10 Good Reasons to Do PGDM Degree

If you have a Tech, BA, BBA or other degree and are still unsure whether to choose an MBA or PGDM for higher education, this article will help you make an informed decision.

https://medium.com/@shantibschooldigital/10-good-reasons-to-do-pgdm-degree-ef480f42a235

Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
Array methods for performing operations
1. Change an existing value
2. Add a new value
3. Remove a value
Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.

import array as arr

#how you would create an array
arr.array()

Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.

from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

variable_name would be the name of the array.
The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

First we included the array module, in this case with import array as arr .
Then, we created a numbers array.
We used arr.array() because of import array as arr .
Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.


import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:


import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python

Alex O'Sullivan

1666082925

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:
1:15 What is an array?
2:53 Is python list same as an array?
3:48 How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
- 10:33 Finding the length of an array
- 11:44 Adding Elements
- 15:06 Removing elements
- 18:32 Array concatenation
- 20:59 Slicing
- 23:26 Looping

Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
Array methods for performing operations
1. Change an existing value
2. Add a new value
3. Remove a value
Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.

import array as arr

#how you would create an array
arr.array()

Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.

from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

variable_name would be the name of the array.
The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

First we included the array module, in this case with import array as arr .
Then, we created a numbers array.
We used arr.array() because of import array as arr .
Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming

津田あすか

1647851880

Pythonで実用的な例を使用したLambda関数

序章

Pythonでラムダ関数に最初に出会ったとき、私は非常に恐れていて、それらが高度なPythonista用であると思いました。初心者のPythonチュートリアルは、その言語の読みやすい構文を称賛していますが、ラムダは確かにユーザーフレンドリーではないようです。

ただし、一般的な構文を理解し、いくつかの単純なユースケースを検討すると、それらを使用することはそれほど怖くありませんでした。

構文

簡単に言うと、ラムダ関数は通常のPython関数と同じですが、定義時に名前がなく、1行のコードに含まれている点が異なります。

lambda argument(s): expression

ラムダ関数は、指定された引数の式を評価します。関数に値（引数）を指定してから、演算（式）を指定します。キーワードlambdaが最初に来る必要があります。完全なコロン（:)は、引数と式を区切ります。

以下のサンプルコードでは、xが引数で、x+xが式です。

#Normal python function
def a_name(x):
    return x+x
#Lambda function
lambda x: x+x

実際のアプリケーションに入る前に、Pythonコミュニティがラムダ関数の良い点と悪い点についていくつかの技術について言及しましょう。

長所

わかりやすい単純な論理演算に適しています。これにより、コードも読みやすくなります。
一度だけ使う機能が欲しいときに便利です。

短所

実行できる式は1つだけです。1つのラムダ関数で複数の独立した操作を行うことはできません。
通常の関数で複数行にまたがる操作def （ネストされた条件付き操作など）には適していません。コードを理解するのに1、2分必要な場合は、代わりに名前付き関数を使用してください。
通常の関数の場合のように、すべての入力、操作、および出力を説明するdoc-stringを記述できないため、問題がありますdef。

この記事の最後では、Lambda関数が正当であるように見えても推奨されない、一般的に使用されるコード例を見ていきます。

ただし、最初に、ラムダ関数を使用する場合の状況を見てみましょう。map（）やfilter（）などの関数を引数として受け取るPythonクラスでは、ラムダ関数を頻繁に使用することに注意してください。これらは高階関数とも呼ばれます。

1.スカラー値

これは、単一の値に対してラムダ関数を実行する場合です。

(lambda x: x*2)(12)
###Results
24

上記のコードでは、関数が作成され、すぐに実行されました。これは、即時に呼び出される関数式またはIIFEの例です。

2.リスト

フィルター（）。これは、特定の基準に適合する値のみを返すPython組み込みライブラリです。構文はfilter(function, iterable)です。iterableは、リスト、セット、またはシリーズオブジェクト（以下で詳しく説明します）などの任意のシーケンスにすることができます。

以下の例では、数値のリストをフィルタリングしevenます。filter関数は「Filterobject」を返すため、値を返すにはリストでカプセル化する必要があることに注意してください。

list_1 = [1,2,3,4,5,6,7,8,9]
filter(lambda x: x%2==0, list_1)
### Results
<filter at 0xf378982348>
list(filter(lambda x: x%2==0, list_1))
###Results
[2, 4, 6, 8]

地図（）。これは、構文が含まれるもう1つの組み込みのPythonライブラリです。map(function, iterable).

