<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223064/scala-if-else-1_xehql4.png" />Source: <a href="https://www.tutorialspoint.com/scala/scala_if_else.htm">Scala - IF ELSE Statements</a></center> 

Like many other applications and programming languages, Scala also has a decision making conditional <code>if-else</code> statements. The <code>if</code> statement conditional block is executed if the condition is found to be True, if not then the <code>else</code> conditional block is implemented (only if, <code>else</code> statement is present).
Generally, the <code>else</code> statement has no condition; it is executed only when the <code>if</code> condition is False. So in short <code>if-else</code> statements consists of boolean expressions which work on a <code>True-False</code> basis.
There are various kinds of <code>if-else</code> statements:
- If statement,
- If-Else statement,
- Nested If-else statement,
- If-Else-If-Else statement.
Before you start learning the different <code>if-else</code> statements, let&#39;s quickly set up the Scala environment for mac users by following the below steps.
Note: Make sure you have Java installed, if not then please follow the instructions provided <a href="http://sourabhbajaj.com/mac-setup/Java/README.html">here</a>.
- \$ brew update
- \$ brew install scala
- \$ brew install sbt
- \$ echo &#39;-J-XX:+CMSClassUnloadingEnabled&#39; &gt;&gt; /usr/local/etc/sbtopts
- \$ echo &#39;-J-Xmx2G&#39; &gt;&gt; /usr/local/etc/sbtopts
 
Once done with the above steps quickly test the installation by typing <code>scala</code> in the terminal, you should see an output as shown below.

<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223064/scala-if-else-2_xrhd1q.png" />
</center> 

Note: In this tutorial, you will be writing the code in the <code>vim</code> editor and executing it from the terminal.
<h2 id="scala-if-statement">Scala if Statement</h2>
The <code>if</code> statement comprises of an expression which is <code>boolean</code> (true or false) in nature. If the expression holds true, then the block of code within the <code>if</code> statement is executed if the expression is false, then the first line of code after the end of the <code>if</code> statement gets executed.
<pre><code>if(boolean expression)
{
execute if boolean expression holds true
}
</code></pre>First, you will define an <code>object</code> or a <code>class</code> named <code>scala_if</code> inside that object you will set your <code>main</code> function and write your main body of the code. You will create an integer variable <code>x</code> with a value 20. Finally, comes the crux of the code which is the <code>if</code> block with a condition <code>x&lt;=20</code> and since <code>x</code> has a value 20, the block inside the <code>if</code> statement will get executed.
 
Let&#39;s save the below code with a name <code>if.scala</code>.
 
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223064/scala-if-else-3_hu9srs.png" />
</center>
To run the above code, all you need to do is type <code>scala if.scala</code> and you should see an output as shown below.
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223064/scala-if-else-4_an7r8l.png" />
</center> 

<h2 id="scala-if-else-statement">Scala if-else Statement</h2>
Similar to the <code>if</code> statement, the <code>if-else</code> statement comprises of an expression which is <code>boolean</code> (true or false) in nature. If the <code>if</code> statement holds true, then the block of code within the <code>if</code> statement is executed, if it is false then the block of code within the <code>else</code> statement gets executed.
<pre><code>if(boolean expression)
{
execute if boolean expression holds true
}
else
{
execute block if boolean expression (if statement) holds false
}
</code></pre>The following code will execute the <code>else</code> statement since variable <code>x</code> has a value greater than 20, the block of code inside the <code>else</code> statement will get executed.
Let&#39;s save the below code with a name of <code>if-else.scala</code>.
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223065/scala-if-else-5_qqwh6t.png" />
</center> 
To run the above code, all you need to do is type <code>scala if-else.scala</code> and you should see an output as shown below.
 
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223064/scala-if-else-6_gkmesv.png" />
</center> 

<h2 id="scala-nested-if-else-statement">Scala Nested if-else Statement</h2>
Much like you usually have an <code>if</code> statement followed by an <code>else</code> statement, the nested <code>if-else</code> statement is also similar in spirit. In nested if-else, you can use one if or else-if statement inside another if or else-if statement.
In the next section, you will be looking at the else-if statements.
<pre><code>if(boolean expression 1)
{
 execute block 1 if boolean expression 1 holds true**
 if(boolean expression 2)
 {
 execute block 2 if boolean expression 1 and 2 holds true
 }
}
</code></pre>The following code uses <code>nested-if</code> statements for comparing four integer variables namely $v$, $x$, $y$, and $z$ and finally, if all the statements are correct, then it prints output on the terminal.
Let&#39;s save the below code with a name <code>nested-if-else.scala</code>.
 
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223065/scala-if-else-7_ir0oxp.png" />
</center> 
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1566223065/scala-if-else-8_cwkrfh.png" />
</center> 

<h2 id="scala-else-if-statement">Scala else-if Statement</h2>
The <code>else-if</code> statement is compelling compared to the <code>if-else</code> statements. Unlike, if-else statements wherein an <code>else</code> is followed by an <code>if</code> statement, <code>else-if</code> is in itself a single statement. It gives you the freedom to test various conditions.
However, there are few rules to keep in mind while using <code>else-if</code> statements:
<ul>
<li>An <code>else-if</code> should come only after an <code>if</code> statement, an <code>if</code> statement can have zero or more <code>else-if&#39;s</code>.
</li>
<li>Similarly, an <code>else</code> should come only after an <code>if</code> and <code>else-if</code> statements, an <code>if</code> statement can have zero or one <code>else</code> statement.
</li>
<li>Once an <code>else-if</code> succeeds, none of the remaining <code>else-if&#39;s</code> or <code>else&#39;s</code> will be considered and will be directly skipped.
</li>
</ul>
<pre><code>if(boolean expression 1)
{
 execute block 1 if boolean expression 1 holds true
}
else-if(boolean expression 2)
{
 execute block 2 if boolean expression 2 holds true
}
else
{
 execute this block if all of the above statements holds false
}
</code></pre><h2 id="conclusion">Conclusion</h2>
Congratulations on finishing the tutorial.
This tutorial was a good starting point for beginners who are interested to learn the conditional IF ELSE statements in Scala.
A small exercise for you all is to write a small piece of code for the <code>else-if</code> statement in Scala and try to understand how these statements are different from the others.
References:
<ul>
<li><a href="https://www.tutorialspoint.com/scala/scala_if_else.htm">Scala - IF ELSE Statements</a></li>
</ul>
Please feel free to ask any questions related to this tutorial in the comments section below.

https://www.datacamp.com/community/tutorials/if-else-in-scala

In this tutorial, you'll learn about conditional IF ELSE statements in the Scala programming language.

IF ELSE in Scala

<h2 id="introduction">Introduction</h2>
<img align ="center" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1565032558/scala_vax3kh.png" />
Scala was designed by Martin Odersky, a professor at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and a German computer scientist. Martin started working on creating Scala in 2001, and it was first released in 2004.
Scala stands for <code>Scalable Language</code>. It is a general-purpose, object-oriented language which has a few commonalities with the Java programming language. It provides support for functional programming. Many of Scala&#39;s design decisions were aimed to address the drawbacks of the Java programming language.
A few features that Scala offers are:
<ul>
<li>Scala&#39;s source code can be compiled to Java bytecode and hence, are executable on a Java Virtual Machine (JVM).
</li>
<li>It provides language interoperability with Java so that libraries written in either language can be referenced in Scala or Java codebase.
</li>
<li>Like Java, Scala also uses a curly-brace syntax.
</li>
<li>It has many features of functional programming languages like Scheme, Standard Machine Learning and Haskell, including currying, type inference, immutability, lazy evaluation, and pattern matching.
</li>
<li>Support for Web application using JavaScript.
</li>
<li>Unlike Java, Scala also supports operator overloading, optional parameters, named parameters, and raw strings.
</li>
</ul>
One Interesting fact about Scala is that it is widely used by companies like Apple, Twitter, Walmart, and Google because of its scalability and the capability of being used in backend operations.
<h3 id="setting-up-scala-in-jupyter-notebook">Setting up Scala in Jupyter Notebook</h3>
Jupyter notebook is the most widely used tools in computer science, especially in the data science domain. It also has support for Scala development with the help of <code>spylon-kernel</code>.
Note: For those of you, who are new to jupyter notebook, please feel free to check out <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook">this</a> comprehensive tutorial.
Setting up the Scala environment in jupyter notebook is not rocket science. So, let&#39;s quickly set it up in just a few steps.
<ul>
<li>First, you will install the <code>spylon-kernel</code>.
 <code>pip install spylon-kernel</code> (Python 2) 
 <code>pip3 install spylon-kernel</code> (Python 3)
</li>
<li>Next, you will create a kernel specification which will allow you to select the <code>scala-kernel</code> in jupyter notebook.
 <code>python -m spylon_kernel install</code> (Python 2) 
 <code>python3 -m spylon_kernel install</code> (Python 3)
</li>
<li>Then, as an important step, you will install <code>pyspark</code> and <code>findspark</code> since <code>spylon-kernel</code> also installs some <code>spark</code> components.
 <code>pip install pyspark findspark</code> (Python 2) 
<code>pip3 install pyspark findspark</code> (Python 3)
</li>
<li>Finally, start your jupyter notebook and choose kernel as <code>spylon-kernel</code> and you are good to go!
</li>
</ul>
<img align ="center" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1565032560/scala2_trsgx2.png" />
<ul>
<li>Let&#39;s run a small piece of code to test whether the Scala kernel was correctly configured or not.</li>
</ul>
<pre><code class="lang-python">val x = 2
val y = 3
x*y
</code></pre>
<pre><code>x: Int = 2
y: Int = 3
res1: Int = 6
</code></pre>Great! So as you can observe from the above output, you were successful in configuring the Scala environment in jupyter notebook.
<h3 id="basic-syntax">Basic Syntax</h3>
Let&#39;s start by printing <code>Hello, World!</code> in Scala.
<pre><code class="lang-python">print(&quot;Hello, World!&quot;)
</code></pre>
<pre><code>Hello, World!
</code></pre>As you can see, it was so simple, just a <code>print</code> command followed by the input as an argument.
Now, let&#39;s take a look at few Syntax related details in Scala:
<ul>
<li>Scala is case-sensitive, which means identifier <code>DataCamp</code> and <code>dataCamp</code> would have a different meaning in Scala.
</li>
<li>In Scala, all class names first letter should be in <code>Upper Case</code>. If many words are combined to form a name of the class, each separate word&#39;s first letter should be in Upper Case. For example, <code>class MyScalaDataCampTutorial</code>.
</li>
<li>The method names in Scala work slightly differently as compared to <code>class names</code> mainly to distinguish the <code>method names</code> from <code>class names</code>. The method names should start with a Lower Case letter (<a href="https://codescracker.com/java/java-basic-syntax.htm">Source</a>). If multiple words are combined to form the name of the method, then each inner word&#39;s first letter should be in Upper Case. For example, <code>def firstDatacampScalaTutorial()</code>
</li>
<li>In Scala, the name of the program file should exactly match the object name. When saving the file, you need to save it using the object name and append <code>.scala</code> to the end of the name. For example, Let&#39;s say <code>ScalaTutorial</code> is the object name. Then the file should be saved as <code>ScalaTutorial.scala</code>.
Note: If the file name and the object name does not match, then your program will not even compile.
</li>
<li>Finally, like most programming languages function, Scala program processing also starts from the <code>main()</code> method, which is a crucial part of every Scala Program.
</li>
</ul>
Below figure shows the reserved words in Scala which cannot be used as constants or variables or as any other identifiers.
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1565032560/scala3_fld81h.png" />
 <figcaption>(<a href="https://www.tutorialspoint.com/scala/scala_basic_syntax.htm" />Source</a>) </figcaption>
</figure>
</center> 

<h3 id="data-types">Data Types</h3>
Data types in Scala are almost identical to many other programming languages like C, C++, Java, Python, etc. with similar memory footprint and precision.
Finally, let&#39;s take a look at a few of the data types that exist in Scala:
<ul>
<li>Int: A 32-bit signed (can take both positive as well as negative values) value has a range from $2^{-31}$ to $2^{31} - 1$
</li>
<li>Byte: An 8-bit signed value has a range from $2^{-7}$ to $2^{7} - 1$
</li>
<li>Short: A 16-bit signed value has a range from $2^{-15}$ to $2^{15} - 1$
</li>
<li>Long: A 64-bit signed value has a range from $2^{-63}$ to $2^{63} - 1$
</li>
<li>Float: Both 32-bit (single-precision float) and 64-bit (double precision float)
</li>
<li>Char: A 16-bit unsigned (can only take positive values) Unicode character.
</li>
</ul>
A few of the other data types are String, Boolean, Null.
<h2 id="conclusion">Conclusion</h2>
Congratulations on finishing this tutorial.
It is a good starting point for beginners who are interested in learning Scala.
There is a plethora of information related to Scala that remains unraveled like if/else, for loops, various operators in Scala, etc., which will be covered in the future tutorials, so stay tuned!
References:
<ul>
<li><a href="https://en.wikipedia.org/wiki/Scala_(programming_language)">Scala (programming language)</a>
</li>
<li><a href="https://www.tutorialspoint.com/scala/index.htm">Scala Tutorial</a>
</li>
</ul>
Please feel free to ask any questions related to this tutorial in the comments section below.

https://www.datacamp.com/community/tutorials/beginners-guide-to-scala

Learn the basics in Scala including syntax, how to set up an environment, and various common data types.

