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How to Install Python on Windows

Python was created by Guido van Rossum and first released in 1991. It is an open source, interpreted programming language, and is simple to use, readable and extensible. You can split your program into modules that can be reused in other Python programs. Python supports several paradigms, such as object-oriented, functional, and procedural with the support of a large comprehensible library.  Python releases for Windows  The latest version of Python is 3.9.0, released on Oct. 5, 2020. It contains many new features and optimization. This is the first version of Python to default to the 64-bit installer on Windows. The installer now also actively disallows installation on Windows 7. Python 3.9 is incompatible with this unsupported version of Windows. If you go to the page:  https://www.python.org/downloads/windows/, you will find all releases of Python.   The installation process is the same, regardless of the version. The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components. There are a number of different installers available for Windows, each with certain advantages and disadvantages. Web-based installers: This installer is very small in size. It could be installed in a local computer with web-access. The installer will download necessary files and install them.  Executable installer: Best option for installing Python locally. The installer contains all important components and installs them itself. This is the best option for development of any kind of Python projects.   The embeddable package: is a minimal package of Python suitable for embedding into a larger application. The embeddable package uses Python as runtime and allows to distribute applications on Windows. Installing Python 3.9.0 for Windows Step 1: Open your browser window and navigate : https://www.python.org/downloads/ Step 2: Click on button download python 3.9.0 to download Python Executable Installer for Windows. It will start downloading the installer, which is around 27 MB in size.( If you have a lower version of Windows, please click Downloads menu button and select a specific version from the downloads page.) Step 3:  Once finished with the download, double click on the downloaded file to run the installer.  Step 4: The setup windows shows following dialog window with two installation options:  A. Install Now B. Customize Installation A. If you select “Install Now”: You will not need to be an administrator. This option installs Python in your user directory shown in the dialog box under Install Now option.  The standard library, test suite, launcher and pip will be installed. If selected, the install directory will be added to your PATH. If you check the install launcher for all users, it will install python launcher for all the users of your system. Step 5: The setup process will be shown in a Python 3.9.0(64-bit) Setup pop-up window.  Step 6: After successful completion of installation the following popup window will be shown with Setup was successful message. This dialog box also gives you the option to Disable path length limit. Clicking the “Disable path length limit” option removes the limitation on the MAX_PATH variable. This change won't break anything, but will allow Python to use long path names. Removing the MAX_PATH Limitation Windows historically has limited path lengths to 260 characters. This meant that paths longer than this would not resolve, and errors would result; this is termed as MAX_PATH Limitation. In the latest versions of Windows, this limitation can be expanded to approximately 32,000 characters.  You must save the file python-3.9 exe somewhere on your computer in case you need to reinstall, modify the settings or uninstall python.  B. Customize installation: This option allows you to select the features to install, the installation location and other options or post-install actions. If you select customized installation: You may be required to provide administrative credentials or approval Python will be installed into the Program Files directory The Python Launcher for Windows will be installed into the Windows directory The standard library can be pre-compiled to bytecode If selected, the install directory will be added to the system PATH Step 5: Step 1 to 4 would be same, but as you select the customized installation, the following dialog box will appear. This window provides a list with several optional features. You may select the respective checkboxes as per the requirement. The meaning of each option is as follows: Documentation: Will download the documentation, which can help you for future references.  Pip: Pip is a tool which helps in installation of additional libraries and dependencies which are not available in the standard library. It is also called the package manager of Python. Starting from the Python 3.4 version, the pip comes bundled with the installation files, but before that we used to install this package manager separately.  IDLE: stands for Integrated Development Learning Environment. This is CLI bundled with python. Along with IDLE editor, enabling this option will also install the “tkinter” toolkit, which is a python default GUI package.  Python test suite: This feature will install all the standard libraries for python application testing.  Py launcher: enables you to launch python CLI through programs like command prompt or windows shell.  For all users: it will install python launcher for all the users of your system Step 6: After clicking on Next button one more dialog window appears on the screen which helps developers to check or uncheck additional features.  This page provides 7 advance options: Install for all users: It will install python launcher for all the users of your system Associate files with Python (requires the py launcer): this option will link all the files Python extensions like .py, .pyd, .pyc or .pyo with the python launcher or editor. Create shortcut for installed applications: It will create a shortcut of application for you Add python to environment variables: PATH is an environment variable in Windows. It basically tells the command-line argument, what folders to look in when attempting to find a file The last three options tell the python installation wizard to install all the debug symbols and binaries along with the bytecode of all the standard libraries, which we will be using with our programs.  Customize installation location: We can change the location of python installation directory. The location you will specify will be added in the environmental variable of our windows 10 operating system.  After selecting the required advance options, click on Install button. After successful completion of installation, the following popup window will be shown with Setup was successful message. Modifying an install Once Python has been installed, you can add or remove features through the Programs and Features tool that is part of Windows. Select the Python entry and choose “Uninstall/Change” to open the installer in maintenance mode. “Modify” allows you to add or remove features by modifying the checkboxes - unchanged checkboxes will not install or remove anything. Some options cannot be changed in this mode, such as the install directory; to modify these, you will need to remove and then reinstall Python completely. “Repair” will verify all the files that should be installed using the current settings and replace any that have been removed or modified. “Uninstall” will remove Python entirely, with the exception of the Python Launcher for Windows, which has its own entry in Programs and Features. Setting Path The definition of “PATH” by Wikipedia is “PATH is an environment variable on Unix-like operating systems, DOS, OS/2, and Microsoft Windows, specifying a set of directories where executable programs are located. In general, each executing process or user session has its own PATH setting” In Windows %PATH%  is a variable which provides a list of directory names separated by semicolon(;). When we enter a command on command shell , the system will search in current windows directory and after that it searches in path. It actually looks for a filename in the path which matches with the command you have given. Once it finds the matching executable file it loads the new process and gets executed.  Python Launcher for Windows The Python launcher for Windows is a utility which helps in locating and executing different Python versions. It allows scripts (or the command-line) to indicate a preference for a specific Python version, and will locate and execute that version. Unlike the PATH variable, the launcher will correctly select the most appropriate version of Python. It will prefer per-user installations over system-wide ones, and orders by language version rather than using the most recently installed version. The launcher was originally specified in PEP 397. A Python launcher is a single executable which uses a number of heuristics to locate a Python executable and launches it with a specified command line. The launcher allows Python scripts or the Windows command-line to specify a particular Python version and will locate and launch that version.Documentation links for python https://www.python.org/doc/:  Browse the docs online or download a copy of your own https://www.python.org/doc/versions/:  document release by version Conclusion We hope this blog “How to install Python on Windows” would help you during the Python installation process. For additional information you can visit the official site of Python: https://www.python.org 

