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emove duplicate rows from dataframe `df1` and calculate their frequency | df1.groupby(['key', 'year']).size().reset_index() |
dictionary `dictionary` in ascending order by its value | sorted(list(dictionary.items()), key=operator.itemgetter(1)) |
erate over dictionary `d` in ascending order of value | sorted(iter(d.items()), key=lambda x: x[1]) |
erate over a python dictionary, ordered by value | sorted(list(dictionary.items()), key=lambda x: x[1]) |
plit 1d array `a` into 2d array at the last eleme | np.split(a, [-1]) |
vert dataframe `df` into a pivot table using column 'order' as index and values of column 'sample' as colum | df.pivot(index='order', columns='sample') |
elect all rows from pandas DataFrame 'df' where the value in column 'A' is greater than 1 or less than 1 in column 'B'. | df[(df['A'] > 1) | (df['B'] < -1)] |
Get the zip output as list from the lists `[1, 2, 3]`, `[4, 5, 6]`, `[7, 8, 9]` | [list(a) for a in zip([1, 2, 3], [4, 5, 6], [7, 8, 9])] |
elect rows of dataframe `df` whose value for column `A` is `foo` | print(df.loc[df['A'] == 'foo']) |
elect rows whose column value in column `column_name` does not equal `some_value` in pandas data frame | df.loc[df['column_name'] != some_value] |
elect rows from a dataframe `df` whose value for column `column_name` is not in `some_values` | df.loc[~df['column_name'].isin(some_values)] |
elect all rows whose values in a column `column_name` equals a scalar `some_value` in pandas data frame object `df` | df.loc[df['column_name'] == some_value] |
Select rows whose value of the B column is one or three in the DataFrame `df` | print(df.loc[df['B'].isin(['one', 'three'])]) |
epeat every character for 7 times in string 'map' | """""".join(map(lambda x: x * 7, 'map')) |
delete an empty directory | os.rmdir() |
ecursively delete all contents in directory `path` | shutil.rmtree(path, ignore_errors=False, onerror=None) |
ecursively remove folder `name` | os.removedirs(name) |
Add row `['8/19/2014', 'Jun', 'Fly', '98765']` to dataframe `df` | df.loc[len(df)] = ['8/19/2014', 'Jun', 'Fly', '98765'] |
list all files in a current directory | glob.glob('*') |
List all the files that doesn't contain the name `hello` | glob.glob('[!hello]*.txt') |
List all the files that matches the pattern `hello*.txt` | glob.glob('hello*.txt') |
evaluate the expression '20<30' | eval('20<30') |
Copy list `old_list` and name it `new_list` | new_list = [x[:] for x in old_list] |
vert scientific notation of variable `a` to decimal | """{:.50f}""".format(float(a[0] / a[1])) |
vert dataframe `df` to integertype sparse objec | df.to_sparse(0) |
display attribute `attr` for each object `obj` in list `my_list_of_objs` | print([obj.attr for obj in my_list_of_objs]) |
he number of True values associated with key 'success' in dictionary `d` | sum(1 if d['success'] else 0 for d in s) |
get the sum of values associated with the key ‘success’ for a list of dictionaries `s` | sum(d['success'] for d in s) |
get complete path of a module named `os` | imp.find_module('os')[1] |
get logical xor of `a` and `b` | (bool(a) != bool(b)) |
get logical xor of `a` and `b` | ((a and (not b)) or ((not a) and b)) |
get logical xor of `a` and `b` | (bool(a) ^ bool(b)) |
get logical xor of `a` and `b` | xor(bool(a), bool(b)) |
get the logical xor of two variables `str1` and `str2` | return (bool(str1) ^ bool(str2)) |
Sort list `my_list` in alphabetical order based on the values associated with key 'name' of each dictionary in the l | my_list.sort(key=operator.itemgetter('name')) |
plit a string `a , b; cdf` using both commas and semicolons as delimeter | re.split('\\s*,\\s*|\\s*;\\s*', 'a , b; cdf') |
