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Introduction

multivariate time series have more than one time-dependent variable. Each variable depends not only on its past values but also has some dependency on other variables.

Vector AutoRegressive (VAR)

Vector AutoRegressive (VAR) is a multivariate forecasting algorithm that is used when two or more time series influence each other.

Let’s understand this be one example. In general univariate forecasting algorithms (AR, ARMA, ARIMA), we predict only one time-dependent variable. Here ‘Money’ is dependent on time.

Now, suppose we have one more feature that depends on time and can also influence another time-dependent variable. Let’s add another feature ‘Spending’.

Here we will predict both ‘Money’ and ‘Spending’. If we plot them, we can see both will be showing similar trends.

The main difference between other autoregressive models (AR, ARMA, and ARIMA) and the VAR model is that former models are unidirectional (predictors variable influence target variable not vice versa) but VAR is bidirectional.

A typical AR(P) model looks like this:

A K dimensional VAR model of order P, denoted as VAR(P), consider K=2, then the equation will be:

For the VAR model, we have multiple time series variables that influence each other and here, it is modelled as a system of equations with one equation per time series variable. Here k represents the count of time series variables.

In matrix form:

The equation for VAR(P) is:

VAR Model in Python

Let us look at the VAR model using the Money and Spending dataset from Kaggle. We combine these datasets into a single dataset that shows that money and spending influence each other. Final combined dataset span from January 2013 to April 2023.

Steps that we need to follow to build the VAR model are:

7. If necessary, invert the earlier transformation.

1. Examine the Data

First import all the required libraries

import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline

Now read the dataset

money_df = pd.read_csv('M2SLMoneyStock.csv') spending_df = pd.read_csv('PCEPersonalSpending.csv') df = money_df.join(spending_df)

2. Check for Stationarity

Before applying the VAR model, all the time series variables in the data should be stationary. Stationarity is a statistical property in which time series show constant mean and variance over time.

One of the common methods to perform a stationarity check is the Augmented Dickey-Fuller test.

In the ADF test, there is a null hypothesis that the time series is considered non-stationary. So, if the p-value of the test is less than the significance level then it rejects the null hypothesis and considers that the time series is stationary.

def adf_test(series,title=''): """ Pass in a time series and an optional title, returns an ADF report """ print(f'Augmented Dickey-Fuller Test: {title}') result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data labels = ['ADF test statistic','p-value','# lags used','# observations'] out = pd.Series(result[0:4],index=labels) for key,val in result[4].items(): out[f'critical value ({key})']=val print(out.to_string()) # .to_string() removes the line "dtype: float64" if result[1] <= 0.05: print("Strong evidence against the null hypothesis") print("Reject the null hypothesis") print("Data has no unit root and is stationary") else: print("Weak evidence against the null hypothesis") print("Fail to reject the null hypothesis") print("Data has a unit root and is non-stationary")

Check both the features whether they are stationary or not.

adf_test(df['Money']) Augmented Dickey-Fuller Test: ADF test statistic 4.239022 p-value 1.000000 # lags used 4.000000 # observations 247.000000 critical value (1%) -3.457105 critical value (5%) -2.873314 critical value (10%) -2.573044 Weak evidence against the null hypothesis Fail to reject the null hypothesis Data has a unit root and is non-stationary

Now check the variable ‘Spending’.

adf_test(df['Spending']) Augmented Dickey-Fuller Test: ADF test statistic 0.149796 p-value 0.969301 # lags used 3.000000 # observations 248.000000 critical value (1%) -3.456996 critical value (5%) -2.873266 critical value (10%) -2.573019 Weak evidence against the null hypothesis Fail to reject the null hypothesis Data has a unit root and is non-stationary

Neither variable is stationary, so we’ll take a first-order difference of the entire DataFrame and re-run the augmented Dickey-Fuller test.

