# Word frequency

(Redirected from Word count)
You are encouraged to solve this task according to the task description, using any language you may know.

Given a text file and an integer   n,   print/display the   n   most common words in the file   (and the number of their occurrences)   in decreasing frequency.

For the purposes of this task:

•   A word is a sequence of one or more contiguous letters.
•   You are free to define what a   letter   is.
•   Underscores, accented letters, apostrophes, hyphens, and other special characters can be handled at your discretion.
•   You may treat a compound word like   well-dressed   as either one word or two.
•   The word   it's   could also be one or two words as you see fit.
•   You may also choose not to support non US-ASCII characters.
•   Assume words will not span multiple lines.
•   Don't worry about normalization of word spelling differences.
•   Treat   color   and   colour   as two distinct words.
•   Uppercase letters are considered equivalent to their lowercase counterparts.
•   Words of equal frequency can be listed in any order.
•   Feel free to explicitly state the thoughts behind the program decisions.

Show example output using Les Misérables from Project Gutenberg as the text file input and display the top   10   most used words.

History

This task was originally taken from programming pearls from Communications of the ACM June 1986 Volume 29 Number 6 where this problem is solved by Donald Knuth using literate programming and then critiqued by Doug McIlroy, demonstrating solving the problem in a 6 line Unix shell script (provided as an example below).

References

## 11l

DefaultDict[String, Int] cnt
cnt[word]++
print(sorted(cnt.items(), key' wordc -> wordc, reverse' 1B)[0.<10])
Output:
[(the, 41045), (of, 19953), (and, 14939), (a, 14527), (to, 13942), (in, 11210), (he, 9646), (was, 8620), (that, 7922), (it, 6659)]


This version uses a character set to match valid characters in a token. Another version could use a pointer to a function returning a boolean to match valid characters (allowing to use functions such as Is_Alphanumeric), but AFAIK there is no "Find_Token" method that uses one.

with Ada.Command_Line;

procedure Word_Frequency is

package String_Counters is new Ada.Containers.Indefinite_Ordered_Maps(String, Natural);
(Natural,
String_Sets.Set,
"=" => String_Sets."=",
"<" => ">");
-- for sorting by decreasing number of occurrences and ascending lexical order

procedure Increment(Key : in String; Element : in out Natural) is
begin
Element := Element + 1;
end Increment;

path : constant String := Ada.Command_Line.Argument(1);
how_many : Natural := 10;
F : TIO.File_Type;
first : Positive;
last : Natural;
from : Positive;
counter : String_Counters.Map;
sorted_counts : Sorted_Counters.Map;
C1 : String_Counters.Cursor;
C2 : Sorted_Counters.Cursor;
tmp_set : String_Sets.Set;
begin
-- read file and count words
TIO.Open(F, name => path, mode => TIO.In_File);
while not TIO.End_Of_File(F) loop
declare
line : constant String := Ada.Characters.Handling.To_Lower(TIO.Get_Line(F));
begin
from := line'First;
loop
exit when last < First;
C1 := counter.Find(line(first .. last));
if String_Counters.Has_Element(C1) then
counter.Update_Element(C1, Increment'Access);
else
counter.Insert(line(first .. last), 1);
end if;
from := last + 1;
end loop;
end;
end loop;
TIO.Close(F);

-- fill Natural -> StringSet Map
C1 := counter.First;
while String_Counters.Has_Element(C1) loop
if sorted_counts.Contains(String_Counters.Element(C1)) then
tmp_set := sorted_counts.Element(String_Counters.Element(C1));
tmp_set.Include(String_Counters.Key(C1));
else
sorted_counts.Include(String_Counters.Element(C1), String_Sets.To_Set(String_Counters.Key(C1)));
end if;
String_Counters.Next(C1);
end loop;

-- output
C2 := sorted_counts.First;
while Sorted_Counters.Has_Element(C2) loop
for Item of Sorted_Counters.Element(C2) loop
TIO.Put(TIO.Standard_Output, " ");
TIO.Put_Line(Item);
end loop;
Sorted_Counters.Next(C2);
how_many := how_many - 1;
exit when how_many = 0;
end loop;
end Word_Frequency;

Output:
$./word_frequency 135-0.txt 41093 the 19954 of 14943 and 14558 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it  ## ALGOL 68 Works with: ALGOL 68G version Any - tested with release 2.8.3.win32 Uses the associative array implementations in ALGOL_68/prelude. # find the n most common words in a file # # use the associative array in the Associate array/iteration task # # but with integer values # PR read "aArrayBase.a68" PR MODE AAKEY = STRING; MODE AAVALUE = INT; AAVALUE init element value = 0; # returns text converted to upper case # OP TOUPPER = ( STRING text )STRING: BEGIN STRING result := text; FOR ch pos FROM LWB result TO UPB result DO IF is lower( result[ ch pos ] ) THEN result[ ch pos ] := to upper( result[ ch pos ] ) FI OD; result END # TOUPPER # ; # returns text converted to an INT or -1 if text is not a number # OP TOINT = ( STRING text )INT: BEGIN INT result := 0; BOOL is numeric := TRUE; FOR ch pos FROM UPB text BY -1 TO LWB text WHILE is numeric DO CHAR c = text[ ch pos ]; is numeric := is numeric AND c >= "0" AND c <= "9"; IF is numeric THEN ( result *:= 10 ) +:= ABS c - ABS "0" FI OD; IF is numeric THEN result ELSE -1 FI END # TOINT # ; # returns TRUE if c is a letter, FALSE otherwise # OP ISLETTER = ( CHAR c )BOOL: IF ( c >= "a" AND c <= "z" ) OR ( c >= "A" AND c <= "Z" ) THEN TRUE ELSE char in string( c, NIL, "ÇåçêëÆôöÿÖØáóÔ" ) FI # ISLETER # ; # get the file name and number of words from then commmand line # STRING file name := "pg-les-misrables.txt"; INT number of words := 10; FOR arg pos TO argc - 1 DO STRING arg upper = TOUPPER argv( arg pos ); IF arg upper = "FILE" THEN file name := argv( arg pos + 1 ) ELIF arg upper = "NUMBER" THEN number of words := TOINT argv( arg pos + 1 ) FI OD; IF FILE input file; open( input file, file name, stand in channel ) /= 0 THEN # failed to open the file # print( ( "Unable to open """ + file name + """", newline ) ) ELSE # file opened OK # print( ( "Processing: ", file name, newline ) ); BOOL at eof := FALSE; BOOL at eol := FALSE; # set the EOF handler for the file # on logical file end( input file, ( REF FILE f )BOOL: BEGIN # note that we reached EOF on the # # latest read # at eof := TRUE; # return TRUE so processing can continue # TRUE END ); # set the end-of-line handler for the file so get word can see line boundaries # on line end( input file , ( REF FILE f )BOOL: BEGIN # note we reached end-of-line # at eol := TRUE; # return FALSE to use the default eol handling # # i.e. just get the next charactefr # FALSE END ); # get the words from the file and store the counts in an associative array # REF AARRAY words := INIT LOC AARRAY; INT word count := 0; CHAR c := " "; WHILE get( input file, ( c ) ); NOT at eof DO WHILE NOT ISLETTER c AND NOT at eof DO get( input file, ( c ) ) OD; STRING word := ""; at eol := FALSE; WHILE ISLETTER c AND NOT at eol AND NOT at eof DO word +:= c; get( input file, ( c ) ) OD; word count +:= 1; words // TOUPPER word +:= 1 OD; close( input file ); print( ( file name, " contains ", whole( word count, 0 ), " words", newline ) ); # find the most used words # [ number of words ]STRING top words; [ number of words ]INT top counts; FOR i TO number of words DO top words[ i ] := ""; top counts[ i ] := 0 OD; REF AAELEMENT w := FIRST words; WHILE w ISNT nil element DO INT count = value OF w; STRING word = key OF w; BOOL found := FALSE; FOR i TO number of words WHILE NOT found DO IF count > top counts[ i ] THEN # found a word that is used nore than a current # # most used word # found := TRUE; # move the other words down one place # FOR move pos FROM number of words BY - 1 TO i + 1 DO top counts[ move pos ] := top counts[ move pos - 1 ]; top words [ move pos ] := top words [ move pos - 1 ] OD; # install the new word # top counts[ i ] := count; top words [ i ] := word FI OD; w := NEXT words OD; print( ( whole( number of words, 0 ), " most used words:", newline ) ); print( ( " count word", newline ) ); FOR i TO number of words DO print( ( whole( top counts[ i ], -6 ), ": ", top words[ i ], newline ) ) OD FI Output: Processing: pg-les-misrables.txt pg-les-misrables.txt contains 578381 words 10 most used words: count word 39333: THE 19154: OF 14628: AND 14229: A 13431: TO 11275: HE 10879: IN 8236: WAS 7527: THAT 6491: IT  ## APL Works with: GNU APL ⍝⍝ NOTE: input text is assumed to be encoded in ISO-8859-1 ⍝⍝ (The suggested example '135-0.txt' of Les Miserables on ⍝⍝ Project Gutenberg is in UTF-8.) ⍝⍝ ⍝⍝ Use Unix 'iconv' if required ⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝ ∇r ← lowerAndStrip s;stripped;mixedCase ⍝⍝ Convert text to lowercase, punctuation and newlines to spaces stripped ← ' abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz*' mixedCase ← ⎕av,' ,.?!;:"''()[]-ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' r ← stripped[mixedCase ⍳ s] ∇ ⍝⍝ Return the _n_ most frequent words and a count of their occurrences ⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝⍝ ∇r ← n wordCount fname ;D;wl;sidx;swv;pv;wc;uw;sortOrder D ← lowerAndStrip (⎕fio['read_file'] fname) ⍝ raw text with newlines wl ← (~ D ∊ ' ') ⊂ D sidx ← ⍒wl swv ← wl[sidx] pv ← +\ 1,~2 ≡/ swv wc ← ∊ ⍴¨ pv ⊂ pv uw ← 1 ⊃¨ pv ⊂ swv sortOrder ← ⍒wc r ← n↑ uw[sortOrder],[0.5]wc[sortOrder] ∇ 5 wordCount '135-0.txt' the of and a to 41042 19952 14938 14526 13942  ## AppleScript (* For simplicity here, words are considered to be uninterrupted sequences of letters and/or digits. The set text is too messy to warrant faffing around with anything more sophisticated. The first letter in each word is upper-cased and the rest lower-cased for case equivalence and presentation. Where more than n words qualify for the top n or fewer places, all are included in the result. *) use AppleScript version "2.4" -- OS X 10.10 (Yosemite) or later use framework "Foundation" use scripting additions on wordFrequency(filePath, n) set |⌘| to current application -- Get the text and "capitalize" it (lower-case except for the first letters in words). set theText to |⌘|'s class "NSString"'s stringWithContentsOfFile:(filePath) usedEncoding:(missing value) |error|:(missing value) set theText to theText's capitalizedStringWithLocale:(|⌘|'s class "NSLocale"'s currentLocale()) -- Yosemite compatible. -- Split it at the non-word characters. set nonWordCharacters to |⌘|'s class "NSCharacterSet"'s alphanumericCharacterSet()'s invertedSet() set theWords to theText's componentsSeparatedByCharactersInSet:(nonWordCharacters) -- Use a counted set to count the individual words' occurrences. set countedSet to |⌘|'s class "NSCountedSet"'s alloc()'s initWithArray:(theWords) -- Build a list of word/frequency records, excluding any empty strings left over from the splitting above. set mutableSet to |⌘|'s class "NSMutableSet"'s setWithSet:(countedSet) tell mutableSet to removeObject:("") script o property discreteWords : mutableSet's allObjects() as list property wordsAndFrequencies : {} end script set discreteWordCount to (count o's discreteWords) repeat with i from 1 to discreteWordCount set thisWord to item i of o's discreteWords set end of o's wordsAndFrequencies to {thisWord:thisWord, frequency:(countedSet's countForObject:(thisWord)) as integer} end repeat -- Convert to NSMutableArray, reverse-sort the result on the frequencies, and convert back to list. set wordsAndFrequencies to |⌘|'s class "NSMutableArray"'s arrayWithArray:(o's wordsAndFrequencies) set descendingByFrequency to |⌘|'s class "NSSortDescriptor"'s sortDescriptorWithKey:("frequency") ascending:(false) tell wordsAndFrequencies to sortUsingDescriptors:({descendingByFrequency}) set o's wordsAndFrequencies to wordsAndFrequencies as list if (discreteWordCount > n) then -- If there are more than n records, check for any immediately following the nth which may have the same frequency as it. set nthHighestFrequency to frequency of item n of o's wordsAndFrequencies set qualifierCount to n repeat with i from (n + 1) to discreteWordCount if (frequency of item i of o's wordsAndFrequencies = nthHighestFrequency) then set qualifierCount to i else exit repeat end if end repeat else -- Otherwise reduce n to the actual number of discrete words. set n to discreteWordCount set qualifierCount to discreteWordCount end if -- Compose a text report from the qualifying words and frequencies. if (qualifierCount = n) then set output to {"The " & n & " most frequently occurring words in the file are:"} else set output to {(qualifierCount as text) & " words share the " & ((n as text) & " highest frequencies in the file:")} end if repeat with i from 1 to qualifierCount set {thisWord:thisWord, frequency:frequency} to item i of o's wordsAndFrequencies set end of output to thisWord & ": " & (tab & frequency) end repeat set astid to AppleScript's text item delimiters set AppleScript's text item delimiters to linefeed set output to output as text set AppleScript's text item delimiters to astid return output end wordFrequency -- Test code: set filePath to POSIX path of ((path to desktop as text) & "www.rosettacode.org:Word frequency:135-0.txt") set n to 10 return wordFrequency(filePath, n)  Output: "The 10 most frequently occurring words in the file are: The: 41092 Of: 19954 And: 14943 A: 14545 To: 13953 In: 11219 He: 9649 Was: 8622 That: 7924 It: 6661"  ## Arturo findFrequency: function [file, count][ freqs: #[] r: {/[[:alpha:]]+/} loop flatten map split.lines read file 'l -> match lower l r 'word [ if not? key? freqs word -> freqs\[word]: 0 freqs\[word]: freqs\[word] + 1 ] freqs: sort.values.descending freqs result: new [] loop 0..dec count 'x [ 'result ++ @[@[get keys freqs x, get values freqs x]] ] return result ] loop findFrequency "https://www.gutenberg.org/files/135/135-0.txt" 10 'pair [ print pair ]  Output: the 41096 of 19955 and 14939 a 14558 to 13954 in 11218 he 9649 was 8622 that 7924 it 6661 ## AutoHotkey URLDownloadToFile, http://www.gutenberg.org/files/135/135-0.txt, % A_temp "\tempfile.txt" FileRead, H, % A_temp "\tempfile.txt" FileDelete, % A_temp "\tempfile.txt" words := [] while pos := RegExMatch(H, "\b[[:alpha:]]+\b", m, A_Index=1?1:pos+StrLen(m)) words[m] := words[m] ? words[m] + 1 : 1 for word, count in words list .= count "t" word "rn" Sort, list, RN loop, parse, list, n, r { result .= A_LoopField "rn" if A_Index = 10 break } MsgBox % "FreqtWordn" result return  Outputs: Freq Word 41036 The 19946 of 14940 and 14589 A 13939 TO 11204 in 9645 HE 8619 WAS 7922 THAT 6659 it ## AWK # syntax: GAWK -f WORD_FREQUENCY.AWK [-v show=x] LES_MISERABLES.TXT # # A word is anything separated by white space. # Therefor "this" and "this." are different. # But "This" and "this" are identical. # As I am "free to define what a letter is" I have chosen to allow # numerics and all special characters as they are usually considered # parts of words in text processing applications. # { nbytes += length($0) + 2 # +2 for CR/LF
nfields += NF
$0 = tolower($0)
for (i=1; i<=NF; i++) {
arr[$i]++ } } END { show = (show == "") ? 10 : show width1 = length(show) PROCINFO["sorted_in"] = "@val_num_desc" for (i in arr) { if (width2 == 0) { width2 = length(arr[i]) } if (n++ >= show) { break } printf("%*d %*d %s\n",width1,n,width2,arr[i],i) } printf("input: %d records, %d bytes, %d words of which %d are unique\n",NR,nbytes,nfields,length(arr)) exit(0) }  Output:  1 40372 the 2 19868 of 3 14472 and 4 14278 a 5 13589 to 6 11024 in 7 9213 he 8 8347 was 9 7250 that 10 6414 his input: 73829 records, 3369772 bytes, 568744 words of which 50394 are unique  ## BASIC ### QB64 This is a rather long code. I fulfilled the requirement with QB64. It "cleans" each word so it takes as a word anything that begins and ends with a letter. It works with arrays. Amazing the speed of QB64 to do this job with such a big file as Les Miserables.txt. OPTION _EXPLICIT ' SUBs and FUNCTIONs DECLARE SUB CountWords (FromString AS STRING) DECLARE SUB QuickSort (lLeftN AS LONG, lRightN AS LONG, iMode AS INTEGER) DECLARE SUB ShowResults () DECLARE SUB ShowCompletion () DECLARE SUB TopCounted () DECLARE FUNCTION InsertWord& (WhichWord AS STRING) DECLARE FUNCTION BinarySearch& (LookFor AS STRING, RetPos AS INTEGER) DECLARE FUNCTION CleanWord$ (WhichWord AS STRING)