How to Install Python on Windows

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How to Install Python on Windows

Python was created by Guido van Rossum and first released in 1991. 

It is an open source, interpreted programming language, and is simple to use, readable and extensible. You can split your program into modules that can be reused in other Python programs. 

Python supports several paradigms, such as object-oriented, functional, and procedural with the support of large comprehensible library.  

Python releases for Windows  

The latest version of Python is 3.9.0, released on Oct. 5, 2020. It contains many new features and optimization. This is the first version of Python to default to the 64-bit installer on Windows. The installer now also actively disallows installation on Windows 7. Python 3.9 is incompatible with this unsupported version of Windows. 

If you go to the page:  https://www.python.org/downloads/windows/, you will find all releases of Python.   The installation process is the same, regardless of the version. 

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components. 

There are a number of different installers available for Windows, each with certain advantages and disadvantages. 

  • Web-based installers:This installer is very small in size. It could be installed in a local computer with web-access. The installer will download necessary files and install them.  
  • Executable installer:Best option for installing Python locally. The installer contains all important components and installs them itself. This is the best option for development of any kind of Python projects.   
  • The embeddable package: is a minimal package of Python suitable for embedding into a larger application. The embeddable package uses Python as runtime and allows to distribute applications on Windows. 

Installing Python 3.9.0 for Windows 

Step 1: Open your browser window and navigate : https://www.python.org/downloads/ 

How to Install Python on Windows

Step 2: Click on button download python 3.9.0 to download Python Executable Installer for Windows. It will start downloading the installer, which is around 27 MB in size.( If you have a lower version of Windows, please click Downloads menu button and select a specific version from the downloads page.) 

Step 3:  Once finished with the download, double click on the downloaded file to run the installer 

Step 4: The setup windows shows following dialog window with two installation options 

A. Install Now 

B. Customize Installation 

How to Install Python on Windows

A. If you select “Install Now”: 

  • You will not need to be an administrator. 
  • This option installs Python in your user directory shown in the dialog box under Install Now option.  
  • The standard library, test suite, launcher and pip will be installed. 
  • If selected, the install directory will be added to your PATH. 
  • If you check the install launcher for all users, it will install python launcher for all the users of your system. 

Step 5: The setup process will be shown in a Python 3.9.0(64-bit) Setup pop-up window.  How to Install Python on Windows

Step 6: After successful completion of installation the following popup window will be shown with Setup was successful message. 

This dialog box also gives you the option to Disable path length limit. 

Clicking the “Disable path length limit” option removes the limitation on the MAX_PATH variable. This change won't break anything, but will allow Python to use long path names. 

Removing the MAX_PATH Limitation 

Windows historically has limited path lengths to 260 characters. This meant that paths longer than this would not resolve, and errors would result; this is termed as MAX_PATH LimitationIn the latest versions of Windows, this limitation can be expanded to approximately 32,000 characters.  

How to Install Python on WindowsYou must save the file python-3.9 exe somewhere on your computer in case you need to reinstall, modify the settings or uninstall python.  

