prompt
stringlengths 4
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stringlengths 2
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Clicking a link using selenium using pytho
|
driver.find_element_by_xpath('xpath').click()
|
que index values in column 'A' in pandas dataframe `ex`
|
ex.groupby(level='A').agg(lambda x: x.index.get_level_values(1).nunique())
|
Create a pandas dataframe of values from a dictionary `d` which contains dictionaries of dictionarie
|
pd.concat(map(pd.DataFrame, iter(d.values())), keys=list(d.keys())).stack().unstack(0)
|
find out the number of nonmatched elements at the same index of list `a` and list `b`
|
sum(1 for i, j in zip(a, b) if i != j)
|
ke all keys lowercase in dictionary `d`
|
d = {(a.lower(), b): v for (a, b), v in list(d.items())}
|
list `list_` based on first element of each tuple and by the length of the second element of each tuple
|
list_.sort(key=lambda x: [x[0], len(x[1]), x[1]])
|
m whitespace in string `s`
|
s.strip()
|
m whitespace (including tabs) in `s` on the left side
|
s = s.lstrip()
|
m whitespace (including tabs) in `s` on the right side
|
s = s.rstrip()
|
m characters ' \t\n\r' in `s`
|
s = s.strip(' \t\n\r')
|
m whitespaces (including tabs) in string `s`
|
print(re.sub('[\\s+]', '', s))
|
Django, filter `Task.objects` based on all entities in ['A', 'P', 'F']
|
Task.objects.exclude(prerequisites__status__in=['A', 'P', 'F'])
|
Change background color in Tkinter
|
root.configure(background='black')
|
vert dict `result` to numpy structured array
|
numpy.array([(key, val) for key, val in result.items()], dtype)
|
Concatenate dataframe `df_1` to dataframe `df_2` sorted by values of the column 'y'
|
pd.concat([df_1, df_2.sort_values('y')])
|
eplace the last occurence of an expression '</div>' with '</bad>' in a string `s`
|
re.sub('(.*)</div>', '\\1</bad>', s)
|
get the maximum of 'salary' and 'bonus' values in a dictionary
|
print(max(d, key=lambda x: (d[x]['salary'], d[x]['bonus'])))
|
Filter Django objects by `author` with ids `1` and `2`
|
Book.objects.filter(author__id=1).filter(author__id=2)
|
plit string 'fooxyzbar' based on caseinsensitive matching using string 'XYZ'
|
re.compile('XYZ', re.IGNORECASE).split('fooxyzbar')
|
get list of sums of neighboring integers in string `example`
|
[sum(map(int, s)) for s in example.split()]
|
Get all the keys from dictionary `y` whose value is `1`
|
[i for i in y if y[i] == 1]
|
verting byte string `c` in unicode string
|
c.decode('unicode_escape')
|
pivot first 2 columns into new columns 'year' and 'value' from a pandas dataframe `x`
|
pd.melt(x, id_vars=['farm', 'fruit'], var_name='year', value_name='value')
|
dd key item3 and value 3 to dictionary `default_data `
|
default_data['item3'] = 3
|
dd key item3 and value 3 to dictionary `default_data `
|
default_data.update({'item3': 3, })
|
dd key value pairs 'item4' , 4 and 'item5' , 5 to dictionary `default_data`
|
default_data.update({'item4': 4, 'item5': 5, })
|
Get the first and last 3 elements of list `l`
|
l[:3] + l[-3:]
|
eset index to default in dataframe `df`
|
df = df.reset_index(drop=True)
|
For each index `x` from 0 to 3, append the element at index `x` of list `b` to the list at index `x` of list a.
|
[a[x].append(b[x]) for x in range(3)]
|
get canonical path of the filename `path`
|
os.path.realpath(path)
|
heck if dictionary `L[0].f.items()` is in dictionary `a3.f.items()`
|
set(L[0].f.items()).issubset(set(a3.f.items()))
|
find all the indexes in a Numpy 2D array where the value is 1
|
zip(*np.where(a == 1))
|
w to find the index of a value in 2d array in Python?
