source_code_classification/model_interpretation_results/errors_analysis/logreg_validation_2.2_chunksize_10_conf_matrix_FP.txt

"ex.groupby(level='A').agg(lambda x: x.index.get_level_values(1).nunique())
pd.concat(map(pd.DataFrame, iter(d.values())), keys=list(d.keys())).stack().unstack(0)
sum(1 for i, j in zip(a, b) if i != j)
d = {(a.lower(), b): v for (a, b), v in list(d.items())}
list_.sort(key=lambda x: [x[0], len(x[1]), x[1]])
s.strip()
s = s.lstrip()
s = s.rstrip()
s = s.strip(' \t\n\r')"
"Task.objects.exclude(prerequisites__status__in=['A', 'P', 'F'])
root.configure(background='black')
numpy.array([(key, val) for key, val in result.items()], dtype)
pd.concat([df_1, df_2.sort_values('y')])
re.sub('(.*)</div>', '\\1</bad>', s)
print(max(d, key=lambda x: (d[x]['salary'], d[x]['bonus'])))
Book.objects.filter(author__id=1).filter(author__id=2)
re.compile('XYZ', re.IGNORECASE).split('fooxyzbar')
[sum(map(int, s)) for s in example.split()]"
"c.decode('unicode_escape')
pd.melt(x, id_vars=['farm', 'fruit'], var_name='year', value_name='value')
default_data['item3'] = 3
default_data.update({'item3': 3, })
default_data.update({'item4': 4, 'item5': 5, })
l[:3] + l[-3:]
df = df.reset_index(drop=True)
[a[x].append(b[x]) for x in range(3)]
os.path.realpath(path)"
"if ('key1' in dict):
    pass
if (key in d):
    pass
Blog.objects.filter(pk__in=[1, 4, 7])
f = open('test/test.pdf', 'rb')
format(12345678.46, ',').replace(',', ' ').replace('.', ',')
pd.merge(frame_1, frame_2, left_on='county_ID', right_on='countyid')
np.isnan(a).sum() / np.prod(a.shape)"
"lst.sort(key=lambda x: x[2], reverse=True)
indices = [i for i, x in enumerate(my_list) if x == 'whatever']
subprocess.call('grep -r PASSED *.log | sort -u | wc -l', shell=True)
len(my_text) - len(my_text.rstrip('?'))
df[df.columns[1:]].replace('[\\$,]', '', regex=True).astype(float)
df1.merge(df2, how='left', on='word')
print(''.join(''.join(i) for i in zip(a2, a1)) + a[-1] if len(a) % 2 else '')
root.attributes('-topmost', True)
root.lift()"
"re.findall('\\d|\\d,\\d\\)', '6,7)')
input('Press Enter to continue...')
""""""
ABC"""""".encode('hex')
db.Doc.update({'_id': b['_id']}, {'$set': {'geolocCountry': myGeolocCountry}})
re.sub('l+', 'l', 'lollll')
rows = soup.findAll('tr')[4::5]
plt.gca().invert_xaxis()
plt.gca().invert_yaxis()
pd.concat([GOOG, AAPL], keys=['GOOG', 'AAPL'], axis=1)"
"i = int(s, 16)
int('0xff', 16)
int('FFFF', 16)
ast.literal_eval('0xdeadbeef')
int('deadbeef', 16)
os.system('screencapture screen.png')
driver.set_window_size(1400, 1000)
unicodedata.normalize('NFKD', 'm\xfasica').encode('ascii', 'ignore')
pandas.concat([df1, df2]).drop_duplicates().reset_index(drop=True)"
"b = {a[i]: a[i + 1] for i in range(0, len(a), 2)}
len(set(a)) == len(a)
print(hashlib.md5(open(full_path, 'rb').read()).hexdigest())
sorted(list(data.items()), key=lambda x: x[1][0])
"""""""""""".join(x.upper() if random.randint(0, 1) else x for x in s)
os.system('GREPDB=""echo 123""; /bin/bash -c ""$GREPDB""')
os.system('/bin/bash -c ""echo hello world""')
getattr(test, a_string)
Image.open('pathToFile').show()"
"weekly = [sum(visitors[x:x + 7]) for x in range(0, len(daily), 7)]
del d[key]
{i: a[i] for i in a if (i != 0)}
lol.pop('hello')
del r[key]
np.linalg.solve(np.dot(a.T, a), np.dot(a.T, b))