これにより、元のリストのすべての値が関数に基づいて変更された変更済みリストが返されます。以下の例では、リスト内のすべての数値を3乗しています。

list_1 = [1,2,3,4,5,6,7,8,9]
cubed = map(lambda x: pow(x,3), list_1)
list(cubed)
###Results
[1, 8, 27, 64, 125, 216, 343, 512, 729]

3.シリーズオブジェクト

Seriesオブジェクトは、データフレーム内の列、言い換えると、対応するインデックスを持つ値のシーケンスです。Lambda関数を使用して、Pandasデータフレーム内の値を操作できます。

家族のメンバーに関するダミーのデータフレームを作成しましょう。

import pandas as pd
df = pd.DataFrame({
    'Name': ['Luke','Gina','Sam','Emma'],
    'Status': ['Father', 'Mother', 'Son', 'Daughter'],
    'Birthyear': [1976, 1984, 2013, 2016],
})

PandasによるApply（）関数を使用したLambda 。この関数は、列のすべての要素に操作を適用します。

各メンバーの現在の年齢を取得するために、現在の年からその誕生年を差し引きます。以下のラムダ関数では、xはbirthyear列の値を指し、式は2021(current year) minus the valueです。

df['age'] = df['Birthyear'].apply(lambda x: 2021-x)

PythonのFilter（）関数を使用したLambda 。これには2つの引数が必要です。1つは条件式を使用したラムダ関数で、2つは反復可能で、これはシリーズオブジェクトです。条件を満たす値のリストを返します。

list(filter(lambda x: x>18, df['age']))
###Results
[45, 37]

PandasによるMap（）関数を使用したLambda 。Mapは、式に基づいて列の値を変更するという点で、apply（）と非常によく似ています。

#Double the age of everyone
df['double_age'] = 
df['age'].map(lambda x: x*2)

特定の基準に基づいて異なる値を返す条件付き操作を実行することもできます。

以下のコードは、Status値が父親または息子の場合は「男性」を返し、それ以外の場合は「女性」を返します。このコンテキストでは、applyとは交換可能であることに注意してください。map

#Conditional Lambda statement
df['Gender'] = df['Status'].map(lambda x: 'Male' if x=='father' or x=='son' else 'Female')

4.データフレームオブジェクトのラムダ

1つの式でデータフレーム全体を変更する場合を除いて、データフレーム全体ではなく、特定の列（シリーズオブジェクト）でLambda関数を使用することがほとんどです。

たとえば、すべての値を小数点以下1桁に丸めます。この場合、round（）は文字列では機能しないため、すべての列はfloatまたはintデータ型である必要があります。

df2.apply(lambda x:round(x,1))
##Returns an error if some 
##columns are not numeric

以下の例では、データフレームに適用を使用し、Lambda関数で変更する列を選択します。式が列単位で適用されるように、ここで使用する必要があることに注意してください。axis=1

#convert to lower-case
df[['Name','Status']] = 
df.apply(lambda x: x[['Name','Status']].str.lower(), axis=1)

推奨されないユースケース

Lambda関数に名前を割り当てます。Lambdaは保存されることを意図していない無名関数を作成するため、これはPEP8pythonスタイルガイドでは推奨されていません。代わりに、def関数を再利用のために保存する場合は、通常の関数を使用してください。

#Bad
triple = lambda x: x*3
#Good
def triple(x):
     return x*3

2.Lambda関数内で関数を渡します。abs関数をmap（）またはapply（）に直接渡すことができるため、Lambdaでは1つの数値引数のみを取るような関数を使用する必要はありません。

#Bad
map(lambda x:abs(x), list_3)
#Good
map(abs, list_3)
#Good
map(lambda x: pow(x, 2), float_nums)

理想的には、ラムダ関数内の関数は2つ以上の引数を取る必要があります。例は次のとおりです。さまざまな組み込みのPython関数を試してpow(number,power)、このコンテキストでLambda関数が必要な関数を確認できます。私はこのノートブックでそうしました。round(number,ndigit).

