| NL: send a signal `signal.sigusr1` to the current process | |
| ref: os.kill(os.getpid(), signal.SIGUSR1) | |
| Output: print ( proc. get _ append ( ) ) | |
| bleu_metric: 14.253628894439752 | |
| ---------- | |
| NL: decode a hex string '4a4b4c' to utf-8. | |
| ref: bytes.fromhex('4a4b4c').decode('utf-8') | |
| Output: print ( str. decode ('utf8') ) | |
| bleu_metric: 23.083362053422253 | |
| ---------- | |
| NL: check if all elements in list `mylist` are identical | |
| ref: all(x == myList[0] for x in myList) | |
| Output: len ( set ( mylist ) ) = = 1 | |
| bleu_metric: 9.360791398475978 | |
| ---------- | |
| NL: format number of spaces between strings `python`, `:` and `very good` to be `20` | |
| ref: print('%*s : %*s' % (20, 'Python', 20, 'Very Good')) | |
| Output: " " " { 0 :, } " " ". format ( 123456789, 16 ) ( lambda x : x. | |
| bleu_metric: 7.030766106716325 | |
| ---------- | |
| NL: how to convert a string from cp-1251 to utf-8? | |
| ref: d.decode('cp1251').encode('utf8') | |
| Output: d. decode ('cp1252'). encode ('utf8') | |
| bleu_metric: 82.82477531331041 | |
| ---------- | |
| NL: get rid of none values in dictionary `kwargs` | |
| ref: res = {k: v for k, v in list(kwargs.items()) if v is not None} | |
| Output: k for k, v in list ( dict. items ( ) ) if v is not none ] | |
| bleu_metric: 54.70605317141546 | |
| ---------- | |
| NL: get rid of none values in dictionary `kwargs` | |
| ref: res = dict((k, v) for k, v in kwargs.items() if v is not None) | |
| Output: k for k, v in list ( dict. items ( ) ) if v is not none ] | |
| bleu_metric: 40.55760587628575 | |
| ---------- | |
| NL: capture final output of a chain of system commands `ps -ef | grep something | wc -l` | |
| ref: subprocess.check_output('ps -ef | grep something | wc -l', shell=True) | |
| Output: subprocess. call ( ['mystring. py ','mystring. py'] ) | |
| bleu_metric: 11.327304007996714 | |
| ---------- | |
| NL: concatenate a list of strings `['a', 'b', 'c']` | |
| ref: """""".join(['a', 'b', 'c']) | |
| Output: ['a ','b ','c ','d'] | |
| bleu_metric: 50.018452828984486 | |
| ---------- | |
| NL: find intersection data between series `s1` and series `s2` | |
| ref: pd.Series(list(set(s1).intersection(set(s2)))) | |
| Output: s1. intersection ( s1, how ='left'). reset _ index ( ) | |
| bleu_metric: 16.132580731955684 | |
| ---------- | |
| NL: sending http headers to `client` | |
| ref: client.send('HTTP/1.0 200 OK\r\n') | |
| Output: server. serve _ forever ( ) | |
| bleu_metric: 6.063168540538235 | |
| ---------- | |
| NL: format a datetime string `when` to extract date only | |
| ref: then = datetime.datetime.strptime(when, '%Y-%m-%d').date() | |
| Output: datetime. datetime. strptime ('2012 - 05 - 29t19 : 30 : 03. 283z ','% y | |
| bleu_metric: 28.12321366714592 | |
| ---------- | |
| NL: split a multi-line string `inputstring` into separate strings | |
| ref: inputString.split('\n') | |
| Output: from functools import reduce reduce ( lambda x, y : x + y, l. split ('' ), x. split ( | |
| bleu_metric: 14.32450175903488 | |
| ---------- | |
| NL: split a multi-line string ` a \n b \r\n c ` by new line character `\n` | |
| ref: ' a \n b \r\n c '.split('\n') | |
| Output: re. split ('\ n \ r \ n ','a \ n b \ n') | |
| bleu_metric: 65.72891674091409 | |
| ---------- | |
| NL: concatenate elements of list `b` by a colon ":" | |
| ref: """:""".join(str(x) for x in b) | |
| Output: ['a ','b'] | |
| bleu_metric: 6.490157276489835 | |
| ---------- | |
| NL: get the first object from a queryset in django model `entry` | |
| ref: Entry.objects.filter()[:1].get() | |
| Output: model. objects. all ( ). order _ by ('- id') [ 0 ] | |
| bleu_metric: 16.1692143534558 | |
| ---------- | |
| NL: calculate sum over all rows of 2d numpy array | |
| ref: a.sum(axis=1) | |
| Output: a. sum ( axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: enable warnings using action 'always' | |
| ref: warnings.simplefilter('always') | |
| Output: warnings. simplefilter ('always') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: concatenate items of list `l` with a space ' ' | |
| ref: print(' '.join(map(str, l))) | |
| Output: print (''. join ( [ str ( x ) for x in l ] ) ) | |
| bleu_metric: 42.5874468039002 | |
| ---------- | |
| NL: run script 'hello.py' with argument 'htmlfilename.htm' on terminal using python executable | |
| ref: subprocess.call(['python.exe', 'hello.py', 'htmlfilename.htm']) | |
| Output: subprocess. call ( ['python ','hello world ','world'] ) | |
| bleu_metric: 48.69018522875219 | |
| ---------- | |
| NL: how can i parse a time string containing milliseconds in it with python? | |
| ref: time.strptime('30/03/09 16:31:32.123', '%d/%m/%y %H:%M:%S.%f') | |
| Output: time. strptime ('2015 / 01 / 01 / 03. 0. 0. 0 ','% d / % m / % | |
| bleu_metric: 31.63951380366603 | |
| ---------- | |
| NL: convert a string `my_string` with dot and comma into a float number `my_float` | |
| ref: my_float = float(my_string.replace(',', '')) | |
| Output: my _ string. replace ( ', ','' ) | |
| bleu_metric: 59.23488237152091 | |
| ---------- | |
| NL: convert a string `123,456.908` with dot and comma into a floating number | |
| ref: float('123,456.908'.replace(',', '')) | |
| Output: " " " { 0 :,. 2f } " " ". format ( 12345678. 456789, 16 | |
| bleu_metric: 11.63901425102663 | |
| ---------- | |
| NL: set pythonpath in python script. | |
| ref: sys.path.append('/path/to/whatever') | |
| Output: os. path. join ( sys. path. append ('/ path / to / my / root') ) | |
| bleu_metric: 51.191407111567585 | |
| ---------- | |
| NL: split string 'words, words, words.' using a regex '(\\w+)' | |
| ref: re.split('(\\W+)', 'Words, words, words.') | |
| Output: re. split ('\ \ w + ','\ \ 1 ', words, words ) | |
| bleu_metric: 39.78163327099985 | |
| ---------- | |
| NL: open a file `output.txt` in append mode | |
| ref: file = open('Output.txt', 'a') | |
| Output: file. write ('whatever') | |
| bleu_metric: 17.234857810563913 | |
| ---------- | |
| NL: download a file "http://www.example.com/songs/mp3.mp3" over http and save to "mp3.mp3" | |
| ref: urllib.request.urlretrieve('http://www.example.com/songs/mp3.mp3', 'mp3.mp3') | |
| Output: urllib. request. urlretrieve ('http : / / www. google. com / file. gz ', ' | |
| bleu_metric: 49.30073739073773 | |
| ---------- | |
| NL: download a file `url` over http and save to `file_name` | |
| ref: u = urllib.request.urlopen(url) | |
| Output: response. close ( ) | |
| bleu_metric: 14.35549295013305 | |
| ---------- | |
| NL: download a file 'http://www.example.com/' over http | |
| ref: response = urllib.request.urlopen('http://www.example.com/') | |
| html = response.read() | |
| Output: urllib. request. urlretrieve ('http : / / www. google. com / ','http : / / | |
| bleu_metric: 42.4884196166917 | |
| ---------- | |
| NL: download a file `url` over http | |
| ref: r = requests.get(url) | |
| Output: response = requests. get ( url ) | |
| bleu_metric: 88.01117367933934 | |
| ---------- | |
| NL: download a file `url` over http and save to "10mb" | |
| ref: response = requests.get(url, stream=True) | |
| Output: urllib. request. urlretrieve ('http : / / example. com / file. ext ','wb ' | |
| bleu_metric: 6.370405230161802 | |
| ---------- | |
| NL: argparse add argument with flag '--version' and version action of '%(prog)s 2.0' to parser `parser` | |
| ref: parser.add_argument('--version', action='version', version='%(prog)s 2.0') | |
| Output: parser. add _ argument ('- - type ', nargs ='*') | |
| bleu_metric: 17.102327939516414 | |
| ---------- | |
| NL: remove key 'c' from dictionary `d` | |
| ref: {i: d[i] for i in d if i != 'c'} | |
| Output: d = { k : v for k, v in list ( d. items ( ) ) if v } | |
| bleu_metric: 9.637411586929977 | |
| ---------- | |
| NL: create new dataframe object by merging columns "key" of dataframes `split_df` and `csv_df` and rename the columns from dataframes `split_df` and `csv_df` with suffix `_left` and `_right` respectively | |
| ref: pd.merge(split_df, csv_df, on=['key'], suffixes=('_left', '_right')) | |
| Output: df. drop _ duplicates ( df. columns ['column _ name']. apply ( lambda x :'column _ name ' | |
| bleu_metric: 8.416754937260349 | |
| ---------- | |
| NL: split a string `s` by space with `4` splits | |
| ref: s.split(' ', 4) | |
| Output: ['x ','y ','x ','z ','x ','d ','x ', ' | |
| bleu_metric: 5.648892151960283 | |
| ---------- | |
| NL: read keyboard-input | |
| ref: input('Enter your input:') | |
| Output: input ('press enter to continue :') | |
| bleu_metric: 42.40125351805037 | |
| ---------- | |
| NL: enable debug mode on flask application `app` | |
| ref: app.run(debug=True) | |
| Output: app. run ( debug = true ) | |
| bleu_metric: 70.71067811865474 | |
| ---------- | |
| NL: python save list `mylist` to file object 'save.txt' | |
| ref: pickle.dump(mylist, open('save.txt', 'wb')) | |
| Output: savefig. save ('filename. txt') | |
| bleu_metric: 13.988120102483691 | |
| ---------- | |
| NL: multiply a matrix `p` with a 3d tensor `t` in scipy | |
| ref: scipy.tensordot(P, T, axes=[1, 1]).swapaxes(0, 1) | |
| Output: scipy. matrix ( [ 1, 2, 3 ], [ 1, 2, 3 ] ). t | |
| bleu_metric: 16.944114083600446 | |
| ---------- | |
| NL: create 3d array of zeroes of size `(3,3,3)` | |
| ref: numpy.zeros((3, 3, 3)) | |
| Output: np. zeros ( ( 3, 3 ) ). transpose ( 2, 3 ) | |
| bleu_metric: 54.166685749344154 | |
| ---------- | |
| NL: cut off the last word of a sentence `content` | |
| ref: """ """.join(content.split(' ')[:-1]) | |
| Output: " " " " " ". join ( content. split ('' ) [ : - 1 ] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert scalar `x` to array | |
| ref: x = np.asarray(x).reshape(1, -1)[(0), :] | |
| Output: x = np. zeros ( x ). reshape ( - 1, 2 ) | |
| bleu_metric: 35.6520799192373 | |
| ---------- | |
| NL: sum all elements of nested list `l` | |
| ref: sum(sum(i) if isinstance(i, list) else i for i in L) | |
| Output: sum ( [ i for i in l ) for i in l ] | |
| bleu_metric: 19.29709182756385 | |
| ---------- | |
| NL: convert hex string '470fc614' to a float number | |
| ref: struct.unpack('!f', '470FC614'.decode('hex'))[0] | |
| Output: struct. unpack ('d ', binascii. unhexlify ('4081637ef7d | |
| bleu_metric: 20.061882169394003 | |
| ---------- | |
| NL: multiple each value by `2` for all keys in a dictionary `my_dict` | |
| ref: my_dict.update((x, y * 2) for x, y in list(my_dict.items())) | |
| Output: dict ( ( k, v ) for k, v in list ( my _ dict. items ( ) ) ) | |
| bleu_metric: 35.25419189217226 | |
| ---------- | |
| NL: running bash script 'sleep.sh' | |
| ref: subprocess.call('sleep.sh', shell=True) | |
| Output: os. system ('echo $ command - flags arguments') | |
| bleu_metric: 14.814869013357976 | |
| ---------- | |
| NL: join elements of list `l` with a comma `,` | |
| ref: """,""".join(l) | |
| Output: " " ", " " ". join ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: make a comma-separated string from a list `mylist` | |
| ref: myList = ','.join(map(str, myList)) | |
| Output: mylist = [ i. split ( ',') for i in mylist ] | |
| bleu_metric: 15.526320210133507 | |
| ---------- | |
| NL: reverse the list that contains 1 to 10 | |
| ref: list(reversed(list(range(10)))) | |
| Output: [ ( 1, 2 ), ( 3, 4 ), ( 2, - 1 ), ( 1, 2 ), ( 3, 4 | |
| bleu_metric: 5.834347180338517 | |
| ---------- | |
| NL: remove substring 'bag,' from a string 'lamp, bag, mirror' | |
| ref: print('lamp, bag, mirror'.replace('bag,', '')) | |
| Output: re. sub ( ', ', ', '. join ( chr ) for ch in asking ) | |
| bleu_metric: 22.93476949918813 | |
| ---------- | |
| NL: reverse the order of words, delimited by `.`, in string `s` | |
| ref: """.""".join(s.split('.')[::-1]) | |
| Output: re. sub ('\ \ b (. + ) (? = \ \ s + \ \ s + ) ','\ \ 1 | |
| bleu_metric: 7.508325052934217 | |
| ---------- | |
| NL: convert epoch time represented as milliseconds `s` to string using format '%y-%m-%d %h:%m:%s.%f' | |
| ref: datetime.datetime.fromtimestamp(s).strftime('%Y-%m-%d %H:%M:%S.%f') | |
| Output: datetime. datetime. now ( ). strftime ('% y - % m - % d % h : % m : % | |
| bleu_metric: 42.22477323756999 | |
| ---------- | |
| NL: parse milliseconds epoch time '1236472051807' to format '%y-%m-%d %h:%m:%s' | |
| ref: time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(1236472051807 / 1000.0)) | |
| Output: datetime. datetime. now ( ). strftime ('% y - % m - % d % h : % m : % | |
| bleu_metric: 32.53778694644052 | |
| ---------- | |
| NL: get the date 7 days before the current date | |
| ref: (datetime.datetime.now() - datetime.timedelta(days=7)).date() | |
| Output: ( datetime. datetime. now ( ) - datetime. timedelta ( days = 1 ) ) | |
| bleu_metric: 69.7353047868514 | |
| ---------- | |
| NL: sum elements at index `column` of each list in list `data` | |
| ref: print(sum(row[column] for row in data)) | |
| Output: [ sum ( item ) for item in zip ( * items ) ] | |
| bleu_metric: 14.009047908905242 | |
| ---------- | |
| NL: sum columns of a list `array` | |
| ref: [sum(row[i] for row in array) for i in range(len(array[0]))] | |
| Output: sum ( i ) | |
| bleu_metric: 0.4295018804111146 | |
| ---------- | |
| NL: encode binary string 'your string' to base64 code | |
| ref: base64.b64encode(bytes('your string', 'utf-8')) | |
| Output: base64. b64encode ('base64') | |
| bleu_metric: 17.22724743406155 | |
| ---------- | |
| NL: combine list of dictionaries `dicts` with the same keys in each list to a single dictionary | |
| ref: dict((k, [d[k] for d in dicts]) for k in dicts[0]) | |
| Output: dict ( ( k, v ) for d in dicts for k, v in list ( d. items ( ) ) ) | |
| bleu_metric: 32.12854967972961 | |
| ---------- | |
| NL: merge a nested dictionary `dicts` into a flat dictionary by concatenating nested values with the same key `k` | |
| ref: {k: [d[k] for d in dicts] for k in dicts[0]} | |
| Output: dict ( ( k, v ) for d in list ( d. items ( ) ) for k, v in list ( d. items ( | |
| bleu_metric: 10.14524997386625 | |
| ---------- | |
| NL: how do i get the url parameter in a flask view | |
| ref: request.args['myParam'] | |
| Output: request. args ['myapp'] | |
| bleu_metric: 58.8837385313777 | |
| ---------- | |
| NL: identify duplicate values in list `mylist` | |
| ref: [k for k, v in list(Counter(mylist).items()) if v > 1] | |
| Output: [ k for k, v in mylist if v. startswith ('mycount') else v for k, v in mylist | |
| bleu_metric: 34.06227682100307 | |
| ---------- | |
| NL: insert directory 'apps' into directory `__file__` | |
| ref: sys.path.insert(1, os.path.join(os.path.dirname(__file__), 'apps')) | |
| Output: urllib. request. urlopen ('http : / / www. example. com ','/ path') | |
| bleu_metric: 9.10451572772839 | |
| ---------- | |
| NL: modify sys.path for python module `subdir` | |
| ref: sys.path.append(os.path.join(os.path.dirname(__file__), 'subdir')) | |
| Output: subprocess. call ( ['start / subdir / bin / 2014 ','inr'] ) | |
| bleu_metric: 8.368553758184737 | |
| ---------- | |
| NL: insert a 'none' value into a sqlite3 table. | |
| ref: db.execute("INSERT INTO present VALUES('test2', ?, 10)", (None,)) | |
| Output: cursor. execute ('select * from table where column in l') | |
| bleu_metric: 13.62767317410906 | |
| ---------- | |
| NL: flatten list `list_of_menuitems` | |
| ref: [image for menuitem in list_of_menuitems for image in menuitem] | |
| Output: [ item for sublist in list _ of _ lists ] | |
| bleu_metric: 17.194656088289214 | |
| ---------- | |
| NL: append elements of a set `b` to a list `a` | |
| ref: a.extend(b) | |
| Output: a. extend ( b ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: append elements of a set to a list in python | |
| ref: a.extend(list(b)) | |
| Output: a. extend ( b ) | |
| bleu_metric: 46.81294641285289 | |
| ---------- | |
| NL: write the data of dataframe `df` into text file `np.txt` | |
| ref: np.savetxt('c:\\data\\np.txt', df.values, fmt='%d') | |
| Output: df. to _ csv ('filename. txt ', header = false ) | |
| bleu_metric: 14.209004647554735 | |
| ---------- | |
| NL: write content of dataframe `df` into text file 'c:\\data\\pandas.txt' | |
| ref: df.to_csv('c:\\data\\pandas.txt', header=None, index=None, sep=' ', mode='a') | |
| Output: df. to _ csv ('c : \ \ data \ \ t. txt ', sep ='\ t ', index | |
| bleu_metric: 37.50344293811855 | |
| ---------- | |
| NL: split a string `x` by last occurrence of character `-` | |
| ref: print(x.rpartition('-')[0]) | |
| Output: x. split ('-') [ : - 1 ] | |
| bleu_metric: 43.353400970949984 | |
| ---------- | |
| NL: get the last part of a string before the character '-' | |
| ref: print(x.rsplit('-', 1)[0]) | |
| Output: re. sub ('(? < = - -. ) ','- ', line ) | |
| bleu_metric: 20.776569671187676 | |
| ---------- | |
| NL: upload file using ftp | |
| ref: ftp.storlines('STOR ' + filename, open(filename, 'r')) | |
| Output: ftp. quit ( ) | |
| bleu_metric: 3.710176016036221 | |
| ---------- | |
| NL: add one to the hidden web element with id 'xyz' with selenium python script | |
| ref: browser.execute_script("document.getElementById('XYZ').value+='1'") | |
| Output: driver. execute _ script ('window. scrollto ( 0, document. body. com') ) | |
| bleu_metric: 10.807499589580493 | |
| ---------- | |
| NL: create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]` | |
| ref: np.maximum([2, 3, 4], [1, 5, 2]) | |
| Output: np. array ( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] ) | |
| bleu_metric: 28.489318277723964 | |
| ---------- | |
| NL: print a list `l` and move first 3 elements to the end of the list | |
| ref: print(l[3:] + l[:3]) | |
| Output: print ( [ l [ 3 : ] for l in l ] ) | |
| bleu_metric: 43.472087194499146 | |
| ---------- | |
| NL: loop over files in directory '.' | |
| ref: for fn in os.listdir('.'): | |
| Output: for ( root, dirs, dirs ) in os. walk ( '.') : if os. path. isdir ( ' | |
| bleu_metric: 26.167475767017738 | |
| ---------- | |
| NL: loop over files in directory `source` | |
| ref: for (root, dirs, filenames) in os.walk(source): | |
| Output: os. listdir ('os') | |
| bleu_metric: 9.081302407530908 | |
| ---------- | |
| NL: create a random list of integers | |
| ref: [int(1000 * random.random()) for i in range(10000)] | |
| Output: random. choice ( [ int ( i ) for i in range ( 10 ) ] ) | |
| bleu_metric: 38.95592996402149 | |
| ---------- | |
| NL: using %f with strftime() in python to get microseconds | |
| ref: datetime.datetime.now().strftime('%H:%M:%S.%f') | |
| Output: datetime. datetime. now ( ). strftime ('% h : % m : % s. % f') | |
| bleu_metric: 69.80972118841675 | |
| ---------- | |
| NL: google app engine execute gql query 'select * from schedule where station = $1' with parameter `foo.key()` | |
| ref: db.GqlQuery('SELECT * FROM Schedule WHERE station = $1', foo.key()) | |
| Output: c. objects. filter ( id = 1 ). order _ by ('foobar') | |
| bleu_metric: 10.30143863225941 | |
| ---------- | |
| NL: filter rows in pandas starting with alphabet 'f' using regular expression. | |
| ref: df.b.str.contains('^f') | |
| Output: df ['a']. str. contains ('c') | |
| bleu_metric: 43.85068972747104 | |
| ---------- | |
| NL: print a 2 dimensional list `tab` as a table with delimiters | |
| ref: print('\n'.join('\t'.join(str(col) for col in row) for row in tab)) | |
| Output: print ('\ n '. join ( map ( str, row ) ) ) | |
| bleu_metric: 28.575895973659073 | |
| ---------- | |
| NL: pandas: delete rows in dataframe `df` based on multiple columns values | |
| ref: df.set_index(list('BC')).drop(tuples, errors='ignore').reset_index() | |
| Output: df. drop ( df. index. map ( lambda x : x. index, axis = 1 ) ) | |
| bleu_metric: 13.21619418680914 | |
| ---------- | |
| NL: format the variables `self.goals` and `self.penalties` using string formatting | |
| ref: """({:d} goals, ${:d})""".format(self.goals, self.penalties) | |
| Output: " " " { } \ \ w { }. { } " " ". format ( date,'user') | |
| bleu_metric: 28.34142738530324 | |
| ---------- | |
| NL: format string "({} goals, ${})" with variables `goals` and `penalties` | |
| ref: """({} goals, ${})""".format(self.goals, self.penalties) | |
| Output: " " " { } { } " " ". format ( {'foo':'bar'} ) | |
| bleu_metric: 34.620457601133026 | |
| ---------- | |
| NL: format string "({0.goals} goals, ${0.penalties})" | |
| ref: """({0.goals} goals, ${0.penalties})""".format(self) | |
| Output: df. set _ index ('name') | |
| bleu_metric: 3.9504404329436813 | |
| ---------- | |
| NL: convert list of lists `l` to list of integers | |
| ref: [int(''.join(str(d) for d in x)) for x in L] | |
| Output: [ int ( x ) for x in l ] | |
| bleu_metric: 18.761335250609946 | |
| ---------- | |
| NL: combine elements of each list in list `l` into digits of a single integer | |
| ref: [''.join(str(d) for d in x) for x in L] | |
| Output: map ( int, l ) | |
| bleu_metric: 2.5915324112223583 | |
| ---------- | |
| NL: convert a list of lists `l` to list of integers | |
| ref: L = [int(''.join([str(y) for y in x])) for x in L] | |
| Output: [ int ( x ) for x in l ] | |
| bleu_metric: 11.473780780974305 | |
| ---------- | |
| NL: write the elements of list `lines` concatenated by special character '\n' to file `myfile` | |
| ref: myfile.write('\n'.join(lines)) | |
| Output: myfile. write ('\ n '. join ( map ( str, mylist ) ) ) | |
| bleu_metric: 60.5478327168454 | |
| ---------- | |
| NL: removing an element from a list based on a predicate 'x' or 'n' | |
| ref: [x for x in ['AAT', 'XAC', 'ANT', 'TTA'] if 'X' not in x and 'N' not in x] | |
| Output: [ x for x in [ x if x! ='b'] if x! ='c'] | |
| bleu_metric: 20.792998340455547 | |
| ---------- | |
| NL: remove duplicate words from a string `text` using regex | |
| ref: text = re.sub('\\b(\\w+)( \\1\\b)+', '\\1', text) | |
| Output: re. sub ('(. * ) \ \ 1 + ','\ \ 1 ', text ) | |
| bleu_metric: 42.691904454956294 | |
| ---------- | |
| NL: count non zero values in each column in pandas data frame | |
| ref: df.astype(bool).sum(axis=1) | |
| Output: df. groupby ( level = 0 ). sum ( ) | |
| bleu_metric: 33.17838167503153 | |
| ---------- | |
| NL: search for string that matches regular expression pattern '(?<!distillr)\\\\acrotray\\.exe' in string 'c:\\somedir\\acrotray.exe' | |
| ref: re.search('(?<!Distillr)\\\\AcroTray\\.exe', 'C:\\SomeDir\\AcroTray.exe') | |
| Output: print ( re. sub ('[ \ \ w _ ] + ','', str1 ). split ( ',') | |
| bleu_metric: 11.01072272272342 | |
| ---------- | |
| NL: split string 'qh qd jc kd js' into a list on white spaces | |
| ref: """QH QD JC KD JS""".split() | |
| Output: ['abcd ','* ','2 ','3 ','4 ','g ','g ', | |
| bleu_metric: 5.682676011595584 | |
| ---------- | |
| NL: search for occurrences of regex pattern '>.*<' in xml string `line` | |
| ref: print(re.search('>.*<', line).group(0)) | |
| Output: print ( doc. group ( 1 ). group ( 1 ) ) | |
| bleu_metric: 22.08517793512327 | |
| ---------- | |
| NL: erase all the contents of a file `filename` | |
| ref: open(filename, 'w').close() | |
| Output: open ('filename ','w'). close ( ) | |
| bleu_metric: 70.98108717509044 | |
| ---------- | |
| NL: convert a string into datetime using the format '%y-%m-%d %h:%m:%s.%f' | |
| ref: datetime.datetime.strptime(string_date, '%Y-%m-%d %H:%M:%S.%f') | |
| Output: datetime. datetime. strptime ('2010 - 11 - 13 10 : 33 : 54. 227806 ','% y | |
| bleu_metric: 23.69248624414322 | |
| ---------- | |
| NL: find the index of a list with the first element equal to '332' within the list of lists `thelist` | |
| ref: [index for index, item in enumerate(thelist) if item[0] == '332'] | |
| Output: [ x [ 0 ] for x in l if x [ 0 ] = ='score'] | |
| bleu_metric: 32.08521724608534 | |
| ---------- | |
| NL: lower a string `text` and remove non-alphanumeric characters aside from space | |
| ref: re.sub('[^\\sa-zA-Z0-9]', '', text).lower().strip() | |
| Output: re. sub ('[ ^ \ \ w. ] + ','', text ). strip ( ) | |
| bleu_metric: 54.08690470924506 | |
| ---------- | |
| NL: remove all non-alphanumeric characters except space from a string `text` and lower it | |
| ref: re.sub('(?!\\s)[\\W_]', '', text).lower().strip() | |
| Output: re. sub ('[ ^ 0 - 9a - za - z ] + ','', s ). strip ( ) | |
| bleu_metric: 38.80387619814469 | |
| ---------- | |
| NL: subscript text 'h20' with '2' as subscripted in matplotlib labels for arrays 'x' and 'y'. | |
| ref: plt.plot(x, y, label='H\u2082O') | |
| Output: plt. plot ( x, y, label ='h \ u2082o') | |
| bleu_metric: 72.76817202342092 | |
| ---------- | |
| NL: subscript text 'h20' with '2' as subscripted in matplotlib labels for arrays 'x' and 'y'. | |
| ref: plt.plot(x, y, label='$H_2O$') | |
| Output: plt. plot ( x, y, label ='h \ u2082o') | |
| bleu_metric: 71.60350546947922 | |
| ---------- | |
| NL: loop over a list `mylist` if sublists length equals 3 | |
| ref: [x for x in mylist if len(x) == 3] | |
| Output: mylist [ : : 2 ] = = mylist [ 0 ] | |
| bleu_metric: 12.640504935009755 | |
| ---------- | |
| NL: initialize a list `lst` of 100 objects object() | |
| ref: lst = [Object() for _ in range(100)] | |
| Output: lst = [ [ ] for i in range ( 3 ) ] | |
| bleu_metric: 28.992166587078206 | |
| ---------- | |
| NL: create list `lst` containing 100 instances of object `object` | |
| ref: lst = [Object() for i in range(100)] | |
| Output: list ( chain. from _ iterable ( l ) ) | |
| bleu_metric: 10.649638629044373 | |
| ---------- | |
| NL: get the content of child tag with`href` attribute whose parent has css `someclass` | |
| ref: self.driver.find_element_by_css_selector('.someclass a').get_attribute('href') | |
| Output: soup. find _ elements _ by _ xpath ( " / / * [ @ type ='submit'] " ). get _ attribute ( | |
| bleu_metric: 10.389889377420038 | |
| ---------- | |
| NL: joining data from dataframe `df1` with data from dataframe `df2` based on matching values of column 'date_time' in both dataframes | |
| ref: df1.merge(df2, on='Date_Time') | |
| Output: pd. merge ( df1, df2, on = ['date _ time ','date _ time'] ) | |
| bleu_metric: 21.22363344155404 | |
| ---------- | |
| NL: use `%s` operator to print variable values `str1` inside a string | |
| ref: 'first string is: %s, second one is: %s' % (str1, 'geo.tif') | |
| Output: print ( str ( a ) +'' + str ( b ) ) | |
| bleu_metric: 6.053698751871955 | |
| ---------- | |
| NL: split a string by a delimiter in python | |
| ref: [x.strip() for x in '2.MATCHES $$TEXT$$ STRING'.split('$$TEXT$$')] | |
| Output: s. split (';') | |
| bleu_metric: 2.904501569386027 | |
| ---------- | |
| NL: check if directory `directory ` exists and create it if necessary | |
| ref: if (not os.path.exists(directory)): | |
| os.makedirs(directory) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 9.597952472216766 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: distutils.dir_util.mkpath(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 40.661103887968814 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: distutils.dir_util.mkpath(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 40.661103887968814 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: os.makedirs(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: replace a separate word 'h3' by 'h1' in a string 'text' | |
| ref: re.sub('\\bH3\\b', 'H1', text) | |
| Output: re. sub ('\ \ w + ','\ \ 1 ', text ) | |
| bleu_metric: 56.31071891586576 | |
| ---------- | |
| NL: substitute ascii letters in string 'aas30dsa20' with empty string '' | |
| ref: re.sub('\\D', '', 'aas30dsa20') | |
| Output: re. sub ('[ a - z ] ','','abcd') | |
| bleu_metric: 51.003234295212685 | |
| ---------- | |
| NL: get digits only from a string `aas30dsa20` using lambda function | |
| ref: """""".join([x for x in 'aas30dsa20' if x.isdigit()]) | |
| Output: " " " " " ". join ( map ( lambda x : x * 7 ) ) | |
| bleu_metric: 39.54603942707659 | |
| ---------- | |
| NL: access a tag called "name" in beautifulsoup `soup` | |
| ref: print(soup.find('name').string) | |
| Output: soup. findall ('div ', {'name':'description'} ) | |
| bleu_metric: 17.457404828304572 | |
| ---------- | |
| NL: get a dictionary `records` of key-value pairs in pymongo cursor `cursor` | |
| ref: records = dict((record['_id'], record) for record in cursor) | |
| Output: dict ( ( x [ 0 ], x [ 1 ] ) for x in cursor. items ( ) ) | |
| bleu_metric: 16.34453553468014 | |
| ---------- | |
| NL: create new matrix object by concatenating data from matrix a and matrix b | |
| ref: np.concatenate((A, B)) | |
| Output: numpy. concatenate ( [ a, b ], axis = 0 ) | |
| bleu_metric: 18.92240568795935 | |
| ---------- | |
| NL: concat two matrices `a` and `b` in numpy | |
| ref: np.vstack((A, B)) | |
| Output: np. sqrt ( ( a. shape [ 0 ], b ) ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: get the characters count in a file `filepath` | |
