Introduction to python cheat sheet for all

Sequence Containers Indexing
Base Types
Python 3 Cheat Sheet
©2012-2015 - Laurent Pointal
License Creative Commons Attribution 4
Latest version on :
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0
783 -192
int
9.23 -1.7e-6
0.0
float
True False
bool
"OnenTwo"
'I'm'
str
"""XtYtZ
1t2t3"""
×10
-6
escaped tab
escaped new line
Multiline string:
Container Types
list [1,5,9] ["x",11,8.9] ["mot"] []
tuple (1,5,9) 11,"y",7.4 ("mot",) ()
dict
{1:"one",3:"three",2:"two",3.14:"π"}
{"key":"value"}
set
{}
{1,9,3,0}
◾ ordered sequences, fast index access, repeatable values
set()
◾ key containers, no a priori order, fast key access, each key is unique
{"key1","key2"}
Non modifiable values (immutables)
Variables assignment
x=1.2+8+sin(y)
y,z,r=9.2,-7.6,0
a…zA…Z_ followed by a…zA…Z_0…9
◽ diacritics allowed but should be avoided
◽ language keywords forbidden
◽ lower/UPPER case discrimination
☝ expression with only comas →tuple
dictionary
collection
integer, float, boolean, string, bytes
Identifiers
☺ a toto x7 y_max BigOne
☹ 8y and for
x+=3
x-=2
increment ⇔ x=x+3
decrement ⇔ x=x-2
Conversions
for lists, tuples, strings, bytes…
int("15") → 15
int("3f",16) → 63 can specify integer number base in 2nd
parameter
int(15.56) → 15 truncate decimal part
float("-11.24e8") → -1124000000.0
round(15.56,1)→ 15.6 rounding to 1 decimal (0 decimal → integer number)
bool(x) False for null x, empty container x , None or False x ; True for other x
str(x)→ "…" representation string of x for display (cf. formatting on the back)
chr(64)→'@' ord('@')→64 code ↔ char
repr(x)→ "…" literal representation string of x
bytes([72,9,64]) → b'Ht@'
list("abc") → ['a','b','c']
dict([(3,"three"),(1,"one")]) → {1:'one',3:'three'}
set(["one","two"]) → {'one','two'}
separator str and sequence of str → assembled str
':'.join(['toto','12','pswd']) → 'toto:12:pswd'
str splitted on whitespaces → list of str
"words with spaces".split() → ['words','with','spaces']
str splitted on separator str → list of str
"1,4,8,2".split(",") → ['1','4','8','2']
sequence of one type → list of another type (via list comprehension)
[int(x) for x in ('1','29','-3')] → [1,29,-3]
type(expression)
lst=[10, 20, 30, 40, 50]
lst[1]→20
lst[-2]→40
0 1 2 3 4
-5 -4 -3 -1
-2 Individual access to items via lst[index]
positive index
negative index
0 1 2 3 5
4
-5 -4 -3 -1
-2
negative slice
positive slice
Access to sub-sequences via lst[start slice:end slice:step]
len(lst)→5
lst[1:3]→[20,30]
lst[::2]→[10,30,50]
lst[-3:-1]→[30,40]
lst[:3]→[10,20,30]
lst[:-1]→[10,20,30,40]
lst[3:]→[40,50]
lst[1:-1]→[20,30,40]
lst[:]→[10,20,30,40,50]
Missing slice indication → from start / up to end.
On mutable sequences (list), remove with del lst[3:5] and modify with assignment lst[1:4]=[15,25]
Conditional Statement
if age<=18:
state="Kid"
elif age>65:
state="Retired"
else:
state="Active"
Boolean Logic Statements Blocks
parent statement:
statement block 1…
⁝
parent statement:
statement block2…
⁝
next statement after block 1
indentation
!
Comparisons : < > <= >= == !=
≠
=
≥
≤
a and b
a or b
not a
logical and
logical or
logical not
one or other
or both
both simulta-
-neously
if logical condition:
statements block
statement block executed only
if a condition is true
Can go with several elif, elif... and only one
final else. Only the block of first true
condition is executed.
lst[-1]→50
lst[0]→10
⇒ last one
⇒ first one
x=None « undefined » constant value
Maths
Operators: + - * / // % **
× ÷
integer ÷ ÷ remainder
ab
from math import sin,pi…
sin(pi/4)→0.707…
cos(2*pi/3)→-0.4999…
sqrt(81)→9.0 √
log(e**2)→2.0
ceil(12.5)→13
floor(12.5)→12
escaped '
☝ floating numbers… approximated values angles in radians
(1+5.3)*2→12.6
abs(-3.2)→3.2
round(3.57,1)→3.6
pow(4,3)→64.0
for variables, functions,
modules, classes… names
Mémento v2.0.6
str (ordered sequences of chars / bytes)
(key/value associations)
☝ pitfall : and and or return value of a or
of b (under shortcut evaluation).
⇒ ensure that a and b are booleans.
(boolean results)
a=b=c=0 assignment to same value
multiple assignments
a,b=b,a values swap
a,*b=seq
*a,b=seq
unpacking of sequence in
item and list
bytes
bytes
b"totoxfe775"
hexadecimal octal
0b010 0xF3
0o642
binary octal hexa
""
empty
dict(a=3,b=4,k="v")
Items count
☝ keys=hashable values (base types, immutables…)
True
False True and False constants ☝ configure editor to insert 4 spaces in
place of an indentation tab.
lst[::-1]→[50,40,30,20,10]
lst[::-2]→[50,30,10]
1) evaluation of right side expression value
2) assignment in order with left side names
=
☝ assignment ⇔ binding of a name with a value
☝ immutables
On mutable sequences (list), remove with
del lst[3] and modify with assignment
lst[4]=25
del x remove name x
b""
@ → matrix × python3.5+numpy
☝ index from 0
(here from 0 to 4)
frozenset immutable set
Priority (…)
☝ usual order of operations
modules math, statistics, random,
decimal, fractions, numpy, etc. (cf. doc)
Modules/Names Imports
from monmod import nom1,nom2 as fct
module truc⇔file truc.py
→direct access to names, renaming with as
import monmod →access via monmod.nom1 …
☝ modules and packages searched in python path (cf sys.path)
?
yes
no
shallow copy of sequence
?
yes no
and
*=
/=
%=
…
☝ with a var x:
if bool(x)==True: ⇔ if x:
if bool(x)==False: ⇔ if not x:
raise ExcClass(…)
Signaling an error:
Errors processing:
try:
normal procesising block
except Exception as e:
error processing block
normal
processing
error
processing
error
processing
raise
raise X()
zero
☝ finally block for final processing
in all cases.
Exceptions on Errors
TYPE -1