df_difference = df.diff() adf_test(df_difference['Money']) Augmented Dickey-Fuller Test: ADF test statistic -7.077471e+00 p-value 4.760675e-10 # lags used 1.400000e+01 # observations 2.350000e+02 critical value (1%) -3.458487e+00 critical value (5%) -2.873919e+00 critical value (10%) -2.573367e+00 Strong evidence against the null hypothesis Reject the null hypothesis Data has no unit root and is stationary adf_test(df_difference['Spending']) Augmented Dickey-Fuller Test: ADF test statistic -8.760145e+00 p-value 2.687900e-14 # lags used 8.000000e+00 # observations 2.410000e+02 critical value (1%) -3.457779e+00 critical value (5%) -2.873609e+00 critical value (10%) -2.573202e+00 Strong evidence against the null hypothesis Reject the null hypothesis Data has no unit root and is stationary 3. Train-Test Split

We will be using the last 1 year of data as a test set (last 12 months).

test_obs = 12 train = df_difference[:-test_obs] test = df_difference[-test_obs:] 4. Grid Search for Order P for i in [1,2,3,4,5,6,7,8,9,10]: model = VAR(train) results = model.fit(i) print('Order =', i) print('AIC: ', results.aic) print('BIC: ', results.bic) print() Order = 1 AIC: 14.178610495220896 Order = 2 AIC: 13.955189367163705 Order = 3 AIC: 13.849518291541038 Order = 4 AIC: 13.827950574458281 Order = 5 AIC: 13.78730034460964 Order = 6 AIC: 13.799076756885809 Order = 7 AIC: 13.797638727913972 Order = 8 AIC: 13.747200843672085 Order = 9 AIC: 13.768071682657098 Order = 10 AIC: 13.806012266239211

As you keep on increasing the value of the P model becomes more complex. AIC penalizes the complex model.

As we can see, AIC begins to drop as we fit the more complex model but, after a certain amount of time AIC begins to increase again. It’s because AIC is punishing these models for being too complex.

VAR(5) returns the lowest score and after that again AIC starts increasing, hence we will build the VAR model of order 5.

5. Fit VAR(5) Model result = model.fit(5) result.summary() Summary of Regression Results ================================== Model: VAR Method: OLS Date: Thu, 29, Jul, 2023 Time: 15:21:45 -------------------------------------------------------------------- No. of Equations: 2.00000 BIC: 14.1131 Nobs: 233.000 HQIC: 13.9187 Log likelihood: -2245.45 FPE: 972321. AIC: 13.7873 Det(Omega_mle): 886628. -------------------------------------------------------------------- Results for equation Money ============================================================================== coefficient std. error t-stat prob ------------------------------------------------------------------------------ const 0.516683 1.782238 0.290 0.772 L1.Money -0.646232 0.068177 -9.479 0.000 L1.Spending -0.107411 0.051388 -2.090 0.037 L2.Money -0.497482 0.077749 -6.399 0.000 L2.Spending -0.192202 0.068613 -2.801 0.005 L3.Money -0.234442 0.081004 -2.894 0.004 L3.Spending -0.178099 0.074288 -2.397 0.017 L4.Money -0.295531 0.075294 -3.925 0.000 L4.Spending -0.035564 0.069664 -0.511 0.610 L5.Money -0.162399 0.066700 -2.435 0.015 L5.Spending -0.058449 0.051357 -1.138 0.255 ============================================================================== 6 Predict Test Data

The VAR .forecast() function requires that we pass in a lag order number of previous observations.

lagged_Values = train.values[-8:]

pred = result.forecast(y=lagged_Values, steps=12)

idx = pd.date_range('2023-01-01', periods=12, freq='MS')

df_forecast=pd.DataFrame(data=pred, index=idx, columns=['money_2d', 'spending_2d'])

7. Invert the transformation

We have to note that the forecasted value is a second-order difference. To get it similar to original data we have to roll back each difference. This can be done by taking the most recent values of the original series’ training data and adding it to a cumulative sun of forecasted values.