3.複数行のコードが読みやすい場合にLambda関数を使用します。例として、ラムダ関数内でif-elseステートメントを使用している場合があります。この記事の前半で、以下の例を使用しました。

#Conditional Lambda statement
df['Gender'] = df['Status'].map(lambda x: 'Male' if x=='father' or x=='son' else 'Female')

以下のコードでも同じ結果が得られます。私はこの方法を好みます。なぜなら、あなたは無限の条件を持つことができ、コードは従うのに十分単純だからです。ベクトル化された条件の詳細については、こちらをご覧ください。

結論

Lambdasが気に入らない多くのプログラマーは、通常、Lambdasをより理解しやすいリスト内包表記、組み込み関数、および標準ライブラリに置き換えることができると主張しています。ジェネレータ式（リスト内包表記と同様）も、map（）およびfilter（）関数の便利な代替手段です。

Lambda関数をコードに含めるかどうかに関係なく、他の人のコードで必然的にLambda関数に遭遇するため、それらが何であるか、およびそれらがどのように使用されるかを理解する必要があります。

ソース：https ：//towardsdatascience.com/lambda-functions-with-practical-examples-in-python-45934f3653a8

#lambda #python

Adolphus Tillman

1647930480

Lambda Functions with Practical Examples in Python

Introduction

When I first came across lambda functions in python, I was very much intimidated and thought they were for advanced Pythonistas. Beginner python tutorials applaud the language for its readable syntax, but lambdas sure didn’t seem user-friendly.

However, once I understood the general syntax and examined some simple use cases, using them was less scary.

Syntax

Simply put, a lambda function is just like any normal python function, except that it has no name when defining it, and it is contained in one line of code.

lambda argument(s): expression

A lambda function evaluates an expression for a given argument. You give the function a value (argument) and then provide the operation (expression). The keyword lambda must come first. A full colon (:) separates the argument and the expression.

In the example code below, x is the argument and x+x is the expression.

#Normal python function
def a_name(x):
    return x+x
#Lambda function
lambda x: x+x

Before we get into practical applications, let’s mention some technicalities on what the python community thinks is good and bad with lambda functions.

Pros

Good for simple logical operations that are easy to understand. This makes the code more readable too.
Good when you want a function that you will use just one time.

Cons

They can only perform one expression. It’s not possible to have multiple independent operations in one lambda function.
Bad for operations that would span more than one line in a normal def function (For example nested conditional operations). If you need a minute or two to understand the code, use a named function instead.
Bad because you can’t write a doc-string to explain all the inputs, operations, and outputs as you would in a normal def function.

At the end of this article, we’ll look at commonly used code examples where Lambda functions are discouraged even though they seem legitimate.

But first, let’s look at situations when to use lambda functions. Note that we use lambda functions a lot with python classes that take in a function as an argument, for example, map() and filter(). These are also called Higher-order functions.

1. Scalar values

This is when you execute a lambda function on a single value.

(lambda x: x*2)(12)
###Results
24

In the code above, the function was created and then immediately executed. This is an example of an immediately invoked function expression or IIFE.

2. Lists

Filter(). This is a Python inbuilt library that returns only those values that fit certain criteria. The syntax is filter(function, iterable). The iterable can be any sequence such as a list, set, or series object (more below).

The example below filters a list for even numbers. Note that the filter function returns a ‘Filter object’ and you need to encapsulate it with a list to return the values.

list_1 = [1,2,3,4,5,6,7,8,9]
filter(lambda x: x%2==0, list_1)
### Results
<filter at 0xf378982348>
list(filter(lambda x: x%2==0, list_1))
###Results
[2, 4, 6, 8]

Map(). This is another inbuilt python library with the syntax map(function, iterable).

