Data types in Python or Different Types of Objects in Python

In the below post we will learn about the Data types in Python or Different Types of Objects in Python.

Python has five standard Data Types:

  • Numbers
  • String
  • List
  • Set
  • Tuple
  • Dictionary
  • Sets
  • ByteArray

Python sets the variable type based on the value that is assigned to it. Unlike more riggers languages, Python will change the variable type if the variable value is set to another value. For example:

var = 123 # This will create a number integer assignment
var = 'john' # the `var` variable is now a string type.

Numbers

Python numbers variables are created by the standard Python method:

var = 382

Most of the time using the standard Python number type is fine. Python will automatically convert a number from one type to another if it needs. But, under certain circumstances that a specific number type is needed (ie. complex, hexidecimal), the format can be forced into a format by using additional syntax in the table below:

Type Format Description
int a = 10 Signed Integer
long a = 345L (L) Long integers, they can also be represented in octal and hexadecimal
float a = 45.67 (.) Floating point real values
complex a = 3.14J (J) Contains integer in the range 0 to 255.

Most of the time Python will do variable conversion automatically. You can also use Python conversion functions (int(), long(), float(), complex()) to convert data from one type to another. In addition, the type function returns information about how your data is stored within a variable.

message = "Good morning"
num = 85
pi = 3.14159

print(type(message))  # This will return a string
print(type(n))  # This will return an integer
print(type(pi))  # This will return a float

String

Create string variables by enclosing characters in quotes. Python uses single quotes ' double quotes " and triple quotes """ to denote literal strings. Only the triple quoted strings """ also will automatically continue across the end of line statement.

firstName = 'john'
lastName = "smith"
message = """This is a string that will span across multiple lines. Using newline characters
and no spaces for the next lines. The end of lines within this string also count as a newline when printed"""

Strings can be accessed as a whole string, or a substring of the complete variable using brackets ‘[]’. Here are a couple examples:

var1 = 'Hello World!'
var2 = 'RhinoPython'

print var1[0] # this will print the first character in the string an `H`
print var2[1:5] # this will print the substring 'hinoP`

Python can use a special syntax to format multiple strings and numbers. The string formatter is quickly covered here because it is seen often and it is important to recognize the syntax.

print "The item {} is repeated {} times".format(element,count))

The {} are placeholders that are substituted by the variables element and count in the final string. This compact syntax is meant to keep the code more readable and compact.

Python is currently transitioning to the format syntax above, but python can use an older syntax, which is being phased out, but is still seen in some example code:

print "The item %i is repeated %i times"% (element,count)

For more information on the string formatter in Python see the article: PyFormat Website

For a more detailed look at string variables in Python, see the Tutorialspoint Python Tutorial on stings. Note: this article does use the older formatting syntax, but has a lot useful information.

String Formatting Operator

One of Python’s coolest features is the string format operator %. This operator is unique to strings and makes up for the pack of having functions from C’s printf() family. Following is a simple example −

#!/usr/bin/python

print "My name is %s and weight is %d kg!" % ('Zara', 21) 

When the above code is executed, it produces the following result −

My name is Zara and weight is 21 kg!

Here is the list of complete set of symbols which can be used along with % −

Format Symbol Conversion
%c character
%s string conversion via str() prior to formatting
%i signed decimal integer
%d signed decimal integer
%u unsigned decimal integer
%o octal integer
%x hexadecimal integer (lowercase letters)
%X hexadecimal integer (UPPERcase letters)
%e exponential notation (with lowercase ‘e’)
%E exponential notation (with UPPERcase ‘E’)
%f floating point real number
%g the shorter of %f and %e
%G the shorter of %f and %E

Other supported symbols and functionality are listed in the following table −

Symbol Functionality
* argument specifies width or precision
left justification
+ display the sign
<sp> leave a blank space before a positive number
# add the octal leading zero ( ‘0’ ) or hexadecimal leading ‘0x’ or ‘0X’, depending on whether ‘x’ or ‘X’ were used.
0 pad from left with zeros (instead of spaces)
% ‘%%’ leaves you with a single literal ‘%’
(var) mapping variable (dictionary arguments)
m.n. m is the minimum total width and n is the number of digits to display after the decimal point (if appl.)
String Special Operators

Assume string variable a holds ‘Hello’ and variable b holds ‘Python’, then −

Operator Description Example
+ Concatenation – Adds values on either side of the operator a + b will give HelloPython
* Repetition – Creates new strings, concatenating multiple copies of the same string a*2 will give -HelloHello
[] Slice – Gives the character from the given index a[1] will give e
[ : ] Range Slice – Gives the characters from the given range a[1:4] will give ell
in Membership – Returns true if a character exists in the given string H in a will give 1
not in Membership – Returns true if a character does not exist in the given string M not in a will give 1
r/R Raw String – Suppresses actual meaning of Escape characters. The syntax for raw strings is exactly the same as for normal strings with the exception of the raw string operator, the letter “r,” which precedes the quotation marks. The “r” can be lowercase (r) or uppercase (R) and must be placed immediately preceding the first quote mark. print r’\n’ prints \n and print R’\n’prints \n
% Format – Performs String formatting See at next section

Built-in String Methods

Python includes the following built-in methods to manipulate strings −

SN Methods with Description
1 capitalize()
Capitalizes first letter of string
2 center(width, fillchar)

Returns a space-padded string with the original string centered to a total of width columns.

3 count(str, beg= 0,end=len(string))

Counts how many times str occurs in string or in a substring of string if starting index beg and ending index end are given.

4 decode(encoding=’UTF-8′,errors=’strict’)

Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding.

5 encode(encoding=’UTF-8′,errors=’strict’)

Returns encoded string version of string; on error, default is to raise a ValueError unless errors is given with ‘ignore’ or ‘replace’.

6 endswith(suffix, beg=0, end=len(string))
Determines if string or a substring of string (if starting index beg and ending index end are given) ends with suffix; returns true if so and false otherwise.
7 expandtabs(tabsize=8)

Expands tabs in string to multiple spaces; defaults to 8 spaces per tab if tabsize not provided.

8 find(str, beg=0 end=len(string))

Determine if str occurs in string or in a substring of string if starting index beg and ending index end are given returns index if found and -1 otherwise.

9 index(str, beg=0, end=len(string))

Same as find(), but raises an exception if str not found.

10 isalnum()

Returns true if string has at least 1 character and all characters are alphanumeric and false otherwise.

11 isalpha()

Returns true if string has at least 1 character and all characters are alphabetic and false otherwise.

12 isdigit()

Returns true if string contains only digits and false otherwise.

13 islower()

Returns true if string has at least 1 cased character and all cased characters are in lowercase and false otherwise.

14 isnumeric()

Returns true if a unicode string contains only numeric characters and false otherwise.

15 isspace()

Returns true if string contains only whitespace characters and false otherwise.

16 istitle()

Returns true if string is properly “titlecased” and false otherwise.

17 isupper()

Returns true if string has at least one cased character and all cased characters are in uppercase and false otherwise.

18 join(seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

19 len(string)

Returns the length of the string

20 ljust(width[, fillchar])

Returns a space-padded string with the original string left-justified to a total of width columns.

21 lower()

Converts all uppercase letters in string to lowercase.

22 lstrip()

Removes all leading whitespace in string.

23 maketrans()

Returns a translation table to be used in translate function.

24 max(str)

Returns the max alphabetical character from the string str.

25 min(str)

Returns the min alphabetical character from the string str.

26 replace(old, new [, max])

Replaces all occurrences of old in string with new or at most max occurrences if max given.

27 rfind(str, beg=0,end=len(string))

Same as find(), but search backwards in string.

28 rindex( str, beg=0, end=len(string))

Same as index(), but search backwards in string.

29 rjust(width,[, fillchar])

Returns a space-padded string with the original string right-justified to a total of width columns.

30 rstrip()

Removes all trailing whitespace of string.

31 split(str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

32 splitlines( num=string.count(‘\n’))

Splits string at all (or num) NEWLINEs and returns a list of each line with NEWLINEs removed.

33 startswith(str, beg=0,end=len(string))

Determines if string or a substring of string (if starting index beg and ending index end are given) starts with substring str; returns true if so and false otherwise.

34 strip([chars])

Performs both lstrip() and rstrip() on string

35 swapcase()

Inverts case for all letters in string.

36 title()

Returns “titlecased” version of string, that is, all words begin with uppercase and the rest are lowercase.

