The value of the attribute will be the name of the downloaded file. There are no restrictions on allowed values, and the browser will automatically detect the correct file extension and add it to the file (.img, .pdf, .txt, .html, etc.).
Text files are displayed in the browser when the content-type is sent as text. You'd have to change the server to send it with a different content-type or use a language such as PHP to send it as a download.
You can export from SQL Server to a text file using C#. You could also perform a similar task using Visual Basic. In this example, we will use the Script task included in SSDT. This option is very useful if you are writing code and you need to integrate this task to the code.
For simple text string operations such as string search and replacement, you can use the built-in string functions (e.g., str.replace(old, new)). For complex pattern search and replacement, you need to master regular expression (regex).
The Get-Content cmdlet gets the content of the item at the location specified by the path, such asthe text in a file or the content of a function. For files, the content is read one line at a timeand returns a collection of objects, each of which represents a line of content.
Specifies the delimiter that Get-Content uses to divide the file into objects while it reads. Thedefault is \\n, the end-of-line character. When reading a text file, Get-Content returns acollection of string objects, each of which ends with an end-of-line character. When you enter adelimiter that does not exist in the file, Get-Content returns the entire file as a single,undelimited object.
In the above syntax, we take the file the user uploads into the HTML and convert its content to a blob object. After that, we create the text file using the blob object and save the file to the local computer.
writetable(T) writestable T to a comma delimited text file. The filename is the workspace variable name of the table, appended with theextension .txt. If writetable cannotconstruct the file name from the input table name, then it writesto the file table.txt.
Write the table to a comma-separated text file named myData.csv and view the file contents. Use the 'QuoteStrings' name-value pair argument to ensure that the commas in the third column are not treated as delimiters.
For Excel files, writetable writes table variables containing datetime arrays as Excel dates. If the table contains datetime arrays with years prior to either 1900 or 1904, then writetable writes the variables as text. For more information on Excel dates, see Differences between the 1900 and the 1904 date system in Excel.
NLTK includes a small selection of texts from the Project Gutenbergelectronic text archive, which containssome 25,000 free electronic books, hosted at We beginby getting the Python interpreter to load the NLTK package,then ask to see nltk.corpus.gutenberg.fileids(), the file identifiers inthis corpus:
In 1, we showed how youcould carry out concordancing of a text such as text1 with thecommand text1.concordance(). However, this assumes that you areusing one of the nine texts obtained as a result of doing fromnltk.book import *. Now that you have started examining data fromnltk.corpus, as in the previous example, you have to employ thefollowing pair of statements to perform concordancing and othertasks from 1:
Let's write a short program to display other information about eachtext, by looping over all the values of fileid corresponding tothe gutenberg file identifiers listed earlier and then computingstatistics for each text. For a compact output display, we will roundeach number to the nearest integer, using round().
This program displays three statistics for each text:average word length, average sentence length, and the number of times each vocabularyitem appears in the text on average (our lexical diversity score).Observe that average word length appears to be a general property of English, sinceit has a recurrent value of 4. (In fact, the average word length is really3 not 4, since the num_chars variable counts space characters.)By contrast average sentence length and lexical diversityappear to be characteristics of particular authors.
The previous example also showed how we can access the \"raw\" text of the book ,not split up into tokens. The raw() function gives us the contents of the filewithout any linguistic processing. So, for example, len(gutenberg.raw('blake-poems.txt'))tells us how many letters occur in the text, including the spaces between words.The sents() function divides the text up into its sentences, where each sentence isa list of words:
Although Project Gutenberg contains thousands of books, it represents establishedliterature. It is important to consider less formal language as well. NLTK'ssmall collection of web text includes content from a Firefox discussion forum,conversations overheard in New York, the movie script of Pirates of the Carribean,personal advertisements, and wine reviews:
The Brown Corpus was the first million-word electroniccorpus of English, created in 1961 at Brown University.This corpus contains text from 500 sources, and the sourceshave been categorized by genre, such as news, editorial, and so on.1.1 gives an example of each genre(for a complete list, see -los.html).