B. Customize installationThis option allows you to select the features to install, the installation location and other options or post-install actions. If you select customized installation: 

  • You may be required to provide administrative credentials or approval 
  • Python will be installed into the Program Files directory 
  • The Python Launcher for Windows will be installed into the Windows directory 
  • The standard library can be pre-compiled to bytecode 
  • If selected, the install directory will be added to the system PATH 

Step 5: Step 1 to 4 would be same, but as you select the customized installation, the following dialog box will appear. 

How to Install Python on Windows

This window provides a list with several optional featuresYou may select the respective checkboxes as per the requirement. The meaning of each option is as follows: 

  • Documentation: Will download the documentation, which can help you for future references.  
  • Pip: Pip is a tool which helps in installation of additional libraries and dependencies which are not available in the standard library. It is also called the package manager of Python. Starting from the Python 3.4 version, the pip comes bundled with the installation files, but before that we used to install this package manager separately.  
  • IDLE: stands for Integrated Development Learning Environment. This is CLI bundled with python. Along with IDLE editor, enabling this option will also install the “tkinter” toolkit, which is a python default GUI package.  
  • Python test suite: This feature will install all the standard libraries for python application testing.  
  • Py launcher: enables you to launch python CLI through programs like command prompt or windows shell.  
  • For all users: it will install python launcher for all the users of your system 

Step 6: After clicking on Next button one more dialog window appears on the screen which helps developers to check or uncheck additional features.  How to Install Python on WindowsThis page provides 7 advance options: 

  • Install for all users: It will install python launcher for all the users of your system 
  • Associate files with Python (requires the py launcer): this option will link all the files Python extensions like .py, .pyd, .pyc or .pyo with the python launcher or editor. 
  • Create shortcut for installed applications: It will create a shortcut of application for you 
  • Add python to environment variables: PATH is an environment variable in Windows. It basically tells the command-line argument, what folders to look in when attempting to find a file 
  • The last three options tell the python installation wizard to install all the debug symbols and binaries along with the bytecode of all the standard libraries, which we will be using with our programs.  
  • Customize installation location: We can change the location of python installation directory. The location you will specify will be added in the environmental variable of our windows 10 operating system.  

After selecting the required advance options, click on Install button. 

After successful completion of installation, the following popup window will be shown with Setup was successful message. 

How to Install Python on Windows

Modifying an install 

Once Python has been installed, you can add or remove features through the Programs and Features tool that is part of Windows. Select the Python entry and choose “Uninstall/Change” to open the installer in maintenance mode. 

“Modify” allows you to add or remove features by modifying the checkboxes - unchanged checkboxes will not install or remove anything. Some options cannot be changed in this mode, such as the install directory; to modify these, you will need to remove and then reinstall Python completely. 

“Repair” will verify all the files that should be installed using the current settings and replace any that have been removed or modified. 

“Uninstall” will remove Python entirely, with the exception of the Python Launcher for Windows, which has its own entry in Programs and Features. 

Setting Path 

The definition of “PATH” by Wikipedia is “PATH is an environment variable on Unix-likeoperating systems, DOS, OS/2, and Microsoft Windows, specifying a set of directories where executable programs are located. In general, each executing process or user session has its own PATH setting 

In Windows %PATH%  is a variable which provides a list of directory names separated by semicolon(;). When we enter a command on command shell , the system will search in current windows directory and after that it searches in path. It actually looks for a filename in the path which matches with the command you have given. Once it finds the matching executable file it loads the new process and getexecuted.  

Python Launcher for Windows 

The Python launcher for Windows is a utility which helps in locating and executing different Python versions. It allows scripts (or the command-line) to indicate a preference for a specific Python version, and will locate and execute that version. 

Unlike the PATH variable, the launcher will correctly select the most appropriate version of Python. It will prefer per-user installations over system-wide ones, and orders by language version rather than using the most recently installed version. 

The launcher was originally specified in PEP 397. 

A Python launcher is a single executable which uses a number of heuristics to locate a Python executable and launches it with a specified command line. 

The launcher allows Python scripts or the Windows command-line to specify a particular Python version and will locate and launch that version.

Documentation links for python 

https://www.python.org/doc/:  Browse the docs online or download a copy of your own 

https://www.python.org/doc/versions/:  document release by version 

Conclusion 

We hope this blog “How to install Python on Windows” would help you during the Python installation process. For additional information you can visit the official site of Python: https://www.python.org 

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Python Map Vs List Comprehension