37 translate(table, deletechars=””)

Translates string according to translation table str(256 chars), removing those in the del string.

38 upper()

Converts lowercase letters in string to uppercase.

39 zfill (width)

Returns original string leftpadded with zeros to a total of width characters; intended for numbers, zfill() retains any sign given (less one zero).

40 isdecimal()

Returns true if a unicode string contains only decimal characters and false otherwise.

List

Lists are a very useful variable type in Python. A list can contain a series of values. List variables are declared by using brackets [ ] following the variable name.

A = [ ] # This is a blank list variable
B = [1, 23, 45, 67] # this list creates an initial list of 4 numbers.
C = [2, 4, 'john'] # lists can contain different variable types.

All lists in Python are zero-based indexed. When referencing a member or the length of a list the number of list elements is always the number shown plus one.

mylist = ['Rhino', 'Grasshopper', 'Flamingo', 'Bongo']
B = len(mylist) # This will return the length of the list which is 3. The index is 0, 1, 2, 3.
print mylist[1] # This will return the value at index 1, which is 'Grasshopper'
print mylist[0:2] # This will return the first 3 elements in the list.

You can assign data to a specific element of the list using an index into the list. The list index starts at zero. Data can be assigned to the elements of an array as follows:

mylist = [0, 1, 2, 3]
mylist[0] = 'Rhino'
mylist[1] = 'Grasshopper'
mylist[2] = 'Flamingo'
mylist[3] = 'Bongo'
print mylist[1]

Lists aren’t limited to a single dimension. Although most people can’t comprehend more than three or four dimensions. You can declare multiple dimensions by separating an with commas. In the following example, the MyTable variable is a two-dimensional array :

MyTable = [[], []]

In a two-dimensional array, the first number is always the number of rows; the second number is the number of columns.

For a detailed look at managing lists, take a look at the article

  • Python Lists – Google developer
  • TutorialPoint Python Lists

Built-in List Functions & Methods:

Python includes the following list functions −

SN Function with Description
1 cmp(list1, list2)

Compares elements of both lists.

2 len(list)

Gives the total length of the list.

3 max(list)

Returns item from the list with max value.

4 min(list)

Returns item from the list with min value.

5 list(seq)

Converts a tuple into list.

Python includes following list methods

SN Methods with Description
1 list.append(obj)

Appends object obj to list

2 list.count(obj)

Returns count of how many times obj occurs in list

3 list.extend(seq)

Appends the contents of seq to list

4 list.index(obj)

Returns the lowest index in list that obj appears

5 list.insert(index, obj)

Inserts object obj into list at offset index

6 list.pop(obj=list[-1])

Removes and returns last object or obj from list

7 list.remove(obj)

Removes object obj from list

8 list.reverse()

Reverses objects of list in place

9 list.sort([func])

Tuple

Tuples are a group of values like a list and are manipulated in similar ways. But, tuples are fixed in size once they are assigned. In Python the fixed size is considered immutable as compared to a list that is dynamic and mutable. Tuples are defined by parenthesis ().

myGroup = ('Rhino', 'Grasshopper', 'Flamingo', 'Bongo')

Here are some advantages of tuples over lists:

  1. Elements to a tuple. Tuples have no append or extend method.
  2. Elements cannot be removed from a tuple.
  3. You can find elements in a tuple, since this doesn’t change the tuple.
  4. You can also use the in operator to check if an element exists in the tuple.
  5. Tuples are faster than lists. If you’re defining a constant set of values and all you’re ever going to do with it is iterate through it, use a tuple instead of a list.
  6. It makes your code safer if you “write-protect” data that does not need to be changed.

It seems tuples are very restrictive, so why are they useful? There are many datastructures in Rhino that require a fixed set of values. For instance a Rhino point is a list of 3 numbers [34.5, 45.7, 0]. If this is set as tuple, then you can be assured the original 3 number structure stays as a point (34.5, 45.7, 0). There are other datastructures such as lines, vectors, domains and other data in Rhino that also require a certain set of values that do not change. Tuples are great for this.

A tuple is a sequence of immutable Python objects. Tuples are sequences, just like lists. The differences between tuples and lists are, the tuples cannot be changed unlike lists and tuples use parentheses, whereas lists use square brackets.

Creating a tuple is as simple as putting different comma-separated values. Optionally you can put these comma-separated values between parentheses also. For example −

tup1 = ('physics', 'chemistry', 1997, 2000);
tup2 = (1, 2, 3, 4, 5 );
tup3 = "a", "b", "c", "d";

The empty tuple is written as two parentheses containing nothing −

tup1 = ();

To write a tuple containing a single value you have to include a comma, even though there is only one value −

tup1 = (50,);

Like string indices, tuple indices start at 0, and they can be sliced, concatenated, and so on.

Accessing Values in Tuples:

To access values in tuple, use the square brackets for slicing along with the index or indices to obtain value available at that index. For example −

#!/usr/bin/python

tup1 = ('physics', 'chemistry', 1997, 2000);
tup2 = (1, 2, 3, 4, 5, 6, 7 );

print "tup1[0]: ", tup1[0]
print "tup2[1:5]: ", tup2[1:5]

When the above code is executed, it produces the following result −

tup1[0]:  physics
tup2[1:5]:  [2, 3, 4, 5]

Updating Tuples

Tuples are immutable which means you cannot update or change the values of tuple elements. You are able to take portions of existing tuples to create new tuples as the following example demonstrates −

#!/usr/bin/python

tup1 = (12, 34.56);
tup2 = ('abc', 'xyz');

# Following action is not valid for tuples
# tup1[0] = 100;

# So let's create a new tuple as follows
tup3 = tup1 + tup2;
print tup3

When the above code is executed, it produces the following result −

(12, 34.56, 'abc', 'xyz')

Delete Tuple Elements

Removing individual tuple elements is not possible. There is, of course, nothing wrong with putting together another tuple with the undesired elements discarded.

To explicitly remove an entire tuple, just use the del statement. For example:

#!/usr/bin/python

tup = ('physics', 'chemistry', 1997, 2000);

print tup
del tup;
print "After deleting tup : "
print tup

This produces the following result. Note an exception raised, this is because after del tup tuple does not exist any more −

('physics', 'chemistry', 1997, 2000)
After deleting tup :
Traceback (most recent call last):
  File "test.py", line 9, in <module>
    print tup;
NameError: name 'tup' is not defined

Basic Tuples Operations

Tuples respond to the + and * operators much like strings; they mean concatenation and repetition here too, except that the result is a new tuple, not a string.

In fact, tuples respond to all of the general sequence operations we used on strings in the prior chapter −

Python Expression Results Description
len((1, 2, 3)) 3 Length
(1, 2, 3) + (4, 5, 6) (1, 2, 3, 4, 5, 6) Concatenation
(‘Hi!’,) * 4 (‘Hi!’, ‘Hi!’, ‘Hi!’, ‘Hi!’) Repetition
3 in (1, 2, 3) True Membership
for x in (1, 2, 3): print x, 1 2 3 Iteration

Indexing, Slicing, and Matrixes

Because tuples are sequences, indexing and slicing work the same way for tuples as they do for strings. Assuming following input −

L = ('spam', 'Spam', 'SPAM!')
Python Expression Results Description
L[2] ‘SPAM!’ Offsets start at zero
L[-2] ‘Spam’ Negative: count from the right
L[1:] [‘Spam’, ‘SPAM!’] Slicing fetches sections

No Enclosing Delimiters

Any set of multiple objects, comma-separated, written without identifying symbols, i.e., brackets for lists, parentheses for tuples, etc., default to tuples, as indicated in these short examples −

#!/usr/bin/python

print 'abc', -4.24e93, 18+6.6j, 'xyz'
x, y = 1, 2;
print "Value of x , y : ", x,y

When the above code is executed, it produces the following result −

abc -4.24e+93 (18+6.6j) xyz
Value of x , y : 1 2

Built-in Tuple Functions

Python includes the following tuple functions −

SN Function with Description
1 cmp(tuple1, tuple2)

Compares elements of both tuples.

2 len(tuple)

Gives the total length of the tuple.

3 max(tuple)

Returns item from the tuple with max value.

4 min(tuple)

Returns item from the tuple with min value.

5 tuple(seq)

Converts a list into tuple.

