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If the file is a text file (.txt), Excel starts the Import Text Wizard. When you are done with the steps, click Finish to complete the import operation. See Text Import Wizard for more information about delimiters and advanced options.

Download text (2) txt

If Excel doesn't convert a particular column of data to the format that you want, then you can convert the data after you import it. For more information, see Convert numbers stored as text to numbers and Convert dates stored as text to dates.

A dialog box appears, reminding you that only the current worksheet will be saved to the new file. If you are certain that the current worksheet is the one that you want to save as a text file, click OK. You can save other worksheets as separate text files by repeating this procedure for each worksheet.

If Excel does not convert a column of data to the format that you want, you can convert the data after you import it. For more information, see Convert numbers stored as text to numbers and Convert dates stored as text to dates.

A second dialog box appears, reminding you that your worksheet may contain features that are not supported by text file formats. If you are interested only in saving the worksheet data into the new text file, click Yes. If you are unsure and would like to know more about which Excel features are not supported by text file formats, click Help for more information.

If you use Get & Transform Data > From Text/CSV, after you choose the text file and click Import, choose a character to use from the list under Delimiter. You can see the effect of your new choice immediately in the data preview, so you can be sure you make the choice you want before you proceed.

If you use the Text Import Wizard to import a text file, you can change the delimiter that is used for the import operation in Step 2 of the Text Import Wizard. In this step, you can also change the way that consecutive delimiters, such as consecutive quotation marks, are handled.

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).

When Chinese or Japanese is selected, you should specify the textdirection (vertical/horizontal/auto) using the text directionhotkey: Windows Key + O. If auto is selected, horizontal will be used whenthe capture width is more than twice the height, otherwise vertical will beused. The text direction also affects how furigana is stripped from Japanese text.

Text Orientation: The orientation of the text that will be captured. This option is only used when Chinese or Japanese is set as the active OCR language. If Auto is selected, horizontal will be used when the capture width is more than twice the height, otherwise vertical will be used. The text direction also affects how furigana is stripped from Japanese text. You may also specify the text orientation in the tray icon menu or with the Text Orientation hotkey.

Folger Digital Texts provides .txt format files for projects and applications where simplicity and/or stability is the highest priority. These ASCII 7-encoded files are the most likely to render properly in the widest number of applications and the least likely to present conversion errors when being incorporated into text analysis tools. However, they also lack formatting, critical editing marks, and special characters. It is important to note that because special characters are not present, accents on words will be missing, which will change the meter of those lines. It is recommended that you use one of the other formats offered via Folger Digital Texts unless using a completely unadorned text is a priority. Use of this content is protected under our Terms of Use.

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 : 041b061a72