' Var
DIM iFile AS INTEGER
DIM iCol AS INTEGER
DIM iFil AS INTEGER
DIM iStep AS INTEGER
DIM iBar AS INTEGER
DIM iBlock AS INTEGER
DIM lIni AS LONG
DIM lEnd AS LONG
DIM lLines AS LONG
DIM lLine AS LONG
DIM lLenF AS LONG
DIM iRuns AS INTEGER
DIM lMaxWords AS LONG
DIM sTimer AS SINGLE
DIM strFile AS STRING
DIM strKey AS STRING
DIM strText AS STRING
DIM strDate AS STRING
DIM strTime AS STRING
DIM strBar AS STRING
DIM lWords AS LONG
DIM strWords AS STRING
CONST TopCount = 10
CONST FALSE = 0, TRUE = NOT FALSE

' Initialize
iFile = FREEFILE
lIni = 1
strDate = DATE$strTime = TIME$
lEnd = 0
lMaxWords = 1000
REDIM strWords(lIni TO lMaxWords) AS STRING
REDIM lWords(lIni TO lMaxWords) AS LONG
REDIM lTopWords(1) AS LONG
REDIM strTopWords(1) AS STRING

' ---Main program loop
$RESIZE:SMOOTH DO DO CLS PRINT "This program will count how many words are in a text file and shows the 10" PRINT "most used of them." PRINT INPUT "Document to open (TXT file) (f=see files): ", strFile IF UCASE$(strFile) = "F" THEN
strFile = ""
FILES
DO: LOOP UNTIL INKEY$<> "" END IF LOOP UNTIL strFile <> "" OPEN strFile FOR BINARY AS #iFile IF LOF(iFile) > 0 THEN iRuns = iRuns + 1 CLOSE #iFile ' Opens the document file to analyze it sTimer = TIMER ON TIMER(1) GOSUB ShowAdvance OPEN strFile FOR INPUT AS #iFile lLenF = LOF(iFile) PRINT "Looking for words in "; strFile; ". File size:"; STR$(lLenF); ". ";: iCol = POS(0): PRINT "Initializing";
COLOR 23: PRINT "...";: COLOR 7

' Count how many lines has the file
lLines = 0
DO WHILE NOT EOF(iFile)
LINE INPUT #iFile, strText
lLines = lLines + 1
LOOP
CLOSE #iFile

' Shows the bar
LOCATE , iCol: PRINT "Initialization complete."
PRINT
PRINT "Processing"; lLines; "lines";: COLOR 23: PRINT "...": COLOR 7
iFil = CSRLIN
iCol = POS(0)
iBar = 80
iBlock = 80 / lLines
IF iBlock = 0 THEN iBlock = 1
PRINT STRING$(iBar, 176) lLine = 0 iStep = lLines * iBlock / iBar IF iStep = 0 THEN iStep = 1 IF iStep > 20 THEN TIMER ON END IF OPEN strFile FOR INPUT AS #iFile DO WHILE NOT EOF(iFile) lLine = lLine + 1 IF (lLine MOD iStep) = 0 THEN strBar = STRING$(iBlock * (lLine / iStep), 219)
LOCATE iFil, 1
PRINT strBar
ShowCompletion
END IF
LINE INPUT #iFile, strText
CountWords strText
strKey = INKEY$LOOP ShowCompletion CLOSE #iFile TIMER OFF LOCATE iFil - 1, 1 PRINT "Done!" + SPACE$(70)
strBar = STRING$(iBar, 219) LOCATE iFil, 1 PRINT strBar LOCATE iFil + 5, 1 PRINT "Finishing";: COLOR 23: PRINT "...";: COLOR 7 ShowResults ' Frees the RAM lMaxWords = 1000 lEnd = 0 REDIM strWords(lIni TO lMaxWords) AS STRING REDIM lWords(lIni TO lMaxWords) AS LONG ELSE CLOSE #iFile KILL strFile PRINT PRINT "Document does not exist." END IF PRINT PRINT "Try again? (Y/n)" DO strKey = UCASE$(INKEY$) LOOP UNTIL strKey = "Y" OR strKey = "N" OR strKey = CHR$(13) OR strKey = CHR$(27) LOOP UNTIL strKey = "N" OR strKey = CHR$(27) OR iRuns >= 99

CLS
IF iRuns >= 99 THEN
PRINT "Maximum runs reached for this session."
END IF

PRINT "End of program"
PRINT "Start date/time: "; strDate; " "; strTime
PRINT "End date/time..: "; DATE$; " "; TIME$
END
' ---End main program

ShowCompletion
RETURN

FUNCTION BinarySearch& (LookFor AS STRING, RetPos AS INTEGER)
' Var
DIM lFound AS LONG
DIM lLow AS LONG
DIM lHigh AS LONG
DIM lMid AS LONG
DIM strLookFor AS STRING
SHARED lIni AS LONG
SHARED lEnd AS LONG
SHARED lMaxWords AS LONG
SHARED strWords() AS STRING
SHARED lWords() AS LONG

' Binary search for stated word in the list
lLow = lIni
lHigh = lEnd
lFound = 0
strLookFor = UCASE$(LookFor) DO WHILE (lFound < 1) AND (lLow <= lHigh) lMid = (lHigh + lLow) / 2 IF strWords(lMid) = strLookFor THEN lFound = lMid ELSEIF strWords(lMid) > strLookFor THEN lHigh = lMid - 1 ELSE lLow = lMid + 1 END IF LOOP ' Should I return the position if not found? IF lFound = 0 AND RetPos THEN IF lEnd < 1 THEN lFound = 1 ELSEIF strWords(lMid) > strLookFor THEN lFound = lMid ELSE lFound = lMid + 1 END IF END IF ' Return the value BinarySearch = lFound END FUNCTION FUNCTION CleanWord$ (WhichWord AS STRING)
' Var
DIM iSeek AS INTEGER
DIM iStep AS INTEGER
DIM bOK AS INTEGER
DIM strWord AS STRING
DIM strChar AS STRING

strWord = WhichWord

' Look for trailing wrong characters
strWord = LTRIM$(RTRIM$(strWord))
IF LEN(strWord) > 0 THEN
iStep = 0
DO
' Determines if step will be forward or backwards
IF iStep = 0 THEN
iStep = -1
ELSE
iStep = 1
END IF

' Sets the initial value of iSeek
IF iStep = -1 THEN
iSeek = LEN(strWord)
ELSE
iSeek = 1
END IF

bOK = FALSE
DO
strChar = MID$(strWord, iSeek, 1) SELECT CASE strChar CASE "A" TO "Z" bOK = TRUE CASE CHR$(129) TO CHR$(154) bOK = TRUE CASE CHR$(160) TO CHR$(165) bOK = TRUE END SELECT ' If it is not a character valid as a letter, please move one space IF NOT bOK THEN iSeek = iSeek + iStep END IF ' If no letter was recognized, then exit the loop IF iSeek < 1 OR iSeek > LEN(strWord) THEN bOK = TRUE END IF LOOP UNTIL bOK IF iStep = -1 THEN ' Reviews if a word was encountered IF iSeek > 0 THEN strWord = LEFT$(strWord, iSeek)
ELSE
strWord = ""
END IF
ELSEIF iStep = 1 THEN
IF iSeek <= LEN(strWord) THEN
strWord = MID$(strWord, iSeek) ELSE strWord = "" END IF END IF LOOP UNTIL iStep = 1 OR strWord = "" END IF ' Return the result CleanWord = strWord END FUNCTION SUB CountWords (FromString AS STRING) ' Var DIM iStart AS INTEGER DIM iLenW AS INTEGER DIM iLenS AS INTEGER DIM iLenD AS INTEGER DIM strString AS STRING DIM strWord AS STRING DIM lWhichWord AS LONG SHARED lEnd AS LONG SHARED lMaxWords AS LONG SHARED strWords() AS STRING SHARED lWords() AS LONG ' Converts to Upper Case and cleans leading and trailing spaces strString = UCASE$(FromString)
strString = LTRIM$(RTRIM$(strString))

' Get words from string
iStart = 1
iLenW = 0
iLenS = LEN(strString)
DO WHILE iStart <= iLenS
iLenW = INSTR(iStart, strString, " ")
IF iLenW = 0 AND iStart <= iLenS THEN
iLenW = iLenS + 1
END IF
strWord = MID$(strString, iStart, iLenW - iStart) ' Will remove mid dashes or apostrophe or "â€" iLenD = INSTR(strWord, "-") IF iLenD < 1 THEN iLenD = INSTR(strWord, "'") IF iLenD < 1 THEN iLenD = INSTR(strWord, "â€") END IF END IF IF iLenD >= 2 THEN strWord = LEFT$(strWord, iLenD - 1)
iLenW = iStart + (iLenD - 1)
END IF
strWord = CleanWord(strWord)

IF strWord <> "" THEN
' Look for the word to be counted
lWhichWord = BinarySearch(strWord, FALSE)

' If the word doesn't exist in the list, add it
IF lWhichWord = 0 THEN
lWhichWord = InsertWord(strWord)
END IF

' Count the word
IF lWhichWord > 0 THEN
lWords(lWhichWord) = lWords(lWhichWord) + 1
END IF
END IF
iStart = iLenW + 1
LOOP

END SUB

' Here a word will be inserted in the list
FUNCTION InsertWord& (WhichWord AS STRING)
' Var
DIM lFound AS LONG
DIM lWord AS LONG
DIM strWord AS STRING
SHARED lIni AS LONG
SHARED lEnd AS LONG
SHARED lMaxWords AS LONG
SHARED strWords() AS STRING
SHARED lWords() AS LONG

' Look for the word in the list
strWord = UCASE$(WhichWord) lFound = BinarySearch(WhichWord, TRUE) IF lFound > 0 THEN ' Add one word lEnd = lEnd + 1 ' Verifies if there is still room for a new word IF lEnd > lMaxWords THEN lMaxWords = lMaxWords + AddWords ' Other words IF lMaxWords > 32767 THEN IF lEnd <= 32767 THEN lMaxWords = 32767 ELSE lFound = -1 END IF END IF IF lFound > 0 THEN REDIM _PRESERVE strWords(lIni TO lMaxWords) AS STRING REDIM _PRESERVE lWords(lIni TO lMaxWords) AS LONG END IF END IF IF lFound > 0 THEN ' Move the words below this IF lEnd > 1 THEN FOR lWord = lEnd TO lFound + 1 STEP -1 strWords(lWord) = strWords(lWord - 1) lWords(lWord) = lWords(lWord - 1) NEXT lWord END IF ' Insert the word in the position strWords(lFound) = strWord lWords(lFound) = 0 END IF END IF InsertWord = lFound END FUNCTION SUB QuickSort (lLeftN AS LONG, lRightN AS LONG, iMode AS INTEGER) ' Var DIM lPivot AS LONG DIM lLeftNIdx AS LONG DIM lRightNIdx AS LONG SHARED lWords() AS LONG SHARED strWords() AS STRING ' Clasifies from highest to lowest lLeftNIdx = lLeftN lRightNIdx = lRightN IF (lRightN - lLeftN) > 0 THEN lPivot = (lLeftN + lRightN) / 2 DO WHILE (lLeftNIdx <= lPivot) AND (lRightNIdx >= lPivot) IF iMode = 0 THEN ' Ascending DO WHILE (lWords(lLeftNIdx) < lWords(lPivot)) AND (lLeftNIdx <= lPivot) lLeftNIdx = lLeftNIdx + 1 LOOP DO WHILE (lWords(lRightNIdx) > lWords(lPivot)) AND (lRightNIdx >= lPivot) lRightNIdx = lRightNIdx - 1 LOOP ELSE ' Descending DO WHILE (lWords(lLeftNIdx) > lWords(lPivot)) AND (lLeftNIdx <= lPivot) lLeftNIdx = lLeftNIdx + 1 LOOP DO WHILE (lWords(lRightNIdx) < lWords(lPivot)) AND (lRightNIdx >= lPivot) lRightNIdx = lRightNIdx - 1 LOOP END IF SWAP lWords(lLeftNIdx), lWords(lRightNIdx) SWAP strWords(lLeftNIdx), strWords(lRightNIdx) lLeftNIdx = lLeftNIdx + 1 lRightNIdx = lRightNIdx - 1 IF (lLeftNIdx - 1) = lPivot THEN lRightNIdx = lRightNIdx + 1 lPivot = lRightNIdx ELSEIF (lRightNIdx + 1) = lPivot THEN lLeftNIdx = lLeftNIdx - 1 lPivot = lLeftNIdx END IF LOOP QuickSort lLeftN, lPivot - 1, iMode QuickSort lPivot + 1, lRightN, iMode END IF END SUB SUB ShowCompletion () ' Var SHARED iFil AS INTEGER SHARED lLine AS LONG SHARED lLines AS LONG SHARED lEnd AS LONG LOCATE iFil + 1, 1 PRINT "Lines analyzed :"; lLine PRINT USING "% of completion: ###%"; (lLine / lLines) * 100 PRINT "Words found....:"; lEnd END SUB SUB ShowResults () ' Var DIM iMaxL AS INTEGER DIM iMaxW AS INTEGER DIM lWord AS LONG DIM lHowManyWords AS LONG DIM strString AS STRING DIM strFileR AS STRING SHARED lIni AS LONG SHARED lEnd AS LONG SHARED lLenF AS LONG SHARED lMaxWords AS LONG SHARED sTimer AS SINGLE SHARED strFile AS STRING SHARED strWords() AS STRING SHARED lWords() AS LONG SHARED strTopWords() AS STRING SHARED lTopWords() AS LONG SHARED iRuns AS INTEGER ' Show results ' Creates file name lWord = INSTR(strFile, ".") IF lWord = 0 THEN lWord = LEN(strFile) strFileR = LEFT$(strFile, lWord)
IF RIGHT$(strFileR, 1) <> "." THEN strFileR = strFileR + "." ' Retrieves the longest word found and the highest count FOR lWord = lIni TO lEnd ' Gets the longest word found IF LEN(strWords(lWord)) > iMaxL THEN iMaxL = LEN(strWords(lWord)) END IF lHowManyWords = lHowManyWords + lWords(lWord) NEXT lWord IF iMaxL > 60 THEN iMaxW = 60 ELSE iMaxW = iMaxL ' Gets top counted TopCounted ' Shows the results CLS PRINT "File analyzed : "; strFile PRINT "Length of file:"; lLenF PRINT "Time lapse....:"; TIMER - sTimer;"seconds" PRINT "Words found...:"; lHowManyWords; "(Unique:"; STR$(lEnd); ")"
PRINT "Longest word..:"; iMaxL
PRINT
PRINT "The"; TopCount; "most used are:"
PRINT STRING$(iMaxW, "-"); "+"; STRING$(80 - (iMaxW + 1), "-")
PRINT " Word"; SPACE$(iMaxW - 5); "| Count" PRINT STRING$(iMaxW, "-"); "+"; STRING$(80 - (iMaxW + 1), "-") strString = "\" + SPACE$(iMaxW - 2) + "\| #########,"
FOR lWord = lIni TO TopCount
PRINT USING strString; strTopWords(lWord); lTopWords(lWord)
NEXT lWord
PRINT STRING$(iMaxW, "-"); "+"; STRING$(80 - (iMaxW + 1), "-")
PRINT "See files "; strFileR + "S" + LTRIM$(STR$(iRuns)); " and "; strFileR + "C" + LTRIM$(STR$(iRuns)); " to see the full list."
END SUB

SUB TopCounted ()
' Var
DIM lWord AS LONG
DIM strFileR AS STRING
DIM iFile AS INTEGER
CONST Descending = 1
SHARED lIni AS LONG
SHARED lEnd AS LONG
SHARED lMaxWords AS LONG
SHARED strWords() AS STRING
SHARED lWords() AS LONG
SHARED strTopWords() AS STRING
SHARED lTopWords() AS LONG
SHARED iRuns AS INTEGER
SHARED strFile AS STRING

' Assigns new dimmentions
REDIM strTopWords(lIni TO TopCount) AS STRING
REDIM lTopWords(lIni TO TopCount) AS LONG

' Saves the current values
lWord = INSTR(strFile, ".")
IF lWord = 0 THEN lWord = LEN(strFile)
strFileR = LEFT$(strFile, lWord) IF RIGHT$(strFileR, 1) <> "." THEN strFileR = strFileR + "."
iFile = FREEFILE
OPEN strFileR + "S" + LTRIM$(STR$(iRuns)) FOR OUTPUT AS #iFile
FOR lWord = lIni TO lEnd
WRITE #iFile, strWords(lWord), lWords(lWord)
NEXT lWord
CLOSE #iFile

' Classifies the counted in descending order
QuickSort lIni, lEnd, Descending

' Now, saves the required values in the arrays
FOR lWord = lIni TO TopCount
strTopWords(lWord) = strWords(lWord)
lTopWords(lWord) = lWords(lWord)
NEXT lWord

' Now, saves the order from the file
OPEN strFileR + "C" + LTRIM$(STR$(iRuns)) FOR OUTPUT AS #iFile
FOR lWord = lIni TO lEnd
WRITE #iFile, strWords(lWord), lWords(lWord)
NEXT lWord
CLOSE #iFile

END SUB

Output:
This program will count how many words are in a text file and shows the 10
most used of them.