Dictionary

Dictionaries in Python are lists of Key:Value pairs. This is a very powerful datatype to hold a lot of related information that can be associated through keys. The main operation of a dictionary is to extract a value based on the key name. Unlike lists, where index numbers are used, dictionaries allow the use of a key to access its members. Dictionaries can also be used to sort, iterate and compare data.

Dictionaries are created by using braces ({}) with pairs separated by a comma (,) and the key values associated with a colon(:). In Dictionaries the Key must be unique. Here is a quick example on how dictionaries might be used:

room_num = {'john': 425, 'tom': 212}
room_num['john'] = 645  # set the value associated with the 'john' key to 645
print (room_num['tom']) # print the value of the 'tom' key.
room_num['isaac'] = 345 # Add a new key 'isaac' with the associated value
print (room_num.keys()) # print out a list of keys in the dictionary
print ('isaac' in room_num) # test to see if 'issac' is in the dictionary.  This returns true.

Each key is separated from its value by a colon (:), the items are separated by commas, and the whole thing is enclosed in curly braces. An empty dictionary without any items is written with just two curly braces, like this: {}.

Keys are unique within a dictionary while values may not be. The values of a dictionary can be of any type, but the keys must be of an immutable data type such as strings, numbers, or tuples.

Accessing Values in Dictionary:

To access dictionary elements, you can use the familiar square brackets along with the key to obtain its value. Following is a simple example −

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}

print "dict['Name']: ", dict['Name']
print "dict['Age']: ", dict['Age']

When the above code is executed, it produces the following result −

dict['Name']:  Zara
dict['Age']:  7

If we attempt to access a data item with a key, which is not part of the dictionary, we get an error as follows −

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}

print "dict['Alice']: ", dict['Alice']

When the above code is executed, it produces the following result −

dict['Alice']:
Traceback (most recent call last):
   File "test.py", line 4, in <module>
      print "dict['Alice']: ", dict['Alice'];
KeyError: 'Alice'

Updating Dictionary

You can update a dictionary by adding a new entry or a key-value pair, modifying an existing entry, or deleting an existing entry as shown below in the simple example −

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}

dict['Age'] = 8; # update existing entry
dict['School'] = "DPS School"; # Add new entry


print "dict['Age']: ", dict['Age']
print "dict['School']: ", dict['School']

When the above code is executed, it produces the following result −

dict['Age']:  8
dict['School']:  DPS School

Delete Dictionary Elements

You can either remove individual dictionary elements or clear the entire contents of a dictionary. You can also delete entire dictionary in a single operation.

To explicitly remove an entire dictionary, just use the del statement. Following is a simple example −

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'}

del dict['Name']; # remove entry with key 'Name'
dict.clear();     # remove all entries in dict
del dict ;        # delete entire dictionary

print "dict['Age']: ", dict['Age']
print "dict['School']: ", dict['School']

This produces the following result. Note that an exception is raised because after del dict dictionary does not exist any more −

dict['Age']:
Traceback (most recent call last):
  File "test.py", line 8, in <module>
    print "dict['Age']: ", dict['Age'];
TypeError: 'type' object is unsubscriptable

Note: del() method is discussed in subsequent section.

Properties of Dictionary Keys

Dictionary values have no restrictions. They can be any arbitrary Python object, either standard objects or user-defined objects. However, same is not true for the keys.

There are two important points to remember about dictionary keys −

(a) More than one entry per key not allowed. Which means no duplicate key is allowed. When duplicate keys encountered during assignment, the last assignment wins. For example −

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Name': 'Manni'}

print "dict['Name']: ", dict['Name']

When the above code is executed, it produces the following result −

dict['Name']:  Manni

(b) Keys must be immutable. Which means you can use strings, numbers or tuples as dictionary keys but something like [‘key’] is not allowed. Following is a simple example:

#!/usr/bin/python

dict = {['Name']: 'Zara', 'Age': 7}

print "dict['Name']: ", dict['Name']

When the above code is executed, it produces the following result −

Traceback (most recent call last):
   File "test.py", line 3, in <module>
      dict = {['Name']: 'Zara', 'Age': 7};
TypeError: list objects are unhashable

Built-in Dictionary Functions & Methods −

Python includes the following dictionary functions −

SN Function with Description
1 cmp(dict1, dict2)

Compares elements of both dict.

2 len(dict)

Gives the total length of the dictionary. This would be equal to the number of items in the dictionary.

3 str(dict)

Produces a printable string representation of a dictionary

4 type(variable)

Returns the type of the passed variable. If passed variable is dictionary, then it would return a dictionary type.

Python includes following dictionary methods −

SN Methods with Description
1 dict.clear()

Removes all elements of dictionary dict

2 dict.copy()

Returns a shallow copy of dictionary dict

3 dict.fromkeys()

Create a new dictionary with keys from seq and values set to value.

4 dict.get(key, default=None)

For key key, returns value or default if key not in dictionary

5 dict.has_key(key)

Returns true if key in dictionary dict, false otherwise

6 dict.items()

Returns a list of dict‘s (key, value) tuple pairs

7 dict.keys()

Returns list of dictionary dict’s keys

8 dict.setdefault(key, default=None)

Similar to get(), but will set dict[key]=default if key is not already in dict

9 dict.update(dict2)

Adds dictionary dict2‘s key-values pairs to dict

10 dict.values()

Returns list of dictionary dict‘s values

Set

A set is a collection which is unordered and unindexed. In Python sets are written with curly brackets.

Example

Create a Set:

thisset = {“apple”“banana”“cherry”}
print(thisset)

Run example »

Note: Sets are unordered, so you cannot be sure in which order the items will appear.


Access Items

You cannot access items in a set by referring to an index, since sets are unordered the items has no index.

But you can loop through the set items using a for loop, or ask if a specified value is present in a set, by using the in keyword.

Example

Loop through the set, and print the values:

thisset = {“apple”“banana”“cherry”}

for x in thisset:
print(x)

Run example »

Example

Check if “banana” is present in the set:

thisset = {“apple”“banana”“cherry”}

print(“banana” in thisset)

Run example »


Change Items

Once a set is created, you cannot change its items, but you can add new items.


Add Items

To add one item to a set use the add() method.

To add more than one item to a set use the update() method.

Example

Add an item to a set, using the add() method:

thisset = {“apple”“banana”“cherry”}

thisset.add(“orange”)

print(thisset)

Run example »

Example

Add multiple items to a set, using the update() method:

thisset = {“apple”“banana”“cherry”}

thisset.update([“orange”“mango”“grapes”])

print(thisset)

Run example »



Get the Length of a Set

To determine how many items a set has, use the len() method.

Example

Get the number of items in a set:

thisset = {“apple”“banana”“cherry”}

print(len(thisset))

Run example »


Remove Item

To remove an item in a set, use the remove(), or the discard() method.

Example

Remove “banana” by using the remove() method:

thisset = {“apple”“banana”“cherry”}

thisset.remove(“banana”)

print(thisset)

Run example »

Note: If the item to remove does not exist, remove() will raise an error.

Example

Remove “banana” by using the discard() method:

thisset = {“apple”“banana”“cherry”}

thisset.discard(“banana”)

print(thisset)

Note: If the item to remove does not exist, discard() will NOT raise an error.

You can also use the pop(), method to remove an item, but this method will remove the last item. Remember that sets are unordered, so you will not know what item that gets removed.

The return value of the pop() method is the removed item.

Example

Remove the last item by using the pop() method:

thisset = {“apple”“banana”“cherry”}

x = thisset.pop()

print(x)

print(thisset)

Note: Sets are unordered, so when using the pop() method, you will not know which item that gets removed.

Example

The clear() method empties the set:

thisset = {“apple”“banana”“cherry”}

thisset.clear()

print(thisset)

Example

The del keyword will delete the set completely:

thisset = {“apple”“banana”“cherry”}

del thisset

print(thisset)


Join Two Sets

There are several ways to join two or more sets in Python.

You can use the union() method that returns a new set containing all items from both sets, or the update() method that inserts all the items from one set into another:

Example

The union() method returns a new set with all items from both sets:

set1 = {“a”“b” , “c”}
set2 = {123}

set3 = set1.union(set2)
print(set3)

Example

The update() method inserts the items in set2 into set1:

set1 = {“a”“b” , “c”}
set2 = {123}

set1.update(set2)
print(set1)

Note: Both union() and update() will exclude any duplicate items.

There are other methods that joins two sets and keeps ONLY the duplicates, or NEVER the duplicates, check the full list of set methods in the bottom of this page.