df_forecast['Money1d'] = (df['Money'].iloc[-test_obs-1]-df['Money'].iloc[-test_obs-2]) + df_forecast['money2d'].cumsum() df_forecast['MoneyForecast'] = df['Money'].iloc[-test_obs-1] + df_forecast['Money1d'].cumsum() df_forecast['Spending1d'] = (df['Spending'].iloc[-test_obs-1]-df['Spending'].iloc[-test_obs-2]) + df_forecast['Spending2d'].cumsum() df_forecast['SpendingForecast'] = df['Spending'].iloc[-test_obs-1] + df_forecast['Spending1d'].cumsum() Plot the Result

Now let’s plot the predicted v/s original values of ‘Money’ and ‘Spending’ for test data.

test_original = df[-test_obs:] test_original.index = pd.to_datetime(test_original.index) test_original['Money'].plot(figsize=(12,5),legend=True) df_forecast['MoneyForecast'].plot(legend=True) test_original['Spending'].plot(figsize=(12,5),legend=True) df_forecast['SpendingForecast'].plot(legend=True)

The original value and predicted values show a similar pattern for both ‘Money’ and ‘Spending’.

Conclusion

In this article, first, we gave a basic understanding of univariate and multivariate analysis followed by intuition behind the VAR model and steps required to implement the VAR model in Python.

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Timestamp To Date In Python

Introduction to Timestamp to Date in Python

The timestamp to date is the conversion of the encoded numerical values into data using python. The timestamp is an encoded or encrypted sequence of the information and converts it into date and time. The python converts from timestamp to date using the installed module or method. It is a method to convert digital information into date using a python programming language. The python language has installed a feature to change timestamp to date using the method. It is a transformation in python technology to keep maintain application information.

Syntax of Timestamp to Date in Python

Given below is the syntax mentioned:

The timestamp to date converts from 1 January 1970, at UTC.

The basic syntax of the timestamp to date method is below:

fromtimestamp()

The “fromtimestamp” method helps to convert into date and date-time. The object of the value keeps inside of the method.

The basic syntax of the timestamp to date with value is below:

fromtimestamp(timestamp value)

fromtimestamp(timestamp value, tz = none)

The “fromtimestamp” method helps to convert into a date. The “timestamp value” is a sequence of the information to convert into a date. The “tz” specifies the time zone of the timestamp. This function is optional.

The syntax of the timestamp to date using python language is below:

datetime.fromtimestamp(timestamp value)

datetime.fromtimestamp(timestamp object)

The “datetime” is a python module to convert into a date, date-time, timestamp, etc. The “fromtimestamp” method helps to convert into a date. The “timestamp value” is an encoded sequence of the information to convert into a date. The python installed the datetime module by default. You do not need to install third software for conversion. The datetime uses either object or value inside of the “fromtimestamp” method.

The syntax of the timestamp to date conversion shows below:

datetime.fromtimestamp(objtmstmp).strftime('%d - %m - %y')

The “datetime” is a python module to convert into date. The “strftime” function shows the only date as per requirement. The function uses to date, month, and time as per the required format.

How to Convert Timestamp to Date in Python?

Install python software or use online IDE for coding.

The following link helps to download python software.

Create a python file using the .py extension. Then, start to write python code.

Filename: main.py

Import the “datetime” file to start timestamp conversion into a date.

from datetime import datetime

Create an object and initialize the value of the timestamp.

objtmstmp = 14590157322

Use the ” fromtimestamp ()” method to place either data or object.

objectdate = datetime.fromtimestamp(objtmstmp)

objectdate = datetime.fromtimestamp(14590157322)

Print the date after conversion of the timestamp.

print(" date" , objectdate)

If you require the type of date, then print the date type of the python.

print("type of date object =", type(objectdate))

Combine the working procedure of the timestamp to date in the python.

from datetime import datetime objtmstmp = 14590157322 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate)) Examples of Timestamp to Date in Python

Given below are the examples of Timestamp to Date in Python:

Example #1

The timestamp to date convert for the 1970 year example and output.