Python has many inbuilt methods, data structures, and modules to get the desired output. When we write a huge code consisting of many lines for a detailed problem statement, it is difficult to debug the code. Python provides the features we need to write code with a fewer number of lines.  Python Map and Python list comprehension are features that work differently but have some similarities. Their perfomance varies with the parameters we are using. Let's discuss what these maps and list comprehension features are, how they work, and their performances. Map function:-  The map function takes an Expression and an Iterable. The output will be an Iterable object where the expression will work on each element of the given Iterable. The output of each expression will be an element of the resultant Iterable.  Syntax  map( expression, iterable) Parameters  It accepts two parameters as arguments the "expression maps "and  "iterable". expression- It is the formula you need to calculate the result. Iterable- It is the iterable whose each element will be calculated on from the expression. Return type  It returns an object of the iterable where the given formula calculates each element (as of 1st argument) applied on the elements of given iterable (2nd argument) of the map function. The output can be converted to an iterable. Example Example 1: Map ( ) using the function as the first argument. #This code will add "2" to each element of the list  def add_two(n):  #parameter is "n"   return n + 2             #add "2" to "n"  #application of formula "add_two( )" on every element of "num"  num = [1, 2, 3, 4] #2nd argument  added_two = map(add_two, num)      #returns "added_two" as object  print(list(added_two))       #object "added_two" is converted to list. #Output: added_two = [3, 4, 5, 6] Example 2: Using Lambda expression as first argument  # Double up all numbers through map and lambda   num = [1, 2, 3, 4] #input list, 2nd argument  added_two = map(lambda i: i + 2, num)      #output list object  print(list(result))        #object is converted to list Iterable  #Output: added_two = [3, 4, 5, 6] map vs. for loop python speed  Map and For Loop can produce the same output, but they work differently. Inside "for loop", we iterate a variable fed to an expression within an Iterable. Whereas "Map" is used to map a function to all the elements of the given Iterable. It is a one-lined code configuration of "for loop". So, if we give the same input and write code to get the same output using "map" and "for loop", we will see from the time of execution on the IDE that their speed of execution is different. Run the below codes in your Python IDE and you will see that "map" is faster than "for loop". So, in map vs for loop python speed , map wins. Example solution with "map" :  #definition of function to cube a given number   def numCube (n) :   return n * n * n   num = [0, 1, 2, 3, 4]   cubedNum = map(cubedNum, num)  #object of iterable  print(list(cubedNum)) #object is converted to Iterable list #output:         [0, 2, 8, 27, 64] Example solution with "for loop" :  num = [0, 1, 2, 3, 4]  #input list  for n in num :   cubedNum = n * n * n         #expression  print(cubedNum)  #output:      #         0  #         1  #         8  #         27  #         64 List Comprehension  Python List Comprehension is used for creating a list where each element is generated by applying a simple formula on the given list.  Syntax  resultant_list = [ for in ]                or resultant_list = [ for in if ]  Parameters  It takes two argument including a list. variable_expression- It is the formula containing a variable and the formula is used on the variable. variable- It is a variable to access each element.  input_list- It is the given list from where each element will be accessed as variable and applied on the formula.  condition- After calculating the expression, if it matches the given Cindy, then only it will appear in the resultant_list. Return type  It always returns a list. Example  Example 1: Python List comprehension without condition # a list comprehension to print cube  num= [-2, -1, 0, 1, 2]  cubedNum = [n**3 for n in num]   print(cubedNum)  #output:      [ -8, -1, 0, 1, 8]  Example 2: Python List comprehension with condition # a list comprehension to print cube if cube is positive  num= [-2, -1, 0, 1, 2]  cubedNum = [n**3 for n in num if n**3 >= 0]   print(cubedNum)      #output list contains only positive cubes #output:  [ 0, 1, 8]  python any list comprehension (use of "any" keyword) The "any ( )" function is very useful tool which works on iterables, and returns true if any of the element in the iterable is true. As list compression generates a list, we can use any() function with a python list comprehension.  Example: Print "not allowed" if any one of the ages is less than 18 ages= [22, 15, 20, 25, 34]   if any([a < 18 for a in ages]):  print("At least one is under-aged and they all are not allowed")  # there is one age 15 which is less than 18  #output:.    At least one is under-aged, and they all are not allowed python reduce vs list comprehension Reduce- Used to reduce the list where a formula is applied recursively on the elements of a list. It accepts two arguments, including expression as the first argument and Iterable as the second argument. This function can be defined in the "functools" module.  Firstly it took the first two elements of the Iterable and applied them to the expression. The calculated result and next element are applied to the expression. It is traversed till the last element of the Iterable, and the final output is thus generated.  