pd.concat([df.drop('b', axis=1), pd.DataFrame(df['b'].tolist())], axis=1)
for i in range(0, 10, 2):
    pass"
"    pass
[{'content': x['content'].lower()} for x in messages]
"""""" """""".join(my_list)
re.sub('(http://\\S+|\\S*[^\\w\\s]\\S*)', '', a)
str(n) == str(n)[::-1]
ftp.storbinary('STOR myfile.txt', open('myfile.txt', 'rb'))
re.sub('.*I', 'I', stri)
int('1,000,000'.replace(',', ''))
pd.merge(df1, df2, left_index=True, right_index=True, how='outer')"
"'Hello\n\n\n'.rstrip('\n')
re.findall('.{,16}\\b', text)
[[X[i][j] for j in range(len(X[i]))] for i in range(len(X))]
'\xd0\xbc\xd0\xb0\xd1\x80\xd0\xba\xd0\xb0'.encode('latin-1')
df.groupby((df.a == 'B').shift(1).fillna(0).cumsum())
urllib.request.urlretrieve('http://search.twitter.com/search.json?q=hi', 'hi.json')
numpy.where((x == 0))[0]
sys.stdout.flush()
str(i)"
"str(a)
L.sort(key=operator.itemgetter(1))
print(str(count) + '    ' + str(conv))
df.fillna(method='ffill', inplace=True)
text.config(state=DISABLED)
sum(map(ord, string))
list(itertools.product(*arrays))
'{:,}'.format(value)
locale.setlocale(locale.LC_ALL, 'en_US')"
"results = [item['value'] for item in test_data]
datetime.datetime.now().isoformat()
datetime.datetime.utcnow().isoformat()
df.apply(' '.join, axis=0)
pd.DataFrame(df.values - df2.values, columns=df.columns)
print(open('myfile.txt', 'U').read())
print(line.decode('utf-16-le').split())
file = io.open('data.txt', 'r', encoding='utf-16-le')
s1 = pd.merge(df1, df2, how='inner', on=['user_id'])"
"pd.merge(df1, df2, on=['A', 'B', 'C', 'D'], how='inner')
dict((v, k) for k, v in map.items())
s.decode('unicode_escape')
[int(i) for i in str_list]
map(int, ['1', '2', '3'])
list(map(int, ['1', '2', '3']))
soup.find_all('a', href=re.compile('http://www\\.iwashere\\.com/'))
soup.find_all('a', href=re.compile('^(?!(?:[a-zA-Z][a-zA-Z0-9+.-]*:|//))'))
subprocess.call(['java', '-jar', 'Blender.jar'])"
"no_integers = [x for x in mylist if not isinstance(x, int)]
tree.xpath("".//a[text()='Example']"")[0].tag
"""""", """""".join([(str(k) + ' ' + str(v)) for k, v in list(a.items())])
print(set(re.sub('[\x00-\x7f]', '', '\xa3\u20ac\xa3\u20ac')))
print(re.sub('[\x00-\x7f]', '', '\xa3100 is worth more than \u20ac100'))
ast.literal_eval(""{'muffin' : 'lolz', 'foo' : 'kitty'}"")
print(t.decode('unicode_escape'))
print(str.encode('cp1252').decode('utf-8').encode('cp1252').decode('utf-8'))
zip(list_a, list_b)"
"driver.find_element_by_name('<check_box_name>').is_selected()
driver.find_element_by_id('<check_box_id>').is_selected()
[(a if a else 2) for a in [0, 1, 0, 3]]
'M\\N{AMPERSAND}M\\N{APOSTROPHE}s'.encode().decode('unicode-escape')
'M\\N{AMPERSAND}M\\N{APOSTROPHE}s'.decode('unicode-escape')
chr(int('fd9b', 16)).encode('utf-8')
print('0x%X' % value)
cleaned = [x for x in your_list if x]
slice(*[(int(i.strip()) if i else None) for i in string_slice.split(':')])"
"zip(it, it, it)
df['x'].str.lower()
jsobj['a']['b']['e'].append({'f': var6, 'g': var7, 'h': var8})
"""""""""""".join(lst)
sum(v for v in list(d.values()) if v > 0)
app.run(debug=True)
df.drop(df.index[[1, 3]], inplace=True)
df.apply(lambda x: x.fillna(x.mean()), axis=0)
[o.my_attr for o in my_list]"
"re.findall('TAA(?:[ATGC]{3})+?TAA', seq)
sorted(s, key=float)
hex(65)