Document to open (TXT file) (f=see files): miserabl.txt
Looking for words in miserabl.txt. File size: 3369775. Initialization complete.

Processing... Done!
Lines analyzed : 72917
% of completion: 100%
Words found....: 23288

Finishing...

Lenght of file: 3369775
Time lapse....: 35 seconds
Words found...: 578614 (Unique: 23538)
Longest word..: 25

The 10 most used are:
---------------------------+------------------------------------------------------------------------
Word                       | Count
---------------------------+------------------------------------------------------------------------
THE                        |     40,751
OF                         |     19,949
AND                        |     14,891
A                          |     14,430
TO                         |     13,923
IN                         |     11,189
HE                         |      9,605
WAS                        |      8,617
THAT                       |      7,833
IT                         |      6.579
---------------------------+------------------------------------------------------------------------
See files miserabl.S1 and miserabl.C1 to see the full list.

Try again? (Y/n)


### BaCon

Removing all punctuation, digits, tabs and carriage returns. So "This", "this" and "this." are the same. Full support for UTF8 characters in words. The code itself could be smaller, but for sake of clarity all has been written explicitly.

' We do not count superfluous spaces as words
OPTION COLLAPSE TRUE

' Optional: use TRE regex library to speed up the program
PRAGMA RE tre INCLUDE <tre/regex.h> LDFLAGS -ltre

' We're using associative arrays
DECLARE frequency ASSOC NUMBER

' Load the text and remove all punctuation, digits, tabs and cr
book$= EXTRACT$(LOAD$("miserables.txt"), "[[:punct:]]|[[:digit:]]|[\t\r]", TRUE) ' Count each word in lowercase FOR word$ IN REPLACE$(book$, NL$, CHR$(32))
INCR frequency(LCASE$(word$))
NEXT

' Sort the associative array and then map the index to a string array
LOOKUP frequency TO term$SIZE x SORT DOWN ' Show results FOR i = 0 TO 9 PRINT term$[i], " : ", frequency(term$[i]) NEXT Output: the : 40440 of : 19903 and : 14738 a : 14306 to : 13630 in : 11083 he : 9452 was : 8605 that : 7535 his : 6434  ## Batch File This takes a very long time per word thus I have chosen to feed it a 200 line sample and go from there. You could cut the length of this down drastically if you didn't need to be able to recall the word at nth position and wished only to display the top 10 words. @echo off call:wordCount 1 2 3 4 5 6 7 8 9 10 42 101 pause>nul exit :wordCount setlocal enabledelayedexpansion set word=100000 set line=0 for /f "delims=" %%i in (input.txt) do ( set /a line+=1 for %%j in (%%i) do ( if not !skip%%j!==true ( echo line !line! ^| word !word:~-5! - "%%~j" type input.txt | find /i /c "%%~j" > count.tmp set /p tmpvar=<count.tmp set tmpvar=000000000!tmpvar! set tmpvar=!tmpvar:~-10! set count[!word!]=!tmpvar! %%~j set "skip%%j=true" set /a word+=1 ) ) ) del count.tmp set wordcount=0 for /f "tokens=1,2 delims= " %%i in ('set count ^| sort /+14 /r') do ( set /a wordcount+=1 for /f "tokens=2 delims==" %%k in ("%%i") do ( set word[!wordcount!]=!wordcount!. %%j - %%k ) ) cls for %%i in (%*) do echo !word[%%i]! endlocal goto:eof Output 1. - 0000000140 I 2. - 0000000140 a 3. - 0000000118 He 4. - 0000000100 the 5. - 0000000080 an 6. - 0000000075 in 7. - 0000000066 at 8. - 0000000062 is 9. - 0000000058 on 10. - 0000000058 as 42. - 0000000010 with 101. - 0000000004 ears  ## Bracmat This solution assumes that words consists of characters that exist in a lowercase and a highercase version. So it won't work with many non-European alphabets. The built-in vap function can take either two or three arguments. The first argument must be the name of a function or a function definition. The second argument must be a string. The two-argument version maps the function to each character in the string. The three-argument version splits the string at each occurrence of the third argument, which must be a single character, and applies the function to the intervening substrings. The output of vap is a space-separated list of results from the function argument. The expression !('($arg:?A [($pivot) ?Z)) must be read as follows: The subexpression '($arg:?A [($pivot) ?Z) is a macro expression. The symbols arg and pivot, which are the right hand sides of $ operators with empty left hand side, are replaced by the actual values of !arg and !pivot. The whole subexpression is made the right hand side of a = operator with empty left hand side, e.g. =a b c d e:?A [2 ?Z. The = operator protects the subexpression against evaluation. By prefixing the expression with the ! unary operator (which normally is used to obtain the value of a variable), the pattern match operation a b c d e:?A [2 ?Z is executed, assigning a b to A and assigning c d e to Z.

The reason for using a macro expression is that the evaluation of a pattern match operation with pattern variable as in !arg:?A [!pivot ?Z is unecessary slow, since !pivot is evaluated up to !pivot+1 times.

  ( 10-most-frequent-words
=     MergeSort                        { Local variable declarations. }
types
sorted-words
frequency
type
most-frequent-words
.   ( MergeSort                      { Definition of function MergeSort. }
=   A N Z pivot
.   !arg:? [?N                 { [?N is a subpattern that counts the number of preceding elements }
& (   !N:>1                           { if N at least 2 ... }
& div$(!N.2):?pivot { divide N by 2 ... } & !('($arg:?A [($pivot) ?Z)) { split list in two halves A and Z ... } & MergeSort$!A+MergeSort$!Z { sort each of A and Z and return sum } | !arg { else just return a single element} ) ) & MergeSort { Sort }$ ( vap                 { Split second argument at each occurrence of third character and apply first argument to each chunk. }
$( (=.low$!arg)      { Return input, lowercased. }
.   str
$( vap { Vaporize second argument in UTF-8 or Latin-1 characters and apply first argument to each of them. }$ ( (
=
.   upp$!arg:low$!arg&\n  { Return newline instead of non-alphabetic character. }
| !arg                  { Return (Euro-centric) alphabetic character.}
)
. get$(!arg,NEW STR) { Read input text as a single string. } ) ) . \n { Split at newlines } ) ) : ?sorted-words { Assign sum of (frequency*lowercasedword) terms to sorted-words. } & :?types { Initialize types as an empty list. } & whl { Loop until right hand side fails. } ' ( !sorted-words:#?frequency*%@?type+?sorted-words { Extract first frequency*type term from sum. } & (!frequency.!type) !types:?types { Prepend (frequency.type) pair to types list} ) & MergeSort$!types                                     { Sort the list of (frequency.type) pairs. }
: (?+[-11+?most-frequent-words|?most-frequent-words)   { Pick the last 10 terms from the sum returned by MergeSort. }
& !most-frequent-words                                   { Return the last 10 terms. }
)
& out$(10-most-frequent-words$"135-0.txt")      { Call 10-most-frequent-words with name of inout file and print result to screen. }

Output

  (6661.it)
+ (7924.that)
+ (8622.was)
+ (9649.he)
+ (11219.in)
+ (13953.to)
+ (14546.a)
+ (14943.and)
+ (19954.of)
+ (41092.the)

## C

Library: GLib

Words are defined by the regular expression "\w+".

#include <stdbool.h>
#include <stdio.h>
#include <glib.h>

typedef struct word_count_tag {
const char* word;
size_t count;
} word_count;

int compare_word_count(const void* p1, const void* p2) {
const word_count* w1 = p1;
const word_count* w2 = p2;
if (w1->count > w2->count)
return -1;
if (w1->count < w2->count)
return 1;
return 0;
}

bool get_top_words(const char* filename, size_t count) {
GError* error = NULL;
GMappedFile* mapped_file = g_mapped_file_new(filename, FALSE, &error);
if (mapped_file == NULL) {
fprintf(stderr, "%s\n", error->message);
g_error_free(error);
return false;
}
const char* text = g_mapped_file_get_contents(mapped_file);
if (text == NULL) {
fprintf(stderr, "File %s is empty\n", filename);
g_mapped_file_unref(mapped_file);
return false;
}
gsize file_size = g_mapped_file_get_length(mapped_file);
// Store word counts in a hash table
GHashTable* ht = g_hash_table_new_full(g_str_hash, g_str_equal,
g_free, g_free);
GRegex* regex = g_regex_new("\\w+", 0, 0, NULL);
GMatchInfo* match_info;
g_regex_match_full(regex, text, file_size, 0, 0, &match_info, NULL);
while (g_match_info_matches(match_info)) {
char* word = g_match_info_fetch(match_info, 0);
char* lower = g_utf8_strdown(word, -1);
g_free(word);
size_t* count = g_hash_table_lookup(ht, lower);
if (count != NULL) {
++*count;
g_free(lower);
} else {
count = g_new(size_t, 1);
*count = 1;
g_hash_table_insert(ht, lower, count);
}
g_match_info_next(match_info, NULL);
}
g_match_info_free(match_info);
g_regex_unref(regex);
g_mapped_file_unref(mapped_file);

// Sort words in decreasing order of frequency
size_t size = g_hash_table_size(ht);
word_count* words = g_new(word_count, size);
GHashTableIter iter;
gpointer key, value;
g_hash_table_iter_init(&iter, ht);
for (size_t i = 0; g_hash_table_iter_next(&iter, &key, &value); ++i) {
words[i].word = key;
words[i].count = *(size_t*)value;
}
qsort(words, size, sizeof(word_count), compare_word_count);

// Print the most common words
if (count > size)
count = size;
printf("Top %lu words\n", count);
printf("Rank\tCount\tWord\n");
for (size_t i = 0; i < count; ++i)
printf("%lu\t%lu\t%s\n", i + 1, words[i].count, words[i].word);
g_free(words);
g_hash_table_destroy(ht);
return true;
}

int main(int argc, char** argv) {
if (argc != 2) {
fprintf(stderr, "usage: %s file\n", argv);
return EXIT_FAILURE;
}
if (!get_top_words(argv, 10))
return EXIT_FAILURE;
return EXIT_SUCCESS;
}

Output:
Top 10 words
Rank	Count	Word
1	41039	the
2	19951	of
3	14942	and
4	14527	a
5	13941	to
6	11209	in
7	9646	he
8	8620	was
9	7922	that
10	6659	it


## C#

Translation of: D
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text.RegularExpressions;

namespace WordCount {
class Program {
static void Main(string[] args) {

var match = Regex.Match(text, "\\w+");
Dictionary<string, int> freq = new Dictionary<string, int>();
while (match.Success) {
string word = match.Value;
if (freq.ContainsKey(word)) {
freq[word]++;
} else {
}

match = match.NextMatch();
}

Console.WriteLine("Rank  Word  Frequency");
Console.WriteLine("====  ====  =========");
int rank = 1;
foreach (var elem in freq.OrderByDescending(a => a.Value).Take(10)) {
Console.WriteLine("{0,2}    {1,-4}    {2,5}", rank++, elem.Key, elem.Value);
}
}
}
}

Output:
Rank  Word  Frequency
====  ====  =========
1    the     41035
2    of      19946
3    and     14940
4    a       14577
5    to      13939
6    in      11204
7    he       9645
8    was      8619
9    that     7922
10    it       6659

## C++

#include <algorithm>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <iterator>
#include <string>
#include <unordered_map>
#include <vector>

int main(int ac, char** av) {
std::ios::sync_with_stdio(false);
int head = (ac > 1) ? std::atoi(av) : 10;
std::istreambuf_iterator<char> it(std::cin), eof;
std::filebuf file;
if (ac > 2) {
if (file.open(av, std::ios::in), file.is_open()) {
it = std::istreambuf_iterator<char>(&file);
} else return std::cerr << "file " << av << " open failed\n", 1;
}
auto alpha = [](unsigned c) { return c-'A' < 26 || c-'a' < 26; };
auto lower = [](unsigned c) { return c | '\x20'; };
std::unordered_map<std::string, int> counts;
std::string word;
for (; it != eof; ++it) {
if (alpha(*it)) {
word.push_back(lower(*it));
} else if (!word.empty()) {
++counts[word];
word.clear();
}
}
if (!word.empty()) ++counts[word]; // if file ends w/o ws
std::vector<std::pair<const std::string,int> const*> out;
for (auto& count : counts) out.push_back(&count);
std::partial_sort(out.begin(),
out.end(), [](auto const* a, auto const* b) {
return a->second > b->second;
});
for (auto const& count : out) {
std::cout << count->first << ' ' << count->second << '\n';
}
return 0;
}

Output:
CALL "C$DELETE" USING "Output.txt" ,0 STOP RUN. Parse-a-Words. INSPECT Input-Record CONVERTING '-.,"();:/[]{}!?|' TO SPACE PERFORM UNTIL Pos > FUNCTION STORED-CHAR-LENGTH(Input-Record) UNSTRING Input-Record DELIMITED BY SPACE INTO Word1 WITH POINTER Pos TALLYING IN Cnt MOVE FUNCTION TRIM(FUNCTION LOWER-CASE(Word1)) TO Word-Record IF Word-Record NOT EQUAL SPACES AND Word-Record IS ALPHABETIC THEN WRITE Word-Record END-IF END-PERFORM. Collect-Totals. MOVE 'F' to Eof OPEN INPUT Word-File OPEN OUTPUT Output-File READ Word-File MOVE Input-Word TO Current-Word MOVE 1 to Current-Word-Cnt PERFORM UNTIL Eof = 'T' READ Word-File AT END MOVE 'T' TO Eof END-READ IF FUNCTION TRIM(Word-Record) EQUAL FUNCTION TRIM(Current-Word) THEN ADD 1 to Current-Word-Cnt ELSE MOVE Current-Word TO Output-Rec-Word MOVE Current-Word-Cnt TO Output-Rec-Word-Cnt WRITE Output-Rec MOVE 1 to Current-Word-Cnt MOVE Word-Record TO Current-Word MOVE SPACES TO Input-Record END-IF END-PERFORM. CLOSE Word-File Output-File. END-PROGRAM.  Output:  Rank Word Frequency 1 the 40551 2 of 19806 3 and 14730 4 a 14351 5 to 13775 6 in 11074 7 he 09480 8 was 08613 9 that 07632 10 his 06446 11 it 06335 12 had 06181 13 is 06097 14 which 05135 15 with 04469  ## Common Lisp (defun count-word (n pathname) (with-open-file (s pathname :direction :input) (loop for line = (read-line s nil nil) while line nconc (list-symb (drop-noise line)) into words finally (return (subseq (sort (pair words) #'> :key #'cdr) 0 n))))) (defun list-symb (s) (let ((*read-eval* nil)) (read-from-string (concatenate 'string "(" s ")")))) (defun drop-noise (s) (delete-if-not #'(lambda (x) (or (alpha-char-p x) (equal x #\space) (equal x #\-))) s)) (defun pair (words &aux (hash (make-hash-table)) ac) (dolist (word words) (incf (gethash word hash 0))) (maphash #'(lambda (e n) (push (,e . ,n) ac)) hash) ac)  Output: > (count-word 10 "c:/temp/135-0.txt") ((THE . 40738) (OF . 19922) (AND . 14878) (A . 14419) (TO . 13702) (IN . 11172) (HE . 9577) (WAS . 8612) (THAT . 7768) (IT . 6467))  ## Crystal require "http/client" require "regex" # Get the text from the internet response = HTTP::Client.get "https://www.gutenberg.org/files/135/135-0.txt" text = response.body text .downcase .scan(/[a-zA-ZáéíóúÁÉÍÓÚâêôäüöàèìòùñ']+/) .reduce({} of String => Int32) { |hash, match| word = match hash[word] = hash.fetch(word, 0) + 1 # using fetch to set a default value (1) to the new found word hash } .to_a # convert the returned hash to an array of tuples (String, Int32) -> {word, sum} .sort { |a, b| b <=> a }[0..9] # sort and get the first 10 elements .each_with_index(1) { |(word, n), i| puts "#{i} \t #{word} \t #{n}" } # print the result  Output: 1 the 41092 2 of 19954 3 and 14943 4 a 14556 5 to 13953 6 in 11219 7 he 9649 8 was 8622 9 that 7924 10 it 6661  ## D import std.algorithm : sort; import std.array : appender, split; import std.range : take; import std.stdio : File, writefln, writeln; import std.typecons : Tuple; import std.uni : toLower; //Container for a word and how many times it has been seen alias Pair = Tuple!(string, "k", int, "v"); void main() { int[string] wcnt; //Read the file line by line foreach (line; File("135-0.txt").byLine) { //Split the words on whitespace foreach (word; line.split) { //Increment the times the word has been seen wcnt[word.toLower.idup]++; } } //Associative arrays cannot be sort, so put the key/value in an array auto wb = appender!(Pair[]); foreach(k,v; wcnt) { wb.put(Pair(k,v)); } Pair[] sw = wb.data.dup; //Sort the array, and display the top ten values writeln("Rank Word Frequency"); int rank=1; foreach (word; sw.sort!"a.v>b.v".take(10)) { writefln("%4s %-10s %9s", rank++, word.k, word.v); } }  Output: Rank Word Frequency 1 the 40368 2 of 19863 3 and 14470 4 a 14277 5 to 13587 6 in 11019 7 he 9212 8 was 8346 9 that 7251 10 his 6414 ## Delphi Translation of: C# program Word_frequency; {$APPTYPE CONSOLE}

uses
System.SysUtils,
System.IOUtils,
System.Generics.Collections,
System.Generics.Defaults,
System.RegularExpressions;

type
TWords = TDictionary<string, Integer>;

