1597791600

# Sum of previous numbers that are greater than current number for given array

Given an array A[], for each element in the array, the task is to find the sum of all the previous elements which are strictly greater than the current element.

Examples:

_Input: _A[] = {2, 6, 4, 1, 7}

_Output: _0 0 6 12 0

Explanation:

For 2 and 6 there is no element greater to it on the left.

For 4 there is 6.

For 1 the sum would be 12.

For 7 there is again no element greater to it.

_Input: _A[] = {7, 3, 6, 2, 1}

Output:_ 0 7 7 16 18_

Explanation:

_For 7 there is no element greater to it on the left. _

For 3 there is 7.

For 6 the sum would be 7.

For 2 it has to be 7 + 3 + 6 = 16.

For 1 the sum would be 7 + 3 + 6 + 2 = 18

Naive Approach: For each element, the idea is to find the elements which are strictly greater than the current element on the left side of it and then find the sum of all those elements.

Below is the implementation of the above approach:

• C++

`// C++ program for the above approach`

`#include <bits/stdc++.h>`

`**using**` `**namespace**` `std;`

`// Max Element of the Array`

`**const**` `**int**` `maxn = 1000000;`

`// Function to find the sum of previous`

`// numbers that are greater than the`

`// current number for the given array`

`**void**` `sumGreater(``**int**` `ar[],` `**int**` `N)`

`{`

`// Loop to iterate over all`

`// the elements of the array`

`**for**` `(``**int**` `i = 0; i < N; i++) {`

`// Store the answer for`

`// the current element`

`**int**` `cur_sum = 0;`

`// Iterate from (current index - 1)`

`// to 0 and check if ar[j] is greater`

`// than the current element and add`

`// it to the cur_sum if so`

`**for**` `(``**int**` `j = i - 1; j >= 0; j--) {`

`**if**` `(ar[j] > ar[i])`

`cur_sum += ar[j];`

`}`

`// Print the answer for`

`// current element`

`cout << cur_sum <<` `" "``;`

`}`

`}`

`// Driver Code`

`**int**` `main()`

`{`

`// Given array arr[]`

`**int**` `ar[] = { 7, 3, 6, 2, 1 };`

`// Size of the array`

`**int**` `N =` `**sizeof**` `ar /` `**sizeof**` `ar[0];`

`// Function call`

`sumGreater(ar, N);`

`**return**` `0;`

`}`

Output:

``````0 7 7 16 18
``````

_Time Complexity: _O(N2)

_Auxiliary Space: _O(1)

Efficient Approach: To optimize the above approach the idea is to use Fenwick Tree. Below are the steps:

1. Traverse the given array and find the sum(say total_sum) of all the elements stored in the Fenwick Tree.
2. Now Consider each element(say arr[i]) as the index of the Fenwick Tree.
3. Now find the sum of all the elements(say curr_sum) which is smaller than the current element using values stored in Tree.
4. The value of total_sum – curr_sum will give the sum of all elements which are strictly greater than the elements on the left side of the current element.
5. Update the current element in the Fenwick Tree.
6. Repeat the above steps for all the elements in the array.

#arrays #competitive programming #tree #binary indexed tree #bit #segment-tree

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
- 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.

1. Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
2. 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
3. Array methods for performing operations
1. Change an existing value
3. Remove a value
4. 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:

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.

Thanks for reading and happy coding!

#python #programming

1670560264

## 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.

1. Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
2. 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
3. Array methods for performing operations
1. Change an existing value
3. Remove a value
4. 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`:

1. 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()
``````
1. 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()
``````
1. 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:

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

1597791600

## Sum of previous numbers that are greater than current number for given array

Given an array A[], for each element in the array, the task is to find the sum of all the previous elements which are strictly greater than the current element.

Examples:

_Input: _A[] = {2, 6, 4, 1, 7}

_Output: _0 0 6 12 0

Explanation:

For 2 and 6 there is no element greater to it on the left.

For 4 there is 6.

For 1 the sum would be 12.

For 7 there is again no element greater to it.

_Input: _A[] = {7, 3, 6, 2, 1}

Output:_ 0 7 7 16 18_

Explanation:

_For 7 there is no element greater to it on the left. _

For 3 there is 7.

For 6 the sum would be 7.

For 2 it has to be 7 + 3 + 6 = 16.

For 1 the sum would be 7 + 3 + 6 + 2 = 18

Naive Approach: For each element, the idea is to find the elements which are strictly greater than the current element on the left side of it and then find the sum of all those elements.

