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Introduction to PySpark CountPySpark Count is a PySpark function that is used to Count the number of elements present in the PySpark data model. This count function is used to return the number of elements in the data. It is an action operation in PySpark that counts the number of Rows in the PySpark data model. It is an important operational data model that is used for further data analysis, counting the number of elements to be used. The count function counts the data and returns the data to the driver in PySpark, making the type action in PySpark. This count function in PySpark is used to count the number of rows that are present in the data frame post/pre-data analysis.
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Syntax:
b.count()
b: The data frame created.
count(): The count operation that counts the data elements present in the data frame model.
Output:
Working of Count in PySpark
The count is an action operation in PySpark that is used to count the number of elements present in the PySpark data model. It is a distributed model in PySpark where actions are distributed, and all the data are brought back to the driver node. The data shuffling operation sometimes makes the count operation costlier for the data model.
When applied to the dataset, the count operation aggregates the data by one of the executors, while the count operation over RDD aggregates the data final result in the driver. This makes up 2 stages in the Data set and a single stage with the RDD. The data will be available by explicitly caching the data, and the data will not be in memory.
Examples of PySpark CountDifferent examples are mentioned below:
But, first, let’s start by creating a sample data frame in PySpark.
Code:
data1 = [{'Name':'Jhon','Sal':25000,'Add':'USA'},{'Name':'Joe','Sal':30000,'Add':'USA'},{'Name':'Tina','Sal':22000,'Add':'IND'},{'Name':'Jhon','Sal':15000,'Add':'USA'}]The data contains the Name, Salary, and Address that will be used as sample data for Data frame creation.
a = sc.parallelize(data1)The sc.parallelize will be used for the creation of RDD with the given Data.
b = spark.createDataFrame(a)Post creation, we will use the createDataFrame method for the creation of Data Frame.
b.show()Output:
Now let us try to count of a number of elements in the data frame by using the Dataframe.count () function. The counts create a DAG and bring the data back to the driver node for functioning.
b.count()This counts up the data elements present in the Data frame and returns the result back to the driver as a result.
Output:
Now let’s try to count the elements by creating a Spark RDD with elements in it. This will make an RDD and count the data elements present in that particular RDD data model.
The RDD we are taking can be of any existing data type, and the count function can work over it.
a = sc.parallelize(["Ar","Br","Cr","Dr"]) a.count()Now let’s try to do this by taking the data type as Integer. This again will make an RDD and count the elements present in that. Note that all the elements are counted using the count function, not only the distinct elements but even if there are duplicate values, those elements will be counted as part of the Count function in the PySpark Data model.
a = sc.parallelize([2,3,4,56,3,2,4,5,3,4,56,4,2]) a.count()Output:
Note: It is an action operation in PySpark. It returns the count of elements present in the PySpark data model. Second, an action operation brings back the data to the driver node, so shuffling of data happens. Finally, it initiates DAG execution in PySpark Data Frame.
Conclusion Recommended ArticlesThis is a guide to PySpark Count. Here we discuss the introduction, working of count in PySpark, and examples for better understanding. You may also have a look at the following articles to learn more –
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Different Function Of Linspace In Matlab With Examples
Introduction to Linspace MATLAB
MATLAB is a technical computing language. MATLAB gets its popularity from providing an easy environment for performing and integrating computing tasks, visualizing & programming.
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Hadoop, Data Science, Statistics & others
Uses of MATLAB include (but not limited to)
Computation
Simulation
Modeling
Data analytics (Analysing and Visualizing data)
Prototyping
Application development
Engineering & Scientific graphics
Linspace Function in MATLABIn this article, we will understand a very useful function of MATLAB called ‘linspace’. This function will generate a vector of values linearly spaced between two endpoints. It will need two inputs for the endpoints and an optional input to specify the number of points to include in the two endpoints.
X = linspace(a1, a2)
Now let us understand this one by one
1. X=linspace(a1,a2)This function will return a row of a vector of 100(default) linearly spaced points between a1 and a2
a1 and a2 can be real or complex
a2 can be either larger or smaller than a1
If a2 is smaller than a1 then the vector contains descending values
Here is an example to understand this:
Example #1X = linspace(-1, 1)
It will generate a vector of 100 evenly spaced vectors for the interval [-1, 1]
Output:
Example #2X = linspace(2, 3)
It will generate a vector of 100 evenly spaced vectors for the interval [2,3]
Output:
Example #3X = linspace(2, 1)
Here a2 is smaller than a1, it will generate a vector of 100 evenly spaced vectors for the interval [2,1] in descending order
Output:
2. X=linspace(a1,a2,n)This function will return a row of a vector of “n” points as specified in input for linearly spaced points between a1 and a2. This function gives control of the number of points and will always include the endpoints specified in the input as well.