| ref: os.stat(filepath).st_size | |
| Output: print ( os. path. join ( get _ list ) ) | |
| bleu_metric: 13.834368456410944 | |
| ---------- | |
| NL: count the occurrences of item "a" in list `l` | |
| ref: l.count('a') | |
| Output: [ x. count ('a') for x in l ] | |
| bleu_metric: 43.78826865860793 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: Counter(l) | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 13.065113298388567 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: [[x, l.count(x)] for x in set(l)] | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 16.443715448412245 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: dict(((x, l.count(x)) for x in set(l))) | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 15.927159921457768 | |
| ---------- | |
| NL: count the occurrences of item "b" in list `l` | |
| ref: l.count('b') | |
| Output: [ ( x, l. count ( x ) ) for x in l ] | |
| bleu_metric: 20.61477352156375 | |
| ---------- | |
| NL: copy file `srcfile` to directory `dstdir` | |
| ref: shutil.copy(srcfile, dstdir) | |
| Output: os. chdir ('copyfile. txt ', dst ) | |
| bleu_metric: 14.949751774990682 | |
| ---------- | |
| NL: find the key associated with the largest value in dictionary `x` whilst key is non-zero value | |
| ref: max(k for k, v in x.items() if v != 0) | |
| Output: max ( x, key = lambda x : ( x ['x'], x ['x'] ) ) | |
| bleu_metric: 9.186672262412122 | |
| ---------- | |
| NL: get the largest key whose not associated with value of 0 in dictionary `x` | |
| ref: (k for k, v in x.items() if v != 0) | |
| Output: ( k for k, v in x. items ( ) if v! = 0 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the largest key in a dictionary `x` with non-zero value | |
| ref: max(k for k, v in x.items() if v != 0) | |
| Output: max ( x, key = lambda x : ( x [ 1 ], x [ 0 ] ) ) | |
| bleu_metric: 11.162938527547608 | |
| ---------- | |
| NL: put the curser at beginning of the file | |
| ref: file.seek(0) | |
| Output: os. system ('with open ( filename ). read ( ) ','rb') | |
| bleu_metric: 8.687475782716618 | |
| ---------- | |
| NL: combine values from column 'b' and column 'a' of dataframe `df` into column 'c' of datafram `df` | |
| ref: df['c'] = np.where(df['a'].isnull, df['b'], df['a']) | |
| Output: df ['c'] = df ['a']. apply ( lambda x : x ['b']. fillna | |
| bleu_metric: 43.50703089837015 | |
| ---------- | |
| NL: remove key 'ele' from dictionary `d` | |
| ref: del d['ele'] | |
| Output: d = { k : v for k, v in list ( d. items ( ) ) if v! ='none'} | |
| bleu_metric: 5.442133807846856 | |
| ---------- | |
| NL: update datetime field in `mymodel` to be the existing `timestamp` plus 100 years | |
| ref: MyModel.objects.update(timestamp=F('timestamp') + timedelta(days=36524.25)) | |
| Output: mymodel. objects. filter ( date _ _ length ='% y - % m - % d') | |
| bleu_metric: 12.351156662075915 | |
| ---------- | |
| NL: merge list `['it']` and list `['was']` and list `['annoying']` into one list | |
| ref: ['it'] + ['was'] + ['annoying'] | |
| Output: [ dict ( zip ( * [ ['one ','two'], ['one ','two'] ] ) ) | |
| bleu_metric: 10.105385745342533 | |
| ---------- | |
| NL: increment a value with leading zeroes in a number `x` | |
| ref: str(int(x) + 1).zfill(len(x)) | |
| Output: print ('\ xe, { 0 : x. 0 } '. format ( x, - 1 ) ) | |
| bleu_metric: 13.471766853689124 | |
| ---------- | |
| NL: check if a pandas dataframe `df`'s index is sorted | |
| ref: all(df.index[:-1] <= df.index[1:]) | |
| Output: df. index. values. tolist ( ) | |
| bleu_metric: 9.980212030371455 | |
| ---------- | |
| NL: convert tuple `t` to list | |
| ref: list(t) | |
| Output: list ( t ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert list `t` to tuple | |
| ref: tuple(l) | |
| Output: tuple ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert tuple `level1` to list | |
| ref: level1 = map(list, level1) | |
| Output: [ ( x + 1 ) for x in a ] | |
| bleu_metric: 11.35935489027116 | |
| ---------- | |
| NL: send the output of pprint object `dataobject` to file `logfile` | |
| ref: pprint.pprint(dataobject, logFile) | |
| Output: sys. stdout. flush ( ) | |
| bleu_metric: 18.67587389639562 | |
| ---------- | |
| NL: get index of rows in column 'boolcol' | |
| ref: df.loc[df['BoolCol']] | |
| Output: df [ ['bool'] ]. index ('matrix2'). index | |
| bleu_metric: 18.710158230410627 | |
| ---------- | |
| NL: create a list containing the indexes of rows where the value of column 'boolcol' in dataframe `df` are equal to true | |
| ref: df.iloc[np.flatnonzero(df['BoolCol'])] | |
| Output: df [ df. index. map ( lambda x : x [ 1 ]. isin ( x ) ) ] | |
| bleu_metric: 14.72821272412462 | |
| ---------- | |
| NL: get list of indexes of rows where column 'boolcol' values match true | |
| ref: df[df['BoolCol'] == True].index.tolist() | |
| Output: [ df ['ratio'] for d in l ] | |
| bleu_metric: 19.62812256638541 | |
| ---------- | |
| NL: get index of rows in dataframe `df` which column 'boolcol' matches value true | |
| ref: df[df['BoolCol']].index.tolist() | |
| Output: df ['column _ name'] = = df. index. str. get ('columnid') | |
| bleu_metric: 18.088201962129936 | |
| ---------- | |
| NL: change working directory to the directory `owd` | |
| ref: os.chdir(owd) | |
| Output: os. chdir ('c : \ \ users \ \ users \ \ desktop \ \ desktop \ \ python') | |
| bleu_metric: 15.553986407412198 | |
| ---------- | |
| NL: insert data from a string `testfield` to sqlite db `c` | |
| ref: c.execute("INSERT INTO test VALUES (?, 'bar')", (testfield,)) | |
| Output: cursor. execute ('insert into table values ( % s, % s ) ', ( ast, ) ) | |
| bleu_metric: 34.29547961820059 | |
| ---------- | |
| NL: decode string "\\x89\\n" into a normal string | |
| ref: """\\x89\\n""".decode('string_escape') | |
| Output: " " " \ \ xc3 \ \ x96 \ xc3 \ x96 \ x93 \ x93 \ | |
| bleu_metric: 29.888108815158 | |
| ---------- | |
| NL: convert a raw string `raw_string` into a normal string | |
| ref: raw_string.decode('string_escape') | |
| Output: raw _ string. decode ('unicode _ escape') | |
| bleu_metric: 35.556702356686955 | |
| ---------- | |
| NL: convert a raw string `raw_byte_string` into a normal string | |
| ref: raw_byte_string.decode('unicode_escape') | |
| Output: raw _ byte _ string. decode ('unicode _ escape') | |
| bleu_metric: 30.620490882364876 | |
| ---------- | |
| NL: split a string `s` with into all strings of repeated characters | |
| ref: [m.group(0) for m in re.finditer('(\\d)\\1*', s)] | |
| Output: re. findall ('\ \ d + (? : \ \ s + ) ','a \ n b') | |
| bleu_metric: 12.792208459604188 | |
| ---------- | |
| NL: scatter a plot with x, y position of `np.random.randn(100)` and face color equal to none | |
| ref: plt.scatter(np.random.randn(100), np.random.randn(100), facecolors='none') | |
| Output: plt. show ( ) | |
| bleu_metric: 0.8278521686370256 | |
| ---------- | |
| NL: do a scatter plot with empty circles | |
| ref: plt.plot(np.random.randn(100), np.random.randn(100), 'o', mfc='none') | |
| Output: plt. show ( ) | |
| bleu_metric: 0.42503347540650505 | |
| ---------- | |
| NL: remove a div with a id `main-content` using beautifulsoup | |
| ref: soup.find('div', id='main-content').decompose() | |
| Output: soup. find _ all ('div ', id ='all') | |
| bleu_metric: 46.28692516962493 | |
| ---------- | |
| NL: filter rows containing key word `ball` in column `ids` | |
| ref: df[df['ids'].str.contains('ball')] | |
| Output: id. idxmax ( word ). drop ( false ) | |
| bleu_metric: 7.630799524006417 | |
| ---------- | |
| NL: convert index at level 0 into a column in dataframe `df` | |
| ref: df.reset_index(level=0, inplace=True) | |
| Output: df. set _ index ('level1'). unstack ( level = 0 ) | |
| bleu_metric: 24.29335519279861 | |
| ---------- | |
| NL: add indexes in a data frame `df` to a column `index1` | |
| ref: df['index1'] = df.index | |
| Output: df. loc [ index ] = df. index. get _ level _ values ( 0 ) | |
| bleu_metric: 24.04315522172745 | |
| ---------- | |
| NL: convert pandas index in a dataframe to columns | |
| ref: df.reset_index(level=['tick', 'obs']) | |
| Output: df. reset _ index ( drop = true ) | |
| bleu_metric: 11.884004683899853 | |
| ---------- | |
| NL: get reverse of list items from list 'b' using extended slicing | |
| ref: [x[::-1] for x in b] | |
| Output: [ [ i [ 0 ] for i in range ( len ( x ) ) ] for i in range ( 0, len ( list ( a ) | |
| bleu_metric: 7.434360163166209 | |
| ---------- | |
| NL: join each element in array `a` with element at the same index in array `b` as a tuple | |
| ref: np.array([zip(x, y) for x, y in zip(a, b)]) | |
| Output: b = np. where ( a = = 0, axis = 0 ) | |
| bleu_metric: 9.027924728401977 | |
| ---------- | |
| NL: zip two 2-d arrays `a` and `b` | |
| ref: np.array(zip(a.ravel(), b.ravel()), dtype='i4,i4').reshape(a.shape) | |
| Output: np. array ( zip ( a. ravel ( ), b. ravel ( ) ), dtype ='i4, i4 | |
| bleu_metric: 67.03200460356393 | |
| ---------- | |
| NL: convert list `list_of_ints` into a comma separated string | |
| ref: """,""".join([str(i) for i in list_of_ints]) | |
| Output: list ( map ( int, list _ of _ lists. split ( ',') ) ) | |
| bleu_metric: 13.283261562346366 | |
| ---------- | |
| NL: send a post request with raw data `data` and basic authentication with `username` and `password` | |
| ref: requests.post(url, data=DATA, headers=HEADERS_DICT, auth=(username, password)) | |
| Output: r = requests. post ( url, data = {'username':'username'} ) | |
| bleu_metric: 37.18698903847989 | |
| ---------- | |
| NL: find last occurrence of character '}' in string "abcd}def}" | |
| ref: 'abcd}def}'.rfind('}') | |
| Output: re. findall ('(? : ^ [ a - z ] * ) ','abcd0') | |
| bleu_metric: 12.962472880491877 | |
| ---------- | |
| NL: iterate ove list `[1, 2, 3]` using list comprehension | |
| ref: print([item for item in [1, 2, 3]]) | |
| Output: [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ] | |
| bleu_metric: 27.073362211548464 | |
| ---------- | |
| NL: extract all the values with keys 'x' and 'y' from a list of dictionaries `d` to list of tuples | |
| ref: [(x['x'], x['y']) for x in d] | |
| Output: [ d ['x'] for d in x if'y'in d ] | |
| bleu_metric: 37.60857585579953 | |
| ---------- | |
| NL: get the filename without the extension from file 'hemanth.txt' | |
| ref: print(os.path.splitext(os.path.basename('hemanth.txt'))[0]) | |
| Output: print ( glob. glob ('liname. txt') [ 0 ] ) | |
| bleu_metric: 31.18776276497533 | |
| ---------- | |
| NL: create a dictionary by adding each two adjacent elements in tuple `x` as key/value pair to it | |
| ref: dict(x[i:i + 2] for i in range(0, len(x), 2)) | |
| Output: dict ( ( k, v ) for k, v in zip ( a, b ) ) | |
| bleu_metric: 12.152094584183624 | |
| ---------- | |
| NL: create a list containing flattened list `[['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]` | |
| ref: values = sum([['A', 'B', 'C'], ['D', 'E', 'F'], ['G', 'H', 'I']], []) | |
| Output: [ ['a ','b ','c ','d'], ['a ','b ','c | |
| bleu_metric: 17.7080331882364 | |
| ---------- | |
| NL: select rows in a dataframe `df` column 'closing_price' between two values 99 and 101 | |
| ref: df = df[(df['closing_price'] >= 99) & (df['closing_price'] <= 101)] | |
| Output: df. loc [ ~ df ['column _ name']. isin ( some _ values ('one ','two | |
| bleu_metric: 10.948000684047626 | |
| ---------- | |
| NL: replace all occurences of newlines `\n` with `<br>` in dataframe `df` | |
| ref: df.replace({'\n': '<br>'}, regex=True) | |
| Output: df. replace ('\ n ','\ n') | |
| bleu_metric: 27.770641036119947 | |
| ---------- | |
| NL: replace all occurrences of a string `\n` by string `<br>` in a pandas data frame `df` | |
| ref: df.replace({'\n': '<br>'}, regex=True) | |
| Output: df. replace ( {'\ n':'< br >'}, regex = true ) | |
| bleu_metric: 89.21616972156079 | |
| ---------- | |
| NL: create a list containing each two adjacent letters in string `word` as its elements | |
| ref: [(x + y) for x, y in zip(word, word[1:])] | |
| Output: [ ('a ','b'), ('b ','c'), ('c ','d') | |
| bleu_metric: 8.840994001530046 | |
| ---------- | |
| NL: get a list of pairs from a string `word` using lambda function | |
| ref: list(map(lambda x, y: x + y, word[:-1], word[1:])) | |
| Output: [ str ( x ) for x in words ] | |
| bleu_metric: 3.8733243551839442 | |
| ---------- | |
| NL: extract a url from a string `mystring` | |
| ref: print(re.findall('(https?://[^\\s]+)', myString)) | |
| Output: print ( re. search ('( https? : / / [ ^ \ \ s ] + ) ', mystring ) ) | |
| bleu_metric: 77.28607577370117 | |
| ---------- | |
| NL: extract a url from a string `mystring` | |
| ref: print(re.search('(?P<url>https?://[^\\s]+)', myString).group('url')) | |
| Output: print ( re. search ('( https? : / / [ ^ \ \ s ] + ) ', mystring ) ) | |
| bleu_metric: 51.916231800616785 | |
| ---------- | |
| NL: remove all special characters, punctuation and spaces from a string `mystring` using regex | |
| ref: re.sub('[^A-Za-z0-9]+', '', mystring) | |
| Output: re. sub ('\ \ w + (? = [ ^. ] * ) ','', mystring ) | |
| bleu_metric: 45.91497693322866 | |
| ---------- | |
| NL: create a datetimeindex containing 13 periods of the second friday of each month starting from date '2016-01-01' | |
| ref: pd.date_range('2016-01-01', freq='WOM-2FRI', periods=13) | |
| Output: df. groupby ('date'). apply ( lambda x : x. year ) | |
| bleu_metric: 8.453429635655233 | |
| ---------- | |
| NL: create multidimensional array `matrix` with 3 rows and 2 columns in python | |
| ref: matrix = [[a, b], [c, d], [e, f]] | |
| Output: matrix = np. concatenate ( ( [ a, b ], [ [ 0, 3, 6 ], [ 7, 8, 9 | |
| bleu_metric: 32.24199399071415 | |
| ---------- | |
| NL: replace spaces with underscore | |
| ref: mystring.replace(' ', '_') | |
| Output: re. sub ('\ \ b (? = \ \ w + ) ','','this is a string ','replace | |
| bleu_metric: 14.117915208981241 | |
| ---------- | |
| NL: get an absolute file path of file 'mydir/myfile.txt' | |
| ref: os.path.abspath('mydir/myfile.txt') | |
| Output: os. path. join ( os. path. join ( root, myfile ) ) | |
| bleu_metric: 23.83041256525615 | |
| ---------- | |
| NL: split string `my_string` on white spaces | |
| ref: """ """.join(my_string.split()) | |
| Output: " " " 42, " " ". split ( ) | |
| bleu_metric: 47.72263780018156 | |
| ---------- | |
| NL: get filename without extension from file `filename` | |
| ref: os.path.splitext(filename)[0] | |
| Output: os. path. splitext ( filename ) [ 0 ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get a list containing the sum of each element `i` in list `l` plus the previous elements | |
| ref: [sum(l[:i]) for i, _ in enumerate(l)] | |
| Output: sum ( x for x in l ) | |
| bleu_metric: 6.924459302580939 | |
| ---------- | |
| NL: split a string `docs/src/scripts/temp` by `/` keeping `/` in the result | |
| ref: """Docs/src/Scripts/temp""".replace('/', '/\x00/').split('\x00') | |
| Output: re. split ('\ \ s + ', string ) | |
| bleu_metric: 7.926805970774756 | |
| ---------- | |
| NL: shuffle columns of an numpy array 'r' | |
| ref: np.random.shuffle(np.transpose(r)) | |
| Output: np. random. shuffle ( np. transpose ( r ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: copy all values in a column 'b' to a new column 'd' in a pandas data frame 'df' | |
| ref: df['D'] = df['B'] | |
| Output: df ['b'] = df ['c']. apply ( lambda x : x. tolist ( ), axis = | |
| bleu_metric: 26.492685902784498 | |
| ---------- | |
| NL: find a value within nested json 'data' where the key inside another key 'b' is unknown. | |
| ref: list(data['A']['B'].values())[0]['maindata'][0]['Info'] | |
| Output: print ( json. dumps ( ['b ','c'] ) ) | |
| bleu_metric: 5.979778160388024 | |
| ---------- | |
| NL: check characters of string `string` are true predication of function `predicate` | |
| ref: all(predicate(x) for x in string) | |
| Output: print ( any ( test _ string ) for substring in string _ string ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: determine number of files on a drive with python | |
| ref: os.statvfs('/').f_files - os.statvfs('/').f_ffree | |
| Output: os. stat ('/ myfile. txt'). os. listdir ('/') | |
| bleu_metric: 37.211067993181466 | |
| ---------- | |
| NL: how to get a single result from a sqlite query in python? | |
| ref: cursor.fetchone()[0] | |
| Output: cursor. fetchone ( ) [ 0 ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert string `user_input` into a list of integers `user_list` | |
| ref: user_list = [int(number) for number in user_input.split(',')] | |
| Output: [ int ( x ) for x in input ( ). split ( ',') ] | |
| bleu_metric: 51.23350305765596 | |
| ---------- | |
| NL: get a list of integers by splitting a string `user` with comma | |
| ref: [int(s) for s in user.split(',')] | |
| Output: map ( int,'re. split ('( \ \ d + ) ','hello world') ) | |
| bleu_metric: 20.80315522738391 | |
| ---------- | |
| NL: sorting a python list by two criteria | |
| ref: sorted(list, key=lambda x: (x[0], -x[1])) | |
| Output: sorted ( list ( range ( 0, len ( s ) ), key = lambda x : ( x [ 0 ], x [ 0 ] ) | |
| bleu_metric: 48.440807796000044 | |
| ---------- | |
| NL: sort a list of objects `ut`, based on a function `cmpfun` in descending order | |
| ref: ut.sort(key=cmpfun, reverse=True) | |
| Output: sorted ( l, key = lambda x : x. modified, reverse = true ) | |
| bleu_metric: 18.107197870881738 | |
| ---------- | |
| NL: reverse list `ut` based on the `count` attribute of each object | |
| ref: ut.sort(key=lambda x: x.count, reverse=True) | |
| Output: ['city ','same2 ','type'] | |
| bleu_metric: 7.453337029678027 | |
| ---------- | |
| NL: sort a list of objects `ut` in reverse order by their `count` property | |
| ref: ut.sort(key=lambda x: x.count, reverse=True) | |
| Output: sorted ( l, key = lambda x : x. count ('c') ) | |
| bleu_metric: 45.34010611888324 | |
| ---------- | |
| NL: click a href button 'send' with selenium | |
| ref: driver.find_element_by_partial_link_text('Send').click() | |
| Output: driver. find _ element _ by _ css _ selector ('send inmail'). click ( ) | |
| bleu_metric: 35.13874939965221 | |
| ---------- | |
| NL: click a href button having text `send inmail` with selenium | |
| ref: driver.findElement(By.linkText('Send InMail')).click() | |
| Output: driver. find _ element _ by _ link _ text ('send inmail') | |
| bleu_metric: 15.175278640994005 | |
| ---------- | |
| NL: click a href button with text 'send inmail' with selenium | |
| ref: driver.find_element_by_link_text('Send InMail').click() | |
| Output: driver. find _ element _ by _ link _ text ('send') | |
| bleu_metric: 15.27678022374703 | |
| ---------- | |
| NL: cast an int `i` to a string and concat to string 'me' | |
| ref: 'ME' + str(i) | |
| Output: str ( i ) | |
| bleu_metric: 36.787944117144235 | |
| ---------- | |
| NL: sorting data in dataframe pandas | |
| ref: df.sort_values(['System_num', 'Dis']) | |
| Output: df. sort _ values ( ['system _ id ','dis'] ) | |
| bleu_metric: 31.671615012203976 | |
| ---------- | |
| NL: prepend the line '#test firstline\n' to the contents of file 'infile' and save as the file 'outfile' | |
| ref: open('outfile', 'w').write('#test firstline\n' + open('infile').read()) | |
| Output: open ('test. txt ','w'). write ('\ n') | |
| bleu_metric: 36.176855814312944 | |
| ---------- | |
| NL: sort a list `l` by length of value in tuple | |
| ref: l.sort(key=lambda t: len(t[1]), reverse=True) | |
| Output: l. sort ( key = lambda x : ( len ( x ), x ) ) | |
| bleu_metric: 37.875123684341865 | |
| ---------- | |
| NL: split string `s` by words that ends with 'd' | |
| ref: re.findall('\\b(\\w+)d\\b', s) | |
| Output: re. split ('( \ \ d + ) ', s ) | |
| bleu_metric: 29.23155541924467 | |
| ---------- | |
| NL: return `true` if string `foobarrrr` contains regex `ba[rzd]` | |
| ref: bool(re.search('ba[rzd]', 'foobarrrr')) | |
| Output: pattern = re. compile ('\ \ b [ a - za - z0 - 9 - ]') | |
| bleu_metric: 12.384901282810546 | |
| ---------- | |
| NL: removing duplicates in list `t` | |
| ref: list(set(t)) | |
| Output: list ( set ( t ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: removing duplicates in list `source_list` | |
| ref: list(set(source_list)) | |
| Output: list ( set ( t ) ) | |
| bleu_metric: 59.69491792019645 | |
| ---------- | |
| NL: removing duplicates in list `abracadabra` | |
| ref: list(OrderedDict.fromkeys('abracadabra')) | |
| Output: [ ('a ','b'), ('b ','c'), ('a ','d') | |
| bleu_metric: 7.555283720961675 | |
| ---------- | |
| NL: convert array `a` into a list | |
| ref: numpy.array(a).reshape(-1).tolist() | |
| Output: list ( a ) | |
| bleu_metric: 5.237603475197421 | |
| ---------- | |
| NL: convert the first row of numpy matrix `a` to a list | |
| ref: numpy.array(a)[0].tolist() | |
| Output: numpy. apply _ along _ axis ( numpy. array ( [ 0, 1, 2 ] ), a ) | |
| bleu_metric: 20.97823227594146 | |
| ---------- | |
| NL: in `soup`, get the content of the sibling of the `td` tag with text content `address:` | |
| ref: print(soup.find(text='Address:').findNext('td').contents[0]) | |
| Output: soup. find _ element _ by _ id ('< id >'). text | |
| bleu_metric: 12.033574792347215 | |
| ---------- | |
| NL: convert elements of each tuple in list `l` into a string separated by character `@` | |
| ref: """ """.join([('%d@%d' % t) for t in l]) | |
| Output: [''. join ( x ) for x in l ] | |
| bleu_metric: 17.41466588478686 | |
| ---------- | |
| NL: convert each tuple in list `l` to a string with '@' separating the tuples' elements | |
| ref: """ """.join([('%d@%d' % (t[0], t[1])) for t in l]) | |
| Output: " " " " " ". join ( [ x for x in l if x [ 0 ] in l ] ) | |
| bleu_metric: 36.45674601639763 | |
| ---------- | |
| NL: get the html from the current web page of a selenium driver | |
| ref: driver.execute_script('return document.documentElement.outerHTML;') | |
| Output: driver. get ('http : / / www. google. com') | |
| bleu_metric: 19.209534151258666 | |
| ---------- | |
| NL: get all matches with regex pattern `\\d+[xx]` in list of string `teststr` | |
| ref: [i for i in teststr if re.search('\\d+[xX]', i)] | |
| Output: [ i for i in teststr if re. search ('\ \ d + [ ^ \ \ ] * \ \ ] ', i ) | |
| bleu_metric: 69.89029944247109 | |
| ---------- | |
| NL: select values from column 'a' for which corresponding values in column 'b' will be greater than 50, and in column 'c' - equal 900 in dataframe `df` | |
| ref: df['A'][(df['B'] > 50) & (df['C'] == 900)] | |
| Output: df [ ( df ['a'] > 1 ) & ( df ['b'] < = 5 ) ] | |
| bleu_metric: 39.042104382208294 | |
| ---------- | |
| NL: sort dictionary `o` in ascending order based on its keys and items | |
| ref: sorted(o.items()) | |
| Output: sorted ( list ( dictionary. items ( ) ), key = lambda x : x [ 1 ] ) | |
| bleu_metric: 24.42195895839763 | |
| ---------- | |
| NL: get sorted list of keys of dict `d` | |
| ref: sorted(d) | |
| Output: sorted ( d, key = d. get ) | |
| bleu_metric: 25.21193618434983 | |
| ---------- | |
| NL: how to sort dictionaries by keys in python | |
| ref: sorted(d.items()) | |
| Output: sorted ( d. items ( ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert string "1" into integer | |
| ref: int('1') | |
| Output: int ('1') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: function to convert strings into integers | |
| ref: int() | |
| Output: map ( int,'1') | |
| bleu_metric: 21.105340631872636 | |
| ---------- | |
| NL: convert items in `t1` to integers | |
| ref: T2 = [map(int, x) for x in T1] | |
| Output: int ('\ n '. join ( str ( i ) for i in str ( n ) ) ) | |
| bleu_metric: 8.607692533178168 | |
| ---------- | |
| NL: call a shell script `./test.sh` using subprocess | |
| ref: subprocess.call(['./test.sh']) | |
| Output: subprocess. call ( ['test. sh ','/ test. sh otherfunc'] ) | |
| bleu_metric: 53.66411241731205 | |
| ---------- | |
| NL: call a shell script `notepad` using subprocess | |
| ref: subprocess.call(['notepad']) | |
| Output: subprocess. call ( ['notepad'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: combine lists `l1` and `l2` by alternating their elements | |
| ref: [val for pair in zip(l1, l2) for val in pair] | |
| Output: [ ( x + y ) for x in l2 for y in zip ( l1, l2 ) ] | |
| bleu_metric: 38.146366463982886 | |
| ---------- | |
| NL: encode string 'data to be encoded' | |
| ref: encoded = base64.b64encode('data to be encoded') | |
| Output: data. encode ('utf8') | |
| bleu_metric: 19.260282250359822 | |
| ---------- | |
| NL: encode a string `data to be encoded` to `ascii` encoding | |
| ref: encoded = 'data to be encoded'.encode('ascii') | |
| Output: data. encode ('ascii') | |
| bleu_metric: 41.51380951089673 | |
| ---------- | |
| NL: parse tab-delimited csv file 'text.txt' into a list | |
| ref: lol = list(csv.reader(open('text.txt', 'rb'), delimiter='\t')) | |
| Output: txt = open ('myfile. txt ','r'). read ( ) | |
| bleu_metric: 20.692050735338164 | |
| ---------- | |
| NL: get attribute `my_str` of object `my_object` | |
| ref: getattr(my_object, my_str) | |
| Output: getattr ( my _ obj,'id') | |
| bleu_metric: 19.72940627795883 | |
| ---------- | |
| NL: group a list of dicts `ld` into one dict by key | |
| ref: print(dict(zip(LD[0], zip(*[list(d.values()) for d in LD])))) | |
| Output: values = [ dict ( k, v ) for k, v in list ( d. items ( ) ) ] | |
| bleu_metric: 17.057966110550765 | |
| ---------- | |
| NL: how do i sum the first value in each tuple in a list of tuples in python? | |
| ref: sum([pair[0] for pair in list_of_pairs]) | |
| Output: sum ( [ item [ 0 ] for item in tuple ] ) | |
| bleu_metric: 41.37441202051881 | |
| ---------- | |
| NL: convert unicode string u"{'code1':1,'code2':1}" into dictionary | |
| ref: d = ast.literal_eval("{'code1':1,'code2':1}") | |
| Output: print ('\ u0420 \ u043e \ u0441 \ u0438 \ u0438 \ u | |
| bleu_metric: 6.351019285070924 | |
| ---------- | |
| NL: find all words in a string `mystring` that start with the `$` sign | |
| ref: [word for word in mystring.split() if word.startswith('$')] | |
| Output: re. findall ('\ \ b [ ^ \ \ w \ \ ] ', mystring ) | |
| bleu_metric: 9.850775379403402 | |
| ---------- | |
| NL: remove any url within string `text` | |
| ref: text = re.sub('^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE) | |
| Output: re. sub ('\ \. \ \ blog _ addr \ \ r \ \ n ','', text ) | |
| bleu_metric: 32.17551945847391 | |
| ---------- | |
| NL: replace all elements in array `a` that are not present in array `[1, 3, 4]` with zeros | |
| ref: np.where(np.in1d(A, [1, 3, 4]).reshape(A.shape), A, 0) | |
| Output: np. where ( np. in1d ( a, b ) ) [ 0 ] | |
| bleu_metric: 22.828499613963487 | |
| ---------- | |
| NL: calculate mean across dimension in a 2d array `a` | |
| ref: np.mean(a, axis=1) | |
| Output: np. mean ( a, axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: running r script '/pathto/myrscript.r' from python | |
| ref: subprocess.call(['/usr/bin/Rscript', '--vanilla', '/pathto/MyrScript.r']) | |
| Output: sys. path. append ('/ path / to / my / my / mypath') | |
| bleu_metric: 7.04483155666154 | |
| ---------- | |
| NL: run r script '/usr/bin/rscript --vanilla /pathto/myrscript.r' | |
| ref: subprocess.call('/usr/bin/Rscript --vanilla /pathto/MyrScript.r', shell=True) | |
| Output: os. path. join ( root, '. / main _ folder') | |
| bleu_metric: 6.726052538373431 | |
| ---------- | |
| NL: add a header to a csv file | |
| ref: writer.writeheader() | |
| Output: writer. writeheader ( ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: replacing nan in the dataframe `df` with row average | |
| ref: df.fillna(df.mean(axis=1), axis=1) | |
| Output: df. fillna ( df. mean ( axis = 1 ), axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert unix timestamp '1347517370' to formatted string '%y-%m-%d %h:%m:%s' | |
| ref: time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(1347517370)) | |
| Output: datetime. strptime ('21 / 11 / 06 16 : 30 ','% d / % m / % y') | |
| bleu_metric: 10.247261229308657 | |
| ---------- | |
| NL: call a base class's class method `do` from derived class `derived` | |
| ref: super(Derived, cls).do(a) | |
| Output: super ( childclass, self ). _ _ init _ _ ( * args, * * kwargs ) | |
| bleu_metric: 11.96655750514248 | |
| ---------- | |
| NL: selecting rows in numpy ndarray 'a', where the value in the first column is 0 and value in the second column is 1 | |
| ref: a[np.where((a[:, (0)] == 0) * (a[:, (1)] == 1))] | |
| Output: a [ np. arange ( 3 ), ( 0, 1, 0 ) ] | |
| bleu_metric: 13.581342454223687 | |
| ---------- | |
| NL: separate words delimited by one or more spaces into a list | |
| ref: re.split(' +', 'hello world sample text') | |
| Output: ['one ','two ','one ','two'] | |
