prompt
stringlengths 4
181
| completion
stringlengths 2
232
|
|---|---|
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) |