NL: send a signal `signal.sigusr1` to the current process
ref: os.kill(os.getpid(), signal.SIGUSR1)
Output: proc.terminate()
bleu_metric: 8.15663954186223
----------
NL: decode a hex string '4a4b4c' to utf-8.
ref: bytes.fromhex('4a4b4c').decode('utf-8')
Output: print(str(a.encode('hexB', 'hex'))
bleu_metric: 13.947196297357516
----------
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: """{:,}""".format(a)
bleu_metric: 7.039722936059098
----------
NL: how to convert a string from cp-1251 to utf-8?
ref: d.decode('cp1251').encode('utf8')
Output: urllib.parse.unquote(s.encode('utf8'))
bleu_metric: 47.38611152748619
----------
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: v for k, v in list(d.items()) if v > 0}
bleu_metric: 64.58251510057832
----------
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: v for k, v in list(d.items()) if v > 0}
bleu_metric: 32.55630358189704
----------
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('echo "foo"', shell=True)
bleu_metric: 33.54121358422117
----------
NL: concatenate a list of strings `['a', 'b', 'c']`
ref: """""".join(['a', 'b', 'c'])
Output: print(('a', 'b', 'c'))
bleu_metric: 42.80768209469581
----------
NL: find intersection data between series `s1` and series `s2`
ref: pd.Series(list(set(s1).intersection(set(s2))))
Output: s1 = pd.DataFrame(s1, s2=None)
bleu_metric: 10.722703083793286
----------
NL: sending http headers to `client`
ref: client.send('HTTP/1.0 200 OK\r\n')
Output: s.send('HTTP/1.0 200 OK\r\n')
bleu_metric: 94.57416090031758
----------
NL: format a datetime string `when` to extract date only
ref: then = datetime.datetime.strptime(when, '%Y-%m-%d').date()
Output: dt.strftime('%m/%d/%Y')
bleu_metric: 10.693730801587432
----------
NL: split a multi-line string `inputstring` into separate strings
ref: inputString.split('\n')
Output: a, b, c = input('string_escape')
bleu_metric: 21.972813874997158
----------
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: a.rstrip().split('\n')
bleu_metric: 38.90205926838098
----------
NL: concatenate elements of list `b` by a colon ":"
ref: """:""".join(str(x) for x in b)
Output: print(repr(a))
bleu_metric: 4.9422115544674705
----------
NL: get the first object from a queryset in django model `entry`
ref: Entry.objects.filter()[:1].get()
Output: Entry.objects.get_values('id')
bleu_metric: 23.929967984761056
----------
NL: calculate sum over all rows of 2d numpy array
ref: a.sum(axis=1)
Output: np.sum(a.shape[1:])
bleu_metric: 24.022110864391532
----------
NL: enable warnings using action 'always'
ref: warnings.simplefilter('always')
Output: os.kill(logging.DEBUG)
bleu_metric: 21.105340631872636
----------
NL: concatenate items of list `l` with a space ' '
ref: print(' '.join(map(str, l)))
Output: print(' '.join(map(str, l))
bleu_metric: 91.69729150999534
----------
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('start "$file.py', shell=True)
bleu_metric: 24.391589282046496
----------
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.strftime('%H:%M:%S,%f'))
bleu_metric: 19.792633055621124
----------
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: 62.69839476367552
----------
NL: convert a string `123,456.908` with dot and comma into a floating number
ref: float('123,456.908'.replace(',', ''))
Output: print(', '.join(str(12345))
bleu_metric: 21.93127055132894
----------
NL: set pythonpath in python script.
ref: sys.path.append('/path/to/whatever')
Output: os.path.abspath(__file__)
bleu_metric: 14.91608246051739
----------
NL: split string 'words, words, words.' using a regex '(\\w+)'
ref: re.split('(\\W+)', 'Words, words, words.')
Output: re.split('\\b(\\w+)', 'Words, words, words.')
bleu_metric: 71.4022021970798
----------
NL: open a file `output.txt` in append mode
ref: file = open('Output.txt', 'a')
Output: f = open('file.txt', 'r')
bleu_metric: 54.7332024239071
----------
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://randomsite.com/', 'rb').read()
bleu_metric: 45.9127788668896
----------
NL: download a file `url` over http and save to `file_name`
ref: u = urllib.request.urlopen(url)
Output: file_name = wget.urlretrieve('http://example.com/file.gz')
bleu_metric: 9.449865252803164
----------
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.com/file.com/file.gz')
bleu_metric: 41.91356463630533
----------
NL: download a file `url` over http
ref: r = requests.get(url)
Output: urllib.request.urlretrieve('http://example.com/file.com/file.gz')
bleu_metric: 7.265857755970445
----------
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.gz', 'file.gz')
bleu_metric: 7.029695662739608
----------
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('--conf', nargs='*')
bleu_metric: 24.648885368725924
----------
NL: remove key 'c' from dictionary `d`
ref: {i: d[i] for i in d if i != 'c'}
Output: d = {'a': 1, 'c': 2, 'c': 3, 'c': 3}
bleu_metric: 10.884267615693712
----------
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: pd.concat([df_1, df_2.columns], axis=1, on=['A_B', 'B'
bleu_metric: 24.624806058504372
----------
NL: split a string `s` by space with `4` splits
ref: s.split(' ', 4)
Output: re.split('\\s+|\\S+', s)
bleu_metric: 23.58844810653421
----------
NL: read keyboard-input
ref: input('Enter your input:')
Output: sys.stdout.flush()
bleu_metric: 17.6625510283176
----------
NL: enable debug mode on flask application `app`
ref: app.run(debug=True)
Output: app.run(debug=True)
bleu_metric: 100.0
----------
NL: python save list `mylist` to file object 'save.txt'
ref: pickle.dump(mylist, open('save.txt', 'wb'))
Output: file.write('myfile.txt', 'wb')
bleu_metric: 42.60520663282873
----------
NL: multiply a matrix `p` with a 3d tensor `t` in scipy
ref: scipy.tensordot(P, T, axes=[1, 1]).swapaxes(0, 1)
Output: np.dot(x.reshape(-1, 3), np.dot(0, 1, 0, 1))
bleu_metric: 22.78371390626513
----------
NL: create 3d array of zeroes of size `(3,3,3)`
ref: numpy.zeros((3, 3, 3))
Output: np.zeros((3, 3))
bleu_metric: 75.33808072882879
----------
NL: cut off the last word of a sentence `content`
ref: """ """.join(content.split(' ')[:-1])
Output: """ """.join(s.split())
bleu_metric: 46.40451719480771
----------
NL: convert scalar `x` to array
ref: x = np.asarray(x).reshape(1, -1)[(0), :]
Output: np.zeros((x, x) for x in s)
bleu_metric: 9.777229794850028
----------
NL: sum all elements of nested list `l`
ref: sum(sum(i) if isinstance(i, list) else i for i in L)
Output: sum(lst)
bleu_metric: 1.841640459320245
----------
NL: convert hex string '470fc614' to a float number
ref: struct.unpack('!f', '470FC614'.decode('hex'))[0]
Output: struct.unpack('d', struct.pack('0x1252', 'F6d0424a'))[0]
bleu_metric: 48.34492705061849
----------
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 my_dict.items())
bleu_metric: 28.26550613903659
----------
NL: running bash script 'sleep.sh'
ref: subprocess.call('sleep.sh', shell=True)
Output: subprocess.call('start command -c 'r', shell=True)
bleu_metric: 55.7853798004205
----------
NL: join elements of list `l` with a comma `,`
ref: """,""".join(l)
Output: [y for x in L for y in x if x.startswith(''.join(',')]
bleu_metric: 22.50265947708922
----------
NL: make a comma-separated string from a list `mylist`
ref: myList = ','.join(map(str, myList))
Output: myList = mylist[:n]
bleu_metric: 10.218289380194193
----------
NL: reverse the list that contains 1 to 10
ref: list(reversed(list(range(10))))
Output: L[::-1]
bleu_metric: 7.575149194183216
----------
NL: remove substring 'bag,' from a string 'lamp, bag, mirror'
ref: print('lamp, bag, mirror'.replace('bag,', ''))
Output: [m.strip() for m in x if m.endswith('@')]
bleu_metric: 10.616735292999413
----------
NL: reverse the order of words, delimited by `.`, in string `s`
ref: """.""".join(s.split('.')[::-1])
Output: sorted(s, key=lambda s: s.split('.') if s else s s[1]))
bleu_metric: 35.65843931149688
----------
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: print(s.strftime('%Y-%m-%d %H:%M:%S'))
bleu_metric: 62.56418230541474
----------
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: time.strftime('%Y-%m-%d %H:%M:%S')
bleu_metric: 59.71758060527691
----------
NL: get the date 7 days before the current date
ref: (datetime.datetime.now() - datetime.timedelta(days=7)).date()
Output: (datetime.date(2010, 12, 31) + relativedelta(months=(+ 1)))
bleu_metric: 23.713320246552005
----------
NL: sum elements at index `column` of each list in list `data`
ref: print(sum(row[column] for row in data))
Output: data[sum(b) for b in zip(*data)]
bleu_metric: 15.844501337268929
----------
NL: sum columns of a list `array`
ref: [sum(row[i] for row in array) for i in range(len(array[0]))]
Output: sum(sum(row[:, (1)] for row in mylist)
bleu_metric: 21.03312756130989
----------
NL: encode binary string 'your string' to base64 code
ref: base64.b64encode(bytes('your string', 'utf-8'))
Output: print(content.encode('base64'))
bleu_metric: 20.291424899466545
----------
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()) if k in d]
bleu_metric: 28.548259613272815
----------
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 k, v in list(d.items()))
bleu_metric: 8.608775825594615
----------
NL: how do i get the url parameter in a flask view
ref: request.args['myParam']
Output: request.url
bleu_metric: 6.856953606192477
----------
NL: identify duplicate values in list `mylist`
ref: [k for k, v in list(Counter(mylist).items()) if v > 1]
Output: set(mylist))
bleu_metric: 2.0605095345578897
----------
NL: insert directory 'apps' into directory `__file__`
ref: sys.path.insert(1, os.path.join(os.path.dirname(__file__), 'apps'))
Output: os.chdir(path)
bleu_metric: 1.4431184819037772
----------
NL: modify sys.path for python module `subdir`
ref: sys.path.append(os.path.join(os.path.dirname(__file__), 'subdir'))
Output: my_path.append('/path/to/my_file.txt')
bleu_metric: 10.285502024406256
----------
NL: insert a 'none' value into a sqlite3 table.
ref: db.execute("INSERT INTO present VALUES('test2', ?, 10)", (None,))
Output: cursor.execute('insert into example values values values)')
bleu_metric: 14.095580788435447
----------
NL: flatten list `list_of_menuitems`
ref: [image for menuitem in list_of_menuitems for image in menuitem]
Output: list_of_lists = [list(f) for f in my_list]
bleu_metric: 14.949751774990682
----------
NL: append elements of a set `b` to a list `a`
ref: a.extend(b)
Output: a.append(b)
bleu_metric: 59.46035575013605
----------
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.write.txt', header=None, delimiter='\t')
bleu_metric: 17.503492126188856
----------
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\\data\\t.txt', sep='\t')
bleu_metric: 45.493235468867745
----------
NL: split a string `x` by last occurrence of character `-`
ref: print(x.rpartition('-')[0])
Output: x.split('\\d+', s)
bleu_metric: 16.460024863157845
----------
NL: get the last part of a string before the character '-'
ref: print(x.rsplit('-', 1)[0])
Output: print(x.rfind('-')[0])
bleu_metric: 60.109065739327065
----------
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.find_element_by_xpath("//li/label/input[contains(..,'polishpottery')]")
bleu_metric: 13.25451214611751
----------
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], [2, 3, 4], [3, 4, 5]])
bleu_metric: 42.651255482235264
----------
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[:5] + l[0])
bleu_metric: 50.02460338549947
----------
NL: loop over files in directory '.'
ref: for fn in os.listdir('.'):
Output: for os.path.join(path, filename)
bleu_metric: 16.07716496330015
----------
NL: loop over files in directory `source`
ref: for (root, dirs, filenames) in os.walk(source):
Output: for os.path.join(path, filename)
bleu_metric: 11.519792081464464
----------
NL: create a random list of integers
ref: [int(1000 * random.random()) for i in range(10000)]
Output: randomList = [random.random() for _ in range(10)]
bleu_metric: 34.3414769312466
----------
NL: using %f with strftime() in python to get microseconds
ref: datetime.datetime.now().strftime('%H:%M:%S.%f')
Output: datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
bleu_metric: 65.15366202853991
----------
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.execute('SELECT * FROM foo WHERE bar = %s AND baz = %s', (param1, param2))
bleu_metric: 24.090844358935918
----------
NL: filter rows in pandas starting with alphabet 'f' using regular expression.
ref: df.b.str.contains('^f')
Output: df.dropna(lambda x: x.replace('\\[^\\w\\s]', 'A')
bleu_metric: 10.727295782787309
----------
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: 26.894705347686966
----------
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[[1, 3]], axis=1)
bleu_metric: 11.446641525222896
----------
NL: format the variables `self.goals` and `self.penalties` using string formatting
ref: """({:d} goals, ${:d})""".format(self.goals, self.penalties)
Output: """{0} {1}""".format(name='bar', 'change!')