TFreqPair = TPair<string, Integer>;

TFreq = TArray<TFreqPair>;

function CreateValueCompare: IComparer<TFreqPair>;
begin
Result := TComparer<TFreqPair>.Construct(
function(const Left, Right: TFreqPair): Integer
begin
Result := Right.Value - Left.Value;
end);
end;

function WordFrequency(const Text: string): TFreq;
var
words: TWords;
match: TMatch;
w: string;
begin
words := TWords.Create();
match := TRegEx.Match(Text, '\w+');
while match.Success do
begin
w := match.Value;
if words.ContainsKey(w) then
words[w] := words[w] + 1
else
match := match.NextMatch();
end;

Result := words.ToArray;
words.Free;
TArray.Sort<TFreqPair>(Result, CreateValueCompare);
end;

var
Text: string;
rank: integer;
Freq: TFreq;
w: TFreqPair;

begin

Freq := WordFrequency(Text);

Writeln('Rank  Word  Frequency');
Writeln('====  ====  =========');

for rank := 1 to 10 do
begin
w := Freq[rank - 1];
Writeln(format('%2d   %6s   %5d', [rank, w.Key, w.Value]));
end;

end.

Output:
Rank  Word  Frequency
====  ====  =========
1      the   41040
2       of   19951
3      and   14942
4        a   14539
5       to   13941
6       in   11209
7       he    9646
8      was    8620
9     that    7922
10       it    6659


## F#

open System.IO
open System.Text.RegularExpressions
[for n in g do yield n.Value.ToLower()]|>List.countBy(id)|>List.sortBy(fun n->(-(snd n)))|>List.take 10|>List.iter(fun n->printfn "%A" n)

Output:
("the", 41088)
("of", 19949)
("and", 14942)
("a", 14596)
("to", 13951)
("in", 11214)
("he", 9648)
("was", 8621)
("that", 7924)
("it", 6661)


## Factor

This program expects stdin to read from a file via the command line. ( e.g. invoking the program in Windows: >factor word-count.factor < input.txt ) The definition of a word here is simply any string surrounded by some combination of spaces, punctuation, or newlines.

USING: ascii io math.statistics prettyprint sequences
splitting ;
IN: rosetta-code.word-count

lines " " join " .,?!:;()\"-" split harvest [ >lower ] map

Output:
{
{ "the" 41021 }
{ "of" 19945 }
{ "and" 14938 }
{ "a" 14522 }
{ "to" 13938 }
{ "in" 11201 }
{ "he" 9600 }
{ "was" 8618 }
{ "that" 7822 }
{ "it" 6532 }
}


## FreeBASIC

 #Include "file.bi"
type tally
as string s
as long l
end type

Sub quicksort(array() As String,begin As Long,Finish As Long)
Dim As Long i=begin,j=finish
Dim As String x =array(((I+J)\2))
While I <= J
While array(I) < X :I+=1:Wend
While array(J) > X :J-=1:Wend
If I<=J Then Swap array(I),array(J): I+=1:J-=1
Wend
If J >begin Then quicksort(array(),begin,J)
If I <Finish Then quicksort(array(),I,Finish)
End Sub

Sub tallysort(array() As tally,begin As Long,Finish As long)
Dim As Long i=begin,j=finish
Dim As tally x =array(((I+J)\2))
While I <= J
While array(I).l > X .l:I+=1:Wend
While array(J).l < X .l:J-=1:Wend
If I<=J Then Swap array(I),array(J): I+=1:J-=1
Wend
If J >begin Then tallysort(array(),begin,J)
If I <Finish Then tallysort(array(),I,Finish)
End Sub

Function loadfile(file As String) As String
Dim As Long  f=Freefile
Open file For Binary Access Read As #f
Dim As String text
If Lof(f) > 0 Then
text = String(Lof(f), 0)
Get #f, , text
End If
Close #f
Return text
End Function

Function String_Split(s_in As String,chars As String,result() As String) As Long
Dim As Long ctr,ctr2,k,n,LC=Len(chars)
Dim As boolean tally(Len(s_in))
#macro check_instring()
n=0
While n<Lc
If chars[n]=s_in[k] Then
tally(k)=true
If (ctr2-1) Then ctr+=1
ctr2=0
Exit While
End If
n+=1
Wend
#endmacro

#macro splice()
If tally(k) Then
If (ctr2-1) Then ctr+=1:result(ctr)=Mid(s_in,k+2-ctr2,ctr2-1)
ctr2=0
End If
#endmacro
'==================  LOOP TWICE =======================
For k  =0 To Len(s_in)-1
ctr2+=1:check_instring()
Next k
If ctr=0 Then
If Len(s_in) Andalso Instr(chars,Chr(s_in)) Then ctr=1':
End If
If ctr Then Redim result(1 To ctr): ctr=0:ctr2=0 Else  Return 0
For k  =0 To Len(s_in)-1
ctr2+=1:splice()
Next k
'===================== Last one ========================
If ctr2>0 Then
Redim Preserve result(1 To ctr+1)
result(ctr+1)=Mid(s_in,k+1-ctr2,ctr2)
End If

Return Ubound(result)
End Function

Redim As String s()
redim as tally t()
dim as string p1,p2,deliminators
dim as long count,jmp
dim as double tm=timer

L=lcase(L)
'get deliminators
for n as long=1 to 96
p1+=chr(n)
next
for n as long=123 to 255
p2+=chr(n)
next

deliminators=p1+p2

string_split(L,deliminators,s())

quicksort(s(),lbound(s),ubound(s))

For n As Long=lbound(s)  To ubound(s)-1
if s(n+1)=s(n) then jmp+=1
if s(n+1)<>s(n) then
count+=1
redim preserve t(1 to count)
t(count).s=s(n)
t(count).l=jmp
jmp=0
end if
Next

tallysort(t(),lbound(t),ubound(t))'sort by frequency
print "frequency","word"
print
for n as long=lbound(t) to lbound(t)+9
print t(n).l,t(n).s
next

Print
print "time for operation  ";timer-tm;" seconds"
sleep
Output:
I saved and reloaded the file as ascii text.
frequency     word

41098        the
19955        of
14939        and
14557        a
13953        to
11219        in
9648         he
8621         was
7923         that
6660         it

time for operation   1.099869600031525 seconds



## Frink

This example shows some of the subtle and non-obvious power of Frink in processing text files in a language-aware and Unicode-aware fashion:

• Frink has a Unicode-aware function, wordList[str], which intelligently enumerates through the words in a string (and correctly handles compound words, hyphenated words, accented characters, etc.) It returns words, spaces, and punctuation marks separately. For the purposes of this program, "words" that do not contain any alphanumeric characters (as decided by the Unicode standard) are filtered out. These are likely punctuation and spaces. There is also a two-argument function, wordList[str, lang] which allows you to specify a language code e.g. "fr" to use the rules of French (or many other human languages) to perform correct word-breaking according to the rules of that language!
• The file fetched from Project Gutenberg is supposed to be encoded in UTF-8 character encoding, but their servers incorrectly send either that it is Windows-1252 encoded or send no character encoding at all, so this program fixes that.
• Frink has a Unicode-aware lowercase function, lc[str] that correctly handles accented characters and may even make a string longer.
• Frink can normalize Unicode characters with its normalizeUnicode function so the same word encoded two different ways in Unicode can be treated consistently. For example, a Unicode string can use various methods to encode what is essentially the same character/glyph. For example, the character ô can be represented as either "\u00F4" or "\u006F\u0302". The former is a "precomposed" character, "LATIN SMALL LETTER O WITH CIRCUMFLEX", and the latter is two Unicode codepoints, an o (LATIN SMALL LETTER O) followed by "COMBINING CIRCUMFLEX ACCENT". (This is usually referred to as a "decomposed" representation.) Unicode normalization rules can convert these "equivalent" encodings into a canonical representation. This makes two different strings which look equivalent to a human (but are very different in their codepoints) be treated as the same to a computer, and these programs will count them the same. Even if the Project Gutenberg document uses precomposed and decomposed representations for the same words, this program will fix it and count them the same! See the [Unicode Normal Forms] specification for more about these normalization rules. Frink implements all of them (NFC, NFD, NFKC, NFKD). NFC is the default in normalizeUnicode[str, encoding=NFC]. They're interesting!

How many other languages in this page do all or any of this correctly?

There are two sample programs below. First, a simple but powerful method that works in old versions of Frink:

d = new dict
for w = select[wordList[read[normalizeUnicode["https://www.gutenberg.org/files/135/135-0.txt", "UTF-8"]]], %r/[[:alnum:]]/ ]
d.increment[lc[w], 1]

println[join["\n", first[reverse[sort[array[d], {|a,b| a@1 <=> b@1}]], 10]]]
Output:
[the, 40802]
[of, 19933]
[and, 14924]
[a, 14450]
[to, 13719]
[in, 11184]
[he, 9636]
[was, 8617]
[that, 7901]
[it, 6641]


Next, a "showing off" one-liner that works in recent versions of Frink that uses the countToArray function which easily creates sorted frequency lists and the formatTable function that formats into a nice table with columns lined up, and still performs full Unicode-aware normalization, capitalization, and word-breaking:

formatTable[first[countToArray[select[wordList[lc[normalizeUnicode[read["https://www.gutenberg.org/files/135/135-0.txt", "UTF-8"]]]], %r/[[:alnum:]]/ ]], 10], "right"]
Output:
 the 36629
of 19602
and 14063
a 13447
to 13345
in 10259
was  8541
that  7303
he  6812


## FutureBasic

Task said: "Feel free to explicitly state the thoughts behind the program decisions." Thus the heavy comments.

include "NSLog.incl"

local fn WordFrequency( textStr as CFStringRef, caseSensitive as Boolean, ascendingOrder as Boolean ) as CFStringRef
'~'1
CFStringRef     wrd
CFDictionaryRef dict

// Depending on the value of the caseSensitive Boolean function parameter above, lowercase incoming text
if caseSensitive == NO then textStr = fn StringLowercaseString( textStr )

// Trim non-alphabetic characters from string, and separate individual words with a space
CFStringRef tempStr = fn ArrayComponentsJoinedByString( fn StringComponentsSeparatedByCharactersInSet( textStr, fn CharacterSetInvertedSet( fn CharacterSetLetterSet ) ), @" " )

// Prepare separators to parse string into array
CFMutableCharacterSetRef separators = fn MutableCharacterSetInit

// Informally, this set is the set of all non-whitespace characters used to separate linguistic units in scripts, such as periods, dashes, parentheses, and so on.
MutableCharacterSetFormUnionWithCharacterSet( separators, fn CharacterSetPunctuationSet )

// A character set containing all the whitespace and newline characters including characters in Unicode General Category Z*, U+000A U+000D, and U+0085.
MutableCharacterSetFormUnionWithCharacterSet( separators, fn CharacterSetWhitespaceAndNewlineSet )

// Create array of separated words
CFArrayRef tempArr = fn StringComponentsSeparatedByCharactersInSet( tempStr, separators )

// Create a counted set with each word and its frequency
CountedSetRef freqencies = fn CountedSetWithArray( tempArr )

// Enumerate each word-frequency pair in the counted set...
EnumeratorRef enumRef = fn CountedSetObjectEnumerator( freqencies )

// .. and use it to create array of words in counted set
CFArrayRef array = fn EnumeratorAllObjects( enumRef )

// Create an empty mutable array
CFMutableArrayRef wordArr = fn MutableArrayWithCapacity( 0 )

// Create word counter
NSInteger totalWords = 0
// Enumerate each unique word, get its frequency, create its own key/value pair dictionary, add each dictionary into master array
for wrd in array
totalWords++
// Create dictionary with frequency and matching word
dict = @{ @"count":fn NumberWithUnsignedInteger( fn CountedSetCountForObject( freqencies, wrd ) ), @"object":wrd }
// Add each dictionary to the master mutable array, checking for a valid word by length
if ( fn StringLength( wrd ) != 0 )
end if
next

// Store the total words as a global application property
AppSetProperty( @"totalWords", fn StringWithFormat( @"%d", totalWords - 1 ) )

// Sort the array in ascending or descending order as determined by the ascendingOrder Boolean function input parameter
SortDescriptorRef descriptors = fn SortDescriptorWithKey( @"count", ascendingOrder )
CFArrayRef sortedArray = fn ArraySortedArrayUsingDescriptors( wordArr, @[descriptors] )

// Create an empty mutable string
CFMutableStringRef mutStr = fn MutableStringWithCapacity( 0 )

// Use each dictionary in sorted array to build the formatted output string
NSInteger count = 1
for dict in sortedArray
MutableStringAppendString( mutStr, fn StringWithFormat( @"%-7d %-7lu %@\n", count, fn StringIntegerValue( fn DictionaryValueForKey( dict, @"count" ) ), fn DictionaryValueForKey( dict, @"object"  ) ) )
count++
next

// Create an immutable output string from mutable the string
CFStringRef resultStr = fn StringWithFormat( @"%@", mutStr )
end fn = resultStr

local fn ParseTextFromWebsite( webSite as CFStringRef )
// Convert incoming string to URL
CFURLRef textURL = fn URLWithString( webSite )
// Read contents of URL into a string
CFStringRef textStr = fn StringWithContentsOfURL( textURL, NSUTF8StringEncoding, NULL )

// Start timer
CFAbsoluteTime startTime = fn CFAbsoluteTimeGetCurrent
// Calculate frequency of words in text and sort by occurrence
CFStringRef frequencyStr = fn WordFrequency( textStr, NO, NO )
// Log results and post post processing time
NSLogClear
NSLog( @"%@", frequencyStr )
NSLog( @"Total unique words in document: %@", fn AppProperty( @"totalWords" ) )
// Stop timer and log elapsed processing time
NSLog( @"Elapsed time: %f milliseconds.", ( fn CFAbsoluteTimeGetCurrent - startTime ) * 1000.0 )
end fn

dispatchglobal
// Pass url for Les Misérables on Project Gutenberg and parse in background
fn ParseTextFromWebsite( @"https://www.gutenberg.org/files/135/135-0.txt" )
dispatchend

HandleEvents
Output:
1       41095   the
2       19955   of
3       14939   and
4       14546   a
5       13954   to
6       11218   in
7       9649    he
8       8622    was
9       7924    that
10      6661    it
11      6470    his
12      6193    is

//-------------------

22900   1       millstones
22901   1       fumbles
22902   1       shunned
22903   1       avoids
22904   1       poitevin
22905   1       muleteer
22906   1       idolizes
22907   1       lapsed
22908   1       reptitalmus
22909   1       bled
22910   1       isabella

Total unique words in document: 22910
Elapsed time: 595.407963 milliseconds.