Code:

from datetime import datetime objtmstmp = 100043 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #2

The timestamp to date convert for the 2000 year example and output.

Code:

from datetime import datetime objtmstmp = 1000000000 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #3

The timestamp to date convert at the 1970 year example and output.

Code:

from datetime import datetime objtmstmp = 2500000000 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #4

The timestamp to date convert with four-digit value example and output.

Code:

from datetime import datetime objtmstmp = 1000 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #5

The timestamp to date convert with a five-digit value example and output.

Code:

from datetime import datetime objtmstmp = 10005 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #6

The timestamp to date converts to change date example and output.

Code:

from datetime import datetime objtmstmp = 1000641 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #7

The timestamp to date converts to change month example and output.

Code:

from datetime import datetime objtmstmp = 10006416 objectdate = datetime.fromtimestamp (objtmstmp) print ("timestamp to date conversion.") print (" date" , objectdate) print ("type of date object =", type (objectdate))

Output:

Example #8

The timestamp to date converts to change current date example and output.

Code:

import datetime; print ("Display current time") current_time = datetime.datetime.now() print ("current time:-", current_time) print ("Display timestamp") time_stamp = current_time.timestamp() print ("timestamp:-", time_stamp)

Output:

Example #9

The timestamp to date converts to change current date example and output.

Code:

from datetime import datetime objtmstmp = 1500000 objectdate = datetime.fromtimestamp(objtmstmp).strftime('%d - %m - %y') print ("timestamp to date conversion.") print (" date (date - month - year):" , objectdate) objectdate = datetime.fromtimestamp(objtmstmp).strftime('%y - %d - %m') print ("timestamp to date conversion.") print (" date (year - date - month):" , objectdate) objectdate = datetime.fromtimestamp(objtmstmp).strftime('%m - %d - %y') print ("timestamp to date conversion.") print (" date (month - date - year ):" , objectdate)

Output:

Conclusion

It helps to save date and time effortlessly in the database. The web application shows the date from the timestamp value using minimum code. The timestamp to date stores date and time together without complexion. It is used for updates, search date, and time without lengthy code.

The Return Statement In Python

The return statement in python is an extremely useful statement used to return the flow of program from the function to the function caller. The keyword return is used to write the return statement.

Since everything in python is an object the return value can be any object such as – numeric (int, float, double) or collections (list, tuple, dictionary) or user defined functions and classes or packages.

The return statement has the following features –

Return statement cannot be used outside the function.

Any code written after return statement is called dead code as it will never be executed.

Return statement can pass any value implicitly or explicitly, if no value is given then None is returned.

Syntax

Following is the syntax of return statement in python –

def some_function(parameters): print(some_function) Example

Following is the simple example of return statement –

def welcome(str): return str + " from TutorialsPoint" print(welcome("Good morning")) Output

Following is an output of the above code –

Good morning from TutorialsPoint

The return statement is useful in multiple ways and the below sections discuss the different use case of return statement along with examples.

Use of return statement in Python

Functions are core of any programming language as they allow for code modularity thereby reducing program complexity. Functions can display the result within itself, but it makes the program complex, hence it is best to pass the result from all the functions to a common place.

It is in this scenario that the return statement is useful as it terminates the currently executing function and passes control of the program to the statement that invoked the function.

Example

In the below code the sum_fibonacci function is used to calculate the sum of the first 15 terms in the fibonacci series. After calculating the sum, it prints and returns the sum to the sum_result variable. This is to show that printing inside the function and returning the value give the same output.

