Trending December 2023 # Steps For Creating Sas Ods With Examples And Faq # Suggested January 2024 # Top 18 Popular

You are reading the article Steps For Creating Sas Ods With Examples And Faq updated in December 2023 on the website Daihoichemgio.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Steps For Creating Sas Ods With Examples And Faq

Introduction to SAS ODS

SAS ODS is the output delivery system that helps produce the output format in the SAS data program to understand the ideas and data reports with additional platform support on multiple PROC statements in the file through different parameters called path representing the html output cases in other filename paths data style is an additional parameter for the default set of styles that represented in SAS environment.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Key Takeaways

SAS ODS denoted the SAS OUTPUT DELIVERY SYSTEM.

It is more user-friendly with the html, pdf, and RTF word document formats.

So it will produce friendly data reports with accuracy and customized data view pattern for user understanding.

It also shared the same output format with other Operating System Environments and Software.

Multiple Proc statements help perform as a single file.

What is SAS ODS?

The SAS output delivery system helps to convert the data format like .html, pdf, etc., which helps to restore most of the areas in the SAS platform. It has two sets of parameters like, Path and Style; here, the path represents html set of input codes that get the appropriate output result cases, including the filename already saved with the .html format in the user location. Then the style represents the default set of methods available in the SAS environment on the proc set of SQL statements captured in the input file. The SAS dataset will be created accordingly to the ODS output statements with required output objects viewed by the record, and the same will be printed on the output console.

How to Use SAS ODS?

Using ODS, the html statement will create the file with an extension called .html format on the desired path based on the user inputs datasets output is produced, and the same will be consumed on the printed screen. It supports other extensions like pdf and word(TRF) format in the RTF file path across the data collections from the default procedure on the tools like ODS TRACE, ODS OUTPUT, SAS metadata, etc. These are some essential tools for printing the datas in the SAS environment. The SAS Graphics editor and other reporting procedures are buried with the procedural output on the demonstration that is exactly queried to retrieve the information.

Syntax of SAS ODS

Below code is the basic syntax for the formats:

ods html file=''; path=""; style=""; proc sql; select column name1, column name2,… from table name conditional statements orders; quit; ods html close;

The above codes are performed like Output Delivery System to access and convert the file systems.

Steps for Creating SAS ODS

Given below are the steps mentioned:

1. Navigate to the below URL and log in to the issue.

3. Then paste the below code for generating the ODS in html format.

4. ODS HTML

5. PATH = ‘/home/u61573544/August5’

6. FILE = ‘August5.html’

7. STYLE = EGDefault;

8. proc SQL;

9. select ID, SEGMENT, DENSITY

10. from MAPSSAS.INDIA

11. where ID in (1,6)

12. Order by ID;

13. quit;

14. ODS HTML CLOSE;

15. We get the above results with the columns like GLC: Province Number, Country Segment Number, and Levels for Reducing.

16. The condition is only satisfied with this query.

18. from MAPSSAS.INDIA

19. where ID in (1,6)

20. Order by ID;

21. And we get an output file with html format on the specified location like below.

How to Create HTML and PDF Output?

The Data step and ODS will help create HTML and PDF reports in the default data table template to perform the user operations. Additionally, it will help write the output object to the HTML destination and the PDF format if the user input conversion is pdf.

Example #1

Example for creating HTML Output using SAS ODS.

Code:

ODS HTML PATH = '/home/u61573544/August5' FILE = 'August61.html' STYLE = EGDefault; proc SQL; select AMOUNT, DATE from SASHELP.BUY where AMOUNT in (-1000,-4000) Order by AMOUNT; quit; ODS HTML CLOSE;

Output:

Example #2

Example for creating PDF Output using SAS ODS.

Code:

ODS PDF STYLE = EGDefault; proc SQL; select AMOUNT, DATE from SASHELP.BUY where AMOUNT in (-1000,-4000) Order by AMOUNT; quit;

Output:

The above example which same as the first example with html format output. Here we need to convert the data format by using the PDF view. We can use the same table with data and orders along with the FILE and STYLE paths.

Frequently Asked Questions

Other FAQs are mentioned below:

Q1. What is SAS ODS?

Answer:

It is one type of conversion in SAS and stands for output delivery system. So it delivers using the ODS keyword, which is available in the SAS.

Q2. What are the formats supported in SAS ODS?

Answer:

Currently, it supports html, pdf, and RTF formats.

Q3. How will you convert the SAS ODS to the HTML format?

Answer:

Using ODS HTML

Specified PATH=’’

FILE=’name.html’

STYLE= “”;

Q4. How will we convert the SAS ODS to PDF format?