If n is 1, the function will return a2 as output
If n is zero or negative, the function will return 1by0 empty matrix
Here is an example to understand this:
Example #1X = linspace(-1, 1, 7 )
It will generate a vector of 7 evenly spaced vectors for the interval [-1, 1]
Output:
Example #2X = linspace(2,3,5)
It will generate a vector of 5 evenly spaced vectors for the interval [2,3]
Output:
Example #3X = linspace(2, 3, 1)
Here n = 1, so the function will return a2 input parameter
Output:
Example #4Here n = 0, so function will return 1X0 empty double row vector
Output:
Vector of evenly spaced Complex numbersX = linspace(2+2i, 3+3i)
Here a1 and a2 are complex numbers, it will generate a vector of complex numbers for 100 evenly spaced points for the interval [2+21, 3+3i]
Output:
X= linspace(1+1i, 5+5i, 4)
It will generate a vector of complex numbers with 4 evenly spaced point for the interval [1+1i, 5+5i]
Output:
The linspace function in MATLAB provides us with an array/matrix comprising the desired number of values starting from and ending at a declared value. The produced array will have exactly the desired number of terms which will be evenly spaced. The values will be in the range of start and end values passed. So, the linspace function will help us in creating an instantiated matrix or array.
Recommended ArticlesThis is a guide to Linspace MATLAB. Here we discuss the introduction, Linspace Function in MATLAB and Vector of evenly spaced Complex numbers with examples and outputs. You can also go through our other suggested articles to learn more–
Learn The Latest Versions Of Pyspark
Introduction to PySpark version
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Versions of PySparkMany versions of PySpark have been released and are available to use for the general public. Some of the latest Spark versions supporting the Python language and having the major changes are given below :
1. Spark Release 2.3.0This is the fourth major release of the 2.x version of Apache Spark. This release includes a number of PySpark performance enhancements including the updates in DataSource and Data Streaming APIs.
Improvements were made regarding the performance and interoperability of python by vectorized execution and fast data serialization.
A new Spark History Server was added in order to provide better scalability for the large applications.
register* for UDFs in SQLContext and Catalog was deprecated in PySpark.
Python na.fill() function now also accepts boolean values and replaces the null values with booleans (in previous versions PySpark ignores it and returns the original DataFrame).
In order to respect session timezone, timestamp behavior was changed for the Panda related functionalities.
From this release, Pandas 0.19.2 or upper version is required for the user to use Panda related functionalities.
Many documentation changes and the test scripts were revised in this release for the Python language.
2. Spark Release 2.4.7This was basically the maintenance release including the bug fixes while maintaining the stability and security of the ongoing software system. Not any specific and major feature was introduced related to the Python API of PySpark in this release. Some of the notable changes that were made in this release are given below:
Now loading of the job UI page takes only 40 sec.
Python Scripts were changes that were failing in certain environments in previous releases.
Now users can compare two dataframes with the same schema (Except for the nullable property).
In the release DockerFile, R language version is upgraded to 4.0.2
Support for the R less than 3.5 version is dropped.
Exception messages at various places were improved.
Error messages were locked when failing in interpreter mode.
Many changes were made in the documentation for the inconsistent AWS variables.
3. Spark Release 3.0.0This is the first release of 3.x version. It brings many new ideas from the 2.x release and continues the same ongoing project in development. It was officially released in June 2023. The top component in this release is SparkSQL as more than 45% of the tickets were resolved on SparkSQL. It benefits all the high level APIs and high level libraries including the DataFrames and SQL. At this stage, Python is the most widely used language on Apache Spark. Millions of users downloaded Apache Spark with the Python language only. Major changes and the features that were introduced in this release are given below:
In this release functionality and usability is improved including the redesign of Pandas UDF APIs.
Various Pythonic error handling were done.
Python 2 support was deprecated in this release.
PySpark SQL exceptions were made more pythonic in this release.
Various changes in the test coverage and documentation of Python UDFs were made.
For K85 Python Bindings, Python 3 was made as the default language.