Beginner’s Guide to Scala

Markdown is a lightweight and popular Markup language which is a writing standard for data scientists and analysts. It is often converted into the corresponding HTML which by the Markdown processor which allows it to be easily shared between different devices and people.
Markup language is similar to Hypertext Markup Language(HTML) made of Markup tags, and it consists of the opening tag &lt;tagname&gt; and closing tag &lt;/tagname&gt;.
In this tutorial, you can see the same result obtained by using Markup tags, and also the Markdown syntax which is supported by Jupyter Notebook.
<ul>
 <li>Headings</li>
 <li>Blockquotes</li>
 <li>Code section</li>
 <li>Mathematical Symbol</li>
 <li>Line Break</li>
 <li>Bold and Italic Text</li>
 <li>Horizontal Lines</li>
 <li>Ordered List</li>
 <li>Unordered List</li>
 <li>Internal and External Link</li>
 <li>Table</li>
 <li>Image</li> </ul>

You need to have Jupyter Notebook, the environment can be set up by using DataCamp&#39;s tutorial: <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook">Jupyter Notebook Tutorial: The Definitive Guide</a>.
Markdown cells can be selected in Jupyter Notebook by using the drop-down or also by the keyboard shortcut &#39;m/M&#39; immediately after inserting a new cell.
<img src="https://i.imgur.com/vl7Fmg9.png" width=500 height=500>
<h2 id="headings">Headings</h2>
The Headings starts with &#39;#,&#39; i.e., hash symbol followed by the space, and there are six Headings with the largest heading only using one hash symbol and the smallest titles using six hash symbols.
# (Header 1, title)
## (Header 2, major headings)
### (Header 3, subheadings)
#### (Header 4)
##### (Header 5)
###### (Header 6)``
Alternatively, the headings can start with Markup Tags, i.e., from h1 to h6 with the following syntaxes.
&lt;h1&gt;Header 1,title&lt;h1&gt; 
&lt;h2&gt;Header 2,major headings&lt;h2&gt; 
&lt;h3&gt;Header 3,subheadings&lt;h3&gt; 
&lt;h4&gt;Header 4&lt;h4&gt; 
&lt;h5&gt;Header 5&lt;h5&gt; 
&lt;h6&gt;Header 6&lt;h6&gt;
Both of the syntaxes above can render the headings from h1 to h6 after clicking the &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/jr4dpLW.png" width=500 height=500 />
<h2 id="blockquotes">Blockquotes</h2>
Blockquotes can hold the large chunk of text and are generally indented. They can be obtained by using Markdown symbol &#39;&gt;&#39; or with &lt;blockquote&gt;text for blockquote&lt;/blockquote&gt; 
<ul>
 <li>>This is good</li>
 <li>&lt;blockquote&gt;This is good&lt;/blockquote&gt;
</ul>



Both of the syntaxes above can render the text in indented form after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/WLTSEiE.png" width=500 height=500>
<h2 id="code-section">Code section</h2>
The Code section is the part that specifies the code of different programming languages and can be rendered where inline code starts with &#39; `inline code goes here` &#39; back-ticks around it, but the block of code starts with three back-ticks &#39; ``` block line code goes here ``` &#39;. Also,the Markup tag for a Code section is &#39; &lt;code&gt;code goes here&lt;code&gt; &#39;.
The inline code example is given below: 
`x =5` 
 
You can see after clicking &quot;Run&quot; the inline code renders with highlighting the code.
<img src="https://i.imgur.com/ovmm4T0.png" width=500 height=500>
Code section examples are given below: 
<ul>
 <li>Using Markdown</li>
```Python 
 str = &quot;This is block level code&quot; 
 print(str) 
```
 <li>Using Markup Tags</li>
&lt;code&gt;Python 
 str = &quot;This is a block level code&quot; 
 print(str) &lt;/code&gt;

 </ul>

Using Markdown, you can get the syntax highlighting of code if programming language name is mentioned after the &#39;```&#39; three ticks and the example is given below:
<img src="https://i.imgur.com/QybdSeC.png" width=500 height=500>
Using Markdown, you will not get syntax highlighting, but code is highlighted:
<img src="https://i.imgur.com/6nFeCfV.png" width=500 height=500>
<h2 id="mathematical-symbol">Mathematical Symbol</h2>
The mathematical symbol in Markdown is included in &#39;\$ mathematical expression goes here \$&#39; enclosed in a dollar symbol and in Markup you can follow this link for more detail:<a href="https://www.w3schools.com/charsets/ref_utf_math.asp"> Mathematical Operators</a>. You can see the example of using the mathematical symbols below. 
\$\sqrt{k}$
The above example will render the mathematical expression in a bold format.
<img src="https://i.imgur.com/3BHMusF.png" width=500 height=500>
<h2 id="line-break">Line Break</h2>
The line break tag starts with &lt;br&gt; tag with no closing tag which breaks the line, and the remaining contents begin with a new line with the example shown below.
<pre><code>The line breaks after using &lt;br&gt; br tags and it is awesome.
</code></pre>After clicking the &#39;Run&#39; in the toolbar, you can see the line break after using the &lt;br&gt; tag. The remaining text starts in a new line.
<img src="https://i.imgur.com/8YZjIjg.png" width=500 height=500>
<h2 id="bold-and-italic-text">Bold and Italic Text</h2>
You can use &lt;b&gt; tags, &#39;**&#39; i.e. &#39;double asterisk&#39; or &#39;__&#39; i.e. &#39;double underscore&#39; to get bold text with the following syntax. 
<ul>

<li>&lt;b&gt;This is bold text &lt;/b&gt;</li> 
<li>** This is bold text</li> 
<li>__ This is bold text</li> 
</ul>
All of the syntaxes above can render bold text after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/3aoz2mz.png" width=500 height=500>
You can use &lt;i&gt; tags, &#39;*&#39; i.e., single asterisk or &#39;_&#39; i.e., single underscore to get the italic text for the following syntax. 
<ul>
 <li>&lt;i&gt;This is italic text &lt;/i&gt;</li> 
 <li>* This is italic text</li> 
 <li>_ This is italic text</li> 
</ul>
All of the syntaxes above can render the italic text after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/42M3r13.png" width=500 height=500>
<h2 id="horizontal-line">Horizontal Line</h2>
You can obtain a horizontal line by using Markdown &#39;---&#39; three hyphens or Markup tags &lt;hr&gt;
Both of the syntax above will render the horizontal line across from one end to another end after clicking &quot;Run&quot;.
<img src="https://i.imgur.com/C4sA03A.png" width=500 height= 500>
<h2 id="ordered-list">Ordered List</h2>
The Ordered List is the numbered list which is obtained by starting &lt;ol&gt; tag and ending with &lt;/ol&gt; tag with the required item in between &lt;li&gt; and &lt;/li&gt; tags. The tag, i.e., &#39;ol,&#39; is the short form for an ordered list and &#39;li&#39; is the short form for the list item. For example, you can see the Ordered List below containing the item for the grocery list.
&lt;ol&gt; 
 &lt;li&gt;Fish&lt;/li&gt; 
 &lt;li&gt;Eggs&lt;/li&gt; 
 &lt;li&gt;Cheese&lt;/li&gt; 
&lt;/ol&gt;
Alternatively, you can list by &#39;1. &#39; i.e., the required number followed by space with the example given below.
<ol>
<li>Fish</li>
<li>Eggs </li>
<li>Cheese</li>
</ol>
Both of the syntaxes above can render the numbered list after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/SCF2HeH.png" width=500 height=500 />
<h2 id="unordered-list">Unordered List</h2>
The Unordered list is a bullet list which is obtained by using the &lt;ul&gt; tag and ending with the &lt;/ul&gt; tag, see the example below:
&lt;ul&gt; 
 &lt;li&gt;Fish&lt;/li&gt; 
 &lt;li&gt;Eggs&lt;/li&gt; 
 &lt;li&gt;Cheese&lt;/li&gt; 
&lt;/ul&gt;
Alternatively, the Unordered list can start with the &#39;-&#39; symbol with space, which gives the black circle symbol and can also start with the &#39;*&#39; symbol with space, which gives the black square symbol.
- &nbsp; Fish 
- &nbsp; Eggs 
- &nbsp; Cheese 
The above example shows the bullet list contains the &#39;-&#39; symbol followed by space with the items which gives the black circle symbol.
Both of the syntaxes above can render the same following result where the items in the list appear in the black circle, after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/WWrydEh.png" width=500 height=500 />
<h2 id="internal-and-external-link">Internal and External Link</h2>
Internal Link in Markdown starts with &lt;a&gt; tag with unique id defined by the attribute &#39;id&#39; which can be linked in the notebook with the example below: 
&lt;a id = &quot;division_ID&quot;text goes here&gt;&lt;/a&gt; 
Also, the id defined above can be linked to the section of the notebook by following the code which makes the link clickable. 
[Section title](#division_ID)
The example of the above can be seen below where the id defined is linked with the section and clickable link obtained after clicking &quot;Run&quot; in the toolbar.
<img src="https://i.imgur.com/aRmHbUb.png" width=500 height=500>
External Link in Markdown starts with &lt;a&gt; and ends with &lt;a&gt; tag, i.e., &lt;a&gt; stands for anchor which defines the link, and it has attribute &#39;href&#39; also called as hyper reference which contains the destination address of the link or URL and texts between tags is visible and is clickable to open the destination address as shown below.
&lt;a&gt; href=&quot;<a href="https://www.google.com">https://www.google.com</a>&quot; &gt;Link to Google&lt;/a&gt;
Alternatively, it could also start with the __[text link](URL for the site)__ where the double underscore is on both sides with the text link enclosed in a square bracket and the URL for the site is enclosed in a parenthesis followed by the URL.
<a href="https://www.google.com">Link to Google</a>
Both of the syntaxes above can render the same result below where the clickable and underlined text can lead to a new page after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/YXUfyMu.png" width=500 height=500 />
<h2 id="table">Table</h2>
The Table contains the information in rows and columns and is built by the combination of &#39;|&#39; i.e. &#39;vertical pipe&#39; to separate each column and &#39;-&#39; i.e., hyphen symbol to create the header where the blank line, i.e., a combination of vertical pipe and dashes to render the table format.
Also, you can vary the cells by roughly aligning with the columns, and the notebook will automatically resize the content in the given cell.
<code>
|Name|Address|Salary|
|-----|-------|------|
|Hanna|Brisbane|4000|
|Adam|Sydney|5000|
 </code>
Alternatively, the Markdown can be used to build tables where &lt; table &gt; is used to define a table with its width in percentages.&lt;tr&gt; sets table row which gives the bold with centered text along with table heading is described by &lt;th&gt; is at the top of the table with the other entries in the table are set by the &lt;td&gt; i.e., table data tag. 
&lt;table style=&quot;width:20%&quot;&gt; 
 &lt;tr&gt; 
 &lt;th&gt;&lt;Name&gt;&lt;/th&gt; 
 &lt;th&gt;&lt;Address&gt;&lt;/th&gt; 
 &lt;th&gt;&lt;Salary&gt;&lt;/th&gt; 
 &lt;/tr&gt; 
 &lt;tr&gt; 
 &lt;td&gt;&lt;Hanna&gt;&lt;/td&gt; 
 &lt;td&gt;&lt;Brisbane&gt;&lt;/td&gt; 
 &lt;td&gt;&lt;4000&gt;&lt;/td&gt; 
 &lt;/tr&gt;
 &lt;tr&gt; 
 &lt;td&gt;&lt;Adam&gt;&lt;/td&gt; 
 &lt;td&gt;&lt;Sydney&gt;&lt;/td&gt; 
 &lt;td&gt;&lt;5000&gt;&lt;/td&gt; 
 &lt;/tr&gt; 
&lt;/table&gt;
Both of the syntaxes above can render the same following result after clicking &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/LZmWNPO.png" width=500 height=700>
<h2 id="images">Images</h2>
You can insert an image from the toolbar by choosing the &#39;Insert Image&#39; from an Edit menu and can browse the required image as shown below.
<img src="https://i.imgur.com/sggzys0.png" width=200 height=200>
<img src="https://i.imgur.com/rr9YIOR.png" width=500 height=500>
The image can contain Markdown tag &lt;img&gt; with source, i.e.src as an attribute which consists the link to image with optional properties like width and height, and the example of it is below.
 &lt;img&gt; &lt;src=&quot;<a href="https://i.imgur.com/WWrydEh.png">https://i.imgur.com/WWrydEh.png</a>&quot; width =&quot;500&quot; height=500 &gt; 
Both of the processes above can render the same image after clicking the &#39;Run&#39; in the toolbar.
<img src="https://i.imgur.com/u7tlPDv.png" width=500 height=700>
<h2 id="conclusion">Conclusion</h2>
In this tutorial, you have learned about different Markup tags, which is defined by Markup language and also syntax related to Markdown cells specific to the Jupyter Notebook which is used side by side along with code to describe the content more effectively. 
If you would like to learn more about Markdown, have a look at <a href="https://www.markdownguide.org/getting-started/">Markdown Guide</a>.