The set() Constructor

It is also possible to use the set() constructor to make a set.

Example

Using the set() constructor to make a set:

thisset = set((“apple”“banana”“cherry”)) # note the double round-brackets
print(thisset)

Run example »


Set Methods

Python has a set of built-in methods that you can use on sets.

Method Description
add() Adds an element to the set
clear() Removes all the elements from the set
copy() Returns a copy of the set
difference() Returns a set containing the difference between two or more sets
difference_update() Removes the items in this set that are also included in another, specified set
discard() Remove the specified item
intersection() Returns a set, that is the intersection of two other sets
intersection_update() Removes the items in this set that are not present in other, specified set(s)
isdisjoint() Returns whether two sets have a intersection or not
issubset() Returns whether another set contains this set or not
issuperset() Returns whether this set contains another set or not
pop() Removes an element from the set
remove() Removes the specified element
symmetric_difference() Returns a set with the symmetric differences of two sets
symmetric_difference_update() inserts the symmetric differences from this set and another
union() Return a set containing the union of sets
update() Update the set with the union of this set and others

Python String Functions

In this below post we will go through the Python String Functions and their their uses. Strings operation and manipulation is very easy in the python . We can create them simply by enclosing characters in quotes. Python treats single quotes the same as double quotes. Creating strings is as simple as assigning a value to a variable. For example −

var1 = 'Hello World!'
var2 = "Python Programming"

Accessing Values in Strings

Python does not support a character type; these are treated as strings of length one, thus also considered a Substring.

To access substrings, use the square brackets for slicing along with the index or indices to obtain your substring. For example −

#!/usr/bin/python

var1 = 'Hello World!'
var2 = "Python Programming"

print "var1[0]: ", var1[0]
print "var2[1:5]: ", var2[1:5]

When the above code is executed, it produces the following result −

var1[0]:  H
var2[1:5]:  ytho

Updating Strings

You can “update” an existing string by (re)assigning a variable to another string. The new value can be related to its previous value or to a completely different string altogether. For example −

#!/usr/bin/python

var1 = 'Hello World!'

print "Updated String :- ", var1[:6] + 'Python'

When the above code is executed, it produces the following result −

Updated String :-  Hello Python

Escape Characters

Following table is a list of escape or non-printable characters that can be represented with backslash notation.

An escape character gets interpreted; in a single quoted as well as double quoted strings.

Backslash
notation
Hexadecimal
character
Description
\a 0x07 Bell or alert
\b 0x08 Backspace
\cx Control-x
\C-x Control-x
\e 0x1b Escape
\f 0x0c Formfeed
\M-\C-x Meta-Control-x
\n 0x0a Newline
\nnn Octal notation, where n is in the range 0.7
\r 0x0d Carriage return
\s 0x20 Space
\t 0x09 Tab
\v 0x0b Vertical tab
\x Character x
\xnn Hexadecimal notation, where n is in the range 0.9, a.f, or A.F

String Special Operators

Assume string variable a holds ‘Hello’ and variable b holds ‘Python’, then −

Operator Description Example
+ Concatenation – Adds values on either side of the operator a + b will give HelloPython
* Repetition – Creates new strings, concatenating multiple copies of the same string a*2 will give -HelloHello
[] Slice – Gives the character from the given index a[1] will give e
[ : ] Range Slice – Gives the characters from the given range a[1:4] will give ell
in Membership – Returns true if a character exists in the given string H in a will give 1
not in Membership – Returns true if a character does not exist in the given string M not in a will give 1
r/R Raw String – Suppresses actual meaning of Escape characters. The syntax for raw strings is exactly the same as for normal strings with the exception of the raw string operator, the letter “r,” which precedes the quotation marks. The “r” can be lowercase (r) or uppercase (R) and must be placed immediately preceding the first quote mark. print r’\n’ prints \n and print R’\n’prints \n
% Format – Performs String formatting See at next section

String Formatting Operator

One of Python’s coolest features is the string format operator %. This operator is unique to strings and makes up for the pack of having functions from C’s printf() family. Following is a simple example −

#!/usr/bin/python

print "My name is %s and weight is %d kg!" % ('Zara', 21) 

When the above code is executed, it produces the following result −

My name is Zara and weight is 21 kg!

Here is the list of complete set of symbols which can be used along with % −

Format Symbol Conversion
%c character
%s string conversion via str() prior to formatting
%i signed decimal integer
%d signed decimal integer
%u unsigned decimal integer
%o octal integer
%x hexadecimal integer (lowercase letters)
%X hexadecimal integer (UPPERcase letters)
%e exponential notation (with lowercase ‘e’)
%E exponential notation (with UPPERcase ‘E’)
%f floating point real number
%g the shorter of %f and %e
%G the shorter of %f and %E

Other supported symbols and functionality are listed in the following table −

Symbol Functionality
* argument specifies width or precision
left justification
+ display the sign
<sp> leave a blank space before a positive number
# add the octal leading zero ( ‘0’ ) or hexadecimal leading ‘0x’ or ‘0X’, depending on whether ‘x’ or ‘X’ were used.
0 pad from left with zeros (instead of spaces)
% ‘%%’ leaves you with a single literal ‘%’
(var) mapping variable (dictionary arguments)
m.n. m is the minimum total width and n is the number of digits to display after the decimal point (if appl.)

Triple Quotes

Python’s triple quotes comes to the rescue by allowing strings to span multiple lines, including verbatim NEWLINEs, TABs, and any other special characters.

The syntax for triple quotes consists of three consecutive single or doublequotes.

#!/usr/bin/python

para_str = """this is a long string that is made up of
several lines and non-printable characters such as
TAB ( \t ) and they will show up that way when displayed.
NEWLINEs within the string, whether explicitly given like
this within the brackets [ \n ], or just a NEWLINE within
the variable assignment will also show up.
"""
print para_str

When the above code is executed, it produces the following result. Note how every single special character has been converted to its printed form, right down to the last NEWLINE at the end of the string between the “up.” and closing triple quotes. Also note that NEWLINEs occur either with an explicit carriage return at the end of a line or its escape code (\n) −

this is a long string that is made up of
several lines and non-printable characters such as
TAB (    ) and they will show up that way when displayed.
NEWLINEs within the string, whether explicitly given like
this within the brackets [
 ], or just a NEWLINE within
the variable assignment will also show up.

Raw strings do not treat the backslash as a special character at all. Every character you put into a raw string stays the way you wrote it −

#!/usr/bin/python

print 'C:\\nowhere'

When the above code is executed, it produces the following result −

C:\nowhere

Now let’s make use of raw string. We would put expression in r’expression’ as follows −

#!/usr/bin/python

print r'C:\\nowhere'

When the above code is executed, it produces the following result −

C:\\nowhere

Unicode String

Normal strings in Python are stored internally as 8-bit ASCII, while Unicode strings are stored as 16-bit Unicode. This allows for a more varied set of characters, including special characters from most languages in the world. I’ll restrict my treatment of Unicode strings to the following −

#!/usr/bin/python

print u'Hello, world!'

When the above code is executed, it produces the following result −

Hello, world!

As you can see, Unicode strings use the prefix u, just as raw strings use the prefix r.

Built-in String Methods

Python includes the following built-in methods to manipulate strings −

SN Methods with Description
1 capitalize()
Capitalizes first letter of string
2 center(width, fillchar)

Returns a space-padded string with the original string centered to a total of width columns.

3 count(str, beg= 0,end=len(string))

Counts how many times str occurs in string or in a substring of string if starting index beg and ending index end are given.

4 decode(encoding=’UTF-8′,errors=’strict’)

Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding.

5 encode(encoding=’UTF-8′,errors=’strict’)

Returns encoded string version of string; on error, default is to raise a ValueError unless errors is given with ‘ignore’ or ‘replace’.

6 endswith(suffix, beg=0, end=len(string))
Determines if string or a substring of string (if starting index beg and ending index end are given) ends with suffix; returns true if so and false otherwise.
7 expandtabs(tabsize=8)

Expands tabs in string to multiple spaces; defaults to 8 spaces per tab if tabsize not provided.

8 find(str, beg=0 end=len(string))

Determine if str occurs in string or in a substring of string if starting index beg and ending index end are given returns index if found and -1 otherwise.

9 index(str, beg=0, end=len(string))

Same as find(), but raises an exception if str not found.

10 isalnum()

Returns true if string has at least 1 character and all characters are alphanumeric and false otherwise.