"modele{} {} {}".format(x,y,r)
"{selection:formatting!conversion}"
◽ Selection :
2
nom
0.nom
4[key]
0[2]
str
Display
print("v=",3,"cm :",x,",",y+4)
print options:
◽ sep=" " items separator, default space
◽ end="n" end of print, default new line
◽ file=sys.stdout print to file, default standard output
items to display : literal values, variables, expressions
loop on dict/set ⇔ loop on keys sequences
use slices to loop on a subset of a sequence
Conditional Loop Statement
statements block executed as long as
condition is true
while logical condition:
statements block
s = 0
i = 1
while i <= 100:
s = s + i**2
i = i + 1
print("sum:",s)
initializations before the loop
condition with a least one variable value (here i)
s= ∑
i=1
i=100
i2
☝ make condition variable change !
statements block executed for each
item of a container or iterator
for var in sequence:
statements block
s = "Some text"
cnt = 0
for c in s:
if c == "e":
cnt = cnt + 1
print("found",cnt,"'e'")
Go over sequence's values
Algo: count
number of e
in the string.
Go over sequence's index
◽ modify item at index
◽ access items around index (before / after)
lst = [11,18,9,12,23,4,17]
lost = []
for idx in range(len(lst)):
val = lst[idx]
if val > 15:
lost.append(val)
lst[idx] = 15
print("modif:",lst,"-lost:",lost)
Algo: limit values greater
than 15, memorizing
of lost values.
☝
beware
of
infinite
loops!
initializations before the loop
loop variable, assignment managed by for statement
values to format
formating directives
Integer Sequences
Files
s = input("Instructions:")
☝ input always returns a string, convert it to required type
(cf. boxed Conversions on the other side).
range(5)→ 0 1 2 3 4 range(2,12,3)→ 2 5 8 11
range(3,8)→ 3 4 5 6 7 range(20,5,-5)→ 20 15 10
range(len(seq))→ sequence of index of values in seq
☝ range provides an immutable sequence of int constructed as needed
range([start,] end [,step])
f = open("file.txt","w",encoding="utf8")
storing data on disk, and reading it back
opening mode
◽ 'r' read
◽ 'w' write
◽ 'a' append
◽ …'+' 'x' 'b' 't'
encoding of
chars for text
files:
utf8 ascii
latin1 …
name of file
on disk
(+path…)
file variable
for operations
f.write("coucou")
f.writelines(list of lines)
writing reading
f.read([n]) → next chars
if n not specified, read up to end !
f.readlines([n]) → list of next lines
f.readline() → next line
with open(…) as f:
for line in f :
# processing ofline
cf. modules os, os.path and pathlib
f.close() ☝ dont forget to close the file after use !
Very common: opening with a guarded block
(automatic closing) and reading loop on lines
of a text file:
Function Definition
def fct(x,y,z):
"""documentation"""
# statements block, res computation, etc.
return res
function name (identifier)
result value of the call, if no computed
result to return: return None
☝ parameters and all
variables of this block exist only in the block and during the function
call (think of a “black box”)
named parameters
Function Call
r = fct(3,i+2,2*i)
☝ read empty string if end of file
len(c)→ items count
min(c) max(c) sum(c)
sorted(c)→ list sorted copy
val in c → boolean, membership operator in (absence not in)
enumerate(c)→ iterator on (index, value)
zip(c1,c2…)→ iterator on tuples containing ci
items at same index
all(c)→ True if all c items evaluated to true, else False
any(c)→ True if at least one item of c evaluated true, else False
☝ modify original list
lst.append(val) add item at end
lst.extend(seq) add sequence of items at end
lst.insert(idx,val) insert item at index
lst.remove(val) remove first item with value val
lst.pop([idx])→value remove & return item at index idx (default last)
lst.sort() lst.reverse() sort / reverse liste in place
"{:+2.3f}".format(45.72793)
→'+45.728'
"{1:>10s}".format(8,"toto")
→' toto'
"{x!r}".format(x="I'm")
→'"I'm"'
☝ start default 0, end not included in sequence, step signed, default 1
◽ Conversion : s (readable text) or r (literal representation)
< > ^ = 0 at start for filling with 0
integer: b binary, c char, d decimal (default), o octal, x or X hexa…
float: e or E exponential, f or F fixed point, g or G appropriate (default),
string: s … % percent
◽ Formatting :
fill char alignment sign mini width.precision~maxwidth type
+ - space
Operations on Dictionaries Operations on Sets
Operators:
| → union (vertical bar char)
& → intersection
- ^ → difference/symmetric diff.
< <= > >= → inclusion relations
Operators also exist as methods.
d.update(d2) update/add
associations
Note: For dictionaries and sets, these
operations use keys.
Specific to ordered sequences containers (lists, tuples, strings, bytes…)
reversed(c)→ inversed iterator c*5→ duplicate c+c2→ concatenate
c.index(val)→ position c.count(val)→ events count
Operations on Lists
d[key]=value
d[key]→ value
d.keys()
d.values()
d.items()
d.clear()
del d[key]
→iterable views on
keys/values/associations
Examples
d.pop(key[,default])→ value
d.popitem()→ (key,value)
d.get(key[,default])→ value
d.setdefault(key[,default])→value
s.update(s2) s.copy()
s.add(key) s.remove(key)
s.discard(key) s.clear()
s.pop()
Loop Control
Go simultaneously over sequence's index and values:
for idx,val in enumerate(lst):
☝
good
habit
:
don't
modify
loop
variable
Advanced: def fct(x,y,z,*args,a=3,b=5,**kwargs):
*args variable positional arguments (→tuple), default values,
**kwargs variable named arguments (→dict)
one argument per
parameter
storage/use of
returned value
Algo:
f.flush() write cache
f.tell()→position
reading/writing progress sequentially in the file, modifiable with:
f.seek(position[,origin])
f.truncate([size]) resize
Advanced:
*sequence
**dict
s.startswith(prefix[,start[,end]])
s.endswith(suffix[,start[,end]]) s.strip([chars])
s.count(sub[,start[,end]]) s.partition(sep)→ (before,sep,after)
s.index(sub[,start[,end]]) s.find(sub[,start[,end]])
s.is…() tests on chars categories (ex. s.isalpha())
s.upper() s.lower() s.title() s.swapcase()
s.casefold() s.capitalize() s.center([width,fill])
s.ljust([width,fill]) s.rjust([width,fill]) s.zfill([width])
s.encode(encoding) s.split([sep]) s.join(seq)
?
yes
no
next
finish
…
Input
import copy
copy.copy(c)→ shallow copy of container
copy.deepcopy(c)→ deep copy of container
☝ this is the use of function
name with parentheses
which does the call
fct()
fct
fct
☝ text mode t by default (read/write str), possible binary
mode b (read/write bytes). Convert from/to required type !
break immediate exit
continue next iteration
☝ else block for normal
loop exit.
Iterative Loop Statement
Operations on Strings
Formatting
Generic Operations on Containers
TYPE -1