https://www.datacamp.com/community/tutorials/markdown-in-jupyter-notebook

In this tutorial, you'll learn how to use and write with different markup tags using Jupyter Notebook.

In this tutorial, you'll learn how to use markdown with Jupyter Notebook. Find code and syntax examples to markdown your code today!

Jupyter Notebook Markdown Tutorial

Markdown in Jupyter Notebook

<a href="https://jupyter.org/">Jupyter Notebooks</a> are an essential part of any Data Science workflow, so much so that many of the organizations like Netflix find them indispensable. The browser-based computing environment, coupled with reproducible document format, has made them the de-facto choice of millions of data scientists and researchers around the globe. Jupyter boasts of a great international community coming from almost every country on earth. The community themselves has developed a lot of kernels.
<a href="https://jupyterlab.readthedocs.io/en/stable/">Jupyter Lab</a> is the next-generation user interface for Project Jupyter offering all the familiar building blocks of the classic Jupyter Notebook like the notebook, terminal, text editor, file browser, rich outputs, etc. However, unlike the classic notebooks, all these features are provided in a flexible and powerful user interface. The basic idea of the Jupyter Lab is to bring all the building blocks that are in the classic notebook, plus some new stuff, under one roof.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql1_ae4e89.png" />
JupyterLab showing its work area with notebooks, text files, terminals, and notebook outputs all capable of interacting with each other.
Let’s first get Jupyter Lab installed and running on our systems.
<h4 id="installation">Installation</h4>
JupyterLab can be installed using <code>conda, pip</code>, or <code>pipenv.</code>
<pre><code>conda install -c conda-forge jupyterlab
or
pip install jupyterlab
or
pipenv install jupyterlab 
pipenv shell
</code></pre>Have a look at the official installation <a href="https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html">documentation</a> for more details.
<h4 id="starting-jupyterlab">Starting JupyterLab</h4>
You can start Jupyter by simply typing the following in the console:
<pre><code>jupyter lab
</code></pre>JupyterLab will open automatically open in the browser with an interface resembling the one below. This means everything is in place, and you are good to go.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql2_lydydc.png" />
The main work area is the place where the actual activity takes place. It comprises of the notebooks, documents, consoles, terminals, etc. Just double click or drag a file on to this area to start working.
<h2 id="sql-interface-to-jupyterlab">SQL Interface to JupyterLab</h2>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql3_piw8ih.png" />
Most of the times, the data that we work with is stored in files called databases, and an essential task of a Data Scientist is to be able to access data from databases and then analyze it. Therefore it is a great idea to have a seamless interface between SQL databases and Jupyter Notebook/Lab so that accessing and manipulating data becomes easier and more efficient. This interface can be achieved in two possible ways:
<h3 id="1-using-ipython-sql-magic-extension">1. Using IPython SQL Magic extension</h3>
Magic commands are a set of convenient functions in Jupyter Notebooks that are designed to solve some of the common problems in standard data analysis. You can see all of the available magics with the help of <code>%lsmagic</code>.
<a href="https://github.com/catherinedevlin/ipython-sql">IPython SQL magic</a> extension makes it possible to write SQL queries directly into code cells as well as read the results straight into pandas DataFrames (<a href="http://news.datascience.org.ua/2019/01/11/unleash-the-power-of-jupyter-notebooks/">Source</a>). This works for both the traditional notebooks as well as the modern Jupyter Labs.
Installing SQL module in the notebook
<pre><code> !pip install ipython-sql
</code></pre>Loading the SQL module
<pre><code> %load_ext sql
</code></pre>The above magic command loads the <code>ipython-sql</code> extension. We can connect to any database which is supported by <a href="https://www.sqlalchemy.org/">SQLAlchemy</a>. Here we will connect to a SQLite database. Enter the following command in the code cell:
<pre><code>%sql sqlite://
</code></pre>If you get the output as <code>‘Connected: @None&#39;,</code> this means the connection has been established.
<ul>
<li>Creating a database</li>
</ul>
Finally, we create a demo table called EMPLOYEES to showcase the function.
<pre><code>%%sql 
CREATE TABLE EMPLOYEE(firstname varchar(50),lastname varchar(50)); 
INSERT INTO EMPLOYEE VALUES(&#39;Tom&#39;,&#39;Mitchell&#39;); 
INSERT INTO EMPLOYEE VALUES(&#39;Jack&#39;,&#39;Ryan&#39;);
</code></pre>We can now execute queries on our database.
<pre><code> %sql SELECT * from EMPLOYEE; 
 ---------------------------------- 
 * sqlite:// 
 Done.
</code></pre>The above query outputs the following table.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql4_xb2uuw.png" />
<ul>
<li>Working with an existing database</li>
</ul>
We can also connect to an existing database using the magic function. For this tutorial, we will be making use of the SQL_SAFI database(Studying African Farmer-led Irrigation (SAFI) database). &quot;The <a href="http://www.safi-research.org/">SAFI Project</a> is a research project looking at farming and irrigation methods used by farmers in Tanzania and Mozambique. This dataset is composed of survey data relating to households and agriculture&quot; (<a href="http://brunalab.org/las6292/2019/01/28/session-3-data-structure/">Source</a>). You can download it from <a href="https://github.com/parulnith/SQL-Interface-within-JupyterLab/blob/master/SQL_SAFI.sqlite">here</a>.
<pre><code> #Specifying the path of the database 
 %sql sqlite:////Users/Parul/Desktop/SQL_SAFI.sqlite 
 -----------------------------------------------------------

 &#39;Connected: @/Users/Parul/Desktop/SQL_SAFI.sqlite&#39;
</code></pre>The above statement opened the database named <code>SQL_SAFI.sqlite</code> that resides in the path: <code>Users/Parul/Desktop.</code> Let&#39;s select the <code>Crops</code> table and display its contents.
<pre><code>%sql select * from Crops
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql5_tzqadk.png" />
Displaying first few rows of the Crops Table
<pre><code> %sql select * from Crops where D_curr_crop = &quot;maize&quot; AND ID &lt;= 3;
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql6_nfmzzp.png" />
The result can also be converted to a pandas dataframe, as follows:
<pre><code>result = %sql select * from Crops where D_curr_crop = &quot;maize&quot; AND ID &lt;= 3; 
dataframe = result.DataFrame()
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql7_n6j2fu.png" />
<pre><code>dataframe.info()
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql8_jkhriq.png" />
<h3 id="2-the-jupyterlab-sql-extension">2. The jupyterlab-sql extension</h3>
Before going further, let’s talk a little about the Jupyter extensions in general.
JupyterLab Extensions
Extensions are one of the most useful features of Jupyter Lab and can enhance the Jupyter lab experience. Jupyter Lab is essentially an extensible environment itself with notebooks, terminal, and editor, all present as an extension.
&quot;JupyterLab extensions are npm packages (the standard package format in Javascript development)&quot;(<a href="https://buildmedia.readthedocs.org/media/pdf/jupyterlab/latest/jupyterlab.pdf">Source</a>). Extensions provide a way to customize and enhance the JupyterLab experience by providing several useful options like new themes, viewers, keyboard shortcuts, advanced settings options, to name a few. There are many community-developed extensions on GitHub. You can search for the GitHub topic <a href="https://github.com/topics/jupyterlab-extension">jupyterlab-extension</a> to find extensions.
To install JupyterLab extensions, you need to have <a href="https://nodejs.org/">Node.js</a> installed which can either be installed from their <a href="https://nodejs.org/en/">website</a> or as follows.
<pre><code>conda install -c conda-forge nodejs 
or 
brew install node #for MacOS users
</code></pre>The <code>jupyterlab-sql extension</code> is one such useful extension that lets you add a SQL user interface to Jupyter Lab. This has two primary advantages:
<ul>
<li>The SQL tables can be explored with a simple point and click.</li>
<li>Tables can also be modified and read with custom queries.</li>
</ul>
You can read more about it at the official <a href="https://github.com/pbugnion/jupyterlab-sql">Github repository</a>.
<h4 id="installation">Installation</h4>
To install <code>jupyterlab-sql</code>, run the following commands in the given order:
<pre><code>pip install jupyterlab_sql
jupyter serverextension enable jupyterlab_sql --py --sys-prefix
jupyter lab build
</code></pre>You will then need to restart any running Jupyter servers.
Also, it is important to note that <code>jupyterlab-sql</code> only works with Python 3.5 and above.
<h4 id="getting-started">Getting Started</h4>
After you restart the Jupyter Lab, a new SQL icon appears in the launcher
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql9_iba4oi.png" />
This means, everything is installed correctly, and you are ready to connect your instance to a database.
<h4 id="connection">Connection</h4>
For connecting to the database, you need to have a valid database URL. <code>jupyterlab-sql</code> has been extensively tested against SQLite, PostgreSQL, and MySQL databases (<a href="https://github.com/pbugnion/jupyterlab-sql">Source</a>). Follow the <a href="https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls">SQLAlchemy guide</a> on URLs. The URL must be a database URL, for instance:
<pre><code>postgres://localhost:5432/postgres 
postgres://username:password@localhost:5432/postgres 
mysql://localhost/employees 
sqlite:// 
sqlite:///myfile.db
</code></pre>Let us connect to the existing SQL_SAFI database, that we used earlier. Click on the SQL icon in the launcher and type in the database url. Press <code>enter</code> to connect.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832500/sql10_cvrmwv.png" />
As soon as the database gets connected, you can view all your tables in the database.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832501/sql11_wpfbqm.png" />
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563832501/sql12_ds2ftm.gif" />
Next, we can also write custom SQL queries to get the desired data from the tables.
<h3 id="conclusion">Conclusion</h3>
Using SQL and JupterLab together takes the data analysis to the next level. The jupyter-sql interface makes it very easy to connect the SQL Server to Jupyter ecosystem and extract the data directly into it, without having to leave the Jupyter interface.
If you would like to learn more about SQL, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/data-driven-decision-making-with-sql">Data-Driven Decision Making in SQL</a> course.

https://www.datacamp.com/community/tutorials/sql-interface-within-jupyterlab

Learn how to use and modify SQL tables within JupyterLabs.