# Defining function to calculate sum of Fibonacci series def sum_fibonacci(terms): first_term = 0 second_term = 1 sum_series = 0 # Finding the sum of first 15 terms of Fibonacci series for i in range(0, terms): sum_series = sum_series + first_term next_term = first_term + second_term first_term = second_term second_term = next_term # Printing the sum inside the function print("Sum of Fibonacci series inside the function is = {}".format(sum_series)) # Returning the sum using return statement return sum_series # Invoking the sum_fibonacci function sum_result = sum_fibonacci(15) print("Sum of Fibonacci series outside the function is = {}".format(sum_result)) Output

The output shows that the sum from inside the function using print statement and the sum from outside the function using return statement is equal.

Sum of Fibonacci series inside the function is = 986 Sum of Fibonacci series outside the function is = 986 Returning a function using return statement

In python, functions are first class objects which means that they can be stored in a variable or can be passed as an argument to another function. Since functions are objects, return statement can be used to return a function as a value from another function. Functions that return a function or take a function as an argument are called higher-order functions.

Example

In this example, finding_sum function contains another function – add inside it. The finding_sum function is called first and receives the first number as the parameter. The add function receives the second number as parameter and returns the sum of the two numbers to finding_sum function. The finding_sum function then returns the add function as a value to sum variable.

# Defining function to return sum of two numbers # Function to get the first number def finding_sum(num1): # Function to get the second number def add(num2): return num1 + num2 # return sum of numbers to add function return add # return value present in add function to finding_sum function sum = finding_sum(5) print("The sum of the two numbers is: {}".format(sum(10))) Output

The output of the program gives the sum of the two numbers – 5 and 10.

The sum of the two numbers is: 15 Returning None using return statement

Functions in python always return a value, even if the return statement is not written explicitly. Hence, python does not have procedures, which in other programming languages are functions without a return statement. If a return statement does not return a result or is omitted from a function, then python will implicitly return default value of None.

Explicit calling of return None should only be considered if the program contains multiple return statement to let other programmers know the termination point of the function.

Example

The program below gives a perfect illustration of using return None. In this program the check_prime() function is used to check if a list contains any prime numbers. If the list contains prime numbers, then all the prime numbers present in the list are printed. However, if there are no prime numbers in the list then None is returned, since this program contains multiple return statements, hence None is called explicitly.

def check_prime(list): prime_list = [] for i in list: counter = 0 for j in range(1, i): if i % j == 0: counter = counter + 1 if counter == 1: prime_list.append(i) if len(prime_list): return prime_list else: return None list = [4, 6, 8, 10, 12] print("The prime numbers in the list are: {}".format(check_prime(list))) Output

The output prints None since there are no prime numbers in the list.

The prime numbers in the list are: [4] Returning multiple values using return statement

The return statement in python can also be used to return multiple values from a single function using a ‘,’ to separate the values. This feature can be especially useful when multiple calculations need to be performed on the same dataset without changing the original dataset. The result from the return statement is a tuple of the values.

Example

In this example the built-in functions of the statistics library are used to compute the mean, median and mode which are returned using a single return statement showing how multiple values can be returned from a function.

import statistics as stat # Defining function to perform different statistical calculations def finding_stats(data): return stat.mean(data), stat.median(data), stat.mode(data) # returning multiple values list_numbers = [5, 7, 13, 17, 17, 19, 33, 47, 83, 89] print("The mean, median and mode of the data is: {}".format(finding_stats(list_numbers))) Output

The output gives the mean, median and mode of the dataset with type as tuple.

The mean, median and mode of the data is: (33, 18.0, 17)