bleu_metric: 28.1073423324667
----------
NL: format string "({} goals, ${})" with variables `goals` and `penalties`
ref: """({} goals, ${})""".format(self.goals, self.penalties)
Output: """{} {}""".format(TotalAmount)
bleu_metric: 30.199592877917368
----------
NL: format string "({0.goals} goals, ${0.penalties})"
ref: """({0.goals} goals, ${0.penalties})""".format(self)
Output: """{0} {1}""".format(name, 20)
bleu_metric: 32.37295639418319
----------
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: 26.91716452870884
----------
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: int(''.join(map(str, list))
bleu_metric: 23.011232462942058
----------
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: 18.058610039563465
----------
NL: write the elements of list `lines` concatenated by special character '\n' to file `myfile`
ref: myfile.write('\n'.join(lines))
Output: with open('myfile.txt', 'w').read().write('\n')
bleu_metric: 30.77874158297155
----------
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 L if x is not None]
bleu_metric: 7.979312731505724
----------
NL: remove duplicate words from a string `text` using regex
ref: text = re.sub('\\b(\\w+)( \\1\\b)+', '\\1', text)
Output: re.sub('(?<=[a-z])\\r?\\w+', '\\1', s)
bleu_metric: 43.796510807866646
----------
NL: count non zero values in each column in pandas data frame
ref: df.astype(bool).sum(axis=1)
Output: df.apply(lambda x: 0).sum() if sum(x) > 1)
bleu_metric: 23.20305803246989
----------
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: re.findall('(?<=\\w+)\\n|[^\\w+)\\n]*', mystring)
bleu_metric: 20.693739176213647
----------
NL: split string 'qh qd jc kd js' into a list on white spaces
ref: """QH QD JC KD JS""".split()
Output: [i.strip() for i in line.split()]
bleu_metric: 21.86976686325118
----------
NL: search for occurrences of regex pattern '>.*<' in xml string `line`
ref: print(re.search('>.*<', line).group(0))
Output: print(re.search('(?<=\\w+)', line))
bleu_metric: 47.71512105691513
----------
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.strptime('2010-11-13 10:33:54.227806', '%Y-%m-%d
bleu_metric: 40.88633662916028
----------
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: [i for i, v in enumerate(L) if v[0] == 'id' in x]
bleu_metric: 33.034466035625115
----------
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-zA-Z0-9]','', text)
bleu_metric: 64.45968952353843
----------
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-Z0-9]', '', text)
bleu_metric: 34.05969875105766
----------
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.show()
bleu_metric: 6.117046115680378
----------
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.show()
bleu_metric: 6.117046115680378
----------
NL: loop over a list `mylist` if sublists length equals 3
ref: [x for x in mylist if len(x) == 3]
Output: [(x + 1) for x in mylist]
bleu_metric: 27.97803653491597
----------
NL: initialize a list `lst` of 100 objects object()
ref: lst = [Object() for _ in range(100)]
Output: [item for item in lst]
bleu_metric: 11.786767588753086
----------
NL: create list `lst` containing 100 instances of object `object`
ref: lst = [Object() for i in range(100)]
Output: [x for x in lst if x is not None]
bleu_metric: 13.386745323687673
----------
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_all('a', {'id':'st,'st_name':'st/content': 'content'}
bleu_metric: 7.478822222889464
----------
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, how='inner', on=['user_id'])
bleu_metric: 18.70222963555319
----------
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('{0}'.format(num))
bleu_metric: 5.68097120736594
----------
NL: split a string by a delimiter in python
ref: [x.strip() for x in '2.MATCHES $$TEXT$$ STRING'.split('$$TEXT$$')]
Output: a.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: if (not os.path.exists(directory)):
    os.makedirs(directory)
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: 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\\1', 'h2'), 'h2'), ('h2',
bleu_metric: 18.81434448407342
----------
NL: substitute ascii letters in string 'aas30dsa20' with empty string ''
ref: re.sub('\\D', '', 'aas30dsa20')
Output: """""".join(str(i) for i in s if i.isdigit())
bleu_metric: 9.408196821068621
----------
NL: get digits only from a string `aas30dsa20` using lambda function
ref: """""".join([x for x in 'aas30dsa20' if x.isdigit()])
Output: map(int, mystring.split(' ', 1)[0]
bleu_metric: 7.1335710240663
----------
NL: access a tag called "name" in beautifulsoup `soup`
ref: print(soup.find('name').string)
Output: soup.findAll(text='Python')
bleu_metric: 22.779746815932338
----------
NL: get a dictionary `records` of key-value pairs in pymongo cursor `cursor`
ref: records = dict((record['_id'], record) for record in cursor)
Output: db.find({'class': {'section': {'}, {'$': {'section': 'Markus': {'}, {
bleu_metric: 4.175415561574825
----------
NL: create new matrix object  by concatenating data from matrix a and matrix b
ref: np.concatenate((A, B))
Output: A = np.array([[i, j]) for i in range(1, len(a), 2))]
bleu_metric: 7.939087147543495
----------
NL: concat two matrices `a` and `b` in numpy
ref: np.vstack((A, B))
Output: np.concatenate((a, b))
bleu_metric: 29.502343631964045
----------
NL: get the characters count in a file `filepath`
ref: os.stat(filepath).st_size
Output: sum(os.path.join(path)
bleu_metric: 17.827531042796256
----------
NL: count the occurrences of item "a" in list `l`
ref: l.count('a')
Output: Counter(x in a for x in a)
bleu_metric: 17.03318603763928
----------
NL: count the occurrences of items in list `l`
ref: Counter(l)
Output: Counter(l)
bleu_metric: 100.0
----------
NL: count the occurrences of items in list `l`
ref: [[x, l.count(x)] for x in set(l)]
Output: Counter(l)
bleu_metric: 2.190091937500515
----------
NL: count the occurrences of items in list `l`
ref: dict(((x, l.count(x)) for x in set(l)))
Output: Counter(l)
bleu_metric: 1.5775358510206974
----------
NL: count the occurrences of item "b" in list `l`
ref: l.count('b')
Output: Counter(x in a for x in set(l)]
bleu_metric: 11.114924776032007
----------
NL: copy file `srcfile` to directory `dstdir`
ref: shutil.copy(srcfile, dstdir)
Output: shutil.copyfile(file_file, file)
bleu_metric: 25.406637407730738
----------
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(a, key=lambda x: x[x])
bleu_metric: 10.970270231176839
----------
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(k, v)
bleu_metric: 8.44727061998314
----------
NL: put the curser at beginning of the file
ref: file.seek(0)
Output: subprocess.call('mv /Foo/', 'r', shell=True)
bleu_metric: 9.134423666564473
----------
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['b'].fillna(df['a'])
bleu_metric: 50.85465237405449
----------
NL: remove key 'ele' from dictionary `d`
ref: del d['ele']
Output: d = {key: val for key, val in list(d.items()) if val!= 42}
bleu_metric: 5.166045454134253
----------
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: datetime.datetime.now().strftime('%Y-%m-%d')
bleu_metric: 10.00116481297629
----------
NL: merge list `['it']` and list `['was']` and list `['annoying']` into one list
ref: ['it'] + ['was'] + ['annoying']
Output: ['a', 'b', 'c', 'd']
bleu_metric: 19.408623270627203
----------
NL: increment a value with leading zeroes in a number `x`
ref: str(int(x) + 1).zfill(len(x))
Output: x ='+ str(x)
bleu_metric: 13.057811073335992
----------
NL: check if a pandas dataframe `df`'s index is sorted
ref: all(df.index[:-1] <= df.index[1:])
Output: df.sort_index()
bleu_metric: 3.1405961937021267
----------
NL: convert tuple `t` to list
ref: list(t)
Output: tuple(t)
bleu_metric: 75.98356856515926
----------
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: list1 = [item[0] for item in L]
bleu_metric: 10.571070857151538
----------
NL: send the output of pprint object `dataobject` to file `logfile`
ref: pprint.pprint(dataobject, logFile)
Output: print(data.read())
bleu_metric: 18.885888592159468
----------
NL: get index of rows in column 'boolcol'
ref: df.loc[df['BoolCol']]
Output: df.index
bleu_metric: 2.5225322607845957
----------
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.columns[df['Value'].isin(id=['Value'] == 'foo']))]
bleu_metric: 14.614460466911597
----------
NL: get list of indexes of rows where column 'boolcol' values match true
ref: df[df['BoolCol'] == True].index.tolist()
Output: [df.index.tolist() for index in l]
bleu_metric: 31.04879110800733
----------
NL: get index of rows in dataframe `df` which column 'boolcol' matches value true
ref: df[df['BoolCol']].index.tolist()
Output: df.loc[df['a'].isin(['a', 'b'])]
bleu_metric: 19.35951733925871
----------
NL: change working directory to the directory `owd`
ref: os.chdir(owd)
Output: os.chdir(os.path.dirname(os.path.abspath(__file__))
bleu_metric: 19.554016376461796
----------
NL: insert data from a string `testfield` to sqlite db `c`
ref: c.execute("INSERT INTO test VALUES (?, 'bar')", (testfield,))
Output: cursor.execute('SELECT * FROM table WHERE bar" FROM table WHERE column IN %s;+$', (1,))
bleu_metric: 24.03479325641683
----------
NL: decode string "\\x89\\n" into a normal string
ref: """\\x89\\n""".decode('string_escape')
Output: """\\xc3\\x85あ""".encode('utf-8')
bleu_metric: 41.902845957302084
----------
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: 65.80370064762462
----------
NL: convert a raw string `raw_byte_string` into a normal string
ref: raw_byte_string.decode('unicode_escape')
Output: raw_string.decode('unicode_escape')
bleu_metric: 88.01117367933934
----------
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('\\w+', s)
bleu_metric: 10.45716776939666
----------
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.scatter(np.random.randn(100), np.random.randn(100), np.randn(100
bleu_metric: 57.932844496915834
----------
NL: remove a div with a id `main-content` using beautifulsoup
ref: soup.find('div', id='main-content').decompose()
Output: soup.findAll(text='div', id='width=True)
bleu_metric: 40.78097038402287
----------
NL: filter rows containing key word `ball` in column `ids`
ref: df[df['ids'].str.contains('ball')]
Output: print(df.query('User')['value'].filter(lambda x: x.year))
bleu_metric: 13.348189438082967
----------
NL: convert index at level 0 into a column in dataframe `df`
ref: df.reset_index(level=0, inplace=True)
Output: df.set_index([' 0', '0'])
bleu_metric: 15.310245441182444
----------
NL: add indexes in a data frame `df` to a column `index1`
ref: df['index1'] = df.index
Output: df.set_index(['index', 'date'])
bleu_metric: 16.451929399933107
----------
NL: convert pandas index in a dataframe to columns
ref: df.reset_index(level=['tick', 'obs'])
Output: df2 = df.set_index(['T', 'level2'])
bleu_metric: 33.23485859915788
----------
NL: get reverse of list items from list 'b' using extended slicing
ref: [x[::-1] for x in b]
Output: [zip(b[::-1]) for b in a]
bleu_metric: 39.392473548207036
----------
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: [(a, b), (b, b)]
bleu_metric: 22.319313765733384
----------
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(a)
bleu_metric: 1.1785360562036349
----------
NL: convert list `list_of_ints` into a comma separated string
ref: """,""".join([str(i) for i in list_of_ints])
Output: print(', '.join(l[:1])
bleu_metric: 22.587773654803488
----------
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: requests.get('https://username/post', json={'data': 'cheers'})
bleu_metric: 9.052524251044028
----------
NL: find last occurrence of character '}' in string "abcd}def}"
ref: 'abcd}def}'.rfind('}')
Output: """{:.5}""".format(p.finditer('_')
bleu_metric: 15.007375127940442
----------
NL: iterate ove list `[1, 2, 3]` using list comprehension
ref: print([item for item in [1, 2, 3]])
Output: [(x + y) for x in [1, 2, 3, 4], [4, 5, 6]]
bleu_metric: 27.199778234956106
----------
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 for d in a if d['name'] == 'pluto']
bleu_metric: 13.446276862221726
----------
NL: get the filename without the extension from file 'hemanth.txt'
ref: print(os.path.splitext(os.path.basename('hemanth.txt'))[0])
Output: os.path.splitext(filename, 'r')
bleu_metric: 19.13913717974136
----------
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(zip(x, y))
bleu_metric: 6.591862820334322
----------
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', 'd'])]
bleu_metric: 22.895991001938675
----------
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[df['column_name'].str[1:]
bleu_metric: 9.220008234645237
----------
NL: replace all occurences of newlines `\n` with `<br>` in dataframe `df`
ref: df.replace({'\n': '<br>'}, regex=True)
Output: df.replace('\n', '>').replace('\r\n')
bleu_metric: 28.577933090423212
----------
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: 100.0
----------
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: print(list(itertools.chain.from_iterable([''.join([''.join(i)) for i in myList
bleu_metric: 6.9872837779786146
----------
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: [item[0] for item in wordids]
bleu_metric: 3.6045344658126863
----------
NL: extract a url from a string `mystring`
ref: print(re.findall('(https?://[^\\s]+)', myString))
Output: print(re.search('(?<=\\S+)', mystring))
bleu_metric: 29.098751790827798
----------
NL: extract a url from a string `mystring`
ref: print(re.search('(?P<url>https?://[^\\s]+)', myString).group('url'))
Output: print(re.search('(?<=\\S+)', mystring))
bleu_metric: 26.19504694379959
----------
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: 54.67160268053085
----------
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: datetime.datetime.strptime('01-01-04', '%d-%b-%Y')
bleu_metric: 15.911783110981517
----------
NL: create multidimensional array `matrix` with 3 rows and 2 columns in python
ref: matrix = [[a, b], [c, d], [e, f]]
Output: np.array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 1
bleu_metric: 11.802861352393501
----------
NL: replace spaces with underscore
ref: mystring.replace(' ', '_')
Output: re.sub('\\([^\\w\\)', '', text)
bleu_metric: 15.604242268653643
----------
NL: get an absolute file path of file 'mydir/myfile.txt'
ref: os.path.abspath('mydir/myfile.txt')
Output: os.path.abspath(mypath)
bleu_metric: 36.304072644520666
----------
NL: split string `my_string` on white spaces
ref: """ """.join(my_string.split())
Output: my_string.split('_')
bleu_metric: 19.77851893672361
----------
NL: get filename without extension from file `filename`
ref: os.path.splitext(filename)[0]
Output: os.path.splitext('/path/to/filename.txt')
bleu_metric: 33.70129264673147
----------
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 lst if x > 2]
bleu_metric: 20.61396573920308
----------
NL: split a string `docs/src/scripts/temp` by `/` keeping `/` in the result
ref: """Docs/src/Scripts/temp""".replace('/', '/\x00/').split('\x00')
Output: result = subprocess.Popen(sys.+)" -DOL)
bleu_metric: 3.19149448369712
----------
NL: shuffle columns of an numpy array 'r'
ref: np.random.shuffle(np.transpose(r))
Output: np.random.shuffle(np.random.randint(0, 1, axis=1))
bleu_metric: 41.3948387915005
----------
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['B']
bleu_metric: 80.03203203844998
----------
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: data[(key_data['Date']['post code']) & (data['Date'])]
bleu_metric: 21.54701803718216
----------
NL: check characters of string `string` are true predication of function `predicate`
ref: all(predicate(x) for x in string)
Output: all(x in string for x in list(string.items()) if x == 'isdit')
bleu_metric: 16.27934873162478
----------
NL: determine number of files on a drive with python
ref: os.statvfs('/').f_files - os.statvfs('/').f_ffree
Output: os.stat(path).st_common(f.st_f)
bleu_metric: 10.276795791404941
----------
NL: how to get a single result from a sqlite query in python?
ref: cursor.fetchone()[0]
Output: cursor.execute('User')[0]
bleu_metric: 44.08231875586727
----------
NL: convert string `user_input` into a list of integers `user_list`
ref: user_list = [int(number) for number in user_input.split(',')]
Output: return int(soup.split(','))
bleu_metric: 34.34281712615472
----------
NL: get a list of integers by splitting  a string `user` with comma
ref: [int(s) for s in user.split(',')]
Output: [int(x) for x in [1, 2, 3, 4, 5]
bleu_metric: 21.79301929852717
----------
NL: sorting a python list by two criteria
ref: sorted(list, key=lambda x: (x[0], -x[1]))
Output: sorted(list, key=lambda x: (x[1], x[0]))
bleu_metric: 80.44410811781832
----------
NL: sort a list of objects `ut`, based on a function `cmpfun` in descending order
ref: ut.sort(key=cmpfun, reverse=True)
Output: sorted(a, key=lambda x: x.count(x.getter(1))
bleu_metric: 10.813005337959174
----------
NL: reverse list `ut` based on the `count` attribute of each object
ref: ut.sort(key=lambda x: x.count, reverse=True)
Output: sum(lst)
bleu_metric: 2.867683507302
----------
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: (-len(x.split('-)))
bleu_metric: 25.0737833894674
----------
NL: click a href button 'send' with selenium
ref: driver.find_element_by_partial_link_text('Send').click()
Output: browser.find_element_by_text('http://www.example.com')
bleu_metric: 14.44788670919441
----------
NL: click a href button having text `send inmail` with selenium
ref: driver.findElement(By.linkText('Send InMail')).click()
Output: browser.find_element_by_class_name('http://www.com/')
bleu_metric: 10.573773767686836
----------
NL: click a href button with text 'send inmail' with selenium
ref: driver.find_element_by_link_text('Send InMail').click()
Output: browser.find_element_by_class_name('http://www.text.com')
bleu_metric: 14.44788670919441
----------
NL: cast an int `i` to a string and concat to string 'me'
ref: 'ME' + str(i)
Output: """""".join(i)
bleu_metric: 31.61487584488943
----------
NL: sorting data in dataframe pandas
ref: df.sort_values(['System_num', 'Dis'])
Output: df.sort('m')
bleu_metric: 13.487005099534619
----------
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('filename', 'w').write('\n').read().write('\n').write('\n')
bleu_metric: 45.445331007629186
----------
NL: sort a list `l` by length of value in tuple
ref: l.sort(key=lambda t: len(t[1]), reverse=True)
Output: print(sorted(lst, key=lambda tup: len(x), len(y)))
bleu_metric: 17.729842264695016
----------
NL: split string `s` by words that ends with 'd'
ref: re.findall('\\b(\\w+)d\\b', s)
Output: [word for word in s.split('.')[0] for s in s.split()]
bleu_metric: 7.987276352377326
----------
NL: return `true` if string `foobarrrr` contains regex `ba[rzd]`
ref: bool(re.search('ba[rzd]', 'foobarrrr'))
Output: re.search('(?<=.)(?=(a))', 'aaa bbb ccc ddd e')
bleu_metric: 23.770841795864293
----------
NL: removing duplicates in list `t`
ref: list(set(t))
Output: list(set(item) for item in L))
bleu_metric: 35.556702356686955
----------
NL: removing duplicates in list `source_list`
ref: list(set(source_list))
Output: list(item)
bleu_metric: 24.795364698947967
----------
NL: removing duplicates in list `abracadabra`
ref: list(OrderedDict.fromkeys('abracadabra'))
Output: list(set(t))
bleu_metric: 19.745471508497758
----------
NL: convert array `a` into a list
ref: numpy.array(a).reshape(-1).tolist()
Output: a.append(a)
bleu_metric: 9.852859740626553
----------
NL: convert the first row of numpy matrix `a` to a list
ref: numpy.array(a)[0].tolist()
Output: numpy.array(a, a[0])
bleu_metric: 44.579146390257485
----------
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.findAll(text='"')
bleu_metric: 12.1241604438414
----------
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(map(lambda x: x[1])
bleu_metric: 37.15744930678825
----------
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: [(x + y) for x, y in zip(l1, l2)]
bleu_metric: 4.866055054993489
----------
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: [m.start() for m in re.findall('\\d+', 'abcd')]
bleu_metric: 31.35886387911532
----------
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[['A', 'B']].fillna(df['A']
bleu_metric: 32.2826123723905
----------
NL: sort dictionary `o` in ascending order based on its keys and items
ref: sorted(o.items())
Output: sorted(list(d.items()), key=lambda v: v[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()), key=lambda x: (x['name'], x['name'], x['name']))
bleu_metric: 21.62882016096312
----------
NL: convert string "1" into integer
ref: int('1')
Output: int('1', 2)
bleu_metric: 58.143073696821936
----------
NL: function to convert strings into integers
ref: int()
Output: int(''.join(map(str, x)))
bleu_metric: 12.729922658368398
----------
NL: convert items in `t1` to integers
ref: T2 = [map(int, x) for x in T1]
Output: int('1')
bleu_metric: 7.448668213629092
----------
NL: call a shell script `./test.sh` using subprocess
ref: subprocess.call(['./test.sh'])
Output: subprocess.call('test.sh otherfunc')
bleu_metric: 34.64490516910983
----------
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, y in zip(l1, l2)]
bleu_metric: 40.59264050928986
----------
NL: encode string 'data to be encoded'
ref: encoded = base64.b64encode('data to be encoded')
Output: data = json.encode('utf-8')
bleu_metric: 21.665290989148364
----------
NL: encode a string `data to be encoded` to `ascii` encoding
ref: encoded = 'data to be encoded'.encode('ascii')
Output: data = 'data2'
bleu_metric: 6.5298221612721505
----------
NL: parse tab-delimited csv file 'text.txt' into a list
ref: lol = list(csv.reader(open('text.txt', 'rb'), delimiter='\t'))
Output: open('demo.txt', 'r').read()
bleu_metric: 18.96773811885661
----------
NL: get attribute `my_str` of object `my_object`
ref: getattr(my_object, my_str)
Output: my_obj.__dict__
bleu_metric: 11.909345107359583
----------
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: dict((k, v) for k, v in list(d.items()) for k, v in list(d.items())}
bleu_metric: 21.161547096559342
----------
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 mylist)
bleu_metric: 32.21542689451684
----------
NL: convert unicode string u"{'code1':1,'code2':1}" into dictionary
ref: d = ast.literal_eval("{'code1':1,'code2':1}")
Output: {'MyString1': '1', '1'}).encode('ascii', 'ignore')
bleu_metric: 14.294565332608489
----------
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('\\[^\\w\\w', mystring)
bleu_metric: 9.573479881751316
----------
NL: remove any url within string `text`
ref: text = re.sub('^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)
Output: text = re.sub('\\r\\r\\r\\n*\\Z', '', text)
bleu_metric: 39.07206775645457
----------
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.isnan(a).any(np.in1d(a), 0, a)
bleu_metric: 20.39361671090609
----------
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.exit(app.exec_())
bleu_metric: 2.642308696142247
----------
NL: run r script '/usr/bin/rscript --vanilla /pathto/myrscript.r'
ref: subprocess.call('/usr/bin/Rscript --vanilla /pathto/MyrScript.r', shell=True)
Output: subprocess.call('/usr/bin/perl', shell=True)
bleu_metric: 48.72501457287311
----------
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=0)
bleu_metric: 86.6397551781362
----------
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('21/11/13 19:19:30 -0400', '%Y/%m/%d
bleu_metric: 12.446578619659645
----------
NL: call a base class's class method `do` from derived class `derived`
ref: super(Derived, cls).do(a)
Output: super(MyClass, self).__init__(*args, **kwargs)
bleu_metric: 14.02577516008147
----------
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(a[0])!= 0]
bleu_metric: 10.315793886636692
----------
NL: separate words delimited by one or more spaces into a list
ref: re.split(' +', 'hello world sample text')
Output: [word for word in my_list if any(x) not in '\n' % x.split()]
bleu_metric: 10.332090908268508
----------
NL: length of longest element in list `words`
ref: len(max(words, key=len))
Output: len(words, key=lambda i: len(word) for word in word_list)
bleu_metric: 30.51327880362441
----------
NL: get the value associated with unicode key 'from_user' of first dictionary in list `result`
ref: result[0]['from_user']
Output: [x for x in list_of_dict if x['success'] not in data]
bleu_metric: 12.879862858915844
----------
NL: retrieve each line from a file 'file.txt' as a list
ref: [line.split() for line in open('File.txt')]
Output: line = [line.strip() for line in open('filename.txt')]
bleu_metric: 63.83964846132485
----------
NL: swap keys with values in a dictionary `a`
ref: res = dict((v, k) for k, v in a.items())
Output: dict((v, k) for k, v in a.items())
bleu_metric: 90.00876262522594
----------
NL: open a file `path/to/file_name.ext` in write mode
ref: new_file = open('path/to/FILE_NAME.ext', 'w')
Output: shutil.copyfile('/to/file.txt', 'path')
bleu_metric: 25.66441742647156
----------
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('A').agg(lambda x: x.nlargest(x.name).nunique())
bleu_metric: 24.81076483975817
----------
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(dict.values())
bleu_metric: 9.663861439684919
----------
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: [key for key, value in list(d.items()) if 'new y' in d]
bleu_metric: 43.11804919179004
----------
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(' '.join(map(list, list)))
bleu_metric: 62.705622531832795
----------
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('\xd0\xd0\xd1\xd0\xd0\xd0\xd0\xd0\xd0\
bleu_metric: 32.278364081487986
----------
NL: encode a latin character in string `sopet\xc3\xb3n` properly
ref: 'Sopet\xc3\xb3n'.encode('latin-1').decode('utf-8')
Output: print('\xc3\xa9'.encode('utf-8'))
bleu_metric: 40.96260380345688
----------
NL: resized image `image` to width, height of `(x, y)` with filter of `antialias`
ref: image = image.resize((x, y), Image.ANTIALIAS)
Output: im = Image.imshow(image, 0, cv2.COLOR_rGR2RGB)
bleu_metric: 14.564246815114087
----------
NL: regex, find "n"s only in the middle of string `s`
ref: re.findall('n(?<=[^n]n)n+(?=[^n])(?i)', s)
Output: re.sub('(?<=n)', '', s)
bleu_metric: 19.97638457636558
----------
NL: display the float `1/3*100` as a percentage
ref: print('{0:.0f}%'.format(1.0 / 3 * 100))
Output: int(round(1.5))
bleu_metric: 8.651705771167139
----------
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: my_list.sort(key=lambda x: x['title'])
bleu_metric: 30.53233854712605
----------
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: print(max(x, key=sum))
bleu_metric: 5.1753387054451085
----------
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('div', attrs={'name': 'description'})
bleu_metric: 27.858167198909346
----------
NL: write records in dataframe `df` to table 'test' in schema 'a_schema'
ref: df.to_sql('test', engine, schema='a_schema')
Output: df.to_csv('test.csv', index=False)
bleu_metric: 20.519505687240557
----------
NL: extract brackets from string `s`
ref: brackets = re.sub('[^(){}[\\]]', '', s)
Output: re.findall('\\[\\]]', s)
bleu_metric: 36.918962549447706
----------
NL: remove duplicate elements from list 'l'
ref: list(dict((x[0], x) for x in L).values())
Output: [set(l) for l in set(lst) for l in L)]
bleu_metric: 13.839002470625022
----------
NL: read a file `file` without newlines
ref: [line.rstrip('\n') for line in file]
Output: f.close()
bleu_metric: 4.4408502586392995
----------
NL: get the position of item 1 in `testlist`
ref: [i for (i, x) in enumerate(testlist) if (x == 1)]
Output: print(list(range(1, len(mylist)) + 1)
bleu_metric: 9.025970847670083
----------
NL: get the position of item 1 in `testlist`
ref: [i for (i, x) in enumerate(testlist) if (x == 1)]
Output: print(list(range(1, len(mylist)) + 1)
bleu_metric: 9.025970847670083
----------
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(list(range(1, len(mylist)) + 1)
bleu_metric: 7.3690983226275435
----------
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(list(range(1, len(mylist)) + 1)
bleu_metric: 8.372912227572577
----------
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(list(range(1, len(mylist)) + 1)
bleu_metric: 7.018371226772391
----------
NL: get the position of item `element` in list `testlist`
ref: print(testlist.index(element))
Output: print(testlist.index(x))
bleu_metric: 70.16879391277371
----------
NL: get the position of item `element` in list `testlist`
ref: try:
    print(testlist.index(element))
except ValueError:
    pass
Output: print(testlist.index(x))
bleu_metric: 34.660486300017936
----------
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[1] for x in L[0])
bleu_metric: 22.078697088920286
----------
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(a, key=lambda x: x[1])
bleu_metric: 21.186050864016664
----------
NL: make a delay of 1 second
ref: time.sleep(1)
Output: time.sleep(1)
bleu_metric: 100.0
----------
NL: convert list of tuples `l` to a string
ref: """, """.join('(' + ', '.join(i) + ')' for i in L)
Output: """""".join(map(str, L))
bleu_metric: 22.382912241881993
----------
NL: django set default value of field `b` equal to '0000000'
ref: b = models.CharField(max_length=7, default='0000000', editable=False)
Output: super(Model, self).save(*args, **kwargs)
bleu_metric: 8.756189878973519
----------
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(list(range(len(a))), key=lambda x: (-len(x)), x[1])
bleu_metric: 21.534597645653108
----------
NL: how do i perform secondary sorting in python?