SQL Interface within JupyterLab

You&#39;ll be using GitHub for this tutorial as it is widely used, however, Bitbucket, Gitlab, etc. are also popular, but Developers, Data Scientists, and Data Analysts mostly use the GitHub to PUSH and do PULL Request.
<ul>
 <li>GIT PUSH</li>
 <li>Using Command line to PUSH to GitHub.</li>
 <li>Using GitHub Desktop to PUSH to your local content to GitHub.</li>
 <li>PULL Request</li>
 <li>PULL Request through Command Line.</li>
 <li>PULL Request through GitHub Desktop.</li>
</ul>

You can easily follow along with all of the materials in the tutorial, even if you are a beginner. However, if you don&#39;t have any concept about Git, then have a look at <a href="https://www.youtube.com/watch?v=HVsySz-h9r4&t=1402s">Git Tutorial for Beginners: Command-Line Fundamentals </a> and set up your environment by using <a href="https://www.datacamp.com/community/tutorials/git-setup"> GIT SETUP: The Definitive Guide</a>.
You do need to have a GitHub account. If you don&#39;t, you can create one <a href="https://github.com/">here</a>.
<h2 id="git-push">Git PUSH</h2>
The <code>git push</code> command is used to transfer or push the commit, which is made on a local branch in your computer to a remote repository like GitHub. The command used for pushing to GitHub is given below. 
<code>git push &#39;remote_name&#39; &#39;branch_name&#39;</code> 
In this tutorial, you&#39;ll be looking two different ways to PUSH to GitHub.
<h2 id="using-command-line-to-push-to-github">Using Command line to PUSH to GitHub</h2>
1. Creating a new repository. 
<ul>
 <li> You need to create a new repository and click on the plus sign.</li>
 <li>Fill up all the required details, i.e., repository name, description and also make the repository public this time as it is free.</li>
</ul>

<img src="https://i.imgur.com/4EWFwBk.png" width="700" height="700"/>
<img src="https://i.imgur.com/9q1fUg1.png" width="700" height="700" />
2. Open your Git Bash. 
<ul>
<li>Git Bash can be downloaded in <a href="https://git-scm.com/downloads">here</a>, and it is a shell used to interface with the operating system which follows the UNIX command.</li>

</ul>


3. Create your local project in your desktop directed towards a current working directory. 
<ul>
 <li><code>pwd</code> stands for &#39;print working directory&#39;, which is used to print the current directory.</li>
 <li>Move to the specific path in your local computer by <code>cd &#39;path_name&#39;</code>. The cd commands stand for &#39;change directory&#39; and it is used to change to the working directory in your operating system, and to locate your file, <code>&#39;path_name&#39;</code>, i.e., <code>C:/Users/Dell/Downloads/FaceDetect-master</code> needs to be given. This command can identify the required file that you are looking to work with.</li>
</ul>
<img src="https://i.imgur.com/soIN2tK.png" width="700" height="700"/> 

4. Initialize the git repository 
<ul>
 <li>Use <code>git init</code> to initialize the repository. It is used to create a new empty repository or directory consisting of files&#39; with the hidden directory. &#39;.git&#39; is created at the top level of your project, which places all of the revision information in one place. </li>
</ul>

<img src="https://i.imgur.com/45YjU3E.png" width="700" height="700"/>
5. Add the file to the new local repository. 
<ul>
 <li>Use <code>git add . </code> in your bash to add all the files to the given folder.</li>
 <li>Use <code>git status</code> in your bash to view all the files which are going to be staged to the first commit.</li>
</ul>
<img src="https://i.imgur.com/ZqYEvxO.png" width="700" height="700"/> 

6. Commit the files staged in your local repository by writing a commit message. 
<ul>
 <li>You can create a commit message by <code>git commit -m &#39;your message&#39;</code>, which adds the change to the local repository.</li>
 <li><code>git commit</code> uses &#39;-m&#39; as a flag for a message to set the commits with the content where the full description is included, and a message is written in an imperative sentence up to 50 characters long and defining &quot;what was changed&quot;, and &quot;why was the change made&quot;.
</ul>
<img src="https://i.imgur.com/ogoyi2W.png" width="700" height="700" /> 

7. Copy your remote repository&#39;s URL from GitHub. 
<ul>
 <li>The HTTPS or URL is copied from the given GitHub account, which is the place of the remote repository.</li>
</ul>

<img src="https://i.imgur.com/5GSsEnY.png" width="700" height="700"/>
8. Add the URL copied, which is your remote repository to where your local content from your repository is pushed. 
<ul>
 <li><code> git remote add origin &#39;your_url_name&#39;</code>
 <li>In the above code, The &#39;origin&#39; is the remote name, and the remote URL is &quot;<a href="https://github.com/Olivia-Smithcoder100/FaceDetection.git">https://github.com/Olivia-Smithcoder100/FaceDetection.git</a>&quot;. You can see the remote as GitHub in this case, and GitHub provides the URL for adding to the remote repository.
</ul>



9. Push the code in your local repository to GitHub 
<ul>
 <li> <code>git push -u origin master</code> is used for pushing local content to GitHub.</li>
 <li>In the code, the origin is your default remote repository name and &#39;-u&#39; flag is upstream, which is equivalent to &#39;-set-upstream.&#39; and the master is the branch, name.upstream is the repository that we have cloned the project.</li>
 <li>Fill in your GitHub username and password.</li>

</ul>

<img src="https://i.imgur.com/gf9qMMK.png" width="700" height="700"/>
10. View your files in your repository hosted on GitHub. 
<ul>
 <li>You can finally see the file hosted on GitHub.</li>
</ul>

<img src="https://i.imgur.com/fNUnnYJ.png" width="700" height="700"/>
<h2 id="using-github-desktop-to-push-to-your-local-content-to-github-">Using GitHub Desktop to PUSH to your local content to GitHub.</h2>
<a href="https://desktop.github.com/">GitHub Desktop</a> is available to download for any operating system, and it gives the GUI(Graphical User Interface) platform to push your local content from your local repository to a remote repository like GitHub.
You need to open your GitHub account in your browser and the process of creating a new repository, i.e., step 1 is the same as mentioned above in &quot;Using Command line to PUSH to GitHub&quot;.
1. Click &quot;Set up in a Desktop&quot;. 
<ul>
 <li>You need to click on the button, as shown below where a pop up comes, and you click on &quot;Open GitHub desktop&quot;.</li>

</ul>

<img src="https://i.imgur.com/jeffvFy.png" width="700" height="700"/>
<img src="https://i.imgur.com/soFJZ6P.png" width="700" height="700"/>
2. Cloning in a GitHub Desktop. 
<ul>
 <li>You can click the &quot;Clone&quot; button, as shown below.</li>

</ul>

<img src="https://i.imgur.com/RG6Kh45.png" width="700" height="700"/>
After cloning a new clone, the folder is created in your local computer where a hidden directory &quot;.git&quot; is also present.
<img src="https://i.imgur.com/Ywk16L8.png" width="700" height="700"/>
3. Copy all the required files from your local computer into the clone folder on your computer. 
<ul>
 <li> You need to copy all the required files, images, README files, etc., to the clone folder.</li>
</ul>

<img src="https://i.imgur.com/IZSXcW0.png" width="700" height="700"/>
4. Move to GitHub Desktop and commit to master 
<ul>
 <li> You can see the files that are added into the clone folder are seen in GitHub Desktop too. Finally, write your message and push &quot;Commit to master&quot;.</li>

</ul>
<img src="https://i.imgur.com/hYVhufe.png" width="700" height="700"/> 

5. Publish branch in GitHub Desktop to upload your all files to GitHub. 
<ul>
 <li> You can click on &quot;Publish Branch&quot; to publish your all local content to GitHub.</li>
</ul>
<img src="https://i.imgur.com/XyB6SNk.png" width="500" height="500" /> 

You can view your repository in GitHub after you have completed all steps. 

<img src="https://i.imgur.com/XY4S0nJ.png" width="700" height="700" /></ul>



<h2 id="pull-request">PULL Request</h2>
If you make a change in a repository, GIT PULL can allow others to view the changes. It is used to acknowledge the change that you&#39;ve made to the repository that you&#39;re working on. Or also called a target repository.
The simple command to PULL from a branch is:
<code>git pull &#39;remote_name&#39; &#39;branch_name&#39;</code>.
The <code>git pull</code> command is a combination of <code>git fetch</code> which fetches the recent commits in the local repository and <code>git merge</code>, which will merge the branch from a remote to a local branch also &#39;remote_name&#39; is the repository name and &#39;branch_name&#39; is the name of the specific branch.
You&quot;ll be looking at two different ways on how to use the PULL request.
<h2 id="pull-request-through-command-line-">PULL Request through Command Line.</h2>
You can see the README files below which contains a typo. The README file has the word &quot;contain&quot; misspelled as &quot;containnns&quot;. The owner of this repository is MNALO, and Olivia is the collaborator. She will solve the error and submit a PULL Request You&#39;ll see the process for making a PULL Request through a particular example given below.
<img src="https://i.imgur.com/ChzbfUZ.png" width="700" height="700"/>
In the file above, you can see a typo in the word &quot;containnns&quot;.
1. Fork the Repository. 
<ul>

 <li> &quot;The &quot;Fork&quot; is a copy of a repository. Forking a repository allows you to freely experiment with changes without affecting the original project.&quot;<a href="https://help.github.com/en/articles/fork-a-repo">(Source)</a>
 </li>
</ul>

<img src="https://i.imgur.com/pX2aItX.png" width="700" height="700"/>
2. Open your bash in your computer. 
<ul>
 <li>You need to move to the required path or folder by using the <code>cd</code> command, and the content can be viewed by using the <code>ls</code> command, which will list all of the present files in the directory and in our case you can see the &#39;README.md&#39; is present.
</ul>

<img src="https://i.imgur.com/amgiEzz.png" width="700" height="700"/>
3. Make a new branch. 
<ul>
 <li>You can create a new branch by using the <code>git checkout -b &#39;branch_name&#39;.</code>
 In the above code, &#39;-b&#39; flag is used to create a new branch, and &#39;branch_name&#39; is used to give the branch a specific name, and with checkout, the branch is switched to the newly created branch.</li>
</ul>