Developing Intelligent Tutoring Systems And Ai’s Role

With AI, teachers can now develop intelligent tutoring systems

According to a research report , artificial intelligence in the global education market is projected to reach USD3.68 billion by 2023, registering a CAGR of 47%. The role of AI in education is huge and imperative in the current scenario. AI along with other disruptive technology has given rise to EdTech and smart learning methods. It has now entered into another significant area, which is Intelligent Tutoring . Intelligent tutoring systems, as the name suggests is an intelligent computer system that can effectively provide instructions to the learners and enables a feedback system with minimal human intervention. In May last year, researchers from Carnegie Mellon University developed a system wherein a teacher can teach computer systems with the help of AI and create intelligent tutoring systems. A report by CMU quotes Ken Koedinger, professor of human-computer interaction psychology saying that initially, it took almost 200 hours of development for each hour of tutored instruction since they programmed production rules by hand and later using a shortcut method reduced the hours to about 40 to 50. Still, it is a long process considering the variety of problems and instructions that exist. The new AI-based intelligent tutoring system is not that lazy to learn and can be programmed in 30 minutes for a 30-minutes session. The paper published on the same states that they used a novel interaction design to create Intelligent Tutoring Systems by training Simulated Learners. It leverages machine learning and developed a user-friendly teaching interface for these machine learning programs. Teachers can demonstrate how to solve a problem in different ways to these computer systems and correct if they respond with errors. Since it uses AI and machine learning, the system can learn to solve problems under a topic by generalizing without receiving any demonstration from teachers. Artificial intelligence is disrupting the education sector and with the arrival of pandemics, the influence increased. Intelligent Tutoring Systems are hailed for their capability to provide one-on-one curriculum and personalized instructions. Intelligent tutoring systems using AI are set to revolutionize the conventional education system that runs on a one-size-fits-all rule and thinks that everybody needs an equal amount of learning time and attention. Every student is different and Intelligent Tutoring Systems are designed to address this. It is customizable according to the needs of each student. Intelligent tutoring systems developed by AI do not require any programming and teachers can mold it according to their teaching techniques. The system developed by CMU has been proved effective in teaching English grammar, chemistry, and algebra and they have experimented with specific problems like multi-column addition to test its capability. Ken Koedinger explains in the report that the machine learning models usually stumble in different places while learning, like the students. Thus, it might also enlighten teachers on understanding the difficulty level of each technique and instruction. Artificial intelligence technology has reigned different industries and education is a relevant one. By enabling the faster and easier development of Intelligent Tutoring Systems, AI is making world-class education accessible to all.

Getting Started With Developing For Tizen Smartwatches

Smartwatches are often pigeonholed as consumer gadgets for tracking fitness goals or streaming music, but innovative businesses are starting to discover some unique use cases for these wrist-worn devices.

Whether it’s to streamline bank branch operations, improve customer service or keep police officers safe, smartwatches deliver instantly accessible, hands-free alerts and notifications that help keep teams nimble and up to speed. For organizations concerned about health and safety, their built-in sensors — GPS, motion and heart-rate — also offer a way to track the location and condition of team members.

App dev for wearables

At the core of every successful enterprise wearables initiative is a well-designed application.

Developing applications for wearables is a new paradigm, even for highly experienced mobile developers. Successful user interactions with watch faces and widgets require a streamlined UX defined by ease-of-use.

In my role on the Samsung Business Services team, we work with enterprise customers to design and develop wearable applications for Samsung’s Tizen-based smartwatches. We also offer a jumpstart seminar for enterprise dev teams looking to create Tizen smartwatch apps internally. In this post, I’ll share some of the key resources and frameworks available for those exploring app development on Tizen.

How to get started with Tizen

Everything you need to get started creating powerful applications for Tizen smartwatches is offered online by the Tizen and Samsung developer communities. Resources available in these online communities include extensive documentation of best practices, code sharing and active user forums.

Tizen’s Studio offers a complete launchpad to build apps for mobile, wearable, displays and appliances. The development environment is built on the Eclipse Integrated Development Environment (IDE), the most common Java IDE. Experienced Android developers generally find the studio environment, menus and user flow intuitive, or even familiar, based on previous exposure to Eclipse IDEs.

Within the studio, developers are given the choice of using Native or Web tools to create applications. Each of these options has distinct use cases.

Native: Native development of wearable native watch and widget applications can be written using the C programming language. This is the best approach to developing applications which require access to low-level components, such as wearable sensors or hardware buttons.