ref: sorted(list5, key=lambda vertex: (degree(vertex), vertex))
Output: sorted(list1, key=lambda x: x[:-1])
bleu_metric: 23.523340210589698
----------
NL: convert a list  into a generator object
ref: (n for n in [1, 2, 3, 5])
Output: [(i * i for i in range(10)) for i in range(10)]
bleu_metric: 7.707532488755381
----------
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: [item for item in my_list if some_condition()]
bleu_metric: 9.65619264674583
----------
NL: open a file `yourfile.txt` in write mode
ref: f = open('yourfile.txt', 'w')
Output: f = open('file.txt', 'r')
bleu_metric: 61.28081331864039
----------
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(map(tuple, map(int, t))
bleu_metric: 3.3785986651715976
----------
NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to list in one line
ref: map(lambda a: a[0], (('aa',), ('bb',), ('cc',)))
Output: [('a', 'b', 'c'), ('d', 'e', 'e']) for e in l]
bleu_metric: 15.687545978719285
----------
NL: python pandas: how to replace a characters in a column of a dataframe?
ref: df['range'].replace(',', '-', inplace=True)
Output: df['AB'].replace(',',', '-', inplace=True)
bleu_metric: 80.377750806414
----------
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)
bleu_metric: 0.24532394983186231
----------
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)
bleu_metric: 0.24532394983186231
----------
NL: unzip list `original`
ref: result = ([a for (a, b) in original], [b for (a, b) in original])
Output: zip(*main_list)
bleu_metric: 0.7985269443272172
----------
NL: unzip list `original` and return a generator
ref: result = ((a for (a, b) in original), (b for (a, b) in original))
Output: [(i, j) for i in range(10)]
bleu_metric: 5.292278815391249
----------
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)
bleu_metric: 0.07639466888110501
----------
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)
bleu_metric: 0.037243160645090284
----------
NL: encode `decimal('3.9')` to a json string
ref: json.dumps(Decimal('3.9'))
Output: json.dumps(ord('x1b2\x92\x03\x9\xa2\x8')
bleu_metric: 22.115966812339867
----------
NL: add key "mynewkey" to dictionary `d` with value "mynewvalue"
ref: d['mynewkey'] = 'mynewvalue'
Output: d['Test'].update({'item': 1, 'item': 4}, {'item3': 1})
bleu_metric: 9.25329498915617
----------
NL: add key 'a' to dictionary `data` with value 1
ref: data.update({'a': 1, })
Output: data['a'] = 'b'
bleu_metric: 17.86189637183548
----------
NL: add key 'a' to dictionary `data` with value 1
ref: data.update(dict(a=1))
Output: data['a'] = 'b'
bleu_metric: 13.078474031415348
----------
NL: add key 'a' to dictionary `data` with value 1
ref: data.update(a=1)
Output: data['a'] = 'b'
bleu_metric: 14.323145079400494
----------
NL: find maximal value in matrix `matrix`
ref: max([max(i) for i in matrix])
Output: max(max(x, key=lambda x: x[1])[0]
bleu_metric: 12.58220555845937
----------
NL: round number `answer` to 2 precision after the decimal point
ref: answer = str(round(answer, 2))
Output: round(1.005, 2)
bleu_metric: 24.5981275183433
----------
NL: extract ip address from an html string
ref: ip = re.findall('[0-9]+(?:\\.[0-9]+){3}', s)
Output: print(re.findall('\\d+(?=(\\.com+)\\.com/', s))
bleu_metric: 29.033332017734637
----------
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[df['A'] > 1]
bleu_metric: 10.739983258001304
----------
NL: append each line in file `myfile` into a list
ref: [x for x in myfile.splitlines() if x != '']
Output: myfile.write('myfile.txt', 'w').write('\n')
bleu_metric: 8.899891914341412
----------
NL: get a list of integers `lst` from a file `filename.txt`
ref: lst = map(int, open('filename.txt').readlines())
Output: [int(i) for i in f.split(',')]
bleu_metric: 12.313871716496207
----------
NL: add color bar with image `mappable` to plot `plt`
ref: plt.colorbar(mappable=mappable, cax=ax3)
Output: plt.savefig('test.png')
bleu_metric: 16.449450056138957
----------
NL: count most frequent 100 words in column 'text' of dataframe `df`
ref: Counter(' '.join(df['text']).split()).most_common(100)
Output: df.join(word).most_common(word)
bleu_metric: 14.732626148274333
----------
NL: python split a string using regex
ref: re.findall('(.+?):(.+?)\\b ?', text)
Output: re.split('(?<=\\.\\d+|\\s)', text)
bleu_metric: 27.16899549919204
----------
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], [4, 5, 6])) for a in range(
bleu_metric: 23.605722704787006
----------
NL: get a value of datetime.today() in the utc time zone
ref: datetime.now(pytz.utc)
Output: datetime.datetime.now().utcnow()
bleu_metric: 34.38931217657843
----------
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: 100.0
----------
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: list23 = [(a - b) for a, b in zip(list1, list2)]
bleu_metric: 11.13613656101029
----------
NL: django response with json `data`
ref: return HttpResponse(data, mimetype='application/json')
Output: json.dumps(data, ensure_ascii=False)
bleu_metric: 18.83283134652959
----------
NL: get all text that is not enclosed within square brackets in string `example_str`
ref: re.findall('(.*?)\\[.*?\\]', example_str)
Output: """\\[\\]]""".split()
bleu_metric: 16.91906599442455
----------
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('\\[(.*?\\]\\]', '', str))
bleu_metric: 41.184938137508176
----------
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.findall('\\w+(?:\\w+)', '\\1', s)
bleu_metric: 38.05918723545142
----------
NL: match regex '\\((.*?)\\)|(\\w)' with string '(zyx)bc'
ref: re.findall('\\((.*?)\\)|(\\w)', '(zyx)bc')
Output: re.match('(?<=\\w+)', 'a\n b\n c')
bleu_metric: 21.2203664532429
----------
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', 'abcdefg123 and again abc')
bleu_metric: 40.304968802021214
----------
NL: formate each string cin list `elements` into pattern '%{0}%'
ref: elements = ['%{0}%'.format(element) for element in elements]
Output: print(' '.join(map(str, list)))
bleu_metric: 9.154148084605266
----------
NL: open a background process 'background-process' with arguments 'arguments'
ref: subprocess.Popen(['background-process', 'arguments'])
Output: subprocess.Popen(['file', '-r', '-r', 'r', 'file'])
bleu_metric: 31.65014630070639
----------
NL: get list of values from dictionary 'mydict' w.r.t. list of keys 'mykeys'
ref: [mydict[x] for x in mykeys]
Output: [k for d in mylist if'mykey' in d]
bleu_metric: 14.807582680058125
----------
NL: convert list `[('name', 'joe'), ('age', 22)]` into a dictionary
ref: dict([('Name', 'Joe'), ('Age', 22)])
Output: dict((name, eval(name)) for name in ['name','sp', 'name'])
bleu_metric: 16.475516456048457
----------
NL: average each two columns of array `data`
ref: data.reshape(-1, j).mean(axis=1).reshape(data.shape[0], -1)
Output: data.sum(axis=1).sum(axis=1)
bleu_metric: 19.628836362845185
----------
NL: double backslash escape all double quotes in string `s`
ref: print(s.encode('unicode-escape').replace('"', '\\"'))
Output: result = result.replace('\\', '')
bleu_metric: 23.514174149586935
----------
NL: split a string into a list of words and whitespace
ref: re.split('(\\W+)', s)
Output: [word for word in mystring.split()]
bleu_metric: 15.620412264187214
----------
NL: plotting stacked barplots on a panda data frame
ref: df.plot(kind='barh', stacked=True)
Output: df.plot(kind='bar', stacked=True)
bleu_metric: 81.53551038173114
----------
NL: reverse the keys and values in a dictionary `mydictionary`
ref: {i[1]: i[0] for i in list(myDictionary.items())}
Output: dict((v, k) for k, v in d.items() if k!= 'None')
bleu_metric: 15.450079547146164
----------
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 for x in mylist if 'abc' in x.startswith('start')]
bleu_metric: 9.601670492244972
----------
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(a, str)
bleu_metric: 59.46035575013605
----------
NL: check if object `o` is a string
ref: (type(o) is str)
Output: isinstance(a, str)
bleu_metric: 23.813694985189848
----------
NL: check if object `o` is a string
ref: isinstance(o, str)
Output: isinstance(a, str)
bleu_metric: 59.46035575013605
----------
NL: check if `obj_to_test` is a string
ref: isinstance(obj_to_test, str)
Output: if (not some_string):
    pass
bleu_metric: 20.556680845025983
----------
NL: append list `list1` to `list2`
ref: list2.extend(list1)
Output: list2.append(list2)
bleu_metric: 32.17294420803808
----------
NL: append list `mylog` to `list1`
ref: list1.extend(mylog)
Output: mylist.append('myD')
bleu_metric: 18.850319022747346
----------
NL: append list `a` to `c`
ref: c.extend(a)
Output: a.append(b)
bleu_metric: 29.071536848410968
----------
NL: append items in list `mylog` to `list1`
ref: for line in mylog:
    list1.append(line)
Output: mylist.append('myD')
bleu_metric: 22.84389301518129
----------
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, b, c) for a, b in zip(a, b)]
bleu_metric: 9.850589025894903
----------
NL: initialize `secret_key` in flask config with `your_secret_string `
ref: app.config['SECRET_KEY'] = 'Your_secret_string'
Output: app.run()
bleu_metric: 9.728049676725325
----------
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.columns, index=['A', 'B'])
bleu_metric: 23.203863908318283
----------
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: zip(*sorted(enumerate(a), key=lambda x: x[1])
bleu_metric: 7.353922010374821
----------
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_xlabel('Temperature (\u2103)')
bleu_metric: 16.54461999338998
----------
NL: remove symbols from a string `s`
ref: re.sub('[^\\w]', ' ', s)
Output: re.sub('[^\\w]', '', s)
bleu_metric: 100.0
----------
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: 52.085526428001614
----------
NL: find octal characters matches from a string `str` using regex
ref: print(re.findall("'\\\\[0-7]{1,3}'", str))
Output: re.findall('[^\\w-\\d]', str)
bleu_metric: 27.1936649292955
----------
NL: split string `input` based on occurrences of regex pattern '[ ](?=[a-z]+\\b)'
ref: re.split('[ ](?=[A-Z]+\\b)', input)
Output: re.split('[a-zA-Z]', '<g+')
bleu_metric: 36.00172012681823
----------
NL: split string `input` at every space followed by an upper-case letter
ref: re.split('[ ](?=[A-Z])', input)
Output: re.split('\\g+', s)
bleu_metric: 24.270940595211844
----------
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: urllib.request.urlretrieve('http://www.com/go/')
bleu_metric: 8.688932567491825
----------
NL: write bytes `bytes_` to a file `filename` in python 3
ref: open('filename', 'wb').write(bytes_)
Output: f.write(open('filename.txt', 'wb')
bleu_metric: 55.95642461260143
----------
NL: get a list from a list `lst` with values mapped into a dictionary `dct`
ref: [dct[k] for k in lst]
Output: print([y for x in lst if 'id' in l])
bleu_metric: 11.75029694362029
----------
NL: find duplicate names in column 'name' of the dataframe `x`
ref: x.set_index('name').index.get_duplicates()
Output: df.groupby('name')['name'].index
bleu_metric: 34.899812526885086
----------
NL: truncate float 1.923328437452 to 3 decimal places
ref: round(1.923328437452, 3)
Output: '{0:.2f}'.format(13.95)
bleu_metric: 10.802314890908066
----------
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=lambda x: order.index(x[2], x[2]])
bleu_metric: 30.17923315990544
----------
NL: place the radial ticks in plot `ax` at 135 degrees
ref: ax.set_rlabel_position(135)
Output: ax.set_xlabel('30')
bleu_metric: 24.446151121745054
----------
NL: check if path `my_path` is an absolute path
ref: os.path.isabs(my_path)
Output: os.path.exists(my_path)
bleu_metric: 65.80370064762462
----------
NL: get number of keys in dictionary `yourdict`
ref: len(list(yourdict.keys()))
Output: len(dict_dict[key] for key in dict_dict)
bleu_metric: 17.194656088289214
----------
NL: count the number of keys in dictionary `yourdictfile`
ref: len(set(open(yourdictfile).read().split()))
Output: sum(1 for d in my_list if d.get('key', 1)])
bleu_metric: 9.449865252803164
----------
NL: pandas dataframe get first row of each group by 'id'
ref: df.groupby('id').first()
Output: df.groupby('id').first()
bleu_metric: 100.0
----------
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.apply(lambda x: df.split(',')[1:-1])
bleu_metric: 13.32809869264623
----------
NL: extract attributes 'src="js/([^"]*\\bjquery\\b[^"]*)"' from string `data`
ref: re.findall('src="js/([^"]*\\bjquery\\b[^"]*)"', data)
Output: re.findall('[a-zA-Z][\\w-]*\\.\\S*\\S*[^a-
bleu_metric: 22.84852347284557
----------
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(map(int, ['1', '3', '2', '3'])
bleu_metric: 15.327565138128369
----------
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('start /im | md5sum', shell=True)
bleu_metric: 6.258382435886439
----------
NL: reverse a priority queue `q` in python without using classes
ref: q.put((-n, n))
Output: (::2 > 0).objects.filter(id=123))
bleu_metric: 13.78592993183041
----------
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.plot(x, y, 'B')
bleu_metric: 1.7617020801492098
----------
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: print(re.findall('(?=(a.*?)\\b', '\\1', data))
bleu_metric: 29.06614574259767
----------
NL: get the length of list `my_list`
ref: len(my_list)
Output: len(my_list)
bleu_metric: 100.0
----------
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: 45.180100180492246
----------
NL: getting the length of `my_string`
ref: len(my_string)
Output: len(my_string)
bleu_metric: 100.0
----------
NL: remove escape character from string "\\a"
ref: """\\a""".decode('string_escape')
Output: print(' '.join(a)
bleu_metric: 14.244775721428216
----------
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 b""".replace('b', 'a', 'baz'))
bleu_metric: 34.4125014551951
----------
NL: remove directory tree '/folder_name'
ref: shutil.rmtree('/folder_name')
Output: os.makedirs(path)
bleu_metric: 18.094495256969623
----------
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: data['hour'] = df['Date'].apply(lambda x: x.year))
bleu_metric: 54.038193355462695
----------
NL: reverse sort counter `x` by values
ref: sorted(x, key=x.get, reverse=True)
Output: sorted(x.sort(x.c))
bleu_metric: 20.35024093540328
----------
NL: reverse sort counter `x` by value
ref: sorted(list(x.items()), key=lambda pair: pair[1], reverse=True)
Output: sorted(x, key=x.get)
bleu_metric: 8.425741883707015
----------
NL: append a numpy array 'b' to a numpy array 'a'
ref: np.vstack((a, b))
Output: a.reshape(a, -1, a1)
bleu_metric: 23.901088824528134
----------
NL: numpy concatenate two arrays `a` and `b` along the first axis
ref: print(concatenate((a, b), axis=0))
Output: print(a.shape[1, 3, 4])
bleu_metric: 15.325316503089068
----------
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(a.shape[1, 3, 4])
bleu_metric: 9.146501935450095
----------
NL: numpy concatenate two arrays `a` and `b` along the first axis
ref: np.array((a, b))
Output: print(a.shape[1, 3, 4])
bleu_metric: 15.911783110981517
----------
NL: fetch address information for host 'google.com' ion port 80
ref: print(socket.getaddrinfo('google.com', 80))
Output: socket.send('HTTP/1.0 200 OK')
bleu_metric: 15.850829785188195
----------
NL: add a column 'day' with value 'sat' to dataframe `df`
ref: df.xs('sat', level='day', drop_level=False)
Output: df['d'].apply(lambda x: 'Y'))
bleu_metric: 11.454635531478054
----------
NL: return a 401 unauthorized in django
ref: return HttpResponse('Unauthorized', status=401)
Output: return HttpResponse(status=204)
bleu_metric: 32.28559902619133
----------
NL: flask set folder 'wherever' as the default template folder
ref: Flask(__name__, template_folder='wherever')
Output: app.path.join(__file__, os.dirname(__file__), 'app'))
bleu_metric: 10.457995018881574
----------
NL: how do i insert into t1 (select * from t2) in sqlalchemy?