<img src="https://i.imgur.com/qCgQIxu.png" width="700" height="700"/>
4. Make a change by using vim from bash or direct replacement from the original README file. 
<ul>
 <li> You can change the word &quot;containnns&quot; to &quot;contains&quot; in the README file, and the changes with the current status can be viewed by using the following command.</li>
<img src="https://i.imgur.com/ZyQApTE.png" width="700" height="700"/>


5. Adding and Committing a file to the repository. 
<ul>
 <li>You need to add and commit by the following commands.</li>
</ul>

<img src="https://i.imgur.com/yYYl30F.png" width="700" height="700"/>
6. Push the repository to the GitHub. 
<ul>
 <li>You need to push the content by <code>git push origin &#39;branch_name&#39; </code></li>
 <li>In the above code, the origin is the remote repository, and &#39;branch_name&#39; is the required branch that you need to upload your local content.
</ul>

<img src="https://i.imgur.com/Eq2viyX.png" width="700" height="700"/>
7. PULL request for a specific branch on GitHub. 
<ul>
 <li>You can move to your repository in GitHub and see that there is a new branch.</li>
<li>
You can now move to step 8, but there is a need for a local repository update with the upstream repository, read this detailed blog on <a href="https://www.digitalocean.com/community/tutorials/how-to-create-a-pull-request-on-github">
 How To Create a Pull Request on GitHub</a> </li>
 <li>Alternatively, you can do <code>git pull-request</code> in the command line and complete the PULL Request to GitHub, where it will force push your current branch to a remote repository.</li>


</ul>

<img src="https://i.imgur.com/SwuYNpF.png" width="700" height="700"/>
8. Open a Pull request 
<ul>

 <li> You need to click the button on &quot;Create pull request,&quot; to finish the action.</li>

</ul></ul>

<img src="https://i.imgur.com/gI0HoHX.png" width="700" height="700"/>
<h2 id="deleting-a-branch-after-the-pull-request-is-merged-">Deleting a Branch after the PULL Request is Merged.</h2>
<ul>
<li>You need to move to the main page of the repository and click &quot;Pull requests&quot;.</li> 

<img src="https://i.imgur.com/i2wqIO5.png" width="700" height="700"/> 

<ul>
<li>You need to click &#39;Closed&#39; to see the lists of all the PULL Requests that you&#39;ve made, but there is only one at the moment which needs to be selected. It is the one related to your branch that you want to delete.</li> 

<img src="https://i.imgur.com/XyIxLdu.png" width="700" height="700"/> 

<ul>
<li>You can now click &#39;Delete branch&#39; to complete the action.</li> 

<img src="https://i.imgur.com/tThThh6.png" width="700" height="700"/> 


 The owner of the repository can view all the commits, pull request, etc., made by collaborators and others. The changes made by someone can be significant, quick fixes for a bug, errors, etc., and are added to the project. 

<img src="https://i.imgur.com/J02pFZh.png" width="700" height="700"/> 

 The owner now clicks &quot;Merge pull request&quot;. Also, he/she will click &quot;Confirm merge&quot; through the following process. 

<img src="https://i.imgur.com/rAuHU5X.png" width="700" height="700"/>
<img src="https://i.imgur.com/qjrf9Hf.png" width="700" height="700"/> 

 The last change made to the README.md file with a corrected typo is below. 

<img src="https://i.imgur.com/uz3VwSQ.png" width="700" height="700"/></ul></ul></ul></ul>

<h2 id="pull-request-through-github-desktop">PULL Request through GitHub Desktop</h2>
The file &quot;imp&quot; contains a typo where MNALO is the owner and Olivia is collaborator follows the following process to create a PULL request from GitHub Desktop. 
1. Cloning and Opening to Desktop. 
<ul>
 <li> A project is cloned and click to &quot;Open in Desktop&quot;. </li> 
</ul>

<img src="https://i.imgur.com/HIY445n.png" width="700" height="700"/>
2. Create a new branch. 
<ul>
 <li> A new branch, &quot;fix-typo-imp&quot; is created. </li> 
</ul>

<img src="https://i.imgur.com/oiofHh0.png" width="700" height="700"/>
3. Make a change in the imp file from the text editor. 
<ul>
 <li> You can change the content of the imp file, fix a typo, and add some text. </li> 
</ul>

4. Commit the changes. 
<ul>
 <li> A commit message written and &quot;Commit to fix-typo-imp&quot; is clicked. </li> 
</ul>

<img src="https://i.imgur.com/7Ft9iz1.png" width="700" height="700"/>
5. Publish the branch. 
<ul>
 <li> You can now publish the branch, which pushes the commit to GitHub. </li> 
</ul>

<img src="https://i.imgur.com/M5QZwdG.png" width="700" height="700"/>
6. Create a PULL Request. 
<ul>
 <li> You can now make a PULL request by clicking &quot;Create pull request&quot;. </li> <li>You can also now write a message and then click &quot;Create pull request&quot; again.</li>

</ul>

<img src="https://i.imgur.com/p3pzYIa.png" width="700" height="700"/>
<img src="https://i.imgur.com/2flAXxO.png" width="700" height="700"/>
The process afterward is the same as above in &quot;PULL Request through Command Line&quot;.</ul></ul></ul>
<h1 id="conclusion">Conclusion</h1>
In this tutorial, you have learned the PUSH and PULL request and also the different ways through which the PUSH and PULL request is done through the command line and GitHub Desktop Applications.
There are many resources that you can learn the Git:
<ol>
<li>Getting Git Right
</li>
<li>Git Basics
</li>
</ol>
If you would like to learn more data science skills, take DataCamp&#39;s intro courses:
<ul>
<li><a href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a></li>
<li><a href="https://www.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a></li>
</ul>

https://www.datacamp.com/community/tutorials/git-push-pull

Learn how to perform Git PUSH and PULL requests through GitHub Desktop and the Command-Line.

GIT Push and Pull

<h2 id="introduction">Introduction</h2>
Like other programming languages, <code>for</code> loops in Python are a little different in the sense that they work more like an iterator and less like a <code>for</code> keyword. In Python, there is not <code>C</code> like syntax <code>for(i=0; i&lt;n; i++)</code> but you use <code>for in n</code>.
They can be used to iterate over a sequence of a <code>list</code>, <code>string</code>, <code>tuple</code>, <code>set</code>, <code>array</code>, <code>data frame</code>.
Given a list of elements, <code>for</code> loop can be used to iterate over each item in that list and execute it.
To iterate over a series of items For loops use the <code>range</code> function. The <code>range</code> function returns a new list with numbers of that specified range based on the length of the sequence.
While iterating over a sequence you can also use the index of elements in the sequence to iterate, but the key is first to calculate the length of the list and then iterate over the series within the range of this length.
The <code>for</code> loops in Python are zero-indexed.
Let&#39;s quickly jump onto the implementation part of it.
<h2 id="implementing-loops">Implementing Loops</h2>
To start with, let&#39;s print numbers ranging from 1-10. Since the <code>for</code> loops in Python are zero-indexed you will need to add one in each iteration; otherwise, it will output values from 0-9.
<pre><code class="lang-python">for i in range(10):
 print (i+1)
</code></pre>
<pre><code>1
2
3
4
5
6
7
8
9
10
</code></pre>Let&#39;s iterate over a string of a word <code>Datacamp</code> using for loop and only print the letter <code>a</code>.
<pre><code class="lang-python">for i in &quot;Datacamp&quot;:
 if i == &#39;a&#39;:
 print (i)
</code></pre>
<pre><code>a
a
a
</code></pre>Let&#39;s say you want to define a list of elements and iterate over those elements one by one.
<pre><code class="lang-python">sequence = [1,2,8,100,200,&#39;datacamp&#39;,&#39;tutorial&#39;]
</code></pre>
<pre><code class="lang-python">for i in sequence:
 print (i)
</code></pre>
<pre><code>1
2
8
100
200
datacamp
tutorial
</code></pre>But what if you want to find the length of the list and then iterate over it? You will use the in-built <code>len</code> function for it and then on the length output you will apply <code>range</code>.
Remember, the <code>range</code> always expects an integer value.
<pre><code class="lang-python">for i in range(len(sequence)):
 print (sequence[i])
</code></pre>
<pre><code>1
2
8
100
200
datacamp
tutorial
</code></pre>Great! But why do you need to use the <code>len</code> function when you can directly use <code>for i in numbers</code>? The answer is simple. What if you would like to modify or work with the indices of the sequence like changing the element of an existing list, then you would need <code>range(len(sequence))</code>.
<pre><code class="lang-python">for i in range(len(sequence)):
 element = sequence[i]
 if type(element) == int:
 sequence[i] = element + 4
</code></pre>
<pre><code class="lang-python">sequence
</code></pre>
<pre><code>[5, 6, 12, 104, 204, &#39;datacamp&#39;, &#39;tutorial&#39;]
</code></pre>Cool, isn&#39;t it? You were able to modify the elements of the list based on the <code>if</code> condition.
Let&#39;s now see how you can print odd numbers between 1 - 20. To accomplish this, you will have to define three things in the <code>range</code> function. The starting point, the ending point and the increment value (or steps) at which the loop will increment over the numbers 1 - 20.
<pre><code class="lang-python">for i in range(1,20,2):
 print (i)
</code></pre>
<pre><code>1
3
5
7
9
11
13
15
17
19
</code></pre><h4 id="nested-for-loop">Nested For loop</h4>
<pre><code class="lang-python">for i in range(11):
 for j in range(i):
 print (i, end=&#39; &#39;)
 print()
</code></pre>
<pre><code>1
2 2
3 3 3
4 4 4 4
5 5 5 5 5
6 6 6 6 6 6
7 7 7 7 7 7 7
8 8 8 8 8 8 8 8
9 9 9 9 9 9 9 9 9
10 10 10 10 10 10 10 10 10 10
</code></pre><h4 id="for-loop-to-iterate-over-rows-and-columns-of-a-dataframe">For loop to iterate over rows and columns of a dataframe</h4>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<pre><code class="lang-python">iris = pd.read_csv(&#39;https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv&#39;)
</code></pre>
<pre><code class="lang-python">iris.head()
</code></pre>
<div>
<style scoped>
 .dataframe tbody tr th:only-of-type {
 vertical-align: middle;
 }

 .dataframe tbody tr th {
 vertical-align: top;
 }

 .dataframe thead th {
 text-align: right;
 }
</style>
<div class="output_wrapper"><table border="1" class="dataframe">
 <thead>
 <tr style="text-align: right;">
 <th></th>
 <th>sepal_length</th>
 <th>sepal_width</th>
 <th>petal_length</th>
 <th>petal_width</th>
 <th>species</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <th>0</th>
 <td>5.1</td>
 <td>3.5</td>
 <td>1.4</td>
 <td>0.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>1</th>
 <td>4.9</td>
 <td>3.0</td>
 <td>1.4</td>
 <td>0.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>2</th>
 <td>4.7</td>
 <td>3.2</td>
 <td>1.3</td>
 <td>0.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>3</th>
 <td>4.6</td>
 <td>3.1</td>
 <td>1.5</td>
 <td>0.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>4</th>
 <td>5.0</td>
 <td>3.6</td>
 <td>1.4</td>
 <td>0.2</td>
 <td>setosa</td>
 </tr>
 </tbody>
</table></div>
</div>




<pre><code class="lang-python">len(iris)
</code></pre>
<pre><code>150
</code></pre><pre><code class="lang-python">for i in range(len(iris)):
 Class = iris.iloc[i,4]
 if Class == &#39;versicolor&#39; and i &lt; 70:
 print (Class)
</code></pre>
<pre><code>versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
</code></pre>Next, let&#39;s add two to every row of columns <code>sepal_length</code>,<code>sepal_width</code>, <code>petal_length</code> and <code>petal_width</code>.
<pre><code class="lang-python">columns = [&#39;sepal_length&#39;,&#39;sepal_width&#39;,&#39;petal_length&#39;,&#39;petal_width&#39;]
</code></pre>
<pre><code class="lang-python">for indices, row in iris.iterrows():
 for column in columns:
 iris.at[indices,column] = row[column] + 2
</code></pre>
<pre><code class="lang-python">iris.head()
</code></pre>
<div>
<style scoped>
 .dataframe tbody tr th:only-of-type {
 vertical-align: middle;
 }