Python Cheat Sheet
by Dave Child (DaveChild) via cheatography.com/1/cs/19/
Python sys Variables
argv Command line args
builtin_module_‐
names
Linked C modules
byteorder Native byte order
check_interval Signal check
frequency
exec_prefix Root directory
executable Name of executable
exitfunc Exit function name
modules Loaded modules
path Search path
platform Current platform
stdin, stdout, stderr File objects for I/O
version_info Python version info
winver Version number
Python sys.argv
sys.argv[0] foo.py
sys.argv[1] bar
sys.argv[2] -c
sys.argv[3] qux
sys.argv[4] --h
sys.argv for the command:
$ python foo.py bar -c qux --h
Python os Variables
altsep Alternative sep
curdir Current dir string
defpath Default search path
devnull Path of null device
extsep Extension separator
linesep Line separator
name Name of OS
pardir Parent dir string
pathsep Patch separator
sep Path separator
Registered OS names: "posix", "nt",
"mac", "os2", "ce", "java", "riscos"
Python Class Special Methods
__new__(cls) __lt__(self, other)
__init__(self, args) __le__(self, other)
__del__(self) __gt__(self, other)
__repr__(self) __ge__(self, other)
__str__(self) __eq__(self, other)
__cmp__(self, other) __ne__(self, other)
__index__(self) __nonzero__(self)
__hash__(self)
__getattr__(self, name)
__getattribute__(self, name)
__setattr__(self, name, attr)
__delattr__(self, name)
__call__(self, args, kwargs)
Python List Methods
append(item) pop(position)
count(item) remove(item)
extend(list) reverse()
index(item) sort()
insert(position, item)
Python String Methods
capitalize() * lstrip()
center(width) partition(sep)
count(sub, start,
end)
replace(old, new)
decode() rfind(sub, start ,end)
encode() rindex(sub, start,
end)
endswith(sub) rjust(width)
expandtabs() rpartition(sep)
find(sub, start, end) rsplit(sep)
index(sub, start,
end)
rstrip()
isalnum() * split(sep)
isalpha() * splitlines()
isdigit() * startswith(sub)
islower() * strip()
isspace() * swapcase() *
Python String Methods (cont)
istitle() * title() *
isupper() * translate(table)
join() upper() *
ljust(width) zfill(width)
lower() *
Methods marked * are locale dependant for
8-bit strings.
Python File Methods
close() readlines(size)
flush() seek(offset)
fileno() tell()
isatty() truncate(size)
next() write(string)
read(size) writelines(list)
readline(size)
Python Indexes and Slices
len(a) 6
a[0] 0
a[5] 5
a[-1] 5
a[-2] 4
a[1:] [1,2,3,4,5]
a[:5] [0,1,2,3,4]
a[:-2] [0,1,2,3]
a[1:3] [1,2]
a[1:-1] [1,2,3,4]
b=a[:] Shallow copy of a
Indexes and Slices of a=[0,1,2,3,4,5]
Python Datetime Methods
today() fromordinal(ordinal)
now(timezoneinfo) combine(date, time)
utcnow() strptime(date, format)
fromtimestamp(timestamp)
utcfromtimestamp(timestamp)
By Dave Child (DaveChild)
cheatography.com/davechild/
aloneonahill.com
Published 19th October, 2011.
Last updated 3rd November, 2020.
Page 1 of 2.
Sponsored by ApolloPad.com
Everyone has a novel in them. Finish
Yours!
https://apollopad.com
TYPE -2

