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sft-ready-neulab-conala

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lear terminal screen on window

os.system('cls')

lear the terminal screen in Linux

os.system('clear')

execute external commands/script `your_own_script` with csh instead of bash

os.system('tcsh your_own_script')

execute command 'echo $0' in Z shell

os.system("zsh -c 'echo $0'")

pdate a list `l1` dictionaries with a key `count` and value from list `l2`

[dict(d, count=n) for d, n in zip(l1, l2)]

eate a list with the sum of respective elements of the tuples of list `l`

[sum(x) for x in zip(*l)]

m each value in a list `l` of tuple

map(sum, zip(*l))

he number of nonnan elements in a numpy ndarray matrix `data`

np.count_nonzero(~np.isnan(data))

Convert each list in list `main_list` into a tuple

map(list, zip(*main_list))

django get the value of key 'title' from POST request `request` if exists, else return empty string ''

request.POST.get('title', '')

heck if string `test.mp3` ends with one of the strings from a tuple `('.mp3', '.avi')`

"""test.mp3""".endswith(('.mp3', '.avi'))

plit a string 's' by space while ignoring spaces within square braces and quotes.

re.findall('\\[[^\\]]*\\]|"[^"]*"|\\S+', s)

get biggest 3 values from each column of the pandas dataframe `data`

data.apply(lambda x: sorted(x, 3))

permanently set the current directory to the 'C:/Users/Name/Desktop'

os.chdir('C:/Users/Name/Desktop')

get all characters between two `$` characters in string `string`

re.findall('\\$([^$]*)\\$', string)

getting the string between 2 '$' characters in '$sin (x)$ is an function of x'

re.findall('\\$(.*?)\\$', '$sin (x)$ is an function of x')

Format a date object `str_data` into iso fomr

datetime.datetime.strptime(str_date, '%m/%d/%Y').date().isoformat()

get element at index 0 of first row and element at index 1 of second row in array `A`

A[[0, 1], [0, 1]]

bset numpy array `a` by column and row, returning the values from the first row, first column and the second row, second column and the third row, first column.

a[np.arange(3), (0, 1, 0)]

Get a list of all keys from dictionary `dictA` where the number of occurrences of value `duck` in that key is more than `1`

[k for k, v in dictA.items() if v.count('duck') > 1]

Create sub matrix of a list of lists `[[2, 3, 4], [2, 3, 4], [2, 3, 4]]` (without numpy)

[[2, 3, 4], [2, 3, 4], [2, 3, 4]]

get an element at index `[1,1]`in a numpy array `arr`

print(arr[1, 1])

Set colorbar range from `0` to `15` for pyplot object `quadmesh` in matplotlib

quadmesh.set_clim(vmin=0, vmax=15)

ead csv file 'my_file.csv' into numpy array

my_data = genfromtxt('my_file.csv', delimiter=',')

ead csv file 'myfile.csv' into array

df = pd.read_csv('myfile.csv', sep=',', header=None)

ead csv file 'myfile.csv' into array

np.genfromtxt('myfile.csv', delimiter=',')

ead csv file 'myfile.csv' into array

np.genfromtxt('myfile.csv', delimiter=',', dtype=None)

ead the first line of a string `my_string`

my_string.splitlines()[0]

w do I read the first line of a string?

my_string.split('\n', 1)[0]

generate a list from a pandas dataframe `df` with the column name and column value

df.values.tolist()

Replace repeated instances of a character '*' with a single instance in a string 'text'

re.sub('\\*\\*+', '*', text)

eplace repeated instances of * with a single instance of *

re.sub('\\*+', '*', text)

ltiply values of dictionary `dict` with their respective values in dictionary `dict2`

dict((k, v * dict2[k]) for k, v in list(dict1.items()) if k in dict2)

Get a random string of length `length`

return ''.join(random.choice(string.lowercase) for i in range(length))

Get total number of values in a nested dictionary `food_colors`

sum(len(x) for x in list(food_colors.values()))

all elements in a nested dictionary `food_colors`

sum(len(v) for v in food_colors.values())

pply logical operator 'AND' to all elements in list `a_list`

all(a_list)

emoving vowel characters 'aeiouAEIOU' from string `text`

"""""".join(c for c in text if c not in 'aeiouAEIOU')

Divide elements in list `a` from elements at the same index in list `b`

[(x / y) for x, y in zip(a, b)]

h regex 'abc(de)fg(123)' on string 'abcdefg123 and again abcdefg123'

re.findall('abc(de)fg(123)', 'abcdefg123 and again abcdefg123')

pply function `log2` to the grouped values by 'type' in dataframe `df`

df.groupby('type').apply(lambda x: np.mean(np.log2(x['v'])))

get geys of dictionary `my_dict` that contain any values from list `lst`

[key for key, value in list(my_dict.items()) if set(value).intersection(lst)]

get list of keys in dictionary `my_dict` whose values contain values from list `lst`

[key for item in lst for key, value in list(my_dict.items()) if item in value]