 .dataframe tbody tr th {
 vertical-align: top;
 }

 .dataframe thead th {
 text-align: right;
 }
</style>
<div class="output_wrapper"><table border="1" class="dataframe">
 <thead>
 <tr style="text-align: right;">
 <th></th>
 <th>sepal_length</th>
 <th>sepal_width</th>
 <th>petal_length</th>
 <th>petal_width</th>
 <th>species</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <th>0</th>
 <td>7.1</td>
 <td>5.5</td>
 <td>3.4</td>
 <td>2.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>1</th>
 <td>6.9</td>
 <td>5.0</td>
 <td>3.4</td>
 <td>2.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>2</th>
 <td>6.7</td>
 <td>5.2</td>
 <td>3.3</td>
 <td>2.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>3</th>
 <td>6.6</td>
 <td>5.1</td>
 <td>3.5</td>
 <td>2.2</td>
 <td>setosa</td>
 </tr>
 <tr>
 <th>4</th>
 <td>7.0</td>
 <td>5.6</td>
 <td>3.4</td>
 <td>2.2</td>
 <td>setosa</td>
 </tr>
 </tbody>
</table></div>
</div>



<h4 id="iterating-over-a-sequence-with-lambda-function">Iterating over a sequence with Lambda function</h4>
Python&#39;s lambda function is fast and powerful as compared to the basic <code>for</code> loop. It is widely used, especially when dealing with Dataframes. You can process your data with the help of <code>Lambda</code> function with very little code.
Although, it sometimes becomes difficult to understand it.
<pre><code class="lang-python">x = [20, 30, 40, 50, 60]
y = []
</code></pre>
<pre><code class="lang-python">for v in x :
 y += [v * 5]
</code></pre>
<pre><code class="lang-python">y
</code></pre>
<pre><code>[100, 150, 200, 250, 300]
</code></pre>Now, let&#39;s try this with a lambda and map function.
<code>Map</code> takes in a function, for example, a lambda function and a sequence <code>x</code> and then returns a new sequence.
<pre><code class="lang-python">y = map(lambda x: x * 5,x)
</code></pre>
<pre><code class="lang-python">y
</code></pre>
<pre><code>&lt;map at 0x11be7cc88&gt;
</code></pre>It returns a generator function and to get the output from the generator; you pass the output as an argument to <code>list</code>.
<pre><code class="lang-python">list(y)
</code></pre>
<pre><code>[100, 150, 200, 250, 300]
</code></pre>Now, let&#39;s take a step back and look at both the <code>for</code> loop way and lambda/map() combination. You will notice that the difference between the two is adding <code>map</code>, <code>lambda</code>, and removal of &quot;for&quot; and &quot;in&quot;. And also, within one line, you were able to code it.
<h2 id="conclusion">Conclusion</h2>
Congratulations on finishing this basic Python For loop tutorial.
For loops are the backbone of every programming language and when it is Python, using For loops are not at all hard to code, and they are similar in spirit to writing an English sentence.
If you want to learn more about <code>for</code> loops in Python, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/python-data-science-toolbox-part-2">Python Data Science Toolbox (Part 2)</a> course.
Please feel free to ask any questions related to this tutorial in the comments section below.

https://www.datacamp.com/community/tutorials/for-loops-in-python

Learn how to implement For Loops in Python for iterating a sequence, or the rows and columns of a pandas dataframe. 

Learn how to iterate over Pandas Dataframe rows and columns with Python for loops. Follow step-by-step code examples today!

For Loop Pandas DataFrame Python: How to iterate over rows & columns using For Loops in Python

For Loops in Python

The following topics are covered in the tutorial.
<ol>
 <li>
 <a href="#create-table">Creating a Table</a> 
 </li>
<li>
 <a href="#user-defined">User Defined Stored Procedure</a> 
 </li>
 <li><a href="#execute">To Execute the Stored Procedure</a> 
 </li>

 <li><a href="#storedproc">Stored Procedure with Parameters</a> 
 </li>
 <li><a href="#modify">Modifying the Stored Procedure</a> </li>
 <li><a href="#delete">Deleting the Stored Procedure</a> </li>
 <li><a href="#security">Adding Security through Encryption</a> </li>
 </ol>

You will be using Microsoft SQL server as the database. The concept covered in this tutorial is the same for most of the Relational Database Management System&#39;s, but syntaxes are different, although the idea used here serves well for all databases.
Stored Procedure is the block of the SQL statement in Relational Database Management System (RDBMS), and it is typically written by the programmer, Database Administrator, Data Analyst, and is saved and re-used for multiple programs. It can also have different types depending on the various RDBMS.
However, the two most crucial Stored Procedures found in any RDBMS are as follows.
<ul>
 <li>User-defined Stored Procedure
 <li>System Stored Procedure
</ul>

Stored Procedure is a prepared compiled code which is stored or cached and then re-used. You can have cached code for re-use which makes maintainability far easier, i.e., it doesn&#39;t need to be changed multiple times, which can maintain security.
<a id="create-table">
<h2 id="creating-a-table-a-">Creating a Table</a></h2>
You will now create a table named <code>table_Employees</code> as follows.
<pre><code class="lang-python">CREATE TABLE table_Employees
(
 EmployeeId INT PRIMARY KEY NOT NULL,
 EmployeeFirstName VARCHAR(25) NOT NULL,
 EmployeeLastName VARCHAR(25) NOT NULL,
 EmployeeGender VARCHAR(25) NOT NULL,
 EmployeeDepartmentID INT

)
</code></pre>
The table consists of information about employees in a particular company and consists of the following columns:
<ol type="i">
 <li>EmployeeId:

 It is the primary key which is unique and is integer datatype, where the value gets auto-incremented.
 <li>EmployeeFirstName: It is the First Name of an employee, which is of character data type and should not be left empty.
 <li>EmployeeLast Name: It is the Last Name of an employee, which is of character data type and should not be left empty.
 <li>EmployeeGender: It shows the Gender of the employee and is also a character type.
 <li>EmployeeDepartmentId: It is the id of the employee of the integer data type.
 </ol>

You can write a query to insert values into the table. The queries to add values to the table is:
<pre><code> INSERT INTO &lt;TABLE_NAME&gt;(&lt;COLUMNS_OF_TABLE&gt;) VALUES(&lt;CORRESPONDING_VALUES_TO_MATCH_COLUMN&gt;)
</code></pre>After inserting the values into the table, table_Employees is created.
<img src="https://i.imgur.com/daB4XTk.png" width=800,height=800>
<a id="user-defined">
<h2 id="to-create-a-user-defined-stored-procedure-a-">To Create a User Defined Stored Procedure.</a></h2>
Stored Procedure let&#39;s you write a query once, and the query has a name which can be saved and executed multiple times if needed. By executing the queries, you can have a folder created as Programmabilty-&gt;Stored Procedure, and there is the file named dbo.uprocGetEmployees.
The syntax for creating a Stored Procedure is: 
<pre><code>CREATE PROCEDURE &lt;&lt;&lt;procedure_name&gt;&gt;&gt;
AS
BEGIN

 &#39;&#39;&#39;Required SQL Queries&#39;&#39;&#39;

END
</code></pre>You can create a Stored Procedure by using the SQL statement as follows.
 <li>CREATE PROCEDURE procedure_name
 <li>CREATE PROC procedure_name
</ol>
Also, you need to create a Stored Procedure with a naming convention other than &quot;sp_.&quot;. The Stored Procedure in the example is created with the name: <code>uprocGetEmployees</code>.
<img src="https://i.imgur.com/10oaWRG.png" width =800,height=800>
<a id="execute">
<h2 id="to-execute-the-stored-procedure-a-">To Execute the Stored Procedure</a></h2>
To execute a Stored procedure, you can use one of three approaches, and run it as follows.
<ul>
 <li>EXEC &lt;<procedure_name>&gt;</li>
 <li>EXECUTE &lt;<proceure_name>&gt;</li>
 <li>&lt;<procedure_name>&gt;</li>
</ul>

In the above program, you can use procedure name uprocGetEmployees and select it to execute the query.
<a id="storedproc">
<h2 id="stored-procedure-with-parameters-a-">Stored Procedure with Parameters</a></h2>
The Stored Procedure can accept single and multiple parameters. You can easily understand single parameters if you first understand multiple parameters.
The syntax for creating a Stored Procedure with multiple parameters is:
<pre><code>CREATE PROCEDURE &lt;&lt;procedure_name&gt;&gt; &lt;&lt;procedure_parameter&gt;&gt;
AS
 BEGIN
 &lt;&lt;sql_query&gt;&gt;
 END
</code></pre>The example shows that there is a change in the folder below. 
EMPLOYEE-&gt;Programmability-&gt;Stored Procedures-&gt;dbo.uprocGetEmployessGenderAndDepartment
<img src="https://i.imgur.com/mhpWxU6.png" width=800,height=800>
The concept here is very similar to the function in general programming languages like Python, Java, etc. The program above has the function or procedure name called as &quot;uprocGetEmployeesGenderAndDepartment&quot;, which has the parameter name @EmployeeGender and @EmployeeDepartmentId which are &quot;called&quot; by passing the required value to our procedure which can be done by one of two ways and it is explained below.
The parameters in this program are @EmployeeGender and @EmployeeDepartmentId which are &quot;called&quot; by passing the value to the parameter.
The value can be specified by one of two ways. You can use either one of them to execute the Stored Procedure with the multiple parameters.
<ul>
<li>By specifying the parameter names in the query and passing required values:</li>
<img src="https://i.imgur.com/mXTRqOP.png" width=300,height=300>
<li start="2">By the position that matches the parameter of Stored Procedure with your query and pass the required value to the parameter:</li></ul>
<img src="https://i.imgur.com/1ch29Nv.png" width=300 height=300>
<a id="modify">
<h2 id="modifying-the-stored-procedure-a-">Modifying the Stored Procedure</a></h2>
You can also modify the Stored Procedure by using the ALTER PROCEDURE command.
<pre><code> ALTER PROCEDURE uprocGetEmployeesGenderAndDepartment
@EmployeeGender nvarchar(25),
@EmployeeDepartmentId int
BEGIN
 SELECT EmployeeFirstName,EmployeeGender,EmployeeDepartmentId FROM table_Employees WHERE
 EmployeeGender = @EmployeeGender AND EmployeeDepartmentId = @EmployeeDepartmentId
END
</code></pre><a id="delete">
<h2 id="deleting-stored-procedure-a-">Deleting Stored Procedure</a></h2>
Stored Procedures can be quickly deleted by using the following commands:
DROP PROC &lt;<procedure_name>&gt;
DROP PROCEDURE &lt;<procedure_name>&gt;
<a id="security">
<h2 id="adding-security-through-encryption-a-">Adding Security Through Encryption</a></h2>
The lock symbol indicated in the picture below to the left shows that Stored Procedure is encrypted which ensures only authorized people can access it.
<img src="https://i.imgur.com/25swIXh.png" width=800 height=800>
<h2 id="conclusion">Conclusion</h2>
You have just completed learning the basics of User Stored Procedures and the topics covered are conceptually similar to any RDBMS. You can learn more on DataCamp, by taking their <a href="https://www.datacamp.com/courses/intermediate-t-sql">Intermediate SQL Server</a> course. Also, the following <a href="https://docs.microsoft.com/en-us/sql/relational-databases/stored-procedures/stored-procedures-database-engine?view=sql-server-2017">Stored Procedures</a> tutorial will serve well in your learning.

https://www.datacamp.com/community/tutorials/user-defined-stored-procedure

Learn how to use and apply the user-defined stored procedure along with examining different types of stored procedures in SQL.