a.append(b).reset_index(drop=True)
pd.concat([a, b], ignore_index=True)
[(i, j) for i in range(1, 3) for j in range(1, 5)]
sorted(iter(mydict.items()), key=itemgetter(1), reverse=True)
pd.date_range('1/1/2014', periods=12, freq='BM')
requests.get('https://kennethreitz.com', verify=False)"
""""""""""""".join(list(OrderedDict.fromkeys('aaabcabccd').keys()))
list(set('aaabcabccd'))
"""""""""""".join(set('aaabcabccd'))
df.loc[(df.loc[:, (df.dtypes != object)] != 0).any(1)]
br.form.add_file(open(filename), 'text/plain', filename)
all(word in d for word in ['somekey', 'someotherkey', 'somekeyggg'])
subprocess.check_output(['espeak', text], stderr=subprocess.STDOUT)
df.fillna(method='ffill', inplace=True)
print(np.linspace(1, 3, num=4, endpoint=False))"
"fn = os.path.join(os.path.dirname(__file__), 'my_file')
e = next(iter(s))
os.system('dir c:\\')
self.treeview.connect('size-allocate', self.treeview_changed)
3 in [1, 2, 3]
datetime.datetime.strptime('10/05/2012', '%d/%m/%Y').strftime('%Y-%m-%d')
s = s.replace('\\', '\\\\')
print(proc.communicate()[0])
pd.concat([pd.DataFrame(l) for l in my_list], axis=1).T"
"plt.savefig('test.png', bbox_inches='tight')
(listone + listtwo)
for item in itertools.chain(listone, listtwo):
    pass
males = df[(df[Gender] == 'Male') & (df[Year] == 2014)]
print('\\')
df.replace('-', np.nan)
df = df.drop('column_name', 1)
df.drop(df.columns[[0, 1, 3]], axis=1)"
"parser = argparse.ArgumentParser(allow_abbrev=False)
feature3 = [d.get('Feature3') for d in df.dic]
df.loc[gb.groups['foo'], ('A', 'B')]
print('[%s, %s, %s]' % (1, 2, 3))
print('[{0}, {1}, {2}]'.format(1, 2, 3))
[v for k, v in list(my_dict.items()) if 'Date' in k]
""""""{0.month}/{0.day}/{0.year}"""""".format(my_date)
df.drop(('col1', 'a'), axis=1)
df.drop('a', level=1, axis=1)"
"map(int, re.findall('\\d+', s))
os.listdir('/home/username/www/')
os.listdir('path')
pd.concat([distancesDF, datesDF.dates], axis=1)
[x[0] for x in a]
[i[0] for i in a]
re.sub('(?<=[a-z])\\r?\\n', ' ', textblock)
gzip.open('file.gz', 'rt', encoding='utf-8')
set(['a', 'b']).issubset(['b', 'a', 'foo', 'bar'])"
"line.translate(None, '!@#$')
line = re.sub('[!@#$]', '', line)
string.replace('1', '')
a = a.replace(char, '')
a = a.replace(char, '')
line = line.translate(string.maketrans('', ''), '!@#$')
pd.concat([df, pd.get_dummies(df, '', '').astype(int)], axis=1)[order]
[3, 4, 1, 2]
globals()['something'] = 'bob'"
"yourdf.drop(['columnheading1', 'columnheading2'], axis=1, inplace=True)
[s.strip() for s in input().split(',')]
[int(d) for d in str(bin(x))[2:]]
max(len(word) for word in i)
len(max(i, key=len))
os.system(my_cmd)
mylist.sort(key=lambda x: x.lower())
mylist.sort(key=str.lower)
mylist.sort()"
"zip(*list_of_tuples)
pd.merge(y, x, on='k')[['a', 'b', 'y']]
[item.strip() for item in my_string.split(',')]
print((obj.__dict__))
dir()
dir()
window.set_position(Gtk.WindowPosition.CENTER)
plt.rc('font', **{'size': '30'})
df.isnull().values.any()"
"x.reset_index().merge(y, how='left', on='state', sort=False).sort('index')
json.loads(request.POST.get('mydata', '{}'))
list(zip(*((iter([1, 2, 3, 4, 5, 6, 7, 8, 9]),) * 3)))
list(grouper(2, [1, 2, 3, 4, 5, 6, 7]))
[input[i:i + n] for i in range(0, len(input), n)]
keys.sort(key=lambda x: map(int, x.split('.')))
