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sort list `lst` based on each element's number of occurrences

sorted ( lst , key = lambda x : ( - 1 * c [ x ] , lst . index ( x ) ) )

Get the value with the maximum length in each column in array `foo`

[ max ( len ( str ( x ) ) for x in line ) for line in zip ( * foo ) ]

get the count of each unique value in column `Country` of dataframe `df` and store in column `Sum of Accidents`

df . Country . value_counts ( ) . reset_index ( name = 'Sum of Accidents' )

calculat the difference between each row and the row previous to it in dataframe `data`

data . set_index ( 'Date' ) . diff ( )

append values `[3, 4]` to a set `a`

a . update ( [ 3 , 4 ] )

set every two-stride far element to -1 starting from second element in array `a`

a [ 1 : : 2 ] = - 1

Get rank of rows from highest to lowest of dataframe `df`, grouped by value in column `group`, according to value in column `value`

df . groupby ( 'group' ) [ 'value' ] . rank ( ascending = False )

convert js date object 'Tue, 22 Nov 2011 06:00:00 GMT' to python datetime

datetime . strptime ( 'Tue, 22 Nov 2011 06:00:00 GMT' , '%a, %d %b %Y %H:%M:%S %Z' )

Convert a binary value '1633837924' to string

struct . pack ( '<I' , 1633837924 )

append string `foo` to list `list`

list . append ( 'foo' )

insert string `foo` at position `0` of list `list`

list . insert ( 0 , 'foo' )

convert keys in dictionary `thedict` into case insensitive

theset = set ( k . lower ( ) for k in thedict )

pad 'dog' up to a length of 5 characters with 'x'

"""{s:{c}^{n}}""" . format ( s = 'dog' , n = 5 , c = 'x' )

check if type of variable `s` is a string

isinstance ( s , str )

check if type of a variable `s` is string

isinstance ( s , str )

Convert list of dictionaries `L` into a flat dictionary

dict ( pair for d in L for pair in list ( d . items ( ) ) )

merge a list of dictionaries in list `L` into a single dict

{ k : v for d in L for k , v in list ( d . items ( ) ) }

sort a pandas data frame according to column `Peak` in ascending and `Weeks` in descending order

df . sort_values ( [ 'Peak' , 'Weeks' ] , ascending = [ True , False ] , inplace = True )

sort a pandas data frame by column `Peak` in ascending and `Weeks` in descending order

df . sort ( [ 'Peak' , 'Weeks' ] , ascending = [ True , False ] , inplace = True )

run the code contained in string "print('Hello')"

eval ( "print('Hello')" )

creating a list of dictionaries [{'A': 1, 'C': 4, 'B': 2, 'D': 4}, {'A': 1, 'C': 4, 'B': 1, 'D': 5}]

[ { 'A' : 1 , 'C' : 4 , 'B' : 2 , 'D' : 4 } , { 'A' : 1 , 'C' : 4 , 'B' : 1 , 'D' : 5 } ]

Creating a list of dictionaries in python

[ { 'A' : 1 , 'C' : 4 , 'B' : 2 , 'D' : 4 } , { 'A' : 1 , 'C' : 4 , 'B' : 1 , 'D' : 5 } ]

get all possible combination of items from 2-dimensional list `a`

list ( itertools . product ( * a ) )

Get sum of values of columns 'Y1961', 'Y1962', 'Y1963' after group by on columns "Country" and "Item_code" in dataframe `df`.

df . groupby ( [ 'Country' , 'Item_Code' ] ) [ [ 'Y1961' , 'Y1962' , 'Y1963' ] ] . sum ( )

create list `done` containing permutations of each element in list `[a, b, c, d]` with variable `x` as tuples

done = [ ( el , x ) for el in [ a , b , c , d ] ]

remove Nan values from array `x`

x = x [ numpy . logical_not ( numpy . isnan ( x ) ) ]

remove first directory from path '/First/Second/Third/Fourth/Fifth'

os . path . join ( * x . split ( os . path . sep ) [ 2 : ] )

Replace `;` with `:` in a string `line`

line = line . replace ( ';' , ':' )

call bash command 'tar c my_dir | md5sum' with pipe

subprocess . call ( 'tar c my_dir | md5sum' , shell = True )

Convert a hex string `437c2123 ` according to ascii value.