This returns a modified list where every value in the original list has been changed based on a function. The example below cubes every number in the list.

list_1 = [1,2,3,4,5,6,7,8,9]
cubed = map(lambda x: pow(x,3), list_1)
list(cubed)
###Results
[1, 8, 27, 64, 125, 216, 343, 512, 729]

3. Series object

A Series object is a column in a data frame, or put another way, a sequence of values with corresponding indices. Lambda functions can be used to manipulate values inside a Pandas dataframe.

Let’s create a dummy dataframe about members of a family.

import pandas as pd
df = pd.DataFrame({
    'Name': ['Luke','Gina','Sam','Emma'],
    'Status': ['Father', 'Mother', 'Son', 'Daughter'],
    'Birthyear': [1976, 1984, 2013, 2016],
})

Lambda with Apply() function by Pandas. This function applies an operation to every element of the column.

To get the current age of each member, we subtract their birth year from the current year. In the lambda function below, x refers to a value in the birthyear column, and the expression is 2021(current year) minus the value.

df['age'] = df['Birthyear'].apply(lambda x: 2021-x)

Lambda with Python’s Filter() function. This takes 2 arguments; one is a lambda function with a condition expression, two an iterable which for us is a series object. It returns a list of values that satisfy the condition.

list(filter(lambda x: x>18, df['age']))
###Results
[45, 37]

Lambda with Map() function by Pandas. Map works very much like apply() in that it modifies values of a column based on the expression.

#Double the age of everyone
df['double_age'] = 
df['age'].map(lambda x: x*2)

We can also perform conditional operations that return different values based on certain criteria.

The code below returns ‘Male’ if the Status value is father or son, and returns ‘Female’ otherwise. Note that apply and map are interchangeable in this context.

#Conditional Lambda statement
df['Gender'] = df['Status'].map(lambda x: 'Male' if x=='father' or x=='son' else 'Female')

4. Lambda on Dataframe object

I mostly use Lambda functions on specific columns (series object) rather than the entire data frame, unless I want to modify the entire data frame with one expression.

For example rounding all values to 1 decimal place, in which case all the columns have to be float or int datatypes because round() can’t work on strings.

df2.apply(lambda x:round(x,1))
##Returns an error if some 
##columns are not numeric

In the example below, we use apply on a dataframe and select the columns to modify in the Lambda function. Note that we must use axis=1 here so that the expression is applied column-wise.

#convert to lower-case
df[['Name','Status']] = 
df.apply(lambda x: x[['Name','Status']].str.lower(), axis=1)

Discouraged use cases

Assigning a name to a Lambda function. This is discouraged in the PEP8 python style guide because Lambda creates an anonymous function that’s not meant to be stored. Instead, use a normal def function if you want to store the function for reuse.

#Bad
triple = lambda x: x*3
#Good
def triple(x):
     return x*3

2. Passing functions inside Lambda functions. Using functions like abs which only take one number- argument is unnecessary with Lambda because you can directly pass the function into map() or apply().

#Bad
map(lambda x:abs(x), list_3)
#Good
map(abs, list_3)
#Good
map(lambda x: pow(x, 2), float_nums)

Ideally, functions inside lambda functions should take two or more arguments. Examples are pow(number,power) and round(number,ndigit). You can experiment with various in-built python functions to see which ones need Lambda functions in this context. I’ve done so in this notebook.

3. Using Lambda functions when multiple lines of code are more readable. An example is when you are using if-else statements inside the lambda function. I used the example below earlier in this article.

#Conditional Lambda statement
df['Gender'] = df['Status'].map(lambda x: 'Male' if x=='father' or x=='son' else 'Female')

The same results can be achieved with the code below. I prefer this way because you can have endless conditions and the code is simple enough to follow. More on vectorized conditions here.

Conclusion

Many programmers who don’t like Lambdas usually argue that you can replace them with the more understandable list comprehensions, built-in functions, and standard libraries. Generator expressions (similar to list comprehensions) are also handy alternatives to the map() and filter() functions.

