually format them as a string 3. Lists and strings do not have exactly the same functionality. Lists have the added power that you can change their elements: However, lists are mutable, and their contents can be modified at any time.
As a result, lists support operations that modify the original value rather than producing a new value. Consolidate your knowledge of strings by trying some of the exercises on strings at the end of this chapter. The concept of "plain text" is a fiction. In this section, we will give an overview of how to use Unicode for processing texts that use non-ASCII character sets.
Unicode supports over a million characters. Each character is assigned a number, called a code point. Within a program, we can manipulate Unicode strings just like normal strings. However, when Unicode characters are stored in files or displayed on a terminal, they must be encoded as a stream of bytes.
Some encodings such as ASCII and Latin-2 use a single byte per code point, so they can only support a small subset of Unicode, enough for a single language. Other encodings such as UTF-8 use multiple bytes and can represent the full range of Unicode characters.
Text in files will be in a particular encoding, so we need some mechanism for translating it into Unicode — translation into Unicode is called decoding. Conversely, to write out Unicode to a file or a terminal, we first need to translate it into a suitable encoding — this translation out of Unicode is called encoding, and is illustrated in 3.
Unicode Decoding and Encoding From a Unicode perspective, characters are abstract entities which can be realized as one or more glyphs. Only glyphs can appear on a screen or be printed on paper. A font is a mapping from characters to glyphs.
Extracting encoded text from files Let's assume that we have a small text file, and that we know how it is encoded. This file is encoded as Latin-2, also known as ISO It takes a parameter to specify the encoding of the file being read or written. So let's open our Polish file with the encoding 'latin2' and inspect the contents of the file: We find the integer ordinal of a character using ord.
If you are sure that you have the correct encoding, but your Python code is still failing to produce the glyphs you expected, you should also check that you have the necessary fonts installed on your system. It may be necessary to configure your locale to render UTF-8 encoded characters, then use print nacute.
We can also see how this character is represented as a sequence of bytes inside a text file: In the following example, we select all characters in the third line of our Polish text outside the ASCII range and print their UTF-8 byte sequence, followed by their code point integer using the standard Unicode convention i.
The next examples illustrate how Python string methods and the re module can work with Unicode characters. We will take a close look at the re module in the following section. The above example also illustrates how regular expressions can use encoded strings.
For example, we can find words ending with ed using endswith 'ed'. We saw a variety of such "word tests" in 4.WRITING EFFECTIVE REPORTS AND ESSAYS Writing Effective Reports A. What are Reports?
It helps the reader to know what the report is about to have a title and can often be separated from and read instead of the whole report, so a brief introduction to the summary could be used. This is what a successful digital transformation looks like, based on research into the characteristics of enterprises that have succeeded with transformations in real life.
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