Answer:

Using ODS PDF

FILE=”PATH”

STYLE=”name”

Q5. How will it support the SAS ODS to RTF format?

Answer:

Using ODS RTF

FILE=’path along with filename.rtf’

STYLE=”name”

Conclusion

The SAS ODS excludes the specific datas that may include source and destination parts. It also sets the default value, excludes ALL with other data lists, and sets it as the default value. It mainly focused on overcoming the data limitation compared to the standard SAS output.

How To Perform Sas Upcase With Function And Character?

Introduction to SAS Upcase

The SAS Upcase is one of the default functions. It performs the copy operation under the character expression, which helps convert the lowercase letters to uppercase letters and return the result value. Also, it validates the string characters, like whether it contains them or not; the dataset helps to differentiate the table rows and columns, which includes the blank space on each set of characters.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Key Takeaways

It converts the lowercase letters into upper case letters.

It can also omit the spaces and delimiter if the SAS inputs change.

The string is the parameter or arguments in the UPCASE(string) method.

If the user inputs Uppercase’s first character, the other characters are lowercase. By using this method, the rest of the characters are changed to uppercase.

So inputs are calculated and distinguished with characters on each string word.

What is SAS Upcase?

SAS Upcase is one of the default functions and performs the copy operations to convert lowercase characters to uppercase. It helps copy the character expression and related areas like variables, operators, and other things like numbers. The Upcase(expression) method is specified with the valid set of expressions concerned with the string character so that it will convert the lowercase letters to uppercase letters with return the altered values. Macro will convert to Uppercase, which is accomplished using the UPCASE() function that takes up the argument as converted uppercase.

SAS Upcase Function

The upcase() is also the macro function that can convert the text to upper-case, which is already in the lowercase of letters. We can use the same process for converting the string variable to the upper case and by using the datastep for data imported successfully. Character functions are most frequently used in the SAS, comparing the string to numeric values. Like that, COMPBL is the function for comparing the multiple blanks into the single blank data containing the single set of records; it occurs in the various spaces from the first and last strings. To make a string variable’s letters in all the uppercase characters by using the STRIP function to remove the leading and trailing spaces for all the areas, like the first and last names of the strings.

SAS Upcase Character

a is the modifier to remove the upper and lower characters from the strings.

kd is the numeric set of values from the modifier.

d is the numerical values removed from the string modifier.

I is used to removing the upper and lower case strings.

k is also specified string characters that keep instead of the existing one.

Similarly, p is the punctuation mark that can be used between the first and last character strings. Omit the space and change the delimiter on the SAS strings. S is a modifier that removes the string spaces with the default one, and U is the string character that drew the uppercase only from the specified characters.

How to Perform SAS Upcase?

Given below shows how to perform SAS Upcase:

1. Navigate to the below URL and log in to the application.

3. And paste the below code for creating the dataset.

4. data work.August15;

5. input string $ 0-9;

6. datalines;

7. Welcome To

8. My Domain January

9. a February March

10. THe April May

11. ;

12. run;

13. We need to perform the Upcase operation in the above dataset. Using the upcase() method, we can pass the parameter arguments like a string in the method below.

SAS Convert to Upcase

The SAS upcase function is mainly used to convert the string character into the uppercase character, passing the string as the function argument. It includes the English alphabet letters, characters, and symbols, which can be called the altered way approach for returning the values. We can remove unwanted spaces, characters, strings, and other variables.

To achieve the upcase function in the SAS processes, we used the input() function to get the user input as the character, so we used the upcase() function to convert the lowercase letters to uppercase. If we used input as the number format, it would omit and reuse the characters also, the decimal format of the input character is calculated additionally, the floating-point of the system is referred to as Width and Decimal, which call it as W.D, can be used in the proper way of the system and right places.

Example of SAS Upcase

Given below is the example mentioned:

Code:

data August15; input vars $0-9; datalines; January February March april May june July august September october November december ; run; proc print data=August15; data Augustne; set August15; vars = UPCASE(vars); run; proc print data=Augustne;

Output:

Explanation:

1. The above example created the dataset and performed the upcase operation in the mentioned SAS user inputs.

3. Initially, we can set the dataset data in the second case.

4. Finally, the upcase function is executed and shows the same results.

FAQ

Given below is the FAQ mentioned:

Q1. What is SAS upcase?

Answer:

It is one of the default SAS methods, and it is used to convert the lower case characters into upper case characters.

Q2. How to use the upcase() in SAS?

Answer:

When we use the upcase() function, which can take only the strings and that will be the excellent case, like the first character of the input is in uppercase and the rest of the characters are in lowercase, etc.