Python Cheat Sheet
by Dave Child (DaveChild) via cheatography.com/1/cs/19/
Python Time Methods
replace() utcoffset()
isoformat() dst()
__str__() tzname()
strftime(format)
Python Date Formatting
%a Abbreviated weekday (Sun)
%A Weekday (Sunday)
%b Abbreviated month name (Jan)
%B Month name (January)
%c Date and time
%d Day (leading zeros) (01 to 31)
%H 24 hour (leading zeros) (00 to 23)
%I 12 hour (leading zeros) (01 to 12)
%j Day of year (001 to 366)
%m Month (01 to 12)
%M Minute (00 to 59)
%p AM or PM
%S Second (00 to 61⁴)
%U Week number¹ (00 to 53)
%w Weekday² (0 to 6)
%W Week number³ (00 to 53)
%x Date
%X Time
%y Year without century (00 to 99)
%Y Year (2008)
%Z Time zone (GMT)
%% A literal "%" character (%)
¹ Sunday as start of week. All days in a new
year preceding the first Sunday are
considered to be in week 0.
² 0 is Sunday, 6 is Saturday.
³ Monday as start of week. All days in a new
year preceding the first Monday are
considered to be in week 0.
⁴ This is not a mistake. Range takes
account of leap and double-leap seconds.
By Dave Child (DaveChild)
cheatography.com/davechild/
aloneonahill.com
Published 19th October, 2011.
Last updated 3rd November, 2020.
Page 2 of 2.
Sponsored by ApolloPad.com
Everyone has a novel in them. Finish
Yours!
https://apollopad.com
TYPE -2

Selecting List Elements
Import libraries
>>> import numpy
>>> import numpy as np
Selective import
>>> from math import pi
>>> help(str)
PythonForDataScience Cheat Sheet
Python Basics
Learn More Python for Data Science Interactively at www.datacamp.com
Variable Assignment
Strings
>>> x=5
>>> x
5
>>> x+2 Sum of two variables
7
>>> x-2 Subtraction of two variables
3
>>> x*2 Multiplication of two variables
10
>>> x**2 Exponentiation of a variable
25
>>> x%2 Remainder of a variable
1
>>> x/float(2) Division of a variable
2.5
Variables and Data Types
str() '5', '3.45', 'True'
int() 5, 3, 1
float() 5.0, 1.0
bool() True, True, True
Variables to strings
Variables to integers
Variables to floats
Variables to booleans
Lists
>>> a = 'is'
>>> b = 'nice'
>>> my_list = ['my', 'list', a, b]
>>> my_list2 = [[4,5,6,7], [3,4,5,6]]
Subset
>>> my_list[1]
>>> my_list[-3]
Slice
>>> my_list[1:3]
>>> my_list[1:]
>>> my_list[:3]
>>> my_list[:]
Subset Lists of Lists
>>> my_list2[1][0]
>>> my_list2[1][:2]
Also see NumPy Arrays
>>> my_list.index(a)
>>> my_list.count(a)
>>> my_list.append('!')
>>> my_list.remove('!')
>>> del(my_list[0:1])
>>> my_list.reverse()
>>> my_list.extend('!')
>>> my_list.pop(-1)
>>> my_list.insert(0,'!')
>>> my_list.sort()
Get the index of an item
Count an item
Append an item at a time
Remove an item
Remove an item
Reverse the list
Append an item
Remove an item
Insert an item
Sort the list
Index starts at 0
Select item at index 1
Select 3rd last item
Select items at index 1 and 2
Select items after index 0
Select items before index 3
Copy my_list
my_list[list][itemOfList]
Libraries
>>> my_string.upper()
>>> my_string.lower()
>>> my_string.count('w')
>>> my_string.replace('e', 'i')
>>> my_string.strip()
>>> my_string = 'thisStringIsAwesome'
>>> my_string
'thisStringIsAwesome'
Numpy Arrays
>>> my_list = [1, 2, 3, 4]
>>> my_array = np.array(my_list)
>>> my_2darray = np.array([[1,2,3],[4,5,6]])
>>> my_array.shape
>>> np.append(other_array)
>>> np.insert(my_array, 1, 5)
>>> np.delete(my_array,[1])
>>> np.mean(my_array)
>>> np.median(my_array)
>>> my_array.corrcoef()
>>> np.std(my_array)
Asking For Help
>>> my_string[3]
>>> my_string[4:9]
Subset
>>> my_array[1]
2
Slice
>>> my_array[0:2]
array([1, 2])
Subset 2D Numpy arrays
>>> my_2darray[:,0]
array([1, 4])
>>> my_list + my_list
['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']
>>> my_list * 2
['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']
>>> my_list2 > 4
True
>>> my_array > 3
array([False, False, False, True], dtype=bool)
>>> my_array * 2
array([2, 4, 6, 8])
>>> my_array + np.array([5, 6, 7, 8])
array([6, 8, 10, 12])
>>> my_string * 2
'thisStringIsAwesomethisStringIsAwesome'
>>> my_string + 'Innit'
'thisStringIsAwesomeInnit'
>>> 'm' in my_string
True DataCamp
Learn Python for Data Science Interactively
Scientific computing
Data analysis
2D plotting
Machine learning
Also see Lists
Get the dimensions of the array
Append items to an array
Insert items in an array
Delete items in an array
Mean of the array
Median of the array
Correlation coefficient
Standard deviation
String to uppercase
String to lowercase
Count String elements
Replace String elements
Strip whitespaces
Select item at index 1
Select items at index 0 and 1
my_2darray[rows, columns]
Install Python
Calculations With Variables
Leading open data science platform
powered by Python
Free IDE that is included
with Anaconda
Create and share
documents with live code,
visualizations, text, ...
Types and Type Conversion
String Operations
List Operations
List Methods
Index starts at 0
String Methods
String Operations
Selecting Numpy Array Elements Index starts at 0
Numpy Array Operations
Numpy Array Functions
TYPE -3