Example 1: Reduce function using lambda #importing functools module where reduce( ) is defined  import functools    num = [ 1 , 2, 5, 3, 9 ]   #summation of elements using functools( )  print("The sum of all list elements is : ")   print(functools.reduce(lambda a,b : a+b, num))  #output: The sum of all list elements is: 20  How to reduce function works?  Step 1: 1 + 2 = 3   Step 2: 3 + 5 = 8   Step 3: 8 + 3 = 11   Step 4: 11 + 9 = 20  Example 2: Reduce function using operator module #importing functools module where reduce( ) is defined  import functools     #importing operator module where numerical operators are defined as function  import operator    num = [ 1 , 2, 5, 3, 9 ]   print ("The sum of the list elements is : ")   # "add" function is used as '+' operator  print (functools.reduce(operator.add, num))  #output: The sum of all list elements is : 20  How does reduce function with operator work? Step 1:    1 add 2 = 3   Step 2:      3 add 5 = 8   Step 3:      8 add 3 = 11   Step 4:       11 add 9 = 20  Python List comprehension- Used to create another list where a formula is applied once on each element of the list. So, we cannot use reduce in place of list comprehension.  We have seen no possible answer to python reduce vs list comprehension because these two can replace each other. Python filter vs list comprehension In python, a filter is a function that is used to filter out some elements from Iterable based on a certain condition. It returns an Iterable object. We can visualize that after printing it, and converting it to a list. It can be done by using list comprehension to create a list with the "if" condition.  When we are using a list with a small number of elements, then the list comprehension and filter method have almost the same execution speed. But when the list size is huge, then list comprehension is slightly faster than a filter. This is because list comprehension directly generates a list, whereas filter function returns an iterable object, which is then converted to a list. However, if we don't convert the object to a list, then the execution time is almost negligible. But to visualize the output, we need a list, not an object.  So, python list comprehension is better than a filter. Example 1: Using list comprehension to print positive numbers from a list. num= [-2, -1, 0, 1, 2]  positive Num = [n for n in num if n > 0]   print(positiveNum)      #output list contains only positive cubes #output: [ 1, 2]  Example 2: Using filter function to print positive numbers from a list. num= [-2, -1, 0, 1, 2]  print(list(filter(lambda n: n>0, num))) # output:    [1, 2] So, in python filter vs. list comprehension, list comprehension has better performance. Python Map VS List Comprehension  Assume that we have a formula. We want to apply that formula to each element of a list and thereby create a new list. Now, using for loop, we can traverse through that given list, pick each item, apply them into the formula and append it to the resultant list. That is a long process that consists of many lines of code. So, in one line, we can use the map function as discussed above.  Now, python list comprehension is a tool for creating a new list. We can use the functionality of lambda expression, filter method, and map function together using only this list comprehension.  List comprehension has a simpler configuration than the map function.  List comprehension can be used together with if condition as replacement of filter method. Map function has no such functionality. However, we can feed the map function output to the filter function.  List comprehension returns a list, whereas the map function returns an object of Iterable.  List comprehension execution is faster than that of map function when the formula expression is huge and complex. Map function is faster than list comprehension when the formula is already defined as a function earlier. So, that map function is used without lambda expression. Comparing Execution Time  Now to examine the execution performance of list comprehension and map function, we will import a module "timeit" to check the execution time. Let us see the result with a variety of methods. Without lambda: Map is faster than List Comprehension when function is already defined in case of map function. Example code 1: This code will print the time taken to evaluate numbers from 1 to 50. Map function is used without lambda. import timeit  # list comprehension  l1 = timeit.timeit( '[ l for l in range(50)]' , number = 999999)  print (l)   #map function  f= 'def num( ) : print (n)'  m1 = timeit.timeit( ' map (num, range(50))' , number = 999999, setup = f )   print (m) With lambda in map: List comprehension is better than map function when we don't define the function beforehand and use lambda expression inside map.   Example code 2: This code will print the time taken to add a number to itself and this is applied for each element of the list. The expression is defined as lambda expression. import timeit  # list comprehension  l2 = timeit.timeit( '[ n+n for n in range(50)]' , number = 999999)  print (l)  #map function  m2 = timeit.timeit( ' map (lambda a: a+a, range(50))' , number = 999999, setup = f )   print (m) Write the above codes on your IDE. You will see that for the first case, m1 is very less than l1. That means the map works faster than list comprehension. For the second case, m2 is greater than l2, implying that list comprehension is faster than map function when map function is used with Lambda expression.  ConclusionThere are no clear answers about which is the better option, in Python Map Vs List comprehension. We should know what the problem statement is, then use the method which suits us better because our main objective is to calculate the solution with optimal time and space consumption. 
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Python Map Vs List Comprehension