Q3. Will SAS upcase() omit the special characters in the user inputs?

Answer:

Yes SAS upcase() method omitted the special characters like operators, expressions, and other symbols in the inputs.

Conclusion

The SAS upcase() function is the default one, converting the user inputs from lowercase characters into uppercase letters. It includes the alphabet, not symbols, characters, operators, and other symbols. Every first character of the input words is Uppercase, and the rest of the characters are in lowercase is the condition for validating and performing the upcase function in SAS.

Tips And Tricks For Creating Inclusive Content For Youtube

What do you mean by inclusive content on YouTube

Inclusive content on YouTube refers to videos that are welcoming and respectful to all viewers, regardless of their race, gender, sexual orientation, religion, ability, or other characteristics. This can include content that is produced with a diverse cast and crew, as well as content that actively works to challenge stereotypes and promote understanding of marginalized groups. Inclusive content can also include closed captions and audio descriptions for viewers with disabilities. Overall, the aim of inclusive content is to create a positive and respectful community for all viewers.

Benefits of Inclusive Content

Inclusive content can have a number of benefits, including −

Increased representation and visibility of marginalized groups − Inclusive content can help to amplify the voices and perspectives of marginalized communities, which can help to combat stereotypes and bias.

Improved engagement and loyalty − Diversifying the perspectives and experiences represented in your content can help to attract and retain a wider range of readers or viewers.

Greater cultural understanding − Inclusive content can help to promote understanding and empathy between different groups, which can lead to a more tolerant and inclusive society.

Better business performance − Companies that are seen as inclusive and diverse are often perceived more positively by consumers, which can lead to better business performance.

Compliance with legal and ethical standards − Inclusive content can help to ensure that companies follow laws and regulations around discrimination and equal opportunity, as well as ethical standards of inclusivity and diversity.

Tips for Creating Inclusive Content for YouTube

Understanding the importance of words is key. They can motivate, but also have the capacity to differentiate and remove. When it comes to composing all-inclusive material, you must be mindful of the language you use.

Using gender-neutral terminology should be taken for granted. Instead of terms such as “mankind” and “salesman”, use “humankind” and “salesperson”. In addition, you should avoid any gender-based presumptions. Please note that politicians are not always men, and a parent does not necessarily have to be a woman. It is best to use a gender-neutral pronoun such as “they” in these situations.

Use inclusive language and avoid stereotypes or offensive language.

Consider the diversity of your audience and represent a wide range of perspectives. If you are looking to promote an inclusive and diverse environment, stock images featuring white, middle-class Americans or Europeans will not be enough. People prefer to be represented in images, which is why learning how to use diverse stock photos is essential. This will make them feel empowered, included, and increase their connection to your company and product.

Use closed captions or subtitles to make your content accessible to viewers with hearing impairments. Add descriptive text to your videos to make them accessible to viewers with visual impairments.

Be mindful of cultural references and avoid appropriating or misrepresenting cultures.

Consider the background and setting of your videos, and make sure they are not discriminatory or offensive.

Avoid using flashing lights or strobe effects, which can trigger seizures or migraines.

Be mindful of your tone and the words you use in your videos, and consider the impact they may have on different viewers.

Avoid industry jargon, abbreviations, and other specialized terms that the average consumer will not understand.

Wrapping Up

To ensure you have all the tools needed to create inclusive content going forward, make sure to commit the steps in this article to memory and add them to your style guide. Take some time to explore the stock photo libraries I mentioned, and keep learning about using inclusive language.

Creating And Visualizing Reports Automatically

Introduction to Kibana Reporting

You can generate a report proper format including the Kibana dashboard, data visualization, and other tools which make your report more meaningful about data. We can also save our project based on the image and pdf format which depends upon the requirements of yours like either in PNG, PDF. Kibana reporting we can generate not only automatically but also manually. In this blog, we will do both generations manually and automatically. Apart from this, we can also generate a report with the help of a script and Watcher. Kibana reporting makes our analytics more powerful by seeing all data in a single place.

Kibana Reporting Tool

To find where is Kibana reporting tool available, we have to follow the below steps:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

1. Open the Kibana and it will take to your first page of the Kibana.

4. Now, we have to look Kibana reporting option in that as shown below in the screenshot.

Features of Kibana Reporting

Below there is a customize feature display option, which provides all details of different kinds of tools charts, visualization, etc. that we can use in reporting.

1. Reporting Permission

To generate a report in Kibana, you must have a granted privilege to create a report which we called resporting_user, and also you have the privilege to access the index data which you want to create report else data will not display.