11 isalpha()

Returns true if string has at least 1 character and all characters are alphabetic and false otherwise.

12 isdigit()

Returns true if string contains only digits and false otherwise.

13 islower()

Returns true if string has at least 1 cased character and all cased characters are in lowercase and false otherwise.

14 isnumeric()

Returns true if a unicode string contains only numeric characters and false otherwise.

15 isspace()

Returns true if string contains only whitespace characters and false otherwise.

16 istitle()

Returns true if string is properly “titlecased” and false otherwise.

17 isupper()

Returns true if string has at least one cased character and all cased characters are in uppercase and false otherwise.

18 join(seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

19 len(string)

Returns the length of the string

20 ljust(width[, fillchar])

Returns a space-padded string with the original string left-justified to a total of width columns.

21 lower()

Converts all uppercase letters in string to lowercase.

22 lstrip()

Removes all leading whitespace in string.

23 maketrans()

Returns a translation table to be used in translate function.

24 max(str)

Returns the max alphabetical character from the string str.

25 min(str)

Returns the min alphabetical character from the string str.

26 replace(old, new [, max])

Replaces all occurrences of old in string with new or at most max occurrences if max given.

27 rfind(str, beg=0,end=len(string))

Same as find(), but search backwards in string.

28 rindex( str, beg=0, end=len(string))

Same as index(), but search backwards in string.

29 rjust(width,[, fillchar])

Returns a space-padded string with the original string right-justified to a total of width columns.

30 rstrip()

Removes all trailing whitespace of string.

31 split(str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

32 splitlines( num=string.count(‘\n’))

Splits string at all (or num) NEWLINEs and returns a list of each line with NEWLINEs removed.

33 startswith(str, beg=0,end=len(string))

Determines if string or a substring of string (if starting index beg and ending index end are given) starts with substring str; returns true if so and false otherwise.

34 strip([chars])

Performs both lstrip() and rstrip() on string

35 swapcase()

Inverts case for all letters in string.

36 title()

Returns “titlecased” version of string, that is, all words begin with uppercase and the rest are lowercase.

37 translate(table, deletechars=””)

Translates string according to translation table str(256 chars), removing those in the del string.

38 upper()

Converts lowercase letters in string to uppercase.

39 zfill (width)

Returns original string leftpadded with zeros to a total of width characters; intended for numbers, zfill() retains any sign given (less one zero).

40 isdecimal()

Returns true if a unicode string contains only decimal characters and false otherwise.

History of Python Comedy, Snake or Programming Language

History of Python

Easy as ABC

Origin of the name Monty Python What do the alphabet and the programming language Python have in common? Right, both start with ABC. If we are talking about ABC in the Python context, it’s clear that the programming language ABC is meant. ABC is a general-purpose programming language and programming environment, which had been developed in the Netherlands, Amsterdam, at the CWI (Centrum Wiskunde & Informatica). The greatest achievement of ABC was to influence the design of Python.

Python was conceptualized in the late 1980s. Guido van Rossum worked that time in a project at the CWI, called Amoeba, a distributed operating system. In an interview with Bill Venners1, Guido van Rossum said: “In the early 1980s, I worked as an implementer on a team building a language called ABC at Centrum voor Wiskunde en Informatica (CWI). I don’t know how well people know ABC’s influence on Python. I try to mention ABC’s influence because I’m indebted to everything I learned during that project and to the people who worked on it.”

Later on in the same Interview, Guido van Rossum continued: “I remembered all my experience and some of my frustration with ABC. I decided to try to design a simple scripting language that possessed some of ABC’s better properties, but without its problems. So I started typing. I created a simple virtual machine, a simple parser, and a simple runtime. I made my own version of the various ABC parts that I liked. I created a basic syntax, used indentation for statement grouping instead of curly braces or begin-end blocks, and developed a small number of powerful data types: a hash table (or dictionary, as we call it), a list, strings, and numbers.”

Comedy, Snake or Programming Language

So, what about the name “Python”: Most people think about snakes, and even the logo depicts two snakes, but the origin of the name has its root in British humour. Guido van Rossum, the creator of Python, wrote in 1996 about the origin of the name of his programming language1: “Over six years ago, in December 1989, I was looking for a ‘hobby’ programming project that would keep me occupied during the week around Christmas. My office … would be closed, but I had a home computer, and not much else on my hands. I decided to write an interpreter for the new scripting language I had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers. I chose Python as a working title for the project, being in a slightly irreverent mood (and a big fan of Monty Python’s Flying Circus).”

The Zen of Python

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • Special cases aren’t special enough to break the rules.
  • Although practicality beats purity.
  • Errors should never pass silently.
  • Unless explicitly silenced.
  • In the face of ambiguity, refuse the temptation to guess.
  • There should be one — and preferably only one — obvious way to do it.
  • Although that way may not be obvious at first unless you’re Dutch.
  • Now is better than never.
  • Although never is often better than *right* now.
  • If the implementation is hard to explain, it’s a bad idea.
  • If the implementation is easy to explain, it may be a good idea.
  • Namespaces are one honking great idea — let’s do more of those!

 

Development Steps of Python

Guido Van Rossum published the first version of Python code (version 0.9.0) at alt.sources in February 1991. This release included already exception handling, functions, and the core data types of list, dict, str and others. It was also object oriented and had a module system.

Python version 1.0 was released in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce, which Guido Van Rossum never liked.

Six and a half years later in October 2000, Python 2.0 was introduced. This release included list comprehensions, a full garbage collector and it was supporting unicode.

Python flourished for another 8 years in the versions 2.x before the next major release as Python 3.0 (also known as “Python 3000” and “Py3K”) was released. Python 3 is not backwards compatible with Python 2.x. The emphasis in Python 3 had been on the removal of duplicate programming constructs and modules, thus fulfilling or coming close to fulfilling the 13th law of the Zen of Python: “There should be one — and preferably only one — obvious way to do it.”

Some changes in Python 3.0:

  • Print is now a function
  • Views and iterators instead of lists
  • The rules for ordering comparisons have been simplified. E.g. a heterogeneous list cannot be sorted, because all the elements of a list must be comparable to each other.
  • There is only one integer type left, i.e. int. long is int as well.
  • The division of two integers returns a float instead of an integer. “//” can be used to have the “old” behaviour.
  • Text Vs. Data Instead Of Unicode Vs. 8-bit

For more on Python 2 & 3 please visit the post Difference between Python 2 and 3

Features of Python Programming Language

Features of Python Programming Language that are listed below.

1) Easy to Learn and Use

Python is easy to learn and use. It is developer-friendly and high level programming language.With minimal syntax and easy to remember keyword makes its special from any  other language.


2) Expressive Language

Python language is more expressive means that it is more understandable and readable.English like keyword is very easy to understand the context of the subject.


3) Interpreted Language

Python is an interpreted language i.e. interpreter executes the code line by line at a time. This makes debugging easy and thus suitable for beginners.It will eliminate the compilation by making machine level language at the time of saving the python files.


4) Cross-platform Language

Python can run equally on different platforms such as Windows, Linux, Unix and Macintosh etc. So, we can say that Python is a portable language.


5) Free and Open Source

Python language is freely available at offical web address.The source-code is also available. Therefore it is open source.It supports most of the platform available in the market.


6) Object-Oriented Language

Python supports object oriented language and concepts of classes and objects come into existence.It is also an object oriented language like C++,Java etc..


7) Extensible

It implies that other languages such as C/C++ can be used to compile the code and thus it can be used further in our python code.


8) Large Standard Library

Python has a large and broad library and provides rich set of module and functions for rapid application development.The most usable feature of the python is that all the standard set of library are readily available.


9) GUI Programming Support

Graphical user interfaces can be developed using Python.It is supported the GUI programming this the interesting fact of Pyhton.


10) Integrated

It can be easily integrated with languages like C, C++, JAVA etc. With the help of the packages it can be readily integrated with above language.

If you have any further clarification on python please give comment bellow Features of Python Programming Language

History of Python Comedy, Snake or Programming Language

History of Python

Easy as ABC

Origin of the name Monty Python What do the alphabet and the programming language Python have in common? Right, both start with ABC. If we are talking about ABC in the Python context, it’s clear that the programming language ABC is meant. ABC is a general-purpose programming language and programming environment, which had been developed in the Netherlands, Amsterdam, at the CWI (Centrum Wiskunde & Informatica). The greatest achievement of ABC was to influence the design of Python.