Whether or not you decide to embrace Lambda functions in your code, you need to understand what they are and how they are used because you will inevitably come across them in other peoples’ code.

Source: https://towardsdatascience.com/lambda-functions-with-practical-examples-in-python-45934f3653a8

#lambda #python

Awesome Rust

1658878980

Git Branchless: Branchless Workflow for Git Built on Rust

Branchless workflow for Git

(This suite of tools is 100% compatible with branches. If you think this is confusing, you can suggest a new name here.)

About

git-branchless is a suite of tools which enhances Git in several ways:

It makes Git easier to use, both for novices and for power users. Examples:

git undo: a general-purpose undo command. See the blog post git undo: We can do better.
The smartlog: a convenient visualization tool.
git restack: to repair broken commit graphs.
Speculative merges: to avoid being caught off-guard by merge conflicts.

It adds more flexibility for power users. Examples:

Patch-stack workflows: strong support for "patch-stack" workflows as used by the Linux and Git projects, as well as at many large tech companies. (This is how Git was "meant" to be used.)
Prototyping and experimenting workflows: strong support for prototyping and experimental work via "divergent" development.
git sync: to rebase all local commit stacks and branches without having to check them out first.
git move: The ability to move subtrees rather than "sticks" while cleaning up old branches, not touching the working copy, etc.
Anonymous branching: reduces the overhead of branching for experimental work.
In-memory operations: to modify the commit graph without having to check out the commits in question.
git next/prev: to quickly jump between commits and branches in a commit stack.
git co -i/--interactive: to interactively select a commit to check out.

It provides faster operations for large repositories and monorepos, particularly at large tech companies. Examples:

See the blog post Lightning-fast rebases with git-move.
Performance tested: benchmarked on torvalds/linux (1M+ commits) and mozilla/gecko-dev (700k+ commits).
Operates in-memory: avoids touching the working copy by default (which can slow down git status or invalidate build artifacts).
Sparse indexes: uses a custom implementation of sparse indexes for fast commit and merge operations.
Segmented changelog DAG: for efficient queries on the commit graph, such as merge-base calculation in O(log n) instead of O(n).
Ahead-of-time compiled: written in an ahead-of-time compiled language with good runtime performance (Rust).
Multithreading: distributes work across multiple CPU cores where appropriate.
To my knowledge, git-branchless provides the fastest implementation of rebase among Git tools and UIs, for the above reasons.

See also the User guide and Design goals.

Demos

Repair

Undo almost anything:

Commits.
Amended commits.
Merges and rebases (e.g. if you resolved a conflict wrongly).
Checkouts.
Branch creations, updates, and deletions.

Why not git reflog?

git reflog is a tool to view the previous position of a single reference (like HEAD), which can be used to undo operations. But since it only tracks the position of a single reference, complicated operations like rebases can be tedious to reverse-engineer. git undo operates at a higher level of abstraction: the entire state of your repository.

git reflog also fundamentally can't be used to undo some rare operations, such as certain branch creations, updates, and deletions. See the architecture document for more details.

What doesn't git undo handle?

git undo relies on features in recent versions of Git to work properly. See the compatibility chart.

Currently, git undo can't undo the following. You can find the design document to handle some of these cases in issue #10.

"Uncommitting" a commit by undoing the commit and restoring its changes to the working copy.
- In stock Git, this can be accomplished with git reset HEAD^.
- This scenario would be better implemented with a custom git uncommit command instead. See issue #3.
Undoing the staging or unstaging of files. This is tracked by issue #10 above.
Undoing back into the middle of a conflict, such that git status shows a message like path/to/file (both modified), so that you can resolve that specific conflict differently. This is tracked by issue #10 above.

Fundamentally, git undo is not intended to handle changes to untracked files.

Comparison to other Git undo tools

gitjk: Requires a shell alias. Only undoes most recent command. Only handles some Git operations (e.g. doesn't handle rebases).
git-extras/git-undo: Only undoes commits at current HEAD.
git-annex undo: Only undoes the most recent change to a given file or directory.
thefuck: Only undoes historical shell commands. Only handles some Git operations (e.g. doesn't handle rebases).