Python
Cheat Sheet
Python 3 is a truly versatile programming language, loved
both by web developers, data scientists and software
engineers. And there are several good reasons for that!
Once you get a hang of it, your development speed and productivity will soar!
• Python is open-source and has a great support community,
• Plus, extensive support libraries.
• Its data structures are user-friendly.
TYPE -4

Python Basics: Getting Started
Main Python Data Types
How to Create a String in Python
Math Operators
How to Store Strings in Variables
Built-in Functions in Python
How to Deﬁne a Function
List
List Comprehensions
Tuples
Dictionaries
If Statements (Conditional Statements) in Python
Python Loops
Class
Dealing with Python Exceptions (Errors)
How to Troubleshoot the Errors
Conclusion
03
04
05
06
07
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Table of Contents

Python Basics: Getting Started
What is IDLE (Integrated Development and Learning)
Most Windows and Mac computers come with Python pre-installed. You can check
that via a Command Line search. The particular appeal of Python is that you can
write a program in any text editor, save it in .py format and then run via a Command
Line. But as you learn to write more complex code or venture into data science, you
might want to switch to an IDE or IDLE.
IDLE (Integrated Development and Learning Environment) comes with every
Python installation. Its advantage over other text editors is that it highlights
important keywords (e.g. string functions), making it easier for you to interpret code.
Shell is the default mode of operation for Python IDLE. In essence, it’s a simple loop
that performs that following four steps:
• Reads the Python statement
• Evaluates the results of it
• Prints the result on the screen
• And then loops back to read the next statement.
Python shell is a great place to test various small code snippets.
Python Cheat Sheet 3
WebsiteSetup.org - Python Cheat Sheet

Main Python Data Types
Every value in Python is called an “object”. And every object has a specific data
type. The three most-used data types are as follows:
Integers (int) — an integer number to represent an object such as “number 3”.
Strings — codify a sequence of characters using a string. For example, the word
“hello”. In Python 3, strings are immutable. If you already defined one, you cannot
change it later on.
While you can modify a string with commands such as replace() or join(), they will
create a copy of a string and apply modification to it, rather than rewrite the original
one.
Plus, another three types worth mentioning are lists, dictionaries, and tuples. All of
them are discussed in the next sections.
For now, let’s focus on the strings.
Floating-point numbers (float) — use them to represent floating-point numbers.
Integers -2, -1, 0, 1, 2, 3, 4, 5
Strings ‘yo’, ‘hey’, ‘Hello!’, ‘what’s up!’
Floating-point numbers -1.25, -1.0, --0.5, 0.0, 0.5, 1.0, 1.25

How to Create a String in Python
Basic Python String
String Concatenation
You can create a string in three ways using single, double or triple quotes. Here’s an
example of every option:
IMP! Whichever option you choose, you should stick to it and use it consistently
within your program.
As the next step, you can use the print() function to output your string in the console
window. This lets you review your code and ensure that all functions well.
Here’s a snippet for that:
my_string = “Let’s Learn Python!”
another_string = ‘It may seem difficult first, but you
can do it!’
a_long_string = ‘’’Yes, you can even master multi-line
strings
that cover more than one line
with some practice’’’
The next thing you can master is concatenation — a way to add two strings
together using the “+” operator. Here’s how it’s done:
Note: You can’t apply + operator to two diﬀerent data types e.g. string + integer. If
you try to do that, you’ll get the following Python error:
string_one = “I’m reading “
string_two = “a new great book!”
string_three = string_one + string_two
TypeError: Can’t convert ‘int’ object to str implicitly
print(“Let’s print out a string!”)