Split a string `string` by multiple separators `,` and `;` | [t.strip() for s in string.split(',') for t in s.split(';')] |
ke a function `f` that calculates the sum of two integer variables `x` and `y` | f = lambda x, y: x + y |
Create list `instancelist` containing 29 objects of type MyCl | instancelist = [MyClass() for i in range(29)] |
Make a dictionary from list `f` which is in the format of four sets of val, key, val | {f[i + 1]: [f[i], f[i + 2]] for i in range(0, len(f), 3)} |
vert bytes string `s` to an unsigned integer | struct.unpack('>q', s)[0] |
atenate a series `students` onto a dataframe `marks` with pand | pd.concat([students, pd.DataFrame(marks)], axis=1) |
Sort list `alist` in ascending order based on each of its elements' attribute `foo` | alist.sort(key=lambda x: x.foo) |
BeautifulSoup select 'div' elements with an id attribute value ending with substring '_answer' in HTML parsed string `soup` | soup.select('div[id$=_answer]') |
ympy solve matrix of linear equations `(([1, 1, 1, 1], [1, 1, 2, 3]))` with variables `(x, y, z)` | linsolve(Matrix(([1, 1, 1, 1], [1, 1, 2, 3])), (x, y, z)) |
best way to extract subset of keyvalue pairs with keys matching 'l', 'm', or 'n' from python dictionary objec | {k: bigdict[k] for k in list(bigdict.keys()) & {'l', 'm', 'n'}} |
extract subset of keyvalue pairs with keys as `('l', 'm', 'n')` from dictionary object `bigdict` | dict((k, bigdict[k]) for k in ('l', 'm', 'n')) |
Get items from a dictionary `bigdict` where the keys are present in `('l', 'm', 'n')` | {k: bigdict.get(k, None) for k in ('l', 'm', 'n')} |
Extract subset of key value pair for keys 'l', 'm', 'n' from `bigdict` in python 3 | {k: bigdict[k] for k in ('l', 'm', 'n')} |
Selenium get the entire `driver` page tex | driver.page_source |
extracting column `1` and `9` from array `data` | data[:, ([1, 9])] |
emove all square brackets from string 'abcd[e]yth[ac]ytwec' | re.sub('\\[.*?\\]', '', 'abcd[e]yth[ac]ytwec') |
w can I resize the root window in Tkinter? | root.geometry('500x500') |
find all substrings in string `mystring` composed only of letters `a` and `b` where each `a` is directly preceded and succeeded by `b` | re.findall('\\b(?:b+a)+b+\\b', mystring) |
vert list `lst` of tuples of floats to list `str_list` of tuples of strings of floats in scientific notation with eight decimal point precisio | str_list = [tuple('{0:.8e}'.format(flt) for flt in sublist) for sublist in lst] |
vert list of sublists `lst` of floats to a list of sublists `str_list` of strings of integers in scientific notation with 8 decimal po | str_list = [['{0:.8e}'.format(flt) for flt in sublist] for sublist in lst] |
Create a tuple `t` containing first element of each tuple in tuple `s` | t = tuple(x[0] for x in s) |
btain the current day of the week in a 3 letter format from a datetime objec | datetime.datetime.now().strftime('%a') |
get the ASCII value of a character 'a' as an | ord('a') |
get the ASCII value of a character u'あ' as an | ord('\u3042') |
get the ASCII value of a character as an | ord() |
decode JSON string `u` to a dictionary | json.load(u) |
Delete mulitple columns `columnheading1`, `columnheading2` in pandas data frame `yourdf` | yourdf.drop(['columnheading1', 'columnheading2'], axis=1, inplace=True) |
get a list of of elements resulting from splitting user input by commas and stripping white space from each resulting string `s` | [s.strip() for s in input().split(',')] |
eate a list containing the digits values from binary string `x` as eleme | [int(d) for d in str(bin(x))[2:]] |
get the max string length in list `i` | max(len(word) for word in i) |