Python was conceptualized in the late 1980s. Guido van Rossum worked that time in a project at the CWI, called Amoeba, a distributed operating system. In an interview with Bill Venners1, Guido van Rossum said: “In the early 1980s, I worked as an implementer on a team building a language called ABC at Centrum voor Wiskunde en Informatica (CWI). I don’t know how well people know ABC’s influence on Python. I try to mention ABC’s influence because I’m indebted to everything I learned during that project and to the people who worked on it.”

Later on in the same Interview, Guido van Rossum continued: “I remembered all my experience and some of my frustration with ABC. I decided to try to design a simple scripting language that possessed some of ABC’s better properties, but without its problems. So I started typing. I created a simple virtual machine, a simple parser, and a simple runtime. I made my own version of the various ABC parts that I liked. I created a basic syntax, used indentation for statement grouping instead of curly braces or begin-end blocks, and developed a small number of powerful data types: a hash table (or dictionary, as we call it), a list, strings, and numbers.”

Comedy, Snake or Programming Language

So, what about the name “Python”: Most people think about snakes, and even the logo depicts two snakes, but the origin of the name has its root in British humour. Guido van Rossum, the creator of Python, wrote in 1996 about the origin of the name of his programming language1: “Over six years ago, in December 1989, I was looking for a ‘hobby’ programming project that would keep me occupied during the week around Christmas. My office … would be closed, but I had a home computer, and not much else on my hands. I decided to write an interpreter for the new scripting language I had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers. I chose Python as a working title for the project, being in a slightly irreverent mood (and a big fan of Monty Python’s Flying Circus).”

The Zen of Python

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • Special cases aren’t special enough to break the rules.
  • Although practicality beats purity.
  • Errors should never pass silently.
  • Unless explicitly silenced.
  • In the face of ambiguity, refuse the temptation to guess.
  • There should be one — and preferably only one — obvious way to do it.
  • Although that way may not be obvious at first unless you’re Dutch.
  • Now is better than never.
  • Although never is often better than *right* now.
  • If the implementation is hard to explain, it’s a bad idea.
  • If the implementation is easy to explain, it may be a good idea.
  • Namespaces are one honking great idea — let’s do more of those!

 

Development Steps of Python

Guido Van Rossum published the first version of Python code (version 0.9.0) at alt.sources in February 1991. This release included already exception handling, functions, and the core data types of list, dict, str and others. It was also object oriented and had a module system.

Python version 1.0 was released in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce, which Guido Van Rossum never liked.

Six and a half years later in October 2000, Python 2.0 was introduced. This release included list comprehensions, a full garbage collector and it was supporting unicode.

Python flourished for another 8 years in the versions 2.x before the next major release as Python 3.0 (also known as “Python 3000” and “Py3K”) was released. Python 3 is not backwards compatible with Python 2.x. The emphasis in Python 3 had been on the removal of duplicate programming constructs and modules, thus fulfilling or coming close to fulfilling the 13th law of the Zen of Python: “There should be one — and preferably only one — obvious way to do it.”

Some changes in Python 3.0:

  • Print is now a function
  • Views and iterators instead of lists
  • The rules for ordering comparisons have been simplified. E.g. a heterogeneous list cannot be sorted, because all the elements of a list must be comparable to each other.
  • There is only one integer type left, i.e. int. long is int as well.
  • The division of two integers returns a float instead of an integer. “//” can be used to have the “old” behaviour.
  • Text Vs. Data Instead Of Unicode Vs. 8-bit

Difference Between JSON vs XML,How to use in the Programming Language.

The JSON vs XML,And basically used in the Programming Language because of simplicity and structured data format.Basically JSON and XML format is useful in sending data over network between different web services because of its simplicity in representation of data.Image result for xml vs json

Those who are the new to these format they can try  for the xml because of its easy user friendly tag structure.Tag is like HTML language tag but no hard fast rules for tag Names,Their is not predefined tag names.You can create your own tag names for your ease of use.

Difference between JSON(Java Script Object Notation) and XML(eXtensible Markup Language). The major difference between are as follows.

No. JSON XML
1) JSON stands for JavaScript Object Notation. XML stands for eXtensible Markup Language.
2) JSON is simple to read and write. XML is less simple than JSON.
3) JSON is easy to learn. XML is less easy than JSON.
4) JSON is data-oriented. XML is document-oriented.
5) JSON doesn’t provide display capabilities. XML provides the capability to display data because it is a markup language.
6) JSON supports array. XML doesn’t support array.
7) JSON is less secured than XML. XML is more secured.
8) JSON files are more human readable than XML. XML files are less human readable.
9) JSON supports only text and number data type. XML support many data types such as text, number, images, charts, graphs etc. Moreover, XML offeres options for transferring the format or structure of the data with actual data.

JSON Example

{“employees”:[
{ “firstName”:”John”, “lastName”:”Doe” },
{ “firstName”:”Anna”, “lastName”:”Smith” },
{ “firstName”:”Peter”, “lastName”:”Jones” }
]}

XML Example

<employees>
<employee>
<firstName>John</firstName> <lastName>Doe</lastName>
</employee>
<employee>
<firstName>Anna</firstName> <lastName>Smith</lastName>
</employee>
<employee>
<firstName>Peter</firstName> <lastName>Jones</lastName>
</employee>
</employees>

 

If you are finding any difficulties with these format with any language you can give your issues in the comment section of Difference Between JSON vs XML,How to use in the Programming Language.

Python Exceptions Handling

Python provides two very important features to handle any unexpected error in your Python programs and to add debugging capabilities in them −

  • Exception Handling: This would be covered in this tutorial. Here is a list standard Exceptions available in Python: Standard Exceptions.
  • Assertions: This would be covered in Assertions in Python tutorial.

List of Standard Exceptions −

EXCEPTION NAME DESCRIPTION
Exception Base class for all exceptions
StopIteration Raised when the next() method of an iterator does not point to any object.
SystemExit Raised by the sys.exit() function.
StandardError Base class for all built-in exceptions except StopIteration and SystemExit.
ArithmeticError Base class for all errors that occur for numeric calculation.
OverflowError Raised when a calculation exceeds maximum limit for a numeric type.
FloatingPointError Raised when a floating point calculation fails.
ZeroDivisionError Raised when division or modulo by zero takes place for all numeric types.
AssertionError Raised in case of failure of the Assert statement.
AttributeError Raised in case of failure of attribute reference or assignment.
EOFError Raised when there is no input from either the raw_input() or input() function and the end of file is reached.
ImportError Raised when an import statement fails.
KeyboardInterrupt Raised when the user interrupts program execution, usually by pressing Ctrl+c.
LookupError Base class for all lookup errors.
IndexError

KeyError

Raised when an index is not found in a sequence.

Raised when the specified key is not found in the dictionary.

NameError Raised when an identifier is not found in the local or global namespace.
UnboundLocalError

EnvironmentError

Raised when trying to access a local variable in a function or method but no value has been assigned to it.

Base class for all exceptions that occur outside the Python environment.

IOError

IOError

Raised when an input/ output operation fails, such as the print statement or the open() function when trying to open a file that does not exist.

Raised for operating system-related errors.

SyntaxError

IndentationError

Raised when there is an error in Python syntax.

Raised when indentation is not specified properly.

SystemError Raised when the interpreter finds an internal problem, but when this error is encountered the Python interpreter does not exit.
SystemExit Raised when Python interpreter is quit by using the sys.exit() function. If not handled in the code, causes the interpreter to exit.
TypeError Raised when an operation or function is attempted that is invalid for the specified data type.
ValueError Raised when the built-in function for a data type has the valid type of arguments, but the arguments have invalid values specified.
RuntimeError Raised when a generated error does not fall into any category.
NotImplementedError Raised when an abstract method that needs to be implemented in an inherited class is not actually implemented.

Assertions in Python

An assertion is a sanity-check that you can turn on or turn off when you are done with your testing of the program.

The easiest way to think of an assertion is to liken it to a raise-if statement (or to be more accurate, a raise-if-not statement). An expression is tested, and if the result comes up false, an exception is raised.

Assertions are carried out by the assert statement, the newest keyword to Python, introduced in version 1.5.

Programmers often place assertions at the start of a function to check for valid input, and after a function call to check for valid output.

The assert Statement

When it encounters an assert statement, Python evaluates the accompanying expression, which is hopefully true. If the expression is false, Python raises an AssertionError exception.