## Go

Translation of: Kotlin
package main

import (
"fmt"
"io/ioutil"
"log"
"regexp"
"sort"
"strings"
)

type keyval struct {
key string
val int
}

func main() {
reg := regexp.MustCompile(\p{Ll}+)
if err != nil {
log.Fatal(err)
}
text := strings.ToLower(string(bs))
matches := reg.FindAllString(text, -1)
groups := make(map[string]int)
for _, match := range matches {
groups[match]++
}
var keyvals []keyval
for k, v := range groups {
keyvals = append(keyvals, keyval{k, v})
}
sort.Slice(keyvals, func(i, j int) bool {
return keyvals[i].val > keyvals[j].val
})
fmt.Println("Rank  Word  Frequency")
fmt.Println("====  ====  =========")
for rank := 1; rank <= 10; rank++ {
word := keyvals[rank-1].key
freq := keyvals[rank-1].val
fmt.Printf("%2d    %-4s    %5d\n", rank, word, freq)
}
}

Output:
Rank  Word  Frequency
====  ====  =========
1    the     41088
2    of      19949
3    and     14942
4    a       14596
5    to      13951
6    in      11214
7    he       9648
8    was      8621
9    that     7924
10    it       6661


## Groovy

Solution:

def topWordCounts = { String content, int n ->
def mapCounts = [:]
content.toLowerCase().split(/\W+/).each {
mapCounts[it] = (mapCounts[it] ?: 0) + 1
}
def top = (mapCounts.sort { a, b -> b.value <=> a.value }.collect{ it })[0..<n]
println "Rank Word Frequency\n==== ==== ========="
(0..<n).each { printf ("%4d %-4s %9d\n", it+1, top[it].key, top[it].value) }
}


Test:

def rawText = "http://www.gutenberg.org/files/135/135-0.txt".toURL().text
topWordCounts(rawText, 10)


Output:

Rank Word Frequency
==== ==== =========
1 the      41036
2 of       19946
3 and      14940
4 a        14589
5 to       13939
6 in       11204
7 he        9645
8 was       8619
9 that      7922
10 it        6659

### Lazy IO with pure Map, arrows

Translation of: Clojure
module Main where

import Control.Category   -- (>>>)
import Data.Char          -- toLower, isSpace
import Data.List          -- sortBy, (Foldable(foldl')), filter -- '
import Data.Ord           -- Down
import System.IO          -- stdin, ReadMode, openFile, hClose
import System.Environment -- getArgs

-- containers
import Data.Map.Strict (Map)
import qualified Data.Map.Strict as M
import qualified Data.IntMap.Strict as IM

-- text
import Data.Text (Text)
import qualified Data.Text as T
import qualified Data.Text.IO as T

frequencies :: Ord a => [a] -> Map a Integer
frequencies = foldl' (\m k -> M.insertWith (+) k 1 m) M.empty -- '
{-# SPECIALIZE frequencies :: [Text] -> Map Text Integer #-}

main :: IO ()
main = do
args <- getArgs
(n,hand,filep) <- case length args of
0 -> return (10,stdin,False)
1 -> return (read $head args,stdin,False) _ -> let (ns:fp:_) = args in fmap (\h -> (read ns,h,True)) (openFile fp ReadMode) T.hGetContents hand >>= (T.map toLower >>> T.split isSpace >>> filter (not <<< T.null) >>> frequencies >>> M.toList >>> sortBy (comparing (Down <<< snd)) -- sort the opposite way >>> take n >>> print) when filep (hClose hand)  Output: $ ./word_count 10 < ~/doc/les_miserables*
[("the",40368),("of",19863),("and",14470),("a",14277),("to",13587),("in",11019),("he",9212),("was",8346),("that",7251),("his",6414)]


### Lazy IO, map of IORefs

Using IORefs as values in the map seems to give a ~2x speedup on large files. The below code is based on https://github.com/composewell/streamly-examples/blob/master/examples/WordFrequency.hs , but still using lazy IO to avoid the extra library dependency (in production you should use a streaming library like streamly/conduit/io-streams):

module Main where

import Data.Char          (isSpace, toLower)
import Data.List          (sortOn, filter)
import Data.Ord           (Down(..))
import System.IO          (stdin, IOMode(..), openFile, hClose)
import System.Environment (getArgs)
import Data.IORef         (IORef(..), newIORef, readIORef, modifyIORef') -- '

-- containers
import Data.HashMap.Strict (HashMap)
import qualified Data.HashMap.Strict as M

-- text
import Data.Text (Text)
import qualified Data.Text as T
import qualified Data.Text.IO as T

frequencies :: [Text] -> IO (HashMap Text (IORef Int))
frequencies = foldM (flip (M.alterF alter)) M.empty
where
alter Nothing    = Just <$> newIORef (1 :: Int) alter (Just ref) = modifyIORef' ref (+ 1) >> return (Just ref) -- ' main :: IO () main = do args <- getArgs when (length args /= 1) (error "expecting 1 arg (number of words to print)") let maxw = read$ head args -- no error handling, to simplify the example
T.hGetContents stdin >>= \contents -> do
freqtable <- frequencies $filter (not . T.null)$ T.split isSpace $T.map toLower contents counts <- let readRef (w, ref) = do cnt <- readIORef ref return (w, cnt) in mapM readRef$ M.toList freqtable
print $take maxw$ sortOn (Down . snd) counts

Output:
$./word_count 10 < ~/doc/les_miserables* [("the",40378),("of",19869),("and",14468),("a",14278),("to",13590),("in",11025),("he",9213),("was",8347),("that",7249),("his",6414)]  ### Lazy IO, short code, but not streaming Or, perhaps a little more simply, though not streaming (will read everything into memory, don't use on big files): import qualified Data.Text.IO as T import qualified Data.Text as T import Data.List (group, sort, sortBy) import Data.Ord (comparing) frequentWords :: T.Text -> [(Int, T.Text)] frequentWords = sortBy (flip$ comparing fst) .
fmap ((,) . length <*> head) . group . sort . T.words . T.toLower

main :: IO ()
main = T.readFile "miserables.txt" >>= (mapM_ print . take 10 . frequentWords)

Output:
(40370,"the")
(19863,"of")
(14470,"and")
(14277,"a")
(13587,"to")
(11019,"in")
(9212,"he")
(8346,"was")
(7251,"that")
(6414,"his")

## J

Text acquisition: store the entire text from the web page http://www.gutenberg.org/files/135/135-0.txt (the plain text UTF-8 link) into a file. This linux example uses ~/downloads/books/LesMis.txt .

Program: Reading from left to right, 10 {. "ten take" from an array computed by words to the right. \:~ "sort descending" by items of the array computed by whatever is to the right. (#;{.)/.~ "tally linked with item" key ;: "words" parses the argument to its right as a j sentence. tolower changes to a common case

Hence the remainder of the j sentence must clean after loading the file.

The parenthesized expression (a.-.Alpha_j_,' ') computes to a vector of the j alphabet excluding [a-zA-Z ] ((e.&(a.-.Alpha_j_,' '))(,:&' '))} substitutes space character for the unwanted characters. 1!:1 reads the file named in the box <

   10{.\:~(#;{.)/.~;:tolower((e.&(a.-.Alpha_j_,' '))(,:&' '))}1!:1<jpath'~/downloads/books/LesMis.txt'
┌─────┬────┐
│41093│the │
├─────┼────┤
│19954│of  │
├─────┼────┤
│14943│and │
├─────┼────┤
│14558│a   │
├─────┼────┤
│13953│to  │
├─────┼────┤
│11219│in  │
├─────┼────┤
│9649 │he  │
├─────┼────┤
│8622 │was │
├─────┼────┤
│7924 │that│
├─────┼────┤
│6661 │it  │
└─────┴────┘



## Java

This is relatively simple in Java.
I used a URL class to download the content, a BufferedReader class to examine the text line-for-line, a Pattern and Matcher to identify words, and a Map to hold to values.

import java.io.BufferedReader;
import java.io.IOException;
import java.net.URI;
import java.net.URISyntaxException;
import java.net.URL;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

void printWordFrequency() throws URISyntaxException, IOException {
URL url = new URI("https://www.gutenberg.org/files/135/135-0.txt").toURL();
Pattern pattern = Pattern.compile("(\\w+)");
Matcher matcher;
String line;
String word;
Map<String, Integer> map = new HashMap<>();
matcher = pattern.matcher(line);
while (matcher.find()) {
word = matcher.group().toLowerCase();
if (map.containsKey(word)) {
map.put(word, map.get(word) + 1);
} else {
map.put(word, 1);
}
}
}
/* print out top 10 */
List<Map.Entry<String, Integer>> list = new ArrayList<>(map.entrySet());
list.sort(Map.Entry.comparingByValue());
Collections.reverse(list);
int count = 1;
for (Map.Entry<String, Integer> value : list) {
System.out.printf("%-20s%,7d%n", value.getKey(), value.getValue());
if (count++ == 10) break;
}
}
}

the                  41,043
of                   19,952
and                  14,938
a                    14,539
to                   13,942
in                   11,208
he                    9,646
was                   8,620
that                  7,922
it                    6,659


An alternate demonstration

Translation of: Kotlin
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.stream.Collectors;

public class WordCount {
public static void main(String[] args) throws IOException {
Path path = Paths.get("135-0.txt");
String text = new String(bytes);
text = text.toLowerCase();

Pattern r = Pattern.compile("\\p{javaLowerCase}+");
Matcher matcher = r.matcher(text);
Map<String, Integer> freq = new HashMap<>();
while (matcher.find()) {
String word = matcher.group();
Integer current = freq.getOrDefault(word, 0);
freq.put(word, current + 1);
}

List<Map.Entry<String, Integer>> entries = freq.entrySet()
.stream()
.sorted((i1, i2) -> Integer.compare(i2.getValue(), i1.getValue()))
.limit(10)
.collect(Collectors.toList());

System.out.println("Rank  Word  Frequency");
System.out.println("====  ====  =========");
int rank = 1;
for (Map.Entry<String, Integer> entry : entries) {
String word = entry.getKey();
Integer count = entry.getValue();
System.out.printf("%2d    %-4s    %5d\n", rank++, word, count);
}
}
}

Output:
Rank  Word  Frequency
====  ====  =========
1    the     41088
2    of      19949
3    and     14942
4    a       14596
5    to      13951
6    in      11214
7    he       9648
8    was      8621
9    that     7924
10    it       6661

## jq

The following solution uses the concept of a "bag of words" (bow), here realized as a JSON object with the words as keys and the frequency of a word as the corresponding value.

To avoid issues with case folding, the "letters" here just the alphabet and hyphen, but a "word" may not begin with hyphen. Thus "the-the" would count as one word, and "-the" would be excluded.

< 135-0.txt jq -nR --argjson n 10 '
def bow(stream):
reduce stream as $word ({}; .[($word|tostring)] += 1);

bow(inputs | gsub("[^-a-zA-Z]"; " ") | splits("  *") | ascii_downcase | select(test("^[a-z][-a-z]*$"))) | to_entries | sort_by(.value) | .[-$n :]
| reverse
| from_entries
'

#### Output

{
"the": 41087,
"of": 19937,
"and": 14932,
"a": 14552,
"to": 13738,
"in": 11209,
"he": 9649,
"was": 8621,
"that": 7923,
"it": 6661
}

## Julia

Works with: Julia version 1.0
using FreqTables

words = split(replace(txt, r"\P{L}"i => " "))
table = sort(freqtable(words); rev=true)
println(table[1:10])

Output:
Dim1   │
───────┼──────
"the"  │ 36671
"of"   │ 19618
"and"  │ 14081
"to"   │ 13541
"a"    │ 13529
"in"   │ 10265
"was"  │  8545
"that" │  7326
"he"   │  6816
"had"  │  6140

## K

Works with: ngn/k
common:{+((!d)o)!n@o:x#>n:#'.d:=("&"\c$"&"|_,/0:y)^,""} {(,'!x),'.x}common[10;"135-0.txt"] (("the";41019) ("of";19898) ("and";14658) (,"a";14517) ("to";13695) ("in";11134) ("he";9405) ("was";8361) ("that";7592) ("his";6446))  (The relatively easy to read output format here is arguably less useful than the table produced by common but it would have been more concise to have common generate it directly.) ## KAP The below program defines the function 'stats' which accepts a filename containing the text. ∇ stats (file) { content ← "[\\h,.\"'\n-]+" regex:split unicode:toLower io:readFile file sorted ← (⍋⊇⊢) content selection ← 1,2≢/sorted words ← selection / sorted {⍵[10↑⍒⍵[;1];]} words ,[0.5] ≢¨ sorted ⊂⍨ +\selection } Output: ┏━━━━━━━━━━━━┓ ┃ "the" 40387┃ ┃ "of" 19913┃ ┃ "and" 14742┃ ┃ "a" 14289┃ ┃ "to" 13819┃ ┃ "in" 11088┃ ┃ "he" 9430┃ ┃ "was" 8597┃ ┃"that" 7516┃ ┃ "his" 6435┃ ┗━━━━━━━━━━━━┛ ## Kotlin The author of the Raku entry has given a good account of the difficulties with this task and, in the absence of any clarification on the various issues, I've followed a similar 'literal' approach. So, after first converting the text to lower case, I've assumed that a word is any sequence of one or more lower-case Unicode letters and obtained the same results as the Raku version. There is no change in the results if the numerals 0-9 are also regarded as letters. // version 1.1.3 import java.io.File fun main(args: Array<String>) { val text = File("135-0.txt").readText().toLowerCase() val r = Regex("""\p{javaLowerCase}+""") val matches = r.findAll(text) val wordGroups = matches.map { it.value } .groupBy { it } .map { Pair(it.key, it.value.size) } .sortedByDescending { it.second } .take(10) println("Rank Word Frequency") println("==== ==== =========") var rank = 1 for ((word, freq) in wordGroups) System.out.printf("%2d %-4s %5d\n", rank++, word, freq) }  Output: Rank Word Frequency ==== ==== ========= 1 the 41088 2 of 19949 3 and 14942 4 a 14596 5 to 13951 6 in 11214 7 he 9648 8 was 8621 9 that 7924 10 it 6661  ## Liberty BASIC dim words$(100000,2)'words$(a,1)=the word, words$(a,2)=the count
dim lines$(150000) open "135-0.txt" for input as #txt while EOF(#txt)=0 and total < 150000 input #txt, lines$(total)
total=total+1
wend
for a = 1 to total
token$= "?" index=0 new=0 while token$ <> ""
new=0
index = index + 1
token$= lower$(word$(lines$(a),index))
token$=replstr$(token$,".","") token$=replstr$(token$,",","")
token$=replstr$(token$,";","") token$=replstr$(token$,"!","")
token$=replstr$(token$,"?","") token$=replstr$(token$,"-","")
token$=replstr$(token$,"_","") token$=replstr$(token$,"~","")
token$=replstr$(token$,"+","") token$=replstr$(token$,"0","")
token$=replstr$(token$,"1","") token$=replstr$(token$,"2","")
token$=replstr$(token$,"3","") token$=replstr$(token$,"4","")
token$=replstr$(token$,"5","") token$=replstr$(token$,"6","")
token$=replstr$(token$,"7","") token$=replstr$(token$,"8","")
token$=replstr$(token$,"9","") token$=replstr$(token$,"/","")
token$=replstr$(token$,"<","") token$=replstr$(token$,">","")
token$=replstr$(token$,":","") for b = 1 to newwordcount if words$(b,1)=token$then num=val(words$(b,2))+1
num$=str$(num)
if len(num$)=1 then num$="0000"+num$if len(num$)=2 then num$="000"+num$
if len(num$)=3 then num$="00"+num$if len(num$)=4 then num$="0"+num$
words$(b,2)=num$
new=1
exit for
end if
next b
if new<>1 then newwordcount=newwordcount+1:words$(newwordcount,1)=token$:words$(newwordcount,2)="00001":print newwordcount;" ";token$
wend
next a
print
sort words$(), 1, newwordcount, 2 print "Count Word" print "===== =================" for a = newwordcount to newwordcount-10 step -1 print words$(a,2);" ";words$(a,1) next a print "-----------------------" print newwordcount;" unique words found." print "End of program" close #txt end Output: Count Word ===== ================= 40292 the 19825 of 14703 and 14249 a 13594 to 122613 11061 in 09436 he 08579 was 07530 that 06428 his ----------------------- 29109 unique words found.  ## Lua Works with: lua version 5.3 -- This program takes two optional command line arguments. The first (arg) -- specifies the input file, or defaults to standard input. The second -- (arg) specifies the number of results to show, or defaults to 10. -- in freq, each key is a word and each value is its count local freq = {} for line in io.lines(arg) do -- %a stands for any letter for word in string.gmatch(string.lower(line), "%a+") do if not freq[word] then freq[word] = 1 else freq[word] = freq[word] + 1 end end end -- in array, each entry is an array whose first value is the count and whose -- second value is the word local array = {} for word, count in pairs(freq) do table.insert(array, {count, word}) end table.sort(array, function (a, b) return a > b end) for i = 1, arg or 10 do io.write(string.format('%7d %s\n', array[i] , array[i])) end  Output: ❯ ./wordcount.lua 135-0.txt 41093 the 19954 of 14943 and 14558 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it  Relevant documentation: io.lines gmatch patterns like %a ## Mathematica / Wolfram Language TakeLargest@WordCounts[Import["https://www.gutenberg.org/files/135/135-0.txt"], IgnoreCase->True]//Dataset  Output: the 41088 of 19936 and 14931 a 14536 to 13738 in 11208 he 9607 was 8621 that 7825 it 6535  ## MATLAB / Octave function [result,count] = word_frequency() URL='https://www.gutenberg.org/files/135/135-0.txt'; text=webread(URL); DELIMITER={' ', ',', ';', ':', '.', '/', '*', '!', '?', '<', '>', '(', ')', '[', ']','{', '}', '&','$','§','"','”','“','-','—','‘','\t','\n','\r'};
words  = sort(strsplit(lower(text),DELIMITER));
flag   = [find(~strcmp(words(1:end-1),words(2:end))),length(words)];
dwords = words(flag);   % get distinct words, and ...
count  = diff([0,flag]);  % ... the corresponding occurance frequency
[tmp,idx] = sort(-count);       % sort according to occurance
result = dwords(idx);
count  = count(idx);
for k  =  1:10,
fprintf(1,'%d\t%s\n',count(k),result{k})
end