Python has many inbuilt methods, data structures,... Read More

Why use the map() function?

The map() in Python is a built-in function. For each item of the iterable (e.g. list, tuple, etc.), the map() calls the defined function and returns a list of results. The first argument in the map() is that you need to pass a function without calling it. This means that there are no parentheses. The operation performed by map() is usually known as a mapping, as it maps every item in an iterable input to an iterable new item. This is achieved by applying map() to all things in the input iterable. Python uses the map concept from functional programming. The map operation considers the mapping function and the data vector as arguments and returns the new vector, which results in the development of the mapping function on each vector object. Syntax: The syntax of map() in python is: map(function, iterable) Let’s assume that you want to use a list of operations or functions to construct a new list for each variable and use those outputs. An example would be to have a list of numbers and to create a new list with their cubes. One way to do this is by going through the list of numbers you are using for loop, and using a function that returns the cube of each number or part. Following is a simple program using the map function in python that returns the cube of a number: # defining a function that returns the cube of a number  def cube(number):    return number**3  # original list  number_list = [1,2,3,4,5,6]  # list that will contain the squared numbers  number_list_cube = []  # using a for loop to iterate over our number_list and create a new list with the cubic values  for number in number_list:    number_list_cube.append(cube(number))  print(number_list_cube)   # output [1,4,9,16,25,36We have a list of numbers, number_list, and we want to create a new list, number_list_cube, that contains the squares of number_list. We use a for loop to loop over number_list, and append the square of each number or number to our number_list_cube list. The built-in python function called map is another way to do this. There are two arguments in the map function: the function we want to apply and the object or sequence to which we want to apply it (such as a list in this case). In other words, the map function maps or applies it to each element in the sequence. Working of map() function in Python: Two inputs as a function and an iterable object used by the map(). The function given to map() is a normal function and is iterated over all the values in the given object. For example, making a list of numbers, and finding the cube for each number. The function is required that will return the double of the number. The function will be as follows: def double(number):  return number+number Now we will create the list of items: number_list = [1,2,3,4,5] Now we will use the map() function to find the double of each item in the number-list.  def double(number):      return number*2  number_list = [1,2,3,4,5]  number_list = map(double, number_list)  print(list(number_list)) Output: [2, 4, 6, 8, 10] We have used list() in the code to iterate the output from the map. The map() function helps to get the double of each number. map() in Python with built-in functions: The map() in Python is a built-in function and can also be used with other built-in functions available in Python. In the example, we are going to make use of the Python pow() built-in function. It is given to map two list objects, one for each base and index parameter. The result is a list containing the power of each number in bases raised to the corresponding number in the index. Example: We have two lists, one is base and another is index. base= [10, 20, 30, 40, 50]  index= [1, 2, 3, 4, 5]  We will use pow() as the function to map().  base=[10, 20, 30, 40, 50]  index=[1, 2, 3, 4, 5]  powers=list(map(pow, base, index))  print(list(powers)) Output: [10, 400, 27000, 2560000, 312500000] map() in Python with Tuple: Tuples in Python are an ordered collection of objects. They are defined by enclosing the elements in parentheses (()). In certain instances, the tuple is identical to the list in terms of indexing, nested objects and repetition, but the tuple is immutable, unlike the lists. Even though tuples are defined using brackets, you still use square brackets to index and slice tuples, just like strings and lists. Example: In the following example we will use the function that will convert the values of the tuple into uppercase. def upperFunc(values):      return values.upper()  values_tuple = ('swift','java','python','go','kotlin')  new_list = map(upperFunc, values_tuple)  print(list(new_list)) Output: ['SWIFT', 'JAVA', 'PYTHON', 'GO', 'KOTLIN'] map() in Python with Dictionary: Dictionaries are the implementation by Python that is more generally known as an associative array. A dictionary is a collection of key-value pairs. It is defined by curly brackets({}). Dictionaries are both mutable and dynamic. They can be updated and deleted as needed. Dictionaries differ from lists as Dictionary elements are accessed via keys while