This is a draft cheat sheet. It is a work in progress and is not finished yet.
Basic of Basic
comment |
#comment |
print |
print() |
assignment sign |
= |
equal sign |
== |
case sensitive |
YES |
indenting matters |
YES |
help(x) or ?x |
INFO on x |
IF condition |
if elif else |
empty_list = [] |
initiate an empty list |
!ls |
list files in wd |
Math Operators
basic 4 |
+ - * / |
exponentiation |
** |
modulo |
% |
floor division |
// |
ZERO Indexing
x[0] |
1st element of x |
x[-1] |
last element of x |
x[a:b] |
from position "a" to "b-1" |
x[a:] |
from position "a" to the last |
x[:b] |
from position "b-1" to the first |
x[::-1] |
read backward 1 step back |
Basic functions
Type & conversion |
type() str() int() float() bool() |
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len() |
define function |
def function_name(parameter): |
output |
return new_value |
docstrings |
""" foo """ after function declared |
LIST
list = [a, b, c] |
compound data type |
add to list |
foo + list |
remove from list |
del(list[x]) |
copy list (explicit) |
list() or list[:] |
List & Dict comprehension
new_nums = [num + 1 for num in nums] |
new_pairs = [(num1, num2) for num1 in range1 for num2 in range2] |
new_dict = {num: num+1 for num in range(x)} |
Most common packages
Pandas |
Dataframe |
Numpy |
Array |
Matplotlib |
Visualisation |
Scikit-learn |
Machine learning |
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STRING methods
find |
<str>.find(<str>, <number>) |
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1st find position; -1 if not found starting from <number> |
case |
upper() lower() |
count |
count("foo") |
LIST methods
append |
list.append(foo) |
index |
list.index(foo) |
count |
list.count(foo) |
remove (1st match) |
list.remove(foo) |
reverse (order) |
list.reverse() |
Dataframe methods
df.columns |
df.head(), df.tail() |
df.shape, df.info() |
df['foo'].value_counts(dropna=FALSE) |
df.describe() |
summary statistic of numeric data |
Iterating
list |
for index, item in enumerate(list_name) |
string |
for chr in "name" |
dictionary |
for key, val in dict_name.items() |
Numpy array |
for val in numpy.nditer(array_name) |
Pandas series |
for label, row in pandas_name.iterrows() |
data |
for chunk in pd.read_csv('data.csv', chunksize = 1000) |
iterate through rows in pandas is not efficient. avoid if possible.
i
an iterable is an object that can return an iterator, while an iterator is an object that keeps state and produces the next value when you call next() on it
iterable -> iter() -> next()
print(*x) to print all elements
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Quirks
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Different types of value string*number is OK but string+number is error
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Multiple assignment a, b = 3, 4 the value of a is 3 and the value of b is 4
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tuple vs. list vs. dictionary ( ) vs. [ ] vs. {}, immutable vs. mutable vs. immutable key
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declare non-local var in local functions
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declare global var in enclosing functions
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generator not stored in memory like list, iterable
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BEWARE
edge case: s[0] causes ERROR if s is empty |
list is ordered vs dictionary is un-ordered |
Functions on function
map() |
applies function over an object |
reduce() |
filter() |
Used together with lambda
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