| bleu_metric: 13.78592993183041 | |
| ---------- | |
| NL: length of longest element in list `words` | |
| ref: len(max(words, key=len)) | |
| Output: len ( max ( words, key = len ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the value associated with unicode key 'from_user' of first dictionary in list `result` | |
| ref: result[0]['from_user'] | |
| Output: ['key1 ','key2 ','value'] | |
| bleu_metric: 18.20705281109213 | |
| ---------- | |
| NL: retrieve each line from a file 'file.txt' as a list | |
| ref: [line.split() for line in open('File.txt')] | |
| Output: print ( line. rstrip ('\ n') ) | |
| bleu_metric: 11.789701057610694 | |
| ---------- | |
| NL: swap keys with values in a dictionary `a` | |
| ref: res = dict((v, k) for k, v in a.items()) | |
| Output: a [ key ] = dict ( ( v ) for k, v in a. items ( ) ) | |
| bleu_metric: 73.78351342269067 | |
| ---------- | |
| NL: open a file `path/to/file_name.ext` in write mode | |
| ref: new_file = open('path/to/FILE_NAME.ext', 'w') | |
| Output: file _ name = os. path. join ( os. path. join ( dir, '.') ) | |
| bleu_metric: 10.713701843513144 | |
| ---------- | |
| NL: how to count distinct values in a column of a pandas group by object? | |
| ref: df.groupby(['col1', 'col2'])['col3'].nunique().reset_index() | |
| Output: df. groupby ( ['col1 ','col2'] ) ['col3']. apply ( lambda x : | |
| bleu_metric: 72.38800804180103 | |
| ---------- | |
| NL: check if any key in the dictionary `dict1` starts with the string `emp$$` | |
| ref: any(key.startswith('EMP$$') for key in dict1) | |
| Output: any ( isinstance ( x, int ( dict. values ( ) ) ) for x in dict _ string in input. lower ( | |
| bleu_metric: 8.839374326825924 | |
| ---------- | |
| NL: create list of values from dictionary `dict1` that have a key that starts with 'emp$$' | |
| ref: [value for key, value in list(dict1.items()) if key.startswith('EMP$$')] | |
| Output: [ dict ( zip ( k, v ), v ) for k, v in d. items ( ) if v. startswith ( ' | |
| bleu_metric: 24.14138031982595 | |
| ---------- | |
| NL: convert a pandas series `sf` into a pandas dataframe `df` with columns `email` and `list` | |
| ref: pd.DataFrame({'email': sf.index, 'list': sf.values}) | |
| Output: pd. concat ( [ df1, df2 ], axis = 1 ) | |
| bleu_metric: 8.12657367724027 | |
| ---------- | |
| NL: print elements of list `list` seperated by tabs `\t` | |
| ref: print('\t'.join(map(str, list))) | |
| Output: print ( [ i. partition ('\ t') for i in list _ of _ lists ] ) | |
| bleu_metric: 25.58058053378779 | |
| ---------- | |
| NL: print unicode string '\xd0\xbf\xd1\x80\xd0\xb8' with utf-8 | |
| ref: print('\xd0\xbf\xd1\x80\xd0\xb8'.encode('raw_unicode_escape')) | |
| Output: print ('\ xc5 \ xa4 \ xd0 \ x80 \ xd0 \ xb0 \ xd0 | |
| bleu_metric: 29.472590166651962 | |
| ---------- | |
| NL: encode a latin character in string `sopet\xc3\xb3n` properly | |
| ref: 'Sopet\xc3\xb3n'.encode('latin-1').decode('utf-8') | |
| Output: " " " \ xc3 \ xa3 \ x9f " " ". encode ('utf - 8') | |
| bleu_metric: 46.255815148030386 | |
| ---------- | |
| NL: resized image `image` to width, height of `(x, y)` with filter of `antialias` | |
| ref: image = image.resize((x, y), Image.ANTIALIAS) | |
| Output: img = np. array ( [ 0, 1, 0, 1, 2, - 1, 0, 1, 2 ] ) | |
| bleu_metric: 7.278748444764005 | |
| ---------- | |
| NL: regex, find "n"s only in the middle of string `s` | |
| ref: re.findall('n(?<=[^n]n)n+(?=[^n])(?i)', s) | |
| Output: re. findall ('(? < = [ ^ \ \ s ] ) (? = [ ^ \ \ s ] + ) ', | |
| bleu_metric: 44.74679573461579 | |
| ---------- | |
| NL: display the float `1/3*100` as a percentage | |
| ref: print('{0:.0f}%'.format(1.0 / 3 * 100)) | |
| Output: print ( ('0. 1 ', 1 ) ) | |
| bleu_metric: 11.406542828786534 | |
| ---------- | |
| NL: sort a list of dictionary `mylist` by the key `title` | |
| ref: mylist.sort(key=lambda x: x['title']) | |
| Output: mylist. sort ( key = lambda x : x ['title'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: sort a list `l` of dicts by dict value 'title' | |
| ref: l.sort(key=lambda x: x['title']) | |
| Output: l. sort ( key = lambda x : x ['title'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: sort a list of dictionaries by the value of keys 'title', 'title_url', 'id' in ascending order. | |
| ref: l.sort(key=lambda x: (x['title'], x['title_url'], x['id'])) | |
| Output: sorted ( l, key = lambda x : x ['name'] ) | |
| bleu_metric: 17.362697834284052 | |
| ---------- | |
| NL: find 10 largest differences between each respective elements of list `l1` and list `l2` | |
| ref: heapq.nlargest(10, range(len(l1)), key=lambda i: abs(l1[i] - l2[i])) | |
| Output: [ ( x, y ) for x, y in zip ( l1, l2 ) ] | |
| bleu_metric: 5.935298261592072 | |
| ---------- | |
| NL: beautifulsoup find all 'span' elements in html string `soup` with class of 'stargryb sp' | |
| ref: soup.find_all('span', {'class': 'starGryB sp'}) | |
| Output: soup. findall ( text ='div ', style ='width = 300px ;') | |
| bleu_metric: 11.970571135993998 | |
| ---------- | |
| NL: write records in dataframe `df` to table 'test' in schema 'a_schema' | |
| ref: df.to_sql('test', engine, schema='a_schema') | |
| Output: df. plot ( row ='test ','test _ txt') | |
| bleu_metric: 32.62478546610937 | |
| ---------- | |
| NL: extract brackets from string `s` | |
| ref: brackets = re.sub('[^(){}[\\]]', '', s) | |
| Output: re. sub ('\ \ [ ( \ \ w + ) \ \ s + \ \ ] ','', s ) | |
| bleu_metric: 47.1113978575501 | |
| ---------- | |
| NL: remove duplicate elements from list 'l' | |
| ref: list(dict((x[0], x) for x in L).values()) | |
| Output: [ k for k, g in l if'l'in k ] | |
| bleu_metric: 6.942420857748657 | |
| ---------- | |
| NL: read a file `file` without newlines | |
| ref: [line.rstrip('\n') for line in file] | |
| Output: file. write ('\ n') | |
| bleu_metric: 30.98188793972842 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: [i for (i, x) in enumerate(testlist) if (x == 1)] | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 9.243620302376062 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: [i for (i, x) in enumerate(testlist) if (x == 1)] | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 9.243620302376062 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: for i in [i for (i, x) in enumerate(testlist) if (x == 1)]: | |
| pass | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 7.565780264883953 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: for i in (i for (i, x) in enumerate(testlist) if (x == 1)): | |
| pass | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 6.0168435849456285 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: gen = (i for (i, x) in enumerate(testlist) if (x == 1)) | |
| for i in gen: | |
| pass | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 5.043459282125431 | |
| ---------- | |
| NL: get the position of item `element` in list `testlist` | |
| ref: print(testlist.index(element)) | |
| Output: print ( testlist. index ( element ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the position of item `element` in list `testlist` | |
| ref: try: | |
| print(testlist.index(element)) | |
| except ValueError: | |
| pass | |
| Output: print ( testlist. index ( element ) ) | |
| bleu_metric: 49.01732980393223 | |
| ---------- | |
| NL: find the first element of the tuple with the maximum second element in a list of tuples `lis` | |
| ref: max(lis, key=lambda item: item[1])[0] | |
| Output: max ( x [ 0 ] for x in rows ) | |
| bleu_metric: 14.988854080083206 | |
| ---------- | |
| NL: get the item at index 0 from the tuple that has maximum value at index 1 in list `lis` | |
| ref: max(lis, key=itemgetter(1))[0] | |
| Output: max ( x, key = lambda x : x [ 1 ] ) [ 0 ] | |
| bleu_metric: 35.27295712700594 | |
| ---------- | |
| NL: make a delay of 1 second | |
| ref: time.sleep(1) | |
| Output: time. sleep ( 0. 1 ) | |
| bleu_metric: 52.53819788848316 | |
| ---------- | |
| NL: convert list of tuples `l` to a string | |
| ref: """, """.join('(' + ', '.join(i) + ')' for i in L) | |
| Output: " " " " " ". join ( l ) | |
| bleu_metric: 14.164334116374599 | |
| ---------- | |
| NL: django set default value of field `b` equal to '0000000' | |
| ref: b = models.CharField(max_length=7, default='0000000', editable=False) | |
| Output: obj. save ('new _ name ','default _ number') | |
| bleu_metric: 10.318679011582763 | |
| ---------- | |
| NL: sort lis `list5` in ascending order based on the degrees value of its elements | |
| ref: sorted(list5, lambda x: (degree(x), x)) | |
| Output: sorted ( lst, key = lambda x : x [ 1 ] ) | |
| bleu_metric: 19.143591202259493 | |
| ---------- | |
| NL: how do i perform secondary sorting in python? | |
| ref: sorted(list5, key=lambda vertex: (degree(vertex), vertex)) | |
| Output: sorted ( list5, key = lambda x : ( sum ( x [ 1 ] ), x ) ) | |
| bleu_metric: 41.26152034907945 | |
| ---------- | |
| NL: convert a list into a generator object | |
| ref: (n for n in [1, 2, 3, 5]) | |
| Output: list ( itertools. combinations ( * ( l ) ) ) | |
| bleu_metric: 9.930751066504135 | |
| ---------- | |
| NL: remove elements from list `oldlist` that have an index number mentioned in list `removelist` | |
| ref: newlist = [v for i, v in enumerate(oldlist) if i not in removelist] | |
| Output: [ x for x in set ( split _ list [ i ] for i in range ( len ( x ) ) if i not in index ] | |
| bleu_metric: 18.860270984795903 | |
| ---------- | |
| NL: open a file `yourfile.txt` in write mode | |
| ref: f = open('yourfile.txt', 'w') | |
| Output: os. system ('your string. txt') | |
| bleu_metric: 23.571525044211057 | |
| ---------- | |
| NL: get attribute 'attr' from object `obj` | |
| ref: getattr(obj, 'attr') | |
| Output: getattr ( obj,'attr') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to tuple | |
| ref: from functools import reduce | |
| reduce(lambda a, b: a + b, (('aa',), ('bb',), ('cc',))) | |
| Output: tuple ( ['a ','b ','c ','d'] ) | |
| bleu_metric: 6.101077050680879 | |
| ---------- | |
| NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to list in one line | |
| ref: map(lambda a: a[0], (('aa',), ('bb',), ('cc',))) | |
| Output: map ( lambda s : ( d ['a'], d ['b'], d ['c'] ), d [ | |
| bleu_metric: 11.200083731986627 | |
| ---------- | |
| NL: python pandas: how to replace a characters in a column of a dataframe? | |
| ref: df['range'].replace(',', '-', inplace=True) | |
| Output: df ['brandname'] = df ['brandname']. replace ('- ', ','in') | |
| bleu_metric: 45.97006398721532 | |
| ---------- | |
| NL: unzip the list `[('a', 1), ('b', 2), ('c', 3), ('d', 4)]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4)]) | |
| Output: zip ( * [ ('a ','b ','c ','d'] ) ) | |
| bleu_metric: 36.501102157810664 | |
| ---------- | |
| NL: unzip the list `[('a', 1), ('b', 2), ('c', 3), ('d', 4)]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4)]) | |
| Output: zip ( * [ ('a ','b ','c ','d'] ) ) | |
| bleu_metric: 36.501102157810664 | |
| ---------- | |
| NL: unzip list `original` | |
| ref: result = ([a for (a, b) in original], [b for (a, b) in original]) | |
| Output: zip ( * list ( range ( 10 ) ) ) | |
| bleu_metric: 4.606184881898074 | |
| ---------- | |
| NL: unzip list `original` and return a generator | |
| ref: result = ((a for (a, b) in original), (b for (a, b) in original)) | |
| Output: [ i for i in range ( 1, len ( l ) ) if i! ='+ 1 ] | |
| bleu_metric: 7.62604991346693 | |
| ---------- | |
| NL: unzip list `[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', )]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e',)]) | |
| Output: zip ( * [ ('a ','b ','c ','d ','e'] ) ) | |
| bleu_metric: 34.87995424158042 | |
| ---------- | |
| NL: unzip list `[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', )]` and fill empty results with none | |
| ref: map(None, *[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e',)]) | |
| Output: zip ( * [ ('a ','b'), ('b ','c ','d'), ( ' | |
| bleu_metric: 35.430131828411845 | |
| ---------- | |
| NL: encode `decimal('3.9')` to a json string | |
| ref: json.dumps(Decimal('3.9')) | |
| Output: json. dumps ( ['1 ','2 ','3'] ) | |
| bleu_metric: 23.58844810653421 | |
| ---------- | |
| NL: add key "mynewkey" to dictionary `d` with value "mynewvalue" | |
| ref: d['mynewkey'] = 'mynewvalue' | |
| Output: df. setdefault ('name ', [ ] ). append ( value ) | |
| bleu_metric: 9.50724412002623 | |
| ---------- | |
| NL: add key 'a' to dictionary `data` with value 1 | |
| ref: data.update({'a': 1, }) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 37.863495563972855 | |
| ---------- | |
| NL: add key 'a' to dictionary `data` with value 1 | |
| ref: data.update(dict(a=1)) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 8.24055698798382 | |
| ---------- | |
| NL: add key 'a' to dictionary `data` with value 1 | |
| ref: data.update(a=1) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 8.24055698798382 | |
| ---------- | |
| NL: find maximal value in matrix `matrix` | |
| ref: max([max(i) for i in matrix]) | |
| Output: max ( matrix, key = lambda x : max ( x ) ) | |
| bleu_metric: 16.943571815930884 | |
| ---------- | |
| NL: round number `answer` to 2 precision after the decimal point | |
| ref: answer = str(round(answer, 2)) | |
| Output: round ( 1. 0005, 3 ) | |
| bleu_metric: 17.51643270174889 | |
| ---------- | |
| NL: extract ip address from an html string | |
| ref: ip = re.findall('[0-9]+(?:\\.[0-9]+){3}', s) | |
| Output: print ( re. findall ('[ ^ a - za - z0 - 9 - ] + (? : \ \. [ a - | |
| bleu_metric: 40.70867848415918 | |
| ---------- | |
| NL: filter dataframe `df` by values in column `a` that appear more than once | |
| ref: df.groupby('A').filter(lambda x: len(x) > 1) | |
| Output: df. filter ( df. filter ( lambda x : x. any ( axis = 1 ) ) ) | |
| bleu_metric: 34.640201116587235 | |
| ---------- | |
| NL: append each line in file `myfile` into a list | |
| ref: [x for x in myfile.splitlines() if x != ''] | |
| Output: myfile. write ('\ n '. join ( [ str ( i ) for i in myfile ] ) ) | |
| bleu_metric: 10.435185733665413 | |
| ---------- | |
| NL: get a list of integers `lst` from a file `filename.txt` | |
| ref: lst = map(int, open('filename.txt').readlines()) | |
| Output: [ int ( x ) for x in lst ] | |
| bleu_metric: 7.020674481218203 | |
| ---------- | |
| NL: add color bar with image `mappable` to plot `plt` | |
| ref: plt.colorbar(mappable=mappable, cax=ax3) | |
| Output: plt. show ( ) | |
| bleu_metric: 11.914378950211951 | |
| ---------- | |
| NL: count most frequent 100 words in column 'text' of dataframe `df` | |
| ref: Counter(' '.join(df['text']).split()).most_common(100) | |
| Output: df. groupby ('text'). apply ( lambda x : x. sum ( ) ). most _ common ( 1 ) | |
| bleu_metric: 21.52643286782715 | |
| ---------- | |
| NL: python split a string using regex | |
| ref: re.findall('(.+?):(.+?)\\b ?', text) | |
| Output: re. split ('(? < = \ \ d ) \ \ d + ', text ) | |
| bleu_metric: 29.769819080461428 | |
| ---------- | |
| NL: generate all 2-element subsets of tuple `(1, 2, 3)` | |
| ref: list(itertools.combinations((1, 2, 3), 2)) | |
| Output: list ( itertools. product ( [ 1, 2, 3 ], [ 4, 5, 6 ] ) ) | |
| bleu_metric: 34.29547961820059 | |
| ---------- | |
| NL: get a value of datetime.today() in the utc time zone | |
| ref: datetime.now(pytz.utc) | |
| Output: pytz. utcnow ( ) | |
| bleu_metric: 23.206041459353084 | |
| ---------- | |
| NL: get a new list `list2`by removing empty list from a list of lists `list1` | |
| ref: list2 = [x for x in list1 if x != []] | |
| Output: list2 = [ x for x in list1 if x [ : ] = = [ ] ] | |
| bleu_metric: 69.3395566222006 | |
| ---------- | |
| NL: create `list2` to contain the lists from list `list1` excluding the empty lists from `list1` | |
| ref: list2 = [x for x in list1 if x] | |
| Output: list ( zip ( a, b ) ) | |
| bleu_metric: 10.320893749383378 | |
| ---------- | |
| NL: django response with json `data` | |
| ref: return HttpResponse(data, mimetype='application/json') | |
| Output: return httpresponse ( json. dumps ( response _ data ), content _ type ='application / json') | |
| bleu_metric: 36.17640392425988 | |
| ---------- | |
| NL: get all text that is not enclosed within square brackets in string `example_str` | |
| ref: re.findall('(.*?)\\[.*?\\]', example_str) | |
| Output: str1. split ('\ \ ) [ ^ \ \ ] ', str. strip ( ) | |
| bleu_metric: 26.91730507369109 | |
| ---------- | |
| NL: use a regex to get all text in a string `example_str` that is not surrounded by square brackets | |
| ref: re.findall('(.*?)(?:\\[.*?\\]|$)', example_str) | |
| Output: re. findall ('\ \ [ (? : \ \ w + ) \ \ ] ', str1 ) | |
| bleu_metric: 35.15147667690756 | |
| ---------- | |
| NL: get whatever is between parentheses as a single match, and any char outside as an individual match in string '(zyx)bc' | |
| ref: re.findall('\\(.+?\\)|\\w', '(zyx)bc') | |
| Output: re. sub ('\ \ ( [ ^ \ \ w ] ) ','', filename ) | |
| bleu_metric: 27.664786097762494 | |
| ---------- | |
| NL: match regex '\\((.*?)\\)|(\\w)' with string '(zyx)bc' | |
| ref: re.findall('\\((.*?)\\)|(\\w)', '(zyx)bc') | |
| Output: re. sub ('(? < = \ \ w ) ( \ \ w + ) ( \ \ w + ) ','\ \ | |
| bleu_metric: 30.955387518700363 | |
| ---------- | |
| NL: match multiple regex patterns with the alternation operator `|` in a string `(zyx)bc` | |
| ref: re.findall('\\(.*?\\)|\\w', '(zyx)bc') | |
| Output: re. findall ('(? < = \ \ w ) ( [ ^ \ \ w \ \ w ] | \ \ w + ) | |
| bleu_metric: 29.552612305356018 | |
| ---------- | |
| NL: formate each string cin list `elements` into pattern '%{0}%' | |
| ref: elements = ['%{0}%'.format(element) for element in elements] | |
| Output: " " " { 0 :. 2f } " " ". format ('foo ','bar') | |
| bleu_metric: 17.414675099971177 | |
| ---------- | |
| NL: open a background process 'background-process' with arguments 'arguments' | |
| ref: subprocess.Popen(['background-process', 'arguments']) | |
| Output: os. system ('some _ command - some _ file') | |
| bleu_metric: 12.107470158860224 | |
| ---------- | |
| NL: get list of values from dictionary 'mydict' w.r.t. list of keys 'mykeys' | |
| ref: [mydict[x] for x in mykeys] | |
| Output: [ key for item in my _ list if item. get ('name ','age') ] | |
| bleu_metric: 9.119675426861836 | |
| ---------- | |
| NL: convert list `[('name', 'joe'), ('age', 22)]` into a dictionary | |
| ref: dict([('Name', 'Joe'), ('Age', 22)]) | |
| Output: dict ( ( ('class ', 2 ), ('class ', 3 ) ) ) | |
| bleu_metric: 24.080231808676874 | |
| ---------- | |
| NL: average each two columns of array `data` | |
| ref: data.reshape(-1, j).mean(axis=1).reshape(data.shape[0], -1) | |
| Output: np. mean ( np. arange ( len ( df. columns ) ), axis = 1 ) | |
| bleu_metric: 18.410768031391818 | |
| ---------- | |
| NL: double backslash escape all double quotes in string `s` | |
| ref: print(s.encode('unicode-escape').replace('"', '\\"')) | |
| Output: print ( s. split ('\ \') ) | |
| bleu_metric: 21.765088513075124 | |
| ---------- | |
| NL: split a string into a list of words and whitespace | |
| ref: re.split('(\\W+)', s) | |
| Output: ['this is a string : ','this is a string ','this is a string ','this is a string ', | |
| bleu_metric: 5.648892151960283 | |
| ---------- | |
| NL: plotting stacked barplots on a panda data frame | |
| ref: df.plot(kind='barh', stacked=True) | |
| Output: df. plot ( kind ='barh ', stacked = true ) | |
| bleu_metric: 83.7117009877792 | |
| ---------- | |
| NL: reverse the keys and values in a dictionary `mydictionary` | |
| ref: {i[1]: i[0] for i in list(myDictionary.items())} | |
| Output: dict ( ( k, v ) for k, v in list ( d. items ( ) ) ) | |
| bleu_metric: 23.690464140247087 | |
| ---------- | |
| NL: finding the index of elements containing substring 'how' and 'what' in a list of strings 'mylist'. | |
| ref: [i for i, j in enumerate(myList) if 'how' in j.lower() or 'what' in j.lower()] | |
| Output: ['x ','y ','red ','var ','var'] | |
| bleu_metric: 6.180827527485682 | |
| ---------- | |
| NL: check if object `obj` is a string | |
| ref: isinstance(obj, str) | |
| Output: isinstance ( obj, str ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: isinstance(o, str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 59.46035575013605 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: (type(o) is str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 23.813694985189848 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: isinstance(o, str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 59.46035575013605 | |
| ---------- | |
| NL: check if `obj_to_test` is a string | |
| ref: isinstance(obj_to_test, str) | |
| Output: print ( obj. isfile ('attr name') ) | |
| bleu_metric: 13.508625657351411 | |
| ---------- | |
| NL: append list `list1` to `list2` | |
| ref: list2.extend(list1) | |
| Output: [ list1. append ( list1 ) for a in range ( 3 ) ] | |
| bleu_metric: 14.879641171245488 | |
| ---------- | |
| NL: append list `mylog` to `list1` | |
| ref: list1.extend(mylog) | |
| Output: list3. append ( list1 ) | |
| bleu_metric: 29.071536848410968 | |
| ---------- | |
| NL: append list `a` to `c` | |
| ref: c.extend(a) | |
| Output: a. append ( a ) | |
| bleu_metric: 54.10822690539396 | |
| ---------- | |
| NL: append items in list `mylog` to `list1` | |
| ref: for line in mylog: | |
| list1.append(line) | |
| Output: list3. append ( list1 ) | |
| bleu_metric: 20.917479021833486 | |
| ---------- | |
| NL: append a tuple of elements from list `a` with indexes '[0][0] [0][2]' to list `b` | |
| ref: b.append((a[0][0], a[0][2])) | |
| Output: a = [ [ ] for i in range ( 0, len ( a ), [ 3, 4 ] ] ) | |
| bleu_metric: 11.556647985416685 | |
| ---------- | |
| NL: initialize `secret_key` in flask config with `your_secret_string ` | |
| ref: app.config['SECRET_KEY'] = 'Your_secret_string' | |
| Output: app. config ['your _ id'] ='your _ config ' | |
| bleu_metric: 43.343660127583234 | |
| ---------- | |
| NL: unpack a series of tuples in pandas into a dataframe with column names 'out-1' and 'out-2' | |
| ref: pd.DataFrame(out.tolist(), columns=['out-1', 'out-2'], index=out.index) | |
| Output: pd. dataframe ( df. values. tolist ( ), columns = ['a ','b'], axis = 1 | |
| bleu_metric: 35.31494509780309 | |
| ---------- | |
| NL: find the index of an element 'msft' in a list `stocks_list` | |
| ref: [x for x in range(len(stocks_list)) if stocks_list[x] == 'MSFT'] | |
| Output: print ( list ( enumerate ( my _ list ) ) ) | |
| bleu_metric: 5.9394573438219 | |
| ---------- | |
| NL: rotate the xtick labels of matplotlib plot `ax` by `45` degrees to make long labels readable | |
| ref: ax.set_xticklabels(labels, rotation=45) | |
| Output: ax. set _ xticklabels ( ax. xaxis. get _ majorticklabels ( ), rotation = 45 ) | |
| bleu_metric: 29.88309778054086 | |
| ---------- | |
| NL: remove symbols from a string `s` | |
| ref: re.sub('[^\\w]', ' ', s) | |
| Output: re. sub ('[ ^ a - z ] ','', s ) | |
| bleu_metric: 74.83293841345244 | |
| ---------- | |
| NL: get the current directory of a script | |
| ref: os.path.basename(os.path.dirname(os.path.realpath(__file__))) | |
| Output: os. path. dirname ( os. path. abspath ( _ _ file _ _ ) ) | |
| bleu_metric: 48.431464491050775 | |
| ---------- | |
| NL: find octal characters matches from a string `str` using regex | |
| ref: print(re.findall("'\\\\[0-7]{1,3}'", str)) | |
| Output: re. findall ('\ \ [ (? < = \ \ d ) \ \ ) ','- ', str ) | |
| bleu_metric: 34.98979283499355 | |
| ---------- | |
| NL: split string `input` based on occurrences of regex pattern '[ ](?=[a-z]+\\b)' | |
| ref: re.split('[ ](?=[A-Z]+\\b)', input) | |
| Output: re. findall ('(? < = [ a - z ] ) (? = [ a - z ] ) (? = [ a | |
| bleu_metric: 16.927318970546587 | |
| ---------- | |
| NL: split string `input` at every space followed by an upper-case letter | |
| ref: re.split('[ ](?=[A-Z])', input) | |
| Output: re. split ('\ \ b ', text ) | |
| bleu_metric: 23.369549958807866 | |
| ---------- | |
| NL: send multipart encoded file `files` to url `url` with headers `headers` and metadata `data` | |
| ref: r = requests.post(url, files=files, headers=headers, data=data) | |
| Output: r = requests. get ( url ) | |
| bleu_metric: 13.273006566054132 | |
| ---------- | |
| NL: write bytes `bytes_` to a file `filename` in python 3 | |
| ref: open('filename', 'wb').write(bytes_) | |
| Output: f. write ( struct. pack ('> i ', filename ) ) | |
| bleu_metric: 21.042990347620457 | |
| ---------- | |
| NL: get a list from a list `lst` with values mapped into a dictionary `dct` | |
| ref: [dct[k] for k in lst] | |
| Output: [ list ( lst [ i ] ) for i in lst ] | |
| bleu_metric: 28.787878181011266 | |
| ---------- | |
| NL: find duplicate names in column 'name' of the dataframe `x` | |
| ref: x.set_index('name').index.get_duplicates() | |
| Output: print ( list ( df. index. get _ duplicates ( ) ) ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: truncate float 1.923328437452 to 3 decimal places | |
| ref: round(1.923328437452, 3) | |
| Output: round ( 1. 005, 2 ) | |
| bleu_metric: 46.71379777282001 | |
| ---------- | |
| NL: sort list `li` in descending order based on the date value in second element of each list in list `li` | |
| ref: sorted(li, key=lambda x: datetime.strptime(x[1], '%d/%m/%Y'), reverse=True) | |
| Output: sorted ( li, key = operator. itemgetter ( 1 ), reverse = true ) | |
| bleu_metric: 19.26332463060877 | |
| ---------- | |
| NL: place the radial ticks in plot `ax` at 135 degrees | |
| ref: ax.set_rlabel_position(135) | |
| Output: ax. set _ xticklabels ( [ ] ) | |
| bleu_metric: 21.834177214239062 | |
| ---------- | |
| NL: check if path `my_path` is an absolute path | |
| ref: os.path.isabs(my_path) | |
| Output: os. path. exists ('my _ path') | |
| bleu_metric: 31.61487584488943 | |
| ---------- | |
| NL: get number of keys in dictionary `yourdict` | |
| ref: len(list(yourdict.keys())) | |
| Output: len ( mydict ) | |
| bleu_metric: 11.141275535087015 | |
| ---------- | |
| NL: count the number of keys in dictionary `yourdictfile` | |
| ref: len(set(open(yourdictfile).read().split())) | |
| Output: sum ( len ( v ) for v in yourdict. values ( ) ) | |
| bleu_metric: 18.767030063813223 | |
| ---------- | |
| NL: pandas dataframe get first row of each group by 'id' | |
| ref: df.groupby('id').first() | |
| Output: df. groupby ('id'). first ( ). first ( ) | |
| bleu_metric: 75.62722459724604 | |
| ---------- | |
| NL: split a list in first column into multiple columns keeping other columns as well in pandas data frame | |
| ref: pd.concat([df[0].apply(pd.Series), df[1]], axis=1) | |
| Output: df [ ['c ','df'] ]. apply ( lambda x : x [ 1 : - 1 ]. split ( | |
| bleu_metric: 19.037861963633805 | |
| ---------- | |
| NL: extract attributes 'src="js/([^"]*\\bjquery\\b[^"]*)"' from string `data` | |
| ref: re.findall('src="js/([^"]*\\bjquery\\b[^"]*)"', data) | |
| Output: " " " \ \ b " " " | |
| bleu_metric: 3.6253194206622026 | |
| ---------- | |
| NL: sum integers contained in strings in list `['', '3.4', '', '', '1.0']` | |
| ref: sum(int(float(item)) for item in [_f for _f in ['', '3.4', '', '', '1.0'] if _f]) | |
| Output: sum ( [ 1, 2, 3, 4, 5 ] ) | |
| bleu_metric: 3.255351208100268 | |
| ---------- | |
| NL: call a subprocess with arguments `c:\\program files\\vmware\\vmware server\\vmware-cmd.bat` that may contain spaces | |
| ref: subprocess.Popen(['c:\\Program Files\\VMware\\VMware Server\\vmware-cmd.bat']) | |
| Output: subprocess. call ( ['shutdown ','/ r ','/ tmp / test. py'] ) | |
| bleu_metric: 16.665685159230854 | |
| ---------- | |
| NL: reverse a priority queue `q` in python without using classes | |
| ref: q.put((-n, n)) | |
| Output: t. start ( ) | |
| bleu_metric: 12.151662434083677 | |
| ---------- | |
| NL: make a barplot of data in column `group` of dataframe `df` colour-coded according to list `color` | |
| ref: df['group'].plot(kind='bar', color=['r', 'g', 'b', 'r', 'g', 'b', 'r']) | |
| Output: df. groupby ('o') ['value']. first ( ) | |
| bleu_metric: 3.5795004593328636 | |
| ---------- | |
| NL: find all matches of regex pattern '([a-fa-f\\d]{32})' in string `data` | |
| ref: re.findall('([a-fA-F\\d]{32})', data) | |
| Output: re. findall ('(? < = [ a - z ] ) ( [ a - z ] ) ','abcd _ string | |
| bleu_metric: 33.336490646495264 | |
| ---------- | |
| NL: get the length of list `my_list` | |
| ref: len(my_list) | |
| Output: len ( my _ list ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: getting the length of array `l` | |
| ref: len(l) | |
| Output: len ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: getting the length of array `s` | |
| ref: len(s) | |
| Output: len ( s ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: getting the length of `my_tuple` | |