String Replication
Math Operators
As the name implies, this command lets you repeat the same string several times.
This is done using * operator. Mind that this operator acts as a replicator only with
string data types. When applied to numbers, it acts as a multiplier.
String replication example:
For reference, here’s a list of other math operations you can apply towards numbers:
And with print ()
And your output will be Alice written ﬁve times in a row.
‘Alice’ * 5 ‘AliceAliceAliceAliceAlice’
print(“Alice” * 5)
Operators Operation Example
** Exponent 2 ** 3 = 8
% Modulus/Remainder 22 % 8 = 6
// Integer division 22 // 8 = 2
/ Division 22 / 8 = 2.75
* Multiplication 3 * 3 = 9
- Subtraction 5 - 2 = 3
+ Addition 2 + 2 = 4

How to Store Strings in Variables
Variables in Python 3 are special symbols that assign a speciﬁc storage location to
a value that’s tied to it. In essence, variables are like special labels that you place on
some value to know where it’s stored.
Strings incorporate data. So you can “pack” them inside a variable. Doing so makes
it easier to work with complex Python programs.
Here’s how you can store a string inside a variable.
Let’s break it down a bit further:
• my_str is the variable name.
• = is the assignment operator.
• “Just a random string” is a value you tie to the variable name.
Now when you print this out, you receive the string output.
See? By using variables, you save yourself heaps of eﬀort as you don’t need to
retype the complete string every time you want to use it.
my_str = “Hello World”
print(my_str)
= Hello World

Built-in Functions in Python
Input() Function
len() Function
You already know the most popular function in Python — print(). Now let’s take a
look at its equally popular cousins that are in-built in the platform.
When you run this short program, the results will look like this:
Output:
input() function is a simple way to prompt the user for some input (e.g. provide their
name). All user input is stored as a string.
Here’s a quick snippet to illustrate this:
len() function helps you ﬁnd the length of any string, list, tuple, dictionary, or another
data type. It’s a handy command to determine excessive values and trim them to
optimize the performance of your program.
Here’s an input function example for a string:
Hi! What’s your name? “Jim”
Nice to meet you, Jim!
How old are you? 25
So, you are already 25 years old, Jim!
The length of the string is: 35
name = input(“Hi! What’s your name? “)
print(“Nice to meet you “ + name + “!”)
age = input(“How old are you “)
print(“So, you are already “ + str(age) + “ years old, “
+ name + “!”)
# testing len()
str1 = “Hope you are enjoying our tutorial!”
print(“The length of the string is :”, len(str1))

ﬁlter()
Use the Filter() function to exclude items in an iterable object (lists, tuples,
dictionaries, etc)
(Optional: The PDF version of the checklist can also include a full table of all the in-built
functions).
ages = [5, 12, 17, 18, 24, 32]
def myFunc(x):
if x < 18:
return False
else:
return True
adults = filter(myFunc, ages)
for x in adults:
print(x)

How to Define a Function
Apart from using in-built functions, Python 3 also allows you to define your own
functions for your program.
To recap, a function is a block of coded instructions that perform a certain action.
Once properly defined, a function can be reused throughout your program i.e. re-use
the same code.
Here’s a quick walkthrough explaining how to define a function in Python:
First, use def keyword followed by the function name():. The parentheses can
contain any parameters that your function should take (or stay empty).
Next, you’ll need to add a second code line with a 4-space indent to specify what
this function should do.
Now, let’s take a look at a defined function with a parameter — an entity, specifying
an argument that a function can accept.
Now, you have to call this function to run the code.
def name():
def name():
print(“What’s your name?”)
def add_numbers(x, y, z):
a = x + y
b = x + z
c = y + z
print(a, b, c)
add_numbers(1, 2, 3)
name.py
def name():
print(“What’s your name?”)
hello()
name()

How to Pass Keyword Arguments to a Function
In this case, you pass the number 1 in for the x parameter, 2 in for the y parameter,
and 3 in for the z parameter. The program will that do the simple math of adding up
the numbers:
Output:
A function can also accept keyword arguments. In this case, you can use
parameters in random order as the Python interpreter will use the provided
keywords to match the values to the parameters.
Here’s a simple example of how you pass a keyword argument to a function.
Output:
# Define function with parameters
def product_info(product name, price):
print(“productname: “ + product name)
print(“Price “ + str(dollars))
# Call function with parameters assigned as above
product_info(“White T-shirt”, 15 dollars)
# Call function with keyword arguments
product_info(productname=”jeans”, price=45)
Productname: White T-shirt
Price: 15
Productname: Jeans
Price: 45
a = 1 + 2
b = 1 + 3
c = 2 + 3
3
Product Name: White T-Shirt
Price: 15
Product Name: Jeans
Price: 45
4
def product_info (product name, price):
print(”Product Name: “ + product_name)
print(”Price: “ + str(price))
5
# Define function with parameters
product_info(”White T-Shirt: “, 15)
# Call function with parameters assigned as above
product_info(productname=”Jeans“, price=45)
# Call function with keyword arguments

Lists
Example lists
How to Add Items to a List
Lists are another cornerstone data type in Python used to specify an ordered
sequence of elements. In short, they help you keep related data together and
perform the same operations on several values at once. Unlike strings, lists are
mutable (=changeable).
Each value inside a list is called an item and these are placed between square
brackets.
Alternatively, you can use list() function to do the same:
You have two ways to add new items to existing lists.
The ﬁrst one is using append() function:
The second option is to insert() function to add an item at the speciﬁed index:
my_list = [1, 2, 3]
my_list2 = [“a”, “b”, “c”]
my_list3 = [“4”, d, “book”, 5]
beta_list = [“apple”, “banana”, “orange”]
beta_list.append(“grape”)
print(beta_list)
beta_list.insert(“2 grape”)
print(beta_list)
alpha_list = list((“1”, “2”, “3”))
print(alpha_list)
2,“grape”