2. Creating a Manual Report

We can also create a report manually. To create a report manual we have to follow the following steps:

Index your data and access that index in the Kibana main dashboard.

We can choose the option according to our requirements:

If we want to make a report of visualization and charts, so we can choose either Pdf or image option.

we can also convert the whole canvas into pdf.

We can also opt for the option of CSV.

Now we can save that file anywhere in our local system.

3. Size of the Report

The size of the report in which we generate either pdf or image form that depends upon through which we generating. If we are using canvas, then it has to determine the size before creating the report depends upon the requirements. But, if you are using any other app then might be it depends upon the size of that app like if we want to reduce the report size we can just decrease the size of the browser.

4. Automatic Report Creation

We can also create a report using POST through HTTP (with the help of the script). We can do for both PDF or CSV.

Steps to create POST link for PDF:

Go to the visualization of the Kibana and choose the time filter option. For an absolute time we have to use the time filter option.

Now the Kibana toolbar will show the share option as shown (Fig. no. 5) above. We have to choose a PDF option and create a PDF link.

Now copy the link showing in the below screenshot.

Fig. no. 6: PDF URL link

5. Steps to Create POST Link for CSV

We have to load the data from the search option and for the absolute time, we can use the time filter option. The Discover option is available in the left toolbar at the top.

Now the Kibana toolbar will show the share option as shown (Fig. no. 5) above. We have to choose the CSV option and create a CSV link.

Now copy the link showing in the below screenshot.

6. Using a Script

We can also generate an automatic link for PDF and CSV through the script. This script requests a POST link and will get the result in the form of JSON and also contain a link to download the report. To get the report through a link we have to give GET request in the script itself.

curl

Code:

Here kbn-version is telling what version we are using currently. And -u elastic will check grant permission of the user to create a report. XPOST is basically for the POST.

Final report result:

After we have done all activities of reporting then our report will look like this. I just uploaded the saved projects. You can see a lot of saved projects which option is shown in fig. no. 3 left side.

Conclusion

The Kibana reporting option is very awesome to visualize all things in a single place to decision making. It has a lot of features that you explore while going through the above mention methods. Kibana has also another option to create reports automatically using Watcher. For that, we have to link the Watcher certificate to the Kibana, so that it can access Watcher.

Python Numpy Tutorial For Beginners: Learn With Examples

What is NumPy in Python?

NumPy is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming. It is a very useful library to perform mathematical and statistical operations in Python. It works perfectly for multi-dimensional arrays and matrix multiplication. It is easy to integrate with C/C++ and Fortran.

For any scientific project, NumPy is the tool to know. It has been built to work with the N-dimensional array, linear algebra, random number, Fourier transform, etc.

NumPy is a programming language that deals with multi-dimensional arrays and matrices. On top of the arrays and matrices, NumPy supports a large number of mathematical operations. In this part, we will review the essential functions that you need to know for the tutorial on ‘TensorFlow.’

Why use NumPy?

NumPy is memory efficiency, meaning it can handle the vast amount of data more accessible than any other library. Besides, NumPy is very convenient to work with, especially for matrix multiplication and reshaping. On top of that, NumPy is fast. In fact, TensorFlow and Scikit learn to use NumPy array to compute the matrix multiplication in the back end.

In this Python NumPy Tutorial, we will learn:

How to Install NumPy

To install NumPy library, please refer our tutorial How to install TensorFlow. NumPy is installed by default with Anaconda.

In remote case, NumPy not installed-

You can install NumPy using Anaconda:

conda install -c anaconda numpy

In Jupyter Notebook :

import sys !conda install --yes --prefix {sys.prefix} numpy Import NumPy and Check Version

The command to import numpy is:

import numpy as np

Above code renames the Numpy namespace to np. This permits us to prefix Numpy function, methods, and attributes with ” np ” instead of typing ” numpy.” It is the standard shortcut you will find in the numpy literature

To check your installed version of NumPy, use the below command:

print (np.__version__)

Output:

1.18.0 What is Python NumPy Array?

NumPy arrays are a bit like Python lists, but still very much different at the same time. For those of you who are new to the topic, let’s clarify what it exactly is and what it’s good for.

As the name kind of gives away, a NumPy array is a central data structure of the numpy library. The library’s name is actually short for “Numeric Python” or “Numerical Python”.