| ref: len(my_tuple) | |
| Output: len ( my _ list ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: getting the length of `my_string` | |
| ref: len(my_string) | |
| Output: len ( my _ string ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: remove escape character from string "\\a" | |
| ref: """\\a""".decode('string_escape') | |
| Output: print ('\ n '. join ('[ _ name _ ]') ) | |
| bleu_metric: 15.071676257541073 | |
| ---------- | |
| NL: replace each 'a' with 'b' and each 'b' with 'a' in the string 'obama' in a single pass. | |
| ref: """obama""".replace('a', '%temp%').replace('b', 'a').replace('%temp%', 'b') | |
| Output: a = re. sub ('[ a - za - z0 - 9 ] ','', line ) | |
| bleu_metric: 6.256610657452671 | |
| ---------- | |
| NL: remove directory tree '/folder_name' | |
| ref: shutil.rmtree('/folder_name') | |
| Output: os. path. dirname ( os. path. join ( dirpath, os. path. dirname ( _ _ file _ _ ) ) | |
| bleu_metric: 6.4718242450883325 | |
| ---------- | |
| NL: create a new column `weekday` in pandas data frame `data` based on the values in column `my_dt` | |
| ref: data['weekday'] = data['my_dt'].apply(lambda x: x.weekday()) | |
| Output: df. apply ( lambda x : x. to _ frame ('new _ column'), axis = 1 ) | |
| bleu_metric: 34.619282106807496 | |
| ---------- | |
| NL: reverse sort counter `x` by values | |
| ref: sorted(x, key=x.get, reverse=True) | |
| Output: sorted ( x, key = itemgetter ( 1 ) ) | |
| bleu_metric: 40.540555470323305 | |
| ---------- | |
| NL: reverse sort counter `x` by value | |
| ref: sorted(list(x.items()), key=lambda pair: pair[1], reverse=True) | |
| Output: sorted ( x, key = itemgetter ( 1 ), reverse = true ) | |
| bleu_metric: 15.53218858512746 | |
| ---------- | |
| NL: append a numpy array 'b' to a numpy array 'a' | |
| ref: np.vstack((a, b)) | |
| Output: np. concatenate ( ( a, b ) ) | |
| bleu_metric: 76.91605673134586 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: print(concatenate((a, b), axis=0)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 82.82477531331041 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the second axis | |
| ref: print(concatenate((a, b), axis=1)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: c = np.r_[(a[None, :], b[None, :])] | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 9.520068110441361 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: np.array((a, b)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 41.70507291634131 | |
| ---------- | |
| NL: fetch address information for host 'google.com' ion port 80 | |
| ref: print(socket.getaddrinfo('google.com', 80)) | |
| Output: os. environ ['localhost : \ \ python27 \ \ python'] | |
| bleu_metric: 9.672649511413093 | |
| ---------- | |
| NL: add a column 'day' with value 'sat' to dataframe `df` | |
| ref: df.xs('sat', level='day', drop_level=False) | |
| Output: df ['value'] = df ['value']. apply ( lambda x : x ['value'] ) | |
| bleu_metric: 8.179879050970094 | |
| ---------- | |
| NL: return a 401 unauthorized in django | |
| ref: return HttpResponse('Unauthorized', status=401) | |
| Output: return httpresponse ( status = 204 ) | |
| bleu_metric: 15.451666492113134 | |
| ---------- | |
| NL: flask set folder 'wherever' as the default template folder | |
| ref: Flask(__name__, template_folder='wherever') | |
| Output: app. run ( debug = true ) | |
| bleu_metric: 16.89983564524028 | |
| ---------- | |
| NL: how do i insert into t1 (select * from t2) in sqlalchemy? | |
| ref: session.execute('INSERT INTO t1 (SELECT * FROM t2)') | |
| Output: session. execute ('select * from from t1 ;') | |
| bleu_metric: 34.9740896964685 | |
| ---------- | |
| NL: sort a list of lists 'c2' such that third row comes first | |
| ref: c2.sort(key=lambda row: row[2]) | |
| Output: sorted ( l, key = lambda x : ( - x [ 1 ], x [ 2 ] ) ) | |
| bleu_metric: 22.169194554222997 | |
| ---------- | |
| NL: sorting a list of lists in python | |
| ref: c2.sort(key=lambda row: (row[2], row[1], row[0])) | |
| Output: [ ( row [ i ], row [ i + 2 ] ) for i in range ( 0, len ( row ), row [ 2 ] | |
| bleu_metric: 26.189293164591476 | |
| ---------- | |
| NL: sorting a list of lists in python | |
| ref: c2.sort(key=lambda row: (row[2], row[1])) | |
| Output: [ ( row [ i ], row [ i + 2 ] ) for i in range ( 0, len ( row ), row [ 2 ] | |
| bleu_metric: 22.303080489498637 | |
| ---------- | |
| NL: set font `arial` to display non-ascii characters in matplotlib | |
| ref: matplotlib.rc('font', **{'sans-serif': 'Arial', 'family': 'sans-serif'}) | |
| Output: ax. set _ ylabel ('y - r ', title ='\ u2022') | |
| bleu_metric: 6.906418747057865 | |
| ---------- | |
| NL: convert datetime column 'date' of pandas dataframe 'df' to ordinal | |
| ref: df['date'].apply(lambda x: x.toordinal()) | |
| Output: df ['date'] = df ['date']. map ( lambda x : x. date ( ) ) | |
| bleu_metric: 51.29497107827519 | |
| ---------- | |
| NL: get html source of selenium webelement `element` | |
| ref: element.get_attribute('innerHTML') | |
| Output: print ( element. get _ attribute ('innerhtml') ) | |
| bleu_metric: 18.69300079996002 | |
| ---------- | |
| NL: get the integer location of a key `bob` in a pandas data frame | |
| ref: df.index.get_loc('bob') | |
| Output: df. reset _ index ( ) | |
| bleu_metric: 19.840332506055578 | |
| ---------- | |
| NL: open a 'gnome' terminal from python script and run 'sudo apt-get update' command. | |
| ref: os.system('gnome-terminal -e \'bash -c "sudo apt-get update; exec bash"\'') | |
| Output: os. system ('echo $ filename. exe') | |
| bleu_metric: 12.044064401053362 | |
| ---------- | |
| NL: add an item with key 'third_key' and value 1 to an dictionary `my_dict` | |
| ref: my_dict.update({'third_key': 1}) | |
| Output: my _ dict ['my _ dict'] = var | |
| bleu_metric: 10.579369505074823 | |
| ---------- | |
| NL: declare an array | |
| ref: my_list = [] | |
| Output: variable = [ ] | |
| bleu_metric: 75.98356856515926 | |
| ---------- | |
| NL: insert item `12` to a list `my_list` | |
| ref: my_list.append(12) | |
| Output: my _ list. append ('{ }, { } '. format ( c. c ) ) | |
| bleu_metric: 12.259149958656423 | |
| ---------- | |
| NL: add an entry 'wuggah' at the beginning of list `mylist` | |
| ref: myList.insert(0, 'wuggah') | |
| Output: mylist = [ 'an ','i ','j'] | |
| bleu_metric: 11.502783619900047 | |
| ---------- | |
| NL: convert a hex-string representation to actual bytes | |
| ref: """\\xF3\\xBE\\x80\\x80""".replace('\\x', '').decode('hex') | |
| Output: " " " \ \ xc3 \ \ x85あ " " ". encode ('hex') | |
| bleu_metric: 24.712086993335753 | |
| ---------- | |
| NL: select the last column of dataframe `df` | |
| ref: df[df.columns[-1]] | |
| Output: df [ df. columns [ - 1 ] ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the first value from dataframe `df` where column 'letters' is equal to 'c' | |
| ref: df.loc[df['Letters'] == 'C', 'Letters'].values[0] | |
| Output: df ['value'] = df. groupby ('value') ['value']. apply ( lambda x : x | |
| bleu_metric: 17.966302274629616 | |
| ---------- | |
| NL: converting two lists `[1, 2, 3]` and `[4, 5, 6]` into a matrix | |
| ref: np.column_stack(([1, 2, 3], [4, 5, 6])) | |
| Output: matrix ( [ [ 1, 2 ], [ 3, 4 ], [ 5, 6 ] ] ) | |
| bleu_metric: 35.61693754184905 | |
| ---------- | |
| NL: get the type of `i` | |
| ref: type(i) | |
| Output: print ( len ( a ) ) | |
| bleu_metric: 22.679164443904003 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the type of variable `variable_name` | |
| ref: print(type(variable_name)) | |
| Output: globals ( ) ['a'] | |
| bleu_metric: 16.784459625186194 | |
| ---------- | |
| NL: get the 5th item of a generator | |
| ref: next(itertools.islice(range(10), 5, 5 + 1)) | |
| Output: next ( ( iter ( range ( n ) ) ) for i in range ( n ) ) | |
| bleu_metric: 18.81478574691708 | |
| ---------- | |
| NL: print a string `word` with string format | |
| ref: print('"{}"'.format(word)) | |
| Output: print ('\ n '. join ('% s = % s'% ( value, ) ) ) | |
| bleu_metric: 16.331948281960493 | |
| ---------- | |
| NL: join a list of strings `list` using a space ' ' | |
| ref: """ """.join(list) | |
| Output: list (''. join ( map ( str, list ) ) ) | |
| bleu_metric: 33.918247054808944 | |
| ---------- | |
| NL: create list `y` containing two empty lists | |
| ref: y = [[] for n in range(2)] | |
| Output: y = [ ( x, y ) for x in zip ( l1, l2 ) ] | |
| bleu_metric: 18.951629567590743 | |
| ---------- | |
| NL: read a file 'c:/name/mydocuments/numbers' into a list `data` | |
| ref: data = [line.strip() for line in open('C:/name/MyDocuments/numbers', 'r')] | |
| Output: new _ list = [ line. rstrip ('\ t') for line in open ('filename. txt') ] | |
| bleu_metric: 37.34651115614923 | |
| ---------- | |
| NL: delete all occurrences of character 'i' in string 'it is icy' | |
| ref: """""".join([char for char in 'it is icy' if char != 'i']) | |
| Output: re. sub ('i ','','it is icy') | |
| bleu_metric: 19.720950806796015 | |
| ---------- | |
| NL: delete all instances of a character 'i' in a string 'it is icy' | |
| ref: re.sub('i', '', 'it is icy') | |
| Output: re. sub ('i ','','it is icy') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: delete all characters "i" in string "it is icy" | |
| ref: """it is icy""".replace('i', '') | |
| Output: " " " a b : b. c '! ='a') | |
| bleu_metric: 23.234178119893244 | |
| ---------- | |
| NL: how to delete all instances of a character in a string in python? | |
| ref: """""".join([char for char in 'it is icy' if char != 'i']) | |
| Output: " " " " " ". join ( [ char for char in'it is icy'if char! ='i'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: drop rows of pandas dataframe `df` having nan in column at index "1" | |
| ref: df.dropna(subset=[1]) | |
| Output: df. drop ( df. index. duplicated ( ) | |
| bleu_metric: 17.996531271765896 | |
| ---------- | |
| NL: get elements from list `mylist`, that have a field `n` value 30 | |
| ref: [x for x in myList if x.n == 30] | |
| Output: mylist = [ i for i in range ( 10 ) if i % 2 = = 0 ] | |
| bleu_metric: 11.709061630923735 | |
| ---------- | |
| NL: converting list of strings `intstringlist` to list of integer `nums` | |
| ref: nums = [int(x) for x in intstringlist] | |
| Output: nums = [ int ( x ) for x in intstringlist ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert list of string numbers into list of integers | |
| ref: map(int, eval(input('Enter the unfriendly numbers: '))) | |
| Output: [ int ( s ) for s in example _ string. split ( ',') ] | |
| bleu_metric: 11.433361115787454 | |
| ---------- | |
| NL: print "." without newline | |
| ref: sys.stdout.write('.') | |
| Output: sys. stdout. write ( '.') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: round off the float that is the product of `2.52 * 100` and convert it to an int | |
| ref: int(round(2.51 * 100)) | |
| Output: int ( round ( 1123. 456789, 0 ) ) | |
| bleu_metric: 39.832871551569504 | |
| ---------- | |
| NL: find all files in directory "/mydir" with extension ".txt" | |
| ref: for file in glob.glob('*.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 25.1696695878184 | |
| ---------- | |
| NL: find all files in directory "/mydir" with extension ".txt" | |
| ref: for file in os.listdir('/mydir'): | |
| if file.endswith('.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 95.90965597935381 | |
| ---------- | |
| NL: find all files in directory "/mydir" with extension ".txt" | |
| ref: for (root, dirs, files) in os.walk('/mydir'): | |
| for file in files: | |
| if file.endswith('.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 50.25808484829805 | |
| ---------- | |
| NL: plot dataframe `df` without a legend | |
| ref: df.plot(legend=False) | |
| Output: df. legend ( ). plot ( kind ='bar') | |
| bleu_metric: 21.305413619585085 | |
| ---------- | |
| NL: loop through the ip address range "192.168.x.x" | |
| ref: for i in range(256): | |
| for j in range(256): | |
| ip = ('192.168.%d.%d' % (i, j)) | |
| print(ip) | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 13.252382555750852 | |
| ---------- | |
| NL: loop through the ip address range "192.168.x.x" | |
| ref: for (i, j) in product(list(range(256)), list(range(256))): | |
| pass | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 5.334065332387164 | |
| ---------- | |
| NL: loop through the ip address range "192.168.x.x" | |
| ref: generator = iter_iprange('192.168.1.1', '192.168.255.255', step=1) | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 61.58601992013119 | |
| ---------- | |
| NL: sum the corresponding decimal values for binary values of each boolean element in list `x` | |
| ref: sum(1 << i for i, b in enumerate(x) if b) | |
| Output: float ( [ x for x in list ( d. values ( ) ) ] ) | |
| bleu_metric: 11.156857921595945 | |
| ---------- | |
| NL: write multiple strings `line1`, `line2` and `line3` in one line in a file `target` | |
| ref: target.write('%r\n%r\n%r\n' % (line1, line2, line3)) | |
| Output: result = [ line. split ( ) for line in open ('filename. txt ','r') ] | |
| bleu_metric: 7.848329424931549 | |
| ---------- | |
| NL: convert list of lists `data` into a flat list | |
| ref: [y for x in data for y in (x if isinstance(x, list) else [x])] | |
| Output: [ a for b in c for a in list _ of _ b ] | |
| bleu_metric: 8.372912227572577 | |
| ---------- | |
| NL: print new line character as `\n` in a string `foo\nbar` | |
| ref: print('foo\nbar'.encode('string_escape')) | |
| Output: print ('\ n '. join ( chr ( i ) for i in text ) ) | |
| bleu_metric: 19.268479640608692 | |
| ---------- | |
| NL: remove last comma character ',' in string `s` | |
| ref: """""".join(s.rsplit(',', 1)) | |
| Output: s = s. replace ( ', ','' ) | |
| bleu_metric: 24.4545047029932 | |
| ---------- | |
| NL: calculate the mean of each element in array `x` with the element previous to it | |
| ref: (x[1:] + x[:-1]) / 2 | |
| Output: np. mean ( x, y ) | |
| bleu_metric: 8.67666492910357 | |
| ---------- | |
| NL: get an array of the mean of each two consecutive values in numpy array `x` | |
| ref: x[:-1] + (x[1:] - x[:-1]) / 2 | |
| Output: np. array ( [ [ 2, 3, 4 ], [ 2, 5, 6 ] ] ) | |
| bleu_metric: 9.410489957951636 | |
| ---------- | |
| NL: load data containing `utf-8` from file `new.txt` into numpy array `arr` | |
| ref: arr = numpy.fromiter(codecs.open('new.txt', encoding='utf-8'), dtype='<U2') | |
| Output: print ( arr. decode ('utf - 8'). read ( ) ) | |
| bleu_metric: 17.11989843271324 | |
| ---------- | |
| NL: reverse sort list of dicts `l` by value for key `time` | |
| ref: l = sorted(l, key=itemgetter('time'), reverse=True) | |
| Output: sorted ( l, key = itemgetter ('date'), reverse = true ) | |
| bleu_metric: 63.481816146288104 | |
| ---------- | |
| NL: sort a list of dictionary `l` based on key `time` in descending order | |
| ref: l = sorted(l, key=lambda a: a['time'], reverse=True) | |
| Output: l = sorted ( l, key = lambda x : l ['time'], reverse = true ) | |
| bleu_metric: 69.63845241054851 | |
| ---------- | |
| NL: get rows of dataframe `df` that match regex '(hel|just)' | |
| ref: df.loc[df[0].str.contains('(Hel|Just)')] | |
| Output: df [ df. str. contains ('-') ] | |
| bleu_metric: 34.22307652991899 | |
| ---------- | |
| NL: find the string in `your_string` between two special characters "[" and "]" | |
| ref: re.search('\\[(.*)\\]', your_string).group(1) | |
| Output: your _ string. findall ( " print ( [ 1, 2, 3 ] + ', your _ string )') | |
| bleu_metric: 10.540925533894598 | |
| ---------- | |
| NL: how to create a list of date string in 'yyyymmdd' format with python pandas? | |
| ref: [d.strftime('%Y%m%d') for d in pandas.date_range('20130226', '20130302')] | |
| Output: df ['date']. strftime ('% y % m % d') | |
| bleu_metric: 25.96554412038301 | |
| ---------- | |
| NL: count number of times string 'brown' occurred in string 'the big brown fox is brown' | |
| ref: """The big brown fox is brown""".count('brown') | |
| Output: s = s. split ('' ) | |
| bleu_metric: 10.206322439745918 | |
| ---------- | |
| NL: decode json string `request.body` to python dict | |
| ref: json.loads(request.body) | |
| Output: json. loads ( request. body ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: download the file from url `url` and save it under file `file_name` | |
| ref: urllib.request.urlretrieve(url, file_name) | |
| Output: file _ name = open ('filename. txt ','filename. txt'). read ( ) | |
| bleu_metric: 8.607692533178168 | |
| ---------- | |
| NL: split string `text` by space | |
| ref: text.split() | |
| Output: re. split ('\ \ s + ', text ) | |
| bleu_metric: 20.333448190047886 | |
| ---------- | |
| NL: split string `text` by "," | |
| ref: text.split(',') | |
| Output: text. split ( ',') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: split string `line` into a list by whitespace | |
| ref: line.split() | |
| Output: [ item. strip ( ) for s in my _ list ] | |
| bleu_metric: 12.300686288463773 | |
| ---------- | |
| NL: replace dot characters '.' associated with ascii letters in list `s` with space ' ' | |
| ref: [re.sub('(?<!\\d)\\.(?!\\d)', ' ', i) for i in s] | |
| Output: re. sub ('[ ^ \ \ w ] + ','', s ) | |
| bleu_metric: 18.60592450912929 | |
| ---------- | |
| NL: sort list `list_of_strings` based on second index of each string `s` | |
| ref: sorted(list_of_strings, key=lambda s: s.split(',')[1]) | |
| Output: sorted ( s, key = lambda s : s. split ( ',') [ 1 ] ) | |
| bleu_metric: 89.21616972156079 | |
| ---------- | |
| NL: call multiple bash function 'vasp' and 'tee tee_output' using '|' | |
| ref: subprocess.check_call('vasp | tee tee_output', shell=True) | |
| Output: subprocess. call ('echo $ grep - data ', shell = true ) | |
| bleu_metric: 28.162897000452098 | |
| ---------- | |
| NL: eliminate all strings from list `lst` | |
| ref: [element for element in lst if isinstance(element, int)] | |
| Output: [ lst for l in lst if not isinstance ( lst, lst ) ] | |
| bleu_metric: 24.692069728555516 | |
| ---------- | |
| NL: get all the elements except strings from the list 'lst'. | |
| ref: [element for element in lst if not isinstance(element, str)] | |
| Output: [ lst [ : : - 1 ] for l in lst ] | |
| bleu_metric: 14.398289657599705 | |
| ---------- | |
| NL: sort a list of dictionaries `list_to_be_sorted` by the value of the dictionary key `name` | |
| ref: newlist = sorted(list_to_be_sorted, key=lambda k: k['name']) | |
| Output: sorted ( list _ of _ dct, key = lambda x : x ['name'] ) | |
| bleu_metric: 46.08662469973653 | |
| ---------- | |
| NL: sort a list of dictionaries `l` by values in key `name` in descending order | |
| ref: newlist = sorted(l, key=itemgetter('name'), reverse=True) | |
| Output: sorted ( l, key = lambda x : ( x ['name'], x ['age'] ) ) | |
| bleu_metric: 29.61516536011624 | |
| ---------- | |
| NL: how do i sort a list of dictionaries by values of the dictionary in python? | |
| ref: list_of_dicts.sort(key=operator.itemgetter('name')) | |
| Output: list _ of _ dicts. sort ( key = operator. itemgetter ('name') ) | |
| bleu_metric: 74.25946367830886 | |
| ---------- | |
| NL: how do i sort a list of dictionaries by values of the dictionary in python? | |
| ref: list_of_dicts.sort(key=operator.itemgetter('age')) | |
| Output: list _ of _ dicts. sort ( key = operator. itemgetter ('name') ) | |
| bleu_metric: 60.5478327168454 | |
| ---------- | |
| NL: how to sort a dataframe by the ocurrences in a column in python (pandas) | |
| ref: df.groupby('prots').sum().sort('scores', ascending=False) | |
| Output: df. groupby ('prod'). sum ( ) | |
| bleu_metric: 31.369171074632213 | |
| ---------- | |
| NL: join together with "," elements inside a list indexed with 'category' within a dictionary `trans` | |
| ref: """,""".join(trans['category']) | |
| Output: [ v for k, v in list ( mydict. items ( ) ) if v. startswith ('l') ] | |
| bleu_metric: 6.878430971169709 | |
| ---------- | |
| NL: concatenate array of strings `['a', 'b', 'c', 'd']` into a string | |
| ref: """""".join(['A', 'B', 'C', 'D']) | |
| Output: " " " " " ". join ( ['a ','b ','c ','d ','g'] | |
| bleu_metric: 51.01157052571188 | |
| ---------- | |
| NL: get json data from restful service 'url' | |
| ref: json.load(urllib.request.urlopen('url')) | |
| Output: urllib. request. urlretrieve ('http : / / search. twitter. com / search. json? q = | |
| bleu_metric: 17.729842264695016 | |
| ---------- | |
| NL: remove all strings from a list a strings `sents` where the values starts with `@$\t` or `#` | |
| ref: [x for x in sents if not x.startswith('@$\t') and not x.startswith('#')] | |
| Output: del my _ list. pop ('\ n') | |
| bleu_metric: 4.122430965890577 | |
| ---------- | |
| NL: django filter by hour | |
| ref: Entry.objects.filter(pub_date__contains='08:00') | |
| Output: entry. objects. filter ( created _ _ lte ='2011 : 00') | |
| bleu_metric: 47.02773871923361 | |
| ---------- | |
| NL: sort a list of dictionary `list` first by key `points` and then by `time` | |
| ref: list.sort(key=lambda item: (item['points'], item['time'])) | |
| Output: sorted ( list ( d. items ( ) ), key = lambda x : x [ 1 ] ) | |
| bleu_metric: 14.223469354841619 | |
| ---------- | |
| NL: convert datetime object `(1970, 1, 1)` to seconds | |
| ref: (t - datetime.datetime(1970, 1, 1)).total_seconds() | |
| Output: datetime. datetime. combine ( datetime. datetime. now ( ), 1 ) | |
| bleu_metric: 20.287417439504075 | |
| ---------- | |
| NL: insert `_suff` before the file extension in `long.file.name.jpg` or replace `_a` with `suff` if it precedes the extension. | |
| ref: re.sub('(\\_a)?\\.([^\\.]*)$', '_suff.\\2', 'long.file.name.jpg') | |
| Output: filename = os. path. join ( base _ name,'a + b') | |
| bleu_metric: 4.1219102274800345 | |
| ---------- | |
| NL: reload a module `module` | |
| ref: import imp | |
| imp.reload(module) | |
| Output: imp. _ _ name _ _ | |
| bleu_metric: 18.370727471078332 | |
| ---------- | |
| NL: convert integer `number` into an unassigned integer | |
| ref: struct.unpack('H', struct.pack('h', number)) | |
| Output: int (''. join ( map ( str, int, x ) ) ) | |
| bleu_metric: 15.69339742398475 | |
| ---------- | |
| NL: convert int values in list `numlist` to float | |
| ref: numlist = [float(x) for x in numlist] | |
| Output: print ( float ( mylist ) ) | |
| bleu_metric: 12.83572790104489 | |
| ---------- | |
| NL: write dataframe `df`, excluding index, to a csv file | |
| ref: df.to_csv(filename, index=False) | |
| Output: df. to _ csv ( filename, index = false ) | |
| bleu_metric: 43.24227075463214 | |
| ---------- | |
| NL: convert a urllib unquoted string `unescaped` to a json data `json_data` | |
| ref: json_data = json.loads(unescaped) | |
| Output: json _ data = json. loads ( json. dumps ( data ) ) | |
| bleu_metric: 31.689002674537857 | |
| ---------- | |
| NL: create a list containing all ascii characters as its elements | |
| ref: [chr(i) for i in range(127)] | |
| Output: [ ord ( c ) for c in range ( 12 ) ] | |
| bleu_metric: 27.560016787236364 | |
| ---------- | |
| NL: write `newfilebytes` to a binary file `newfile` | |
| ref: newFile.write(struct.pack('5B', *newFileBytes)) | |
| Output: f. write ('myfile') | |
| bleu_metric: 10.455877726641399 | |
| ---------- | |
| NL: python regex - check for a capital letter with a following lowercase in string `string` | |
| ref: re.sub('^[A-Z0-9]*(?![a-z])', '', string) | |
| Output: re. sub ('(? < = \ \ w ) ( [ a - z ] ) ','\ \ 1 ', string ) | |
| bleu_metric: 51.55802176328614 | |
| ---------- | |
| NL: get the last key of dictionary `dict` | |
| ref: list(dict.keys())[-1] | |
| Output: list ( dict. items ( ) ) | |
| bleu_metric: 34.69313394445662 | |
| ---------- | |
| NL: write line "hi there" to file `f` | |
| ref: print('hi there', file=f) | |
| Output: print ('hi there ', file = f ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: write line "hi there" to file `myfile` | |
| ref: f = open('myfile', 'w') | |
| f.write('hi there\n') | |
| f.close() | |
| Output: print ('myfile. txt ','w') | |
| bleu_metric: 16.739062621563587 | |
| ---------- | |
| NL: write line "hello" to file `somefile.txt` | |
| ref: with open('somefile.txt', 'a') as the_file: | |
| the_file.write('Hello\n') | |
| Output: print ('hello { 0 : myfile. txt ','% ( line ) ) | |
| bleu_metric: 20.43236176252964 | |
| ---------- | |
| NL: convert unicode string `s` to ascii | |
| ref: s.encode('iso-8859-15') | |
| Output: s. encode ('ascii ','ignore') | |
| bleu_metric: 44.534504264163466 | |
| ---------- | |
| NL: django get maximum value associated with field 'added' in model `authorizedemail` | |
| ref: AuthorizedEmail.objects.filter(group=group).order_by('-added')[0] | |
| Output: employees. objects. filter ( id ='id'). order _ by ('id') | |
| bleu_metric: 26.946924595891623 | |
| ---------- | |
| NL: find all numbers and dots from a string `text` using regex | |
| ref: re.findall('Test([0-9.]*[0-9]+)', text) | |
| Output: re. findall ('\ \ d + (? = [ ^ \ \ d ] * ) \ \ d + ) ', text ) | |
| bleu_metric: 36.1187685428593 | |
| ---------- | |
| NL: python regex to find all numbers and dots from 'text' | |
| ref: re.findall('Test([\\d.]*\\d+)', text) | |
| Output: re. findall ('\ \ d + (? = [ ^ \ \ d ] * ) ', text ) | |
| bleu_metric: 53.011675073675754 | |
| ---------- | |
| NL: execute script 'script.ps1' using 'powershell.exe' shell | |
| ref: os.system('powershell.exe', 'script.ps1') | |
| Output: os. system ('test. sh otherfunc') | |
| bleu_metric: 32.787776781958996 | |
| ---------- | |
| NL: sort a list of tuples `b` by third item in the tuple | |
| ref: b.sort(key=lambda x: x[1][2]) | |
| Output: b. sort ( key = lambda x : x [ 1 ] ) | |
| bleu_metric: 77.08276169003516 | |
| ---------- | |
| NL: get a list of all keys in cassandra database `cf` with pycassa | |
| ref: list(cf.get_range().get_keys()) | |
| Output: ##quen. get ( ). get ('2') | |
| bleu_metric: 18.295654224495205 | |
| ---------- | |
| NL: create a datetime with the current date & time | |
| ref: datetime.datetime.now() | |
| Output: datetime. datetime. now ( ). date ( ) | |
| bleu_metric: 61.90429960963545 | |
| ---------- | |
| NL: get the index of an integer `1` from a list `lst` if the list also contains boolean items | |
| ref: next(i for i, x in enumerate(lst) if not isinstance(x, bool) and x == 1) | |
| Output: next ( i for i, e in enumerate ( lst ) if e! = 1 ) | |
| bleu_metric: 42.75902729039988 | |
| ---------- | |
| NL: subtract 13 from every number in a list `a` | |
| ref: a[:] = [(x - 13) for x in a] | |
| Output: [ ( a - b ) for a, b in zip ( a, b ) ] | |
| bleu_metric: 13.229527092256335 | |
| ---------- | |
| NL: choose a random file from the directory contents of the c drive, `c:\\` | |
| ref: random.choice(os.listdir('C:\\')) | |
| Output: random. choice ( os. listdir ('\ \') ) | |
| bleu_metric: 77.51410336522927 | |
| ---------- | |
| NL: get the highest element in absolute value in a numpy matrix `x` | |
| ref: max(x.min(), x.max(), key=abs) | |
| Output: max ( x. min ( ), x. max ( ), key = abs ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get all urls within text `s` | |
| ref: re.findall('"(http.*?)"', s, re.MULTILINE | re.DOTALL) | |
| Output: re. findall ('(? = re. com / (? = " [ ^ " ] * " | [ ^ " ] * " | |
| bleu_metric: 20.539509719773488 | |
| ---------- | |
| NL: match urls whose domain doesn't start with `t` from string `document` using regex | |
| ref: re.findall('http://[^t][^s"]+\\.html', document) | |
| Output: re. search ('(? < = \ \ w ) (? : \ \. )? ( (? : \ \. \ \ | |
| bleu_metric: 10.625281384457024 | |
| ---------- | |
| NL: split a string `mystring` considering the spaces ' ' | |
| ref: mystring.replace(' ', '! !').split('!') | |
| Output: mystring. split ('' ) | |
| bleu_metric: 15.586404917762792 | |
| ---------- | |
| NL: open file `path` with mode 'r' | |
| ref: open(path, 'r') | |
| Output: os. path. join ( os. path. abspath ( '.') ) | |
| bleu_metric: 12.257617673006655 | |
| ---------- | |
| NL: sum elements at the same index in list `data` | |
| ref: [[sum(item) for item in zip(*items)] for items in zip(*data)] | |
| Output: [ sum ( item ) for item in zip ( * items ) ] | |
| bleu_metric: 50.621900464488824 | |
| ---------- | |
| NL: add a new axis to array `a` | |
| ref: a[:, (np.newaxis)] | |
| Output: numpy. zeros ( ( 2, 2, 3 ), numpy. zeros ( 2, 4 ) ) | |
| bleu_metric: 7.289334177359764 | |
| ---------- | |
| NL: send a signal `signal.SIGUSR1` to the current process | |
| ref: os.kill(os.getpid(), signal.SIGUSR1) | |
| Output: print ( proc. get _ append ( ) ) | |
| bleu_metric: 14.253628894439752 | |
| ---------- | |
| NL: decode a hex string '4a4b4c' to UTF-8. | |