ref: session.execute('INSERT INTO t1 (SELECT * FROM t2)')
Output: db.session.commit()
bleu_metric: 9.01760872773444
----------
NL: sort a list of lists 'c2' such that third row comes first
ref: c2.sort(key=lambda row: row[2])
Output: sorted(lst, key=lambda x: x[2]))
bleu_metric: 32.22538601891173
----------
NL: sorting a list of lists in python
ref: c2.sort(key=lambda row: (row[2], row[1], row[0]))
Output: [sorted(item) for item in data]
bleu_metric: 3.9177831075306493
----------
NL: sorting a list of lists in python
ref: c2.sort(key=lambda row: (row[2], row[1]))
Output: [sorted(item) for item in data]
bleu_metric: 5.9428726447571005
----------
NL: set font `arial` to display non-ascii characters in matplotlib
ref: matplotlib.rc('font', **{'sans-serif': 'Arial', 'family': 'sans-serif'})
Output: plt.setp(loc=('\u2022', 'ignore')
bleu_metric: 7.494955959649734
----------
NL: convert  datetime column 'date' of pandas dataframe 'df' to ordinal
ref: df['date'].apply(lambda x: x.toordinal())
Output: df['date'] = df['date'].dt.strftime('%d%m%Y')
bleu_metric: 26.66090188234886
----------
NL: get html source of selenium webelement `element`
ref: element.get_attribute('innerHTML')
Output: driver.find_element_by_xpath("//*[@id='submit']/text()")
bleu_metric: 12.173115521158184
----------
NL: get the integer location of a key `bob` in a pandas data frame
ref: df.index.get_loc('bob')
Output: df.columns = df['state'].get_dict()
bleu_metric: 15.76777026245066
----------
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('start "$file"')
bleu_metric: 14.726128825673209
----------
NL: add an item with key 'third_key' and value 1 to an dictionary `my_dict`
ref: my_dict.update({'third_key': 1})
Output: default_dict.update({'key1': 1, 'position': 2})
bleu_metric: 35.95818888322724
----------
NL: declare an array
ref: my_list = []
Output: intarray = array('i')
bleu_metric: 15.619699684601276
----------
NL: insert item `12` to a list `my_list`
ref: my_list.append(12)
Output: my_list.insert(0, k)
bleu_metric: 24.446151121745054
----------
NL: add an entry 'wuggah' at the beginning of list `mylist`
ref: myList.insert(0, 'wuggah')
Output: a.append('b')
bleu_metric: 21.64910073203448
----------
NL: convert a hex-string representation to actual bytes
ref: """\\xF3\\xBE\\x80\\x80""".replace('\\x', '').decode('hex')
Output: binascii.unhexlify('\x00\x80')[2:]
bleu_metric: 4.9663342844865035
----------
NL: select the last column of dataframe `df`
ref: df[df.columns[-1]]
Output: df.drop(df.columns[-1]]
bleu_metric: 69.30977286178778
----------
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[df['column'] == 'B']
bleu_metric: 25.294817005134707
----------
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: np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
bleu_metric: 53.858386640481605
----------
NL: get the type of `i`
ref: type(i)
Output: print(isinstance(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: print(get_name)
bleu_metric: 24.795364698947967
----------
NL: get the 5th item of a generator
ref: next(itertools.islice(range(10), 5, 5 + 1))
Output: next(it)
bleu_metric: 2.7474047213893544
----------
NL: print a string `word` with string format
ref: print('"{}"'.format(word))
Output: print('Hello, {0}'.format(word))
bleu_metric: 59.978201631280236
----------
NL: join a list of strings `list` using a space ' '
ref: """ """.join(list)
Output: [')','','','','','','','','','','','','','', '
bleu_metric: 5.987016463560546
----------
NL: create list `y` containing two empty lists
ref: y = [[] for n in range(2)]
Output: listy = [[] for i in range(3)]
bleu_metric: 49.35578819979933
----------
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: data = [r[i] for i in open('myfile.txt')]
bleu_metric: 20.807540769186392
----------
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', '', 'I', 'it is icy')
bleu_metric: 22.408895662889165
----------
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: re.sub('[^i]', '', 'I', text)
bleu_metric: 22.722445488928127
----------
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([i for i in range(100) if i!= 'i'])
bleu_metric: 59.20060650358378
----------
NL: drop rows of pandas dataframe `df` having nan in column at index "1"
ref: df.dropna(subset=[1])
Output: df.drop(df.index[[1, 3]], inplace=True)
bleu_metric: 12.91802583371725
----------
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: 12.58220555845937
----------
NL: converting list of strings `intstringlist` to list of integer `nums`
ref: nums = [int(x) for x in intstringlist]
Output: [int(x) for x in intstringlist]
bleu_metric: 83.9234989944319
----------
NL: convert list of string numbers into list of integers
ref: map(int, eval(input('Enter the unfriendly numbers: ')))
Output: [int(i) for i in str_list]
bleu_metric: 7.290792403968443
----------
NL: print "." without newline
ref: sys.stdout.write('.')
Output: print('\n'.join(str(x))
bleu_metric: 15.911783110981517
----------
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(math.pack('100', struct.pack('100', int(2)))
bleu_metric: 12.920725243713852
----------
NL: find all files in directory "/mydir" with extension ".txt"
ref: for file in glob.glob('*.txt'):
    pass
Output: results += [each for each in os.listdir('myfile.txt') if each.endswith('.txt'):
 
bleu_metric: 17.995816286711673
----------
NL: find all files in directory "/mydir" with extension ".txt"
ref: for file in os.listdir('/mydir'):
    if file.endswith('.txt'):
        pass
Output: results += [each for each in os.listdir('myfile.txt') if each.endswith('.txt'):
 
bleu_metric: 46.726782846422246
----------
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: results += [each for each in os.listdir('myfile.txt') if each.endswith('.txt'):
 
bleu_metric: 31.01992438627778
----------
NL: plot dataframe `df` without a legend
ref: df.plot(legend=False)
Output: plt.legend(loc=2, fontsize='y')
bleu_metric: 13.380161378318956
----------
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: for i in range(0, 10)
bleu_metric: 2.18006837039135
----------
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: for i in range(0, 10)
bleu_metric: 3.8730052084654263
----------
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: for i in range(0, 10)
bleu_metric: 2.8194161881384923
----------
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: sum(map(int, x))
bleu_metric: 10.55672713033355
----------
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: f.write('\n')
bleu_metric: 8.523846965585486
----------
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: [item for sublist in l for item in sublist]
bleu_metric: 6.735737120156013
----------
NL: print new line character as `\n` in a string `foo\nbar`
ref: print('foo\nbar'.encode('string_escape'))
Output: print('\n'.join(parts))
bleu_metric: 23.92121113361141
----------
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: x = np.mean(a, axis=1)
bleu_metric: 9.241605197022109
----------
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.mean(np.arange(len(x)) + 3).mean(axis=1))
bleu_metric: 7.6047020452233385
----------
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: data = numpy.loadtxt('file.txt', 'utf-8')
bleu_metric: 25.88323054958255
----------
NL: reverse sort list of dicts `l` by value for key `time`
ref: l = sorted(l, key=itemgetter('time'), reverse=True)
Output: sorted(lst, key=lambda d: (d['time'], d['time'], d['time'], reverse=True
bleu_metric: 17.7946690947194
----------
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: order['time'], reverse=True)
bleu_metric: 80.40514736345938
----------
NL: get rows of dataframe `df` that match regex '(hel|just)'
ref: df.loc[df[0].str.contains('(Hel|Just)')]
Output: df[df.str.startswith('(?=.)', '|#')]
bleu_metric: 29.906271972271124
----------
NL: find the string in `your_string` between two special characters "[" and "]"
ref: re.search('\\[(.*)\\]', your_string).group(1)
Output: re.search('(.*?)\\[^\\w\\s]', my_string)
bleu_metric: 35.5402294764931
----------
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: pd.to_datetime(str(d.values, '%Y-%m-%dT%H:%M:%
bleu_metric: 12.00990216299384
----------
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: """ """.join(['Pplit', 'aaa'])
bleu_metric: 31.276700211004304
----------
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('file.txt', filename)
bleu_metric: 15.53712569276035
----------
NL: split string `text` by space
ref: text.split()
Output: text = re.split('\\s+')
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 item in mylist]
bleu_metric: 14.283632578659287
----------
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(list_of_tuples, key=lambda s: s.split('_')[1]))
bleu_metric: 70.64070210210579
----------
NL: call multiple bash function 'vasp' and 'tee tee_output' using '|'
ref: subprocess.check_call('vasp | tee tee_output', shell=True)
Output: subprocess.call('zsh -c "echo $0'")
bleu_metric: 15.384750052099497
----------
NL: eliminate all strings from list `lst`
ref: [element for element in lst if isinstance(element, int)]
Output: """""".join(lst)
bleu_metric: 9.980554701686325
----------
NL: get all the elements except strings from the list 'lst'.
ref: [element for element in lst if not isinstance(element, str)]
Output: [x for x in lst if x[1] == 'C' in x]
bleu_metric: 15.047149445189383
----------
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 k: k['name']))
bleu_metric: 73.36726069437738
----------
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(d, key=operator.itemgetter('name', 'age'))
bleu_metric: 35.3774707378071
----------
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.sort(key=operator.itemgetter('name', 'age'))
bleu_metric: 69.91461503583824
----------
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.sort(key=operator.itemgetter('name', 'age'))
bleu_metric: 69.91461503583824
----------
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.sort_values(['Peak', 'Weeks'], ascending=[True, True])
bleu_metric: 15.057522385331268
----------
NL: join together with "," elements inside a list indexed with 'category' within a dictionary `trans`
ref: """,""".join(trans['category'])
Output: [item['one'] for item in list_of_dicts]
bleu_metric: 11.133037663423181
----------
NL: concatenate array of strings `['a', 'b', 'c', 'd']` into a string
ref: """""".join(['A', 'B', 'C', 'D'])
Output: """""".join(['a', 'b', 'c', 'd'])
bleu_metric: 61.82527625446008
----------
NL: get json data from restful service 'url'
ref: json.load(urllib.request.urlopen('url'))
Output: print(json.dumps(url, data=data))
bleu_metric: 18.76960278961121
----------
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('@$\t') and not x.startswith('#')]
Output: [x for x in my_list if x.startswith('#')]
bleu_metric: 36.8758127348874
----------
NL: django filter by hour
ref: Entry.objects.filter(pub_date__contains='08:00')
Output: Entry.objects.filter(created__year='2011', {'_date': '2011'})
bleu_metric: 30.05180557366462
----------
NL: sort a list of dictionary `list` first by key `points` and then by `time`
ref: list.sort(key=lambda item: (item['points'], item['time']))
Output: list.sort(key=lambda x: x['time'])
bleu_metric: 40.633530232106814
----------
NL: convert datetime object `(1970, 1, 1)` to seconds
ref: (t - datetime.datetime(1970, 1, 1)).total_seconds()
Output: datetime.datetime.now().strftime('%H:%M:%S')
bleu_metric: 15.415064977510756
----------
NL: insert `_suff` before the file extension in `long.file.name.jpg` or replace `_a` with `suff` if it precedes the extension.
ref: re.sub('(\\_a)?\\.([^\\.]*)$', '_suff.\\2', 'long.file.name.jpg')
Output: os.path.splitext('/home/to/somefile.txt')
bleu_metric: 3.7217110227905947
----------
NL: reload a module `module`
ref: import imp
imp.reload(module)
Output: importlib.import_module('foo.b')
bleu_metric: 17.03318603763928
----------
NL: convert integer `number` into an unassigned integer
ref: struct.unpack('H', struct.pack('h', number))
Output: int('1 + str(n)[-1]
bleu_metric: 9.01224794878171
----------
NL: convert int values in list `numlist` to float
ref: numlist = [float(x) for x in numlist]
Output: float(map(int, ['1', '3'], ['0', '4']])
bleu_metric: 7.039774105336835
----------
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: 100.0
----------
NL: convert a urllib unquoted string `unescaped` to a json data `json_data`
ref: json_data = json.loads(unescaped)
Output: json.dumps({'hi': 'foo'})
bleu_metric: 14.247788801610147
----------
NL: create a list containing all ascii characters as its elements
ref: [chr(i) for i in range(127)]
Output: [int(x) for x in list('\u0420', '\u0420']
bleu_metric: 11.71291646655074
----------
NL: write `newfilebytes` to a binary file `newfile`
ref: newFile.write(struct.pack('5B', *newFileBytes))
Output: open('file', 'wb').write('\n'.join(write))
bleu_metric: 16.973628531343277
----------
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('[^A-Z][\\w-]*', '', s)
bleu_metric: 31.75248595372921
----------
NL: get the last key of dictionary `dict`
ref: list(dict.keys())[-1]
Output: max(dict.keys())
bleu_metric: 50.17507694885614
----------
NL: write line "hi there" to file `f`
ref: print('hi there', file=f)
Output: f.write('hi there\n')
bleu_metric: 35.41698766144061
----------
NL: write line "hi there" to file `myfile`
ref: f = open('myfile', 'w')
f.write('hi there\n')
f.close()
Output: print('myfile.txt', open('myfile.txt'))
bleu_metric: 16.098380502753933
----------
NL: write line "hello" to file `somefile.txt`
ref: with open('somefile.txt', 'a') as the_file:
    the_file.write('Hello\n')
Output: f.write('foo\n')
bleu_metric: 14.805399561478843
----------
NL: convert unicode string `s` to ascii
ref: s.encode('iso-8859-15')
Output: s.decode('ascii', 'ignore')
bleu_metric: 24.022110864391532
----------
NL: django get maximum value associated with field 'added' in model `authorizedemail`
ref: AuthorizedEmail.objects.filter(group=group).order_by('-added')[0]
Output: MyModel.objects.filter(userprofile__in=['title', 'Bob'])
bleu_metric: 23.364722604693753
----------
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+', '', re.findall('\\d+', s))
bleu_metric: 20.563241139732103
----------
NL: python regex  to find all numbers and dots from 'text'
ref: re.findall('Test([\\d.]*\\d+)', text)
Output: re.findall('\\d+', s)
bleu_metric: 27.703746325324747
----------
NL: execute script 'script.ps1' using 'powershell.exe' shell
ref: os.system('powershell.exe', 'script.ps1')
Output: subprocess.call('start "$file.py', shell=True)
bleu_metric: 14.400124446705304
----------
NL: sort a list of tuples `b` by third item in the tuple
ref: b.sort(key=lambda x: x[1][2])
Output: sorted(b, key=lambda x: x[1])
bleu_metric: 56.20639922239585
----------
NL: get a list of all keys in cassandra database `cf` with pycassa
ref: list(cf.get_range().get_keys())
Output: df.query('foo')
bleu_metric: 13.532330504290599
----------
NL: create a datetime with the current date & time
ref: datetime.datetime.now()
Output: datetime.datetime.now().isoformat()
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: [i for i, x in enumerate(lst) if x == 0]
bleu_metric: 42.57822397135907
----------
NL: subtract 13 from every number in a list `a`
ref: a[:] = [(x - 13) for x in a]
Output: [(x - x) for x in a]
bleu_metric: 51.44168902788183
----------
NL: choose a random file from the directory contents of the c drive, `c:\\`
ref: random.choice(os.listdir('C:\\'))
Output: os.path.join(os.path.join(dir, '..'))
bleu_metric: 23.41812326184747
----------
NL: get the highest element in absolute value in a numpy matrix `x`
ref: max(x.min(), x.max(), key=abs)
Output: max(x.max(), x.max())
bleu_metric: 56.52866525800724
----------
NL: get all urls within text `s`
ref: re.findall('"(http.*?)"', s, re.MULTILINE | re.DOTALL)
Output: re.findall('\\b(?:[^\\w+)\\s]', s)
bleu_metric: 24.180681260144148
----------
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.compile('^[^a-zA-Z0-9-]*\\.\\.com/', my_string
bleu_metric: 12.913533075470381
----------
NL: split a string `mystring` considering the spaces ' '
ref: mystring.replace(' ', '! !').split('!')