The syntax for assert is −

assert Expression[, Arguments]

If the assertion fails, Python uses ArgumentExpression as the argument for the AssertionError. AssertionError exceptions can be caught and handled like any other exception using the try-except statement, but if not handled, they will terminate the program and produce a traceback.

Example

Here is a function that converts a temperature from degrees Kelvin to degrees Fahrenheit. Since zero degrees Kelvin is as cold as it gets, the function bails out if it sees a negative temperature −

#!/usr/bin/python
def KelvinToFahrenheit(Temperature):
   assert (Temperature >= 0),"Colder than absolute zero!"
   return ((Temperature-273)*1.8)+32
print KelvinToFahrenheit(273)
print int(KelvinToFahrenheit(505.78))
print KelvinToFahrenheit(-5)

When the above code is executed, it produces the following result −

32.0
451
Traceback (most recent call last):
File "test.py", line 9, in 
print KelvinToFahrenheit(-5)
File "test.py", line 4, in KelvinToFahrenheit
assert (Temperature >= 0),"Colder than absolute zero!"
AssertionError: Colder than absolute zero!

What is Exception?

An exception is an event, which occurs during the execution of a program that disrupts the normal flow of the program’s instructions. In general, when a Python script encounters a situation that it cannot cope with, it raises an exception. An exception is a Python object that represents an error.

When a Python script raises an exception, it must either handle the exception immediately otherwise it terminates and quits.

Handling an exception

If you have some suspicious code that may raise an exception, you can defend your program by placing the suspicious code in a try: block. After the try: block, include an except: statement, followed by a block of code which handles the problem as elegantly as possible.

Syntax

Here is simple syntax of try….except…else blocks −

try:
   You do your operations here;
   ......................
except ExceptionI:
   If there is ExceptionI, then execute this block.
except ExceptionII:
   If there is ExceptionII, then execute this block.
   ......................
else:
   If there is no exception then execute this block.

Here are few important points about the above-mentioned syntax −

  • A single try statement can have multiple except statements. This is useful when the try block contains statements that may throw different types of exceptions.
  • You can also provide a generic except clause, which handles any exception.
  • After the except clause(s), you can include an else-clause. The code in the else-block executes if the code in the try: block does not raise an exception.
  • The else-block is a good place for code that does not need the try: block’s protection.

Example

This example opens a file, writes content in the, file and comes out gracefully because there is no problem at all −

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   fh.write("This is my test file for exception handling!!")
except IOError:
   print "Error: can\'t find file or read data"
else:
   print "Written content in the file successfully"
   fh.close()

This produces the following result −

Written content in the file successfully

Example

This example tries to open a file where you do not have write permission, so it raises an exception −

#!/usr/bin/python

try:
   fh = open("testfile", "r")
   fh.write("This is my test file for exception handling!!")
except IOError:
   print "Error: can\'t find file or read data"
else:
   print "Written content in the file successfully"

This produces the following result −

Error: can't find file or read data

The except Clause with No Exceptions

You can also use the except statement with no exceptions defined as follows −

try:
   You do your operations here;
   ......................
except:
   If there is any exception, then execute this block.
   ......................
else:
   If there is no exception then execute this block.

This kind of a try-except statement catches all the exceptions that occur. Using this kind of try-except statement is not considered a good programming practice though, because it catches all exceptions but does not make the programmer identify the root cause of the problem that may occur.

The except Clause with Multiple Exceptions

You can also use the same except statement to handle multiple exceptions as follows −

try:
   You do your operations here;
   ......................
except(Exception1[, Exception2[,...ExceptionN]]]):
   If there is any exception from the given exception list, 
   then execute this block.
   ......................
else:
   If there is no exception then execute this block.

The try-finally Clause

You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute, whether the try-block raised an exception or not. The syntax of the try-finally statement is this −

try:
   You do your operations here;
   ......................
   Due to any exception, this may be skipped.
finally:
   This would always be executed.
   ......................

You cannot use else clause as well along with a finally clause.

Example

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   fh.write("This is my test file for exception handling!!")
finally:
   print "Error: can\'t find file or read data"

If you do not have permission to open the file in writing mode, then this will produce the following result:

Error: can't find file or read data

Same example can be written more cleanly as follows −

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   try:
      fh.write("This is my test file for exception handling!!")
   finally:
      print "Going to close the file"
      fh.close()
except IOError:
   print "Error: can\'t find file or read data"

When an exception is thrown in the try block, the execution immediately passes to the finally block. After all the statements in the finally block are executed, the exception is raised again and is handled in the except statements if present in the next higher layer of the try-except statement.

Argument of an Exception

An exception can have an argument, which is a value that gives additional information about the problem. The contents of the argument vary by exception. You capture an exception’s argument by supplying a variable in the except clause as follows −

try:
   You do your operations here;
   ......................
except ExceptionType, Argument:
   You can print value of Argument here...

If you write the code to handle a single exception, you can have a variable follow the name of the exception in the except statement. If you are trapping multiple exceptions, you can have a variable follow the tuple of the exception.

This variable receives the value of the exception mostly containing the cause of the exception. The variable can receive a single value or multiple values in the form of a tuple. This tuple usually contains the error string, the error number, and an error location.

Example

Following is an example for a single exception −

#!/usr/bin/python

# Define a function here.
def temp_convert(var):
   try:
      return int(var)
   except ValueError, Argument:
      print "The argument does not contain numbers\n", Argument

# Call above function here.
temp_convert("xyz");

This produces the following result −

The argument does not contain numbers
invalid literal for int() with base 10: 'xyz'

Raising an Exceptions

You can raise exceptions in several ways by using the raise statement. The general syntax for the raise statement is as follows.

Syntax

raise [Exception [, args [, traceback]]]

Here, Exception is the type of exception (for example, NameError) and argument is a value for the exception argument. The argument is optional; if not supplied, the exception argument is None.

The final argument, traceback, is also optional (and rarely used in practice), and if present, is the traceback object used for the exception.

Example

An exception can be a string, a class or an object. Most of the exceptions that the Python core raises are classes, with an argument that is an instance of the class. Defining new exceptions is quite easy and can be done as follows −

def functionName( level ):
   if level < 1:
      raise "Invalid level!", level
      # The code below to this would not be executed
      # if we raise the exception

Note: In order to catch an exception, an “except” clause must refer to the same exception thrown either class object or simple string. For example, to capture above exception, we must write the except clause as follows −

try:
   Business Logic here...
except "Invalid level!":
   Exception handling here...
else:
   Rest of the code here...

User-Defined Exceptions

Python also allows you to create your own exceptions by deriving classes from the standard built-in exceptions.

Here is an example related to RuntimeError. Here, a class is created that is subclassed from RuntimeError. This is useful when you need to display more specific information when an exception is caught.

In the try block, the user-defined exception is raised and caught in the except block. The variable e is used to create an instance of the class Networkerror.

class Networkerror(RuntimeError):
   def __init__(self, arg):
      self.args = arg

So once you defined above class, you can raise the exception as follows −

try:
   raise Networkerror("Bad hostname")
except Networkerror,e:
   print e.args

Python Strings

Strings are amongst the most popular types in Python. We can create them simply by enclosing characters in quotes. Python treats single quotes the same as double quotes. Creating strings is as simple as assigning a value to a variable. For example −

var1 = 'Hello World!'
var2 = "Python Programming"

Accessing Values in Strings

Python does not support a character type; these are treated as strings of length one, thus also considered a substring.

To access substrings, use the square brackets for slicing along with the index or indices to obtain your substring. For example −

#!/usr/bin/python

var1 = 'Hello World!'
var2 = "Python Programming"

print "var1[0]: ", var1[0]
print "var2[1:5]: ", var2[1:5]

When the above code is executed, it produces the following result −

var1[0]:  H
var2[1:5]:  ytho

Updating Strings

You can “update” an existing string by (re)assigning a variable to another string. The new value can be related to its previous value or to a completely different string altogether. For example −

#!/usr/bin/python

var1 = 'Hello World!'

print "Updated String :- ", var1[:6] + 'Python'

When the above code is executed, it produces the following result −

Updated String :-  Hello Python

Escape Characters

Following table is a list of escape or non-printable characters that can be represented with backslash notation.

An escape character gets interpreted; in a single quoted as well as double quoted strings.