| ref: bytes.fromhex('4a4b4c').decode('utf-8') | |
| Output: print ( str. decode ('utf8') ) | |
| bleu_metric: 23.083362053422253 | |
| ---------- | |
| NL: check if all elements in list `myList` are identical | |
| ref: all(x == myList[0] for x in myList) | |
| Output: len ( set ( mylist ) ) = = 1 | |
| bleu_metric: 9.360791398475978 | |
| ---------- | |
| NL: format number of spaces between strings `Python`, `:` and `Very Good` to be `20` | |
| ref: print('%*s : %*s' % (20, 'Python', 20, 'Very Good')) | |
| Output: " " " { 0 :, } " " ". format ( 123456789, 16 ) ( lambda x : x. | |
| bleu_metric: 7.030766106716325 | |
| ---------- | |
| NL: How to convert a string from CP-1251 to UTF-8? | |
| ref: d.decode('cp1251').encode('utf8') | |
| Output: d. decode ('cp1252'). encode ('utf8') | |
| bleu_metric: 82.82477531331041 | |
| ---------- | |
| NL: get rid of None values in dictionary `kwargs` | |
| ref: res = {k: v for k, v in list(kwargs.items()) if v is not None} | |
| Output: k for k, v in list ( dict. items ( ) ) if v is not none ] | |
| bleu_metric: 54.70605317141546 | |
| ---------- | |
| NL: get rid of None values in dictionary `kwargs` | |
| ref: res = dict((k, v) for k, v in kwargs.items() if v is not None) | |
| Output: k for k, v in list ( dict. items ( ) ) if v is not none ] | |
| bleu_metric: 40.55760587628575 | |
| ---------- | |
| NL: capture final output of a chain of system commands `ps -ef | grep something | wc -l` | |
| ref: subprocess.check_output('ps -ef | grep something | wc -l', shell=True) | |
| Output: subprocess. call ( ['mystring. py ','mystring. py'] ) | |
| bleu_metric: 11.327304007996714 | |
| ---------- | |
| NL: concatenate a list of strings `['a', 'b', 'c']` | |
| ref: """""".join(['a', 'b', 'c']) | |
| Output: ['a ','b ','c ','d'] | |
| bleu_metric: 50.018452828984486 | |
| ---------- | |
| NL: find intersection data between series `s1` and series `s2` | |
| ref: pd.Series(list(set(s1).intersection(set(s2)))) | |
| Output: s1. intersection ( s1, how ='left'). reset _ index ( ) | |
| bleu_metric: 16.132580731955684 | |
| ---------- | |
| NL: sending http headers to `client` | |
| ref: client.send('HTTP/1.0 200 OK\r\n') | |
| Output: server. serve _ forever ( ) | |
| bleu_metric: 6.063168540538235 | |
| ---------- | |
| NL: Format a datetime string `when` to extract date only | |
| ref: then = datetime.datetime.strptime(when, '%Y-%m-%d').date() | |
| Output: datetime. datetime. strptime ('2012 - 05 - 29t19 : 30 : 03. 283z ','% y | |
| bleu_metric: 28.12321366714592 | |
| ---------- | |
| NL: split a multi-line string `inputString` into separate strings | |
| ref: inputString.split('\n') | |
| Output: from functools import reduce reduce ( lambda x, y : x + y, l. split ('' ), x. split ( | |
| bleu_metric: 14.32450175903488 | |
| ---------- | |
| NL: Split a multi-line string ` a \n b \r\n c ` by new line character `\n` | |
| ref: ' a \n b \r\n c '.split('\n') | |
| Output: re. split ('\ n \ r \ n ','a \ n b \ n') | |
| bleu_metric: 65.72891674091409 | |
| ---------- | |
| NL: concatenate elements of list `b` by a colon ":" | |
| ref: """:""".join(str(x) for x in b) | |
| Output: ['a ','b'] | |
| bleu_metric: 6.490157276489835 | |
| ---------- | |
| NL: get the first object from a queryset in django model `Entry` | |
| ref: Entry.objects.filter()[:1].get() | |
| Output: model. objects. all ( ). order _ by ('- id') [ 0 ] | |
| bleu_metric: 16.1692143534558 | |
| ---------- | |
| NL: Calculate sum over all rows of 2D numpy array | |
| ref: a.sum(axis=1) | |
| Output: a. sum ( axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: enable warnings using action 'always' | |
| ref: warnings.simplefilter('always') | |
| Output: warnings. simplefilter ('always') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: concatenate items of list `l` with a space ' ' | |
| ref: print(' '.join(map(str, l))) | |
| Output: print (''. join ( [ str ( x ) for x in l ] ) ) | |
| bleu_metric: 42.5874468039002 | |
| ---------- | |
| NL: run script 'hello.py' with argument 'htmlfilename.htm' on terminal using python executable | |
| ref: subprocess.call(['python.exe', 'hello.py', 'htmlfilename.htm']) | |
| Output: subprocess. call ( ['python ','hello world ','world'] ) | |
| bleu_metric: 48.69018522875219 | |
| ---------- | |
| NL: How can I parse a time string containing milliseconds in it with python? | |
| ref: time.strptime('30/03/09 16:31:32.123', '%d/%m/%y %H:%M:%S.%f') | |
| Output: time. strptime ('2015 / 01 / 01 / 03. 0. 0. 0 ','% d / % m / % | |
| bleu_metric: 31.63951380366603 | |
| ---------- | |
| NL: convert a string `my_string` with dot and comma into a float number `my_float` | |
| ref: my_float = float(my_string.replace(',', '')) | |
| Output: my _ string. replace ( ', ','' ) | |
| bleu_metric: 59.23488237152091 | |
| ---------- | |
| NL: convert a string `123,456.908` with dot and comma into a floating number | |
| ref: float('123,456.908'.replace(',', '')) | |
| Output: " " " { 0 :,. 2f } " " ". format ( 12345678. 456789, 16 | |
| bleu_metric: 11.63901425102663 | |
| ---------- | |
| NL: set pythonpath in python script. | |
| ref: sys.path.append('/path/to/whatever') | |
| Output: os. path. join ( sys. path. append ('/ path / to / my / root') ) | |
| bleu_metric: 51.191407111567585 | |
| ---------- | |
| NL: split string 'Words, words, words.' using a regex '(\\W+)' | |
| ref: re.split('(\\W+)', 'Words, words, words.') | |
| Output: re. split ('\ \ w + ','\ \ 1 ', words, words ) | |
| bleu_metric: 39.78163327099985 | |
| ---------- | |
| NL: open a file `Output.txt` in append mode | |
| ref: file = open('Output.txt', 'a') | |
| Output: file. write ('whatever') | |
| bleu_metric: 17.234857810563913 | |
| ---------- | |
| NL: download a file "http://www.example.com/songs/mp3.mp3" over HTTP and save to "mp3.mp3" | |
| ref: urllib.request.urlretrieve('http://www.example.com/songs/mp3.mp3', 'mp3.mp3') | |
| Output: urllib. request. urlretrieve ('http : / / www. google. com / file. gz ', ' | |
| bleu_metric: 49.30073739073773 | |
| ---------- | |
| NL: download a file `url` over HTTP and save to `file_name` | |
| ref: u = urllib.request.urlopen(url) | |
| Output: response. close ( ) | |
| bleu_metric: 14.35549295013305 | |
| ---------- | |
| NL: download a file 'http://www.example.com/' over HTTP | |
| ref: response = urllib.request.urlopen('http://www.example.com/') | |
| html = response.read() | |
| Output: urllib. request. urlretrieve ('http : / / www. google. com / ','http : / / | |
| bleu_metric: 42.4884196166917 | |
| ---------- | |
| NL: download a file `url` over HTTP | |
| ref: r = requests.get(url) | |
| Output: response = requests. get ( url ) | |
| bleu_metric: 88.01117367933934 | |
| ---------- | |
| NL: download a file `url` over HTTP and save to "10MB" | |
| ref: response = requests.get(url, stream=True) | |
| Output: urllib. request. urlretrieve ('http : / / example. com / file. ext ','wb ' | |
| bleu_metric: 6.370405230161802 | |
| ---------- | |
| NL: argparse add argument with flag '--version' and version action of '%(prog)s 2.0' to parser `parser` | |
| ref: parser.add_argument('--version', action='version', version='%(prog)s 2.0') | |
| Output: parser. add _ argument ('- - type ', nargs ='*') | |
| bleu_metric: 17.102327939516414 | |
| ---------- | |
| NL: remove key 'c' from dictionary `d` | |
| ref: {i: d[i] for i in d if i != 'c'} | |
| Output: d = { k : v for k, v in list ( d. items ( ) ) if v } | |
| bleu_metric: 9.637411586929977 | |
| ---------- | |
| NL: Create new DataFrame object by merging columns "key" of dataframes `split_df` and `csv_df` and rename the columns from dataframes `split_df` and `csv_df` with suffix `_left` and `_right` respectively | |
| ref: pd.merge(split_df, csv_df, on=['key'], suffixes=('_left', '_right')) | |
| Output: df. drop _ duplicates ( df. columns ['column _ name']. apply ( lambda x :'column _ name ' | |
| bleu_metric: 8.416754937260349 | |
| ---------- | |
| NL: Split a string `s` by space with `4` splits | |
| ref: s.split(' ', 4) | |
| Output: ['x ','y ','x ','z ','x ','d ','x ', ' | |
| bleu_metric: 5.648892151960283 | |
| ---------- | |
| NL: read keyboard-input | |
| ref: input('Enter your input:') | |
| Output: input ('press enter to continue :') | |
| bleu_metric: 42.40125351805037 | |
| ---------- | |
| NL: enable debug mode on Flask application `app` | |
| ref: app.run(debug=True) | |
| Output: app. run ( debug = true ) | |
| bleu_metric: 70.71067811865474 | |
| ---------- | |
| NL: python save list `mylist` to file object 'save.txt' | |
| ref: pickle.dump(mylist, open('save.txt', 'wb')) | |
| Output: savefig. save ('filename. txt') | |
| bleu_metric: 13.988120102483691 | |
| ---------- | |
| NL: Multiply a matrix `P` with a 3d tensor `T` in scipy | |
| ref: scipy.tensordot(P, T, axes=[1, 1]).swapaxes(0, 1) | |
| Output: scipy. matrix ( [ 1, 2, 3 ], [ 1, 2, 3 ] ). t | |
| bleu_metric: 16.944114083600446 | |
| ---------- | |
| NL: Create 3d array of zeroes of size `(3,3,3)` | |
| ref: numpy.zeros((3, 3, 3)) | |
| Output: np. zeros ( ( 3, 3 ) ). transpose ( 2, 3 ) | |
| bleu_metric: 54.166685749344154 | |
| ---------- | |
| NL: cut off the last word of a sentence `content` | |
| ref: """ """.join(content.split(' ')[:-1]) | |
| Output: " " " " " ". join ( content. split ('' ) [ : - 1 ] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert scalar `x` to array | |
| ref: x = np.asarray(x).reshape(1, -1)[(0), :] | |
| Output: x = np. zeros ( x ). reshape ( - 1, 2 ) | |
| bleu_metric: 35.6520799192373 | |
| ---------- | |
| NL: sum all elements of nested list `L` | |
| ref: sum(sum(i) if isinstance(i, list) else i for i in L) | |
| Output: sum ( [ i for i in l ) for i in l ] | |
| bleu_metric: 19.29709182756385 | |
| ---------- | |
| NL: convert hex string '470FC614' to a float number | |
| ref: struct.unpack('!f', '470FC614'.decode('hex'))[0] | |
| Output: struct. unpack ('d ', binascii. unhexlify ('4081637ef7d | |
| bleu_metric: 20.061882169394003 | |
| ---------- | |
| NL: Multiple each value by `2` for all keys in a dictionary `my_dict` | |
| ref: my_dict.update((x, y * 2) for x, y in list(my_dict.items())) | |
| Output: dict ( ( k, v ) for k, v in list ( my _ dict. items ( ) ) ) | |
| bleu_metric: 35.25419189217226 | |
| ---------- | |
| NL: running bash script 'sleep.sh' | |
| ref: subprocess.call('sleep.sh', shell=True) | |
| Output: os. system ('echo $ command - flags arguments') | |
| bleu_metric: 14.814869013357976 | |
| ---------- | |
| NL: Join elements of list `l` with a comma `,` | |
| ref: """,""".join(l) | |
| Output: " " ", " " ". join ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: make a comma-separated string from a list `myList` | |
| ref: myList = ','.join(map(str, myList)) | |
| Output: mylist = [ i. split ( ',') for i in mylist ] | |
| bleu_metric: 15.526320210133507 | |
| ---------- | |
| NL: reverse the list that contains 1 to 10 | |
| ref: list(reversed(list(range(10)))) | |
| Output: [ ( 1, 2 ), ( 3, 4 ), ( 2, - 1 ), ( 1, 2 ), ( 3, 4 | |
| bleu_metric: 5.834347180338517 | |
| ---------- | |
| NL: remove substring 'bag,' from a string 'lamp, bag, mirror' | |
| ref: print('lamp, bag, mirror'.replace('bag,', '')) | |
| Output: re. sub ( ', ', ', '. join ( chr ) for ch in asking ) | |
| bleu_metric: 22.93476949918813 | |
| ---------- | |
| NL: Reverse the order of words, delimited by `.`, in string `s` | |
| ref: """.""".join(s.split('.')[::-1]) | |
| Output: re. sub ('\ \ b (. + ) (? = \ \ s + \ \ s + ) ','\ \ 1 | |
| bleu_metric: 7.508325052934217 | |
| ---------- | |
| NL: convert epoch time represented as milliseconds `s` to string using format '%Y-%m-%d %H:%M:%S.%f' | |
| ref: datetime.datetime.fromtimestamp(s).strftime('%Y-%m-%d %H:%M:%S.%f') | |
| Output: datetime. datetime. now ( ). strftime ('% y - % m - % d % h : % m : % | |
| bleu_metric: 42.22477323756999 | |
| ---------- | |
| NL: parse milliseconds epoch time '1236472051807' to format '%Y-%m-%d %H:%M:%S' | |
| ref: time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(1236472051807 / 1000.0)) | |
| Output: datetime. datetime. now ( ). strftime ('% y - % m - % d % h : % m : % | |
| bleu_metric: 32.53778694644052 | |
| ---------- | |
| NL: get the date 7 days before the current date | |
| ref: (datetime.datetime.now() - datetime.timedelta(days=7)).date() | |
| Output: ( datetime. datetime. now ( ) - datetime. timedelta ( days = 1 ) ) | |
| bleu_metric: 69.7353047868514 | |
| ---------- | |
| NL: sum elements at index `column` of each list in list `data` | |
| ref: print(sum(row[column] for row in data)) | |
| Output: [ sum ( item ) for item in zip ( * items ) ] | |
| bleu_metric: 14.009047908905242 | |
| ---------- | |
| NL: sum columns of a list `array` | |
| ref: [sum(row[i] for row in array) for i in range(len(array[0]))] | |
| Output: sum ( i ) | |
| bleu_metric: 0.4295018804111146 | |
| ---------- | |
| NL: encode binary string 'your string' to base64 code | |
| ref: base64.b64encode(bytes('your string', 'utf-8')) | |
| Output: base64. b64encode ('base64') | |
| bleu_metric: 17.22724743406155 | |
| ---------- | |
| NL: combine list of dictionaries `dicts` with the same keys in each list to a single dictionary | |
| ref: dict((k, [d[k] for d in dicts]) for k in dicts[0]) | |
| Output: dict ( ( k, v ) for d in dicts for k, v in list ( d. items ( ) ) ) | |
| bleu_metric: 32.12854967972961 | |
| ---------- | |
| NL: Merge a nested dictionary `dicts` into a flat dictionary by concatenating nested values with the same key `k` | |
| ref: {k: [d[k] for d in dicts] for k in dicts[0]} | |
| Output: dict ( ( k, v ) for d in list ( d. items ( ) ) for k, v in list ( d. items ( | |
| bleu_metric: 10.14524997386625 | |
| ---------- | |
| NL: How do I get the url parameter in a Flask view | |
| ref: request.args['myParam'] | |
| Output: request. args ['myapp'] | |
| bleu_metric: 58.8837385313777 | |
| ---------- | |
| NL: identify duplicate values in list `mylist` | |
| ref: [k for k, v in list(Counter(mylist).items()) if v > 1] | |
| Output: [ k for k, v in mylist if v. startswith ('mycount') else v for k, v in mylist | |
| bleu_metric: 34.06227682100307 | |
| ---------- | |
| NL: Insert directory 'apps' into directory `__file__` | |
| ref: sys.path.insert(1, os.path.join(os.path.dirname(__file__), 'apps')) | |
| Output: urllib. request. urlopen ('http : / / www. example. com ','/ path') | |
| bleu_metric: 9.10451572772839 | |
| ---------- | |
| NL: modify sys.path for python module `subdir` | |
| ref: sys.path.append(os.path.join(os.path.dirname(__file__), 'subdir')) | |
| Output: subprocess. call ( ['start / subdir / bin / 2014 ','inr'] ) | |
| bleu_metric: 8.368553758184737 | |
| ---------- | |
| NL: Insert a 'None' value into a SQLite3 table. | |
| ref: db.execute("INSERT INTO present VALUES('test2', ?, 10)", (None,)) | |
| Output: cursor. execute ('select * from table where column in l') | |
| bleu_metric: 13.62767317410906 | |
| ---------- | |
| NL: flatten list `list_of_menuitems` | |
| ref: [image for menuitem in list_of_menuitems for image in menuitem] | |
| Output: [ item for sublist in list _ of _ lists ] | |
| bleu_metric: 17.194656088289214 | |
| ---------- | |
| NL: append elements of a set `b` to a list `a` | |
| ref: a.extend(b) | |
| Output: a. extend ( b ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Append elements of a set to a list in Python | |
| ref: a.extend(list(b)) | |
| Output: a. extend ( b ) | |
| bleu_metric: 46.81294641285289 | |
| ---------- | |
| NL: write the data of dataframe `df` into text file `np.txt` | |
| ref: np.savetxt('c:\\data\\np.txt', df.values, fmt='%d') | |
| Output: df. to _ csv ('filename. txt ', header = false ) | |
| bleu_metric: 14.209004647554735 | |
| ---------- | |
| NL: write content of DataFrame `df` into text file 'c:\\data\\pandas.txt' | |
| ref: df.to_csv('c:\\data\\pandas.txt', header=None, index=None, sep=' ', mode='a') | |
| Output: df. to _ csv ('c : \ \ data \ \ t. txt ', sep ='\ t ', index | |
| bleu_metric: 37.50344293811855 | |
| ---------- | |
| NL: Split a string `x` by last occurrence of character `-` | |
| ref: print(x.rpartition('-')[0]) | |
| Output: x. split ('-') [ : - 1 ] | |
| bleu_metric: 43.353400970949984 | |
| ---------- | |
| NL: get the last part of a string before the character '-' | |
| ref: print(x.rsplit('-', 1)[0]) | |
| Output: re. sub ('(? < = - -. ) ','- ', line ) | |
| bleu_metric: 20.776569671187676 | |
| ---------- | |
| NL: upload file using FTP | |
| ref: ftp.storlines('STOR ' + filename, open(filename, 'r')) | |
| Output: ftp. quit ( ) | |
| bleu_metric: 3.710176016036221 | |
| ---------- | |
| NL: add one to the hidden web element with id 'XYZ' with selenium python script | |
| ref: browser.execute_script("document.getElementById('XYZ').value+='1'") | |
| Output: driver. execute _ script ('window. scrollto ( 0, document. body. com') ) | |
| bleu_metric: 10.807499589580493 | |
| ---------- | |
| NL: create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]` | |
| ref: np.maximum([2, 3, 4], [1, 5, 2]) | |
| Output: np. array ( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] ) | |
| bleu_metric: 28.489318277723964 | |
| ---------- | |
| NL: print a list `l` and move first 3 elements to the end of the list | |
| ref: print(l[3:] + l[:3]) | |
| Output: print ( [ l [ 3 : ] for l in l ] ) | |
| bleu_metric: 43.472087194499146 | |
| ---------- | |
| NL: loop over files in directory '.' | |
| ref: for fn in os.listdir('.'): | |
| if os.path.isfile(fn): | |
| pass | |
| Output: for ( root, dirs, dirs ) in os. walk ( '.') : if os. path. isdir ( ' | |
| bleu_metric: 46.596572764176955 | |
| ---------- | |
| NL: loop over files in directory `source` | |
| ref: for (root, dirs, filenames) in os.walk(source): | |
| for f in filenames: | |
| pass | |
| Output: os. listdir ('os') | |
| bleu_metric: 4.9782661836656885 | |
| ---------- | |
| NL: create a random list of integers | |
| ref: [int(1000 * random.random()) for i in range(10000)] | |
| Output: random. choice ( [ int ( i ) for i in range ( 10 ) ] ) | |
| bleu_metric: 38.95592996402149 | |
| ---------- | |
| NL: Using %f with strftime() in Python to get microseconds | |
| ref: datetime.datetime.now().strftime('%H:%M:%S.%f') | |
| Output: datetime. datetime. now ( ). strftime ('% h : % m : % s. % f') | |
| bleu_metric: 69.80972118841675 | |
| ---------- | |
| NL: Google App Engine execute GQL query 'SELECT * FROM Schedule WHERE station = $1' with parameter `foo.key()` | |
| ref: db.GqlQuery('SELECT * FROM Schedule WHERE station = $1', foo.key()) | |
| Output: c. objects. filter ( id = 1 ). order _ by ('foobar') | |
| bleu_metric: 10.30143863225941 | |
| ---------- | |
| NL: filter rows in pandas starting with alphabet 'f' using regular expression. | |
| ref: df.b.str.contains('^f') | |
| Output: df ['a']. str. contains ('c') | |
| bleu_metric: 43.85068972747104 | |
| ---------- | |
| NL: print a 2 dimensional list `tab` as a table with delimiters | |
| ref: print('\n'.join('\t'.join(str(col) for col in row) for row in tab)) | |
| Output: print ('\ n '. join ( map ( str, row ) ) ) | |
| bleu_metric: 28.575895973659073 | |
| ---------- | |
| NL: pandas: delete rows in dataframe `df` based on multiple columns values | |
| ref: df.set_index(list('BC')).drop(tuples, errors='ignore').reset_index() | |
| Output: df. drop ( df. index. map ( lambda x : x. index, axis = 1 ) ) | |
| bleu_metric: 13.21619418680914 | |
| ---------- | |
| NL: format the variables `self.goals` and `self.penalties` using string formatting | |
| ref: """({:d} goals, ${:d})""".format(self.goals, self.penalties) | |
| Output: " " " { } \ \ w { }. { } " " ". format ( date,'user') | |
| bleu_metric: 28.34142738530324 | |
| ---------- | |
| NL: format string "({} goals, ${})" with variables `goals` and `penalties` | |
| ref: """({} goals, ${})""".format(self.goals, self.penalties) | |
| Output: " " " { } { } " " ". format ( {'foo':'bar'} ) | |
| bleu_metric: 34.620457601133026 | |
| ---------- | |
| NL: format string "({0.goals} goals, ${0.penalties})" | |
| ref: """({0.goals} goals, ${0.penalties})""".format(self) | |
| Output: df. set _ index ('name') | |
| bleu_metric: 3.9504404329436813 | |
| ---------- | |
| NL: convert list of lists `L` to list of integers | |
| ref: [int(''.join(str(d) for d in x)) for x in L] | |
| Output: [ int ( x ) for x in l ] | |
| bleu_metric: 18.761335250609946 | |
| ---------- | |
| NL: combine elements of each list in list `L` into digits of a single integer | |
| ref: [''.join(str(d) for d in x) for x in L] | |
| Output: map ( int, l ) | |
| bleu_metric: 2.5915324112223583 | |
| ---------- | |
| NL: convert a list of lists `L` to list of integers | |
| ref: L = [int(''.join([str(y) for y in x])) for x in L] | |
| Output: [ int ( x ) for x in l ] | |
| bleu_metric: 11.473780780974305 | |
| ---------- | |
| NL: write the elements of list `lines` concatenated by special character '\n' to file `myfile` | |
| ref: myfile.write('\n'.join(lines)) | |
| Output: myfile. write ('\ n '. join ( map ( str, mylist ) ) ) | |
| bleu_metric: 60.5478327168454 | |
| ---------- | |
| NL: removing an element from a list based on a predicate 'X' or 'N' | |
| ref: [x for x in ['AAT', 'XAC', 'ANT', 'TTA'] if 'X' not in x and 'N' not in x] | |
| Output: [ x for x in [ x if x! ='b'] if x! ='c'] | |
| bleu_metric: 20.792998340455547 | |
| ---------- | |
| NL: Remove duplicate words from a string `text` using regex | |
| ref: text = re.sub('\\b(\\w+)( \\1\\b)+', '\\1', text) | |
| Output: re. sub ('(. * ) \ \ 1 + ','\ \ 1 ', text ) | |
| bleu_metric: 42.691904454956294 | |
| ---------- | |
| NL: count non zero values in each column in pandas data frame | |
| ref: df.astype(bool).sum(axis=1) | |
| Output: df. groupby ( level = 0 ). sum ( ) | |
| bleu_metric: 33.17838167503153 | |
| ---------- | |
| NL: search for string that matches regular expression pattern '(?<!Distillr)\\\\AcroTray\\.exe' in string 'C:\\SomeDir\\AcroTray.exe' | |
| ref: re.search('(?<!Distillr)\\\\AcroTray\\.exe', 'C:\\SomeDir\\AcroTray.exe') | |
| Output: print ( re. sub ('[ \ \ w _ ] + ','', str1 ). split ( ',') | |
| bleu_metric: 11.01072272272342 | |
| ---------- | |
| NL: split string 'QH QD JC KD JS' into a list on white spaces | |
| ref: """QH QD JC KD JS""".split() | |
| Output: ['abcd ','* ','2 ','3 ','4 ','g ','g ', | |
| bleu_metric: 5.682676011595584 | |
| ---------- | |
| NL: search for occurrences of regex pattern '>.*<' in xml string `line` | |
| ref: print(re.search('>.*<', line).group(0)) | |
| Output: print ( doc. group ( 1 ). group ( 1 ) ) | |
| bleu_metric: 22.08517793512327 | |
| ---------- | |
| NL: erase all the contents of a file `filename` | |
| ref: open(filename, 'w').close() | |
| Output: open ('filename ','w'). close ( ) | |
| bleu_metric: 70.98108717509044 | |
| ---------- | |
| NL: convert a string into datetime using the format '%Y-%m-%d %H:%M:%S.%f' | |
| ref: datetime.datetime.strptime(string_date, '%Y-%m-%d %H:%M:%S.%f') | |
| Output: datetime. datetime. strptime ('2010 - 11 - 13 10 : 33 : 54. 227806 ','% y | |
| bleu_metric: 23.69248624414322 | |
| ---------- | |
| NL: find the index of a list with the first element equal to '332' within the list of lists `thelist` | |
| ref: [index for index, item in enumerate(thelist) if item[0] == '332'] | |
| Output: [ x [ 0 ] for x in l if x [ 0 ] = ='score'] | |
| bleu_metric: 32.08521724608534 | |
| ---------- | |
| NL: lower a string `text` and remove non-alphanumeric characters aside from space | |
| ref: re.sub('[^\\sa-zA-Z0-9]', '', text).lower().strip() | |
| Output: re. sub ('[ ^ \ \ w. ] + ','', text ). strip ( ) | |
| bleu_metric: 54.08690470924506 | |
| ---------- | |
| NL: remove all non-alphanumeric characters except space from a string `text` and lower it | |
| ref: re.sub('(?!\\s)[\\W_]', '', text).lower().strip() | |
| Output: re. sub ('[ ^ 0 - 9a - za - z ] + ','', s ). strip ( ) | |
| bleu_metric: 38.80387619814469 | |
| ---------- | |
| NL: subscript text 'H20' with '2' as subscripted in matplotlib labels for arrays 'x' and 'y'. | |
| ref: plt.plot(x, y, label='H\u2082O') | |
| Output: plt. plot ( x, y, label ='h \ u2082o') | |
| bleu_metric: 72.76817202342092 | |
| ---------- | |
| NL: subscript text 'H20' with '2' as subscripted in matplotlib labels for arrays 'x' and 'y'. | |
| ref: plt.plot(x, y, label='$H_2O$') | |
| Output: plt. plot ( x, y, label ='h \ u2082o') | |
| bleu_metric: 71.60350546947922 | |
| ---------- | |
| NL: loop over a list `mylist` if sublists length equals 3 | |
| ref: [x for x in mylist if len(x) == 3] | |
| Output: mylist [ : : 2 ] = = mylist [ 0 ] | |
| bleu_metric: 12.640504935009755 | |
| ---------- | |
| NL: initialize a list `lst` of 100 objects Object() | |
| ref: lst = [Object() for _ in range(100)] | |
| Output: lst = [ [ ] for i in range ( 3 ) ] | |
| bleu_metric: 28.992166587078206 | |
| ---------- | |
| NL: create list `lst` containing 100 instances of object `Object` | |
| ref: lst = [Object() for i in range(100)] | |
| Output: list ( chain. from _ iterable ( l ) ) | |
| bleu_metric: 10.649638629044373 | |
| ---------- | |
| NL: get the content of child tag with`href` attribute whose parent has css `someclass` | |
| ref: self.driver.find_element_by_css_selector('.someclass a').get_attribute('href') | |
| Output: soup. find _ elements _ by _ xpath ( " / / * [ @ type ='submit'] " ). get _ attribute ( | |
| bleu_metric: 10.389889377420038 | |
| ---------- | |
| NL: joining data from dataframe `df1` with data from dataframe `df2` based on matching values of column 'Date_Time' in both dataframes | |
| ref: df1.merge(df2, on='Date_Time') | |
| Output: pd. merge ( df1, df2, on = ['date _ time ','date _ time'] ) | |
| bleu_metric: 21.22363344155404 | |
| ---------- | |
| NL: use `%s` operator to print variable values `str1` inside a string | |
| ref: 'first string is: %s, second one is: %s' % (str1, 'geo.tif') | |
| Output: print ( str ( a ) +'' + str ( b ) ) | |
| bleu_metric: 6.053698751871955 | |
| ---------- | |
| NL: Split a string by a delimiter in python | |
| ref: [x.strip() for x in '2.MATCHES $$TEXT$$ STRING'.split('$$TEXT$$')] | |
| Output: s. split (';') | |
| bleu_metric: 2.904501569386027 | |
| ---------- | |
| NL: check if directory `directory ` exists and create it if necessary | |
| ref: if (not os.path.exists(directory)): | |
| os.makedirs(directory) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 9.597952472216766 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: os.makedirs(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: distutils.dir_util.mkpath(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 40.661103887968814 | |
| ---------- | |
| NL: check if a directory `path` exists and create it if necessary | |
| ref: os.makedirs(path) | |
| Output: os. makedirs ( path ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Replace a separate word 'H3' by 'H1' in a string 'text' | |
| ref: re.sub('\\bH3\\b', 'H1', text) | |
| Output: re. sub ('\ \ w + ','\ \ 1 ', text ) | |
| bleu_metric: 56.31071891586576 | |
| ---------- | |
| NL: substitute ASCII letters in string 'aas30dsa20' with empty string '' | |
| ref: re.sub('\\D', '', 'aas30dsa20') | |
| Output: re. sub ('[ a - z ] ','','abcd') | |
| bleu_metric: 51.003234295212685 | |
| ---------- | |
| NL: get digits only from a string `aas30dsa20` using lambda function | |
| ref: """""".join([x for x in 'aas30dsa20' if x.isdigit()]) | |
| Output: " " " " " ". join ( map ( lambda x : x * 7 ) ) | |
| bleu_metric: 39.54603942707659 | |
| ---------- | |
| NL: access a tag called "name" in beautifulsoup `soup` | |
| ref: print(soup.find('name').string) | |
| Output: soup. findall ('div ', {'name':'description'} ) | |
| bleu_metric: 17.457404828304572 | |
| ---------- | |
| NL: get a dictionary `records` of key-value pairs in PyMongo cursor `cursor` | |
| ref: records = dict((record['_id'], record) for record in cursor) | |
| Output: dict ( ( x [ 0 ], x [ 1 ] ) for x in cursor. items ( ) ) | |
| bleu_metric: 16.34453553468014 | |
| ---------- | |
| NL: Create new matrix object by concatenating data from matrix A and matrix B | |
| ref: np.concatenate((A, B)) | |
| Output: numpy. concatenate ( [ a, b ], axis = 0 ) | |
| bleu_metric: 18.92240568795935 | |
| ---------- | |
| NL: concat two matrices `A` and `B` in numpy | |
| ref: np.vstack((A, B)) | |
| Output: np. sqrt ( ( a. shape [ 0 ], b ) ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: Get the characters count in a file `filepath` | |
| ref: os.stat(filepath).st_size | |
| Output: print ( os. path. join ( get _ list ) ) | |
| bleu_metric: 13.834368456410944 | |
| ---------- | |