Output: re.split('\\s+', myString)
bleu_metric: 21.02813554778581
----------
NL: open file `path` with mode 'r'
ref: open(path, 'r')
Output: os.path.join(path)
bleu_metric: 23.356898886410004
----------
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(i) for i in zip(*data)]
bleu_metric: 33.38084565798161
----------
NL: add a new axis to array `a`
ref: a[:, (np.newaxis)]
Output: ax.set_zlabel('a')
bleu_metric: 14.873743701255318
----------
NL: send a signal `signal.SIGUSR1` to the current process
ref: os.kill(os.getpid(), signal.SIGUSR1)
Output: sys.stdout.flush()
bleu_metric: 11.578838804156227
----------
NL: decode a hex string '4a4b4c' to UTF-8.
ref: bytes.fromhex('4a4b4c').decode('utf-8')
Output: print(str(a.encode('hex'))
bleu_metric: 13.68218676135829
----------
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: """{:,}""".format(a)
bleu_metric: 7.039722936059098
----------
NL: How to convert a string from CP-1251 to UTF-8?
ref: d.decode('cp1251').encode('utf8')
Output: d.decode('utf8')
bleu_metric: 44.205744126521374
----------
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: {i: v for i, v in list(d.items()) if v > 0}
bleu_metric: 45.78553260521078
----------
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: {i: v for i, v in list(d.items()) if v > 0}
bleu_metric: 23.068994361104984
----------
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('echo "foo"', shell=True)
bleu_metric: 33.54121358422117
----------
NL: concatenate a list of strings `['a', 'b', 'c']`
ref: """""".join(['a', 'b', 'c'])
Output: print(('a', 'b', 'c'))
bleu_metric: 42.80768209469581
----------
NL: find intersection data between series `s1` and series `s2`
ref: pd.Series(list(set(s1).intersection(set(s2))))
Output: s1 = pd.DataFrame(s1, s2=None)
bleu_metric: 10.722703083793286
----------
NL: sending http headers to `client`
ref: client.send('HTTP/1.0 200 OK\r\n')
Output: s.send('HTTP/1.0 200 OK\r\n')
bleu_metric: 94.57416090031758
----------
NL: Format a datetime string `when` to extract date only
ref: then = datetime.datetime.strptime(when, '%Y-%m-%d').date()
Output: datetime.strptime('2012-11-13 10:33:54.227806', '%Y-%m-%d
bleu_metric: 44.26225967370315
----------
NL: split a multi-line string `inputString` into separate strings
ref: inputString.split('\n')
Output: a, b, c = input('string_escape')
bleu_metric: 21.972813874997158
----------
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: line.rstrip('\n\n')
bleu_metric: 27.97842714002644
----------
NL: concatenate elements of list `b` by a colon ":"
ref: """:""".join(str(x) for x in b)
Output: print(repr(a))
bleu_metric: 4.9422115544674705
----------
NL: get the first object from a queryset in django model `Entry`
ref: Entry.objects.filter()[:1].get()
Output: Entry.objects.get_values('id')
bleu_metric: 23.929967984761056
----------
NL: Calculate sum over all rows of 2D numpy array
ref: a.sum(axis=1)
Output: np.sum(a.shape[1:])
bleu_metric: 24.022110864391532
----------
NL: enable warnings using action 'always'
ref: warnings.simplefilter('always')
Output: os.kill(logging.DEBUG)
bleu_metric: 21.105340631872636
----------
NL: concatenate items of list `l` with a space ' '
ref: print(' '.join(map(str, l)))
Output: print(' '.join(map(str, l))
bleu_metric: 91.69729150999534
----------
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('start "$file.py', shell=True)
bleu_metric: 24.391589282046496
----------
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.strftime('%H:%M:%S,%f'))
bleu_metric: 19.792633055621124
----------
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: 62.69839476367552
----------
NL: convert a string `123,456.908` with dot and comma into a floating number
ref: float('123,456.908'.replace(',', ''))
Output: print(', '.join(str(12345))
bleu_metric: 21.93127055132894
----------
NL: set pythonpath in python script.
ref: sys.path.append('/path/to/whatever')
Output: os.path.abspath(__file__)
bleu_metric: 14.91608246051739
----------
NL: split string 'Words, words, words.' using a regex '(\\W+)'
ref: re.split('(\\W+)', 'Words, words, words.')
Output: re.split('\\b(\\w+)', 'Words, words, words.')
bleu_metric: 71.4022021970798
----------
NL: open a file `Output.txt` in append mode
ref: file = open('Output.txt', 'a')
Output: f = open('file.txt', 'r')
bleu_metric: 54.7332024239071
----------
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://randomsite.com/', 'rb').read()
bleu_metric: 45.9127788668896
----------
NL: download a file `url` over HTTP and save to `file_name`
ref: u = urllib.request.urlopen(url)
Output: urllib.request.urlretrieve('http://example.com/file.gz')
bleu_metric: 19.228544753133757
----------
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.com/file.com/file.gz')
bleu_metric: 41.91356463630533
----------
NL: download a file `url` over HTTP
ref: r = requests.get(url)
Output: r = requests.get(url)
bleu_metric: 100.0
----------
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.com/file.gz')
bleu_metric: 7.265857755970445
----------
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('--conf', nargs='*')
bleu_metric: 24.648885368725924
----------
NL: remove key 'c' from dictionary `d`
ref: {i: d[i] for i in d if i != 'c'}
Output: d = {'a': 1, 'c': 2, 'c': 3, 'c': 3}
bleu_metric: 10.884267615693712
----------
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: pd.concat([df_1, df_2.columns], axis=1, on=['A_B', 'B'
bleu_metric: 24.624806058504372
----------
NL: Split a string `s` by space with `4` splits
ref: s.split(' ', 4)
Output: [s.split('\t')[-1] for s in s.split()]
bleu_metric: 19.696330878717074
----------
NL: read keyboard-input
ref: input('Enter your input:')
Output: sys.stdout.flush()
bleu_metric: 17.6625510283176
----------
NL: enable debug mode on Flask application `app`
ref: app.run(debug=True)
Output: app.run(debug=True)
bleu_metric: 100.0
----------
NL: python save list `mylist` to file object 'save.txt'
ref: pickle.dump(mylist, open('save.txt', 'wb'))
Output: file.write('myfile.txt', 'wb')
bleu_metric: 42.60520663282873
----------
NL: Multiply a matrix `P` with a 3d tensor `T` in scipy
ref: scipy.tensordot(P, T, axes=[1, 1]).swapaxes(0, 1)
Output: np.dot(x, y)
bleu_metric: 4.172184841899331
----------
NL: Create 3d array of zeroes of size `(3,3,3)`
ref: numpy.zeros((3, 3, 3))
Output: np.zeros((3, 3))
bleu_metric: 75.33808072882879
----------
NL: cut off the last word of a sentence `content`
ref: """ """.join(content.split(' ')[:-1])
Output: """ """.join(s.split())
bleu_metric: 46.40451719480771
----------
NL: convert scalar `x` to array
ref: x = np.asarray(x).reshape(1, -1)[(0), :]
Output: np.zeros((x, x) for x in s)
bleu_metric: 9.777229794850028
----------
NL: sum all elements of nested list `L`
ref: sum(sum(i) if isinstance(i, list) else i for i in L)
Output: sum(sum(x) for x in list)
bleu_metric: 18.410840441136763
----------
NL: convert hex string '470FC614' to a float number
ref: struct.unpack('!f', '470FC614'.decode('hex'))[0]
Output: struct.unpack('d', struct.pack('Q', 16)).decode('hex'))
bleu_metric: 43.149269702667276
----------
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 my_dict.items())
bleu_metric: 28.26550613903659
----------
NL: running bash script 'sleep.sh'
ref: subprocess.call('sleep.sh', shell=True)
Output: subprocess.call('start command -c 'r', shell=True)
bleu_metric: 55.7853798004205
----------
NL: Join elements of list `l` with a comma `,`
ref: """,""".join(l)
Output: [(x + x) for x, y in zip(l1, l2)]
bleu_metric: 8.039313477786735
----------
NL: make a comma-separated string from a list `myList`
ref: myList = ','.join(map(str, myList))
Output: myList = mylist[:n]
bleu_metric: 10.218289380194193
----------
NL: reverse the list that contains 1 to 10
ref: list(reversed(list(range(10))))
Output: L[::-1]
bleu_metric: 7.575149194183216
----------
NL: remove substring 'bag,' from a string 'lamp, bag, mirror'
ref: print('lamp, bag, mirror'.replace('bag,', ''))
Output: [m.strip() for m in x if m.endswith('@')]
bleu_metric: 10.616735292999413
----------
NL: Reverse the order of words, delimited by `.`, in string `s`
ref: """.""".join(s.split('.')[::-1])
Output: sorted(s, s.split())
bleu_metric: 9.489232285686615
----------
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: print(s.strftime('%Y-%m-%d %H:%M:%S'))
bleu_metric: 62.56418230541474
----------
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: time.strftime('%Y-%m-%d %H:%M:%S')
bleu_metric: 59.71758060527691
----------
NL: get the date 7 days before the current date
ref: (datetime.datetime.now() - datetime.timedelta(days=7)).date()
Output: (datetime.date(2010, 12, 31) + relativedelta(months=(+ 1)))
bleu_metric: 23.713320246552005
----------
NL: sum elements at index `column` of each list in list `data`
ref: print(sum(row[column] for row in data))
Output: data[sum(b) for b in zip(*data)]
bleu_metric: 15.844501337268929
----------
NL: sum columns of a list `array`
ref: [sum(row[i] for row in array) for i in range(len(array[0]))]
Output: sum(sum(row[:, (1)] for row in mylist)
bleu_metric: 21.03312756130989
----------
NL: encode binary string 'your string' to base64 code
ref: base64.b64encode(bytes('your string', 'utf-8'))
Output: print(content.encode('base64'))
bleu_metric: 20.291424899466545
----------
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()) if k in d]
bleu_metric: 28.548259613272815
----------
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 k, v in list(d.items()))
bleu_metric: 8.608775825594615
----------
NL: How do I get the url parameter in a Flask view
ref: request.args['myParam']
Output: request.url
bleu_metric: 6.856953606192477
----------
NL: identify duplicate values in list `mylist`
ref: [k for k, v in list(Counter(mylist).items()) if v > 1]
Output: set(mylist))
bleu_metric: 2.0605095345578897
----------
NL: Insert directory 'apps' into directory `__file__`
ref: sys.path.insert(1, os.path.join(os.path.dirname(__file__), 'apps'))
Output: os.chdir(path)
bleu_metric: 1.4431184819037772
----------
NL: modify sys.path for python module `subdir`
ref: sys.path.append(os.path.join(os.path.dirname(__file__), 'subdir'))
Output: my_path.append('/path/to/my_file.txt')
bleu_metric: 10.285502024406256
----------
NL: Insert a 'None' value into a SQLite3 table.
ref: db.execute("INSERT INTO present VALUES('test2', ?, 10)", (None,))
Output: cursor.execute('INSERT INTO table VALUES (%s, %s)', (None,))
bleu_metric: 53.301554843359774
----------
NL: flatten list `list_of_menuitems`
ref: [image for menuitem in list_of_menuitems for image in menuitem]
Output: list_of_lists = [list(f) for f in my_list]
bleu_metric: 14.949751774990682
----------
NL: append elements of a set `b` to a list `a`
ref: a.extend(b)
Output: a.append(b)
bleu_metric: 59.46035575013605
----------
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.write.txt', header=None, delimiter='\t')
bleu_metric: 17.503492126188856
----------
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\\data\\data\\t.txt', sep='\t')
bleu_metric: 46.606880690561546
----------
NL: Split a string `x` by last occurrence of character `-`
ref: print(x.rpartition('-')[0])
Output: x.split('=')[-1].rsplit()
bleu_metric: 21.468316165048364
----------
NL: get the last part of a string before the character '-'
ref: print(x.rsplit('-', 1)[0])
Output: print(x.rfind('-')[0])
bleu_metric: 60.109065739327065
----------
NL: upload file using FTP
ref: ftp.storlines('STOR ' + filename, open(filename, 'r'))
Output: webbrowser.open('http://example.com/file.gz')
bleu_metric: 12.9205540673015
----------
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.find_element_by_xpath("//li/label/input[contains(..,'polishpottery')]")
bleu_metric: 13.25451214611751
----------
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], [2, 3, 4], [3, 4, 5]])
bleu_metric: 42.651255482235264
----------
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[:5] + l[0])
bleu_metric: 50.02460338549947
----------
NL: loop over files in directory '.'
ref: for fn in os.listdir('.'):
    if os.path.isfile(fn):
        pass
Output: for os.path.join(path, filename)
bleu_metric: 13.004471619759379
----------
NL: loop over files in directory `source`
ref: for (root, dirs, filenames) in os.walk(source):
    for f in filenames:
        pass
Output: for os.path.join(path, filename)
bleu_metric: 7.344980037582256
----------
NL: create a random list of integers
ref: [int(1000 * random.random()) for i in range(10000)]
Output: randomList = [random.random() for _ in range(10)]
bleu_metric: 34.3414769312466
----------
NL: Using %f with strftime() in Python to get microseconds
ref: datetime.datetime.now().strftime('%H:%M:%S.%f')
Output: datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
bleu_metric: 65.15366202853991
----------
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: G.execute('SELECT * FROM foo WHERE bar = %s AND baz = %s', (param1, param2))
bleu_metric: 24.090844358935918
----------
NL: filter rows in pandas starting with alphabet 'f' using regular expression.
ref: df.b.str.contains('^f')
Output: df.dropna(lambda x: x.replace('\\[^\\w\\s]', 'A')
bleu_metric: 10.727295782787309
----------
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: 26.894705347686966
----------
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[[1, 3]], axis=1)
bleu_metric: 11.446641525222896
----------
NL: format the variables `self.goals` and `self.penalties` using string formatting
ref: """({:d} goals, ${:d})""".format(self.goals, self.penalties)
Output: """{0} {1}""".format(name='bar', 'change!')