Creating a NumPy Array

Simplest way to create an array in Numpy is to use Python List

myPythonList = [1,9,8,3]

To convert python list to a numpy array by using the object np.array.

numpy_array_from_list = np.array(myPythonList)

To display the contents of the list

numpy_array_from_list

Output:

array([1, 9, 8, 3])

In practice, there is no need to declare a Python List. The operation can be combined.

a = np.array([1,9,8,3])

NOTE: Numpy documentation states use of np.ndarray to create an array. However, this the recommended method.

You can also create a numpy array from a Tuple.

Mathematical Operations on an Array

You could perform mathematical operations like additions, subtraction, division and multiplication on an array. The syntax is the array name followed by the operation (+.-,*,/) followed by the operand

Example:

numpy_array_from_list + 10

Output:

array([11, 19, 18, 13])

This operation adds 10 to each element of the numpy array.

Shape of Array

You can check the shape of the array with the object shape preceded by the name of the array. In the same way, you can check the type with dtypes.

import numpy as np a = np.array([1,2,3]) print(a.shape) print(a.dtype) (3,) int64

An integer is a value without decimal. If you create an array with decimal, then the type will change to float.

#### Different type b = np.array([1.1,2.0,3.2]) print(b.dtype) float64 2 Dimension Array

You can add a dimension with a “,”coma

Note that it has to be within the bracket []

### 2 dimension c = np.array([(1,2,3), (4,5,6)]) print(c.shape) (2, 3) 3 Dimension Array

Higher dimension can be constructed as follow:

### 3 dimension d = np.array([ [[1, 2,3], [4, 5, 6]], [[7, 8,9], [10, 11, 12]] ]) print(d.shape) (2, 2, 3)

Objective Code

Create array array([1,2,3])

print the shape array([.]).shape

What is numpy.zeros()?

numpy.zeros() or np.zeros Python function is used to create a matrix full of zeroes. numpy.zeros() in Python can be used when you initialize the weights during the first iteration in TensorFlow and other statistic tasks.

numpy.zeros() function Syntax

numpy.zeros(shape, dtype=float, order='C')

Python numpy.zeros() Parameters

Here,

Shape: is the shape of the numpy zero array

Dtype: is the datatype in numpy zeros. It is optional. The default value is float64

Order: Default is C which is an essential row style for numpy.zeros() in Python.

Python numpy.zeros() Example

import numpy as np np.zeros((2,2))

Output:

array([[0., 0.], [0., 0.]])

Example of numpy zero with Datatype

import numpy as np np.zeros((2,2), dtype=np.int16)

Output:

array([[0, 0], [0, 0]], dtype=int16) What is numpy.ones()?

np.ones() function is used to create a matrix full of ones. numpy.ones() in Python can be used when you initialize the weights during the first iteration in TensorFlow and other statistic tasks.

Python numpy.ones() Syntax

numpy.ones(shape, dtype=float, order='C')

Python numpy.ones() Parameters

Here,

Shape: is the shape of the chúng tôi Python Array

Dtype: is the datatype in numpy ones. It is optional. The default value is float64

Order: Default is C which is an essential row style.

Python numpy.ones() 2D Array with Datatype Example

import numpy as np np.ones((1,2,3), dtype=np.int16)

Output:

array([[[1, 1, 1], [1, 1, 1]]], dtype=int16)

numpy.reshape() function in Python

Python NumPy Reshape function is used to shape an array without changing its data. In some occasions, you may need to reshape the data from wide to long. You can use the np.reshape function for this.

Syntax of np.reshape()

numpy.reshape(a, newShape, order='C')

Here,

a: Array that you want to reshape

newShape: The new desires shape

Order: Default is C which is an essential row style.

Example of NumPy Reshape

import numpy as np e = np.array([(1,2,3), (4,5,6)]) print(e) e.reshape(3,2)

Output:

[[1 2 3] [4 5 6]] array([[1, 2], [3, 4], [5, 6]])

numpy.flatten() in Python

Python NumPy Flatten function is used to return a copy of the array in one-dimension. When you deal with some neural network like convnet, you need to flatten the array. You can use the np.flatten() functions for this.

Syntax of np.flatten()

numpy.flatten(order='C')

Order: Default is C which is an essential row style.

Example of NumPy Flatten

e.flatten()

Output:

array([1, 2, 3, 4, 5, 6])

What is numpy.hstack() in Python?

Numpy.hstack is a function in Python that is used to horizontally stack sequences of input arrays in order to make a single array. With hstack() function, you can append data horizontally. It is a very convenient function in NumPy.

Lets study hstack in Python with an example:

Example:

## Horitzontal Stack import numpy as np f = np.array([1,2,3]) g = np.array([4,5,6]) print('Horizontal Append:', np.hstack((f, g)))

Output:

Horizontal Append: [1 2 3 4 5 6]

What is numpy.vstack() in Python?