Below is the implementation of the above approach:

• C++

`// C++ program for the above approach`

`#include <bits/stdc++.h>`

`**using**` `**namespace**` `std;`

`// Max Element of the Array`

`**const**` `**int**` `maxn = 1000000;`

`// Function to find the sum of previous`

`// numbers that are greater than the`

`// current number for the given array`

`**void**` `sumGreater(``**int**` `ar[],` `**int**` `N)`

`{`

`// Loop to iterate over all`

`// the elements of the array`

`**for**` `(``**int**` `i = 0; i < N; i++) {`

`// Store the answer for`

`// the current element`

`**int**` `cur_sum = 0;`

`// Iterate from (current index - 1)`

`// to 0 and check if ar[j] is greater`

`// than the current element and add`

`// it to the cur_sum if so`

`**for**` `(``**int**` `j = i - 1; j >= 0; j--) {`

`**if**` `(ar[j] > ar[i])`

`cur_sum += ar[j];`

`}`

`// Print the answer for`

`// current element`

`cout << cur_sum <<` `" "``;`

`}`

`}`

`// Driver Code`

`**int**` `main()`

`{`

`// Given array arr[]`

`**int**` `ar[] = { 7, 3, 6, 2, 1 };`

`// Size of the array`

`**int**` `N =` `**sizeof**` `ar /` `**sizeof**` `ar[0];`

`// Function call`

`sumGreater(ar, N);`

`**return**` `0;`

`}`

Output:

``````0 7 7 16 18
``````

_Time Complexity: _O(N2)

_Auxiliary Space: _O(1)

Efficient Approach: To optimize the above approach the idea is to use Fenwick Tree. Below are the steps:

1. Traverse the given array and find the sum(say total_sum) of all the elements stored in the Fenwick Tree.
2. Now Consider each element(say arr[i]) as the index of the Fenwick Tree.
3. Now find the sum of all the elements(say curr_sum) which is smaller than the current element using values stored in Tree.
4. The value of total_sum – curr_sum will give the sum of all elements which are strictly greater than the elements on the left side of the current element.
5. Update the current element in the Fenwick Tree.
6. Repeat the above steps for all the elements in the array.

#arrays #competitive programming #tree #binary indexed tree #bit #segment-tree

1596631020

## Sum of prime numbers in range [L, R] from given Array for Q queries

Given an array arr[] of the size of N followed by an array of Q queries, of the following two types:

• Query Type 1: Given two integers L and R, find the sum of prime elements from index L to R where 0 <= L <= R <= N-1.
• Query Type 2: Given two integers i and X, change arr[i] = X where 0 <= i <= n-1.

Note:_ Every first index of the subquery determines the type of query to be answered._

**Example: **

_Input: _arr[] = {1, 3, 5, 7, 9, 11}, Q = { { 1, 1, 3}, {2, 1, 10}, {1, 1, 3 } }

_Output: _

15

12

_Explanation: _

First query is of type 1, so answer is (3 + 5 + 7), = 15

Second query is of type 2, so arr[1] = 10

Third query is of type 1, where arr[1] = 10, which is not prime hence answer is (5 + 7) = 12

Input:_ arr[] = {1, 2, 35, 7, 14, 11}, Q = { {2, 4, 3}, {1, 4, 5 } }_

Output:_ 14_

Explanation:

First query is of type 2, So update arr[4] = 3

Second query is of type 1, since arr[4] = 3, which is prime. So answer is (3 + 11) = 14

**Naive Approach: **The idea is to iterate for each query between L to R and perform the required operation on the given array.

_Time Complexity: _O(Q * N * (O(sqrt(max(arr[i]))

**Approach: ** To optimize the problem use Segment tree and Sieve Of Eratosthenes.

• First, create a boolean array that will mark the prime numbers.
• Now while making the segment tree only add those array elements as leaf nodes which are prime.
• C++
• Python3