Backslash
notation
Hexadecimal
character
Description
\a 0x07 Bell or alert
\b 0x08 Backspace
\cx Control-x
\C-x Control-x
\e 0x1b Escape
\f 0x0c Formfeed
\M-\C-x Meta-Control-x
\n 0x0a Newline
\nnn Octal notation, where n is in the range 0.7
\r 0x0d Carriage return
\s 0x20 Space
\t 0x09 Tab
\v 0x0b Vertical tab
\x Character x
\xnn Hexadecimal notation, where n is in the range 0.9, a.f, or A.F

String Special Operators

Assume string variable a holds ‘Hello’ and variable b holds ‘Python’, then −

Operator Description Example
+ Concatenation – Adds values on either side of the operator a + b will give HelloPython
* Repetition – Creates new strings, concatenating multiple copies of the same string a*2 will give -HelloHello
[] Slice – Gives the character from the given index a[1] will give e
[ : ] Range Slice – Gives the characters from the given range a[1:4] will give ell
in Membership – Returns true if a character exists in the given string H in a will give 1
not in Membership – Returns true if a character does not exist in the given string M not in a will give 1
r/R Raw String – Suppresses actual meaning of Escape characters. The syntax for raw strings is exactly the same as for normal strings with the exception of the raw string operator, the letter “r,” which precedes the quotation marks. The “r” can be lowercase (r) or uppercase (R) and must be placed immediately preceding the first quote mark. print r’\n’ prints \n and print R’\n’prints \n
% Format – Performs String formatting See at next section

String Formatting Operator

One of Python’s coolest features is the string format operator %. This operator is unique to strings and makes up for the pack of having functions from C’s printf() family. Following is a simple example −

#!/usr/bin/python

print "My name is %s and weight is %d kg!" % ('Zara', 21) 

When the above code is executed, it produces the following result −

My name is Zara and weight is 21 kg!

Here is the list of complete set of symbols which can be used along with % −

Format Symbol Conversion
%c character
%s string conversion via str() prior to formatting
%i signed decimal integer
%d signed decimal integer
%u unsigned decimal integer
%o octal integer
%x hexadecimal integer (lowercase letters)
%X hexadecimal integer (UPPERcase letters)
%e exponential notation (with lowercase ‘e’)
%E exponential notation (with UPPERcase ‘E’)
%f floating point real number
%g the shorter of %f and %e
%G the shorter of %f and %E

Other supported symbols and functionality are listed in the following table −

Symbol Functionality
* argument specifies width or precision
left justification
+ display the sign
<sp> leave a blank space before a positive number
# add the octal leading zero ( ‘0’ ) or hexadecimal leading ‘0x’ or ‘0X’, depending on whether ‘x’ or ‘X’ were used.
0 pad from left with zeros (instead of spaces)
% ‘%%’ leaves you with a single literal ‘%’
(var) mapping variable (dictionary arguments)
m.n. m is the minimum total width and n is the number of digits to display after the decimal point (if appl.)

Triple Quotes

Python’s triple quotes comes to the rescue by allowing strings to span multiple lines, including verbatim NEWLINEs, TABs, and any other special characters.

The syntax for triple quotes consists of three consecutive single or doublequotes.

#!/usr/bin/python

para_str = """this is a long string that is made up of
several lines and non-printable characters such as
TAB ( \t ) and they will show up that way when displayed.
NEWLINEs within the string, whether explicitly given like
this within the brackets [ \n ], or just a NEWLINE within
the variable assignment will also show up.
"""
print para_str

When the above code is executed, it produces the following result. Note how every single special character has been converted to its printed form, right down to the last NEWLINE at the end of the string between the “up.” and closing triple quotes. Also note that NEWLINEs occur either with an explicit carriage return at the end of a line or its escape code (\n) −

this is a long string that is made up of
several lines and non-printable characters such as
TAB (    ) and they will show up that way when displayed.
NEWLINEs within the string, whether explicitly given like
this within the brackets [
 ], or just a NEWLINE within
the variable assignment will also show up.

Raw strings do not treat the backslash as a special character at all. Every character you put into a raw string stays the way you wrote it −

#!/usr/bin/python

print 'C:\\nowhere'

When the above code is executed, it produces the following result −

C:\nowhere

Now let’s make use of raw string. We would put expression in r’expression’ as follows −

#!/usr/bin/python

print r'C:\\nowhere'

When the above code is executed, it produces the following result −

C:\\nowhere

Unicode String

Normal strings in Python are stored internally as 8-bit ASCII, while Unicode strings are stored as 16-bit Unicode. This allows for a more varied set of characters, including special characters from most languages in the world. I’ll restrict my treatment of Unicode strings to the following −

#!/usr/bin/python

print u'Hello, world!'

When the above code is executed, it produces the following result −

Hello, world!

As you can see, Unicode strings use the prefix u, just as raw strings use the prefix r.

Built-in String Methods

Python includes the following built-in methods to manipulate strings −

SN Methods with Description
1 capitalize()
Capitalizes first letter of string
2 center(width, fillchar)

Returns a space-padded string with the original string centered to a total of width columns.

3 count(str, beg= 0,end=len(string))

Counts how many times str occurs in string or in a substring of string if starting index beg and ending index end are given.

4 decode(encoding=’UTF-8′,errors=’strict’)

Decodes the string using the codec registered for encoding. encoding defaults to the default string encoding.

5 encode(encoding=’UTF-8′,errors=’strict’)

Returns encoded string version of string; on error, default is to raise a ValueError unless errors is given with ‘ignore’ or ‘replace’.

6 endswith(suffix, beg=0, end=len(string))
Determines if string or a substring of string (if starting index beg and ending index end are given) ends with suffix; returns true if so and false otherwise.
7 expandtabs(tabsize=8)

Expands tabs in string to multiple spaces; defaults to 8 spaces per tab if tabsize not provided.

8 find(str, beg=0 end=len(string))

Determine if str occurs in string or in a substring of string if starting index beg and ending index end are given returns index if found and -1 otherwise.

9 index(str, beg=0, end=len(string))

Same as find(), but raises an exception if str not found.

10 isalnum()

Returns true if string has at least 1 character and all characters are alphanumeric and false otherwise.

11 isalpha()

Returns true if string has at least 1 character and all characters are alphabetic and false otherwise.

12 isdigit()

Returns true if string contains only digits and false otherwise.

13 islower()

Returns true if string has at least 1 cased character and all cased characters are in lowercase and false otherwise.

14 isnumeric()

Returns true if a unicode string contains only numeric characters and false otherwise.

15 isspace()

Returns true if string contains only whitespace characters and false otherwise.

16 istitle()

Returns true if string is properly “titlecased” and false otherwise.

17 isupper()

Returns true if string has at least one cased character and all cased characters are in uppercase and false otherwise.

18 join(seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

19 len(string)

Returns the length of the string

20 ljust(width[, fillchar])

Returns a space-padded string with the original string left-justified to a total of width columns.

21 lower()

Converts all uppercase letters in string to lowercase.

22 lstrip()

Removes all leading whitespace in string.

23 maketrans()

Returns a translation table to be used in translate function.

24 max(str)

Returns the max alphabetical character from the string str.

25 min(str)

Returns the min alphabetical character from the string str.

26 replace(old, new [, max])

Replaces all occurrences of old in string with new or at most max occurrences if max given.

27 rfind(str, beg=0,end=len(string))

Same as find(), but search backwards in string.

28 rindex( str, beg=0, end=len(string))

Same as index(), but search backwards in string.

29 rjust(width,[, fillchar])

Returns a space-padded string with the original string right-justified to a total of width columns.

30 rstrip()

Removes all trailing whitespace of string.

31 split(str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

32 splitlines( num=string.count(‘\n’))

Splits string at all (or num) NEWLINEs and returns a list of each line with NEWLINEs removed.

33 startswith(str, beg=0,end=len(string))

Determines if string or a substring of string (if starting index beg and ending index end are given) starts with substring str; returns true if so and false otherwise.

34 strip([chars])

Performs both lstrip() and rstrip() on string

35 swapcase()

Inverts case for all letters in string.

36 title()

Returns “titlecased” version of string, that is, all words begin with uppercase and the rest are lowercase.

37 translate(table, deletechars=””)

Translates string according to translation table str(256 chars), removing those in the del string.

38 upper()

Converts lowercase letters in string to uppercase.

39 zfill (width)

Returns original string leftpadded with zeros to a total of width characters; intended for numbers, zfill() retains any sign given (less one zero).

40 isdecimal()

Returns true if a unicode string contains only decimal characters and false otherwise.