The reason is simple: the moment Python accesses the content of a list from the computer's memory, it is already at the first element; we have to tell it how many elements forward to go. Thus, zero steps forward leaves it at the first element. This practice of counting from zero is initially confusing, but typical of modern programming languages.
You'll quickly get the hang of it if you've mastered the system of counting centuries where 19XY is a year in the 20th century, or if you live in a country where the floors of a building are numbered from 1, and so walking up n-1 flights of stairs takes you to level n. This time it is not a syntax error, because the program fragment is syntactically correct.
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Instead, it is a runtime error , and it produces a Traceback message that shows the context of the error, followed by the name of the error, IndexError , and a brief explanation. Let's take a closer look at slicing, using our artificial sentence again. Here we verify that the slice includes sent elements at indexes 5, 6, and By convention, m:n means elements m … n As the next example shows, we can omit the first number if the slice begins at the start of the list , and we can omit the second number if the slice goes to the end :.
We can modify an element of a list by assigning to one of its index values. In the next example, we put sent on the left of the equals sign. We can also replace an entire slice with new material. A consequence of this last change is that the list only has four elements, and accessing a later value generates an error. Your Turn: Take a few minutes to define a sentence of your own and modify individual words and groups of words slices using the same methods used earlier. Check your understanding by trying the exercises on lists at the end of this chapter.
From the start of 1 , you have had access to texts called text1 , text2 , and so on. It saved a lot of typing to be able to refer to a ,word book with a short name like this! In general, we can make up names for anything we care to calculate. We did this ourselves in the previous sections, e. Python will evaluate the expression, and save its result to the variable. This process is called assignment.
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It does not generate any output; you have to type the variable on a line of its own to inspect its contents. The equals sign is slightly misleading, since information is moving from the right side to the left. It might help to think of it as a left-arrow. The name of the variable can be anything you like, e. It must start with a letter, and can include numbers and underscores. Here are some examples of variables and assignments:. Python expressions can be split across multiple lines, so long as this happens within any kind of brackets. Python uses the " It doesn't matter how much indentation is used in these continuation lines, but some indentation usually makes them easier to read.
It is good to choose meaningful variable names to remind you — and to help anyone else who reads your Python code — what your code is meant to do.
The only restriction is that a variable name cannot be any of Python's reserved words, such as def , if , not , and import. If you use a reserved word, Python will produce a syntax error:. We will often use variables to hold intermediate steps of a computation, especially when this makes the code easier to follow. Thus len set text1 could also be written:. Take care with your choice of names or identifiers for Python variables.
First, you should start the name with a letter, optionally followed by digits 0 to 9 or letters. Thus, abc23 is fine, but 23abc will cause a syntax error.
Names are case-sensitive, which means that myVar and myvar are distinct variables. Variable names cannot contain whitespace, but you can separate words using an underscore, e. Be careful not to insert a hyphen instead of an underscore: my-var is wrong, since Python interprets the " - " as a minus sign. Some of the methods we used to access the elements of a list also work with individual words, or strings.
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For example, we can assign a string to a variable , index a string , and slice a string :. We can join the words of a list to make a single string, or split a string into a list, as follows:. We will come back to the topic of strings in 3. For the time being, we have two important building blocks — lists and strings — and are ready to get back to some language analysis. Let's return to our exploration of the ways we can bring our computational resources to bear on large quantities of text.
We began this discussion in 1 , and saw how to search for words in context, how to compile the vocabulary of a text, how to generate random text in the same style, and so on. In this section we pick up the question of what makes a text distinct, and use automatic methods to find characteristic words and expressions of a text. As in 1 , you can try new features of the Python language by copying them into the interpreter, and you'll learn about these features systematically in the following section. Before continuing further, you might like to check your understanding of the last section by predicting the output of the following code.
You can use the interpreter to check whether you got it right.
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If you're not sure how to do this task, it would be a good idea to review the previous section before continuing further. How can we automatically identify the words of a text that are most informative about the topic and genre of the text? Imagine how you might go about finding the 50 most frequent words of a book. One method would be to keep a tally for each vocabulary item, like that shown in 3. The tally would need thousands of rows, and it would be an exceedingly laborious process — so laborious that we would rather assign the task to a machine.
Figure 3. The table in 3. In general, it could count any kind of observable event. It is a "distribution" because it tells us how the total number of word tokens in the text are distributed across the vocabulary items.
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Since we often need frequency distributions in language processing, NLTK provides built-in support for them. Let's use a FreqDist to find the 50 most frequent words of Moby Dick :. When we first invoke FreqDist , we pass the name of the text as an argument. We can inspect the total number of words "outcomes" that have been counted up — , in the case of Moby Dick.
Your Turn: Try the preceding frequency distribution example for yourself, for text2. Be careful to use the correct parentheses and uppercase letters. If you get an error message NameError: name 'FreqDist' is not defined , you need to start your work with from nltk.
1.1 Getting Started with Python
Do any words produced in the last example help us grasp the topic or genre of this text? Only one word, whale , is slightly informative! It occurs over times. The rest of the words tell us nothing about the text; they're just English "plumbing. We can generate a cumulative frequency plot for these words, using fdist1.
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These 50 words account for nearly half the book!