`// C++ program for the above approach`

`#include <bits/stdc++.h>`

`**using**` `**namespace**` `std;`

`**int**` `**const**` `MAX = 1000001;`

`**bool**` `prime[MAX];`

`// Function to find the prime numbers`

`**void**` `SieveOfEratosthenes()`

`{`

`// Create a boolean array prime[]`

`// and initialize all entries it as true`

`// A value in prime[i] will`

`// finally be false if i is Not a prime`

`**memset**``(prime,` `**true**``,` `**sizeof**``(prime));`

`**for**` `(``**int**` `p = 2; p * p <= MAX; p++) {`

`// Check if prime[p] is not`

`// changed, then it is a prime`

`**if**` `(prime[p] ==` `**true**``) {`

`// Update all multiples of p`

`// greater than or equal to`

`// the square of it numbers`

`// which are multiple of p`

`// and are less than p^2 are`

`// already been marked`

`**for**` `(``**int**` `i = p * p; i <= MAX; i += p)`

`prime[i] =` `**false**``;`

`}`

`}`

`}`

`// Function to get the middle`

`// index from corner indexes`

`**int**` `getMid(``**int**` `s,` `**int**` `e)`

`{`

`**return**` `s + (e - s) / 2;`

`}`

`// Function to get the sum of`

`// values in the given range`

`// of the array`

`**int**` `getSumUtil(``**int**``* st,` `**int**` `ss,`

`**int**` `se,` `**int**` `qs,`

`**int**` `qe,` `**int**` `si)`

`{`

`// If segment of this node is a`

`// part of given range, then`

`// return the sum of the segment`

`**if**` `(qs <= ss && qe >= se)`

`**return**` `st[si];`

`// If segment of this node is`

`// outside the given range`

`**if**` `(se < qs || ss > qe)`

`**return**` `0;`

`// If a part of this segment`

`// overlaps with the given range`

`**int**` `mid = getMid(ss, se);`

`**return**` `getSumUtil(st, ss, mid,`

`qs, qe,`

`2 * si + 1)`

`+ getSumUtil(st, mid + 1,`

`se, qs, qe,`

`2 * si + 2);`

`}`

`// Function to update the nodes which`

`// have the given index in their range`

`**void**` `updateValueUtil(``**int**``* st,` `**int**` `ss,`

`**int**` `se,` `**int**` `i,`

`**int**` `diff,` `**int**` `si)`

`{`

`// If the input index lies`

`// outside the range of`

`// this segment`

`**if**` `(i < ss || i > se)`

`**return**``;`

`// If the input index is in`

`// range of this node, then update`

`// the value of the node and its children`

`st[si] = st[si] + diff;`

`**if**` `(se != ss) {`

`**int**` `mid = getMid(ss, se);`

`updateValueUtil(st, ss, mid, i,`

`diff, 2 * si + 1);`

`updateValueUtil(st, mid + 1,`

`se, i, diff,`

`2 * si + 2);`

`}`

`}`

`// Function to update a value in`

`// input array and segment tree`

`**void**` `updateValue(``**int**` `arr[],` `**int**``* st,`

`**int**` `n,` `**int**` `i,`

`**int**` `new_val)`

`{`

`// Check for erroneous input index`

`**if**` `(i < 0 || i > n - 1) {`

`cout <<` `"-1"``;`

`**return**``;`

`}`

`// Get the difference between`

`// new value and old value`

`**int**` `diff = new_val - arr[i];`

`**int**` `prev_val = arr[i];`

`// Update the value in array`

`arr[i] = new_val;`

`// Update the values of`

`// nodes in segment tree`

`// only if either previous`

`// value or new value`

`// or both are prime`

`**if**` `(prime[new_val]`

`|| prime[prev_val]) {`

`// If only new value is prime`

`**if**` `(!prime[prev_val])`

`updateValueUtil(st, 0, n - 1,`

`i, new_val, 0);`

`// If only new value is prime`

`**else**` `**if**` `(!prime[new_val])`

`updateValueUtil(st, 0, n - 1,`

`i, -prev_val, 0);`

`// If both are prime`

`**else**`

`updateValueUtil(st, 0, n - 1,`

`i, diff, 0);`

`}`

`}`

`// Return sum of elements in range`

`// from index qs (quey start) to qe`

`// (query end). It mainly uses getSumUtil()`

`**int**` `getSum(``**int**``* st,` `**int**` `n,` `**int**` `qs,` `**int**` `qe)`