How to Remove an Item from a List
Combine Two Lists
Create a Nested List
Again, you have several ways to do so. First, you can use remove() function:
Secondly, you can use the pop() function. If no index is speciﬁed, it will remove the
last item.
The last option is to use del keyword to remove a speciﬁc item:
P.S. You can also apply del towards the entire list to scrap it.
To mash up two lists use the + operator.
You can also create a list of your lists when you have plenty of them :)
beta_list.remove(“apple”)
print(beta_list)
beta_list.pop()
print(beta_list)
del beta_list [1]
print(beta_list)
my_list = [1, 2, 3]
my_list2 = [“a”, “b”, “c”]
combo_list = my_list + my_list2
combo_list
[1, 2, 3, ‘a’, ‘b’, ‘c’]
my_nested_list = [my_list, my_list2]
my_nested_list
[[1, 2, 3], [‘a’, ‘b’, ‘c’]]

Sort a List
Slice a List
Change Item Value on Your List
Loop Through the List
Use the sort() function to organize all items in your list.
Now, if you want to call just a few elements from your list (e.g. the ﬁrst 4 items),
you need to specify a range of index numbers separated by a colon [x:y]. Here’s an
example:
You can easily overwrite a value of one list items:
Using for loop you can multiply the usage of certain items, similarly to what *
operator does. Here’s an example:
Output:
alpha_list = [34, 23, 67, 100, 88, 2]
alpha_list.sort()
alpha_list
[2, 23, 34, 67, 88, 100]
alpha_list[0:4]
[2, 23, 34, 67]
beta_list[1] = “pear”
print(beta_list)
for x in range(1,4):
beta_list += [‘fruit’]
print(beta_list)
[‘apple’, ‘pear’, ‘cherry’]

Copy a List
Use the built-in copy() function to replicate your data:
Alternatively, you can copy a list with the list() method:
beta_list = beta_list.copy()
print(beta_list)
beta_list = list (beta_list)
print(beta_list)

List Comprehensions
Tuples
List comprehensions are a handy option for creating lists based on existing lists.
When using them you can build by using strings and tuples as well.
Tuples are similar to lists — they allow you to display an ordered sequence of
elements. However, they are immutable and you can’t change the values stored in a
tuple.
The advantage of using tuples over lists is that the former are slightly faster. So it’s
a nice way to optimize your code.
Output:
(1, 3, 5, 7, 9)
The process is similar to slicing lists.
Note: Once you create a tuple, you can’t add new items to it or change it in any other way!
List comprehensions examples
How to Create a Tuple
How to Slide a Tuple
Here’s a more complex example that features math operators, integers, and the
range() function:
list_variable = [x for x in iterable]
my_tuple = (1, 2, 3, 4, 5)
my_tuple[0:3]
(1, 2, 3)
numbers = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
print(numbers[1:11:2])
number_list = [x ** 2 for x in range(10) if x % 2 == 0]
print(number_list)

Convert Tuple to a List
Dictionaries
How to Create a Python Dictionary
Since Tuples are immutable, you can’t change them. What you can do though is
convert a tuple into a list, make an edit and then convert it back to a tuple.
Here’s how to accomplish this:
A dictionary holds indexes with keys that are mapped to certain values. These
key-value pairs oﬀer a great way of organizing and storing data in Python. They are
mutable, meaning you can change the stored information.
A key value can be either a string, Boolean, or integer. Here’s an example dictionary
illustrating this:
Here’s a quick example showcasing how to make an empty dictionary.
Option 1: new_dict = {}
Option 2: other_dict= dict()
And you can use the same two approaches to add values to your dictionary:
x = (“apple”, “orange”, “pear”)
y = list(x)
y[1] = “grape”
x = tuple(y)
print(x)
Customer 1= {‘username’: ‘john-sea’, ‘online’: false,
‘friends’:100}
new_dict = {
“brand”: “Honda”,
“model”: “Civic”,
“year”: 1995
}
print(new_dict)

You can access any of the values in your dictionary the following way:
You can also use the following methods to accomplish the same.
• dict.keys() isolates keys
• dict.values() isolates values
• dict.items() returns items in a list format of (key, value) tuple pairs
To change one of the items, you need to refer to it by its key name:
Again to implement looping, use for loop command.
Note: In this case, the return values are the keys of the dictionary. But, you can also return
values using another method.
How to Access a Value in a Dictionary
Change Item Value
Loop Through the Dictionary
x = new_dict[“brand”]
#Change the “year” to 2020:
new_dict= {
“brand”: “Honda”,
“model”: “Civic”,
“year”: 1995
}
new_dict[“year”] = 2020
#print all key names in the dictionary
for x in new_dict:
print(x)
#print all values in the dictionary
for x in new_dict:
print(new_dict[x])
#loop through both keys and values
for x, y in my_dict.items():
print(x, y)

The goal of a conditional statement is to check if it’s True or False.
For more complex operations, you can create nested if statements. Here’s how it
looks:
Just like other programming languages, Python supports the basic logical
conditions from math:
• Equals: a == b
• Not Equals: a != b
• Less than: a < b
• Less than or equal to a <= b
• Greater than: a > b
• Greater than or equal to: a >= b
You can leverage these conditions in various ways. But most likely, you’ll use them in
“if statements” and loops.
Output:
That’s True!
If Statement Example
Nested If Statements
If Statements (Conditional
Statements) in Python
if 5 > 1:
print(“That’s True!”)
x = 35
if x > 20:
print(“Above twenty,”)
if x > 30:
print(“and also above 30!”)