|
np.where(a == 1)
|
Collapse hierarchical column index to level 0 in dataframe `df`
|
df.columns = df.columns.get_level_values(0)
|
eate a matrix from a list `[1, 2, 3]`
|
x = scipy.matrix([1, 2, 3]).transpose()
|
dd character '@' after word 'get' in string `text`
|
text = re.sub('(\\bget\\b)', '\\1@', text)
|
get a numpy array that contains the element wise minimum of three 3x1 array
|
np.array([np.arange(3), np.arange(2, -1, -1), np.ones((3,))]).min(axis=0)
|
dd a column 'new_col' to dataframe `df` for index in range
|
df['new_col'] = list(range(1, len(df) + 1))
|
et environment variable 'DEBUSSY' equal to 1
|
os.environ['DEBUSSY'] = '1'
|
Get a environment variable `DEBUSSY`
|
print(os.environ['DEBUSSY'])
|
et environment variable 'DEBUSSY' to '1'
|
os.environ['DEBUSSY'] = '1'
|
pdate dictionary `b`, overwriting values where keys are identical, with contents of dictionary `d`
|
b.update(d)
|
get all the values in column `b` from pandas data frame `df`
|
df['b']
|
ke a line plot with errorbars, `ebar`, from data `x, y, err` and set color of the errorbars to `y` (yellow)
|
ebar = plt.errorbar(x, y, yerr=err, ecolor='y')
|
find all files with extension '.c' in directory `folder`
|
results += [each for each in os.listdir(folder) if each.endswith('.c')]
|
dd unicode string '1' to UTF8 decoded string '\xc2\xa3'
|
print('\xc2\xa3'.decode('utf8') + '1')
|
lowercase the string obtained by replacing the occurrences of regex pattern '(?<=[az])([AZ])' in string `s` with eplacement '\\1'
|
re.sub('(?<=[a-z])([A-Z])', '-\\1', s).lower()
|
Setting stacksize in a python scrip
|
os.system('ulimit -s unlimited; some_executable')
|
format a string `num` using string formatting
|
"""{0:.3g}""".format(num)
|
ppend the first element of array `a` to array `a`
|
numpy.append(a, a[0])
|
eturn the column for value 38.15 in dataframe `df`
|
df.ix[:, (df.loc[0] == 38.15)].columns
|
erge 2 dataframes `df1` and `df2` with same values in a column 'revenue' with and index 'date'
|
df2['revenue'] = df2.CET.map(df1.set_index('date')['revenue'])
|
load a json data `json_string` into variable `json_data`
|
json_data = json.loads(json_string)
|
vert radians 1 to degree
|
math.cos(math.radians(1))
|
he number of integers in list `a`
|
sum(isinstance(x, int) for x in a)
|
eplacing '\u200b' with '*' in a string using regular expressio
|
'used\u200b'.replace('\u200b', '*')
|
function 'SudsMove' simultaneously
|
threading.Thread(target=SudsMove).start()
|
m of squares values in a list `l`
|
sum(i * i for i in l)
|
alculate the sum of the squares of each value in list `l`
|
sum(map(lambda x: x * x, l))
|
Create a dictionary `d` from list `iterable`
|
d = dict(((key, value) for (key, value) in iterable))
|
Create a dictionary `d` from list `iterable`
|
d = {key: value for (key, value) in iterable}
|
Create a dictionary `d` from list of key value pairs `iterable`
|
d = {k: v for (k, v) in iterable}
|
d off entries in dataframe `df` column `Alabama_exp` to two decimal places, and entries in column `Credit_exp` to three decimal place
|
df.round({'Alabama_exp': 2, 'Credit_exp': 3})
|
Make function `WRITEFUNCTION` output nothing in curl `p`
|
p.setopt(pycurl.WRITEFUNCTION, lambda x: None)
|
eturn a random word from a word list 'words'
|
print(random.choice(words))
|
Find a max value of the key `count` in a nested dictionary `d`
|
max(d, key=lambda x: d[x]['count'])
|
get list of string elements in string `data` delimited by commas, putting `0` in place of empty string