Three best practices for successful wearables development

Developing applications for wearables requires developers to think differently about the user interface and device components; you can’t simply transpose a smartphone UX to wearables. Great experiences for a smartwatch are designed specifically for the screen size and format; in the case of the Galaxy Watch, it’s a circular 42mm or 46mm display. Users of wearable devices typically interact with a single quick touch, a gesture or a turn of the bezel. Successful smartwatch experiences are intuitive and simple.

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In addition to recasting how you think about design flow, developers need to think about the new possibilities and limitations of the hardware components when creating Galaxy Watch applications. Smartwatches are inherently small devices, which means they have a small central processing unit and a limited battery size. Consider these factors when designing apps which pair to a smartphone or interact with a remote back end database. Application processes that require constant communication over a network could sacrifice battery life or performance.

Success criteria for wearable design include a simple UI which is optimized for easy user interactions and analytics for continual improvement.

Efficient UI: A great user interface for a wearable widget or watch face is much simpler than an optimized UX for smartphone apps. Information and notifications will be read by users with a quick glance, which means scannability is paramount. Optimize readability through consistent, intentional design and careful testing. Wearable design best practices include using no more than one button per page or creating clear visual cues for users to access a menu of more options.

Intuitive flow: Users need the ability to quickly control widgets or watch face applications by interacting with the device bezel. User frustration can easily arise when tasks require complicated interactions with the bezel. Intuitive flow requires responsive design and easy-to-use app components, including clear connectivity between the bezel component and on-screen application options. In some cases where users need to complete a complex task or navigate through multiple options, creating menus or headers on-screen can simplify flow.

Analytics: To ensure future success of enterprise wearable applications, developers should create and monitor analytics to measure the quality of UX and continually improve the product post-deployment. Analytics can reveal opportunities to simplify the flow of user interactions and response to notifications or events.

Creating successful enterprise apps for the Galaxy Watch

Business adoption of wearables and other smart mobile devices continues to accelerate. There’s never been a better time to get started developing mobile applications for a rich ecosystem of connected devices, and the Tizen and Samsung developer communities offer rich resources and accessible options for developers to expand their portfolio to wearables.

The latest iteration of Galaxy smartwatches offers an unprecedented number of use cases and opportunities for businesses in many verticals, including public safety and finance. With improved sensors and accuracy to track movement, heart rate and user location, the next generation of smartwatch apps can empower businesses to drive better employee and customer experiences.

From healthcare to hospitality, see how wearables are transforming the workplace.

How Does Sprintf Work In Python?

Definition of sprintf in Python

The sprintf is a function to display the output of the given input using a python programming language.

The sprintf is a print function to shows the output of the format strings in the python language.

It is a coding element to assigns the “f get” method and displays string format output.

The sprintf is a print element to contain string – buffer data of the application and display in the string format using python technology.

The sprintf is a function similar to print, vprint for display buffer output hustle-free.

The python programming language is using the sprintf function to declare argument and constructor elements.

It is an output function to displays all data types like string and array elements.

Syntax:

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The sprintf python works with different data types, lengths of the data, and width.

It has used the percentage sign (%) before the type of the data.

The basic syntax of a sprintf shows below.

% [FLAG WIDTH. (DOT) PRECISION] TYPE

The sprintf python is using the “print” keyword to display output.

print("% [flag width . (dot) precision] type" % (value or object))

The sprintf is used precision and type depends on the data type of the variable. This syntax helps to assign a signed decimal number. The length value of a decimal is 2.

%2d

This syntax helps to assign a binary number. The length value of binary is 4.

%4b

This syntax helps to assign a floating number. The length value of a decimal is 2.1.

%2.1f or %2.1F

This syntax helps to assign ASCII values.

This syntax helps to assign unsigned decimal values.

This syntax helps to assign an octal number.

This syntax helps to assign a hexadecimal number.