| NL: count the occurrences of item "a" in list `l` | |
| ref: l.count('a') | |
| Output: [ x. count ('a') for x in l ] | |
| bleu_metric: 43.78826865860793 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: Counter(l) | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 13.065113298388567 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: [[x, l.count(x)] for x in set(l)] | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 16.443715448412245 | |
| ---------- | |
| NL: count the occurrences of items in list `l` | |
| ref: dict(((x, l.count(x)) for x in set(l))) | |
| Output: [ sum ( x ) for x in l ] | |
| bleu_metric: 15.927159921457768 | |
| ---------- | |
| NL: count the occurrences of item "b" in list `l` | |
| ref: l.count('b') | |
| Output: [ ( x, l. count ( x ) ) for x in l ] | |
| bleu_metric: 20.61477352156375 | |
| ---------- | |
| NL: copy file `srcfile` to directory `dstdir` | |
| ref: shutil.copy(srcfile, dstdir) | |
| Output: os. chdir ('copyfile. txt ', dst ) | |
| bleu_metric: 14.949751774990682 | |
| ---------- | |
| NL: find the key associated with the largest value in dictionary `x` whilst key is non-zero value | |
| ref: max(k for k, v in x.items() if v != 0) | |
| Output: max ( x, key = lambda x : ( x ['x'], x ['x'] ) ) | |
| bleu_metric: 9.186672262412122 | |
| ---------- | |
| NL: get the largest key whose not associated with value of 0 in dictionary `x` | |
| ref: (k for k, v in x.items() if v != 0) | |
| Output: ( k for k, v in x. items ( ) if v! = 0 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the largest key in a dictionary `x` with non-zero value | |
| ref: max(k for k, v in x.items() if v != 0) | |
| Output: max ( x, key = lambda x : ( x [ 1 ], x [ 0 ] ) ) | |
| bleu_metric: 11.162938527547608 | |
| ---------- | |
| NL: Put the curser at beginning of the file | |
| ref: file.seek(0) | |
| Output: os. system ('with open ( filename ). read ( ) ','rb') | |
| bleu_metric: 8.687475782716618 | |
| ---------- | |
| NL: combine values from column 'b' and column 'a' of dataframe `df` into column 'c' of datafram `df` | |
| ref: df['c'] = np.where(df['a'].isnull, df['b'], df['a']) | |
| Output: df ['c'] = df ['a']. apply ( lambda x : x ['b']. fillna | |
| bleu_metric: 43.50703089837015 | |
| ---------- | |
| NL: remove key 'ele' from dictionary `d` | |
| ref: del d['ele'] | |
| Output: d = { k : v for k, v in list ( d. items ( ) ) if v! ='none'} | |
| bleu_metric: 5.442133807846856 | |
| ---------- | |
| NL: Update datetime field in `MyModel` to be the existing `timestamp` plus 100 years | |
| ref: MyModel.objects.update(timestamp=F('timestamp') + timedelta(days=36524.25)) | |
| Output: mymodel. objects. filter ( date _ _ length ='% y - % m - % d') | |
| bleu_metric: 12.351156662075915 | |
| ---------- | |
| NL: merge list `['it']` and list `['was']` and list `['annoying']` into one list | |
| ref: ['it'] + ['was'] + ['annoying'] | |
| Output: [ dict ( zip ( * [ ['one ','two'], ['one ','two'] ] ) ) | |
| bleu_metric: 10.105385745342533 | |
| ---------- | |
| NL: increment a value with leading zeroes in a number `x` | |
| ref: str(int(x) + 1).zfill(len(x)) | |
| Output: print ('\ xe, { 0 : x. 0 } '. format ( x, - 1 ) ) | |
| bleu_metric: 13.471766853689124 | |
| ---------- | |
| NL: check if a pandas dataframe `df`'s index is sorted | |
| ref: all(df.index[:-1] <= df.index[1:]) | |
| Output: df. index. values. tolist ( ) | |
| bleu_metric: 9.980212030371455 | |
| ---------- | |
| NL: Convert tuple `t` to list | |
| ref: list(t) | |
| Output: list ( t ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Convert list `t` to tuple | |
| ref: tuple(l) | |
| Output: tuple ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Convert tuple `level1` to list | |
| ref: level1 = map(list, level1) | |
| Output: [ ( x + 1 ) for x in a ] | |
| bleu_metric: 11.35935489027116 | |
| ---------- | |
| NL: send the output of pprint object `dataobject` to file `logFile` | |
| ref: pprint.pprint(dataobject, logFile) | |
| Output: sys. stdout. flush ( ) | |
| bleu_metric: 18.67587389639562 | |
| ---------- | |
| NL: get index of rows in column 'BoolCol' | |
| ref: df.loc[df['BoolCol']] | |
| Output: df [ ['bool'] ]. index ('matrix2'). index | |
| bleu_metric: 18.710158230410627 | |
| ---------- | |
| NL: Create a list containing the indexes of rows where the value of column 'BoolCol' in dataframe `df` are equal to True | |
| ref: df.iloc[np.flatnonzero(df['BoolCol'])] | |
| Output: df [ df. index. map ( lambda x : x [ 1 ]. isin ( x ) ) ] | |
| bleu_metric: 14.72821272412462 | |
| ---------- | |
| NL: get list of indexes of rows where column 'BoolCol' values match True | |
| ref: df[df['BoolCol'] == True].index.tolist() | |
| Output: [ df ['ratio'] for d in l ] | |
| bleu_metric: 19.62812256638541 | |
| ---------- | |
| NL: get index of rows in dataframe `df` which column 'BoolCol' matches value True | |
| ref: df[df['BoolCol']].index.tolist() | |
| Output: df ['column _ name'] = = df. index. str. get ('columnid') | |
| bleu_metric: 18.088201962129936 | |
| ---------- | |
| NL: change working directory to the directory `owd` | |
| ref: os.chdir(owd) | |
| Output: os. chdir ('c : \ \ users \ \ users \ \ desktop \ \ desktop \ \ python') | |
| bleu_metric: 15.553986407412198 | |
| ---------- | |
| NL: insert data from a string `testfield` to sqlite db `c` | |
| ref: c.execute("INSERT INTO test VALUES (?, 'bar')", (testfield,)) | |
| Output: cursor. execute ('insert into table values ( % s, % s ) ', ( ast, ) ) | |
| bleu_metric: 34.29547961820059 | |
| ---------- | |
| NL: decode string "\\x89\\n" into a normal string | |
| ref: """\\x89\\n""".decode('string_escape') | |
| Output: " " " \ \ xc3 \ \ x96 \ xc3 \ x96 \ x93 \ x93 \ | |
| bleu_metric: 29.888108815158 | |
| ---------- | |
| NL: convert a raw string `raw_string` into a normal string | |
| ref: raw_string.decode('string_escape') | |
| Output: raw _ string. decode ('unicode _ escape') | |
| bleu_metric: 35.556702356686955 | |
| ---------- | |
| NL: convert a raw string `raw_byte_string` into a normal string | |
| ref: raw_byte_string.decode('unicode_escape') | |
| Output: raw _ byte _ string. decode ('unicode _ escape') | |
| bleu_metric: 30.620490882364876 | |
| ---------- | |
| NL: split a string `s` with into all strings of repeated characters | |
| ref: [m.group(0) for m in re.finditer('(\\d)\\1*', s)] | |
| Output: re. findall ('\ \ d + (? : \ \ s + ) ','a \ n b') | |
| bleu_metric: 12.792208459604188 | |
| ---------- | |
| NL: scatter a plot with x, y position of `np.random.randn(100)` and face color equal to none | |
| ref: plt.scatter(np.random.randn(100), np.random.randn(100), facecolors='none') | |
| Output: plt. show ( ) | |
| bleu_metric: 0.8278521686370256 | |
| ---------- | |
| NL: do a scatter plot with empty circles | |
| ref: plt.plot(np.random.randn(100), np.random.randn(100), 'o', mfc='none') | |
| Output: plt. show ( ) | |
| bleu_metric: 0.42503347540650505 | |
| ---------- | |
| NL: remove a div with a id `main-content` using beautifulsoup | |
| ref: soup.find('div', id='main-content').decompose() | |
| Output: soup. find _ all ('div ', id ='all') | |
| bleu_metric: 46.28692516962493 | |
| ---------- | |
| NL: filter rows containing key word `ball` in column `ids` | |
| ref: df[df['ids'].str.contains('ball')] | |
| Output: id. idxmax ( word ). drop ( false ) | |
| bleu_metric: 7.630799524006417 | |
| ---------- | |
| NL: convert index at level 0 into a column in dataframe `df` | |
| ref: df.reset_index(level=0, inplace=True) | |
| Output: df. set _ index ('level1'). unstack ( level = 0 ) | |
| bleu_metric: 24.29335519279861 | |
| ---------- | |
| NL: Add indexes in a data frame `df` to a column `index1` | |
| ref: df['index1'] = df.index | |
| Output: df. loc [ index ] = df. index. get _ level _ values ( 0 ) | |
| bleu_metric: 24.04315522172745 | |
| ---------- | |
| NL: convert pandas index in a dataframe to columns | |
| ref: df.reset_index(level=['tick', 'obs']) | |
| Output: df. reset _ index ( drop = true ) | |
| bleu_metric: 11.884004683899853 | |
| ---------- | |
| NL: Get reverse of list items from list 'b' using extended slicing | |
| ref: [x[::-1] for x in b] | |
| Output: [ [ i [ 0 ] for i in range ( len ( x ) ) ] for i in range ( 0, len ( list ( a ) | |
| bleu_metric: 7.434360163166209 | |
| ---------- | |
| NL: join each element in array `a` with element at the same index in array `b` as a tuple | |
| ref: np.array([zip(x, y) for x, y in zip(a, b)]) | |
| Output: b = np. where ( a = = 0, axis = 0 ) | |
| bleu_metric: 9.027924728401977 | |
| ---------- | |
| NL: zip two 2-d arrays `a` and `b` | |
| ref: np.array(zip(a.ravel(), b.ravel()), dtype='i4,i4').reshape(a.shape) | |
| Output: np. array ( zip ( a. ravel ( ), b. ravel ( ) ), dtype ='i4, i4 | |
| bleu_metric: 67.03200460356393 | |
| ---------- | |
| NL: convert list `list_of_ints` into a comma separated string | |
| ref: """,""".join([str(i) for i in list_of_ints]) | |
| Output: list ( map ( int, list _ of _ lists. split ( ',') ) ) | |
| bleu_metric: 13.283261562346366 | |
| ---------- | |
| NL: Send a post request with raw data `DATA` and basic authentication with `username` and `password` | |
| ref: requests.post(url, data=DATA, headers=HEADERS_DICT, auth=(username, password)) | |
| Output: r = requests. post ( url, data = {'username':'username'} ) | |
| bleu_metric: 37.18698903847989 | |
| ---------- | |
| NL: Find last occurrence of character '}' in string "abcd}def}" | |
| ref: 'abcd}def}'.rfind('}') | |
| Output: re. findall ('(? : ^ [ a - z ] * ) ','abcd0') | |
| bleu_metric: 12.962472880491877 | |
| ---------- | |
| NL: Iterate ove list `[1, 2, 3]` using list comprehension | |
| ref: print([item for item in [1, 2, 3]]) | |
| Output: [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ] | |
| bleu_metric: 27.073362211548464 | |
| ---------- | |
| NL: extract all the values with keys 'x' and 'y' from a list of dictionaries `d` to list of tuples | |
| ref: [(x['x'], x['y']) for x in d] | |
| Output: [ d ['x'] for d in x if'y'in d ] | |
| bleu_metric: 37.60857585579953 | |
| ---------- | |
| NL: get the filename without the extension from file 'hemanth.txt' | |
| ref: print(os.path.splitext(os.path.basename('hemanth.txt'))[0]) | |
| Output: print ( glob. glob ('liname. txt') [ 0 ] ) | |
| bleu_metric: 31.18776276497533 | |
| ---------- | |
| NL: create a dictionary by adding each two adjacent elements in tuple `x` as key/value pair to it | |
| ref: dict(x[i:i + 2] for i in range(0, len(x), 2)) | |
| Output: dict ( ( k, v ) for k, v in zip ( a, b ) ) | |
| bleu_metric: 12.152094584183624 | |
| ---------- | |
| NL: create a list containing flattened list `[['A', 'B', 'C'], ['D', 'E', 'F'], ['G', 'H', 'I']]` | |
| ref: values = sum([['A', 'B', 'C'], ['D', 'E', 'F'], ['G', 'H', 'I']], []) | |
| Output: [ ['a ','b ','c ','d'], ['a ','b ','c | |
| bleu_metric: 17.7080331882364 | |
| ---------- | |
| NL: select rows in a dataframe `df` column 'closing_price' between two values 99 and 101 | |
| ref: df = df[(df['closing_price'] >= 99) & (df['closing_price'] <= 101)] | |
| Output: df. loc [ ~ df ['column _ name']. isin ( some _ values ('one ','two | |
| bleu_metric: 10.948000684047626 | |
| ---------- | |
| NL: replace all occurences of newlines `\n` with `<br>` in dataframe `df` | |
| ref: df.replace({'\n': '<br>'}, regex=True) | |
| Output: df. replace ('\ n ','\ n') | |
| bleu_metric: 27.770641036119947 | |
| ---------- | |
| NL: replace all occurrences of a string `\n` by string `<br>` in a pandas data frame `df` | |
| ref: df.replace({'\n': '<br>'}, regex=True) | |
| Output: df. replace ( {'\ n':'< br >'}, regex = true ) | |
| bleu_metric: 89.21616972156079 | |
| ---------- | |
| NL: create a list containing each two adjacent letters in string `word` as its elements | |
| ref: [(x + y) for x, y in zip(word, word[1:])] | |
| Output: [ ('a ','b'), ('b ','c'), ('c ','d') | |
| bleu_metric: 8.840994001530046 | |
| ---------- | |
| NL: Get a list of pairs from a string `word` using lambda function | |
| ref: list(map(lambda x, y: x + y, word[:-1], word[1:])) | |
| Output: [ str ( x ) for x in words ] | |
| bleu_metric: 3.8733243551839442 | |
| ---------- | |
| NL: extract a url from a string `myString` | |
| ref: print(re.findall('(https?://[^\\s]+)', myString)) | |
| Output: print ( re. search ('( https? : / / [ ^ \ \ s ] + ) ', mystring ) ) | |
| bleu_metric: 77.28607577370117 | |
| ---------- | |
| NL: extract a url from a string `myString` | |
| ref: print(re.search('(?P<url>https?://[^\\s]+)', myString).group('url')) | |
| Output: print ( re. search ('( https? : / / [ ^ \ \ s ] + ) ', mystring ) ) | |
| bleu_metric: 51.916231800616785 | |
| ---------- | |
| NL: remove all special characters, punctuation and spaces from a string `mystring` using regex | |
| ref: re.sub('[^A-Za-z0-9]+', '', mystring) | |
| Output: re. sub ('\ \ w + (? = [ ^. ] * ) ','', mystring ) | |
| bleu_metric: 45.91497693322866 | |
| ---------- | |
| NL: create a DatetimeIndex containing 13 periods of the second friday of each month starting from date '2016-01-01' | |
| ref: pd.date_range('2016-01-01', freq='WOM-2FRI', periods=13) | |
| Output: df. groupby ('date'). apply ( lambda x : x. year ) | |
| bleu_metric: 8.453429635655233 | |
| ---------- | |
| NL: Create multidimensional array `matrix` with 3 rows and 2 columns in python | |
| ref: matrix = [[a, b], [c, d], [e, f]] | |
| Output: matrix = np. concatenate ( ( [ a, b ], [ [ 0, 3, 6 ], [ 7, 8, 9 | |
| bleu_metric: 32.24199399071415 | |
| ---------- | |
| NL: replace spaces with underscore | |
| ref: mystring.replace(' ', '_') | |
| Output: re. sub ('\ \ b (? = \ \ w + ) ','','this is a string ','replace | |
| bleu_metric: 14.117915208981241 | |
| ---------- | |
| NL: get an absolute file path of file 'mydir/myfile.txt' | |
| ref: os.path.abspath('mydir/myfile.txt') | |
| Output: os. path. join ( os. path. join ( root, myfile ) ) | |
| bleu_metric: 23.83041256525615 | |
| ---------- | |
| NL: split string `my_string` on white spaces | |
| ref: """ """.join(my_string.split()) | |
| Output: " " " 42, " " ". split ( ) | |
| bleu_metric: 47.72263780018156 | |
| ---------- | |
| NL: get filename without extension from file `filename` | |
| ref: os.path.splitext(filename)[0] | |
| Output: os. path. splitext ( filename ) [ 0 ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get a list containing the sum of each element `i` in list `l` plus the previous elements | |
| ref: [sum(l[:i]) for i, _ in enumerate(l)] | |
| Output: sum ( x for x in l ) | |
| bleu_metric: 6.924459302580939 | |
| ---------- | |
| NL: split a string `Docs/src/Scripts/temp` by `/` keeping `/` in the result | |
| ref: """Docs/src/Scripts/temp""".replace('/', '/\x00/').split('\x00') | |
| Output: re. split ('\ \ s + ', string ) | |
| bleu_metric: 7.926805970774756 | |
| ---------- | |
| NL: shuffle columns of an numpy array 'r' | |
| ref: np.random.shuffle(np.transpose(r)) | |
| Output: np. random. shuffle ( np. transpose ( r ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: copy all values in a column 'B' to a new column 'D' in a pandas data frame 'df' | |
| ref: df['D'] = df['B'] | |
| Output: df ['b'] = df ['c']. apply ( lambda x : x. tolist ( ), axis = | |
| bleu_metric: 26.492685902784498 | |
| ---------- | |
| NL: find a value within nested json 'data' where the key inside another key 'B' is unknown. | |
| ref: list(data['A']['B'].values())[0]['maindata'][0]['Info'] | |
| Output: print ( json. dumps ( ['b ','c'] ) ) | |
| bleu_metric: 5.979778160388024 | |
| ---------- | |
| NL: check characters of string `string` are true predication of function `predicate` | |
| ref: all(predicate(x) for x in string) | |
| Output: print ( any ( test _ string ) for substring in string _ string ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: determine number of files on a drive with python | |
| ref: os.statvfs('/').f_files - os.statvfs('/').f_ffree | |
| Output: os. stat ('/ myfile. txt'). os. listdir ('/') | |
| bleu_metric: 37.211067993181466 | |
| ---------- | |
| NL: how to get a single result from a SQLite query in python? | |
| ref: cursor.fetchone()[0] | |
| Output: cursor. fetchone ( ) [ 0 ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert string `user_input` into a list of integers `user_list` | |
| ref: user_list = [int(number) for number in user_input.split(',')] | |
| Output: [ int ( x ) for x in input ( ). split ( ',') ] | |
| bleu_metric: 51.23350305765596 | |
| ---------- | |
| NL: Get a list of integers by splitting a string `user` with comma | |
| ref: [int(s) for s in user.split(',')] | |
| Output: map ( int,'re. split ('( \ \ d + ) ','hello world') ) | |
| bleu_metric: 20.80315522738391 | |
| ---------- | |
| NL: Sorting a Python list by two criteria | |
| ref: sorted(list, key=lambda x: (x[0], -x[1])) | |
| Output: sorted ( list ( range ( 0, len ( s ) ), key = lambda x : ( x [ 0 ], x [ 0 ] ) | |
| bleu_metric: 48.440807796000044 | |
| ---------- | |
| NL: sort a list of objects `ut`, based on a function `cmpfun` in descending order | |
| ref: ut.sort(key=cmpfun, reverse=True) | |
| Output: sorted ( l, key = lambda x : x. modified, reverse = true ) | |
| bleu_metric: 18.107197870881738 | |
| ---------- | |
| NL: reverse list `ut` based on the `count` attribute of each object | |
| ref: ut.sort(key=lambda x: x.count, reverse=True) | |
| Output: ['city ','same2 ','type'] | |
| bleu_metric: 7.453337029678027 | |
| ---------- | |
| NL: sort a list of objects `ut` in reverse order by their `count` property | |
| ref: ut.sort(key=lambda x: x.count, reverse=True) | |
| Output: sorted ( l, key = lambda x : x. count ('c') ) | |
| bleu_metric: 45.34010611888324 | |
| ---------- | |
| NL: click a href button 'Send' with selenium | |
| ref: driver.find_element_by_partial_link_text('Send').click() | |
| Output: driver. find _ element _ by _ css _ selector ('send inmail'). click ( ) | |
| bleu_metric: 35.13874939965221 | |
| ---------- | |
| NL: click a href button having text `Send InMail` with selenium | |
| ref: driver.findElement(By.linkText('Send InMail')).click() | |
| Output: driver. find _ element _ by _ link _ text ('send inmail') | |
| bleu_metric: 15.175278640994005 | |
| ---------- | |
| NL: click a href button with text 'Send InMail' with selenium | |
| ref: driver.find_element_by_link_text('Send InMail').click() | |
| Output: driver. find _ element _ by _ link _ text ('send') | |
| bleu_metric: 15.27678022374703 | |
| ---------- | |
| NL: cast an int `i` to a string and concat to string 'ME' | |
| ref: 'ME' + str(i) | |
| Output: str ( i ) | |
| bleu_metric: 36.787944117144235 | |
| ---------- | |
| NL: Sorting data in DataFrame Pandas | |
| ref: df.sort_values(['System_num', 'Dis']) | |
| Output: df. sort _ values ( ['system _ id ','dis'] ) | |
| bleu_metric: 31.671615012203976 | |
| ---------- | |
| NL: prepend the line '#test firstline\n' to the contents of file 'infile' and save as the file 'outfile' | |
| ref: open('outfile', 'w').write('#test firstline\n' + open('infile').read()) | |
| Output: open ('test. txt ','w'). write ('\ n') | |
| bleu_metric: 36.176855814312944 | |
| ---------- | |
| NL: sort a list `l` by length of value in tuple | |
| ref: l.sort(key=lambda t: len(t[1]), reverse=True) | |
| Output: l. sort ( key = lambda x : ( len ( x ), x ) ) | |
| bleu_metric: 37.875123684341865 | |
| ---------- | |
| NL: split string `s` by words that ends with 'd' | |
| ref: re.findall('\\b(\\w+)d\\b', s) | |
| Output: re. split ('( \ \ d + ) ', s ) | |
| bleu_metric: 29.23155541924467 | |
| ---------- | |
| NL: return `True` if string `foobarrrr` contains regex `ba[rzd]` | |
| ref: bool(re.search('ba[rzd]', 'foobarrrr')) | |
| Output: pattern = re. compile ('\ \ b [ a - za - z0 - 9 - ]') | |
| bleu_metric: 12.384901282810546 | |
| ---------- | |
| NL: Removing duplicates in list `t` | |
| ref: list(set(t)) | |
| Output: list ( set ( t ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Removing duplicates in list `source_list` | |
| ref: list(set(source_list)) | |
| Output: list ( set ( t ) ) | |
| bleu_metric: 59.69491792019645 | |
| ---------- | |
| NL: Removing duplicates in list `abracadabra` | |
| ref: list(OrderedDict.fromkeys('abracadabra')) | |
| Output: [ ('a ','b'), ('b ','c'), ('a ','d') | |
| bleu_metric: 7.555283720961675 | |
| ---------- | |
| NL: Convert array `a` into a list | |
| ref: numpy.array(a).reshape(-1).tolist() | |
| Output: list ( a ) | |
| bleu_metric: 5.237603475197421 | |
| ---------- | |
| NL: Convert the first row of numpy matrix `a` to a list | |
| ref: numpy.array(a)[0].tolist() | |
| Output: numpy. apply _ along _ axis ( numpy. array ( [ 0, 1, 2 ] ), a ) | |
| bleu_metric: 20.97823227594146 | |
| ---------- | |
| NL: In `soup`, get the content of the sibling of the `td` tag with text content `Address:` | |
| ref: print(soup.find(text='Address:').findNext('td').contents[0]) | |
| Output: soup. find _ element _ by _ id ('< id >'). text | |
| bleu_metric: 12.033574792347215 | |
| ---------- | |
| NL: convert elements of each tuple in list `l` into a string separated by character `@` | |
| ref: """ """.join([('%d@%d' % t) for t in l]) | |
| Output: [''. join ( x ) for x in l ] | |
| bleu_metric: 17.41466588478686 | |
| ---------- | |
| NL: convert each tuple in list `l` to a string with '@' separating the tuples' elements | |
| ref: """ """.join([('%d@%d' % (t[0], t[1])) for t in l]) | |
| Output: " " " " " ". join ( [ x for x in l if x [ 0 ] in l ] ) | |
| bleu_metric: 36.45674601639763 | |
| ---------- | |
| NL: get the html from the current web page of a Selenium driver | |
| ref: driver.execute_script('return document.documentElement.outerHTML;') | |
| Output: driver. get ('http : / / www. google. com') | |
| bleu_metric: 19.209534151258666 | |
| ---------- | |
| NL: Get all matches with regex pattern `\\d+[xX]` in list of string `teststr` | |
| ref: [i for i in teststr if re.search('\\d+[xX]', i)] | |
| Output: [ i for i in teststr if re. search ('\ \ d + [ ^ \ \ ] * \ \ ] ', i ) | |
| bleu_metric: 69.89029944247109 | |
| ---------- | |
| NL: select values from column 'A' for which corresponding values in column 'B' will be greater than 50, and in column 'C' - equal 900 in dataframe `df` | |
| ref: df['A'][(df['B'] > 50) & (df['C'] == 900)] | |
| Output: df [ ( df ['a'] > 1 ) & ( df ['b'] < = 5 ) ] | |
| bleu_metric: 39.042104382208294 | |
| ---------- | |
| NL: Sort dictionary `o` in ascending order based on its keys and items | |
| ref: sorted(o.items()) | |
| Output: sorted ( list ( dictionary. items ( ) ), key = lambda x : x [ 1 ] ) | |
| bleu_metric: 24.42195895839763 | |
| ---------- | |
| NL: get sorted list of keys of dict `d` | |
| ref: sorted(d) | |
| Output: sorted ( d, key = d. get ) | |
| bleu_metric: 25.21193618434983 | |
| ---------- | |
| NL: How to sort dictionaries by keys in Python | |
| ref: sorted(d.items()) | |
| Output: sorted ( d. items ( ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert string "1" into integer | |
| ref: int('1') | |
| Output: int ('1') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: function to convert strings into integers | |
| ref: int() | |
| Output: map ( int,'1') | |
| bleu_metric: 21.105340631872636 | |
| ---------- | |
| NL: convert items in `T1` to integers | |
| ref: T2 = [map(int, x) for x in T1] | |
| Output: int ('\ n '. join ( str ( i ) for i in str ( n ) ) ) | |
| bleu_metric: 8.607692533178168 | |
| ---------- | |
| NL: call a shell script `./test.sh` using subprocess | |
| ref: subprocess.call(['./test.sh']) | |
| Output: subprocess. call ( ['test. sh ','/ test. sh otherfunc'] ) | |
| bleu_metric: 53.66411241731205 | |
| ---------- | |
| NL: call a shell script `notepad` using subprocess | |
| ref: subprocess.call(['notepad']) | |
| Output: subprocess. call ( ['notepad'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: combine lists `l1` and `l2` by alternating their elements | |
| ref: [val for pair in zip(l1, l2) for val in pair] | |
| Output: [ ( x + y ) for x in l2 for y in zip ( l1, l2 ) ] | |
| bleu_metric: 38.146366463982886 | |
| ---------- | |
| NL: encode string 'data to be encoded' | |
| ref: encoded = base64.b64encode('data to be encoded') | |
| Output: data. encode ('utf8') | |
| bleu_metric: 19.260282250359822 | |
| ---------- | |
| NL: encode a string `data to be encoded` to `ascii` encoding | |
| ref: encoded = 'data to be encoded'.encode('ascii') | |
| Output: data. encode ('ascii') | |
| bleu_metric: 41.51380951089673 | |
| ---------- | |
| NL: parse tab-delimited CSV file 'text.txt' into a list | |
| ref: lol = list(csv.reader(open('text.txt', 'rb'), delimiter='\t')) | |
| Output: txt = open ('myfile. txt ','r'). read ( ) | |
| bleu_metric: 20.692050735338164 | |
| ---------- | |
| NL: Get attribute `my_str` of object `my_object` | |
| ref: getattr(my_object, my_str) | |
| Output: getattr ( my _ obj,'id') | |
| bleu_metric: 19.72940627795883 | |
| ---------- | |
| NL: group a list of dicts `LD` into one dict by key | |
| ref: print(dict(zip(LD[0], zip(*[list(d.values()) for d in LD])))) | |
| Output: values = [ dict ( k, v ) for k, v in list ( d. items ( ) ) ] | |
| bleu_metric: 17.057966110550765 | |
| ---------- | |
| NL: How do I sum the first value in each tuple in a list of tuples in Python? | |
| ref: sum([pair[0] for pair in list_of_pairs]) | |
| Output: sum ( [ item [ 0 ] for item in tuple ] ) | |
| bleu_metric: 41.37441202051881 | |
| ---------- | |
| NL: convert unicode string u"{'code1':1,'code2':1}" into dictionary | |
| ref: d = ast.literal_eval("{'code1':1,'code2':1}") | |
| Output: print ('\ u0420 \ u043e \ u0441 \ u0438 \ u0438 \ u | |
| bleu_metric: 6.351019285070924 | |
| ---------- | |
| NL: find all words in a string `mystring` that start with the `$` sign | |
| ref: [word for word in mystring.split() if word.startswith('$')] | |
| Output: re. findall ('\ \ b [ ^ \ \ w \ \ ] ', mystring ) | |
| bleu_metric: 9.850775379403402 | |
| ---------- | |
| NL: remove any url within string `text` | |
| ref: text = re.sub('^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE) | |
| Output: re. sub ('\ \. \ \ blog _ addr \ \ r \ \ n ','', text ) | |
| bleu_metric: 32.17551945847391 | |
| ---------- | |
| NL: replace all elements in array `A` that are not present in array `[1, 3, 4]` with zeros | |
| ref: np.where(np.in1d(A, [1, 3, 4]).reshape(A.shape), A, 0) | |
| Output: np. where ( np. in1d ( a, b ) ) [ 0 ] | |
| bleu_metric: 22.828499613963487 | |
| ---------- | |
| NL: calculate mean across dimension in a 2d array `a` | |
| ref: np.mean(a, axis=1) | |
| Output: np. mean ( a, axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: running r script '/pathto/MyrScript.r' from python | |
| ref: subprocess.call(['/usr/bin/Rscript', '--vanilla', '/pathto/MyrScript.r']) | |
| Output: sys. path. append ('/ path / to / my / my / mypath') | |
| bleu_metric: 7.04483155666154 | |
| ---------- | |
| NL: run r script '/usr/bin/Rscript --vanilla /pathto/MyrScript.r' | |
| ref: subprocess.call('/usr/bin/Rscript --vanilla /pathto/MyrScript.r', shell=True) | |
| Output: os. path. join ( root, '. / main _ folder') | |
| bleu_metric: 6.726052538373431 | |
| ---------- | |
| NL: add a header to a csv file | |
| ref: writer.writeheader() | |
| Output: writer. writeheader ( ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: replacing nan in the dataframe `df` with row average | |
| ref: df.fillna(df.mean(axis=1), axis=1) | |
| Output: df. fillna ( df. mean ( axis = 1 ), axis = 1 ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Convert unix timestamp '1347517370' to formatted string '%Y-%m-%d %H:%M:%S' | |
| ref: time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(1347517370)) | |
| Output: datetime. strptime ('21 / 11 / 06 16 : 30 ','% d / % m / % y') | |
| bleu_metric: 10.247261229308657 | |
| ---------- | |
| NL: Call a base class's class method `do` from derived class `Derived` | |
| ref: super(Derived, cls).do(a) | |
| Output: super ( childclass, self ). _ _ init _ _ ( * args, * * kwargs ) | |
| bleu_metric: 11.96655750514248 | |
| ---------- | |
| NL: selecting rows in Numpy ndarray 'a', where the value in the first column is 0 and value in the second column is 1 | |
| ref: a[np.where((a[:, (0)] == 0) * (a[:, (1)] == 1))] | |
| Output: a [ np. arange ( 3 ), ( 0, 1, 0 ) ] | |
| bleu_metric: 13.581342454223687 | |
| ---------- | |
| NL: separate words delimited by one or more spaces into a list | |
| ref: re.split(' +', 'hello world sample text') | |
| Output: ['one ','two ','one ','two'] | |
| bleu_metric: 13.78592993183041 | |
| ---------- | |
| NL: length of longest element in list `words` | |
| ref: len(max(words, key=len)) | |