|
[(int(x) if x else 0) for x in data.split(',')]
|
plit string `s` into a list of strings based on ',' then replace empty strings with zero
|
""",""".join(x or '0' for x in s.split(','))
|
egular expression match nothing
|
re.compile('$^')
|
egular expression syntax for not to match anything
|
re.compile('.\\A|.\\A*|.\\A+')
|
eate a regular expression object with a pattern that will match nothing
|
re.compile('a^')
|
drop all columns in dataframe `df` that holds a maximum value bigger than 0
|
df.columns[df.max() > 0]
|
heck if date `yourdatetime` is equal to today's date
|
yourdatetime.date() == datetime.today().date()
|
print bold text 'Hello'
|
print('\x1b[1m' + 'Hello')
|
emove 20 symbols in front of '.' in string 'unique12345678901234567890.mkv'
|
re.sub('.{20}(.mkv)', '\\1', 'unique12345678901234567890.mkv')
|
Define a list with string values `['a', 'c', 'b', 'obj']`
|
['a', 'c', 'b', 'obj']
|
bstitute multiple whitespace with single whitespace in string `mystring`
|
""" """.join(mystring.split())
|
print a floating point number 2.345e67 without any truncatio
|
print('{:.100f}'.format(2.345e-67))
|
Check if key 'key1' in `dict`
|
('key1' in dict)
|
Check if key 'a' in `d`
|
('a' in d)
|
Check if key 'c' in `d`
|
('c' in d)
|
Check if a given key 'key1' exists in dictionary `dict`
|
if ('key1' in dict):
pass
|
Check if a given key `key` exists in dictionary `d`
|
if (key in d):
pass
|
eate a django query for a list of values `1, 4, 7`
|
Blog.objects.filter(pk__in=[1, 4, 7])
|
ead a binary file 'test/test.pdf'
|
f = open('test/test.pdf', 'rb')
|
ert ' ' between every three digit before '.' and replace ',' with '.' in 12345678.46
|
format(12345678.46, ',').replace(',', ' ').replace('.', ',')
|
Join pandas data frame `frame_1` and `frame_2` with left join by `county_ID` and right join by `countyid`
|
pd.merge(frame_1, frame_2, left_on='county_ID', right_on='countyid')
|
alculate ratio of sparsity in a numpy array `a`
|
np.isnan(a).sum() / np.prod(a.shape)
|
everse sort items in default dictionary `cityPopulation` by the third item in each key's list of value
|
sorted(iter(cityPopulation.items()), key=lambda k_v: k_v[1][2], reverse=True)
|
Sort dictionary `u` in ascending order based on second elements of its value
|
sorted(list(u.items()), key=lambda v: v[1])
|
everse sort dictionary `d` based on its value
|
sorted(list(d.items()), key=lambda k_v: k_v[1], reverse=True)
|
g a defaultdict `d` by value
|
sorted(list(d.items()), key=lambda k_v: k_v[1])
|
pen a file 'bundledresource.jpg' in the same directory as a python scrip
|
f = open(os.path.join(__location__, 'bundled-resource.jpg'))
|
pen the file 'words.txt' in 'rU' mode
|
f = open('words.txt', 'rU')
|
divide the values with same keys of two dictionary `d1` and `d2`
|
{k: (float(d2[k]) / d1[k]) for k in d2}
|
divide the value for each key `k` in dict `d2` by the value for the same key `k` in dict `d1`
|
{k: (d2[k] / d1[k]) for k in list(d1.keys()) & d2}
|
divide values associated with each key in dictionary `d1` from values associated with the same key in dictionary `d2`
|
dict((k, float(d2[k]) / d1[k]) for k in d2)
|
write dataframe `df` to csv file `filename` with dates formatted as yearmonthday `%Y%m%d`
|
df.to_csv(filename, date_format='%Y%m%d')
|
emove a key 'key' from a dictionary `my_dict`
|
my_dict.pop('key', None)
|
eplace NaN values in array `a` with zero
|
b = np.where(np.isnan(a), 0, a)
|
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