User Defined Stored Procedures in SQL

As a manager of a company, you always have finite or limited resources, and top management&#39;s expectation is for you to make the most out of it. From time productivity to capital utilization, land to labor, and from supply chain to production-almost everything you do is to optimize productivity. 
A majority of airlines optimize flight schedules to get the highest revenue and lowest cost. These airlines schedules involve a lot of situations and restrictions such as the number of planes at a particular location, fuel, crew, and type of route (popular and profitable routes). These situations and restrictions are known as the constraints of flying planes on the most popular and profitable route. Linear programming is used to find the solution for the given constrained problem.
In this tutorial, you are going to learn about linear programming, and the following topics will be covered:
<ul>
<li>Introduction to Linear Programming</li>
<li>Basic Concepts of LPP<ul>
<li>Decision variable</li>
<li>Constraints</li>
<li>Objective function</li>
<li>Optimal solution</li>
<li>Feasible solution</li>
<li>Infeasible solution</li>
<li>Feasible region</li>
<li>Non-negativity constraints</li>
</ul>
</li>
<li>LPP Problem Formulation(With Example)</li>
<li>Graphical Method to solve LPP in Spreadsheet</li>
<li>Use-cases of LPP</li>
<li>Conclusion</li>
</ul>
<h2 id="introduction-to-linear-programming">Introduction to Linear Programming</h2>
Mathematically, linear programming optimizes (minimizes or maximizes) the linear objective of several variables subject to the given conditions/constraints that satisfies a set of linear inequalities. Linear programming can be applied in planning economic activities such as transportation of goods and services, manufacturing products, optimizing the electric power systems, and network flows. LP problems can be solved using different techniques such as Graphical, Simplex, and Karmakar&#39;s method.
<h2 id="basic-concepts-of-lpp">Basic Concepts of LPP</h2>
Let&#39;s see the basic terminologies of linear programming:
<ul>
<li>Decision variables are the variables which will be used as a function of the objective function. These variables decide your output. The decision maker can control the value of an objective function using the decision variable. When you solve any linear programming problem, you first need to identify the decision variables.
</li>
<li>Constraints are a set of restrictions or situational conditions. Constraints can be in equalities or inequalities form. Constraints restrict the value of decision variables.
</li>
<li>The objective function is a profit or cost function that maximizes or minimize. It is the main target of making decisions.
</li>
<li>The optimal solution is one of the feasible solutions where the objective function is either maximum or minimum, for example, maximum profit or minimum cost. It is the best value of the objective function.
</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200754/image1_dg9p0t.png" />
<ul>
<li>A feasible solution is the set of possible values for decision variables that meets all of the constraints. LP problem is feasible if at least one solution is feasible.
</li>
<li>The infeasible solution is the set of possible values for decision variables that do not meet all the constraints, i.e., there is no optimal solution. LP problem is an infeasible solution if no solution exists that meets all of the constraints.
</li>
<li>The feasible region is a region which covered from all the possible set of values that meet the constraints or intersection of all the constraints. It includes all the inequalities, equalities, and integer constraints.
</li>
<li>Non-negativity constraints for decision variables which accept only non-negative values. Such constraints are greater than or equal to zero.
</li>
</ul>
<h2 id="lpp-problem-formulation-with-example-">LPP Problem Formulation(With Example)</h2>
Formulating linear programming models involves the following steps:
<ul>
<li>Identify the decision variables</li>
<li>Determine the objective function</li>
<li>Identify the constraints</li>
<li>Define the non-negative constraints</li>
</ul>
In a linear programming problem, the decision variables, objective function, and constraints all have to be a linear function
Problem Statement: A furniture dealer deals in only two items–tables and chairs. He has Rs 50,000 to invest and has storage space of at most 60 pieces. A table costs Rs 2500 and a chair Rs 500. He estimates that from the sale of one table, he can make a profit of Rs 250 and from the sale of one chair a profit of Rs 75. He wants to know how many tables and chairs he should buy from the available money to maximize his total profit, assuming that he can sell all the items which he buys. (<a href="https://www.slideshare.net/Zenblade/applications-of-linear-programming">Source</a>)
In this example, you observe that –
<ol>
<li>The dealer can invest his money in buying tables or chairs or a combination of both. Further, he would earn different profits by following different investment strategies. (<a href="https://www.slideshare.net/Zenblade/applications-of-linear-programming">Source</a>)</li>
<li>His investment is limited to a maximum of Rs 50,000, and so is his storage space, which is for a maximum of 60 pieces. (<a href="https://www.slideshare.net/Zenblade/applications-of-linear-programming">Source</a>)</li>
</ol>
Mathematical Formulation:
Let’s suppose he decides to purchase tables only and no chairs. So, he can buy 50000/2500 = 20 tables. In this case, he will earn a profit of 250 x 20 = Rs. 5000. Or he decides to purchase chairs only and no tables. So, he can buy 50000/500 = 100 chairs.
In this case, he will earn a profit of 75 x 60 = Rs. 4500.
There could be many combinations of tables and chairs he could buy.
Let’s suppose he purchases X number of tables and Y number of chairs. So X and Y must be non-negative.
<ol>
<li>The dealer has a maximum amount of Rs. 50000 to purchase tables and chairs hence –
2500 X + 500 Y &lt;= 50000 (Investment constraints) or
5 * X + Y &lt;= 100 ………………………………………………………. Eq.1
</li>
</ol>
<ol>
<li>The maximum number of units he can store is 60.
X + Y &lt;= 60 ………………………………………………………. Eq.2
</li>
</ol>
<ol>
<li>The dealer wants to purchase in such quantities so he can maximize his profit Z, hence the equation would be –
Max Z : 250 X + 75 Y
</li>
</ol>
Objective function:
 Max Z: 250 X + 75 Y
Subjected to constraints:
<pre><code>5 * X + Y &lt;= 100,
X + Y &lt;= 60 and
X,Y &gt;= 0
</code></pre>Source: <a href="http://ncert.nic.in/ncerts/l/lemh206.pdf">http://ncert.nic.in/ncerts/l/lemh206.pdf</a>
<h2 id="graphical-method-to-solve-lpp-in-spreadsheet">Graphical Method to solve LPP in Spreadsheet</h2>
Linear Programming Example
Objective function:
 Max Z: 250 X + 75 Y
Subjected to constraints:
<pre><code>5 * X + Y &lt;= 100,

X + Y &lt;= 60 and

Where X,Y &gt;= 0
</code></pre>Step 1: Let’s write the function in excel like shown below.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image2_wfgpcv.png" />
Step 2: After writing objective function and constraints in the spreadsheet, let&#39;s calculate values for constraints C1 and C2. You can calculate values of by putting another variable value to zero. Like for C1, at X = 0, Y’s value would be Y = 100 and at Y = 0, X’s value would be X = 20. Similarly, for constraints C2.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image3_f6dpdx.png" />
Step-3: Now, after calculating values, let’s plot the graph. Select C1 constraints values, go to insert and plot the scatter chart with smoothed lines. You might see your chart like this.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image4_fdo0qw.png" />
If it looks like this, don’t worry, follow the below steps to change it. Right-click on the chart, go to Select Data, you will see two series there, remove Series 2 and Edit series 1.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image5_y6jgde.png" />
Name series as C1 and change its X value to C1’s A column values and Y value to C1’s B column values. And Click on, Ok. You will see your chart like the one below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image6_cru6fc.png" />
Step-4: 4. Again, go to Select Data by right-clicking on the chart and add another series. Name it C2 and in X values select constraints C2 X column values and in Y values select constraints C2 Y column values. Your chart will look like the one below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image7_y6ffab.png" />
Step-5: Now the graph has been plotted, and you need to look at the feasible region:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image8_yrytpj.png" />
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image9_ozkzen.png" />
Now you have 4 points (O, A, B, C) at feasible region area, you need to calculate objective function values at all the points to see which point gives you the maximum value of the objective.
Step-6: To calculate objective function values, Follow the below steps:
Calculate the objective function for each value of point:
<ul>
<li>At point O the coordinate values of (X, Y) are (0, 0) Hence objective function value would be = (2500+750) = 0</li>
<li>At point A the coordinate values of (X, Y) are (0, 60) Hence objective function value would be = (2500+753) = 4500</li>
<li>At point B the coordinate values of (X, Y) are (20, 0) Hence objective function value would be = (2503+750) = 5000</li>
<li>At point C To find the coordinate values at point Z, you need to find the intersection point of constrains line C1 and C2. You will use MMULT() and MINVERSE() function to calculate the intersection of two equations.
</li>
<li>MMULT(MINVERSE(array),array) # This gives the values of the solution of equations with two variables Or, =MMULT(MINVERSE(AB17:AC18), AE17:AE18), Before using this formula, select X and Y values cells yellow highlighted cells and use this function to calculate intersection point of two equation and press CTRL+SHIFT+ENTER
</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image10_aeqvg8.png" />
The final values of Z:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1563200755/image11_edjtnb.png" />
As you can see here in this linear maximization problem, you have got Z’s maximum value at Point B, and the maximum value is Rs. 6250. Hence, in order to maximize profit, the dealer must purchase 10 tables and 50 chairs.
<h2 id="use-cases-of-lpp">Use-cases of LPP</h2>
Linear programming is applied to find optimal solutions for operations research. LP can find the most optimum solution in given constraints and restrictions. LP is applicable in all kinds of problems such as economic activities in agriculture, engineering, manufacturing, energy, logistics, and supply chain.
<ul>
<li>Engineers apply linear programming in the manufacturing and production industry to optimize the available resources, which allows them to gain maximum profits.
</li>
<li>Logistics and transportation industry uses linear programming to find the shortest path/route, travel time, and pricing strategy.
</li>
<li>Engineers apply linear programming in the energy sector. They optimize the electric load, shortest distribution lines, and also optimize the electrical power grid design.
</li>
<li>In the agriculture sector, linear programming can be used by engineers to determine the type of crop and quantity of crop they should grow to increase revenue efficiently. 
</li>
<li>In the food industry, linear programming can help health managers and nutritionists to plan dietary needs, and low-cost food item set in order to keep away from noncommunicable disease.
</li>
</ul>
<h2 id="conclusion">Conclusion</h2>
Congratulations, you have made it to the end of this tutorial!
You have covered a lot of details about linear programming. You learned what linear programming is, basic concepts, and terminologies used in LP, LP-problem formulation, solving LP problems using the graphical method, and use cases of the LP problem.
Hopefully, you can now utilize the linear programming concepts to make decisions in your organization or optimize your results for decision makers. Thanks for reading this tutorial!
If you would like to learn more is spreadsheets, take the following DataCamp courses:
<ul>
<li><a href="https://www.datacamp.com/courses/data-analysis-with-spreadsheets">Data Analysis with Spreadsheets</a></li>
<li><a href="https://www.datacamp.com/courses/financial-analytics-in-spreadsheets">Financial Analytics in Spreadsheets</a></li>
</ul>

https://www.datacamp.com/community/tutorials/linear-programming-with-spreadsheets

Learn how to use spreadsheets to solve linear programming problems graphically.