Numpy.vstack is a function in Python which is used to vertically stack sequences of input arrays in order to make a single array. With vstack() function, you can append data vertically.

Lets study it with an example:

Example:

## Vertical Stack import numpy as np f = np.array([1,2,3]) g = np.array([4,5,6]) print('Vertical Append:', np.vstack((f, g)))

Output:

Vertical Append: [[1 2 3] [4 5 6]]

After studying NumPy vstack and hstack, let’s learn an example to generate random numbers in NumPy.

Generate Random Numbers using NumPy

To generate random numbers for Gaussian distribution, use:

numpy.random.normal(loc, scale, size)

Here,

Loc: the mean. The center of distribution

Scale: standard deviation.

Size: number of returns

Example:

## Generate random nmber from normal distribution normal_array = np.random.normal(5, 0.5, 10) print(normal_array) [5.56171852 4.84233558 4.65392767 4.946659 4.85165567 5.61211317 4.46704244 5.22675736 4.49888936 4.68731125]

If plotted the distribution will be similar to following plot

Example to Generate Random Numbers using NumPy

NumPy Asarray Function

The asarray()function is used when you want to convert an input to an array. The input could be a lists, tuple, ndarray, etc.

Syntax:

numpy.asarray(data, dtype=None, order=None)[source]

Here,

data: Data that you want to convert to an array

dtype: This is an optional argument. If not specified, the data type is inferred from the input data

Order: Default is C which is an essential row style. Other option is F (Fortan-style)

Example:

Consider the following 2-D matrix with four rows and four columns filled by 1

import numpy as np A = np.matrix(np.ones((4,4)))

If you want to change the value of the matrix, you cannot. The reason is, it is not possible to change a copy.

np.array(A)[2]=2 print(A) [[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]]

Matrix is immutable. You can use asarray if you want to add modification in the original array. Let’s see if any change occurs when you want to change the value of the third rows with the value 2.

np.asarray(A)[2]=2 print(A)

Code Explanation:

np.asarray(A): converts the matrix A to an array

[2]: select the third rows

Output:

[[1. 1. 1. 1.] [1. 1. 1. 1.] [2. 2. 2. 2.] # new value [1. 1. 1. 1.]] What is numpy.arange()?

numpy.arange() is an inbuilt numpy function that returns an ndarray object containing evenly spaced values within a defined interval. For instance, you want to create values from 1 to 10; you can use np.arange() in Python function.

Syntax:

numpy.arange(start, stop, step, dtype)

Python NumPy arange Parameters:

Start: Start of interval for np.arange in Python function.

Stop: End of interval.

Step: Spacing between values. Default step is 1.

Dtype: Is a type of array output for NumPy arange in Python.

Example:

import numpy np np.arange(1, 11)

Output:

array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

Example:

If you want to change the step in this NumPy arange function in Python example, you can add a third number in the parenthesis. It will change the step.

import numpy np np.arange(1, 14, 4)

Output:

array([ 1, 5, 9, 13]) NumPy Linspace Function

Linspace gives evenly spaced samples.

Syntax:

numpy.linspace(start, stop, num, endpoint)

Here,

Start: Starting value of the sequence

Stop: End value of the sequence

Num: Number of samples to generate. Default is 50

Endpoint: If True (default), stop is the last value. If False, stop value is not included.

Example:

For instance, it can be used to create 10 values from 1 to 5 evenly spaced.

import numpy as np np.linspace(1.0, 5.0, num=10)

Output:

array([1. , 1.44444444, 1.88888889, 2.33333333, 2.77777778, 3.22222222, 3.66666667, 4.11111111, 4.55555556, 5. ])

If you do not want to include the last digit in the interval, you can set endpoint to false

np.linspace(1.0, 5.0, num=5, endpoint=False)

Output:

array([1. , 1.8, 2.6, 3.4, 4.2]) LogSpace NumPy Function in Python

LogSpace returns even spaced numbers on a log scale. Logspace has the same parameters as np.linspace.

Syntax:

numpy.logspace(start, stop, num, endpoint)

Example:

np.logspace(3.0, 4.0, num=4)

Output:

array([ 1000. , 2154.43469003, 4641.58883361, 10000. ])

Finaly, if you want to check the memory size of an element in an array, you can use itemsize

x.itemsize

Output:

Each element takes 16 bytes.

Indexing and Slicing in Python

Example:

## Slice import numpy as np e = np.array([(1,2,3), (4,5,6)]) print(e) [[1 2 3] [4 5 6]]

Remember with numpy the first array/column starts at 0.