bleu_metric: 28.1073423324667
----------
NL: format string "({} goals, ${})" with variables `goals` and `penalties`
ref: """({} goals, ${})""".format(self.goals, self.penalties)
Output: """{} {}""".format(TotalAmount)
bleu_metric: 30.199592877917368
----------
NL: format string "({0.goals} goals, ${0.penalties})"
ref: """({0.goals} goals, ${0.penalties})""".format(self)
Output: """{0} {1}""".format(name, 20)
bleu_metric: 32.37295639418319
----------
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: 26.91716452870884
----------
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: int(''.join(map(str, x)))
bleu_metric: 25.049995525110475
----------
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: 18.058610039563465
----------
NL: write the elements of list `lines` concatenated by special character '\n' to file `myfile`
ref: myfile.write('\n'.join(lines))
Output: with open('myfile.txt', 'w').read().write('\n')
bleu_metric: 30.77874158297155
----------
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 L if x is not None]
bleu_metric: 7.979312731505724
----------
NL: Remove duplicate words from a string `text` using regex
ref: text = re.sub('\\b(\\w+)( \\1\\b)+', '\\1', text)
Output: re.sub('(?<=[a-z])\\r?\\w+', '\\1', s)
bleu_metric: 43.796510807866646
----------
NL: count non zero values in each column in pandas data frame
ref: df.astype(bool).sum(axis=1)
Output: df.apply(lambda x: 0).sum() if sum(x) > 1)
bleu_metric: 23.20305803246989
----------
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: re.search('(?<=\\S+)', re.compile('\\.\\s+(?:\\.\\d+)', re
bleu_metric: 30.06266967828333
----------
NL: split string 'QH QD JC KD JS' into a list on white spaces
ref: """QH QD JC KD JS""".split()
Output: [s.strip() for s in re.split(',')]
bleu_metric: 14.962848372546667
----------
NL: search for occurrences of regex pattern '>.*<' in xml string `line`
ref: print(re.search('>.*<', line).group(0))
Output: print(re.search('(?<=\\w+)', line))
bleu_metric: 47.71512105691513
----------
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.strptime('2010-11-13 10:33:54.283Z', '%Y-%m-%d
bleu_metric: 40.88633662916028
----------
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: [i for i, v in enumerate(L) if v[0] == 'id' in x]
bleu_metric: 33.034466035625115
----------
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-zA-Z0-9]','', text)
bleu_metric: 64.45968952353843
----------
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-Z0-9]', '', text)
bleu_metric: 34.05969875105766
----------
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.show()
bleu_metric: 6.117046115680378
----------
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.show()
bleu_metric: 6.117046115680378
----------
NL: loop over a list `mylist` if sublists length equals 3
ref: [x for x in mylist if len(x) == 3]
Output: [(x + 1) for x in mylist]
bleu_metric: 27.97803653491597
----------
NL: initialize a list `lst` of 100 objects Object()
ref: lst = [Object() for _ in range(100)]
Output: [item for item in lst]
bleu_metric: 11.786767588753086
----------
NL: create list `lst` containing 100 instances of object `Object`
ref: lst = [Object() for i in range(100)]
Output: [x for x in lst if x is not None]
bleu_metric: 13.386745323687673
----------
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_all('a', {'id':'st,'st_name':'st/content': 'content'}
bleu_metric: 7.478822222889464
----------
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: s1 = pd.merge(df1, df2, how='inner', on=['user_id'])
bleu_metric: 17.17218426146756
----------
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('{0}'.format(num))
bleu_metric: 5.68097120736594
----------
NL: Split a string by a delimiter in python
ref: [x.strip() for x in '2.MATCHES $$TEXT$$ STRING'.split('$$TEXT$$')]
Output: a.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: if (not os.path.exists(directory)):
    os.makedirs(directory)
bleu_metric: 100.0
----------
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('\\b(?<!\\w+)', '\\1', text)
bleu_metric: 45.79927441960449
----------
NL: substitute ASCII letters in string 'aas30dsa20' with empty string ''
ref: re.sub('\\D', '', 'aas30dsa20')
Output: """{0}""".format(a)
bleu_metric: 13.681843708157734
----------
NL: get digits only from a string `aas30dsa20` using lambda function
ref: """""".join([x for x in 'aas30dsa20' if x.isdigit()])
Output: map(int, mystring.split(' ', 1)[0]
bleu_metric: 7.1335710240663
----------
NL: access a tag called "name" in beautifulsoup `soup`
ref: print(soup.find('name').string)
Output: soup.findAll(text='Python')
bleu_metric: 22.779746815932338
----------
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((k, v) for k, v in {'class': {'Foo': 'k': {'Foo': '}}
bleu_metric: 8.882389583994751
----------
NL: Create new matrix object  by concatenating data from matrix A and matrix B
ref: np.concatenate((A, B))
Output: A = np.dot(a, b, c)
bleu_metric: 17.77835117834348
----------
NL: concat two matrices `A` and `B` in numpy
ref: np.vstack((A, B))
Output: np.concatenate((a, b))
bleu_metric: 29.502343631964045
----------
NL: Get the characters count in a file `filepath`
ref: os.stat(filepath).st_size
Output: sum(os.path.join(path)
bleu_metric: 17.827531042796256
----------
NL: count the occurrences of item "a" in list `l`
ref: l.count('a')
Output: Counter(x in a for x in a)
bleu_metric: 17.03318603763928
----------
NL: count the occurrences of items in list `l`
ref: Counter(l)
Output: Counter(l)
bleu_metric: 100.0
----------
NL: count the occurrences of items in list `l`
ref: [[x, l.count(x)] for x in set(l)]
Output: Counter(l)
bleu_metric: 2.190091937500515
----------
NL: count the occurrences of items in list `l`
ref: dict(((x, l.count(x)) for x in set(l)))
Output: Counter(l)
bleu_metric: 1.5775358510206974
----------
NL: count the occurrences of item "b" in list `l`
ref: l.count('b')
Output: Counter(x in a for x in set(l)]
bleu_metric: 11.114924776032007
----------
NL: copy file `srcfile` to directory `dstdir`
ref: shutil.copy(srcfile, dstdir)
Output: shutil.copyfile(file_file, file)
bleu_metric: 25.406637407730738
----------
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(a, key=lambda x: x[x])
bleu_metric: 10.970270231176839
----------
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(k, v)
bleu_metric: 8.44727061998314
----------
NL: Put the curser at beginning of the file
ref: file.seek(0)
Output: subprocess.call('mv /Foo/', 'r', shell=True)
bleu_metric: 9.134423666564473
----------
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['b'].fillna(df['a'])
bleu_metric: 50.85465237405449
----------
NL: remove key 'ele' from dictionary `d`
ref: del d['ele']
Output: d = {key: val for key, val in list(d.items()) if val!= 42}
bleu_metric: 5.166045454134253
----------
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: dt = datetime.datetime.now().strftime('%Y-%m-%d')
bleu_metric: 10.316767912798015
----------
NL: merge list `['it']` and list `['was']` and list `['annoying']` into one list
ref: ['it'] + ['was'] + ['annoying']
Output: ['a', 'b', 'c', 'd']
bleu_metric: 19.408623270627203
----------
NL: increment a value with leading zeroes in a number `x`
ref: str(int(x) + 1).zfill(len(x))
Output: x ='+ str(x)
bleu_metric: 13.057811073335992
----------
NL: check if a pandas dataframe `df`'s index is sorted
ref: all(df.index[:-1] <= df.index[1:])
Output: df.sort_index()
bleu_metric: 3.1405961937021267
----------
NL: Convert tuple `t` to list
ref: list(t)
Output: tuple(t)
bleu_metric: 75.98356856515926
----------
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: list1 = [item[0] for item in L]
bleu_metric: 10.571070857151538
----------
NL: send the output of pprint object `dataobject` to file `logFile`
ref: pprint.pprint(dataobject, logFile)
Output: print(data.read())
bleu_metric: 18.885888592159468
----------
NL: get index of rows in column 'BoolCol'
ref: df.loc[df['BoolCol']]
Output: df.index
bleu_metric: 2.5225322607845957
----------
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.groupby('id').apply(lambda x: x.sum() == 'foo'))
bleu_metric: 7.779637090949697
----------
NL: get list of indexes of rows where column 'BoolCol' values match True
ref: df[df['BoolCol'] == True].index.tolist()
Output: [df.index for index, g in enumerate(tuple, values)]
bleu_metric: 11.679849482425555
----------
NL: get index of rows in dataframe `df` which column 'BoolCol' matches value True
ref: df[df['BoolCol']].index.tolist()
Output: df.loc[df['BoolCol'].isin(['id', 'Fruit']).tolist()])
bleu_metric: 35.63899712694302
----------
NL: change working directory to the directory `owd`
ref: os.chdir(owd)
Output: os.chdir(os.path.dirname(os.path.abspath(__file__))
bleu_metric: 19.554016376461796
----------
NL: insert data from a string `testfield` to sqlite db `c`
ref: c.execute("INSERT INTO test VALUES (?, 'bar')", (testfield,))
Output: cursor.execute('SELECT * FROM table WHERE bar" FROM table WHERE column IN %s;+$', (1,))
bleu_metric: 24.03479325641683
----------
NL: decode string "\\x89\\n" into a normal string
ref: """\\x89\\n""".decode('string_escape')
Output: """\\xc3\\x85あ""".encode('utf-8')
bleu_metric: 41.902845957302084
----------
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: 65.80370064762462
----------
NL: convert a raw string `raw_byte_string` into a normal string
ref: raw_byte_string.decode('unicode_escape')
Output: raw_string.decode('unicode_escape')
bleu_metric: 88.01117367933934
----------
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('\\w+', s)
bleu_metric: 10.45716776939666
----------
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.scatter(np.random.randn(100), np.random.randn(100), np.randn(100
bleu_metric: 57.932844496915834
----------
NL: remove a div with a id `main-content` using beautifulsoup
ref: soup.find('div', id='main-content').decompose()
Output: soup.findAll(text='div', id='width=True)
bleu_metric: 40.78097038402287
----------
NL: filter rows containing key word `ball` in column `ids`
ref: df[df['ids'].str.contains('ball')]
Output: print(df.query('User')['value'].filter(lambda x: x.year))
bleu_metric: 13.348189438082967
----------
NL: convert index at level 0 into a column in dataframe `df`
ref: df.reset_index(level=0, inplace=True)
Output: df.set_index([' 0', '0'])
bleu_metric: 15.310245441182444
----------
NL: Add indexes in a data frame `df` to a column `index1`
ref: df['index1'] = df.index
Output: df.set_index(['Company', 'date'])
bleu_metric: 15.911783110981517
----------
NL: convert pandas index in a dataframe to columns
ref: df.reset_index(level=['tick', 'obs'])
Output: df2 = df.set_index(['T', 'level2'])
bleu_metric: 33.23485859915788
----------
NL: Get reverse of list items from list 'b' using extended slicing
ref: [x[::-1] for x in b]
Output: [::-1] for i in a]
bleu_metric: 51.95895250321166
----------
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: [(a, b), (b, b)]
bleu_metric: 22.319313765733384
----------
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(a)
bleu_metric: 1.1785360562036349
----------
NL: convert list `list_of_ints` into a comma separated string
ref: """,""".join([str(i) for i in list_of_ints])
Output: print(', '.join(l[:1])
bleu_metric: 22.587773654803488
----------
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: requests.get('https://username/post', json={'cheers': 'cheers'})
bleu_metric: 8.839374326825924
----------
NL: Find last occurrence of character '}' in string "abcd}def}"
ref: 'abcd}def}'.rfind('}')
Output: """{:.5}""".format(p.finditer('_') if '\\1'})
bleu_metric: 9.943312565640671
----------
NL: Iterate ove list `[1, 2, 3]` using list comprehension
ref: print([item for item in [1, 2, 3]])
Output: [(x + y) for x in range(3)]
bleu_metric: 10.524953179645163
----------
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 for d in a if d['name'] == 'pluto']
bleu_metric: 13.446276862221726
----------
NL: get the filename without the extension from file 'hemanth.txt'
ref: print(os.path.splitext(os.path.basename('hemanth.txt'))[0])
Output: os.path.splitext(filename, 'r')
bleu_metric: 19.13913717974136
----------
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(zip(x, y))
bleu_metric: 6.591862820334322
----------
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'], ['d', 'g', 'g', 'g'])]
bleu_metric: 22.895991001938675
----------
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[df['column_name'].str[1:]
bleu_metric: 9.220008234645237
----------
NL: replace all occurences of newlines `\n` with `<br>` in dataframe `df`
ref: df.replace({'\n': '<br>'}, regex=True)
Output: df.replace('\n', '>').replace('\r\n')
bleu_metric: 28.577933090423212
----------
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: 100.0
----------
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: print(list(itertools.chain.from_iterable([''.join([''.join(i)) for i in myList
bleu_metric: 6.9872837779786146
----------
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: [item[0] for item in wordids]
bleu_metric: 3.6045344658126863
----------
NL: extract a url from a string `myString`
ref: print(re.findall('(https?://[^\\s]+)', myString))
Output: print(re.search('https://url.com/', mystring))
bleu_metric: 22.139039509717414
----------
NL: extract a url from a string `myString`
ref: print(re.search('(?P<url>https?://[^\\s]+)', myString).group('url'))
Output: print(re.search('https://url.com/', mystring))
bleu_metric: 19.3178688753789
----------
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: 54.67160268053085
----------
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.date_of_datetime(df.strftime('%Y-%m-%d')
bleu_metric: 9.119026367194746
----------
NL: Create multidimensional array `matrix` with 3 rows and 2 columns in python
ref: matrix = [[a, b], [c, d], [e, f]]
Output: np.array([[0, 0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0
bleu_metric: 11.802861352393501
----------
NL: replace spaces with underscore
ref: mystring.replace(' ', '_')
Output: re.sub('\\([^\\w\\)', '', text)
bleu_metric: 15.604242268653643
----------
NL: get an absolute file path of file 'mydir/myfile.txt'
ref: os.path.abspath('mydir/myfile.txt')
Output: os.path.abspath(mypath)
bleu_metric: 36.304072644520666
----------
NL: split string `my_string` on white spaces
ref: """ """.join(my_string.split())
Output: my_string.split('_')
bleu_metric: 19.77851893672361
----------
NL: get filename without extension from file `filename`
ref: os.path.splitext(filename)[0]
Output: os.path.splitext('/path/to/filename.txt')
bleu_metric: 33.70129264673147
----------
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 lst if x > 2]
bleu_metric: 20.61396573920308
----------
NL: split a string `Docs/src/Scripts/temp` by `/` keeping `/` in the result
ref: """Docs/src/Scripts/temp""".replace('/', '/\x00/').split('\x00')
Output: s.split('\t'))
bleu_metric: 4.851225711700038
----------
NL: shuffle columns of an numpy array 'r'
ref: np.random.shuffle(np.transpose(r))
Output: np.random.shuffle(np.random.randint(0, 1, axis=1))
bleu_metric: 41.3948387915005
----------
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['B']
bleu_metric: 80.03203203844998
----------
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: data[(key=lambda x: x['Date']) & (x['Date']['count'])]
bleu_metric: 17.180939455924943
----------
NL: check characters of string `string` are true predication of function `predicate`
ref: all(predicate(x) for x in string)
Output: all(x in string for x in list(string.items()) if x == 'isdit')
bleu_metric: 16.27934873162478
----------
NL: determine number of files on a drive with python
ref: os.statvfs('/').f_files - os.statvfs('/').f_ffree
Output: os.stat(path).st_common(f.st_f)
bleu_metric: 10.276795791404941
----------
NL: how to get a single result from a SQLite query in python?
ref: cursor.fetchone()[0]
Output: cursor.execute('SELECT * FROM foo WHERE bar = %s' % (f, data))
bleu_metric: 8.282282660969601
----------
NL: convert string `user_input` into a list of integers `user_list`
ref: user_list = [int(number) for number in user_input.split(',')]
Output: return int(soup.split(','))
bleu_metric: 34.34281712615472
----------
NL: Get a list of integers by splitting  a string `user` with comma
ref: [int(s) for s in user.split(',')]
Output: [int(x) for x in (int(x)] for x in s.split('')]
bleu_metric: 32.12854967972961
----------
NL: Sorting a Python list by two criteria
ref: sorted(list, key=lambda x: (x[0], -x[1]))
Output: sorted(list, key=lambda x: (x[1], x[0]))
bleu_metric: 80.44410811781832
----------
NL: sort a list of objects `ut`, based on a function `cmpfun` in descending order
ref: ut.sort(key=cmpfun, reverse=True)
Output: sorted(a, key=lambda x: x.count(x.getter(1))
bleu_metric: 10.813005337959174
----------
NL: reverse list `ut` based on the `count` attribute of each object
ref: ut.sort(key=lambda x: x.count, reverse=True)
Output: sum(lst)
bleu_metric: 2.867683507302
----------
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: (-len(x.split('-)))
bleu_metric: 25.0737833894674
----------
NL: click a href button 'Send' with selenium
ref: driver.find_element_by_partial_link_text('Send').click()
Output: browser.find_element_by_text('http://www.text.com')
bleu_metric: 14.44788670919441
----------
NL: click a href button having text `Send InMail` with selenium
ref: driver.findElement(By.linkText('Send InMail')).click()
Output: browser.find_element_by_class_name('http://www.text.com')
bleu_metric: 11.088142201985505
----------
NL: click a href button with text 'Send InMail' with selenium
ref: driver.find_element_by_link_text('Send InMail').click()
Output: browser.find_element_by_text('http://www.text.com')
bleu_metric: 14.44788670919441
----------
NL: cast an int `i` to a string and concat to string 'ME'
ref: 'ME' + str(i)
Output: """""".join(i)
bleu_metric: 31.61487584488943
----------
NL: Sorting data in DataFrame Pandas
ref: df.sort_values(['System_num', 'Dis'])
Output: df.sort_values(['System_num', 'Dis'])
bleu_metric: 100.0
----------
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('filename', 'w').write('\n').read().write('\n').write('\n')
bleu_metric: 45.445331007629186
----------
NL: sort a list `l` by length of value in tuple
ref: l.sort(key=lambda t: len(t[1]), reverse=True)
Output: print(sorted(lst, key=lambda tup: len(x), len(y)))
bleu_metric: 17.729842264695016
----------
NL: split string `s` by words that ends with 'd'
ref: re.findall('\\b(\\w+)d\\b', s)
Output: [word for word in s.split('.')[0] for s in s.split()]
bleu_metric: 7.987276352377326
----------
NL: return `True` if string `foobarrrr` contains regex `ba[rzd]`
ref: bool(re.search('ba[rzd]', 'foobarrrr'))
Output: re.findall('(?<=\\d+', 'aaa')
bleu_metric: 18.49304691034943
----------
NL: Removing duplicates in list `t`
ref: list(set(t))
Output: list(set(item) for item in L))
bleu_metric: 35.556702356686955
----------
NL: Removing duplicates in list `source_list`
ref: list(set(source_list))
Output: list(item)
bleu_metric: 24.795364698947967
----------
NL: Removing duplicates in list `abracadabra`
ref: list(OrderedDict.fromkeys('abracadabra'))
Output: list(set(t))
bleu_metric: 19.745471508497758
----------
NL: Convert array `a` into a list
ref: numpy.array(a).reshape(-1).tolist()
Output: a.append(a)
bleu_metric: 9.852859740626553
----------
NL: Convert the first row of numpy matrix `a` to a list
ref: numpy.array(a)[0].tolist()
Output: numpy.array(a, a[0])
bleu_metric: 44.579146390257485
----------
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.findAll(text='Python')
bleu_metric: 11.86327327630691
----------
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(map(lambda x: x[1])
bleu_metric: 37.15744930678825
----------
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: [(x + y) for x, y in zip(l1, l2)]
bleu_metric: 4.866055054993489
----------
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: [m.start() for m in re.findall('\\d+', 'abc test test test)]
bleu_metric: 29.15471806136295
----------
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[['A', 'B']].fillna(df['A']
bleu_metric: 32.2826123723905
----------
NL: Sort dictionary `o` in ascending order based on its keys and items
ref: sorted(o.items())
Output: sorted(list(d.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()), key=lambda x: (x['name'], x['name'], x['name']))
bleu_metric: 21.62882016096312
----------
NL: convert string "1" into integer
ref: int('1')
Output: int('1', 2)
bleu_metric: 58.143073696821936
----------
NL: function to convert strings into integers
ref: int()
Output: int(''.join(map(str, x)))
bleu_metric: 12.729922658368398
----------
NL: convert items in `T1` to integers
ref: T2 = [map(int, x) for x in T1]
Output: int('1')
bleu_metric: 7.448668213629092
----------
NL: call a shell script `./test.sh` using subprocess
ref: subprocess.call(['./test.sh'])
Output: subprocess.call('test.sh otherfunc')
bleu_metric: 34.64490516910983
----------
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, y in zip(l1, l2)]
bleu_metric: 40.59264050928986
----------
NL: encode string 'data to be encoded'
ref: encoded = base64.b64encode('data to be encoded')
Output: data = json.encode('utf-8')
bleu_metric: 21.665290989148364
----------
NL: encode a string `data to be encoded` to `ascii` encoding
ref: encoded = 'data to be encoded'.encode('ascii')
Output: data = 'data2'
bleu_metric: 6.5298221612721505
----------
NL: parse tab-delimited CSV file 'text.txt' into a list
ref: lol = list(csv.reader(open('text.txt', 'rb'), delimiter='\t'))
Output: csv.writerow(open('filename.txt', 'r').read()
bleu_metric: 26.04837362077714
----------
NL: Get attribute `my_str` of object `my_object`
ref: getattr(my_object, my_str)
Output: my_obj.get('id')
bleu_metric: 19.96019880774733
----------
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: dict(zip(d.items()) for d in L)}
bleu_metric: 21.362647244490635
----------
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 mylist)
bleu_metric: 32.21542689451684
----------
NL: convert unicode string u"{'code1':1,'code2':1}" into dictionary
ref: d = ast.literal_eval("{'code1':1,'code2':1}")
Output: {'MyString1': '1', '1'}).encode('ascii', 'ignore')
bleu_metric: 14.294565332608489
----------
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('\\[^\\w\\w', mystring)
bleu_metric: 9.573479881751316
----------
NL: remove any url within string `text`
ref: text = re.sub('^https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE)
Output: text = re.sub('\\r\\r\\r\\n*\\Z', '', text)
bleu_metric: 39.07206775645457
----------
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.isnan(a).any(np.in1d(a), 0, a)
bleu_metric: 20.39361671090609
----------
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: subprocess.call('path/to/test.py', shell=True)
bleu_metric: 12.706804596574397
----------
NL: run r script '/usr/bin/Rscript --vanilla /pathto/MyrScript.r'
ref: subprocess.call('/usr/bin/Rscript --vanilla /pathto/MyrScript.r', shell=True)
Output: subprocess.call('/usr/bin/perl', shell=True)
bleu_metric: 48.72501457287311
----------
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=0)
bleu_metric: 86.6397551781362
----------
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('21/11/13 19:19:30 -0400', '%Y/%m/%Y
bleu_metric: 11.771247290341437
----------
NL: Call a base class's class method `do` from derived class `Derived`
ref: super(Derived, cls).do(a)
Output: super(MyClass, self).__init__(*args, **kwargs)
bleu_metric: 14.02577516008147
----------
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(a[0])!= 0]
bleu_metric: 10.315793886636692
----------
NL: separate words delimited by one or more spaces into a list
ref: re.split(' +', 'hello world sample text')
Output: [word for word in my_list if any(x) not in '\n' % x.split()]
bleu_metric: 10.332090908268508
----------
NL: length of longest element in list `words`
ref: len(max(words, key=len))
Output: len(words, key=lambda i: len(word) for word in word_list)
bleu_metric: 30.51327880362441
----------
NL: get the value associated with unicode key 'from_user' of first dictionary in list `result`
ref: result[0]['from_user']
Output: [x for x in list_of_dict if x['success'] not in data]
bleu_metric: 12.879862858915844
----------
NL: Retrieve each line from a file 'File.txt' as a list
ref: [line.split() for line in open('File.txt')]
Output: line = [line.strip() for line in open('filename.txt')]
bleu_metric: 63.83964846132485
----------
NL: swap keys with values in a dictionary `a`
ref: res = dict((v, k) for k, v in a.items())
Output: dict((v, k) for k, v in a.items())
bleu_metric: 90.00876262522594
----------
NL: Open a file `path/to/FILE_NAME.ext` in write mode
ref: new_file = open('path/to/FILE_NAME.ext', 'w')
Output: shutil.copyfile(path, '/to/file.txt')
bleu_metric: 20.55825140665624
----------
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('A').agg(lambda x: x.nlargest(x.name).nunique())
bleu_metric: 24.81076483975817
----------
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(dict.values())
bleu_metric: 9.663861439684919
----------
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: [key for key, value in list(d.items()) if 'new y' in d]
bleu_metric: 43.11804919179004
----------
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(' '.join(map(list, list)))
bleu_metric: 62.705622531832795
----------
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('\xd0\xd0\xd1\xd0\xd0\xd0\xd0\xd0\xd0\
bleu_metric: 32.278364081487986
----------
NL: Encode a latin character in string `Sopet\xc3\xb3n` properly
ref: 'Sopet\xc3\xb3n'.encode('latin-1').decode('utf-8')
Output: print('\xc3\xa9'.encode('utf-8'))
bleu_metric: 40.96260380345688
----------
NL: resized image `image` to width, height of `(x, y)` with filter of `ANTIALIAS`
ref: image = image.resize((x, y), Image.ANTIALIAS)
Output: cv2.imshow(image, 0, cv2.COLOR_BGR2RGB)
bleu_metric: 12.162636437767492
----------
NL: regex, find "n"s only in the middle of string `s`
ref: re.findall('n(?<=[^n]n)n+(?=[^n])(?i)', s)
Output: re.sub('(?<=n)', '', s)
bleu_metric: 19.97638457636558
----------
NL: display the float `1/3*100` as a percentage
ref: print('{0:.0f}%'.format(1.0 / 3 * 100))
Output: int(round(1.5))
bleu_metric: 8.651705771167139
----------
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: my_list.sort(key=lambda x: x['title'])
bleu_metric: 30.53233854712605
----------
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: print(max(x, key=sum))
bleu_metric: 5.1753387054451085
----------
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('div', attrs={'name': 'description'})
bleu_metric: 27.858167198909346
----------
NL: write records in dataframe `df` to table 'test' in schema 'a_schema'
ref: df.to_sql('test', engine, schema='a_schema')
Output: df.to_csv('test.csv', index=False)
bleu_metric: 20.519505687240557
----------
NL: Extract brackets from string `s`
ref: brackets = re.sub('[^(){}[\\]]', '', s)
Output: re.findall('\\[\\w\\]', s)
bleu_metric: 28.25241115931726
----------
NL: remove duplicate elements from list 'L'
ref: list(dict((x[0], x) for x in L).values())
Output: [set(x) for x in set(tuple(x) for x in L)]
bleu_metric: 35.03419928300855
----------
NL: read a file `file` without newlines
ref: [line.rstrip('\n') for line in file]
Output: f.close()
bleu_metric: 4.4408502586392995
----------
NL: get the position of item 1 in `testlist`
ref: [i for (i, x) in enumerate(testlist) if (x == 1)]
Output: print(list(range(1, len(mylist)) + 1)
bleu_metric: 9.025970847670083
----------
NL: get the position of item 1 in `testlist`
ref: [i for (i, x) in enumerate(testlist) if (x == 1)]
Output: print(list(range(1, len(mylist)) + 1)
bleu_metric: 9.025970847670083
----------
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(list(range(1, len(mylist)) + 1)
bleu_metric: 7.3690983226275435
----------
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(list(range(1, len(mylist)) + 1)
bleu_metric: 8.372912227572577
----------
NL: get the position of item 1 in `testlist`
ref: gen = (i for (i, x) in enumerate(testlist) if (x == 1))
Output: print(list(range(1, len(mylist)) + 1)
bleu_metric: 10.364607550945996
----------
NL: get the position of item `element` in list `testlist`
ref: print(testlist.index(element))
Output: print(testlist.index(x))
bleu_metric: 70.16879391277371
----------
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(x))
bleu_metric: 8.18227813664567
----------
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[1] for x in L[0])
bleu_metric: 22.078697088920286
----------
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(a, key=lambda x: x[1])
bleu_metric: 21.186050864016664
----------
NL: Make a delay of 1 second
ref: time.sleep(1)
Output: time.sleep(5)
bleu_metric: 59.46035575013605
----------
NL: convert list of tuples `L` to a string
ref: """, """.join('(' + ', '.join(i) + ')' for i in L)
Output: """""".join(map(str, L))
bleu_metric: 22.382912241881993
----------
NL: Django set default value of field `b` equal to '0000000'
ref: b = models.CharField(max_length=7, default='0000000', editable=False)
Output: super(Model, self).save(*args, **kwargs)
bleu_metric: 8.756189878973519
----------
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(list(range(len(a))), key=lambda x: (-len(x)), x[1]))
bleu_metric: 21.691223219717052
----------
NL: How do I perform secondary sorting in python?