"""437c2123""" . decode ( 'hex' )

Get a list of all fields in class `User` that are marked `required`

[ k for k , v in User . _fields . items ( ) if v . required ]

remove column by index `[:, 0:2]` in dataframe `df`

df = df . ix [ : , 0 : 2 ]

change a string of integers `x` separated by spaces to a list of int

x = map ( int , x . split ( ) )

convert a string of integers `x` separated by spaces to a list of integers

x = [ int ( i ) for i in x . split ( ) ]

find element by css selector "input[onclick*='1 Bedroom Deluxe']"

driver . find_element_by_css_selector ( "input[onclick*='1 Bedroom Deluxe']" )

Python / Remove special character from string

re . sub ( '[^a-zA-Z0-9-_*.]' , '' , my_string )

display a pdf file that has been downloaded as `my_pdf.pdf`

webbrowser . open ( 'file:///my_pdf.pdf' )

replace backslashes in string `result` with empty string ''

result = result . replace ( '\\' , '' )

remove backslashes from string `result`

result . replace ( '\\' , '' )

replace value '-' in any column of pandas dataframe to "NaN"

df . replace ( '-' , 'NaN' )

convert datetime object to date object in python

datetime . datetime . now ( ) . date ( )

How do I convert datetime to date (in Python)?

datetime . datetime . now ( ) . date ( )

get all sub-elements of an element `a` in an elementtree

[ elem . tag for elem in a . iter ( ) ]

get all sub-elements of an element tree `a` excluding the root element

[ elem . tag for elem in a . iter ( ) if elem is not a ]

How can I split and parse a string in Python?

"""2.7.0_bf4fda703454""" . split ( '_' )

move dictionaries in list `lst` to the end of the list if value of key 'language' in each dictionary is not equal to 'en'

sorted ( lst , key = lambda x : x [ 'language' ] != 'en' )

check if all values of a dictionary `your_dict` are zero `0`

all ( value == 0 for value in list ( your_dict . values ( ) ) )

produce a pivot table as dataframe using column 'Y' in datafram `df` to form the axes of the resulting dataframe

df . pivot_table ( 'Y' , rows = 'X' , cols = 'X2' )

call `doSomething()` in a try-except without handling the exception

try : doSomething ( ) except : pass

call `doSomething()` in a try-except without handling the exception

try : doSomething ( ) except Exception : pass

get a sum of 4d array `M`

M . sum ( axis = 0 ) . sum ( axis = 0 )

Convert a datetime object `dt` to microtime

time . mktime ( dt . timetuple ( ) ) + dt . microsecond / 1000000.0

select all rows in dataframe `df` where the values of column 'columnX' is bigger than or equal to `x` and smaller than or equal to `y`

df [ ( x <= df [ 'columnX' ] ) & ( df [ 'columnX' ] <= y ) ]

sort a list of lists `L` by index 2 of the inner list

sorted ( L , key = itemgetter ( 2 ) )

sort a list of lists `l` by index 2 of the inner list

l . sort ( key = ( lambda x : x [ 2 ] ) )

sort list `l` by index 2 of the item

sorted ( l , key = ( lambda x : x [ 2 ] ) )

sort a list of lists `list_to_sort` by indices 2,0,1 of the inner list

sorted_list = sorted ( list_to_sort , key = itemgetter ( 2 , 0 , 1 ) )

find rows of 2d array in 3d numpy array 'arr' if the row has value '[[0, 3], [3, 0]]'

np . argwhere ( np . all ( arr == [ [ 0 , 3 ] , [ 3 , 0 ] ] , axis = ( 1 , 2 ) ) )

From multiIndexed dataframe `data` select columns `a` and `c` within each higher order column `one` and `two`

data . loc [ : , ( list ( itertools . product ( [ 'one' , 'two' ] , [ 'a' , 'c' ] ) ) ) ]

select only specific columns 'a' and 'c' from a dataframe 'data' with multiindex columns

data . loc [ : , ( [ ( 'one' , 'a' ) , ( 'one' , 'c' ) , ( 'two' , 'a' ) , ( 'two' , 'c' ) ] ) ]

match a sharp, followed by letters (including accent characters) in string `str1` using a regex

hashtags = re . findall ( '#(\\w+)' , str1 , re . UNICODE )

Rename file from `src` to `dst`

os . rename ( src , dst )

Get all texts and tags from a tag `strong` from etree tag `some_tag` using lxml

print ( etree . tostring ( some_tag . find ( 'strong' ) ) )