Visualize

Visualize your commit history with the smartlog (git sl):

Why not `git log --graph`?

git log --graph only shows commits which have branches attached with them. If you prefer to work without branches, then git log --graph won't work for you.

To support users who rewrite their commit graph extensively, git sl also points out commits which have been abandoned and need to be repaired (descendants of commits marked with rewritten as abcd1234). They can be automatically fixed up with git restack, or manually handled.

Manipulate

Edit your commit graph without fear:

Why not `git rebase -i`?

Interactive rebasing with git rebase -i is fully supported, but it has a couple of shortcomings:

git rebase -i can only repair linear series of commits, not trees. If you modify a commit with multiple children, then you have to be sure to rebase all of the other children commits appropriately.
You have to commit to a plan of action before starting the rebase. For some use-cases, it can be easier to operate on individual commits at a time, rather than an entire series of commits all at once.

When you use git rebase -i with git-branchless, you will be prompted to repair your commit graph if you abandon any commits.

Installation

See https://github.com/arxanas/git-branchless/wiki/Installation.

Short version: run cargo install --locked git-branchless, then run git branchless init in your repository.

Status

git-branchless is currently in alpha. Be prepared for breaking changes, as some of the workflows and architecture may change in the future. It's believed that there are no major bugs, but it has not yet been comprehensively battle-tested. You can see the known issues in the issue tracker.

git-branchless follows semantic versioning. New 0.x.y versions, and new major versions after reaching 1.0.0, may change the on-disk format in a backward-incompatible way.

To be notified about new versions, select Watch » Custom » Releases in Github's notifications menu at the top of the page. Or use GitPunch to deliver notifications by email.

Related tools

There's a lot of promising tooling developing in this space. See Related tools for more information.

Contributing

Thanks for your interest in contributing! If you'd like, I'm happy to set up a call to help you onboard.

For code contributions, check out the Runbook to understand how to set up a development workflow, and the Coding guidelines. You may also want to read the Architecture documentation.

For contributing documentation, see the Wiki style guide.

Contributors should abide by the Code of Conduct.

Download details:
Author: arxanas
Source code: https://github.com/arxanas/git-branchless
License: GPL-2.0 license

#rust #rustlang #git

10 Good Reasons to Do PGDM Degree

What is GEEK

Buddha Community

Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.

Table of Contents

What are Python Arrays?

What's the Difference between Python Lists and Python Arrays?

When to Use Python Arrays

How to Use Arrays in Python

How to Define Arrays in Python

How to Find the Length of an Array in Python

Array Indexing and How to Access Individual Items in an Array in Python

How to Search Through an Array in Python

How to Loop through an Array in Python

How to Slice an Array in Python

Methods For Performing Operations on Arrays in Python

How to Change the Value of an Item in an Array

How to Add a New Value to an Array

How to Remove a Value from an Array

Conclusion

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

Table of Contents

What are Python Arrays?

What's the Difference between Python Lists and Python Arrays?

When to Use Python Arrays

How to Use Arrays in Python

How to Define Arrays in Python

How to Find the Length of an Array in Python

Array Indexing and How to Access Individual Items in an Array in Python

How to Search Through an Array in Python

How to Loop through an Array in Python

How to Slice an Array in Python

Methods For Performing Operations on Arrays in Python

How to Change the Value of an Item in an Array

How to Add a New Value to an Array

How to Remove a Value from an Array

Conclusion

Pythonで実用的な例を使用したLambda関数

序章

構文

1.スカラー値

2.リスト

3.シリーズオブジェクト

4.データフレームオブジェクトのラムダ

推奨されないユースケース

結論

Lambda Functions with Practical Examples in Python

Introduction

Syntax

1. Scalar values

2. Lists

3. Series object

4. Lambda on Dataframe object

Discouraged use cases

Conclusion

Git Branchless: Branchless Workflow for Git Built on Rust

Branchless workflow for Git

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Demos

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Installation

Status

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