`{`

`// Check for erroneous input values`

`**if**` `(qs < 0 || qe > n - 1 || qs > qe) {`

`cout <<` `"-1"``;`

`**return**` `-1;`

`}`

`**return**` `getSumUtil(st, 0, n - 1,`

`qs, qe, 0);`

`}`

`// Function that constructs Segment Tree`

`**int**` `constructSTUtil(``**int**` `arr[],` `**int**` `ss,`

`**int**` `se,` `**int**``* st,`

`**int**` `si)`

`{`

`// If there is one element in`

`// array, store it in current node of`

`// segment tree and return`

`**if**` `(ss == se) {`

`// Only add those elements in segment`

`// tree which are prime`

`**if**` `(prime[arr[ss]])`

`st[si] = arr[ss];`

`**else**`

`st[si] = 0;`

`**return**` `st[si];`

`}`

`// If there are more than one`

`// elements, then recur for left and`

`// right subtrees and store the`

`// sum of values in this node`

`**int**` `mid = getMid(ss, se);`

`st[si]`

`= constructSTUtil(arr, ss, mid,`

`st, si * 2 + 1)`

`+ constructSTUtil(arr, mid + 1,`

`se, st,`

`si * 2 + 2);`

`**return**` `st[si];`

`}`

`// Function to construct segment`

`// tree from given array`

`**int**``* constructST(``**int**` `arr[],` `**int**` `n)`

`{`

`// Allocate memory for the segment tree`

`// Height of segment tree`

`**int**` `x = (``**int**``)(``**ceil**``(log2(n)));`

`// Maximum size of segment tree`

`**int**` `max_size = 2 * (``**int**``)``**pow**``(2, x) - 1;`

`// Allocate memory`

`**int**``* st =` `**new**` `**int**``[max_size];`

`// Fill the allocated memory st`

`constructSTUtil(arr, 0, n - 1, st, 0);`

`// Return the constructed segment tree`

`**return**` `st;`

`}`

`// Driver code`

`**int**` `main()`

`{`

`**int**` `arr[] = { 1, 3, 5, 7, 9, 11 };`

`**int**` `n =` `**sizeof**``(arr) /` `**sizeof**``(arr[0]);`

`**int**` `Q[3][3]`

`= { { 1, 1, 3 },`

`{ 2, 1, 10 },`

`{ 1, 1, 3 } };`

`// Function call`

`SieveOfEratosthenes();`

`// Build segment tree from given array`

`**int**``* st = constructST(arr, n);`

`// Print sum of values in`

`// array from index 1 to 3`

`cout << getSum(st, n, 1, 3) << endl;`

`// Update: set arr[1] = 10`

`// and update corresponding`

`// segment tree nodes`

`updateValue(arr, st, n, 1, 10);`

`// Find sum after the value is updated`

`cout << getSum(st, n, 1, 3) << endl;`

`**return**` `0;`

`}`

Output:

``````15
12
``````

Time Complexity:_ O(Q * log N) _

Auxiliary Space:_ O(N)_

Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.

#advanced data structure #arrays #dynamic programming #hash #mathematical #tree #array-range-queries #prime number #segment-tree #sieve

1676389586

## PyFCM

Python client for FCM - Firebase Cloud Messaging (Android, iOS and Web)

Firebase Cloud Messaging (FCM) is the new version of GCM. It inherits the reliable and scalable GCM infrastructure, plus new features. GCM users are strongly recommended to upgrade to FCM.

Using FCM, you can notify a client app that new email or other data is available to sync. You can send notifications to drive user reengagement and retention. For use cases such as instant messaging, a message can transfer a payload of up to 4KB to a client app.

## Quickstart

Install using pip:

``````pip install pyfcm

OR

pip install git+https://github.com/olucurious/PyFCM.git
``````

PyFCM supports Android, iOS and Web.

### Features

• All FCM functionality covered

### Examples

Send notifications using the `FCMNotification` class:

``````# Send to single device.

# OR initialize with proxies

proxy_dict = {
"http"  : "http://127.0.0.1",
"https" : "http://127.0.0.1",
}

registration_id = "<device registration_id>"
message_title = "Uber update"
result = push_service.notify_single_device(registration_id=registration_id, message_title=message_title, message_body=message_body)

# Send to multiple devices by passing a list of ids.
registration_ids = ["<device registration_id 1>", "<device registration_id 2>", ...]
message_title = "Uber update"
message_body = "Hope you're having fun this weekend, don't forget to check today's news"
result = push_service.notify_multiple_devices(registration_ids=registration_ids, message_title=message_title, message_body=message_body)

print result``````

Send a data message.

``````# With FCM, you can send two types of messages to clients:
# 1. Notification messages, sometimes thought of as "display messages."
# 2. Data messages, which are handled by the client app.