## First column print('First row:', e[0]) ## Second col print('Second row:', e[1])

Output:

First row: [1 2 3] Second row: [4 5 6]

In Python, like many other languages,

The values before the comma stand for the rows

The value on the rights stands for the columns.

If you want to select a column, you need to add : before the column index.

: means you want all the rows from the selected column.

print('Second column:', e[:,1]) Second column: [2 5]

To return the first two values of the second row. You use : to select all columns up to the second

## Second Row, two values print(e[1, :2]) [4 5] Statistical Functions in Python

NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc from the given elements in the array. The functions are explained as follows −

Numpy is equipped with the robust statistical function as listed below

Function Numpy

Min np.min()

Max np.max()

Mean np.mean()

Median np.median()

Standard deviation np.std()

Consider the following Array:

Example:

import numpy as np normal_array = np.random.normal(5, 0.5, 10) print(normal_array)

Output:

[5.56171852 4.84233558 4.65392767 4.946659 4.85165567 5.61211317 4.46704244 5.22675736 4.49888936 4.68731125]

Example of NumPy Statistical function

### Min print(np.min(normal_array)) ### Max print(np.max(normal_array)) ### Mean print(np.mean(normal_array)) ### Median print(np.median(normal_array)) ### Sd print(np.std(normal_array))

Output:

4.467042435266913 5.612113171990201 4.934841002270593 4.846995625786663 0.3875019367395316 What is numpy dot product?

Numpy.dot product is a powerful library for matrix computation. For instance, you can compute the dot product with chúng tôi chúng tôi product is the dot product of a and b. numpy.dot() in Python handles the 2D arrays and perform matrix multiplications.

Syntax:

numpy.dot(x, y, out=None)

Parameters

Here,

x,y: Input arrays. x and y both should be 1-D or 2-D for the np.dot() function to work

out: This is the output argument for 1-D array scalar to be returned. Otherwise ndarray should be returned.

Returns

The function numpy.dot() in Python returns a Dot product of two arrays x and y. The dot() function returns a scalar if both x and y are 1-D; otherwise, it returns an array. If ‘out’ is given then it is returned.

Raises

Dot product in Python raises a ValueError exception if the last dimension of x does not have the same size as the second last dimension of y.

Example:

## Linear algebra ### Dot product: product of two arrays f = np.array([1,2]) g = np.array([4,5]) ### 1*4+2*5 np.dot(f, g)

Output:

14 Matrix Multiplication in Python

The Numpy matmul() function is used to return the matrix product of 2 arrays. Here is how it works

1) 2-D arrays, it returns normal product

3) 1-D array is first promoted to a matrix, and then the product is calculated

Syntax:

numpy.matmul(x, y, out=None)

Here,

x,y: Input arrays. scalars not allowed

out: This is optional parameter. Usually output is stored in ndarray

Example:

In the same way, you can compute matrices multiplication with np.matmul

### Matmul: matruc product of two arrays h = [[1,2],[3,4]] i = [[5,6],[7,8]] ### 1*5+2*7 = 19 np.matmul(h, i)

Output:

array([[19, 22], [43, 50]]) Determinant

Last but not least, if you need to compute the determinant, you can use np.linalg.det(). Note that numpy takes care of the dimension.

Example:

## Determinant 2*2 matrix ### 5*8-7*6np.linalg.det(i)

Output:

-2.000000000000005 Summary

NumPy is an open source library available in Python, which helps in mathematical, scientific, engineering, and data science programming.

numpy.zeros() or np.zeros Python function is used to create a matrix full of zeroes.

numpy.ones() in Python can be used when you initialize the weights during the first iteration in TensorFlow and other statistic tasks.

Python NumPy Reshape function is used to shape an array without changing its data.

Python NumPy Flatten function is used to return a copy of the array in one-dimension.

Numpy.hstack is a function in Python that is used to horizontally stack sequences of input arrays in order to make a single array.

Numpy.vstack is a function in Python which is used to vertically stack sequences of input arrays in order to make a single array.

numpy.arange() is an inbuilt numpy function that returns an ndarray object containing evenly spaced values within a defined interval.

Numpy.dot product is a powerful library for matrix computation.

The Numpy matmul() function is used to return the matrix product of 2 arrays.

Architecture And Packages In Javafx Api With Examples

Introduction to JavaFX API

Web development, programming languages, Software testing & others

Architecture of JavaFX API

Below is the architecture of JavaFX API.

In the GUI application, a scene graph is considered as the starting point of its construction. It consists of all the application primitives known as a node. Prism in this architecture is a higher performance hardware-accelerated graphical pipeline which helps in rendering the JavaFX graphics. Here, both two dimensional and three-dimensional graphics can be rendered.