Sum elements of tuple `b` to their respective elements of each tuple in list `a`

c = [[(i + j) for i, j in zip(e, b)] for e in a]

get the common prefix from comparing two absolute paths '/usr/var' and '/usr/var2/log'

os.path.commonprefix(['/usr/var', '/usr/var2/log'])

get relative path of path '/usr/var' regarding path '/usr/var/log/'

print(os.path.relpath('/usr/var/log/', '/usr/var'))

filter dataframe `grouped` where the length of each group `x` is bigger than 1

grouped.filter(lambda x: len(x) > 1)

dictionary of lists `myDict` by the third item in each l

sorted(list(myDict.items()), key=lambda e: e[1][2])

Format string `hello {name}, how are you {name}, welcome {name}` to be interspersed by `name` three times, specifying the value as `john` only once

"""hello {name}, how are you {name}, welcome {name}""".format(name='john')

eorder indexed rows `['Z', 'C', 'A']` based on a list in pandas data frame `df`

df.reindex(['Z', 'C', 'A'])

heck if any values in a list `input_list` is a l

any(isinstance(el, list) for el in input_list)

get the size of list `items`

len(items)

get the size of a list `[1,2,3]`

len([1, 2, 3])

get the size of object `items`

items.__len__()

function to get the size of objec

len()

get the size of list `s`

len(s)

each row in a pandas dataframe `df` in descending order

df.sort(axis=1, ascending=False)

Fastest way to sort each row in a pandas dataframe

df.sort(df.columns, axis=1, ascending=False)

get count of rows in each series grouped by column 'col5' and column 'col2' of dataframe `df`

df.groupby(['col5', 'col2']).size().groupby(level=1).max()

heck if string 'x' is in list `['x', 'd', 'a', 's', 'd', 's']`

'x' in ['x', 'd', 'a', 's', 'd', 's']

Delete an item with key key from `mydict`

mydict.pop('key', None)

Delete an item with key `key` from `mydict`

del mydict[key]

Delete an item with key `key` from `mydict`

try: del mydict[key] except KeyError: pass try: del mydict[key] except KeyError: pass

pecify multiple positional arguments with argparse

parser.add_argument('input', nargs='+')

Plot using the color code `#112233` in matplotlib pyplo

pyplot.plot(x, y, color='#112233')

p html from string

re.sub('<[^<]+?>', '', text)

lign values in array `b` to the order of corresponding values in array `a`

a[np.in1d(a, b)]

plit string jvm.args= Dappdynamics.com=true, Dsomeotherparam=false, on the first occurrence of delimiter '='

"""jvm.args= -Dappdynamics.com=true, -Dsomeotherparam=false,""".split('=', 1)

print numbers in list `list` with precision of 3 decimal place

print('[%s]' % ', '.join('%.3f' % val for val in list))

format print output of list of floats `l` to print only up to 3 decimal po

print('[' + ', '.join('%5.3f' % v for v in l) + ']')

print a list of floating numbers `l` using string formatting

print([('%5.3f' % val) for val in l])

Change the current directory one level up

os.chdir('..')

print a unicode string `text`

print(text.encode('windows-1252'))

vert string representation `s2` of binary string rep of integer to floating point number

struct.unpack('d', struct.pack('Q', int(s2, 0)))[0]

vert a binary '0b1110' to a float number

float(int('-0b1110', 0))

vert a binary `b8` to a float number

struct.unpack('d', b8)[0]

plot a bar graph from the column 'color' in the DataFrame 'df'

df.colour.value_counts().plot(kind='bar')

plot categorical data in series `df` with kind `bar` using pandas and matplotlib

df.groupby('colour').size().plot(kind='bar')

p and split each line `line` on white space

line.strip().split(' ')

pply functions `mean` and `std` to each column in dataframe `df`

df.groupby(lambda idx: 0).agg(['mean', 'std'])

dictionary `tag_weight` in reverse order by values cast to integer

sorted(list(tag_weight.items()), key=lambda x: int(x[1]), reverse=True)

find the largest integer less than `x`

int(math.ceil(x)) - 1

heck if the string `myString` is empty

if (not myString): pass

heck if string `some_string` is empty

if (not some_string): pass

heck if string `my_string` is empty

if (not my_string): pass

heck if string `my_string` is empty

if some_string: pass

erate over a dictionary `d` in sorted order

it = iter(sorted(d.items()))

erate over a dictionary `d` in sorted order

for (key, value) in sorted(d.items()): pass

erate over a dictionary `dict` in sorted order

return sorted(dict.items())

erate over a dictionary `dict` in sorted order

return iter(sorted(dict.items()))

erate over a dictionary `foo` in sorted order

for (k, v) in sorted(foo.items()): pass

erate over a dictionary `foo` sorted by the key

for k in sorted(foo.keys()): pass

gn the index of the last occurence of `x` in list `s` to the variable `last`

last = len(s) - s[::-1].index(x) - 1

atenating values in `list1` to a string

str1 = ''.join(list1)

atenating values in list `L` to a string, separate by space

' '.join((str(x) for x in L))

atenating values in `list1` to a string

str1 = ''.join((str(e) for e in list1))

atenating values in list `L` to a string

makeitastring = ''.join(map(str, L))

emove None value from list `L`

[x for x in L if x is not None]

elect a random element from array `[1, 2, 3]`

random.choice([1, 2, 3])

eating a 5x6 matrix filled with `None` and save it as `x`

x = [[None for _ in range(5)] for _ in range(6)]