The Reuters Corpus contains 10,788 news documents totaling 1.3 million words.The documents have been classified into 90 topics, and groupedinto two sets, called \"training\" and \"test\"; thus, the text withfileid 'test/14826' is a document drawn from the test set. This split is fortraining and testing algorithms that automatically detect the topic of a document,as we will see in chap-data-intensive.
In 1, we looked atthe Inaugural Address Corpus,but treated it as a single text. The graph in fig-inauguralused \"word offset\" as one of the axes; this is the numerical index of theword in the corpus, counting from the first word of the first address.However, the corpus is actually a collection of 55 texts, one for each presidential address.An interesting property of this collection is its time dimension:
Many text corpora contain linguistic annotations, representing POS tags,named entities, syntactic structures, semantic roles, and so forth. NLTK providesconvenient ways to access several of these corpora, and has data packages containing corporaand corpus samples, freely downloadable for use in teaching and research.1.2 lists some of the corpora. For information aboutdownloading them, see more examples of how to access NLTK corpora,please consult the Corpus HOWTO at
We have seen a variety of corpus structures so far; these aresummarized in 1.3.The simplest kind lacks any structure: it is just a collection of texts.Often, texts are grouped into categories that might correspond to genre, source, author, language, etc.Sometimes these categories overlap, notably in the case of topical categories as a text can berelevant to more than one topic. Occasionally, text collections have temporal structure,news collections being the most common example.
If you have your own collection of text files that you would like to access usingthe above methods, you can easily load them with the help of NLTK'sPlaintextCorpusReader. Check the location of your files on your file system; inthe following example, we have taken this to be the directory/usr/share/dict. Whatever the location, set this to be the value ofcorpus_root .The second parameter of the PlaintextCorpusReader initializer can be a list of fileids, like ['a.txt', 'test/b.txt'],or a pattern that matches all fileids, like '[abc]/.*\\.txt'(see 3.4 for informationabout regular expressions).
When the texts of a corpus are divided into severalcategories, by genre, topic, author, etc, we can maintain separatefrequency distributions for each category. This will allow us tostudy systematic differences between the categories. In the previoussection we achieved this using NLTK's ConditionalFreqDist datatype. A conditional frequency distribution is a collection offrequency distributions, each one for a different \"condition\". Thecondition will often be the category of the text. 2.1depicts a fragment of a conditional frequency distribution having justtwo conditions, one for news text and one for romance text.
A frequency distribution counts observable events,such as the appearance of words in a text. A conditionalfrequency distribution needs to pair each event with a condition.So instead of processing a sequence of words ,we have to process a sequence of pairs :
In the plot() and tabulate() methods, we canoptionally specify which conditions to display with a conditions= parameter.When we omit it, we get all the conditions. Similarly, we can limit thesamples to display with a samples= parameter. This makes it possible toload a large quantity of data into a conditional frequency distribution, and thento explore it by plotting or tabulating selected conditions and samples. It alsogives us full control over the order of conditions and samples in any displays.For example, we can tabulate the cumulative frequency data just for twolanguages, and for words less than 10 characters long, as shown below.We interpret the last cell on the top row to mean that 1,638 words of theEnglish text have 9 or fewer letters.
In 2.2, we treat each word as a condition, and for each onewe effectively create a frequency distribution over the followingwords. The function generate_model() contains a simple loop togenerate text. When we call the function, we choose a word (such as'living') as our initial context, then once inside the loop, weprint the current value of the variable word, and reset wordto be the most likely token in that context (using max()); nexttime through the loop, we use that word as our new context. As youcan see by inspecting the output, this simple approach to textgeneration tends to get stuck in loops; another method would be torandomly choose the next word from among the available words. 59ce067264