%x OR %X

This syntax helps to assign scientific notation of a lowercase.

This syntax helps to assign scientific notation of an uppercase.

This syntax helps to return the type of data format.

%%type How does sprintf work in Python?

Download python software from the respective website. Create a page with the dot (.) py extension. The file name is the “function.py” to write a python program. Create a variable with initializing the required data type value.

Varble_name = 34

Use the print keyword for the string format of the sprint python.

print (write sprint format)

Use percentage sign to return value.

print (" text : %d ")

Display length of the variable or value before data type.

print (" text : %2d ")

Display the type of the variable value in the format helping of the sprint. Use parameter of the percentage sign to end of the sprintf function end. Add variable to interconnect return function and application data.

print (" text : %2d " % (Varble_name))

Use direct value in sprint function to display decimal value.

print("decimal number : %2d " % (7))

Use float type with sprintf formatted value in the return function.

print("Float number : %5.2f" % ( 23.11))

Combine the working procedure of the sprintf python to better understanding.

x = 34 print ("decimal number : %2d " % (x)) print ("decimal number : %2d " % (7)) print ("Float number : %5.2f" % ( 23.11)) Examples Example #1 – Basic

Code:

e_var = 34 print ("decimal number: %2d " % (e_var)) print ("decimal number: %2d " % (7)) print ("Float number: %5.2f" % ( 23.11)) print ("Float number: %5.4f" % (e_var)) print ("Octal number: %5o" % (e_var)) print ("Octal number: %3o" % (42))

Output:

Example #2 – With different types

Code:

e_var = 341234673 print ("decimal number: %d " % (e_var)) print ("Float number: %f" % (e_var)) print ("Float number: %F" % (e_var)) print ("unsigned decimal number: %u" % (e_var)) print ("Octal number: %o" % (e_var)) print ("first string value: %s" % (e_var)) print ("second string value: %s" % ("string data")) print ("first hexadecimal value: %x" % (e_var)) print ("second hexadecimal value: %X" % (e_var)) print ("ASCII value: %c" % ("A")) print ("lowercase scientific notation: %e" % (e_var)) print ("uppercase scientific notation: %E" % (e_var)) print ("first value: %g" % (e_var)) print ("second value: %G" % (e_var))

Output:

Example #3 – With positive and negative value e_var = 341234673 f_var = -341234673 print ("decimal number: %d " % (e_var)) print ("decimal number: %d n " % (f_var)) print ("unsigned decimal number: %u" % (e_var)) print ("unsigned decimal number: %u n" % (f_var)) print ("Octal number: %o" % (e_var)) print ("Octal number: %o n" % (f_var)) print ("first hexadecimal value: %x " % (e_var)) print ("first hexadecimal value: %x n" % (f_var)) print ("second hexadecimal value: %X " % (e_var)) print ("second hexadecimal value: %X n" % (f_var)) print ("lowercase scientific notation: %e" % (e_var)) print ("lowercase scientific notation: %e n" % (f_var)) print ("uppercase scientific notation: %E" % (e_var)) print ("uppercase scientific notation: %E n" % (f_var)) print ("first value: %g" % (e_var)) print ("second value: %G" % (f_var))

Output:

Example #4 – With different length

Code:

e_var = 341234673 f_var = -341234673 print ("decimal number: %2d " % (e_var)) print ("unsigned decimal number: %1u n" % (f_var)) print ("Octal number: %2o n" % (e_var)) print ("first hexadecimal value: %1x " % (e_var)) print ("second hexadecimal value: %5X n" % (e_var)) print ("lowercase scientific notation: %2e" % (e_var)) print ("uppercase scientific notation: %1E n" % (f_var)) print ("Float value: %2.1f" % (e_var)) print ("Float value: %1.2f n" % (f_var)) print ("Octal value: %2o" % (e_var))

Output:

Conclusion

It is easy to return data in any format per application requirement.

It helps to create web applications attractive, understandable, and user-friendly.