| Output: len ( max ( words, key = len ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the value associated with unicode key 'from_user' of first dictionary in list `result` | |
| ref: result[0]['from_user'] | |
| Output: ['key1 ','key2 ','value'] | |
| bleu_metric: 18.20705281109213 | |
| ---------- | |
| NL: Retrieve each line from a file 'File.txt' as a list | |
| ref: [line.split() for line in open('File.txt')] | |
| Output: print ( line. rstrip ('\ n') ) | |
| bleu_metric: 11.789701057610694 | |
| ---------- | |
| NL: swap keys with values in a dictionary `a` | |
| ref: res = dict((v, k) for k, v in a.items()) | |
| Output: a [ key ] = dict ( ( v ) for k, v in a. items ( ) ) | |
| bleu_metric: 73.78351342269067 | |
| ---------- | |
| NL: Open a file `path/to/FILE_NAME.ext` in write mode | |
| ref: new_file = open('path/to/FILE_NAME.ext', 'w') | |
| Output: file _ name = os. path. join ( os. path. join ( dir, '.') ) | |
| bleu_metric: 10.713701843513144 | |
| ---------- | |
| NL: How to count distinct values in a column of a pandas group by object? | |
| ref: df.groupby(['col1', 'col2'])['col3'].nunique().reset_index() | |
| Output: df. groupby ( ['col1 ','col2'] ) ['col3']. apply ( lambda x : | |
| bleu_metric: 72.38800804180103 | |
| ---------- | |
| NL: Check if any key in the dictionary `dict1` starts with the string `EMP$$` | |
| ref: any(key.startswith('EMP$$') for key in dict1) | |
| Output: any ( isinstance ( x, int ( dict. values ( ) ) ) for x in dict _ string in input. lower ( | |
| bleu_metric: 8.839374326825924 | |
| ---------- | |
| NL: create list of values from dictionary `dict1` that have a key that starts with 'EMP$$' | |
| ref: [value for key, value in list(dict1.items()) if key.startswith('EMP$$')] | |
| Output: [ dict ( zip ( k, v ), v ) for k, v in d. items ( ) if v. startswith ( ' | |
| bleu_metric: 24.14138031982595 | |
| ---------- | |
| NL: convert a pandas series `sf` into a pandas dataframe `df` with columns `email` and `list` | |
| ref: pd.DataFrame({'email': sf.index, 'list': sf.values}) | |
| Output: pd. concat ( [ df1, df2 ], axis = 1 ) | |
| bleu_metric: 8.12657367724027 | |
| ---------- | |
| NL: print elements of list `list` seperated by tabs `\t` | |
| ref: print('\t'.join(map(str, list))) | |
| Output: print ( [ i. partition ('\ t') for i in list _ of _ lists ] ) | |
| bleu_metric: 25.58058053378779 | |
| ---------- | |
| NL: print unicode string '\xd0\xbf\xd1\x80\xd0\xb8' with utf-8 | |
| ref: print('\xd0\xbf\xd1\x80\xd0\xb8'.encode('raw_unicode_escape')) | |
| Output: print ('\ xc5 \ xa4 \ xd0 \ x80 \ xd0 \ xb0 \ xd0 | |
| bleu_metric: 29.472590166651962 | |
| ---------- | |
| NL: Encode a latin character in string `Sopet\xc3\xb3n` properly | |
| ref: 'Sopet\xc3\xb3n'.encode('latin-1').decode('utf-8') | |
| Output: " " " \ xc3 \ xa3 \ x9f " " ". encode ('utf - 8') | |
| bleu_metric: 46.255815148030386 | |
| ---------- | |
| NL: resized image `image` to width, height of `(x, y)` with filter of `ANTIALIAS` | |
| ref: image = image.resize((x, y), Image.ANTIALIAS) | |
| Output: img = np. array ( [ 0, 1, 0, 1, 2, - 1, 0, 1, 2 ] ) | |
| bleu_metric: 7.278748444764005 | |
| ---------- | |
| NL: regex, find "n"s only in the middle of string `s` | |
| ref: re.findall('n(?<=[^n]n)n+(?=[^n])(?i)', s) | |
| Output: re. findall ('(? < = [ ^ \ \ s ] ) (? = [ ^ \ \ s ] + ) ', | |
| bleu_metric: 44.74679573461579 | |
| ---------- | |
| NL: display the float `1/3*100` as a percentage | |
| ref: print('{0:.0f}%'.format(1.0 / 3 * 100)) | |
| Output: print ( ('0. 1 ', 1 ) ) | |
| bleu_metric: 11.406542828786534 | |
| ---------- | |
| NL: sort a list of dictionary `mylist` by the key `title` | |
| ref: mylist.sort(key=lambda x: x['title']) | |
| Output: mylist. sort ( key = lambda x : x ['title'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: sort a list `l` of dicts by dict value 'title' | |
| ref: l.sort(key=lambda x: x['title']) | |
| Output: l. sort ( key = lambda x : x ['title'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: sort a list of dictionaries by the value of keys 'title', 'title_url', 'id' in ascending order. | |
| ref: l.sort(key=lambda x: (x['title'], x['title_url'], x['id'])) | |
| Output: sorted ( l, key = lambda x : x ['name'] ) | |
| bleu_metric: 17.362697834284052 | |
| ---------- | |
| NL: find 10 largest differences between each respective elements of list `l1` and list `l2` | |
| ref: heapq.nlargest(10, range(len(l1)), key=lambda i: abs(l1[i] - l2[i])) | |
| Output: [ ( x, y ) for x, y in zip ( l1, l2 ) ] | |
| bleu_metric: 5.935298261592072 | |
| ---------- | |
| NL: BeautifulSoup find all 'span' elements in HTML string `soup` with class of 'starGryB sp' | |
| ref: soup.find_all('span', {'class': 'starGryB sp'}) | |
| Output: soup. findall ( text ='div ', style ='width = 300px ;') | |
| bleu_metric: 11.970571135993998 | |
| ---------- | |
| NL: write records in dataframe `df` to table 'test' in schema 'a_schema' | |
| ref: df.to_sql('test', engine, schema='a_schema') | |
| Output: df. plot ( row ='test ','test _ txt') | |
| bleu_metric: 32.62478546610937 | |
| ---------- | |
| NL: Extract brackets from string `s` | |
| ref: brackets = re.sub('[^(){}[\\]]', '', s) | |
| Output: re. sub ('\ \ [ ( \ \ w + ) \ \ s + \ \ ] ','', s ) | |
| bleu_metric: 47.1113978575501 | |
| ---------- | |
| NL: remove duplicate elements from list 'L' | |
| ref: list(dict((x[0], x) for x in L).values()) | |
| Output: [ k for k, g in l if'l'in k ] | |
| bleu_metric: 6.942420857748657 | |
| ---------- | |
| NL: read a file `file` without newlines | |
| ref: [line.rstrip('\n') for line in file] | |
| Output: file. write ('\ n') | |
| bleu_metric: 30.98188793972842 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: [i for (i, x) in enumerate(testlist) if (x == 1)] | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 9.243620302376062 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: [i for (i, x) in enumerate(testlist) if (x == 1)] | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 9.243620302376062 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: for i in [i for (i, x) in enumerate(testlist) if (x == 1)]: | |
| pass | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 7.565780264883953 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: for i in (i for (i, x) in enumerate(testlist) if (x == 1)): | |
| pass | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 6.0168435849456285 | |
| ---------- | |
| NL: get the position of item 1 in `testlist` | |
| ref: gen = (i for (i, x) in enumerate(testlist) if (x == 1)) | |
| Output: print ( [ item for item in mylist if item. isdigit ( ) ] ) | |
| bleu_metric: 8.28910840961223 | |
| ---------- | |
| NL: get the position of item `element` in list `testlist` | |
| ref: print(testlist.index(element)) | |
| Output: print ( testlist. index ( element ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the position of item `element` in list `testlist` | |
| ref: gen = (i for (i, x) in enumerate(testlist) if (x == 1)) | |
| Output: print ( testlist. index ( element ) ) | |
| bleu_metric: 7.150780327539586 | |
| ---------- | |
| NL: find the first element of the tuple with the maximum second element in a list of tuples `lis` | |
| ref: max(lis, key=lambda item: item[1])[0] | |
| Output: max ( x [ 0 ] for x in rows ) | |
| bleu_metric: 14.988854080083206 | |
| ---------- | |
| NL: get the item at index 0 from the tuple that has maximum value at index 1 in list `lis` | |
| ref: max(lis, key=itemgetter(1))[0] | |
| Output: max ( x, key = lambda x : x [ 1 ] ) [ 0 ] | |
| bleu_metric: 35.27295712700594 | |
| ---------- | |
| NL: Make a delay of 1 second | |
| ref: time.sleep(1) | |
| Output: time. sleep ( 0. 1 ) | |
| bleu_metric: 52.53819788848316 | |
| ---------- | |
| NL: convert list of tuples `L` to a string | |
| ref: """, """.join('(' + ', '.join(i) + ')' for i in L) | |
| Output: " " " " " ". join ( l ) | |
| bleu_metric: 14.164334116374599 | |
| ---------- | |
| NL: Django set default value of field `b` equal to '0000000' | |
| ref: b = models.CharField(max_length=7, default='0000000', editable=False) | |
| Output: obj. save ('new _ name ','default _ number') | |
| bleu_metric: 10.318679011582763 | |
| ---------- | |
| NL: Sort lis `list5` in ascending order based on the degrees value of its elements | |
| ref: sorted(list5, lambda x: (degree(x), x)) | |
| Output: sorted ( lst, key = lambda x : x [ 1 ] ) | |
| bleu_metric: 19.143591202259493 | |
| ---------- | |
| NL: How do I perform secondary sorting in python? | |
| ref: sorted(list5, key=lambda vertex: (degree(vertex), vertex)) | |
| Output: sorted ( list5, key = lambda x : ( sum ( x [ 1 ] ), x ) ) | |
| bleu_metric: 41.26152034907945 | |
| ---------- | |
| NL: convert a list into a generator object | |
| ref: (n for n in [1, 2, 3, 5]) | |
| Output: list ( itertools. combinations ( * ( l ) ) ) | |
| bleu_metric: 9.930751066504135 | |
| ---------- | |
| NL: remove elements from list `oldlist` that have an index number mentioned in list `removelist` | |
| ref: newlist = [v for i, v in enumerate(oldlist) if i not in removelist] | |
| Output: [ x for x in set ( split _ list [ i ] for i in range ( len ( x ) ) if i not in index ] | |
| bleu_metric: 18.860270984795903 | |
| ---------- | |
| NL: Open a file `yourfile.txt` in write mode | |
| ref: f = open('yourfile.txt', 'w') | |
| Output: os. system ('your string. txt') | |
| bleu_metric: 23.571525044211057 | |
| ---------- | |
| NL: get attribute 'attr' from object `obj` | |
| ref: getattr(obj, 'attr') | |
| Output: getattr ( obj,'attr') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to tuple | |
| ref: from functools import reduce | |
| reduce(lambda a, b: a + b, (('aa',), ('bb',), ('cc',))) | |
| Output: tuple ( ['a ','b ','c ','d'] ) | |
| bleu_metric: 6.101077050680879 | |
| ---------- | |
| NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to list in one line | |
| ref: map(lambda a: a[0], (('aa',), ('bb',), ('cc',))) | |
| Output: map ( lambda s : ( d ['a'], d ['b'], d ['c'] ), d [ | |
| bleu_metric: 11.200083731986627 | |
| ---------- | |
| NL: Python Pandas: How to replace a characters in a column of a dataframe? | |
| ref: df['range'].replace(',', '-', inplace=True) | |
| Output: df ['brandname'] = df ['brandname']. replace ('- ', ','in') | |
| bleu_metric: 45.97006398721532 | |
| ---------- | |
| NL: unzip the list `[('a', 1), ('b', 2), ('c', 3), ('d', 4)]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4)]) | |
| Output: zip ( * [ ('a ','b ','c ','d'] ) ) | |
| bleu_metric: 36.501102157810664 | |
| ---------- | |
| NL: unzip the list `[('a', 1), ('b', 2), ('c', 3), ('d', 4)]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4)]) | |
| Output: zip ( * [ ('a ','b ','c ','d'] ) ) | |
| bleu_metric: 36.501102157810664 | |
| ---------- | |
| NL: unzip list `original` | |
| ref: result = ([a for (a, b) in original], [b for (a, b) in original]) | |
| Output: zip ( * list ( range ( 10 ) ) ) | |
| bleu_metric: 4.606184881898074 | |
| ---------- | |
| NL: unzip list `original` and return a generator | |
| ref: result = ((a for (a, b) in original), (b for (a, b) in original)) | |
| Output: [ i for i in range ( 1, len ( l ) ) if i! ='+ 1 ] | |
| bleu_metric: 7.62604991346693 | |
| ---------- | |
| NL: unzip list `[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', )]` | |
| ref: zip(*[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e',)]) | |
| Output: zip ( * [ ('a ','b ','c ','d ','e'] ) ) | |
| bleu_metric: 34.87995424158042 | |
| ---------- | |
| NL: unzip list `[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', )]` and fill empty results with None | |
| ref: map(None, *[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e',)]) | |
| Output: zip ( * [ ('a ','b'), ('b ','c ','d'), ( ' | |
| bleu_metric: 35.430131828411845 | |
| ---------- | |
| NL: encode `Decimal('3.9')` to a JSON string | |
| ref: json.dumps(Decimal('3.9')) | |
| Output: json. dumps ( ['1 ','2 ','3'] ) | |
| bleu_metric: 23.58844810653421 | |
| ---------- | |
| NL: Add key "mynewkey" to dictionary `d` with value "mynewvalue" | |
| ref: d['mynewkey'] = 'mynewvalue' | |
| Output: df. setdefault ('name ', [ ] ). append ( value ) | |
| bleu_metric: 9.50724412002623 | |
| ---------- | |
| NL: Add key 'a' to dictionary `data` with value 1 | |
| ref: data.update({'a': 1, }) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 37.863495563972855 | |
| ---------- | |
| NL: Add key 'a' to dictionary `data` with value 1 | |
| ref: data.update(dict(a=1)) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 8.24055698798382 | |
| ---------- | |
| NL: Add key 'a' to dictionary `data` with value 1 | |
| ref: data.update(a=1) | |
| Output: a ['a']. append ( {'a': 1,'b': 2 } ) | |
| bleu_metric: 8.24055698798382 | |
| ---------- | |
| NL: find maximal value in matrix `matrix` | |
| ref: max([max(i) for i in matrix]) | |
| Output: max ( matrix, key = lambda x : max ( x ) ) | |
| bleu_metric: 16.943571815930884 | |
| ---------- | |
| NL: Round number `answer` to 2 precision after the decimal point | |
| ref: answer = str(round(answer, 2)) | |
| Output: round ( 1. 0005, 3 ) | |
| bleu_metric: 17.51643270174889 | |
| ---------- | |
| NL: extract ip address from an html string | |
| ref: ip = re.findall('[0-9]+(?:\\.[0-9]+){3}', s) | |
| Output: print ( re. findall ('[ ^ a - za - z0 - 9 - ] + (? : \ \. [ a - | |
| bleu_metric: 40.70867848415918 | |
| ---------- | |
| NL: filter dataframe `df` by values in column `A` that appear more than once | |
| ref: df.groupby('A').filter(lambda x: len(x) > 1) | |
| Output: df. filter ( df. filter ( lambda x : x. any ( axis = 1 ) ) ) | |
| bleu_metric: 34.640201116587235 | |
| ---------- | |
| NL: append each line in file `myfile` into a list | |
| ref: [x for x in myfile.splitlines() if x != ''] | |
| Output: myfile. write ('\ n '. join ( [ str ( i ) for i in myfile ] ) ) | |
| bleu_metric: 10.435185733665413 | |
| ---------- | |
| NL: Get a list of integers `lst` from a file `filename.txt` | |
| ref: lst = map(int, open('filename.txt').readlines()) | |
| Output: [ int ( x ) for x in lst ] | |
| bleu_metric: 7.020674481218203 | |
| ---------- | |
| NL: add color bar with image `mappable` to plot `plt` | |
| ref: plt.colorbar(mappable=mappable, cax=ax3) | |
| Output: plt. show ( ) | |
| bleu_metric: 11.914378950211951 | |
| ---------- | |
| NL: count most frequent 100 words in column 'text' of dataframe `df` | |
| ref: Counter(' '.join(df['text']).split()).most_common(100) | |
| Output: df. groupby ('text'). apply ( lambda x : x. sum ( ) ). most _ common ( 1 ) | |
| bleu_metric: 21.52643286782715 | |
| ---------- | |
| NL: Python split a string using regex | |
| ref: re.findall('(.+?):(.+?)\\b ?', text) | |
| Output: re. split ('(? < = \ \ d ) \ \ d + ', text ) | |
| bleu_metric: 29.769819080461428 | |
| ---------- | |
| NL: generate all 2-element subsets of tuple `(1, 2, 3)` | |
| ref: list(itertools.combinations((1, 2, 3), 2)) | |
| Output: list ( itertools. product ( [ 1, 2, 3 ], [ 4, 5, 6 ] ) ) | |
| bleu_metric: 34.29547961820059 | |
| ---------- | |
| NL: get a value of datetime.today() in the UTC time zone | |
| ref: datetime.now(pytz.utc) | |
| Output: pytz. utcnow ( ) | |
| bleu_metric: 23.206041459353084 | |
| ---------- | |
| NL: Get a new list `list2`by removing empty list from a list of lists `list1` | |
| ref: list2 = [x for x in list1 if x != []] | |
| Output: list2 = [ x for x in list1 if x [ : ] = = [ ] ] | |
| bleu_metric: 69.3395566222006 | |
| ---------- | |
| NL: Create `list2` to contain the lists from list `list1` excluding the empty lists from `list1` | |
| ref: list2 = [x for x in list1 if x] | |
| Output: list ( zip ( a, b ) ) | |
| bleu_metric: 10.320893749383378 | |
| ---------- | |
| NL: Django response with JSON `data` | |
| ref: return HttpResponse(data, mimetype='application/json') | |
| Output: return httpresponse ( json. dumps ( response _ data ), content _ type ='application / json') | |
| bleu_metric: 36.17640392425988 | |
| ---------- | |
| NL: get all text that is not enclosed within square brackets in string `example_str` | |
| ref: re.findall('(.*?)\\[.*?\\]', example_str) | |
| Output: str1. split ('\ \ ) [ ^ \ \ ] ', str. strip ( ) | |
| bleu_metric: 26.91730507369109 | |
| ---------- | |
| NL: Use a regex to get all text in a string `example_str` that is not surrounded by square brackets | |
| ref: re.findall('(.*?)(?:\\[.*?\\]|$)', example_str) | |
| Output: re. findall ('\ \ [ (? : \ \ w + ) \ \ ] ', str1 ) | |
| bleu_metric: 35.15147667690756 | |
| ---------- | |
| NL: get whatever is between parentheses as a single match, and any char outside as an individual match in string '(zyx)bc' | |
| ref: re.findall('\\(.+?\\)|\\w', '(zyx)bc') | |
| Output: re. sub ('\ \ ( [ ^ \ \ w ] ) ','', filename ) | |
| bleu_metric: 27.664786097762494 | |
| ---------- | |
| NL: match regex '\\((.*?)\\)|(\\w)' with string '(zyx)bc' | |
| ref: re.findall('\\((.*?)\\)|(\\w)', '(zyx)bc') | |
| Output: re. sub ('(? < = \ \ w ) ( \ \ w + ) ( \ \ w + ) ','\ \ | |
| bleu_metric: 30.955387518700363 | |
| ---------- | |
| NL: match multiple regex patterns with the alternation operator `|` in a string `(zyx)bc` | |
| ref: re.findall('\\(.*?\\)|\\w', '(zyx)bc') | |
| Output: re. findall ('(? < = \ \ w ) ( [ ^ \ \ w \ \ w ] | \ \ w + ) | |
| bleu_metric: 29.552612305356018 | |
| ---------- | |
| NL: formate each string cin list `elements` into pattern '%{0}%' | |
| ref: elements = ['%{0}%'.format(element) for element in elements] | |
| Output: " " " { 0 :. 2f } " " ". format ('foo ','bar') | |
| bleu_metric: 17.414675099971177 | |
| ---------- | |
| NL: Open a background process 'background-process' with arguments 'arguments' | |
| ref: subprocess.Popen(['background-process', 'arguments']) | |
| Output: os. system ('some _ command - some _ file') | |
| bleu_metric: 12.107470158860224 | |
| ---------- | |
| NL: get list of values from dictionary 'mydict' w.r.t. list of keys 'mykeys' | |
| ref: [mydict[x] for x in mykeys] | |
| Output: [ key for item in my _ list if item. get ('name ','age') ] | |
| bleu_metric: 9.119675426861836 | |
| ---------- | |
| NL: convert list `[('Name', 'Joe'), ('Age', 22)]` into a dictionary | |
| ref: dict([('Name', 'Joe'), ('Age', 22)]) | |
| Output: dict ( ( ('class ', 2 ), ('class ', 3 ) ) ) | |
| bleu_metric: 24.080231808676874 | |
| ---------- | |
| NL: average each two columns of array `data` | |
| ref: data.reshape(-1, j).mean(axis=1).reshape(data.shape[0], -1) | |
| Output: np. mean ( np. arange ( len ( df. columns ) ), axis = 1 ) | |
| bleu_metric: 18.410768031391818 | |
| ---------- | |
| NL: double backslash escape all double quotes in string `s` | |
| ref: print(s.encode('unicode-escape').replace('"', '\\"')) | |
| Output: print ( s. split ('\ \') ) | |
| bleu_metric: 21.765088513075124 | |
| ---------- | |
| NL: split a string into a list of words and whitespace | |
| ref: re.split('(\\W+)', s) | |
| Output: ['this is a string : ','this is a string ','this is a string ','this is a string ', | |
| bleu_metric: 5.648892151960283 | |
| ---------- | |
| NL: plotting stacked barplots on a panda data frame | |
| ref: df.plot(kind='barh', stacked=True) | |
| Output: df. plot ( kind ='barh ', stacked = true ) | |
| bleu_metric: 83.7117009877792 | |
| ---------- | |
| NL: reverse the keys and values in a dictionary `myDictionary` | |
| ref: {i[1]: i[0] for i in list(myDictionary.items())} | |
| Output: dict ( ( k, v ) for k, v in list ( d. items ( ) ) ) | |
| bleu_metric: 23.690464140247087 | |
| ---------- | |
| NL: finding the index of elements containing substring 'how' and 'what' in a list of strings 'myList'. | |
| ref: [i for i, j in enumerate(myList) if 'how' in j.lower() or 'what' in j.lower()] | |
| Output: ['x ','y ','red ','var ','var'] | |
| bleu_metric: 6.180827527485682 | |
| ---------- | |
| NL: check if object `obj` is a string | |
| ref: isinstance(obj, str) | |
| Output: isinstance ( obj, str ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: isinstance(o, str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 59.46035575013605 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: (type(o) is str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 23.813694985189848 | |
| ---------- | |
| NL: check if object `o` is a string | |
| ref: isinstance(o, str) | |
| Output: isinstance ( s, str ) | |
| bleu_metric: 59.46035575013605 | |
| ---------- | |
| NL: check if `obj_to_test` is a string | |
| ref: isinstance(obj_to_test, str) | |
| Output: print ( obj. isfile ('attr name') ) | |
| bleu_metric: 13.508625657351411 | |
| ---------- | |
| NL: append list `list1` to `list2` | |
| ref: list2.extend(list1) | |
| Output: [ list1. append ( list1 ) for a in range ( 3 ) ] | |
| bleu_metric: 14.879641171245488 | |
| ---------- | |
| NL: append list `mylog` to `list1` | |
| ref: list1.extend(mylog) | |
| Output: list3. append ( list1 ) | |
| bleu_metric: 29.071536848410968 | |
| ---------- | |
| NL: append list `a` to `c` | |
| ref: c.extend(a) | |
| Output: a. append ( a ) | |
| bleu_metric: 54.10822690539396 | |
| ---------- | |
| NL: append items in list `mylog` to `list1` | |
| ref: for line in mylog: | |
| list1.append(line) | |
| Output: list3. append ( list1 ) | |
| bleu_metric: 20.917479021833486 | |
| ---------- | |
| NL: append a tuple of elements from list `a` with indexes '[0][0] [0][2]' to list `b` | |
| ref: b.append((a[0][0], a[0][2])) | |
| Output: a = [ [ ] for i in range ( 0, len ( a ), [ 3, 4 ] ] ) | |
| bleu_metric: 11.556647985416685 | |
| ---------- | |
| NL: Initialize `SECRET_KEY` in flask config with `Your_secret_string ` | |
| ref: app.config['SECRET_KEY'] = 'Your_secret_string' | |
| Output: app. config ['your _ id'] ='your _ config ' | |
| bleu_metric: 43.343660127583234 | |
| ---------- | |
| NL: unpack a series of tuples in pandas into a DataFrame with column names 'out-1' and 'out-2' | |
| ref: pd.DataFrame(out.tolist(), columns=['out-1', 'out-2'], index=out.index) | |
| Output: pd. dataframe ( df. values. tolist ( ), columns = ['a ','b'], axis = 1 | |
| bleu_metric: 35.31494509780309 | |
| ---------- | |
| NL: find the index of an element 'MSFT' in a list `stocks_list` | |
| ref: [x for x in range(len(stocks_list)) if stocks_list[x] == 'MSFT'] | |
| Output: print ( list ( enumerate ( my _ list ) ) ) | |
| bleu_metric: 5.9394573438219 | |
| ---------- | |
| NL: rotate the xtick labels of matplotlib plot `ax` by `45` degrees to make long labels readable | |
| ref: ax.set_xticklabels(labels, rotation=45) | |
| Output: ax. set _ xticklabels ( ax. xaxis. get _ majorticklabels ( ), rotation = 45 ) | |
| bleu_metric: 29.88309778054086 | |
| ---------- | |
| NL: remove symbols from a string `s` | |
| ref: re.sub('[^\\w]', ' ', s) | |
| Output: re. sub ('[ ^ a - z ] ','', s ) | |
| bleu_metric: 74.83293841345244 | |
| ---------- | |
| NL: Get the current directory of a script | |
| ref: os.path.basename(os.path.dirname(os.path.realpath(__file__))) | |
| Output: os. path. dirname ( os. path. abspath ( _ _ file _ _ ) ) | |
| bleu_metric: 48.431464491050775 | |
| ---------- | |
| NL: Find octal characters matches from a string `str` using regex | |
| ref: print(re.findall("'\\\\[0-7]{1,3}'", str)) | |
| Output: re. findall ('\ \ [ (? < = \ \ d ) \ \ ) ','- ', str ) | |
| bleu_metric: 34.98979283499355 | |
| ---------- | |
| NL: split string `input` based on occurrences of regex pattern '[ ](?=[A-Z]+\\b)' | |
| ref: re.split('[ ](?=[A-Z]+\\b)', input) | |
| Output: re. findall ('(? < = [ a - z ] ) (? = [ a - z ] ) (? = [ a | |
| bleu_metric: 16.927318970546587 | |
| ---------- | |
| NL: Split string `input` at every space followed by an upper-case letter | |
| ref: re.split('[ ](?=[A-Z])', input) | |
| Output: re. split ('\ \ b ', text ) | |
| bleu_metric: 23.369549958807866 | |
| ---------- | |
| NL: send multipart encoded file `files` to url `url` with headers `headers` and metadata `data` | |
| ref: r = requests.post(url, files=files, headers=headers, data=data) | |
| Output: r = requests. get ( url ) | |
| bleu_metric: 13.273006566054132 | |
| ---------- | |
| NL: write bytes `bytes_` to a file `filename` in python 3 | |
| ref: open('filename', 'wb').write(bytes_) | |
| Output: f. write ( struct. pack ('> i ', filename ) ) | |
| bleu_metric: 21.042990347620457 | |
| ---------- | |
| NL: get a list from a list `lst` with values mapped into a dictionary `dct` | |
| ref: [dct[k] for k in lst] | |
| Output: [ list ( lst [ i ] ) for i in lst ] | |
| bleu_metric: 28.787878181011266 | |
| ---------- | |
| NL: find duplicate names in column 'name' of the dataframe `x` | |
| ref: x.set_index('name').index.get_duplicates() | |
| Output: print ( list ( df. index. get _ duplicates ( ) ) ) | |
| bleu_metric: 19.923405658137924 | |
| ---------- | |
| NL: truncate float 1.923328437452 to 3 decimal places | |
| ref: round(1.923328437452, 3) | |
| Output: round ( 1. 005, 2 ) | |
| bleu_metric: 46.71379777282001 | |
| ---------- | |
| NL: sort list `li` in descending order based on the date value in second element of each list in list `li` | |
| ref: sorted(li, key=lambda x: datetime.strptime(x[1], '%d/%m/%Y'), reverse=True) | |
| Output: sorted ( li, key = operator. itemgetter ( 1 ), reverse = true ) | |
| bleu_metric: 19.26332463060877 | |
| ---------- | |
| NL: place the radial ticks in plot `ax` at 135 degrees | |
| ref: ax.set_rlabel_position(135) | |
| Output: ax. set _ xticklabels ( [ ] ) | |
| bleu_metric: 21.834177214239062 | |
| ---------- | |
| NL: check if path `my_path` is an absolute path | |
| ref: os.path.isabs(my_path) | |
| Output: os. path. exists ('my _ path') | |
| bleu_metric: 31.61487584488943 | |
| ---------- | |
| NL: get number of keys in dictionary `yourdict` | |
| ref: len(list(yourdict.keys())) | |
| Output: len ( mydict ) | |
| bleu_metric: 11.141275535087015 | |
| ---------- | |
| NL: count the number of keys in dictionary `yourdictfile` | |
| ref: len(set(open(yourdictfile).read().split())) | |
| Output: sum ( len ( v ) for v in yourdict. values ( ) ) | |
| bleu_metric: 18.767030063813223 | |
| ---------- | |
| NL: pandas dataframe get first row of each group by 'id' | |
| ref: df.groupby('id').first() | |
| Output: df. groupby ('id'). first ( ). first ( ) | |
| bleu_metric: 75.62722459724604 | |
| ---------- | |
| NL: split a list in first column into multiple columns keeping other columns as well in pandas data frame | |
| ref: pd.concat([df[0].apply(pd.Series), df[1]], axis=1) | |
| Output: df [ ['c ','df'] ]. apply ( lambda x : x [ 1 : - 1 ]. split ( | |
| bleu_metric: 19.037861963633805 | |
| ---------- | |
| NL: extract attributes 'src="js/([^"]*\\bjquery\\b[^"]*)"' from string `data` | |
| ref: re.findall('src="js/([^"]*\\bjquery\\b[^"]*)"', data) | |
| Output: " " " \ \ b " " " | |
| bleu_metric: 3.6253194206622026 | |
| ---------- | |
| NL: Sum integers contained in strings in list `['', '3.4', '', '', '1.0']` | |
| ref: sum(int(float(item)) for item in [_f for _f in ['', '3.4', '', '', '1.0'] if _f]) | |
| Output: sum ( [ 1, 2, 3, 4, 5 ] ) | |
| bleu_metric: 3.255351208100268 | |
| ---------- | |
| NL: Call a subprocess with arguments `c:\\Program Files\\VMware\\VMware Server\\vmware-cmd.bat` that may contain spaces | |
| ref: subprocess.Popen(['c:\\Program Files\\VMware\\VMware Server\\vmware-cmd.bat']) | |
| Output: subprocess. call ( ['shutdown ','/ r ','/ tmp / test. py'] ) | |
| bleu_metric: 16.665685159230854 | |
| ---------- | |
| NL: reverse a priority queue `q` in python without using classes | |
| ref: q.put((-n, n)) | |
| Output: t. start ( ) | |
| bleu_metric: 12.151662434083677 | |
| ---------- | |
| NL: make a barplot of data in column `group` of dataframe `df` colour-coded according to list `color` | |
| ref: df['group'].plot(kind='bar', color=['r', 'g', 'b', 'r', 'g', 'b', 'r']) | |
| Output: df. groupby ('o') ['value']. first ( ) | |
| bleu_metric: 3.5795004593328636 | |
| ---------- | |
| NL: find all matches of regex pattern '([a-fA-F\\d]{32})' in string `data` | |
| ref: re.findall('([a-fA-F\\d]{32})', data) | |
| Output: re. findall ('(? < = [ a - z ] ) ( [ a - z ] ) ','abcd _ string | |
| bleu_metric: 33.336490646495264 | |
| ---------- | |
| NL: Get the length of list `my_list` | |
| ref: len(my_list) | |
| Output: len ( my _ list ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: Getting the length of array `l` | |
| ref: len(l) | |
| Output: len ( l ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Getting the length of array `s` | |
| ref: len(s) | |
| Output: len ( s ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Getting the length of `my_tuple` | |
| ref: len(my_tuple) | |