GWT offers services for managing surfaces, windows, timers and event queues. It connects the platform of JavaFX API and native OS. WebView is the JavaFX component that helps in processing the content using a technology known as Web Kit. It is an internal web browser engine which is open-source. This component provides several web technologies such as HTML5, DOM, JavaScript, CSS and SVG. The media engine in JavaFX is based on an engine called a streamer which is open-source. This engine supports the both the video playback and audio content.

Packages in JavaFX API

The important JavaFX API packages include:

javafx.animation: A set of classes will be provided that is for transition-related animations.

javafx.application: Application life-cycle classes of the package will be provided.

javafx.beans: Interfaces that explain the observability generic form is explained in this package.

javafx.beans.binding: Binding characteristics are explained in this package.

javafx.beans.property: Read-only and writable properties along with numerous implementations are provided in this package.

javafx.beans.value: ObservableValue interface and WritableValue interface, along with all the sub-interfaces, are provided in this package.

javafx.collections: All the JavaFX collections and their utilities are available in this package.

javafx.concurrent: A set of classes will be provided for the javafx.task.

javafx.css: An API that makes the properties stylable with the help of CSS and supports pseudo-class state is provided in this package.

javafx.embed.swing: A set of classes that helps in using JavaFX within the swing applications is provided.

javafx.embed.swt: A set of classes that helps in using JavaFX within the SWT applications is provided.

javafx.event: A basic framework is provided for the FX events, their delivery as well as handling.

javafx.fxml: In order to load a hierarchy of object from markup, this package contains all the classes.

javafx.geometry: This package consists of 2D classes set that define as well as perform operations on objects that relate to 2-D geometry.

javafx.print: This package offers the JavaFX printing API public classes.

javafx.scene: This package contains the base classes’ core set for the scene graph API in JavaFX.

javafx.scene.canvas: This package offers a class set for canvas which is a rendering API’s immediate mode style.

javafx.scene.chart: This offers several chart components, which is very useful for data visualization.

javafx.scene.control: User interface controls in JavaFX are the specialized nodes which are available in the scene graph of JavaFX. It is especially appropriate for reusing several application contexts.

javafx.scene.effect: This package offers different classes for the attachment of graphical filter effects to the nodes of the JavaFX scene graph.

Examples of JavaFX API

Given below are the examples of JavaFX API:

Example #1

Code:

import javafx.application.Application; import javafx.scene.Scene; import javafx.event.ActionEvent; import javafx.event.EventHandler; import javafx.scene.canvas.*; import javafx.scene.web.*; import javafx.scene.layout.*; import javafx.scene.image.*; import java.io.*; import javafx.geometry.*; import javafx.scene.Group; import javafx.scene.control.* ; import javafx.scene.layout.* ; import javafx.stage.Stage ; import javafx.scene.paint.*; import javafx.scene.shape.Circle; public class BackgroundClassProgram extends Application { public void start(Stage st) { st.setTitle("Sample creation of background. . .") ; Circle c = new Circle(); c.setCenterX(311.0f); c.setCenterY(126.0f); c.setRadius(112.0f); HBox hb = new HBox(c); hb.setSpacing(11); hb.setAlignment(Pos.CENTER); Scene sc = new Scene(hb, 290, 280) ; BackgroundFill bf = new BackgroundFill(Color.RED , CornerRadii.EMPTY , Insets.EMPTY) ; Background bg = new Background(bf); hb.setBackground(bg); st.setScene(sc); st.show(); } public static void main(String args[]) { launch(args); } }

Output:

For every program, we have to first import the necessary packages and classes. In this program also, all the necessary classes are imported. Then only the appropriate functions can be used for the display of background colours.

Example #2

JavaFX program that displays a timer with the help of API packages.

import java.util.Timer ; import java.util.TimerTask ; public class TimerProgramSample { public static void main(String[] args) { System.out.println("Timer starts now...") ; Timer tmr = new Timer() ; tmr.schedule(new TimerTask() { @Override public void run() { System.out.println("Timer starts. . . .") ; } }, 3000) ; Timer tt = new Timer() ; tt.scheduleAtFixedRate(new TimerTask() { @Override public void run() { System.out.println("Timer is working fine. . . .") ; } }, 0, 3000) ; } }

Output:

Conclusion

JavaFX is a library that is used for building GUI related applications. It offers an API in order to design GUI applications that run on nearly all devices that have the support of Java. In this article, different aspects of JavaFX API such as architecture, packages and examples are shown in detail.