keys.sort(key=lambda x: [int(y) for y in x.split('.')])
img.transpose(2, 0, 1).reshape(3, -1)
df['BrandName'].replace(['ABC', 'AB'], 'A')"
"df.sub(df.mean(axis=1), axis=0)
"""""""""""".join([i for i in s if i.isalpha()])
l = (int(x) for x in s.split())
""""""42 0"""""".split()
map(int, '42 0'.split())
[i for i, elem in enumerate(bool_list, 1) if elem]
data.groupby(data['date'].map(lambda x: x.year))
np.in1d(b, a).nonzero()[0]
time.strftime('%l:%M%p %z on %b %d, %Y')"
"l1.sort(key=lambda x: int(x[0]))
sorted([[1, 'mike'], [1, 'bob']])
""""""Abc"""""".translate(maketrans('abcABC', 'defDEF'))
""""""<br/>"""""".join([('%s:: %s' % (key, value)) for key, value in list(d.items())])
self.writer.writerow([str(s).encode('utf-8') for s in row])
os.system('cls')
os.system('clear')
os.system('tcsh your_own_script')
os.system(""zsh -c 'echo $0'"")"
"platform.system()
import platform
platform.release()
print(os.name)
[x for x in my_list if not x.startswith('#')]
""""""Day old bread, 50% sale {0}"""""".format('today')
min(list, key=lambda x: float('inf') if math.isnan(x[1]) else x[1])
a = [(sum(x) / len(x)) for x in zip(*a)]
logging.info('Log message', extra={'app_name': 'myapp'})"
"df.fillna(0)
df.toPandas().to_csv('mycsv.csv')
df.write.csv('mycsv.csv')
sum(x[1] for x in structure)
df.groupby('STNAME')['COUNTY_POP'].agg(lambda x: x.nlargest(3).sum())
datetime.strptime('21/11/06 16:30', '%d/%m/%y %H:%M')
os.path.dirname(os.path.abspath(__file__))
re.sub('(.)', '\\1\\1', text.read(), 0, re.S)
"""""""""""".join(('a', 'b', 'c', 'd', 'g', 'x', 'r', 'e'))"
"apple.decode('iso-8859-1').encode('utf8')
df.to_csv('filename.csv', header=False)
print('{0}:<15}}{1}:<15}}{2}:<8}}'.format('1', '2', '3'))
max(ld, key=lambda d: d['size'])
""""""{0}\\w{{2}}b{1}\\w{{2}}quarter"""""".format('b', 'a')
user = models.ForeignKey('User', unique=True)
re.compile('^([^A]*)AA([^A]|AA)*$')
b = np.concatenate((a, a), axis=0)
sorted(l, key=lambda x: x.replace('0', 'Z'))"
"e = root.xpath('.//a[starts-with(text(),""TEXT A"")]')
e = root.xpath('.//a[text()=""TEXT A""]')
c = [b[i] for i in index]
np.dot(a[:, (None)], b[(None), :])
np.outer(a, b)
subprocess.call(['./abc.py', arg1, arg2])
df[['value']].fillna(df.groupby('group').transform('mean'))
re.sub('(.)(?=.)', '\\1-', s)
re.sub('(?<=.)(?=.)', '-', str)"
"print(collections.Counter(s).most_common(1)[0])
float(re.findall('(?:^|_)' + par + '(\\d+\\.\\d*)', dir)[0])
re.findall('[^a]', 'abcd')
print([item for item in dir(adfix) if not item.startswith('__')])
[x[0] for x in rows]
res_list = [x[0] for x in rows]
pd.concat([x] * 5, ignore_index=True)
pd.concat([x] * 5)
sorted_list_of_keyvalues = sorted(list(ips_data.items()), key=item[1]['data_two'])"
"""""""\\xc3\\x85あ"""""".encode('utf-8').decode('unicode_escape')