Output:
41039   the
19950   of
14942   and
14523   a
13941   to
11208   in
9605    he
8620    was
7824    that
6533    it


## Nim

import tables, strutils, sequtils, httpclient

proc take[T](s: openArray[T], n: int): seq[T] = s[0 ..< min(n, s.len)]

var client = newHttpClient()
var text = client.getContent("https://www.gutenberg.org/files/135/135-0.txt")

var wordFrequencies = text.toLowerAscii.splitWhitespace.toCountTable
wordFrequencies.sort
for (word, count) in toSeq(wordFrequencies.pairs).take(10):
echo alignLeft($count, 8), word  Output: 40377 the 19870 of 14469 and 14278 a 13590 to 11025 in 9213 he 8347 was 7249 that 6414 his ## Objeck use System.IO.File; use Collection; use RegEx; class Rosetta { function : Main(args : String[]) ~ Nil { if(args->Size() <> 1) { return; }; input := FileReader->ReadFile(args); filter := RegEx->New("\\w+"); words := filter->Find(input); word_counts := StringMap->New(); each(i : words) { word := words->Get(i)->As(String); if(word <> Nil & word->Size() > 0) { word := word->ToLower(); if(word_counts->Has(word)) { count := word_counts->Find(word)->As(IntHolder); count->Set(count->Get() + 1); } else { word_counts->Insert(word, IntHolder->New(1)); }; }; }; count_words := IntMap->New(); words := word_counts->GetKeys(); each(i : words) { word := words->Get(i)->As(String); count := word_counts->Find(word)->As(IntHolder); count_words->Insert(count->Get(), word); }; counts := count_words->GetKeys(); counts->Sort(); index := 1; "Rank\tWord\tFrequency"->PrintLine(); "====\t====\t===="->PrintLine(); for(i := count_words->Size() - 1; i >= 0; i -= 1;) { if(count_words->Size() - 10 <= i) { count := counts->Get(i); word := count_words->Find(count)->As(String); "{$index}\t{$word}\t{$count}"->PrintLine();
index += 1;
};
};
}
}

Output:

Rank    Word    Frequency
====    ====    ====
1       the     41036
2       of      19946
3       and     14940
4       a       14589
5       to      13939
6       in      11204
7       he      9645
8       was     8619
9       that    7922
10      it      6659


## OCaml

let () =
let n =
try int_of_string Sys.argv.(1)
with _ -> 10
in
let ic = open_in "135-0.txt" in
let h = Hashtbl.create 97 in
let w = Str.regexp "[^A-Za-zéèàêâôîûœ]+" in
try
while true do
let line = input_line ic in
let words = Str.split w line in
List.iter (fun word ->
let word = String.lowercase_ascii word in
match Hashtbl.find_opt h word with
| None -> Hashtbl.add h word 1
| Some x -> Hashtbl.replace h word (succ x)
) words
done
with End_of_file ->
close_in ic;
let l = Hashtbl.fold (fun word count acc -> (word, count)::acc) h [] in
let s = List.sort (fun (_, c1) (_, c2) -> compare c2 c1) l in
let r = List.init n (fun i -> List.nth s i) in
List.iter (fun (word, count) ->
Printf.printf "%d  %s\n" count word
) r

Output:
$ocaml str.cma word_freq.ml 41092 the 19954 of 14943 and 14554 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it  ## Perl Translation of: Raku use strict; use warnings; use utf8; my$top = 10;

open my $fh, '<', 'ref/word-count.txt'; (my$text = join '', <$fh>) =~ tr/A-Z/a-z/; my @matcher = ( qr/[a-z]+/, # simple 7-bit ASCII qr/\w+/, # word characters with underscore qr/[a-z0-9]+/, # word characters without underscore ); for my$reg (@matcher) {
print "\nTop $top using regex: " .$reg\n";
my @matches = $text =~ /$reg/g;
my %words;
for my $w (@matches) {$words{$w}++ }; my$c = 0;
for my $w ( sort {$words{$b} <=>$words{$a} } keys %words ) { printf "%-7s %6d\n",$w, $words{$w};
last if ++$c >=$top;
}
}

Output:
Top 10 using regex: (?^:[a-z]+)
the      41089
of       19949
and      14942
a        14608
to       13951
in       11214
he        9648
was       8621
that      7924
it        6661

Top 10 using regex: (?^:\w+)
the      41036
of       19946
and      14940
a        14589
to       13939
in       11204
he        9645
was       8619
that      7922
it        6659

Top 10 using regex: (?^:[a-z0-9]+)
the      41089
of       19949
and      14942
a        14608
to       13951
in       11214
he        9648
was       8621
that      7924
it        6661


## Phix

without javascript_semantics
constant subs = '\t'&"\r\n_.,\"\'!;:?][()|=<>#/*{}+@%&$", reps = repeat(' ',length(subs)), fn = open("135-0.txt","r") string text = lower(substitute_all(get_text(fn),subs,reps)) close(fn) sequence words = append(sort(split(text,no_empty:=true)),"") constant wf = new_dict() string last = words integer count = 1 for i=2 to length(words) do if words[i]!=last then setd({count,last},0,wf) count = 0 last = words[i] end if count += 1 end for count = 10 function visitor(object key, object /*data*/, object /*user_data*/) ?key count -= 1 return count>0 end function traverse_dict(routine_id("visitor"),0,wf,true)  Output: loading... {40743,"the"} {19925,"of"} {14881,"and"} {14474,"a"} {13704,"to"} {11174,"in"} {9623,"he"} {8613,"was"} {7867,"that"} {6612,"it"}  ## Phixmonti include ..\Utilitys.pmt "loading..." ? "135-0.txt" "r" fopen var fn " " true while fn fgets number? if drop fn fclose false else lower " " chain chain true endif endwhile "process..." ? len for var i i get dup 96 > swap 123 < and not if 32 i set endif endfor split sort "count..." ? ( ) var words "" var prev 1 var n len for var i i get dup prev == if drop n 1 + var n else words ( n prev ) 0 put var words var prev 1 var n endif endfor drop words sort 10 for -1 * get ? endfor drop Output: loading... process... count... [41093, "the"] [19954, "of"] [14943, "and"] [14558, "a"] [13953, "to"] [11219, "in"] [9649, "he"] [8622, "was"] [7924, "that"] [6661, "it"] === Press any key to exit === ## PHP <?php preg_match_all('/\w+/', file_get_contents($argv), $words);$frecuency = array_count_values($words); arsort($frecuency);

echo "Rank\tWord\tFrequency\n====\t====\t=========\n";
$i = 1; foreach ($frecuency as $word =>$count) {
echo $i . "\t" .$word . "\t" . $count . "\n"; if ($i >= 10) {
break;
}
$i++; }  Output: Rank Word Frequency ==== ==== ========= 1 the 36636 2 of 19615 3 and 14079 4 to 13535 5 a 13527 6 in 10256 7 was 8543 8 that 7324 9 he 6814 10 had 6139  ## Picat To get the book proper, the header and footer are removed. Here are some tests with different sets of characters to split the words (split_char/1). main => NTop = 10, File = "les_miserables.txt", Chars = read_file_chars(File), % Remove the Project Gutenberg header/footer find(Chars,"*** START OF THE PROJECT GUTENBERG EBOOK LES MISÉRABLES ***",_,HeaderEnd), find(Chars,"*** END OF THE PROJECT GUTENBERG EBOOK LES MISÉRABLES ***",FooterStart,_), Book = [to_lowercase(C) : C in slice(Chars,HeaderEnd+1,FooterStart-1)], % Split into words (different set of split characters) member(SplitType,[all,space_punct,space]), println(split_type=SplitType), split_chars(SplitType,SplitChars), Words = split(Book,SplitChars), println(freq(Words).to_list.sort_down(2).take(NTop)), nl, fail. freq(L) = Freq => Freq = new_map(), foreach(E in L) Freq.put(E,Freq.get(E,0)+1) end. % different set of split chars split_chars(all,"\n\r \t,;!.?()[]”\"-“—-__‘’*"). split_chars(space_punct,"\n\r \t,;!.?"). split_chars(space,"\n\r \t"). Output: split_type = all [the = 40907,of = 19830,and = 14872,a = 14487,to = 13872,in = 11157,he = 9645,was = 8618,that = 7908,it = 6626] split_type = space_punct [the = 40193,of = 19779,and = 14668,a = 14227,to = 13538,in = 11033,he = 9455,was = 8604,that = 7576,” = 6578] split_type = space [the = 40193,of = 19747,and = 14402,a = 14222,to = 13512,in = 10964,he = 9211,was = 8345,that = 7235,his = 6414] It is a slightly different result if the the header/footer are not removed: split_type = all [the = 41094,of = 19952,and = 14939,a = 14545,to = 13954,in = 11218,he = 9647,was = 8620,that = 7922,it = 6641] split_type = space_punct [the = 40378,of = 19901,and = 14734,a = 14284,to = 13620,in = 11094,he = 9457,was = 8606,that = 7590,” = 6578] split_type = space [the = 40378,of = 19869,and = 14468,a = 14278,to = 13590,in = 11025,he = 9213,was = 8347,that = 7249,his = 6414] ## PicoLisp (setq *Delim " ^I^J^M-_.,\"'*[]?!&@#$%^:;")
(setq *Skip (chop *Delim))

(de word+ NIL
(prog1
(lowc (till *Delim T))
(while (member (peek) *Skip) (char)) ) )

(off B)
(in "135-0.txt"
(until (eof)
(let W (word+)
(if (idx 'B W T) (inc (car @)) (set W 1)) ) ) )
(for L (head 10 (flip (by val sort (idx 'B))))
(println L (val L)) )
Output:
"the" 41088
"of" 19949
"and" 14942
"a" 14545
"to" 13950
"in" 11214
"he" 9647
"was" 8620
"that" 7924
"it" 6661


## Prolog

Works with: SWI Prolog
print_top_words(File, N):-
re_split("\\w+", String, Words),
lower_case(Words, Lower),
sort(1, @=<, Lower, Sorted),
merge_words(Sorted, Counted),
sort(2, @>, Counted, Top_words),
writef("Top %w words:\nRank\tCount\tWord\n", [N]),
print_top_words(Top_words, N, 1).

lower_case([_], []):-!.
lower_case([_, Word|Words], [Lower - 1|Rest]):-
string_lower(Word, Lower),
lower_case(Words, Rest).

merge_words([], []):-!.
merge_words([Word - C1, Word - C2|Words], Result):-
!,
C is C1 + C2,
merge_words([Word - C|Words], Result).
merge_words([W|Words], [W|Rest]):-
merge_words(Words, Rest).

print_top_words([], _, _):-!.
print_top_words(_, 0, _):-!.
print_top_words([Word - Count|Rest], N, R):-
writef("%w\t%w\t%w\n", [R, Count, Word]),
N1 is N - 1,
R1 is R + 1,
print_top_words(Rest, N1, R1).

main:-
print_top_words("135-0.txt", 10).

Output:
Top 15 words:
Rank	Count	Word
1	41040	the
2	19951	of
3	14942	and
4	14539	a
5	13941	to
6	11209	in
7	9646	he
8	8620	was
9	7922	that
10	6659	it


## PureBasic

EnableExplicit

Structure wordcount
wkey$count.i EndStructure Define token.c, word$, idx.i, start.i, arg$NewMap wordmap.i() NewList wordlist.wordcount() If OpenConsole("") arg$ = ProgramParameter(0)
If arg$= "" : End 1 : EndIf start = ElapsedMilliseconds() If ReadFile(0, arg$, #PB_Ascii)
While Not Eof(0)
Select token
Case 'A' To 'Z', 'a' To 'z'
word$+ LCase(Chr(token)) Default If word$
wordmap(word$) + 1 word$ = ""
EndIf
EndSelect
Wend
CloseFile(0)
ForEach wordmap()
wordlist()\wkey$= MapKey(wordmap()) wordlist()\count = wordmap() Next SortStructuredList(wordlist(), #PB_Sort_Descending, OffsetOf(wordcount\count), TypeOf(wordcount\count)) PrintN("Elapsed milliseconds: " + Str(ElapsedMilliseconds() - start)) PrintN("File: " + GetFilePart(arg$))
PrintN(~"Rank\tCount\t\t  Word")
If FirstElement(wordlist())
For idx = 1 To 10
Print(RSet(Str(idx), 2))
Print(~"\t")
Print(wordlist()\wkey$) Print(~"\t\t") PrintN(RSet(Str(wordlist()\count), 6)) If NextElement(wordlist()) = 0 Break EndIf Next EndIf EndIf Input() EndIf End Output: Elapsed milliseconds: 462 File: 135-0.txt Rank Count Word 1 the 41093 2 of 19954 3 and 14943 4 a 14558 5 to 13953 6 in 11219 7 he 9649 8 was 8622 9 that 7924 10 it 6661  ## Python ### Collections #### Python2.7 import collections import re import string import sys def main(): counter = collections.Counter(re.findall(r"\w+",open(sys.argv).read().lower())) print counter.most_common(int(sys.argv)) if __name__ == "__main__": main()  Output: $ python wordcount.py 135-0.txt 10
[('the', 41036), ('of', 19946), ('and', 14940), ('a', 14589), ('to', 13939),
('in', 11204), ('he', 9645), ('was', 8619), ('that', 7922), ('it', 6659)]


#### Python3.6

from collections import Counter
from re import findall

les_mis_file = 'les_mis_135-0.txt'

def _count_words(fname):
with open(fname) as f:
words = findall(r'\w+', text.lower())
return Counter(words)

def most_common_words_in_file(fname, n):
counts = _count_words(fname)
for word, count in [['WORD', 'COUNT']] + counts.most_common(n):
print(f'{word:>10} {count:>6}')

if __name__ == "__main__":
n = int(input('How many?: '))
most_common_words_in_file(les_mis_file, n)

Output:
How many?: 10
WORD  COUNT
the  41036
of  19946
and  14940
a  14586
to  13939
in  11204
he   9645
was   8619
that   7922
it   6659

### Sorted and groupby

Works with: Python version 3.7
"""
Word count task from Rosetta Code
http://www.rosettacode.org/wiki/Word_count#Python
"""
from itertools import (groupby,
starmap)
from operator import itemgetter
from pathlib import Path
from typing import (Iterable,
List,
Tuple)

FILEPATH = Path('lesMiserables.txt')
COUNT = 10

def main():
words_and_counts = most_frequent_words(FILEPATH)
print(*words_and_counts[:COUNT], sep='\n')

def most_frequent_words(filepath: Path,
*,
encoding: str = 'utf-8') -> List[Tuple[str, int]]:
"""
A list of word-frequency pairs sorted by their occurrences.
The words are read from the given file.
"""
def word_and_frequency(word: str,
words_group: Iterable[str]) -> Tuple[str, int]:
return word, capacity(words_group)

words = file_contents.lower().split()
grouped_words = groupby(sorted(words))
words_and_frequencies = starmap(word_and_frequency, grouped_words)
return sorted(words_and_frequencies, key=itemgetter(1), reverse=True)

def capacity(iterable: Iterable) -> int:
"""Returns a number of elements in an iterable"""
return sum(1 for _ in iterable)

if __name__ == '__main__':
main()