Validation sets were added to fit with Gradient Boosted trees in Python.
Parity was maintained in the ML function between Python and Scala programming language.
Various exceptions in the Python UDFs were improved as complaints by the Python users.
Now a multiclass logistic regression in PySpark correctly returns a LogisticRegressionSummary from this release.
4. Spark Release 3.0.1
Double catching was fixed in KMeans and BiKMeans.
Apache Arrow 1.0.0 was supported in SparkR.
For the overflow conditions, silent changes were made for timestamp parsing.
Revisiting keywords based on ANSI SQL standard was done.
Regression was done in handling the NaN values in Sql COUNT.
Changes were made for the Spark producing incorrect results in group by clause.
Grouping problems were resolved as per the case sensitivity in panda UDFs.
MLlibs acceleration docs were improved in this release.
Issues related to the LEFT JOIN found in the regression of 3.0.0 producing unexpected results were resolved.
5. Spark Release 3.1.1
Spark Release 3.1.1 would now be considered as the new official release of Apache Spark including the bug fixes and new features introduced in it. Though it was planned to be released in early January 2023, there is no official documentation of it available on its official site as of now.
ConclusionAbove description clearly explains the various versions of PySpark. Apache Spark is used widely in the IT industry. Python is a high level, general purpose and one of the most widely used languages. In order to implement the key features of Python in Spark framework and to use the building blocks of Spark with Python language, Python Spark (PySpark) is a precious gift of Apache Spark for the IT industry.
Recommended ArticlesThis is a guide to PySpark version. Here we discuss Some of the latest Spark versions supporting the Python language and having the major changes. You may also have a look at the following articles to learn more –
Lists Of Options In Tkinter Grid With Various Examples
Introduction to Tkinter Grid
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Syntax:
widget.grid(options_of_grid) Lists of Options in Tkinter GridBelow mentioned are the different options provided:
column: This option is used to put the widget in a column that is leftmost column. The default column is 0.
columnspan: This option keeps track of how many columns it will take to occupy widgets, and by default, this is 1.
ipadx and ipady: These two options are used for how many pixels on the interface to pad the widgets in horizontal and vertical, respectively, but it will be used in padding inside the widgets border.
padx and pady: These two options are similar to the above options, but these are used to pad outside widget borders in horizontal and vertical padding the pixels.
row: This option is when the widget is to put in a row; by default, the first row is empty.
rowspan: This option will be used to tell how many rowwidgets are occupied, and the default value is 1.
sticky: This option is used when a cell cannot fit in the widget, which means when the cell is larger than the widget, then this option is used to know which sides and corners of the widgets the cell can stick to. By default, widgets are always centered in the cell. Sticky uses similar to compass directions to stick the cells to the widgets like North, south, east, west and all the combination of these four.
Examples of Tkinter GridIn this article, we will see the Python grid() method in Tkinter. This method is usually used to design and manage the widgets in the graphical interface. This is a geometry manager to manage the widgets in a table-like structure; usually, a 2-dimensional table is used to manage these widgets in the parent widget, which is in turn split into row and column.
As we saw, that grid is one of the modules or class of geometry manager that is available in Tkinter. There are geometry managers like “pack”, which is also very good and powerful, but it’s very difficult to understand, and another geometry manager is “place”, which gives us complete control of positioning or placing each element. The grid manager is best known for its flexibility, easy to understand, easy to use, and mix features, which makes the grid manager powerful than any other geometry manager.
Now let us see a few examples to understand the grid geometry manager with the following code below:
Example #1 import tkinter as tk courses = ['C','C++','Python','Java','Unix','DevOps'] r = ['course'] for c in courses: tk.Label(text=c, width=15).grid(column=0) tk.Label(text=r, relief=tk.RIDGE, width=15).grid(column=1) tk.mainloop()Output:
Explanation: In the above example, firstly, we need to import Tkinter and then we need to declare the parent cell as “tk”, in which we want the grid layout of the widgets in row and column-wise. So in the above code, we have taken a column as the courses and each row is kept for each course; to do this, we have taken a list of programming languages as a column, and these are labeled as courses for each language row-wise. Hence the grid manages to display it in a two-dimensional table. You can even modify the above code to look it more attractive by using ridges which means it looks like boxes are drawn for every row in relief as you can see in the output, and you can also give some background color by using “big”, and you can have the tabs in the sunken mode for relief.