ref: sorted(list5, key=lambda vertex: (degree(vertex), vertex))
Output: sorted(list1, key=lambda x: x[:-1])
bleu_metric: 23.523340210589698
----------
NL: convert a list  into a generator object
ref: (n for n in [1, 2, 3, 5])
Output: [(i * i for i in range(10)) for i in range(10)]
bleu_metric: 7.707532488755381
----------
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: [item for item in my_list if some_condition()]
bleu_metric: 9.65619264674583
----------
NL: Open a file `yourfile.txt` in write mode
ref: f = open('yourfile.txt', 'w')
Output: f = open('file.txt', 'rU')
bleu_metric: 57.3057404379869
----------
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(map(tuple, map(int, t))
bleu_metric: 3.3785986651715976
----------
NL: convert tuple of tuples `(('aa',), ('bb',), ('cc',))` to list in one line
ref: map(lambda a: a[0], (('aa',), ('bb',), ('cc',)))
Output: [('a', 'b', 'c'), ('d', 'e', 'e']) for e in l]
bleu_metric: 15.687545978719285
----------
NL: Python Pandas: How to replace a characters in a column of a dataframe?
ref: df['range'].replace(',', '-', inplace=True)
Output: df.replace('-', inplace=True)
bleu_metric: 43.27858737448645
----------
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)
bleu_metric: 0.24532394983186231
----------
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)
bleu_metric: 0.24532394983186231
----------
NL: unzip list `original`
ref: result = ([a for (a, b) in original], [b for (a, b) in original])
Output: zip(*main_list)
bleu_metric: 0.7985269443272172
----------
NL: unzip list `original` and return a generator
ref: result = ((a for (a, b) in original), (b for (a, b) in original))
Output: [(i, j) for i in range(10)]
bleu_metric: 5.292278815391249
----------
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)
bleu_metric: 0.07639466888110501
----------
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)
bleu_metric: 0.037243160645090284
----------
NL: encode `Decimal('3.9')` to a JSON string
ref: json.dumps(Decimal('3.9'))
Output: json.dumps(str(x))
bleu_metric: 32.39454232489392
----------
NL: Add key "mynewkey" to dictionary `d` with value "mynewvalue"
ref: d['mynewkey'] = 'mynewvalue'
Output: d['mykey'] = {'mykey': 1}, {'section': 3})
bleu_metric: 14.908960803395837
----------
NL: Add key 'a' to dictionary `data` with value 1
ref: data.update({'a': 1, })
Output: data['a'] = 'b'
bleu_metric: 17.86189637183548
----------
NL: Add key 'a' to dictionary `data` with value 1
ref: data.update(dict(a=1))
Output: data['a'] = 'b'
bleu_metric: 13.078474031415348
----------
NL: Add key 'a' to dictionary `data` with value 1
ref: data.update(a=1)
Output: data['a'] = 'b'
bleu_metric: 14.323145079400494
----------
NL: find maximal value in matrix `matrix`
ref: max([max(i) for i in matrix])
Output: max(max(x, key=lambda x: x[1])[0]
bleu_metric: 12.58220555845937
----------
NL: Round number `answer` to 2 precision after the decimal point
ref: answer = str(round(answer, 2))
Output: round(1.005, 2)
bleu_metric: 24.5981275183433
----------
NL: extract ip address from an html string
ref: ip = re.findall('[0-9]+(?:\\.[0-9]+){3}', s)
Output: print(re.findall('\\d+(?=(\\.com+)\\.com/', s))
bleu_metric: 29.033332017734637
----------
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[df['A'] > 1].filter(lambda x: x.isnull().values())]
bleu_metric: 29.586757494947246
----------
NL: append each line in file `myfile` into a list
ref: [x for x in myfile.splitlines() if x != '']
Output: myfile.write('myfile.txt', 'w').write('\n')
bleu_metric: 8.899891914341412
----------
NL: Get a list of integers `lst` from a file `filename.txt`
ref: lst = map(int, open('filename.txt').readlines())
Output: [int(i) for i in f.split(',')]
bleu_metric: 12.313871716496207
----------
NL: add color bar with image `mappable` to plot `plt`
ref: plt.colorbar(mappable=mappable, cax=ax3)
Output: plt.savefig('test.png')
bleu_metric: 16.449450056138957
----------
NL: count most frequent 100 words in column 'text' of dataframe `df`
ref: Counter(' '.join(df['text']).split()).most_common(100)
Output: df.join(word).most_common(word)
bleu_metric: 14.732626148274333
----------
NL: Python split a string using regex
ref: re.findall('(.+?):(.+?)\\b ?', text)
Output: re.split('(?<=\\.\\d+|\\s)', text)
bleu_metric: 27.16899549919204
----------
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], [4, 5, 6])) for a in range(
bleu_metric: 23.605722704787006
----------
NL: get a value of datetime.today() in the UTC time zone
ref: datetime.now(pytz.utc)
Output: datetime.datetime.utcnow().date()
bleu_metric: 18.70361278311548
----------
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: 100.0
----------
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: list23 = [(a - b) for a, b in zip(list1, list2)]
bleu_metric: 11.13613656101029
----------
NL: Django response with JSON `data`
ref: return HttpResponse(data, mimetype='application/json')
Output: json.dumps(data, ensure_data)
bleu_metric: 16.66900658055424
----------
NL: get all text that is not enclosed within square brackets in string `example_str`
ref: re.findall('(.*?)\\[.*?\\]', example_str)
Output: """\\[\\]]""".split()
bleu_metric: 16.91906599442455
----------
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('\\[(.*?\\]\\]', '', str))
bleu_metric: 41.184938137508176
----------
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.findall('\\w+(?:\\w+)', '\\1', s)
bleu_metric: 38.05918723545142
----------
NL: match regex '\\((.*?)\\)|(\\w)' with string '(zyx)bc'
ref: re.findall('\\((.*?)\\)|(\\w)', '(zyx)bc')
Output: re.match('(?<=\\w+)', 'a\n b\n c')
bleu_metric: 21.2203664532429
----------
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', 'abcdefg123 and again abc')
bleu_metric: 40.304968802021214
----------
NL: formate each string cin list `elements` into pattern '%{0}%'
ref: elements = ['%{0}%'.format(element) for element in elements]
Output: print(' '.join(map(str, list)))
bleu_metric: 9.154148084605266
----------
NL: Open a background process 'background-process' with arguments 'arguments'
ref: subprocess.Popen(['background-process', 'arguments'])
Output: subprocess.Popen(['file', '-r', '-r', '-r', 'file.file'])
bleu_metric: 28.631840575309536
----------
NL: get list of values from dictionary 'mydict' w.r.t. list of keys 'mykeys'
ref: [mydict[x] for x in mykeys]
Output: [k for d in mylist if'mykey' in d]
bleu_metric: 14.807582680058125
----------
NL: convert list `[('Name', 'Joe'), ('Age', 22)]` into a dictionary
ref: dict([('Name', 'Joe'), ('Age', 22)])
Output: dict(map(int, ['ip', 'at', '7', 'D'])
bleu_metric: 17.760449184092803
----------
NL: average each two columns of array `data`
ref: data.reshape(-1, j).mean(axis=1).reshape(data.shape[0], -1)
Output: data.sum(axis=1).sum(axis=1)
bleu_metric: 19.628836362845185
----------
NL: double backslash escape all double quotes in string `s`
ref: print(s.encode('unicode-escape').replace('"', '\\"'))
Output: result = result.replace('\\', '')
bleu_metric: 23.514174149586935
----------
NL: split a string into a list of words and whitespace
ref: re.split('(\\W+)', s)
Output: [word for word in mystring.split()]
bleu_metric: 15.620412264187214
----------
NL: plotting stacked barplots on a panda data frame
ref: df.plot(kind='barh', stacked=True)
Output: df.plot(kind='bar', stacked=True)
bleu_metric: 81.53551038173114
----------
NL: reverse the keys and values in a dictionary `myDictionary`
ref: {i[1]: i[0] for i in list(myDictionary.items())}
Output: dict((v, k) for k, v in my_dict.items())
bleu_metric: 19.54411146921455
----------
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 for x in mylist if 'abc' in x]
bleu_metric: 4.965473926212578
----------
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(a, str)
bleu_metric: 59.46035575013605
----------
NL: check if object `o` is a string
ref: (type(o) is str)
Output: isinstance(a, str)
bleu_metric: 23.813694985189848
----------
NL: check if object `o` is a string
ref: isinstance(o, str)
Output: isinstance(a, str)
bleu_metric: 59.46035575013605
----------
NL: check if `obj_to_test` is a string
ref: isinstance(obj_to_test, str)
Output: if (not some_string):
    pass
bleu_metric: 20.556680845025983
----------
NL: append list `list1` to `list2`
ref: list2.extend(list1)
Output: list2.append(list2)
bleu_metric: 32.17294420803808
----------
NL: append list `mylog` to `list1`
ref: list1.extend(mylog)
Output: mylist.append('myD')
bleu_metric: 18.850319022747346
----------
NL: append list `a` to `c`
ref: c.extend(a)
Output: a.append(b)
bleu_metric: 29.071536848410968
----------
NL: append items in list `mylog` to `list1`
ref: for line in mylog:
    list1.append(line)
Output: mylist.append('myD')
bleu_metric: 22.84389301518129
----------
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, b, c) for a, b in zip(a, b)]
bleu_metric: 9.850589025894903
----------
NL: Initialize `SECRET_KEY` in flask config with `Your_secret_string `
ref: app.config['SECRET_KEY'] = 'Your_secret_string'
Output: app.config['your_key'] ='my_key_key'
bleu_metric: 57.31224484094262
----------
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.concat([df1, df2], axis=1)
bleu_metric: 5.130188327095793
----------
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: sorted(list_to_index, key=lambda x: x[0].index(x_list)
bleu_metric: 7.022279615553664
----------
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_xlabel('Temperature (\u2103)')
bleu_metric: 16.54461999338998
----------
NL: remove symbols from a string `s`
ref: re.sub('[^\\w]', ' ', s)
Output: re.sub('[^\\w]', '', s)
bleu_metric: 100.0
----------
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: 52.085526428001614
----------
NL: Find octal characters matches from a string `str` using regex
ref: print(re.findall("'\\\\[0-7]{1,3}'", str))
Output: re.findall('[^\\w-\\d]', str)
bleu_metric: 27.1936649292955
----------
NL: split string `input` based on occurrences of regex pattern '[ ](?=[A-Z]+\\b)'
ref: re.split('[ ](?=[A-Z]+\\b)', input)
Output: re.split('(?<=\\w+)', input)
bleu_metric: 39.847572134903544
----------
NL: Split string `input` at every space followed by an upper-case letter
ref: re.split('[ ](?=[A-Z])', input)
Output: re.split('(?<=\\w)([A-Z])', input)
bleu_metric: 59.75969817602503
----------
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: urllib.request.urlretrieve('http://www.com/go/')
bleu_metric: 8.688932567491825
----------
NL: write bytes `bytes_` to a file `filename` in python 3
ref: open('filename', 'wb').write(bytes_)
Output: f.write(open('filename.txt', 'wb')
bleu_metric: 55.95642461260143
----------
NL: get a list from a list `lst` with values mapped into a dictionary `dct`
ref: [dct[k] for k in lst]
Output: print([y for x in lst if 'id' in l])
bleu_metric: 11.75029694362029
----------
NL: find duplicate names in column 'name' of the dataframe `x`
ref: x.set_index('name').index.get_duplicates()
Output: df.groupby('name')['name'].index
bleu_metric: 34.899812526885086
----------
NL: truncate float 1.923328437452 to 3 decimal places
ref: round(1.923328437452, 3)
Output: '{0:.2f}'.format(13.95)
bleu_metric: 10.802314890908066
----------
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=lambda x: order.index(x[2], x[2]])
bleu_metric: 30.17923315990544
----------
NL: place the radial ticks in plot `ax` at 135 degrees
ref: ax.set_rlabel_position(135)
Output: ax.set_xlabel('30')
bleu_metric: 24.446151121745054
----------
NL: check if path `my_path` is an absolute path
ref: os.path.isabs(my_path)
Output: os.path.exists(my_path)
bleu_metric: 65.80370064762462
----------
NL: get number of keys in dictionary `yourdict`
ref: len(list(yourdict.keys()))
Output: len(dict_dict[key] for key in dict_dict)
bleu_metric: 17.194656088289214
----------
NL: count the number of keys in dictionary `yourdictfile`
ref: len(set(open(yourdictfile).read().split()))
Output: sum(1 for d in my_list if d.get('key', 1)])
bleu_metric: 9.449865252803164
----------
NL: pandas dataframe get first row of each group by 'id'
ref: df.groupby('id').first()
Output: df.groupby('id').first()
bleu_metric: 100.0
----------
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.apply(lambda x: df.split(',')[1:-1])
bleu_metric: 13.32809869264623
----------
NL: extract attributes 'src="js/([^"]*\\bjquery\\b[^"]*)"' from string `data`
ref: re.findall('src="js/([^"]*\\bjquery\\b[^"]*)"', data)
Output: re.findall('[a-zA-Z][\\w-]*\\.\\S*\\S*[^a-
bleu_metric: 22.84852347284557
----------
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(map(int, ['1', '3', '2', '3'])
bleu_metric: 15.327565138128369
----------
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('start /home/bin/bash -c "echo "echo "dev/subdir/* somedir/', shell
bleu_metric: 6.784909540835388
----------
NL: reverse a priority queue `q` in python without using classes
ref: q.put((-n, n))
Output: (::2 > 0).objects.filter(id=123))
bleu_metric: 13.78592993183041
----------
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.plot(x, y, 'B')
bleu_metric: 1.7617020801492098
----------
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: print(re.findall('(?=(a.*?)\\b', '\\1', data))
bleu_metric: 29.06614574259767
----------
NL: Get the length of list `my_list`
ref: len(my_list)
Output: len(my_list)
bleu_metric: 100.0
----------
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: 45.180100180492246
----------
NL: Getting the length of `my_string`
ref: len(my_string)
Output: len(my_string)
bleu_metric: 100.0
----------
NL: remove escape character from string "\\a"
ref: """\\a""".decode('string_escape')
Output: print(' '.join(a)
bleu_metric: 14.244775721428216
----------
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 b""".replace('b', 'a', 'baz'))
bleu_metric: 34.4125014551951
----------
NL: remove directory tree '/folder_name'
ref: shutil.rmtree('/folder_name')
Output: os.makedirs(path)
bleu_metric: 18.094495256969623
----------
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: data['hour'] = df['Date'].apply(lambda x: x.year))
bleu_metric: 54.038193355462695
----------
NL: reverse sort Counter `x` by values
ref: sorted(x, key=x.get, reverse=True)
Output: sorted(x, key=x.getter(x))
bleu_metric: 55.573034753918414
----------
NL: reverse sort counter `x` by value
ref: sorted(list(x.items()), key=lambda pair: pair[1], reverse=True)
Output: sorted(x, key=x.get)
bleu_metric: 8.425741883707015
----------
NL: append a numpy array 'b' to a numpy array 'a'
ref: np.vstack((a, b))
Output: a.reshape(a, -1, a1)
bleu_metric: 23.901088824528134
----------
NL: numpy concatenate two arrays `a` and `b` along the first axis
ref: print(concatenate((a, b), axis=0))
Output: print(a.shape[1, 3, 4])
bleu_metric: 15.325316503089068
----------
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(a.shape[1, 3, 4])
bleu_metric: 9.146501935450095
----------
NL: numpy concatenate two arrays `a` and `b` along the first axis
ref: np.array((a, b))
Output: print(a.shape[1, 3, 4])
bleu_metric: 15.911783110981517
----------
NL: fetch address information for host 'google.com' ion port 80
ref: print(socket.getaddrinfo('google.com', 80))
Output: socket.send('HTTP/1.0 200 OK')
bleu_metric: 15.850829785188195
----------
NL: add a column 'day' with value 'sat' to dataframe `df`
ref: df.xs('sat', level='day', drop_level=False)
Output: df['d'].apply(lambda x: 'Y'))
bleu_metric: 11.454635531478054
----------
NL: return a 401 unauthorized in django
ref: return HttpResponse('Unauthorized', status=401)
Output: return HttpResponse(status=204)
bleu_metric: 32.28559902619133
----------
NL: Flask set folder 'wherever' as the default template folder
ref: Flask(__name__, template_folder='wherever')
Output: app.path.join(__file__, os.abspath(__file__))
bleu_metric: 11.502783619900047
----------
NL: How do I INSERT INTO t1 (SELECT * FROM t2) in SQLAlchemy?