Output:
('the', 40372)
('of', 19868)
('and', 14472)
('a', 14278)
('to', 13589)
('in', 11024)
('he', 9213)
('was', 8347)
('that', 7250)
('his', 6414)

### Collections, Sorted and Lambda

#!/usr/bin/python3
import collections
import re

count = 10

with open("135-0.txt") as f:

word_freq = sorted(
collections.Counter(sorted(re.split(r"\W+", text.lower()))).items(),
key=lambda c: c,
reverse=True,
)

for i in range(len(word_freq)):
print("[{:2d}] {:>10} : {}".format(i + 1, word_freq[i], word_freq[i]))
if i == count - 1:
break

Output:
[ 1]        the : 41039
[ 2]         of : 19951
[ 3]        and : 14942
[ 4]          a : 14527
[ 5]         to : 13941
[ 6]         in : 11209
[ 7]         he : 9646
[ 8]        was : 8620
[ 9]       that : 7922
         it : 6659

## R

### Version 1

I chose to remove apostrophes only if they're followed by an s (so "mom" and "mom's" will show up as the same word but "they" and "they're" won't). I also chose not to remove hyphens.

wordcount<-function(file,n){
punctuation=c("","~","!","@","#","$","%","^","&","*","(",")","_","+","=","{","[","}","]","|","\\",":",";","\"","<",",",">",".","?","/","'s") wordlist=scan(file,what=character()) wordlist=tolower(wordlist) for(i in 1:length(punctuation)){ wordlist=gsub(punctuation[i],"",wordlist,fixed=T) } df=data.frame("Word"=sort(unique(wordlist)),"Count"=rep(0,length(unique(wordlist)))) for(i in 1:length(unique(wordlist))){ df[i,2]=length(which(wordlist==df[i,1])) } df=df[order(df[,2],decreasing = T),] row.names(df)=1:nrow(df) return(df[1:n,]) }  Output: > wordcount("MobyDick.txt",10) Read 212793 items Word Count 1 the 14346 2 of 6590 3 and 6340 4 a 4611 5 to 4572 6 in 4130 7 that 2903 8 his 2516 9 it 2308 10 i 1845  ### Version 2 This version is purely functional using the native pipe operator in R 4.1+ and runs in less than a second. word_frequency_pipeline <- function(file=NULL, n=10) { file |> vroom::vroom_lines() |> stringi::stri_split_boundaries(type="word", skip_word_none=T, skip_word_number=T) |> unlist() |> tolower() |> table() |> sort(decreasing = T) |> (\(.) .[1:n])() |> data.frame() }  Output: > word_frequency_pipeline("~/../Downloads/135-0.txt") Var1 Freq 1 the 41042 2 of 19952 3 and 14938 4 a 14526 5 to 13942 6 in 11208 7 he 9605 8 was 8620 9 that 7824 10 it 6533  ## Racket #lang racket (define (all-words f (case-fold string-downcase)) (map case-fold (regexp-match* #px"\\w+" (file->string f)))) (define (l.|l| l) (cons (car l) (length l))) (define (counts l (>? >)) (sort (map l.|l| (group-by values l)) >? #:key cdr)) (module+ main (take (counts (all-words "data/les-mis.txt")) 10))  Output: '(("the" . 41036) ("of" . 19946) ("and" . 14940) ("a" . 14589) ("to" . 13939) ("in" . 11204) ("he" . 9645) ("was" . 8619) ("that" . 7922) ("it" . 6659)) ## Raku (formerly Perl 6) Works with: Rakudo version 2022.07 Note: much of the following exposition is no longer critical to the task as the requirements have been updated, but is left here for historical and informational reasons. This is slightly trickier than it appears initially. The task specifically states: "A word is a sequence of one or more contiguous letters", so contractions and hyphenated words are broken up. Initially we might reach for a regex matcher like /\w+/ , but \w includes underscore, which is not a letter but a punctuation connector; and this text is full of underscores since that is how Project Gutenberg texts denote italicized text. The underscores are not actually parts of the words though, they are markup. We might try /A-Za-z/ as a matcher but this text is bursting with French words containing various diacritics. Those are letters, so words will be incorrectly split up; (Misérables will be counted as 'mis' and 'rables', probably not what we want.) Actually, in this case /A-Za-z/ returns very nearly the correct answer. Unfortunately, the name "Alèthe" appears once (only once!) in the text, gets incorrectly split into Al & the, and incorrectly reports 41089 occurrences of "the". The text has several words like "Panathenæa", "ça", "aérostiers" and "Keksekça" so the counts for 'a' are off too. The other 8 of the top 10 are "correct" using /A-Za-z/, but it is mostly by accident. A more accurate regex matcher would be some kind of Unicode aware /\w/ minus underscore. It may also be useful, depending on your requirements, to recognize contractions with embedded apostrophes, hyphenated words, and hyphenated words broken across lines. Here is a sample that shows the result when using various different matchers. sub MAIN ($filename, UInt $top = 10) { my$file = $filename.IO.slurp.lc.subst(/ (<[\w]-[_]>'-')\n(<[\w]-[_]>) /, {$0 ~ $1}, :g ); my @matcher = rx/ <[a..z]>+ /, # simple 7-bit ASCII rx/ \w+ /, # word characters with underscore rx/ <[\w]-[_]>+ /, # word characters without underscore rx/ [<[\w]-[_]>+]+ % < ' - '- > / # word characters without underscore but with hyphens and contractions ; for @matcher ->$reg {
say "\nTop $top using regex: ",$reg.raku;
my @words = $file.comb($reg).Bag.sort(-*.value)[^$top]; my$length = max @words».key».chars;
printf "%-{$length}s %d\n", .key, .value for @words; } }  Output: Passing in the file name and 10: Top 10 using regex: rx/ <[a..z]>+ / the 41089 of 19949 and 14942 a 14608 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: rx/ \w+ / the 41035 of 19946 and 14940 a 14577 to 13939 in 11204 he 9645 was 8619 that 7922 it 6659 Top 10 using regex: rx/ <[\w]-[_]>+ / the 41088 of 19949 and 14942 a 14596 to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: rx/ <[\w]-[_]>+[["'"|'-'|"'-"]<[\w]-[_]>+]* / the 41081 of 19930 and 14934 a 14587 to 13735 in 11204 he 9607 was 8620 that 7825 it 6535 It can be difficult to figure out what words the different regexes do or don't match. Here are the three more complex regexes along with a list of "words" that are treated as being different using this regex as opposed to /a..z/. IE: It is lumped in as one of the top 10 word counts using /a..z/ but not with this regex. Top 10 using regex: rx/ \w+ / the 41035 alèthe _the _the_ of 19946 of_ _of_ and 14940 _and_ paternoster_and a 14577 _ça aïe ça keksekça aérostiers _a poréa panathenæa to 13939 to_ _to in 11204 _in he 9645 _he was 8619 _was that 7922 _that it 6659 _it Top 10 using regex: rx/ <[\w]-[_]>+ / the 41088 alèthe of 19949 and 14942 a 14596 poréa ça aérostiers panathenæa aïe keksekça to 13951 in 11214 he 9648 was 8621 that 7924 it 6661 Top 10 using regex: rx/ <[\w]-[_]>+[["'"|'-'|"'-"]<[\w]-[_]>+]* / the 41081 will-o'-the-wisps alèthe skip-the-gutter police-agent-ja-vert-was-found-drowned-un-der-a-boat-of-the-pont-au-change jean-the-screw will-o'-the-wisp of 19930 chromate-of-lead-colored die-of-hunger die-of-cold-if-you-have-bread police-agent-ja-vert-was-found-drowned-un-der-a-boat-of-the-pont-au-change unheard-of die-of-hunger-if-you-have-a-fire and 14934 come-and-see so-and-so cock-and-bull hide-and-seek sambre-and-meuse a 14587 keksekça l'a ça now-a-days vis-a-vis a-dreaming police-agent-ja-vert-was-found-drowned-un-der-a-boat-of-the-pont-au-change poréa panathenæa aérostiers a-hunting aïe die-of-hunger-if-you-have-a-fire to 13735 to-morrow to-day hand-to-hand to-night well-to-do face-to-face in 11204 in-pace son-in-law father-in-law whippers-in general-in-chief sons-in-law he 9607 he's he'll was 8620 police-agent-ja-vert-was-found-drowned-un-der-a-boat-of-the-pont-au-change that 7825 that's pick-me-down-that it 6535 it's it'll One nice thing is this isn't special cased. It will work out of the box for any text / language. Russian? No problem. $ raku wf 14741-0.txt 5
Top 5 using regex: rx/ <[a..z]>+ /
the	176
of	119
gutenberg	93
project	87
to	80

Top 5 using regex: rx/ \w+ /
и	860
в	579
не	290
на	222
ты	195

Top 5 using regex: rx/ <[\w]-[_]>+ /
и	860
в	579
не	290
на	222
ты	195

Top 5 using regex: rx/ <[\w]-[_]>+[["'"|'-'|"'-"]<[\w]-[_]>+]* /
и	860
в	579
не	290
на	222
ты	195

Greek? Sure, why not.

}

println(s"\nSuccessfully completed without errors. [total ${scala.compat.Platform.currentTime - executionStart} ms]") }  Output: Rank Word Frequency ==== ======== ====== 1 the 41036 2 of 19946 3 and 14940 4 a 14589 5 to 13939 6 in 11204 7 he 9645 8 was 8619 9 that 7922 10 it 6659 Successfully completed without errors. [total 4528 ms] ## Seed7 The Seed7 program uses the function getHttp, to get the file 135-0.txt directly from Gutemberg. The library scanfile.s7i provides getSimpleSymbol, to get words from a fle. The words are converted to lower case, to assure that "The" and "the" are considered the same. $ include "seed7_05.s7i";
include "gethttp.s7i";
include "strifile.s7i";
include "scanfile.s7i";
include "chartype.s7i";
include "console.s7i";

const type: wordHash is hash [string] integer;
const type: countHash is hash [integer] array string;

const proc: main is func
local
var file: inFile is STD_NULL;
var string: aWord is "";
var wordHash: numberOfWords is wordHash.EMPTY_HASH;
var countHash: countWords is countHash.EMPTY_HASH;
var array integer: countKeys is 0 times 0;
var integer: index is 0;
var integer: number is 0;
begin
OUT := STD_CONSOLE;
inFile := openStrifile(getHttp("www.gutenberg.org/files/135/135-0.txt"));
while hasNext(inFile) do
aWord := lower(getSimpleSymbol(inFile));
if aWord <> "" and aWord in letter_char then
if aWord in numberOfWords then
incr(numberOfWords[aWord]);
else
numberOfWords @:= [aWord] 1;
end if;
end if;
end while;
countWords := flip(numberOfWords);
countKeys := sort(keys(countWords));
writeln("Word    Frequency");
for index range length(countKeys) downto length(countKeys) - 9 do
number := countKeys[index];
for aWord range sort(countWords[number]) do
end for;
end for;
end func;
Output:
Word    Frequency
the     41036
of      19946
and     14940
a       14589
to      13939
in      11204
he      9645
was     8619
that    7922
it      6659


## Sidef

var count = Hash()
var file = File(ARGV \\ '135-0.txt')

file.open_r.each { |line|
line.lc.scan(/[\pL]+/).each { |word|
count{word} := 0 ++
}
}

var top = count.sort_by {|_,v| v }.last(10).flip

top.each { |pair|
say "#{pair.key}\t-> #{pair.value}"
}

Output:
the	-> 41088
of	-> 19949
and	-> 14942
a	-> 14596
to	-> 13951
in	-> 11214
he	-> 9648
was	-> 8621
that	-> 7924
it	-> 6661