elif keyword prompts your program to try another condition if the previous one(s)
was not true. Here’s an example:
else keyword helps you add some additional ﬁlters to your condition clause. Here’s
how an if-elif-else combo looks:
If statements can’t be empty. But if that’s your case, add the pass statement to avoid
having an error:
Not keyword let’s you check for the opposite meaning to verify whether the value is
NOT True:
Elif Statements
If Else Statements
Pass Statements
If-Not-Statements
a = 45
b = 45
if b > a:
print(“b is greater than a”)
elif a == b:
print(“a and b are equal”)
if age < 4:
ticket_price = 0
elif age < 18:
ticket_price = 10
else: ticket_price = 15
a = 33
b = 200
if b > a:
pass
new_list = [1, 2, 3, 4]
x = 10
if x not in new_list:
print(“’x’ isn’t on the list, so this is True!”)

Python has two simple loop commands that are good to know:
• for loops
• while loops
Let’s take a look at each of these.
As already illustrated in the other sections of this Python checklist, for loop is a
handy way for iterating over a sequence such as a list, tuple, dictionary, string, etc.
Here’s an example showing how to loop through a string:
While loop enables you to execute a set of statements as long as the condition for
them is true.
You can also stop the loop from running even if the condition is met. For that, use
the break statement both in while and for loops:
Plus, you’ve already seen other examples for lists and dictionaries.
Python Loops
For Loop
While Loops
How to Break a Loop
for x in “apple”:
print(x)
#print as long as x is less than 8
i = 1
while i< 8:
print(x)
i += 1
i = 1
while i < 8:
print(i)
if i == 4:
break
i += 1

Since Python is an object-oriented programming language almost every element of
it is an object — with its methods and properties.
Class acts as a blueprint for creating diﬀerent objects. Objects are an instance of a
class, where the class is manifested in some program.
Let’s create a class named TestClass, with one property named z:
As a next step, you can create an object using your class. Here’s how it’s done:
Further, you can assign diﬀerent attributes and methods to your object. The
example is below:
Class
How to Create a Class
How To Create an Object
class TestClass:
z = 5
class car(object):
“””docstring”””
def __init__(self, color, doors, tires):
“””Constructor”””
self.color = color
self.doors = doors
self.tires = tires
def brake(self):
“””
Stop the car
“””
return “Braking”
def drive(self):
“””
Drive the car
“””
return “I’m driving!”
p1 = TestClass()
print(p1.x)

Python has a list of in-built exceptions (errors) that will pop up whenever you make
a mistake in your code. As a newbie, it’s good to know how to fix these.
Every object can be further sub-classified. Here’s an example
• AttributeError — pops up when an attribute reference or assignment fails.
• IOError — emerges when some I/O operation (e.g. an open() function) fails
for an I/O-related reason, e.g., “file not found” or “disk full”.
• ImportError — comes up when an import statement cannot locate the
module definition. Also, when a from… import can’t find a name that must be
imported.
• IndexError — emerges when a sequence subscript is out of range.
• KeyError — raised when a dictionary key isn’t found in the set of existing keys.
• KeyboardInterrupt — lights up when the user hits the interrupt key (such
as Control-C or Delete).
• NameError — shows up when a local or global name can’t be found.
Dealing with Python Exceptions (Errors)
How to Create a Subclass
The Most Common Python Exceptions
class Car(Vehicle):
“””
The Car class
“””
def brake(self):
“””
Override brake method
“””
return “The car class is breaking slowly!”
if __name__ == “__main__”:
car = Car(“yellow”, 2, 4, “car”)
car.brake()
‘The car class is breaking slowly!’
car.drive()
“I’m driving a yellow car!”

• OSError — indicated a system-related error.
• SyntaxError — pops up when a parser encounters a syntax error.
• TypeError — comes up when an operation or function is applied to an object
of inappropriate type.
• ValueError — raised when a built-in operation/function gets an argument
that has the right type but not an appropriate value, and the situation is not
described by a more precise exception such as IndexError.
• ZeroDivisionError — emerges when the second argument of a division or
modulo operation is zero.
Python has a useful statement, design just for the purpose of handling exceptions —
try/except statement. Here’s a code snippet showing how you can catch KeyErrors
in a dictionary using this statement:
You can also detect several exceptions at once with a single statement. Here’s an
example for that:
How to Troubleshoot the Errors
my_dict = {“a”:1, “b”:2, “c”:3}
try:
value = my_dict[“d”]
except KeyError:
print(“That key does not exist!”)
my_dict = {“a”:1, “b”:2, “c”:3}
try:
value = my_dict[“d”]
except IndexError:
print(“This index does not exist!”)
except KeyError:
print(“This key is not in the dictionary!”)
except:
print(“Some other problem happened!”)

my_dict = {“a”:1, “b”:2, “c”:3}
try:
value = my_dict[“a”]
except KeyError:
print(“A KeyError occurred!”)
else:
print(“No error occurred!”)
Adding an else clause will help you confirm that no errors
were found:
try/except with else clause
Conclusions
Now you know the core Python concepts!
By no means is this Python checklist comprehensive. But it includes all the key data
types, functions and commands you should learn as a beginner.
As always, we welcome your feedback in the comment section below!

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