This Video on “Java If-Else Statement” will help you learn the fundamentals of the if-else conditional statement in java. To make things better, the video will include practical examples.
#java #programming #developer
OpenJDk or Open Java Development Kit is a free, open-source framework of the Java Platform, Standard Edition (or Java SE). It contains the virtual machine, the Java Class Library, and the Java compiler. The difference between the Oracle OpenJDK and Oracle JDK is that OpenJDK is a source code reference point for the open-source model. Simultaneously, the Oracle JDK is a continuation or advanced model of the OpenJDK, which is not open source and requires a license to use.
In this article, we will be installing OpenJDK on Centos 8.
#tutorials #alternatives #centos #centos 8 #configuration #dnf #frameworks #java #java development kit #java ee #java environment variables #java framework #java jdk #java jre #java platform #java sdk #java se #jdk #jre #open java development kit #open source #openjdk #openjdk 11 #openjdk 8 #openjdk runtime environment
Learn Java 8 and object oriented programming with this complete Java course for beginners.
⌨️ (0:00:00) 1 - Basic Java keywords explained
⌨️ (0:21:59) 2 - Basic Java keywords explained - Coding Session
⌨️ (0:35:45) 3 - Basic Java keywords explained - Debriefing
⌨️ (0:43:41) 4 - Packages, import statements, instance members, default constructor
⌨️ (0:59:01) 5 - Access and non-access modifiers
⌨️ (1:11:59) 6 - Tools: IntelliJ Idea, Junit, Maven
⌨️ (1:22:53) 7 - If/else statements and booleans
⌨️ (1:42:20) 8 - Loops: for, while and do while loop
⌨️ (1:56:57) 9 - For each loop and arrays
⌨️ (2:14:21) 10 - Arrays and enums
⌨️ (2:41:37) 11 - Enums and switch statement
⌨️ (3:07:21) 12 - Switch statement cont.
⌨️ (3:20:39) 13 - Logging using slf4j and logback
⌨️ (3:51:19) 14 - Public static void main
⌨️ (4:11:35) 15 - Checked and Unchecked Exceptions
⌨️ (5:05:36) 16 - Interfaces
⌨️ (5:46:54) 17 - Inheritance
⌨️ (6:20:20) 18 - Java Object finalize() method
⌨️ (6:36:57) 19 - Object clone method. [No lesson 20]
⌨️ (7:16:04) 21 - Number ranges, autoboxing, and more
⌨️ (7:53:00) 22 - HashCode and Equals
⌨️ (8:38:16) 23 - Java Collections
⌨️ (9:01:12) 24 - ArrayList
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=grEKMHGYyns&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=9
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#java #java 8 #learn java 8 #learn java 8 - full tutorial for beginners #beginners #java course for beginners.
In this tutorial, you will learn how to make better use of built-in functions for Strings in Java to program more quickly, effectively, and aesthetically.
Firstly, of course, we have to initialize our string. What is a string used for?
#java #tutorial #java strings #java tutorial for beginners #java string #string tutorial
When we’re doing data analysis with Python, we might sometimes want to add a column to a pandas DataFrame based on the values in other columns of the DataFrame.
Although this sounds straightforward, it can get a bit complicated if we try to do it using an if-else conditional. Thankfully, there’s a simple, great way to do this using numpy!
To learn how to use it, let’s look at a specific data analysis question. We’ve got a dataset of more than 4,000 Dataquest tweets. Do tweets with attached images get more likes and retweets? Let’s do some analysis to find out!
We’ll start by importing pandas and numpy, and loading up our dataset to see what it looks like. (If you’re not already familiar with using pandas and numpy for data analysis, check out our interactive numpy and pandas course).
import pandas as pd import numpy as np df = pd.read_csv('dataquest_tweets_csv.csv') df.head()
We can see that our dataset contains a bit of information about each tweet, including:
date— the date the tweet was posted
time— the time of day the tweet was posted
tweet— the actual text of the tweet
mentions— any other twitter users mentioned in the tweet
photos— the url of any images included in the tweet
replies_count— the number of replies on the tweet
retweets_count— the number of retweets of the tweet
likes_count— the number of likes on the tweet
We can also see that the
photos data is formatted a bit oddly.
For our analysis, we just want to see whether tweets with images get more interactions, so we don’t actually need the image URLs. Let’s try to create a new column called
hasimage that will contain Boolean values —
True if the tweet included an image and
False if it did not.
To accomplish this, we’ll use numpy’s built-in
[where()](https://numpy.org/doc/stable/reference/generated/numpy.where.html) function. This function takes three arguments in sequence: the condition we’re testing for, the value to assign to our new column if that condition is true, and the value to assign if it is false. It looks like this:
np.where(condition, value if condition is true, value if condition is false)
In our data, we can see that tweets without images always have the value
 in the
photos column. We can use information and
np.where() to create our new column,
hasimage, like so:
df['hasimage'] = np.where(df['photos']!= '', True, False) df.head()
Above, we can see that our new column has been appended to our data set, and it has correctly marked tweets that included images as
True and others as
#data science tutorials #add column #beginner #conditions #dataframe #if else #pandas #python #tutorial #tutorials #twitter
When we’re programming in R (or any other language, for that matter), we often want to control when and how particular parts of our code are executed. We can do that using control structures like if-else statements, for loops, and while loops.
Control structures are blocks of code that determine how other sections of code are executed based on specified parameters. You can think of these as a bit like the instructions a parent might give a child before leaving the house:
“If I’m not home by 8pm, make yourself dinner.”
Control structures set a condition and tell R what to do when that condition is met or not met. And unlike some kids, R will always do what we tell it to! You can learn more about control structures in the R documentation if you would like.
In this tutorial, we assume you’re familiar with basic data structures, and arithmetic operations in R.
Not quite there yet? Check out our Introductory R Programming course that’s part of our Data Analyst in R path. It’s free to start learning, there are no prerequisites, and there’s nothing to install — you can start learning in your browser right now.
Start learning R today with our Introduction to R course — no credit card required!
(This tutorial is based on our intermediate R programming course, so check that out as well! It’s interactive and will allow you to write and run code right in your browser.)
In order to use control structures, we need to create statements that will turn out to be either
FALSE. In the kids example above, the statement “It’s 8pm. Are my parents home yet?” yields
TRUE (“Yes”) or
FALSE (“No”). In R, the most fundamental way to evaluate something as
FALSE is through comparison operators.
Below are six essential comparison operators for working with control structures in R:
==means equality. The statement
x == aframed as a question means “Does the value of
xequal the value of
!=means “not equal”. The statement
x == bmeans “Does the value of
xnot equal the value of
<means “less than”. The statement
x < cmeans “Is the value of
xless than the value of
<=means “less than or equal”. The statement
x <= dmeans “Is the value of
xless or equal to the value of
>means “greater than”. The statement
x >e means “Is the value of
xgreater than the value of
>=means “greater than or equal”. The statement
x >= fmeans “Is the value of
xgreater than or equal to the value of
#data science tutorials #beginner #for loop #for loops #if #if else #learn r #r #r tutorial #rstats #tutorial #tutorials #while loop #while loops