get the maximum string length in nested list `i` | len(max(i, key=len)) |
execute os command `my_cmd` | os.system(my_cmd) |
list `mylist` alphabetically | mylist.sort(key=lambda x: x.lower()) |
list `mylist` in alphabetical order | mylist.sort(key=str.lower) |
a list of strings 'mylist'. | mylist.sort() |
a list of strings `list` | list.sort() |
Set multi index on columns 'Company' and 'date' of data frame `df` in pandas. | df.set_index(['Company', 'date'], inplace=True) |
get the attribute `x` from object `your_obj` | getattr(your_obj, x) |
emove first word in string `s` | s.split(' ', 1)[1] |
ave xlsxwriter file in 'app/smth1/smth2/Expenses01.xlsx' path and assign to variable `workbook` | workbook = xlsxwriter.Workbook('app/smth1/smth2/Expenses01.xlsx') |
ave xlsxwriter file to 'C:/Users/Steven/Documents/demo.xlsx' path | workbook = xlsxwriter.Workbook('C:/Users/Steven/Documents/demo.xlsx') |
hange legend size to 'xsmall' in upperleft locatio | pyplot.legend(loc=2, fontsize='x-small') |
hange legend font size with matplotlib.pyplot to 6 | plot.legend(loc=2, prop={'size': 6}) |
plit list `l` into `n` sized l | [l[i:i + n] for i in range(0, len(l), n)] |
plit a list `l` into evenly sized chunks `n` | [l[i:i + n] for i in range(0, len(l), n)] |
heck if character '' exists in a dataframe `df` cell 'a' | df['a'].str.contains('-') |
emove all non word, whitespace, or apostrophe characters from string `doesn't this mean it technically works?` | re.sub("[^\\w' ]", '', "doesn't this mean it -technically- works?") |
find all digits between two characters `\xab` and `\xbb`in a string `text` | print(re.findall('\\d+', '\n'.join(re.findall('\xab([\\s\\S]*?)\xbb', text)))) |
plot data of column 'index' versus column 'A' of dataframe `monthly_mean` after resetting its index | monthly_mean.reset_index().plot(x='index', y='A') |
get the output of a subprocess command `echo foo` in command line | subprocess.check_output('echo "foo"', shell=True) |
Encode each value to 'UTF8' in the list `EmployeeList` | [x.encode('UTF8') for x in EmployeeList] |
mbine two columns `foo` and `bar` in a pandas data frame | pandas.concat([df['foo'].dropna(), df['bar'].dropna()]).reindex_like(df) |
generate a list of consecutive integers from 0 to 8 | list(range(9)) |
vert list `myintegers` into a unicode string | """""".join(chr(i) for i in myintegers) |
herit from class `Executive` | super(Executive, self).__init__(*args) |
Remove the string value `item` from a list of strings `my_sequence` | [item for item in my_sequence if item != 'item'] |
andomly select an item from list `foo` | random.choice(foo) |
heck if all of the following items in list `['a', 'b']` are in a list `['a', 'b', 'c']` | set(['a', 'b']).issubset(['a', 'b', 'c']) |
Check if all the items in a list `['a', 'b']` exists in another list `l` | set(['a', 'b']).issubset(set(l)) |
et the stdin of the process 'grep f' to be b'one\ntwo\nthree\nfour\nfive\nsix\n' | p = Popen(['grep', 'f'], stdout=PIPE, stdin=PIPE, stderr=STDOUT)
grep_stdout = p.communicate(input='one\ntwo\nthree\nfour\nfive\nsix\n')[0] |
et the stdin of the process 'grep f' to be 'one\ntwo\nthree\nfour\nfive\nsix\n' | p = subprocess.Popen(['grep', 'f'], stdout=subprocess.PIPE, stdin=subprocess.PIPE)
p.stdin.write('one\ntwo\nthree\nfour\nfive\nsix\n')
p.communicate()[0]
p.stdin.close() |
vert a list of tuples `list_of_tuples` into list of l | [list(t) for t in zip(*list_of_tuples)] |
group a list `list_of_tuples` of tuples by value | zip(*list_of_tuples) |
erge pandas dataframe `x` with columns 'a' and 'b' and dataframe `y` with column 'y' | pd.merge(y, x, on='k')[['a', 'b', 'y']] |