Example #2 from tkinter import * root = Tk() btn_column = Button(root, text="This is column 2") btn_column.grid(column=2) btn_columnspan = Button(root, text="With columnspan of 4") btn_columnspan.grid(columnspan=4) btn_ipadx = Button(root, text="padding horizontally ipadx of 3") btn_ipadx.grid(ipadx=3) btn_ipady = Button(root, text="padding vertically ipady of 3") btn_ipady.grid(ipady=3) btn_padx = Button(root, text="padx 2") btn_padx.grid(padx=4) btn_pady = Button(root, text="pady of 2") btn_pady.grid(pady=2) btn_row = Button(root, text="This is row 2") btn_row.grid(row=2) btn_rowspan = Button(root, text="With Rowspan of 3") btn_rowspan.grid(rowspan=3) btn_sticky = Button(root, text="Sticking to north-east") btn_sticky.grid(sticky=NE) root.mainloop()Output:
Explanation: In the above program, we have imported Tkinket, and we have imported “*”, which means all the methods or functions in the Tkinter can be imported. Then we are declaring the parent cell as “root” as this is a master widget in which other widgets are placed, and we are calling the “Tk()” method from Tkinter, which is used for creating the main window. At the end of the program, we write “root. mainloop”, where mainloop() is a method from Tkinter again which is used to run your GUI application; when it is ready, it waits for the event to occur and then processes this event until the window is closed. In the above code, we have used a button widget to demonstrate all the grid() class options. So each option is displayed using this widget with their working, or we can say how the options layout of grid works.
ConclusionIn this article, we have seen how to develop the GUI in Python using Tkinter. This Tkinter, in turn, has different geometry managers to make the GUI layout look attractive ad we can use them according to our requirements. The geometry managers are grid() which is very powerful and most widely used, pack() is also used, but as it is a little hard to understand than a grid(), place() manager is used to control the layout. The above article explains about the grid() geometry manager along with the options used in it.
Recommended ArticlesThis is a guide to Tkinter Grid. Here we discuss the Introduction and lists of options in Tkinter Grid along with different examples and code implementation. You may also have a look at the following articles to learn more –
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Complete Guide To How Does C Ftell() Working With Examples
Introduction to C ftell()
The C ftell() function is used to return the current position of the specified file stream. The ftell() function is a built-in function in c. Some times in program while we reading or writing the data from or to the file we need to get the current position of the file to read data from a specific location or to write the data to a specific location, so to get the current location of the file pointer we can use ftell() function and the later to change or move the pointer location we can use the fseek() function(), which is also a built-in function. The ftell() function accepts file pointer which points to the specific file, so this function returns the current position of that specific file and this function also can be used to return the size of the file by moving the pointer to the end of the file with the help of SEEK_END constant value.
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The Syntax of the ftell() function in CFollowing is the syntax to call the ftell() function in c –
long int ftell(FILE *fstream);Parameters –
*fstream – *fstream parameter specifies the FILE type pointer which points to specific FILE object.
Return value –
The return value of the function as is int, it returns the current location of the file pointer pointing, otherwise returns -1L if any error occurs.
Working and Examples of ftell() function in CNext, we write the C code to understand the ftell() function working more clearly with the following example where we use ftell() function to get the current location of the file pointed by the pointer, as below –
Example #1Code:
void main() { char fdata[50]; FILE *fstream = fopen(“data.txt”,”r”); printf(“The current location of th pointer before reading from the file is : %ldn”, ftell(fstream)); fscanf(fstream,”%s”,fdata); printf(“The current data read from the file is : %sn”, fdata); printf(“The current location of th pointer after reading from the file is : %ldn”, ftell(fstream)); }
Output:
As in the above code, the file “data.txt” is opened and the fstream is a FILE type pointer which is pointing to this file, if any operation needs to perform-like read, write, append, etc, we can perform with the help of this FILE pointer(fstream). When the new file is open the file pointer always points to the starting position of the file that is 0 in the file. Farther in the code the ftell() function is used before and after reading some data from the file. So before reading the data the ftell() return the pointer location is 0, after reading data “This” which is of four lengths the ftell() return the pointer location is 4, which are correct.