## Simula

COMMENT COMPILE WITH
$cim -m64 word-count.sim ; BEGIN COMMENT ----- CLASSES FOR GENERAL USE ; ! ABSTRACT HASH KEY TYPE ; CLASS HASHKEY; VIRTUAL: PROCEDURE HASH IS INTEGER PROCEDURE HASH;; PROCEDURE EQUALTO IS BOOLEAN PROCEDURE EQUALTO(K); REF(HASHKEY) K;; BEGIN END HASHKEY; ! ABSTRACT HASH VALUE TYPE ; CLASS HASHVAL; BEGIN ! THERE IS NOTHING REQUIRED FOR THE VALUE TYPE ; END HASHVAL; CLASS HASHMAP; BEGIN CLASS INNERHASHMAP(N); INTEGER N; BEGIN INTEGER PROCEDURE INDEX(K); REF(HASHKEY) K; BEGIN INTEGER I; IF K == NONE THEN ERROR("HASHMAP.INDEX: NONE IS NOT A VALID KEY"); I := MOD(K.HASH,N); LOOP: IF KEYTABLE(I) == NONE OR ELSE KEYTABLE(I).EQUALTO(K) THEN INDEX := I ELSE BEGIN I := IF I+1 = N THEN 0 ELSE I+1; GO TO LOOP; END; END INDEX; ! PUT SOMETHING IN ; PROCEDURE PUT(K,V); REF(HASHKEY) K; REF(HASHVAL) V; BEGIN INTEGER I; IF V == NONE THEN ERROR("HASHMAP.PUT: NONE IS NOT A VALID VALUE"); I := INDEX(K); IF KEYTABLE(I) == NONE THEN BEGIN IF SIZE = N THEN ERROR("HASHMAP.PUT: TABLE FILLED COMPLETELY"); KEYTABLE(I) :- K; VALTABLE(I) :- V; SIZE := SIZE+1; END ELSE VALTABLE(I) :- V; END PUT; ! GET SOMETHING OUT ; REF(HASHVAL) PROCEDURE GET(K); REF(HASHKEY) K; BEGIN INTEGER I; IF K == NONE THEN ERROR("HASHMAP.GET: NONE IS NOT A VALID KEY"); I := INDEX(K); IF KEYTABLE(I) == NONE THEN GET :- NONE ! ERROR("HASHMAP.GET: KEY NOT FOUND"); ELSE GET :- VALTABLE(I); END GET; PROCEDURE CLEAR; BEGIN INTEGER I; FOR I := 0 STEP 1 UNTIL N-1 DO BEGIN KEYTABLE(I) :- NONE; VALTABLE(I) :- NONE; END; SIZE := 0; END CLEAR; ! DATA MEMBERS OF CLASS HASHMAP ; REF(HASHKEY) ARRAY KEYTABLE(0:N-1); REF(HASHVAL) ARRAY VALTABLE(0:N-1); INTEGER SIZE; END INNERHASHMAP; PROCEDURE PUT(K,V); REF(HASHKEY) K; REF(HASHVAL) V; BEGIN IF IMAP.SIZE >= 0.75 * IMAP.N THEN BEGIN COMMENT RESIZE HASHMAP ; REF(INNERHASHMAP) NEWIMAP; REF(ITERATOR) IT; NEWIMAP :- NEW INNERHASHMAP(2 * IMAP.N); IT :- NEW ITERATOR(THIS HASHMAP); WHILE IT.MORE DO BEGIN REF(HASHKEY) KEY; KEY :- IT.NEXT; NEWIMAP.PUT(KEY, IMAP.GET(KEY)); END; IMAP.CLEAR; IMAP :- NEWIMAP; END; IMAP.PUT(K, V); END; REF(HASHVAL) PROCEDURE GET(K); REF(HASHKEY) K; GET :- IMAP.GET(K); PROCEDURE CLEAR; IMAP.CLEAR; INTEGER PROCEDURE SIZE; SIZE := IMAP.SIZE; REF(INNERHASHMAP) IMAP; IMAP :- NEW INNERHASHMAP(16); END HASHMAP; CLASS ITERATOR(H); REF(HASHMAP) H; BEGIN INTEGER POS,KEYCOUNT; BOOLEAN PROCEDURE MORE; MORE := KEYCOUNT < H.SIZE; REF(HASHKEY) PROCEDURE NEXT; BEGIN INSPECT H DO INSPECT IMAP DO BEGIN WHILE KEYTABLE(POS) == NONE DO POS := POS+1; NEXT :- KEYTABLE(POS); KEYCOUNT := KEYCOUNT+1; POS := POS+1; END; END NEXT; END ITERATOR; COMMENT ----- PROBLEM SPECIFIC CLASSES ; HASHKEY CLASS TEXTHASHKEY(T); VALUE T; TEXT T; BEGIN INTEGER PROCEDURE HASH; BEGIN INTEGER I; T.SETPOS(1); WHILE T.MORE DO I := 31*I+RANK(T.GETCHAR); HASH := I; END HASH; BOOLEAN PROCEDURE EQUALTO(K); REF(HASHKEY) K; EQUALTO := T = K QUA TEXTHASHKEY.T; END TEXTHASHKEY; HASHVAL CLASS COUNTER; BEGIN INTEGER COUNT; END COUNTER; REF(INFILE) INF; REF(HASHMAP) MAP; REF(TEXTHASHKEY) KEY; REF(COUNTER) VAL; REF(ITERATOR) IT; TEXT LINE, WORD; INTEGER I, J, MAXCOUNT, LINENO; INTEGER ARRAY MAXCOUNTS(1:10); REF(TEXTHASHKEY) ARRAY MAXWORDS(1:10); WORD :- BLANKS(1000); MAP :- NEW HASHMAP; COMMENT MAP WORDS TO COUNTERS ; INF :- NEW INFILE("135-0.txt"); INF.OPEN(BLANKS(4096)); WHILE NOT INF.LASTITEM DO BEGIN BOOLEAN INWORD; PROCEDURE SAVE; BEGIN IF WORD.POS > 1 THEN BEGIN KEY :- NEW TEXTHASHKEY(WORD.SUB(1, WORD.POS - 1)); VAL :- MAP.GET(KEY); IF VAL == NONE THEN BEGIN VAL :- NEW COUNTER; MAP.PUT(KEY, VAL); END; VAL.COUNT := VAL.COUNT + 1; WORD := " "; WORD.SETPOS(1); END; END SAVE; LINENO := LINENO + 1; LINE :- COPY(INF.IMAGE).STRIP; INF.INIMAGE; COMMENT SEARCH WORDS IN LINE ; COMMENT A WORD IS ANY SEQUENCE OF LETTERS ; INWORD := FALSE; LINE.SETPOS(1); WHILE LINE.MORE DO BEGIN CHARACTER CH; CH := LINE.GETCHAR; IF CH >= 'a' AND CH <= 'z' THEN CH := CHAR(RANK(CH) - RANK('a') + RANK('A')); IF CH >= 'A' AND CH <= 'Z' THEN BEGIN IF NOT INWORD THEN BEGIN SAVE; INWORD := TRUE; END; WORD.PUTCHAR(CH); END ELSE BEGIN IF INWORD THEN BEGIN SAVE; INWORD := FALSE; END; END; END; SAVE; COMMENT LAST WORD ; END; INF.CLOSE; COMMENT FIND 10 MOST COMMON WORDS ; IT :- NEW ITERATOR(MAP); WHILE IT.MORE DO BEGIN KEY :- IT.NEXT; VAL :- MAP.GET(KEY); FOR I := 1 STEP 1 UNTIL 10 DO BEGIN IF VAL.COUNT >= MAXCOUNTS(I) THEN BEGIN FOR J := 10 STEP -1 UNTIL I + 1 DO BEGIN MAXCOUNTS(J) := MAXCOUNTS(J - 1); MAXWORDS(J) :- MAXWORDS(J - 1); END; MAXCOUNTS(I) := VAL.COUNT; MAXWORDS(I) :- KEY; GO TO BREAK; END; END; BREAK: END; COMMENT OUTPUT 10 MOST COMMON WORDS ; FOR I := 1 STEP 1 UNTIL 10 DO BEGIN IF MAXWORDS(I) =/= NONE THEN BEGIN OUTINT(MAXCOUNTS(I), 10); OUTTEXT(" "); OUTTEXT(MAXWORDS(I) QUA TEXTHASHKEY.T); OUTIMAGE; END; END; END Output:  41089 THE 19949 OF 14942 AND 14608 A 13951 TO 11214 IN 9648 HE 8621 WAS 7924 THAT 6661 IT 6 garbage collection(s) in 0.2 seconds.  ## Smalltalk The ASCII text file is from https://www.gutenberg.org/files/135/old/lesms10.txt. ### Cuis Smalltalk, ASCII Works with: Cuis version 6.0 (StandardFileStream new open: 'lesms10.txt' forWrite: false) contents asLowercase substrings asBag sortedCounts first: 10.  Output: an OrderedCollection(40543 -> 'the' 19796 -> 'of' 14448 -> 'and' 14380 -> 'a' 13582 -> 'to' 11006 -> 'in' 9221 -> 'he' 8351 -> 'was' 7258 -> 'that' 6420 -> 'his')  ### Squeak Smalltalk, ASCII Works with: Squeak version 6.0 (StandardFileStream readOnlyFileNamed: 'lesms10.txt') contents asLowercase substrings asBag sortedCounts first: 10.  Output: {40543->'the' . 19796->'of' . 14448->'and' . 14380->'a' . 13582->'to' . 11006->'in' . 9221->'he' . 8351->'was' . 7258->'that' . 6420->'his'}  ## Swift import Foundation func printTopWords(path: String, count: Int) throws { // load file contents into a string let text = try String(contentsOfFile: path, encoding: String.Encoding.utf8) var dict = Dictionary<String, Int>() // split text into words, convert to lowercase and store word counts in dict let regex = try NSRegularExpression(pattern: "\\w+") regex.enumerateMatches(in: text, range: NSRange(text.startIndex..., in: text)) { (match, _, _) in guard let match = match else { return } let word = String(text[Range(match.range, in: text)!]).lowercased() dict[word, default: 0] += 1 } // sort words by number of occurrences let wordCounts = dict.sorted(by: {$0.1 > $1.1}) // print the top count words print("Rank\tWord\tCount") for (i, (word, n)) in wordCounts.prefix(count).enumerated() { print("\(i + 1)\t\(word)\t\(n)") } } do { try printTopWords(path: "135-0.txt", count: 10) } catch { print(error.localizedDescription) }  Output: Rank Word Count 1 the 41039 2 of 19951 3 and 14942 4 a 14527 5 to 13941 6 in 11209 7 he 9646 8 was 8620 9 that 7922 10 it 6659  ## Tcl lassign$argv head
while { [gets stdin line] >= 0 } {
foreach word [regexp -all -inline {[A-Za-z]+} $line] { dict incr wordcount [string tolower$word]
}
}

set sorted [lsort -stride 2 -index 1 -int -decr $wordcount] foreach {word count} [lrange$sorted 0 [expr {$head * 2 - 1}]] { puts "$count\t$word" }  ./wordcount-di.tcl 10 < 135-0.txt Output: 41093 the 19954 of 14943 and 14558 a 13953 to 11219 in 9649 he 8622 was 7924 that 6661 it  ## TMG McIlroy's Unix TMG: /* Input format: N text */ /* Only lowercase letters can constitute a word in text. */ /* (c) 2020, Andrii Makukha, 2-clause BSD licence. */ progrm: readn/error table(freq) table(chain) [firstword = ~0] loop: not(!<<>>) output | [j=777] batch/loop loop; /* Main loop */ /* To use less stack, divide input into batches. */ /* (Avoid interpreting entire input as a single "sentence".) */ batch: [j<=0?] succ | word/skip [j--] skip batch; skip: string(other); not: params(1) (any($1) fail | ());
error:  diag(( ={ <ERROR: input must start with a number> * } ));

/* Process a word */
word:   smark any(letter) string(letter) scopy
locate/new
[freq[k]++] newmax;
locate: find(freq, k);
new:    enter(freq, k)
[freq[k] = 1] newmax
[firstword = firstword==~0 ? k : firstword]
enter(chain, i) [chain[i]=prevword] [prevword=k];
newmax: [max = max<freq[k] ? freq[k] : max];

/* Output logic */
output: [next=max]
outmax: [max=next] [next=0] [max>0?] [j = prevword] cycle/outmax;
cycle:  [i = j] [k = freq[i]] [n>0?]
( [max==freq[i]?] parse(wn)
| [(freq[i]<max) & (next<freq[i])?] [next = freq[i]]
| ())
[i != firstword?] [j = chain[i]] cycle;
wn:     getnam(freq, i) [k = freq[i]] decimal(k) [n--]
= { 2 < > 1 * };

int1:     [n = n*12+i] inta\int1;
inta:     char(i) [i<72?] [(i =- 60)>=0?];

/* Variables */
firstword:  0;  /* First word's index to know where to stop output */
k: 0;
i: 0;
j: 0;
n: 0;           /* Number of most frequent words to display */
max:  0;        /* Current highest number of occurrences */
next: 0;        /* Next highest number of occurrences */

/* Tables */
freq:   0;
chain:  0;

/* Character classes */
letter:   <<abcdefghijklmnopqrstuvwxyz>>;
other:   !<<abcdefghijklmnopqrstuvwxyz>>;

Unix TMG didn't have tolower builtin. Therefore, you would use it together with tr:

cat file | tr A-Z a-z > file1; ./a.out file1


Additionally, because 1972 TMG only understood ASCII characters, you might want to strip down the diacritics (e.g., é → e):

cat file | uni2ascii -B | tr A-Z a-z > file1; ./a.out file1


## Transd

#lang transd

MainModule: {
_start: (λ locals: cnt 0
(with fs FileStream() words String()
(open-r fs "/mnt/text/Literature/Miserables.txt")
(textin fs words)

(with v ( -|
(split (tolower words))
(group-by)
(regroup-by (λ v Vector<String>() -> Int() (size v))))

(for i in v :rev do (lout (get (get (snd i) 0) 0) ":\t " (fst i))
(+= cnt 1) (if (> cnt 10) break))
)))
}

Output:
the:     40379
of:      19869
and:	 14468
a:       14278
to:      13590
in:      11025
he:      9213
was:     8347
that:    7249
his:     6414


## UNIX Shell

Works with: Bash
Works with: zsh

This is derived from Doug McIlroy's original 6-line note in the ACM article cited in the task.

#!/bin/sh
<"$1" tr -cs A-Za-z '\n' | tr A-Z a-z | LC_ALL=C sort | uniq -c | sort -rn | head -n "$2"


Output:
$./wordcount.sh 135-0.txt 10 41089 the 19949 of 14942 and 14608 a 13951 to 11214 in 9648 he 8621 was 7924 that 6661 it  ### Original + URL import This is Doug McIlroy's original solution but follows other solutions in importing the task's text file from the web and directly specifying the 10 most commonly used words. curl "https://www.gutenberg.org/files/135/135-0.txt" | tr -cs A-Za-z '\n' | tr A-Z a-z | sort | uniq -c | sort -rn | sed 10q  Output: 41096 the 19955 of 14939 and 14558 a 13954 to 11218 in 9649 he 8622 was 7924 that 6661 it ## VBA In order to use it, you have to adapt the PATHFILE Const. Option Explicit Private Const PATHFILE As String = "C:\HOME\VBA\ROSETTA" Sub Main() Dim arr Dim Dict As Object Dim Book As String, temp As String Dim T# T = Timer Book = ExtractTxt(PATHFILE & "\les miserables.txt") temp = RemovePunctuation(Book) temp = UCase(temp) arr = Split(temp, " ") Set Dict = CreateObject("Scripting.Dictionary") FillDictionary Dict, arr Erase arr SortDictByFreq Dict, arr DisplayTheTopMostUsedWords arr, 10 Debug.Print "Words different in this book : " & Dict.Count Debug.Print "-------------------------" Debug.Print "" Debug.Print "Optionally : " Debug.Print "Frequency of the word MISERABLE : " & DisplayFrequencyOf("MISERABLE", Dict) Debug.Print "Frequency of the word DISASTER : " & DisplayFrequencyOf("DISASTER", Dict) Debug.Print "Frequency of the word ROSETTA_CODE : " & DisplayFrequencyOf("ROSETTA_CODE", Dict) Debug.Print "-------------------------" Debug.Print "Execution Time : " & Format(Timer - T, "0.000") & " sec." End Sub Private Function ExtractTxt(strFile As String) As String 'http://rosettacode.org/wiki/File_input/output#VBA Dim i As Integer i = FreeFile Open strFile For Input As #i ExtractTxt = Input(LOF(1), #i) Close #i End Function Private Function RemovePunctuation(strBook As String) As String Dim T, i As Integer, temp As String Const PUNCT As String = """,;:!?." T = Split(StrConv(PUNCT, vbUnicode), Chr(0)) temp = strBook For i = LBound(T) To UBound(T) - 1 temp = Replace(temp, T(i), " ") Next temp = Replace(temp, "--", " ") temp = Replace(temp, "...", " ") temp = Replace(temp, vbCrLf, " ") RemovePunctuation = Replace(temp, " ", " ") End Function Private Sub FillDictionary(d As Object, a As Variant) Dim L As Long For L = LBound(a) To UBound(a) If a(L) <> "" Then _ d(a(L)) = d(a(L)) + 1 Next End Sub Private Sub SortDictByFreq(d As Object, myArr As Variant) Dim K Dim L As Long ReDim myArr(1 To d.Count, 1 To 2) For Each K In d.keys L = L + 1 myArr(L, 1) = K myArr(L, 2) = CLng(d(K)) Next SortArray myArr, LBound(myArr), UBound(myArr), 2 End Sub Private Sub SortArray(a, Le As Long, Ri As Long, Col As Long) Dim ref As Long, L As Long, r As Long, temp As Variant ref = a((Le + Ri) \ 2, Col) L = Le r = Ri Do Do While a(L, Col) < ref L = L + 1 Loop Do While ref < a(r, Col) r = r - 1 Loop If L <= r Then temp = a(L, 1) a(L, 1) = a(r, 1) a(r, 1) = temp temp = a(L, 2) a(L, 2) = a(r, 2) a(r, 2) = temp L = L + 1 r = r - 1 End If Loop While L <= r If L < Ri Then SortArray a, L, Ri, Col If Le < r Then SortArray a, Le, r, Col End Sub Private Sub DisplayTheTopMostUsedWords(arr As Variant, Nb As Long) Dim L As Long, i As Integer i = 1 Debug.Print "Rank Word Frequency" Debug.Print "==== ======= =========" For L = UBound(arr) To UBound(arr) - Nb + 1 Step -1 Debug.Print Left(CStr(i) & " ", 5) & Left(arr(L, 1) & " ", 8) & " " & Format(arr(L, 2), "0 000") i = i + 1 Next End Sub Private Function DisplayFrequencyOf(Word As String, d As Object) As Long If d.Exists(Word) Then _ DisplayFrequencyOf = d(Word) End Function Output: Words different in this book : 25884 ------------------------- Rank Word Frequency ==== ======= ========= 1 THE 40 831 2 OF 19 807 3 AND 14 860 4 A 14 453 5 TO 13 641 6 IN 11 133 7 HE 9 598 8 WAS 8 617 9 THAT 7 807 10 IT 6 517 Optionally : Frequency of the word MISERABLE : 35 Frequency of the word DISASTER : 12 Frequency of the word ROSETTA_CODE : 0 ------------------------- Execution Time : 7,785 sec. ## Wren Translation of: Go Library: Wren-str Library: Wren-sort Library: Wren-fmt Library: Wren-pattern I've taken the view that 'letter' means either a letter or digit for Unicode codepoints up to 255. I haven't included underscore, hyphen nor apostrophe as these usually separate compound words. Not very quick (runs in about 47 seconds on my system) though this is partially due to Wren not having regular expressions and the string pattern matching module being written in Wren itself rather than C. If the Go example is re-run today (21 October 2020), then the output matches this Wren example precisely though it appears that the text file has changed since the former was written more than 2 years ago. import "io" for File import "/str" for Str import "/sort" for Sort import "/fmt" for Fmt import "/pattern" for Pattern var fileName = "135-0.txt" var text = File.read(fileName).trimEnd() var groups = {} // match runs of A-z, a-z, 0-9 and any non-ASCII letters with code-points < 256 var p = Pattern.new("+1&w") var lines = text.split("\n") for (line in lines) { var ms = p.findAll(line) for (m in ms) { var t = Str.lower(m.text) groups[t] = groups.containsKey(t) ? groups[t] + 1 : 1 } } var keyVals = groups.toList Sort.quick(keyVals, 0, keyVals.count - 1) { |i, j| (j.value - i.value).sign } System.print("Rank Word Frequency") System.print("==== ==== =========") for (rank in 1..10) { var word = keyVals[rank-1].key var freq = keyVals[rank-1].value Fmt.print("$2d    $-4s$5d", rank, word, freq)
}
Output:
Rank  Word  Frequency
====  ====  =========
1    the     41092
2    of      19954
3    and     14943
4    a       14546
5    to      13953
6    in      11219
7    he       9649
8    was      8622
9    that     7924
10    it       6661


## XQuery

let $maxentries := 10,$uri := 'https://www.gutenberg.org/files/135/135-0.txt'
return
<words in="{$uri}" top="{$maxentries}"> {
(
let $doc := unparsed-text($uri),
$tokens := ( tokenize($doc, '\W+')[normalize-space()]
! lower-case(.)
! normalize-unicode(., 'NFC')
)
return
for $token in$tokens
let $key :=$token
group by $key let$count := count($token) order by$count descending
return <word key="{$key}" count="{$count}"/>
the,41089
of,19949
and,14942
a,14608
to,13951
in,11214
he,9648
was,8621
that,7924
it,6661