""""""\\xc3\\x85あ"""""".encode('utf-8')
[j for i in zip(a, b) for j in i]
[j for i in zip(a, b) for j in i]
print([s.replace('8', '') for s in lst])
"""""","""""".join('Hello')
Content.objects.all().order_by('?')[:100]
A[np.arange(A.shape[0])[:, (None)], B]
df.pivot_table(index='saleid', columns='upc', aggfunc='size', fill_value=0)"
"{key: val for key, val in list(myDict.items()) if val != 42}
return len(s.encode('utf-8'))
os.kill(process.pid, signal.SIGKILL)
df[pd.isnull(df).any(axis=1)]
url.split('&')[-1].replace('=', '') + '.html'
parser.ParseFile(open('sample.xml', 'rb'))
sys.exit()
setattr(self, attr, group)
urllib.parse.unquote(urllib.parse.unquote(some_string))"
"pd.concat(g for _, g in df.groupby('ID') if len(g) > 1)
x = numpy.delete(x, 2, axis=1)
x = numpy.delete(x, 0, axis=0)
pd.concat((df1, df2), axis=1).mean(axis=1)
np.mean(np.array([old_set, new_set]), axis=0)
scatter(x, y, s=500, color='green', marker='h')
result = [item for word in words for item in word.split(',')]
datetime.datetime.strptime('2012-05-29T19:30:03.283Z', '%Y-%m-%dT%H:%M:%S.%fZ')
sum(item['one'] for item in list(tadas.values()))"
"a.encode('ascii', 'ignore')
files = [f for f in os.listdir('.') if re.match('[0-9]+.*\\.jpg', f)]
np.zeros((6, 9, 20)) + np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])[(None), :, (None)]
np.zeros((6, 9, 20)) + np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape((1, 9, 1))
os.system('start excel.exe <path/to/file>')
print(max(x, key=sum))
sum(len(y) for y in x if len(y) > 1)
re.sub('(\\d+)', '""\\1""', 'This is number 1 and this is number 22')
numpy.dot(numpy.dot(a, m), a)"
"sorted(list_of_dct, key=lambda x: order.index(list(x.values())[0]))
return s[0].upper() + s[1:]
"""""""""""".join([1, 2, 3, 4])
line = line.decode('utf-8', 'ignore').encode('utf-8')
os.system(command)
c.execute('SELECT * FROM foo WHERE bar = %s AND baz = %s', (param1, param2))
dateobj = datetime.datetime.strptime(datestr, '%Y-%m-%d').date()
os.kill(os.getpid(), signal.SIGUSR1)
bytes.fromhex('4a4b4c').decode('utf-8')"
"pd.merge(split_df, csv_df, on=['key'], suffixes=('_left', '_right'))
s.split(' ', 4)
input('Enter your input:')
app.run(debug=True)
pickle.dump(mylist, open('save.txt', 'wb'))
scipy.tensordot(P, T, axes=[1, 1]).swapaxes(0, 1)
numpy.zeros((3, 3, 3))
"""""" """""".join(content.split(' ')[:-1])
x = np.asarray(x).reshape(1, -1)[(0), :]"
"db.GqlQuery('SELECT * FROM Schedule WHERE station = $1', foo.key())
df.b.str.contains('^f')
print('\n'.join('\t'.join(str(col) for col in row) for row in tab))
df.set_index(list('BC')).drop(tuples, errors='ignore').reset_index()
""""""({:d} goals, ${:d})"""""".format(self.goals, self.penalties)
""""""({} goals, ${})"""""".format(self.goals, self.penalties)
""""""({0.goals} goals, ${0.penalties})"""""".format(self)