List elements are accessed by their position in the list, via indexing. The dictionary is an iterator, you can use it inside the map() function. Example: def dictFunc(values):      return values*10  values_dict = {1,2,3,4,5}  finalitems = map(dictFunc, values_dict)  print(list(finalitems)) Output: [10, 20, 30, 40, 50] map() in Python with Set: Sets in Python are unordered and the elements of a set are unique i.e. they cannot be the same. You can modify a set, but the elements in the set must be immutable. Like dictionary set() is also an iterator, you can use it inside the map() function. Example: def setFunc(values):      return values*10  values_set = {1,2,3,4,5}  finalitems = map(setFunc, values_set)  print(list(finalitems)) Output: [10, 20, 30, 40, 50] map() in Python with Lambda function: A Lambda Function is an anonymous function that means a function without a name. A Lambda function contains multiple arguments with one expression. We use the def keyword to define a normal function in Python. Similarly, we use the lambda keyword to define an anonymous function in Python. Lambda functions are syntactically restricted to a single expression. We can use the lambda function inside the map() function in Python. Example: numbers = (1,2,3,4,5)  result = map(lambda x: x+x, numbers)  print(list(result)) Output: [2, 4, 6, 8, 10] map() in Python with Other Functional Tools: We can perform more complex transformations on iterables by using map() together with functional tools like filter() and reduce(). map() and filter(): In some problems we need to process an input iterable and return another iterable by filtering unnecessary values in an iterable input. The filter() of Python is a good choice in this situation. Filter() returns the iterable input items for which the function returns true. If no function is passed, filter() uses the identity function. This implies filter() monitors each item in iterable for its true value and filters all falsy items out. Example: If we want to calculate the square root of all the values in a list and if our list have negative values, we’ll get an error because the square root isn’t defined for negative numbers. In order to prevent this problem, filter() is used to filter out all negative values and then find the square root of the remaining positive values. import math  def is_positive(num):  return num >= 0  def filtered_sqrt(numbers):  filtered_iter = map(math.sqrt, filter(is_positive, numbers))  return list(filtered_iter)  filtered_sqrt([100, 9, 225, -36, 0]) map() and reduce(): Another function that exists in functool module of Python is reduce() in the standard library of Python. Reduce() is another key functional method in Python that is helpful when an iterable feature needs to be added to and reduced to a single cumulative value. This form of action is generally called reducing or folding. Example: We will use map() and reduce() to calculate the total size of all the files that live in your home directory. import functools  import operator  import os  import os.path  files = os.listdir(os.path.expanduser(“~”))  functools.reduce(operator.add, map(os.path.getsize, files)) We will call os.path.expanduser("~") to get the path to your home directory. Then you call os.listdir() on that path to get a list with the paths of all the files that live there. The call to map() uses os.path.getsize() to get the size of each file. You eventually use reduce() to obtain the total sum of the size of each file using operator.add(). The end result is the total file size in bytes of your home directory. Generator Expressions instead of map() in Python: map() in python returns a map object, an iterator that returns items on request. The natural substitution for map() is therefore a generator expression. Generator expressions return generator objects, which are also iterators, which yield items on request. A tiny difference occurs between a list comprehension and a generator expression. The list uses a pair of square brackets ([]) while generator expressions use a pair of parentheses (()). If you want to turn a list comprehension into a generator expression, you just need to replace the square brackets with parentheses. def square(number):  return number ** 2  numbers = [1, 2, 3, 4, 5]  gen_exp = (square(x) for x in numbers)  print(list(gen_exp)) Output: [1, 4, 9, 16, 25] Generator expressions are commonly used as arguments in function calls.  Conclusion:map() in python allows to perform operations on iterables. Map() usually helps you to convert and process iterables without directly using a loop. Python map() is a built-in-function that applies a function to all the input items of an iterator. For example, a list, tuple, etc. can be an iterator, and it returns a map object that can be iterated. The map() function will add all the items inside the iterator to the specified function, and then return an iterable map object, i.e. tuple, list, etc. map() in Python is an embedded function that can also be used in Python with other integrated functions. 
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Why use the map() function?