# Client app is responsible for processing data messages. Data messages have only custom key-value pairs. (Python dict)
# Data messages let developers send up to 4KB of custom key-value pairs.

data_message = {
"Nick" : "Mario",
"body" : "great match!",
"Room" : "PortugalVSDenmark"
}
# To multiple devices
result = push_service.notify_multiple_devices(registration_ids=registration_ids, message_body=message_body, data_message=data_message)
# To a single device
result = push_service.notify_single_device(registration_id=registration_id, message_body=message_body, data_message=data_message)

# Sending a data message only payload, do NOT include message_body also do NOT include notification body
# To multiple devices
result = push_service.multiple_devices_data_message(registration_ids=registration_ids, data_message=data_message)
# To a single device
result = push_service.single_device_data_message(registration_id=registration_id, data_message=data_message)

# To send extra kwargs (notification keyword arguments not provided in any of the methods),
# pass it as a key value in a dictionary to the method being used
'android_channel_id': 2
}

# To process background notifications in iOS 10, set content_available
result = push_service.notify_single_device(registration_id=registration_id, data_message=data_message, content_available=True)

# To support rich notifications on iOS 10, set
extra_kwargs = {
'mutable_content': True
}

# Use notification messages when you want FCM to handle displaying a notification on your app's behalf.
# Use data messages when you just want to process the messages only in your app.
# PyFCM can send a message including both notification and data payloads.
# In such cases, FCM handles displaying the notification payload, and the client app handles the data payload.``````

Send a low priority message.

``````# The default is low_priority == False
result = push_service.notify_multiple_devices(registration_ids=registration_ids, message_body=message, low_priority=True)``````

Get valid registration ids (useful for cleaning up invalid registration ids in your database)

``````registration_ids = ['reg id 1', 'reg id 2', 'reg id 3', 'reg id 4', ...]
valid_registration_ids = push_service.clean_registration_ids(registration_ids)
# Shoutout to @baali for this``````

Appengine users should define their environment

``````push_service = FCMNotification(api_key="<api-key>", proxy_dict=proxy_dict, env='app_engine')
result = push_service.notify_multiple_devices(registration_ids=registration_ids, message_body=message, low_priority=True)``````

Manage subscriptions to a topic

``````push_service = FCMNotification(SERVER_KEY)
tokens = [
<registration_id_1>,
<registration_id_2>,
]

subscribed = push_service.subscribe_registration_ids_to_topic(tokens, 'test')
# returns True if successful, raises error if unsuccessful

unsubscribed = push_service.unsubscribe_registration_ids_from_topic(tokens, 'test')
# returns True if successful, raises error if unsuccessful``````

Sending a message to a topic.

``````# Send a message to devices subscribed to a topic.
result = push_service.notify_topic_subscribers(topic_name="news", message_body=message)

# Conditional topic messaging
topic_condition = "'TopicA' in topics && ('TopicB' in topics || 'TopicC' in topics)"
result = push_service.notify_topic_subscribers(message_body=message, condition=topic_condition)
# FCM first evaluates any conditions in parentheses, and then evaluates the expression from left to right.
# In the above expression, a user subscribed to any single topic does not receive the message. Likewise,
# a user who does not subscribe to TopicA does not receive the message. These combinations do receive it:
# TopicA and TopicB
# TopicA and TopicC
# Conditions for topics support two operators per expression, and parentheses are supported.

Other argument options

``````collapse_key (str, optional): Identifier for a group of messages
that can be collapsed so that only the last message gets sent
when delivery can be resumed. Defaults to `None`.
delay_while_idle (bool, optional): If `True` indicates that the
message should not be sent until the device becomes active.
time_to_live (int, optional): How long (in seconds) the message
should be kept in FCM storage if the device is offline. The
maximum time to live supported is 4 weeks. Defaults to ``None``
which uses the FCM default of 4 weeks.
low_priority (boolean, optional): Whether to send notification with
the low priority flag. Defaults to `False`.
restricted_package_name (str, optional): Package name of the
application where the registration IDs must match in order to
receive the message. Defaults to `None`.
dry_run (bool, optional): If `True` no message will be sent but
request will be tested.
``````

Get response data.

``````# Response from PyFCM.
response_dict = {
'multicast_ids': list(), # List of Unique ID (number) identifying the multicast message.
'success': 0, #Number of messages that were processed without an error.
'failure': 0, #Number of messages that could not be processed.
'canonical_ids': 0, #Number of results that contain a canonical registration token.
'results': list(), #Array of dict objects representing the status of the messages processed.
'topic_message_id': None or str
}

# registration_id: Optional string specifying the canonical registration token for the client app that the message
# was processed and sent to. Sender should use this value as the registration token for future requests. Otherwise,
# the messages might be rejected.
# error: String specifying the error that occurred when processing the message for the recipient``````