[int(''.join(str(d) for d in x)) for x in L]
[''.join(str(d) for d in x) for x in L]"
"re.sub('[^\\sa-zA-Z0-9]', '', text).lower().strip()
re.sub('(?!\\s)[\\W_]', '', text).lower().strip()
plt.plot(x, y, label='H\u2082O')
plt.plot(x, y, label='$H_2O$')
[x for x in mylist if len(x) == 3]
lst = [Object() for _ in range(100)]
lst = [Object() for i in range(100)]
self.driver.find_element_by_css_selector('.someclass a').get_attribute('href')
df1.merge(df2, on='Date_Time')"
"sorted(d.items())
int('1')
int()
T2 = [map(int, x) for x in T1]
subprocess.call(['./test.sh'])
subprocess.call(['notepad'])
[val for pair in zip(l1, l2) for val in pair]
encoded = base64.b64encode('data to be encoded')
encoded = 'data to be encoded'.encode('ascii')"
"writer.writeheader()
df.fillna(df.mean(axis=1), axis=1)
time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(1347517370))
super(Derived, cls).do(a)
a[np.where((a[:, (0)] == 0) * (a[:, (1)] == 1))]
re.split(' +', 'hello world sample text')
len(max(words, key=len))
result[0]['from_user']
[line.split() for line in open('File.txt')]"
"new_file = open('path/to/FILE_NAME.ext', 'w')
df.groupby(['col1', 'col2'])['col3'].nunique().reset_index()
any(key.startswith('EMP$$') for key in dict1)
[value for key, value in list(dict1.items()) if key.startswith('EMP$$')]
pd.DataFrame({'email': sf.index, 'list': sf.values})
print('\t'.join(map(str, list)))
print('\xd0\xbf\xd1\x80\xd0\xb8'.encode('raw_unicode_escape'))
'Sopet\xc3\xb3n'.encode('latin-1').decode('utf-8')
image = image.resize((x, y), Image.ANTIALIAS)"
"subprocess.Popen(['background-process', 'arguments'])
[mydict[x] for x in mykeys]
dict([('Name', 'Joe'), ('Age', 22)])
data.reshape(-1, j).mean(axis=1).reshape(data.shape[0], -1)
print(s.encode('unicode-escape').replace('""', '\\""'))
re.split('(\\W+)', s)
df.plot(kind='barh', stacked=True)
{i[1]: i[0] for i in list(myDictionary.items())}
[i for i, j in enumerate(myList) if 'how' in j.lower() or 'what' in j.lower()]"
"pd.concat([df[0].apply(pd.Series), df[1]], axis=1)
re.findall('src=""js/([^""]*\\bjquery\\b[^""]*)""', data)
sum(int(float(item)) for item in [_f for _f in ['', '3.4', '', '', '1.0'] if _f])
subprocess.Popen(['c:\\Program Files\\VMware\\VMware Server\\vmware-cmd.bat'])
q.put((-n, n))
df['group'].plot(kind='bar', color=['r', 'g', 'b', 'r', 'g', 'b', 'r'])
re.findall('([a-fA-F\\d]{32})', data)
len(my_list)
len(l)"
"print(concatenate((a, b), axis=1))
c = np.r_[(a[None, :], b[None, :])]
np.array((a, b))
print(socket.getaddrinfo('google.com', 80))
df.xs('sat', level='day', drop_level=False)
return HttpResponse('Unauthorized', status=401)
Flask(__name__, template_folder='wherever')
session.execute('INSERT INTO t1 (SELECT * FROM t2)')
c2.sort(key=lambda row: row[2])"