Serialize dictionary `data` and its keys to a JSON formatted string

json . dumps ( { str ( k ) : v for k , v in data . items ( ) } )

parse UTF-8 encoded HTML response `response` to BeautifulSoup object

soup = BeautifulSoup ( response . read ( ) . decode ( 'utf-8' ) )

delete file `filename`

os . remove ( filename )

get the next value greatest to `2` from a list of numbers `num_list`

min ( [ x for x in num_list if x > 2 ] )

Replace each value in column 'prod_type' of dataframe `df` with string 'responsive'

df [ 'prod_type' ] = 'responsive'

sort list `lst` with positives coming before negatives with values sorted respectively

sorted ( lst , key = lambda x : ( x < 0 , x ) )

get the date 6 months from today

six_months = ( date . today ( ) + relativedelta ( months = ( + 6 ) ) )

get the date 1 month from today

( date ( 2010 , 12 , 31 ) + relativedelta ( months = ( + 1 ) ) )

get the date 2 months from today

( date ( 2010 , 12 , 31 ) + relativedelta ( months = ( + 2 ) ) )

calculate the date six months from the current date

print ( ( datetime . date . today ( ) + datetime . timedelta ( ( ( 6 * 365 ) / 12 ) ) ) . isoformat ( ) )

get a list of keys of dictionary `things` sorted by the value of nested dictionary key 'weight'

sorted ( list ( things . keys ( ) ) , key = lambda x : things [ x ] [ 'weight' ] , reverse = True )

get all the values from a numpy array `a` excluding index 3

a [ np . arange ( len ( a ) ) != 3 ]

delete all elements from a list `x` if a function `fn` taking value as parameter returns `0`

[ x for x in lst if fn ( x ) != 0 ]

set dataframe `df` index using column 'month'

df . set_index ( 'month' )

read lines from a csv file `./urls-eu.csv` into a list of lists `arr`

arr = [ line . split ( ',' ) for line in open ( './urls-eu.csv' ) ]

list comprehension that produces integers between 11 and 19

[ i for i in range ( 100 ) if i > 10 if i < 20 ]

Get only digits from a string `strs`

"""""" . join ( [ c for c in strs if c . isdigit ( ) ] )

split a string `yas` based on tab '\t'

re . split ( '\\t+' , yas . rstrip ( '\t' ) )

scalar multiply matrix `a` by `b`

( a . T * b ) . T

remove trailing newline in string "test string\n"

'test string\n' . rstrip ( )

remove trailing newline in string 'test string \n\n'

'test string \n\n' . rstrip ( '\n' )

remove newline in string `s`

s . strip ( )

remove newline in string `s` on the right side

s . rstrip ( )

remove newline in string `s` on the left side

s . lstrip ( )

remove newline in string 'Mac EOL\r'

'Mac EOL\r' . rstrip ( '\r\n' )

remove newline in string 'Windows EOL\r\n' on the right side

'Windows EOL\r\n' . rstrip ( '\r\n' )

remove newline in string 'Unix EOL\n' on the right side

'Unix EOL\n' . rstrip ( '\r\n' )

remove newline in string "Hello\n\n\n" on the right side

'Hello\n\n\n' . rstrip ( '\n' )

split string `text` into chunks of 16 characters each

re . findall ( '.{,16}\\b' , text )

Get a list comprehension in list of lists `X`

[ [ X [ i ] [ j ] for j in range ( len ( X [ i ] ) ) ] for i in range ( len ( X ) ) ]

convert unicode string '\xd0\xbc\xd0\xb0\xd1\x80\xd0\xba\xd0\xb0' to byte string

'\xd0\xbc\xd0\xb0\xd1\x80\xd0\xba\xd0\xb0' . encode ( 'latin-1' )

split dataframe `df` where the value of column `a` is equal to 'B'

df . groupby ( ( df . a == 'B' ) . shift ( 1 ) . fillna ( 0 ) . cumsum ( ) )

save json output from a url ‘http://search.twitter.com/search.json?q=hi’ to file ‘hi.json’ in Python 2

urllib . request . urlretrieve ( 'http://search.twitter.com/search.json?q=hi' , 'hi.json' )

Find indices of elements equal to zero from numpy array `x`

numpy . where ( ( x == 0 ) ) [ 0 ]

flush output of python print

sys . stdout . flush ( )

convert `i` to string

str ( i )

convert `a` to string

a . __str__ ( )