The map() in Python is a built-in function. Fo... Read More

What is nested lambda function? What are its characteristics give an example?

The lambda function was initially introduced in the Python programming language because of the demand from LISP programmers. The term lambda originates from the Lambda Calculus, a form of mathematical calculus invented by Alonzo Church.If the mention of Calculus has scared you, relax! You don’t have to master Calculus for using lambda functions. Lambda expressions are simple, concise and easy-to-understand – even a newbie can get the lambda syntax easily.SyntaxLambda functions are one-liner functions. Every lambda function you define in python has three essentials – the keyword lambda, arguments and the expression. Here is the syntax of how we define a lambda function in python:lambda arguments: expressionKeyword: lambdaArguments: The user can define the number of arguments separated by a commaExpression: The expression is an arithmetic expression that uses the arguments defined by the userWhat Is a Lambda Function?A lambda function is an anonymous function, i.e., a function without a name. It allows writing functions without naming them. Unlike the regular way of defining python functions, where we use the keyword def, we use the lambda keyword to define a lambda function.# a function in python defined using the keyword – lambda lambda a, b: a + bis similar to writing a regular function in python,# a function in python defined using the keyword – def  def sum(a, b):     return a + bAdding two numbers using Python lambda functions:sum = lambda a, b: a + b print("Result = ", sum(6,4))Output:Result = 10Characteristics of Lambda functions in Python:Lambda functions can take multiple arguments but return just one value in the form of an expression.The lambda functions are throw-away functions, i.e., they are required where they have been created, and called anywhere in a function when required.Syntactically, they are a one-line version of a function and hence cannot contain multiple-line expressions.Lambda expressions can be passed as arguments to other functions.Lambda functions do not have an explicit return statement, but always contain an expression that is executed and returned.Attention: You can use lambda functions wherever there is a need for function objects. However, they cannot access variables other than those present in their parameter list. Also, global variables cannot be accessed using the lambda function.What is a nested Lambda Function?A lambda function inside a lambda function is called a nested lambda function. Python allows lambda nesting, i.e., you can create another lambda function inside a pre-existing lambda function. For nesting lambdas, you will need to define two lambda functions – an outer and an inner lambda function. When the outer lambda is called, the inner lambda creates a function. The outer lambda then returns the called function.Here is a small code to illustrate the nested lambda function:Adding two numbers using Python lambda functions:add = lambda a=20: lambda b: a + b x = add() print(x) print(x(30))Output:50Here is a step-by-step explanation of the above code.Step-1: Define an outer lambda function (lambda a =10) & an inner lambda function (lambda b: a + b)Step-2: Assign both the lambda functions in a variable name sum.Step-3: Call the outer lambda function, and assign it to a variable xStep-4: Print x by assigning a value.The assigned value of 30 is added up with the outer lambda function and finally, sum=50 is displayed in the output.Where Are Nested Lambda Functions Useful?Nested lambda functions are significant when there is a need for function objects. Nesting allows hiding the mechanics of iteration operations, especially when working on complex projects. Nested lambdas can be easily embedded in places where def functions are restricted. Syntactically, they are useful in places where python wouldn’t allow a function to be defined using a regular def expression. Besides, nested lambda statements are also used along with sort methods to arrange the list elements in a sequence.Attention: With nested lambdas, recursion can occur and may also result in a runtime error.RuntimeError: maximum recursion depth exceeded errorHow to Use Nested Lambda with the Sort MethodIn general, sort() in python has two parameters, key, and reverse. The key value is None, and the reverse value is False, by default. Using the lambda function in the sort method, the key extends its capabilities and becomes more versatile. Here is a simple code to make you understand the importance of the lambda function with the sort method.Sorting without using lambda function:check_list = [[4, "Cherry"], [3, "Banana"], [1, "Apple"], [2, "Dragon Fruit"]] check_list.sort() print(check_list)Output:[[1, 'Apple'], [2, 'Dragon Fruit'], [3, 'Banana'], [4, 'Cherry']]Look at both the codes carefully. The inner list doesn’t get sorted using the regular sort() method but gets sorted using the lambda functions in alphabetical order. Therefore, the lambda function is of the utmost importance when there is a need to sort the inner list sequentially in a pre-defined list.Lambda functions are also used with python in-built functions like filter(), map(), and reduce(). Let’s have a look at each in detail:Use of Filter Function with lambda – filter ()The filter () function in Python is an excellent way of filtering out all the elements of a sequence for which the function is True or False. The function includes only those values that satisfy the result and thus constructs a new list from those elements of the list for which the function returns True. Here is a small code that returns the list of even numbers from an input list:# Python code to illustrate filter() with lambda() # Finding the even numbers from a given list li = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] result = list(filter(lambda x: (x%2 ==0), li)) print(result)Output:[2, 4, 6, 8, 10, 12, 14]From the output of the above program, we see the output returning only the true values. Only those values in the range that are even numbers i.e., divisible by 2, are included in the new list, and the rest are explicitly removed.  Attention Please: If the sequence is a string, tuple, or Unicode, the output will be the respective same type, otherwise, the result is always a list.Use of Map Function with lambda – map()The map() function in Python applies a unary function to each element in the list. After applying the specified function for each item in the list, the map() function returns the new list. The function calls each item in the sequence and returns a list of return values containing the results, in the same order.Here is a small code that returns the list of cubes from an input list:# Python code to illustrate map() with lambda() # Cubing the list of numbers from a given list nums= [1, 3, 5, 6, 4, 2] result = list(map(lambda x: x**3, nums)) print(result)Output:[1, 27, 125, 216, 64, 8]From the output of the above program, we can see that all the elements have been evaluated for each of the elements in the list. The corresponding cubes of all the elements in the list are added to the new list sequentially.Attention Please: Map() function also allows to pass more than one sequence if required. Just keep in mind two things when passing multiple sequences. Firstly, the number of arguments must be identical to the number of sequences. Secondly, each argument must be called from the corresponding item from each sequence. Use of Reduce Function with lambda – reduce()The reduce() function combines the sequence elements using a binary function. It returns a single value generated by calling the function on the first two elements in the sequence. Once the two have been called, the result then gets added to the next item, and so on. The process continues until all the elements in the sequence get evaluated.Pro-tip: Don’t forget to import reduce from the functools module.Here is a small code that returns the list of the sum from an input list:# Python code showing the use of reduce() with lambda() # Finding the sum from a given list lis = [1, 2, 3, 4, 5] result = reduce ((lambda a, b: a + b), lis) print(result)Output:15The result of the first-two elements gets added to the next element. Here is what goes behind the scene à ((((1+2) + 3) + 4) + 5) = 15. The process is continued till the last element of the sequence is reached.Attention Please: If there is a single-value sequence, then the value of the sequence is returned. However, an exception is raised in an empty list.How to Test Lambda FunctionsLambda functions, no matter how simple they sound, also need to be tested at times. Testing a python lambda function is the same as testing a regular function. Unittest and doctest – python standard libraries are used to test lambda functions.Python Lambda FunctionsLambda functions in python are an effective way to define functions anonymously. The lambda functions being executed and returned in a single-line add a significant advantage with throw-away functions. However, lambda functions are not recommended if the expressions become complicated to read or interpret. If it is strenuous to get all the logic inside lambdas, use a def instead.
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What is nested lambda function? What are its chara...

The lambda function was initially introduced in th... Read More

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