| Output: len ( my _ list ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: Getting the length of `my_string` | |
| ref: len(my_string) | |
| Output: len ( my _ string ) | |
| bleu_metric: 30.739407647563223 | |
| ---------- | |
| NL: remove escape character from string "\\a" | |
| ref: """\\a""".decode('string_escape') | |
| Output: print ('\ n '. join ('[ _ name _ ]') ) | |
| bleu_metric: 15.071676257541073 | |
| ---------- | |
| NL: replace each 'a' with 'b' and each 'b' with 'a' in the string 'obama' in a single pass. | |
| ref: """obama""".replace('a', '%temp%').replace('b', 'a').replace('%temp%', 'b') | |
| Output: a = re. sub ('[ a - za - z0 - 9 ] ','', line ) | |
| bleu_metric: 6.256610657452671 | |
| ---------- | |
| NL: remove directory tree '/folder_name' | |
| ref: shutil.rmtree('/folder_name') | |
| Output: os. path. dirname ( os. path. join ( dirpath, os. path. dirname ( _ _ file _ _ ) ) | |
| bleu_metric: 6.4718242450883325 | |
| ---------- | |
| NL: create a new column `weekday` in pandas data frame `data` based on the values in column `my_dt` | |
| ref: data['weekday'] = data['my_dt'].apply(lambda x: x.weekday()) | |
| Output: df. apply ( lambda x : x. to _ frame ('new _ column'), axis = 1 ) | |
| bleu_metric: 34.619282106807496 | |
| ---------- | |
| NL: reverse sort Counter `x` by values | |
| ref: sorted(x, key=x.get, reverse=True) | |
| Output: sorted ( x, key = itemgetter ( 1 ) ) | |
| bleu_metric: 40.540555470323305 | |
| ---------- | |
| NL: reverse sort counter `x` by value | |
| ref: sorted(list(x.items()), key=lambda pair: pair[1], reverse=True) | |
| Output: sorted ( x, key = itemgetter ( 1 ), reverse = true ) | |
| bleu_metric: 15.53218858512746 | |
| ---------- | |
| NL: append a numpy array 'b' to a numpy array 'a' | |
| ref: np.vstack((a, b)) | |
| Output: np. concatenate ( ( a, b ) ) | |
| bleu_metric: 76.91605673134586 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: print(concatenate((a, b), axis=0)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 82.82477531331041 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the second axis | |
| ref: print(concatenate((a, b), axis=1)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: c = np.r_[(a[None, :], b[None, :])] | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 9.520068110441361 | |
| ---------- | |
| NL: numpy concatenate two arrays `a` and `b` along the first axis | |
| ref: np.array((a, b)) | |
| Output: print ( concatenate ( ( a, b ), axis = 1 ) ) | |
| bleu_metric: 41.70507291634131 | |
| ---------- | |
| NL: fetch address information for host 'google.com' ion port 80 | |
| ref: print(socket.getaddrinfo('google.com', 80)) | |
| Output: os. environ ['localhost : \ \ python27 \ \ python'] | |
| bleu_metric: 9.672649511413093 | |
| ---------- | |
| NL: add a column 'day' with value 'sat' to dataframe `df` | |
| ref: df.xs('sat', level='day', drop_level=False) | |
| Output: df ['value'] = df ['value']. apply ( lambda x : x ['value'] ) | |
| bleu_metric: 8.179879050970094 | |
| ---------- | |
| NL: return a 401 unauthorized in django | |
| ref: return HttpResponse('Unauthorized', status=401) | |
| Output: return httpresponse ( status = 204 ) | |
| bleu_metric: 15.451666492113134 | |
| ---------- | |
| NL: Flask set folder 'wherever' as the default template folder | |
| ref: Flask(__name__, template_folder='wherever') | |
| Output: app. run ( debug = true ) | |
| bleu_metric: 16.89983564524028 | |
| ---------- | |
| NL: How do I INSERT INTO t1 (SELECT * FROM t2) in SQLAlchemy? | |
| ref: session.execute('INSERT INTO t1 (SELECT * FROM t2)') | |
| Output: session. execute ('select * from from t1 ;') | |
| bleu_metric: 34.9740896964685 | |
| ---------- | |
| NL: sort a list of lists 'c2' such that third row comes first | |
| ref: c2.sort(key=lambda row: row[2]) | |
| Output: sorted ( l, key = lambda x : ( - x [ 1 ], x [ 2 ] ) ) | |
| bleu_metric: 22.169194554222997 | |
| ---------- | |
| NL: Sorting a list of lists in Python | |
| ref: c2.sort(key=lambda row: (row[2], row[1], row[0])) | |
| Output: [ ( row [ i ], row [ i + 2 ] ) for i in range ( 0, len ( row ), row [ 2 ] | |
| bleu_metric: 26.189293164591476 | |
| ---------- | |
| NL: Sorting a list of lists in Python | |
| ref: c2.sort(key=lambda row: (row[2], row[1])) | |
| Output: [ ( row [ i ], row [ i + 2 ] ) for i in range ( 0, len ( row ), row [ 2 ] | |
| bleu_metric: 22.303080489498637 | |
| ---------- | |
| NL: set font `Arial` to display non-ascii characters in matplotlib | |
| ref: matplotlib.rc('font', **{'sans-serif': 'Arial', 'family': 'sans-serif'}) | |
| Output: ax. set _ ylabel ('y - r ', title ='\ u2022') | |
| bleu_metric: 6.906418747057865 | |
| ---------- | |
| NL: Convert DateTime column 'date' of pandas dataframe 'df' to ordinal | |
| ref: df['date'].apply(lambda x: x.toordinal()) | |
| Output: df ['date'] = df ['date']. map ( lambda x : x. date ( ) ) | |
| bleu_metric: 51.29497107827519 | |
| ---------- | |
| NL: get html source of Selenium WebElement `element` | |
| ref: element.get_attribute('innerHTML') | |
| Output: print ( element. get _ attribute ('innerhtml') ) | |
| bleu_metric: 18.69300079996002 | |
| ---------- | |
| NL: Get the integer location of a key `bob` in a pandas data frame | |
| ref: df.index.get_loc('bob') | |
| Output: df. reset _ index ( ) | |
| bleu_metric: 19.840332506055578 | |
| ---------- | |
| NL: open a 'gnome' terminal from python script and run 'sudo apt-get update' command. | |
| ref: os.system('gnome-terminal -e \'bash -c "sudo apt-get update; exec bash"\'') | |
| Output: os. system ('echo $ filename. exe') | |
| bleu_metric: 12.044064401053362 | |
| ---------- | |
| NL: add an item with key 'third_key' and value 1 to an dictionary `my_dict` | |
| ref: my_dict.update({'third_key': 1}) | |
| Output: my _ dict ['my _ dict'] = var | |
| bleu_metric: 10.579369505074823 | |
| ---------- | |
| NL: declare an array | |
| ref: my_list = [] | |
| Output: variable = [ ] | |
| bleu_metric: 75.98356856515926 | |
| ---------- | |
| NL: Insert item `12` to a list `my_list` | |
| ref: my_list.append(12) | |
| Output: my _ list. append ('{ }, { } '. format ( c. c ) ) | |
| bleu_metric: 12.259149958656423 | |
| ---------- | |
| NL: add an entry 'wuggah' at the beginning of list `myList` | |
| ref: myList.insert(0, 'wuggah') | |
| Output: mylist = [ 'an ','i ','j'] | |
| bleu_metric: 11.502783619900047 | |
| ---------- | |
| NL: convert a hex-string representation to actual bytes | |
| ref: """\\xF3\\xBE\\x80\\x80""".replace('\\x', '').decode('hex') | |
| Output: " " " \ \ xc3 \ \ x85あ " " ". encode ('hex') | |
| bleu_metric: 24.712086993335753 | |
| ---------- | |
| NL: select the last column of dataframe `df` | |
| ref: df[df.columns[-1]] | |
| Output: df [ df. columns [ - 1 ] ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the first value from dataframe `df` where column 'Letters' is equal to 'C' | |
| ref: df.loc[df['Letters'] == 'C', 'Letters'].values[0] | |
| Output: df ['value'] = df. groupby ('value') ['value']. apply ( lambda x : x | |
| bleu_metric: 17.966302274629616 | |
| ---------- | |
| NL: converting two lists `[1, 2, 3]` and `[4, 5, 6]` into a matrix | |
| ref: np.column_stack(([1, 2, 3], [4, 5, 6])) | |
| Output: matrix ( [ [ 1, 2 ], [ 3, 4 ], [ 5, 6 ] ] ) | |
| bleu_metric: 35.61693754184905 | |
| ---------- | |
| NL: get the type of `i` | |
| ref: type(i) | |
| Output: print ( len ( a ) ) | |
| bleu_metric: 22.679164443904003 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: determine the type of variable `v` | |
| ref: type(v) | |
| Output: type ( v ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: get the type of variable `variable_name` | |
| ref: print(type(variable_name)) | |
| Output: globals ( ) ['a'] | |
| bleu_metric: 16.784459625186194 | |
| ---------- | |
| NL: get the 5th item of a generator | |
| ref: next(itertools.islice(range(10), 5, 5 + 1)) | |
| Output: next ( ( iter ( range ( n ) ) ) for i in range ( n ) ) | |
| bleu_metric: 18.81478574691708 | |
| ---------- | |
| NL: Print a string `word` with string format | |
| ref: print('"{}"'.format(word)) | |
| Output: print ('\ n '. join ('% s = % s'% ( value, ) ) ) | |
| bleu_metric: 16.331948281960493 | |
| ---------- | |
| NL: join a list of strings `list` using a space ' ' | |
| ref: """ """.join(list) | |
| Output: list (''. join ( map ( str, list ) ) ) | |
| bleu_metric: 33.918247054808944 | |
| ---------- | |
| NL: create list `y` containing two empty lists | |
| ref: y = [[] for n in range(2)] | |
| Output: y = [ ( x, y ) for x in zip ( l1, l2 ) ] | |
| bleu_metric: 18.951629567590743 | |
| ---------- | |
| NL: read a file 'C:/name/MyDocuments/numbers' into a list `data` | |
| ref: data = [line.strip() for line in open('C:/name/MyDocuments/numbers', 'r')] | |
| Output: new _ list = [ line. rstrip ('\ t') for line in open ('filename. txt') ] | |
| bleu_metric: 37.34651115614923 | |
| ---------- | |
| NL: delete all occurrences of character 'i' in string 'it is icy' | |
| ref: """""".join([char for char in 'it is icy' if char != 'i']) | |
| Output: re. sub ('i ','','it is icy') | |
| bleu_metric: 19.720950806796015 | |
| ---------- | |
| NL: delete all instances of a character 'i' in a string 'it is icy' | |
| ref: re.sub('i', '', 'it is icy') | |
| Output: re. sub ('i ','','it is icy') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: delete all characters "i" in string "it is icy" | |
| ref: """it is icy""".replace('i', '') | |
| Output: " " " a b : b. c '! ='a') | |
| bleu_metric: 23.234178119893244 | |
| ---------- | |
| NL: How to delete all instances of a character in a string in python? | |
| ref: """""".join([char for char in 'it is icy' if char != 'i']) | |
| Output: " " " " " ". join ( [ char for char in'it is icy'if char! ='i'] ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Drop rows of pandas dataframe `df` having NaN in column at index "1" | |
| ref: df.dropna(subset=[1]) | |
| Output: df. drop ( df. index. duplicated ( ) | |
| bleu_metric: 17.996531271765896 | |
| ---------- | |
| NL: get elements from list `myList`, that have a field `n` value 30 | |
| ref: [x for x in myList if x.n == 30] | |
| Output: mylist = [ i for i in range ( 10 ) if i % 2 = = 0 ] | |
| bleu_metric: 11.709061630923735 | |
| ---------- | |
| NL: converting list of strings `intstringlist` to list of integer `nums` | |
| ref: nums = [int(x) for x in intstringlist] | |
| Output: nums = [ int ( x ) for x in intstringlist ] | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: convert list of string numbers into list of integers | |
| ref: map(int, eval(input('Enter the unfriendly numbers: '))) | |
| Output: [ int ( s ) for s in example _ string. split ( ',') ] | |
| bleu_metric: 11.433361115787454 | |
| ---------- | |
| NL: print "." without newline | |
| ref: sys.stdout.write('.') | |
| Output: sys. stdout. write ( '.') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: round off the float that is the product of `2.52 * 100` and convert it to an int | |
| ref: int(round(2.51 * 100)) | |
| Output: int ( round ( 1123. 456789, 0 ) ) | |
| bleu_metric: 39.832871551569504 | |
| ---------- | |
| NL: Find all files in directory "/mydir" with extension ".txt" | |
| ref: os.chdir('/mydir') | |
| for file in glob.glob('*.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 45.30799450827704 | |
| ---------- | |
| NL: Find all files in directory "/mydir" with extension ".txt" | |
| ref: for file in os.listdir('/mydir'): | |
| if file.endswith('.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 95.90965597935381 | |
| ---------- | |
| NL: Find all files in directory "/mydir" with extension ".txt" | |
| ref: for (root, dirs, files) in os.walk('/mydir'): | |
| for file in files: | |
| if file.endswith('.txt'): | |
| pass | |
| Output: for file in os. listdir ('/ mydir') : if file. endswith ( '. txt') : | |
| bleu_metric: 50.25808484829805 | |
| ---------- | |
| NL: plot dataframe `df` without a legend | |
| ref: df.plot(legend=False) | |
| Output: df. legend ( ). plot ( kind ='bar') | |
| bleu_metric: 21.305413619585085 | |
| ---------- | |
| NL: loop through the IP address range "192.168.x.x" | |
| ref: for i in range(256): | |
| for j in range(256): | |
| ip = ('192.168.%d.%d' % (i, j)) | |
| print(ip) | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 13.252382555750852 | |
| ---------- | |
| NL: loop through the IP address range "192.168.x.x" | |
| ref: for (i, j) in product(list(range(256)), list(range(256))): | |
| pass | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 5.334065332387164 | |
| ---------- | |
| NL: loop through the IP address range "192.168.x.x" | |
| ref: generator = iter_iprange('192.168.1.1', '192.168.255.255', step=1) | |
| Output: 192. setsockopt ('192. 168. 1. 1 ','192. 168. 1. 255') | |
| bleu_metric: 61.58601992013119 | |
| ---------- | |
| NL: Sum the corresponding decimal values for binary values of each boolean element in list `x` | |
| ref: sum(1 << i for i, b in enumerate(x) if b) | |
| Output: float ( [ x for x in list ( d. values ( ) ) ] ) | |
| bleu_metric: 11.156857921595945 | |
| ---------- | |
| NL: write multiple strings `line1`, `line2` and `line3` in one line in a file `target` | |
| ref: target.write('%r\n%r\n%r\n' % (line1, line2, line3)) | |
| Output: result = [ line. split ( ) for line in open ('filename. txt ','r') ] | |
| bleu_metric: 7.848329424931549 | |
| ---------- | |
| NL: Convert list of lists `data` into a flat list | |
| ref: [y for x in data for y in (x if isinstance(x, list) else [x])] | |
| Output: [ a for b in c for a in list _ of _ b ] | |
| bleu_metric: 8.372912227572577 | |
| ---------- | |
| NL: Print new line character as `\n` in a string `foo\nbar` | |
| ref: print('foo\nbar'.encode('string_escape')) | |
| Output: print ('\ n '. join ( chr ( i ) for i in text ) ) | |
| bleu_metric: 19.268479640608692 | |
| ---------- | |
| NL: remove last comma character ',' in string `s` | |
| ref: """""".join(s.rsplit(',', 1)) | |
| Output: s = s. replace ( ', ','' ) | |
| bleu_metric: 24.4545047029932 | |
| ---------- | |
| NL: calculate the mean of each element in array `x` with the element previous to it | |
| ref: (x[1:] + x[:-1]) / 2 | |
| Output: np. mean ( x, y ) | |
| bleu_metric: 8.67666492910357 | |
| ---------- | |
| NL: get an array of the mean of each two consecutive values in numpy array `x` | |
| ref: x[:-1] + (x[1:] - x[:-1]) / 2 | |
| Output: np. array ( [ [ 2, 3, 4 ], [ 2, 5, 6 ] ] ) | |
| bleu_metric: 9.410489957951636 | |
| ---------- | |
| NL: load data containing `utf-8` from file `new.txt` into numpy array `arr` | |
| ref: arr = numpy.fromiter(codecs.open('new.txt', encoding='utf-8'), dtype='<U2') | |
| Output: print ( arr. decode ('utf - 8'). read ( ) ) | |
| bleu_metric: 17.11989843271324 | |
| ---------- | |
| NL: reverse sort list of dicts `l` by value for key `time` | |
| ref: l = sorted(l, key=itemgetter('time'), reverse=True) | |
| Output: sorted ( l, key = itemgetter ('date'), reverse = true ) | |
| bleu_metric: 63.481816146288104 | |
| ---------- | |
| NL: Sort a list of dictionary `l` based on key `time` in descending order | |
| ref: l = sorted(l, key=lambda a: a['time'], reverse=True) | |
| Output: l = sorted ( l, key = lambda x : l ['time'], reverse = true ) | |
| bleu_metric: 69.63845241054851 | |
| ---------- | |
| NL: get rows of dataframe `df` that match regex '(Hel|Just)' | |
| ref: df.loc[df[0].str.contains('(Hel|Just)')] | |
| Output: df [ df. str. contains ('-') ] | |
| bleu_metric: 34.22307652991899 | |
| ---------- | |
| NL: find the string in `your_string` between two special characters "[" and "]" | |
| ref: re.search('\\[(.*)\\]', your_string).group(1) | |
| Output: your _ string. findall ( " print ( [ 1, 2, 3 ] + ', your _ string )') | |
| bleu_metric: 10.540925533894598 | |
| ---------- | |
| NL: How to create a list of date string in 'yyyymmdd' format with Python Pandas? | |
| ref: [d.strftime('%Y%m%d') for d in pandas.date_range('20130226', '20130302')] | |
| Output: df ['date']. strftime ('% y % m % d') | |
| bleu_metric: 25.96554412038301 | |
| ---------- | |
| NL: count number of times string 'brown' occurred in string 'The big brown fox is brown' | |
| ref: """The big brown fox is brown""".count('brown') | |
| Output: s = s. split ('' ) | |
| bleu_metric: 10.206322439745918 | |
| ---------- | |
| NL: decode json string `request.body` to python dict | |
| ref: json.loads(request.body) | |
| Output: json. loads ( request. body ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: download the file from url `url` and save it under file `file_name` | |
| ref: urllib.request.urlretrieve(url, file_name) | |
| Output: file _ name = open ('filename. txt ','filename. txt'). read ( ) | |
| bleu_metric: 8.607692533178168 | |
| ---------- | |
| NL: split string `text` by space | |
| ref: text.split() | |
| Output: re. split ('\ \ s + ', text ) | |
| bleu_metric: 20.333448190047886 | |
| ---------- | |
| NL: split string `text` by "," | |
| ref: text.split(',') | |
| Output: text. split ( ',') | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Split string `line` into a list by whitespace | |
| ref: line.split() | |
| Output: [ item. strip ( ) for s in my _ list ] | |
| bleu_metric: 12.300686288463773 | |
| ---------- | |
| NL: replace dot characters '.' associated with ascii letters in list `s` with space ' ' | |
| ref: [re.sub('(?<!\\d)\\.(?!\\d)', ' ', i) for i in s] | |
| Output: re. sub ('[ ^ \ \ w ] + ','', s ) | |
| bleu_metric: 18.60592450912929 | |
| ---------- | |
| NL: sort list `list_of_strings` based on second index of each string `s` | |
| ref: sorted(list_of_strings, key=lambda s: s.split(',')[1]) | |
| Output: sorted ( s, key = lambda s : s. split ( ',') [ 1 ] ) | |
| bleu_metric: 89.21616972156079 | |
| ---------- | |
| NL: call multiple bash function ‘vasp’ and ‘tee tee_output’ using ‘|’ | |
| ref: subprocess.check_call('vasp | tee tee_output', shell=True) | |
| Output: subprocess. call ( ['ls ','- t ','- t'] ) | |
| bleu_metric: 11.96655750514248 | |
| ---------- | |
| NL: eliminate all strings from list `lst` | |
| ref: [element for element in lst if isinstance(element, int)] | |
| Output: [ lst for l in lst if not isinstance ( lst, lst ) ] | |
| bleu_metric: 24.692069728555516 | |
| ---------- | |
| NL: get all the elements except strings from the list 'lst'. | |
| ref: [element for element in lst if not isinstance(element, str)] | |
| Output: [ lst [ : : - 1 ] for l in lst ] | |
| bleu_metric: 14.398289657599705 | |
| ---------- | |
| NL: Sort a list of dictionaries `list_to_be_sorted` by the value of the dictionary key `name` | |
| ref: newlist = sorted(list_to_be_sorted, key=lambda k: k['name']) | |
| Output: sorted ( list _ of _ dct, key = lambda x : x ['name'] ) | |
| bleu_metric: 46.08662469973653 | |
| ---------- | |
| NL: sort a list of dictionaries `l` by values in key `name` in descending order | |
| ref: newlist = sorted(l, key=itemgetter('name'), reverse=True) | |
| Output: sorted ( l, key = lambda x : ( x ['name'], x ['age'] ) ) | |
| bleu_metric: 29.61516536011624 | |
| ---------- | |
| NL: How do I sort a list of dictionaries by values of the dictionary in Python? | |
| ref: list_of_dicts.sort(key=operator.itemgetter('name')) | |
| Output: list _ of _ dicts. sort ( key = operator. itemgetter ('name') ) | |
| bleu_metric: 74.25946367830886 | |
| ---------- | |
| NL: How do I sort a list of dictionaries by values of the dictionary in Python? | |
| ref: list_of_dicts.sort(key=operator.itemgetter('age')) | |
| Output: list _ of _ dicts. sort ( key = operator. itemgetter ('name') ) | |
| bleu_metric: 60.5478327168454 | |
| ---------- | |
| NL: How to sort a Dataframe by the ocurrences in a column in Python (pandas) | |
| ref: df.groupby('prots').sum().sort('scores', ascending=False) | |
| Output: df. groupby ('prod'). sum ( ) | |
| bleu_metric: 31.369171074632213 | |
| ---------- | |
| NL: join together with "," elements inside a list indexed with 'category' within a dictionary `trans` | |
| ref: """,""".join(trans['category']) | |
| Output: [ v for k, v in list ( mydict. items ( ) ) if v. startswith ('l') ] | |
| bleu_metric: 6.878430971169709 | |
| ---------- | |
| NL: concatenate array of strings `['A', 'B', 'C', 'D']` into a string | |
| ref: """""".join(['A', 'B', 'C', 'D']) | |
| Output: " " " " " ". join ( ['a ','b ','c ','d ','g'] | |
| bleu_metric: 51.01157052571188 | |
| ---------- | |
| NL: get json data from restful service 'url' | |
| ref: json.load(urllib.request.urlopen('url')) | |
| Output: urllib. request. urlretrieve ('http : / / search. twitter. com / search. json? q = | |
| bleu_metric: 17.729842264695016 | |
| ---------- | |
| NL: Remove all strings from a list a strings `sents` where the values starts with `@$\t` or `#` | |
| ref: [x for x in sents if not x.startswith('@$\t') and not x.startswith('#')] | |
| Output: del my _ list. pop ('\ n') | |
| bleu_metric: 4.122430965890577 | |
| ---------- | |
| NL: django filter by hour | |
| ref: Entry.objects.filter(pub_date__contains='08:00') | |
| Output: entry. objects. filter ( created _ _ lte ='2011 : 00') | |
| bleu_metric: 47.02773871923361 | |
| ---------- | |
| NL: sort a list of dictionary `list` first by key `points` and then by `time` | |
| ref: list.sort(key=lambda item: (item['points'], item['time'])) | |
| Output: sorted ( list ( d. items ( ) ), key = lambda x : x [ 1 ] ) | |
| bleu_metric: 14.223469354841619 | |
| ---------- | |
| NL: convert datetime object `(1970, 1, 1)` to seconds | |
| ref: (t - datetime.datetime(1970, 1, 1)).total_seconds() | |
| Output: datetime. datetime. combine ( datetime. datetime. now ( ), 1 ) | |
| bleu_metric: 20.287417439504075 | |
| ---------- | |
| NL: insert `_suff` before the file extension in `long.file.name.jpg` or replace `_a` with `suff` if it precedes the extension. | |
| ref: re.sub('(\\_a)?\\.([^\\.]*)$', '_suff.\\2', 'long.file.name.jpg') | |
| Output: filename = os. path. join ( base _ name,'a + b') | |
| bleu_metric: 4.1219102274800345 | |
| ---------- | |
| NL: reload a module `module` | |
| ref: import imp | |
| imp.reload(module) | |
| Output: imp. _ _ name _ _ | |
| bleu_metric: 18.370727471078332 | |
| ---------- | |
| NL: Convert integer `number` into an unassigned integer | |
| ref: struct.unpack('H', struct.pack('h', number)) | |
| Output: int (''. join ( map ( str, int, x ) ) ) | |
| bleu_metric: 15.69339742398475 | |
| ---------- | |
| NL: convert int values in list `numlist` to float | |
| ref: numlist = [float(x) for x in numlist] | |
| Output: print ( float ( mylist ) ) | |
| bleu_metric: 12.83572790104489 | |
| ---------- | |
| NL: write dataframe `df`, excluding index, to a csv file | |
| ref: df.to_csv(filename, index=False) | |
| Output: df. to _ csv ( filename, index = false ) | |
| bleu_metric: 43.24227075463214 | |
| ---------- | |
| NL: convert a urllib unquoted string `unescaped` to a json data `json_data` | |
| ref: json_data = json.loads(unescaped) | |
| Output: json _ data = json. loads ( json. dumps ( data ) ) | |
| bleu_metric: 31.689002674537857 | |
| ---------- | |
| NL: Create a list containing all ascii characters as its elements | |
| ref: [chr(i) for i in range(127)] | |
| Output: [ ord ( c ) for c in range ( 12 ) ] | |
| bleu_metric: 27.560016787236364 | |
| ---------- | |
| NL: write `newFileBytes` to a binary file `newFile` | |
| ref: newFile.write(struct.pack('5B', *newFileBytes)) | |
| Output: f. write ('myfile') | |
| bleu_metric: 10.455877726641399 | |
| ---------- | |
| NL: python regex - check for a capital letter with a following lowercase in string `string` | |
| ref: re.sub('^[A-Z0-9]*(?![a-z])', '', string) | |
| Output: re. sub ('(? < = \ \ w ) ( [ a - z ] ) ','\ \ 1 ', string ) | |
| bleu_metric: 51.55802176328614 | |
| ---------- | |
| NL: get the last key of dictionary `dict` | |
| ref: list(dict.keys())[-1] | |
| Output: list ( dict. items ( ) ) | |
| bleu_metric: 34.69313394445662 | |
| ---------- | |
| NL: write line "hi there" to file `f` | |
| ref: print('hi there', file=f) | |
| Output: print ('hi there ', file = f ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: write line "hi there" to file `myfile` | |
| ref: f = open('myfile', 'w') | |
| f.write('hi there\n') | |
| Output: print ('myfile. txt ','w') | |
| bleu_metric: 25.556664439163445 | |
| ---------- | |
| NL: write line "Hello" to file `somefile.txt` | |
| ref: with open('somefile.txt', 'a') as the_file: | |
| the_file.write('Hello\n') | |
| Output: print ('hello { 0 : myfile. txt ','% ( line ) ) | |
| bleu_metric: 20.43236176252964 | |
| ---------- | |
| NL: convert unicode string `s` to ascii | |
| ref: s.encode('iso-8859-15') | |
| Output: s. encode ('ascii ','ignore') | |
| bleu_metric: 44.534504264163466 | |
| ---------- | |
| NL: Django get maximum value associated with field 'added' in model `AuthorizedEmail` | |
| ref: AuthorizedEmail.objects.filter(group=group).order_by('-added')[0] | |
| Output: employees. objects. filter ( id ='id'). order _ by ('id') | |
| bleu_metric: 26.946924595891623 | |
| ---------- | |
| NL: Find all numbers and dots from a string `text` using regex | |
| ref: re.findall('Test([0-9.]*[0-9]+)', text) | |
| Output: re. findall ('\ \ d + (? = [ ^ \ \ d ] * ) \ \ d + ) ', text ) | |
| bleu_metric: 36.1187685428593 | |
| ---------- | |
| NL: python regex to find all numbers and dots from 'text' | |
| ref: re.findall('Test([\\d.]*\\d+)', text) | |
| Output: re. findall ('\ \ d + (? = [ ^ \ \ d ] * ) ', text ) | |
| bleu_metric: 53.011675073675754 | |
| ---------- | |
| NL: execute script 'script.ps1' using 'powershell.exe' shell | |
| ref: os.system('powershell.exe', 'script.ps1') | |
| Output: os. system ('test. sh otherfunc') | |
| bleu_metric: 32.787776781958996 | |
| ---------- | |
| NL: Sort a list of tuples `b` by third item in the tuple | |
| ref: b.sort(key=lambda x: x[1][2]) | |
| Output: b. sort ( key = lambda x : x [ 1 ] ) | |
| bleu_metric: 77.08276169003516 | |
| ---------- | |
| NL: get a list of all keys in Cassandra database `cf` with pycassa | |
| ref: list(cf.get_range().get_keys()) | |
| Output: ##quen. get ( ). get ('2') | |
| bleu_metric: 18.295654224495205 | |
| ---------- | |
| NL: create a datetime with the current date & time | |
| ref: datetime.datetime.now() | |
| Output: datetime. datetime. now ( ). date ( ) | |
| bleu_metric: 61.90429960963545 | |
| ---------- | |
| NL: get the index of an integer `1` from a list `lst` if the list also contains boolean items | |
| ref: next(i for i, x in enumerate(lst) if not isinstance(x, bool) and x == 1) | |
| Output: next ( i for i, e in enumerate ( lst ) if e! = 1 ) | |
| bleu_metric: 42.75902729039988 | |
| ---------- | |
| NL: subtract 13 from every number in a list `a` | |
| ref: a[:] = [(x - 13) for x in a] | |
| Output: [ ( a - b ) for a, b in zip ( a, b ) ] | |
| bleu_metric: 13.229527092256335 | |
| ---------- | |
| NL: choose a random file from the directory contents of the C drive, `C:\\` | |
| ref: random.choice(os.listdir('C:\\')) | |
| Output: random. choice ( os. listdir ('\ \') ) | |
| bleu_metric: 77.51410336522927 | |
| ---------- | |
| NL: get the highest element in absolute value in a numpy matrix `x` | |
| ref: max(x.min(), x.max(), key=abs) | |
| Output: max ( x. min ( ), x. max ( ), key = abs ) | |
| bleu_metric: 100.0 | |
| ---------- | |
| NL: Get all urls within text `s` | |
| ref: re.findall('"(http.*?)"', s, re.MULTILINE | re.DOTALL) | |
| Output: re. findall ('(? = re. com / (? = " [ ^ " ] * " | [ ^ " ] * " | |
| bleu_metric: 20.539509719773488 | |
| ---------- | |
| NL: match urls whose domain doesn't start with `t` from string `document` using regex | |
| ref: re.findall('http://[^t][^s"]+\\.html', document) | |
| Output: re. search ('(? < = \ \ w ) (? : \ \. )? ( (? : \ \. \ \ | |
| bleu_metric: 10.625281384457024 | |
| ---------- | |
| NL: split a string `mystring` considering the spaces ' ' | |
| ref: mystring.replace(' ', '! !').split('!') | |
| Output: mystring. split ('' ) | |
| bleu_metric: 15.586404917762792 | |
| ---------- | |
| NL: open file `path` with mode 'r' | |
| ref: open(path, 'r') | |
| Output: os. path. join ( os. path. abspath ( '.') ) | |
| bleu_metric: 12.257617673006655 | |
| ---------- | |
| NL: sum elements at the same index in list `data` | |
| ref: [[sum(item) for item in zip(*items)] for items in zip(*data)] | |
| Output: [ sum ( item ) for item in zip ( * items ) ] | |
| bleu_metric: 50.621900464488824 | |
| ---------- | |
| NL: add a new axis to array `a` | |
| ref: a[:, (np.newaxis)] | |
| Output: numpy. zeros ( ( 2, 2, 3 ), numpy. zeros ( 2, 4 ) ) | |
| bleu_metric: 7.289334177359764 | |
| ---------- | |