Next, we write the C code to understand the ftell() function working where we use ftell() function to get the total length of the file by using the file pointer, as below –
Example #2Code:
void main() { char fdata[50]; int length; FILE *fstream = fopen(“data.txt”,”r”); printf(“The current location of th pointer before seek is : %ldn”, ftell(fstream)); fseek(fstream, 0, SEEK_END); length = ftell(fstream); printf(“The total length the file is : %ldn”, length); printf(“The current location of th pointer after seek is : %ldn”, ftell(fstream)); }
Output:
As in the above code, the file “data.txt” is open which stores the data “This is the file data.” of length 22 and the fstream is a FILE type pointer which is pointing to this file. Farther in the code the fseek() function is used to move the pointer to the end of the file with the help of SEEK_END constant value and then after moved with the help of ftell() function return the pointer location which is 22 that is the last location or index pointing by the point and that is what the length of the file.
Example #3Code:
void main() { int i; FILE *fstream = fopen( “data1.txt”,”r” ); i = ftell(fstream); if(i == -1L) { printf( “A file error has occurred!!n” ); } printf( “The current location of the pointer is : %ldn”, ftell(fstream) ); }
Output:
As in the above code, the file “data1.txt” is trying to open but that file does not exist. The fstream FILE type pointer is trying to point to this file as the file does not exist the fopen() function return 0 and so the ftell(fstream) function return -1L, as because the error occurs to open the file.
ConclusionThe ftell() function is a built-in function in C, which is used to return the current position of the file stream. The ftell() function accepts one parameter of File type pointer which points to the file.
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Count Inversions In An Array
The inversions of an array indicate; how many changes are required to convert the array into its sorted form. When an array is already sorted, it needs 0 inversions, and in another case, the number of inversions will be maximum, if the array is reversed.
To solve this problem, we will follow the Merge sort approach to reduce the time complexity, and make it in Divide and Conquer algorithm.
Input and Output Input: A sequence of numbers. (1, 5, 6, 4, 20). Output: The number of inversions required to arrange the numbers into ascending order. Here the number of inversions are 2. First inversion: (1, 5, 4, 6, 20) Second inversion: (1, 4, 5, 6, 20) Algorithmmerge(array, tempArray, left, mid, right)
Input: Two arrays, who have merged, the left, right and the mid indexes.
Output: The merged array in sorted order.
Begin i := left, j := mid, k := right count := 0 while i <= mid -1 and j <= right, do if array[i] <= array[j], then tempArray[k] := array[i] increase i and k by 1 else tempArray[k] := array[j] increase j and k by 1 count := count + (mid - i) done while left part of the array has some extra element, do tempArray[k] := array[i] increase i and k by 1 done while right part of the array has some extra element, do tempArray[k] := array[j] increase j and k by 1 done return count EndmergeSort(array, tempArray, left, right)
Input: Given an array and temporary array, left and right index of the array.
Output − Number of inversions after sorting.
Begin count := 0 mid := (right + left)/2 count := mergeSort(array, tempArray, left, mid) count := count + mergeSort(array, tempArray, mid+1, right) count := count + merge(array, tempArray, left, mid+1, right) return count using namespace std; int merge(intarr[], int temp[], int left, int mid, int right) { int i, j, k; int count = 0; i = left; //i to locate first array location j = mid; k = left; //i to locate merged array location while ((i <= mid - 1) && (j <= right)) { if (arr[i] <= arr[j]) { //when left item is less than right item temp[k++] = arr[i++]; }else{ temp[k++] = arr[j++]; count += (mid - i); //find how many convertion is performed } } while (i <= mid - 1) //if first list has remaining item, add them in the list temp[k++] = arr[i++]; while (j <= right) //if second list has remaining item, add them in the list temp[k++] = arr[j++]; for (i=left; i <= right; i++) arr[i] = temp[i]; //store temp Array to main array return count; } intmergeSort(intarr[], int temp[], int left, int right) { int mid, count = 0; mid = (right + left)/2; //find mid index of the array count = mergeSort(arr, temp, left, mid); //merge sort left sub array count += mergeSort(arr, temp, mid+1, right); //merge sort right sub array count += merge(arr, temp, left, mid+1, right); //merge two sub arrays } return count; } intarrInversion(intarr[], int n) { int temp[n]; return mergeSort(arr, temp, 0, n - 1); } int main() { intarr[] = {1, 5, 6, 4, 20}; int n = 5; cout<< "Number of inversions are "<<arrInversion(arr, n); } Output Number of inversions are 2Update the detailed information about Working Of Count In Pyspark With Examples on the Daihoichemgio.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!