ref: session.execute('INSERT INTO t1 (SELECT * FROM t2)')
Output: session.commit()
bleu_metric: 6.117046115680378
----------
NL: sort a list of lists 'c2' such that third row comes first
ref: c2.sort(key=lambda row: row[2])
Output: sorted(lst, key=lambda x: x[2]))
bleu_metric: 32.22538601891173
----------
NL: Sorting a list of lists in Python
ref: c2.sort(key=lambda row: (row[2], row[1], row[0]))
Output: [sorted(item) for item in data]
bleu_metric: 3.9177831075306493
----------
NL: Sorting a list of lists in Python
ref: c2.sort(key=lambda row: (row[2], row[1]))
Output: [sorted(item) for item in data]
bleu_metric: 5.9428726447571005
----------
NL: set font `Arial` to display non-ascii characters in matplotlib
ref: matplotlib.rc('font', **{'sans-serif': 'Arial', 'family': 'sans-serif'})
Output: matplotlib.rcParams['font.update({'font.size': 'C','size'})
bleu_metric: 21.87310059835298
----------
NL: Convert  DateTime column 'date' of pandas dataframe 'df' to ordinal
ref: df['date'].apply(lambda x: x.toordinal())
Output: df['date'] = df['date'].dt.date.tolist()
bleu_metric: 35.13874939965221
----------
NL: get html source of Selenium WebElement `element`
ref: element.get_attribute('innerHTML')
Output: driver.find_element_by_xpath("//*[contains(text(), 'text')]")
bleu_metric: 12.173115521158184
----------
NL: Get the integer location of a key `bob` in a pandas data frame
ref: df.index.get_loc('bob')
Output: df.columns = df['state'].get_dict()
bleu_metric: 15.76777026245066
----------
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('start "$file"')
bleu_metric: 14.726128825673209
----------
NL: add an item with key 'third_key' and value 1 to an dictionary `my_dict`
ref: my_dict.update({'third_key': 1})
Output: default_dict.update({'key1': 1, 'position': 2})
bleu_metric: 35.95818888322724
----------
NL: declare an array
ref: my_list = []
Output: intarray = array('i')
bleu_metric: 15.619699684601276
----------
NL: Insert item `12` to a list `my_list`
ref: my_list.append(12)
Output: my_list.insert(0, k)
bleu_metric: 24.446151121745054
----------
NL: add an entry 'wuggah' at the beginning of list `myList`
ref: myList.insert(0, 'wuggah')
Output: a.append(c)
bleu_metric: 13.597602315271134
----------
NL: convert a hex-string representation to actual bytes
ref: """\\xF3\\xBE\\x80\\x80""".replace('\\x', '').decode('hex')
Output: binascii.unhexlify('\x00\x80')[2:]
bleu_metric: 4.9663342844865035
----------
NL: select the last column of dataframe `df`
ref: df[df.columns[-1]]
Output: df.drop(df.columns[-1]]
bleu_metric: 69.30977286178778
----------
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[df['A'] == 'B']
bleu_metric: 25.294817005134707
----------
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: np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
bleu_metric: 53.858386640481605
----------
NL: get the type of `i`
ref: type(i)
Output: print(isinstance(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: print(get_name)
bleu_metric: 24.795364698947967
----------
NL: get the 5th item of a generator
ref: next(itertools.islice(range(10), 5, 5 + 1))
Output: next(it)
bleu_metric: 2.7474047213893544
----------
NL: Print a string `word` with string format
ref: print('"{}"'.format(word))
Output: print('Hello, {0}'.format(word))
bleu_metric: 59.978201631280236
----------
NL: join a list of strings `list` using a space ' '
ref: """ """.join(list)
Output: [')','','','','','','','','','','','','','', '
bleu_metric: 5.987016463560546
----------
NL: create list `y` containing two empty lists
ref: y = [[] for n in range(2)]
Output: listy = [[] for i in range(3)]
bleu_metric: 49.35578819979933
----------
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: data = [int(i) for i in f]
bleu_metric: 6.360743006525592
----------
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', '', 'I', 'it is icy')
bleu_metric: 22.408895662889165
----------
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: re.sub('[^i]', '', 'I', text)
bleu_metric: 22.722445488928127
----------
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([i for i in range(100) if i!= 'i'])
bleu_metric: 59.20060650358378
----------
NL: Drop rows of pandas dataframe `df` having NaN in column at index "1"
ref: df.dropna(subset=[1])
Output: df.drop(df.index[[1, 3]], inplace=True)
bleu_metric: 12.91802583371725
----------
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: 12.58220555845937
----------
NL: converting list of strings `intstringlist` to list of integer `nums`
ref: nums = [int(x) for x in intstringlist]
Output: [int(x) for x in intstringlist]
bleu_metric: 83.9234989944319
----------
NL: convert list of string numbers into list of integers
ref: map(int, eval(input('Enter the unfriendly numbers: ')))
Output: [int(i) for i in str_list]
bleu_metric: 7.290792403968443
----------
NL: print "." without newline
ref: sys.stdout.write('.')
Output: print('\n'.join(str(x))
bleu_metric: 15.911783110981517
----------
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(math.pack('100', struct.pack('100', int(2)))
bleu_metric: 12.920725243713852
----------
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 os.endswith('.txt'):
    if file.end
bleu_metric: 31.41808768186965
----------
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 os.endswith('.txt'):
    if file.end
bleu_metric: 74.59429832584496
----------
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 os.endswith('.txt'):
    if file.end
bleu_metric: 38.5828207641196
----------
NL: plot dataframe `df` without a legend
ref: df.plot(legend=False)
Output: plt.legend(loc=2, fontsize='y')
bleu_metric: 13.380161378318956
----------
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: for i in range(0, 10)
bleu_metric: 2.18006837039135
----------
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: for i in range(0, 10)
bleu_metric: 3.8730052084654263
----------
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: for i in range(0, 10)
bleu_metric: 2.8194161881384923
----------
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: sum(map(int, x))
bleu_metric: 10.55672713033355
----------
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: f.write('\n')
bleu_metric: 8.523846965585486
----------
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: [item for sublist in l for item in sublist]
bleu_metric: 6.735737120156013
----------
NL: Print new line character as `\n` in a string `foo\nbar`
ref: print('foo\nbar'.encode('string_escape'))
Output: 'Hello\n\n'.rstrip('\n')
bleu_metric: 16.15322840648722
----------
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: x = np.mean(a, axis=1)
bleu_metric: 9.241605197022109
----------
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.mean(np.arange(len(x)) + 3).mean(axis=1))
bleu_metric: 7.6047020452233385
----------
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: data = numpy.loadtxt('file.txt', 'utf-8')
bleu_metric: 25.88323054958255
----------
NL: reverse sort list of dicts `l` by value for key `time`
ref: l = sorted(l, key=itemgetter('time'), reverse=True)
Output: sorted(lst, key=lambda d: (d['time'], d['time'], d['time'], reverse=True
bleu_metric: 17.7946690947194
----------
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: (d['time'], reverse=True)
bleu_metric: 76.97570474571566
----------
NL: get rows of dataframe `df` that match regex '(Hel|Just)'
ref: df.loc[df[0].str.contains('(Hel|Just)')]
Output: df[df.str.startswith('(?=.)', '|#')]
bleu_metric: 29.906271972271124
----------
NL: find the string in `your_string` between two special characters "[" and "]"
ref: re.search('\\[(.*)\\]', your_string).group(1)
Output: re.search('(.*?)\\[^\\w\\s]', my_string)
bleu_metric: 35.5402294764931
----------
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: d = pd.DataFrame({'%Y': 'Y%m%d': 'Y%Y': 'Y%m%Y
bleu_metric: 22.920768276757666
----------
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: """ """.join(['Pplit', 'aaa'])
bleu_metric: 31.276700211004304
----------
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('file.txt', filename)
bleu_metric: 15.53712569276035
----------
NL: split string `text` by space
ref: text.split()
Output: text = re.split('\\s+')
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: [line.strip() for line in open('   a')]
bleu_metric: 11.794224053267104
----------
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(list_of_tuples, key=lambda s: s.split('_')[1]))
bleu_metric: 70.64070210210579
----------
NL: call multiple bash function ‘vasp’ and ‘tee tee_output’ using ‘|’
ref: subprocess.check_call('vasp | tee tee_output', shell=True)
Output: subprocess.call('start "$z', shell=True)
bleu_metric: 48.80217780091719
----------
NL: eliminate all strings from list `lst`
ref: [element for element in lst if isinstance(element, int)]
Output: """""".join(lst)
bleu_metric: 9.980554701686325
----------
NL: get all the elements except strings from the list 'lst'.
ref: [element for element in lst if not isinstance(element, str)]
Output: [x for x in lst if x[1] == 'C' in x]
bleu_metric: 15.047149445189383
----------
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 k: k['name']))
bleu_metric: 73.36726069437738
----------
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(d, key=operator.itemgetter('name', 'age'))
bleu_metric: 35.3774707378071
----------
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.sort(key=operator.itemgetter('name', 'age'))
bleu_metric: 69.91461503583824
----------
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.sort(key=operator.itemgetter('name', 'age'))
bleu_metric: 69.91461503583824
----------
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.sort_values(['Peak', 'Weeks'], ascending=[True, True])
bleu_metric: 15.057522385331268
----------
NL: join together with "," elements inside a list indexed with 'category' within a dictionary `trans`
ref: """,""".join(trans['category'])
Output: [item['one'] for item in list_of_dicts]
bleu_metric: 11.133037663423181
----------
NL: concatenate array of strings `['A', 'B', 'C', 'D']` into a string
ref: """""".join(['A', 'B', 'C', 'D'])
Output: """""".join(['a', 'b', 'c', 'd'])
bleu_metric: 61.82527625446008
----------
NL: get json data from restful service 'url'
ref: json.load(urllib.request.urlopen('url'))
Output: print(json.dumps(url, data=data))
bleu_metric: 18.76960278961121
----------
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('@$\t') and not x.startswith('#')]
Output: [x for x in L if x.startswith('#')]
bleu_metric: 36.8758127348874
----------
NL: django filter by hour
ref: Entry.objects.filter(pub_date__contains='08:00')
Output: Entry.objects.filter(created__year='2011', {'_date': '2011'})
bleu_metric: 30.05180557366462
----------
NL: sort a list of dictionary `list` first by key `points` and then by `time`
ref: list.sort(key=lambda item: (item['points'], item['time']))
Output: list.sort(key=lambda x: x['time'])
bleu_metric: 40.633530232106814
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NL: convert datetime object `(1970, 1, 1)` to seconds
ref: (t - datetime.datetime(1970, 1, 1)).total_seconds()
Output: datetime.datetime.now().strftime('%H:%M:%S')
bleu_metric: 15.415064977510756
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NL: insert `_suff` before the file extension in `long.file.name.jpg` or replace `_a` with `suff` if it precedes the extension.
ref: re.sub('(\\_a)?\\.([^\\.]*)$', '_suff.\\2', 'long.file.name.jpg')
Output: os.path.splitext('/home/to/somefile.txt')
bleu_metric: 3.7217110227905947
----------
NL: reload a module `module`
ref: import imp
imp.reload(module)
Output: importlib.import_module('foo.b')
bleu_metric: 17.03318603763928
----------
NL: Convert integer `number` into an unassigned integer
ref: struct.unpack('H', struct.pack('h', number))
Output: int('1 + str(n)[-1]
bleu_metric: 9.01224794878171
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NL: convert int values in list `numlist` to float
ref: numlist = [float(x) for x in numlist]
Output: float(map(int, ['1', '3'], ['0', '4']])
bleu_metric: 7.039774105336835
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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: 100.0
----------
NL: convert a urllib unquoted string `unescaped` to a json data `json_data`
ref: json_data = json.loads(unescaped)
Output: json.dumps({'hi': 'foo'})
bleu_metric: 14.247788801610147
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NL: Create a list containing all ascii characters as its elements
ref: [chr(i) for i in range(127)]
Output: [int(x) for x in list('\u0420', '\u0420']
bleu_metric: 11.71291646655074
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NL: write `newFileBytes` to a binary file `newFile`
ref: newFile.write(struct.pack('5B', *newFileBytes))
Output: f.write(struct.pack('i', 'rb')
bleu_metric: 43.76982305624316
----------
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('[^A-Z][\\w-]*', '', s)
bleu_metric: 31.75248595372921
----------
NL: get the last key of dictionary `dict`
ref: list(dict.keys())[-1]
Output: max(dict.keys())
bleu_metric: 50.17507694885614
----------
NL: write line "hi there" to file `f`
ref: print('hi there', file=f)
Output: f.write('hi there\n')
bleu_metric: 35.41698766144061
----------
NL: write line "hi there" to file `myfile`
ref: f = open('myfile', 'w')
f.write('hi there\n')
Output: print('myfile.txt', open('myfile.txt'))
bleu_metric: 20.889533351386476
----------
NL: write line "Hello" to file `somefile.txt`
ref: with open('somefile.txt', 'a') as the_file:
    the_file.write('Hello\n')
Output: file.write('foo\n')
bleu_metric: 14.805399561478843
----------
NL: convert unicode string `s` to ascii
ref: s.encode('iso-8859-15')
Output: s.decode('ascii', 'ignore')
bleu_metric: 24.022110864391532
----------
NL: Django get maximum value associated with field 'added' in model `AuthorizedEmail`
ref: AuthorizedEmail.objects.filter(group=group).order_by('-added')[0]
Output: MyModel.objects.filter(userprofile__pk=['title', 'title'])
bleu_metric: 23.364722604693753
----------
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+', '', re.findall('\\d+', s))
bleu_metric: 20.563241139732103
----------
NL: python regex  to find all numbers and dots from 'text'
ref: re.findall('Test([\\d.]*\\d+)', text)
Output: re.findall('\\d+', s)
bleu_metric: 27.703746325324747
----------
NL: execute script 'script.ps1' using 'powershell.exe' shell
ref: os.system('powershell.exe', 'script.ps1')
Output: subprocess.call('start "$file.py', shell=True)
bleu_metric: 14.400124446705304
----------
NL: Sort a list of tuples `b` by third item in the tuple
ref: b.sort(key=lambda x: x[1][2])
Output: sorted(a, key=lambda x: (sum(x[1], x[0]))
bleu_metric: 32.99895472527792
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NL: get a list of all keys in Cassandra database `cf` with pycassa
ref: list(cf.get_range().get_keys())
Output: ndndndndndndndndndndndndndndndndndndndndndndndndndndndndndndndnd
bleu_metric: 0.000516664614247128
----------
NL: create a datetime with the current date & time
ref: datetime.datetime.now()
Output: datetime.datetime.now().isoformat()
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: [i for i, x in enumerate(lst) if x == 0]
bleu_metric: 42.57822397135907
----------
NL: subtract 13 from every number in a list `a`
ref: a[:] = [(x - 13) for x in a]
Output: [(x - x) for x in a]
bleu_metric: 51.44168902788183
----------
NL: choose a random file from the directory contents of the C drive, `C:\\`
ref: random.choice(os.listdir('C:\\'))
Output: os.stat(os.path.join(dir, '..'))
bleu_metric: 25.958657290343435
----------
NL: get the highest element in absolute value in a numpy matrix `x`
ref: max(x.min(), x.max(), key=abs)
Output: max(x.max(), x.max())
bleu_metric: 56.52866525800724
----------
NL: Get all urls within text `s`
ref: re.findall('"(http.*?)"', s, re.MULTILINE | re.DOTALL)
Output: re.findall('\\b(?=.)', s)
bleu_metric: 24.628487110705017
----------
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.compile('^[^a-zA-Z0-9-]*\\.\\.com/', my_string
bleu_metric: 12.913533075470381
----------
NL: split a string `mystring` considering the spaces ' '
ref: mystring.replace(' ', '! !').split('!')
Output: re.split('\\s+', myString)
bleu_metric: 21.02813554778581
----------
NL: open file `path` with mode 'r'
ref: open(path, 'r')
Output: os.path.join(path)
bleu_metric: 23.356898886410004
----------
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(i) for i in zip(*data)]
bleu_metric: 33.38084565798161
----------
NL: add a new axis to array `a`
ref: a[:, (np.newaxis)]
Output: ax.set_zlabel('a')
bleu_metric: 14.873743701255318
----------