Linear Programming with Spreadsheets

When someone is taught pretty much any programming language, one of the first things that arise are conditionals. That is the classical IF-ELSEIF-ELSE statements, which allow programs to perform different operations based on logical conditions. However, this may not be the first thing that one thinks of in Excel. Nevertheless, they are there, and they are quite important when you are working on Excel reports with a certain amount of complexity. In particular, this tutorial we will cover how IF-ELSE statements are structured in Excel, as well as, some more advanced functions like COUNTIF() that come in handy in certain situations.
<h3 id="basic-if-statements">Basic IF Statements</h3>
To begin, let&#39;s start with an overview of how to write a basic IF statement in Excel. The syntax is as follows:
<code>IF(condition, value_if_true, value_if_false)</code>
Where:
<ul>
<li>condition: value or a logical operation that evaluates to TRUE or FALSE</li>
<li>value_if_true: The value to return if the condition is TRUE</li>
<li>value_if_false: The value to return if the condition is FALSE</li>
</ul>
With that said, let&#39;s jump into a simple example of how to use a basic conditional using a toy table:
<iframe width="560" height="315" src="https://www.youtube.com/embed/BX7BdcYbeFQ" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Simple right? For those of you familiar with R, you probably have noticed that this syntax is essentially the same as the ifelse() function. However, there are a few things that are a bit different. For instance, logical operators. In this simple example, I have used the &quot;&gt;&quot; logical operator, which is a standard way of indicating greater than. Nevertheless, others aren&#39;t so standard. This includes the not equal to operator, which is written as &quot;&lt;&gt;&quot;
A complete account of all the logical operators used in Excel can be seen below:
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Comparison Operator</th>
<th>What does it mean?</th>
<th>Simple Example</th>
</tr>
</thead>
<tbody>
<tr>
<td>=</td>
<td>equal</td>
<td>A1 = B1</td>
</tr>
<tr>
<td>&gt;</td>
<td>greater than</td>
<td>A1 &gt; B1</td>
</tr>
<tr>
<td>&gt;=</td>
<td>greater than or equal to</td>
<td>A1 &gt;= B1</td>
</tr>
<tr>
<td>&lt;</td>
<td>less than</td>
<td>A1 &lt; B1</td>
</tr>
<tr>
<td>&lt;=</td>
<td>less than or equal to</td>
<td>A1 &lt;= B1</td>
</tr>
<tr>
<td>&lt;&gt;</td>
<td>not equal to</td>
<td>A1 &lt;&gt; B1</td>
</tr>
</tbody>
</table></div>
<h4 id="or-and-and-not-">OR(), AND() and NOT()</h4>
Comparison operators are not the only components of Excel conditional statements that may differ from those that you may use in Python, R, or Matlab. In fact, the way that you define boolean operations like OR, AND, and NOT is different. For example, in Excel, you would write an OR expression like this:
<code>IF(OR(value_1 = k, value_2 = y), value_if_true, value_if_false)</code>
To see all boolean operators in action inside Excel, look at the example below:
<iframe width="560" height="315" src="https://www.youtube.com/embed/u0wxJoUcz2g" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<h4 id="nested-if-statements">Nested IF Statements</h4>
Just as you can nest IF-ELSEIF-ELSE statements in Python, R, or Matlab, you can also do it in Excel. To do so, it is as simple as adding another IF statement inside a previous IF statement when this one evaluates to TRUE or FALSE. This allows you to test more conditions and return more results based on these conditions. Excel can nest up to 64 IF statements. However, I strongly advise against nesting too many IF statements as you need to be careful with the logic, and debugging it or changing it down the line can become very cumbersome.
With that said, here is a simple use case of nested IFs inside Excel itself:
<iframe width="560" height="315" src="https://www.youtube.com/embed/ZFoN87AHz_U" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<h3 id="countif-and-countifs-">COUNTIF() and COUNTIFS()</h3>
Aside from traditional IF statements, Excel has other functions that can perform operations based on a set of given conditions. In this section, I am going to focus on conditional counting functions, and, more specifically, in the functions COUNTIF() and COUNTIFS().
COUNTIF() allows the user to count cells that meet a single criterion. Its syntax is as follows:
<code>COUNTIF(cell_range_to_count, criteria)</code>
Where:
<ul>
<li>cell_range_to_count: The specific range of cells that you wish to count</li>
<li>criteria: The criteria that controls which cells should be counted. These can be very diverse, for example:<ul>
<li>COUNTIF(A1:A10, 10) - This would count how many cells in the range from cell A1 to cell A10 are equal to 10</li>
<li>COUNTIF(A1:A10, B5) - This would count how many cells in the range from cell A1 to cell A10 are equal to the value in cell B5</li>
<li>COUNTIF(A1:A10, &quot;Gandalf&quot;) - This would count how many cells in the range from cell A1 to cell A10 are equal to &quot;Gandalf&quot;</li>
<li>COUNTIF(A1:A10, &quot;&gt;20&quot;) - This would count how many cells in the range from cell A1 to cell A10 are greater than 20</li>
<li>COUNTIF(A1:A10, &quot;&lt;&gt;Gandalf&quot;) - This would count how many cells in the range from cell A1 to cell A10 are not equal to &quot;Gandalf&quot;</li>
</ul>
</li>
</ul>
COUNTIFS, as you might have guessed, differs from COUNTIF in that it allows the user to count cells that meet multiple criteria. Its syntax is as follows:
<code>COUNTIFS(cell_range_to_count_1, criteria_1, cell_range_to_count_2, criteria_2,...cell_range_to_count_n, criteria_n)</code>
It is important to note that for each additional range that you specify in COUNTIFS, you must have the same number of rows and columns as the original range (cell_range_to_count_1) otherwise you will get an error.
These two functions are fantastic to use with business reports. One of the best examples for me being how easy they make counting the number of appointments our sales representatives make every month and where are they located in our sales funnel.
In the videos below, I&#39;ll illustrate how these two functions work in action. First, let&#39;s start with just counting the total appointments of a set of fictional salespeople:
<iframe width="560" height="315" src="https://www.youtube.com/embed/i7B3Sa9DRLc" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Wasn&#39;t that easy? You just tallied how many appointments each of our fictitious salespeople had in total. In theory, you could have done this as well using just basic IF statements, but it would have required quite a bit more labor for the same exact result.
Now, let&#39;s say you also want to tally the appointments that each of these salespeople had each month. In this case, COUNTIFS() is the answer; you can see it in action in the video below:
Note: You may have noticed that I covered the range that I wanted to count in the last video in $ signs. This is done in order to lock it down and prevent it from changing when I drag the formula down.
<iframe width="560" height="315" src="https://www.youtube.com/embed/Yjk8oMNbdGo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Voilá, we tallied the appointments of our dummy salespeople by month without breaking a sweat. It is worth mentioning that you could do this in a more &quot;efficient&quot; way if you play with the $ signs to lock down certain cells and ranges from moving as you drag the formulas around (i.e., I could have just written the formula in F2 and then dragged it around to the rest). However, I wanted to be more explicit writing the COUNTIFS formulas for January and February to provide more visibility. If you are curious though, I encourage you to try to rewrite the COUNTIFS() formula in F2, so that you can drag it around the remaining cells while getting the same results. This may be a useful exercise as it is very convenient to play with locking down ranges and cells to drag formulas when your Excel business reports get large. Here is the data table used in this <a href="https://s3.amazonaws.com/assets.datacamp.com/email/batch/screenshots/conditional_functions_spreadsheets.xlsx">tutorial</a>.
<h3 id="sumif-and-sumifs-">SUMIF() and SUMIFS()</h3>
We have seen how you can count cells that meet a single criterion or multiple criteria with COUNTIF() and COUNTIFS(), but what if you need to add the cells instead of just counting them? Enter SUMIF() and SUMIFS(). These functions are particularly useful when you have to add the revenue that a given sales person has generated in total or in a given month, for example. The syntax of both functions is as follows:
<code>SUMIF(criteria_range, criteria, (optional) sum_range)</code>
<code>SUMIFS(sum_range, criteria_range_1, criteria_1, criterion_range_2, criteria_2...criteria_range_n, criteria_n)</code>
Where:
<ul>
<li>criteria_range: This is the cell range in which the criterion will be checked against.</li>
<li>criteria: The conditional criteria used to determine which cells will be added together.</li>
<li>sum_range: The cells that would be added together. If it is not supplied in COUNTIF(), the cells in criteria_range are added together.</li>
</ul>
Now, without further ado, let&#39;s jump into an example that uses both SUMIF() and SUMIFS() to calculate the total revenue and the revenue per month generated by our fake sales representatives:
<iframe width="560" height="315" src="https://www.youtube.com/embed/N4c6wF_qC1Y" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

It is curious to note that, in the case of SUMIFS(), sum_range is a required argument and it is the first argument, but it is optional and the last argument in SUMIF(). This can cause confusion when you are using both formulas as you can see when I was typing the SUMIFS function for February. Therefore, I advise caution when you are working with both formulas. Be sure to always check that you are adding the correct cell ranges.
<h3 id="iferror-">IFERROR()</h3>
For those of you that know a programming/scripting language, you know that there is such as thing as try/catch blocks for error handling. In Excel, we have the IFERROR() function to do exactly that. The IFERROR function allows the user to &quot;catch&quot; errors such as #N/A, #VALUE! or #REF!, and provide a more meaningful output. The syntax of the IRERROR function is as follows:
<code>IFERROR(value, value_if_error)</code>
Where:
<ul>
<li>value: The value or formula to check for an error</li>
<li>value_if_error: The value to output when the function encounters an error.</li>
</ul>
Let&#39;s see how it works in action in the following video:
<iframe width="560" height="315" src="https://www.youtube.com/embed/2j98PxkFhfM" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

When you are working with business sales reports, it is usually a good practice to cover certain calculations in IFERROR functions. At times, you will get situations when new salespeople will have very few appointments, qualified opportunities or closed deals, which can cause divisions by 0 when calculating things like the percentage of appointments that turn into qualified opportunities for a given sales person. This can output nasty-looking errors that will probably make your report look dirty and harder to read for decision makers. However, if you cover these calculations with IFERROR() statements, you can substitute those errors for something much nicer on the eyes like a dash, which would make your report more presentable. Furthermore, IFERROR() statements can go beyond just outputting nicer error messages. You can also nest these statements to perform things like chained <a href="https://www.datacamp.com/community/tutorials/data-wrangling-with-vlookup">VLOOKUPs</a>.
<h3 id="conclusion">Conclusion</h3>
Congratulations! You are now aware of several ways in which you can leverage conditionals in Excel. In this tutorial, I have endeavored to cover those functions that I see myself using often when I am tasked to complete Excel business reports. However, if you are still hungry to learn more about the topic, there are other functions out there such as SWITCH(), AVERAGEIF() or AVERAGEIFS() that I haven&#39;t talked about here, and that you may find of interest.
Furthermore, there are things like using wildcards in functions such as COUNTIFS() that are perfectly valid ways to count partial string matches. For instance, let&#39;s say you have an Excel file with a list of a restaurant&#39;s menu items, and you want to count how many of them mention the word &quot;fish&quot;. In this case, you could write <code>COUNTIF(cell_range,&quot;*fish&quot;)</code> and this function would count items like swordfish or dolphinfish. I strongly encourage you to learn more about wildcards as they can be very useful and you never know when they could come in handy in your next Excel project.
Lastly, you may also want to check out the <a href="https://www.datacamp.com/courses/data-analysis-with-spreadsheets">Data Analysis with Spreadsheets</a> course here at DataCamp since it has a section dedicated to conditional function, which may be of further reference.

https://www.datacamp.com/community/tutorials/conditional-functions-in-spreadsheets

Learn when and how to use conditional functions in spreadsheets.

Conditional Functions in Spreadsheets

<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide1_ol5h4k.png" />
 <figcaption>(<a href="https://www.indianaiexpert.com/best-python-ide-2019/">Source</a>) </figcaption>
</figure>
</center> 

As you start your coding journey, many of you prefer coding in a text editor like emacs, notepad++, etc., wherein you write a code and then open a terminal window to execute your code. When there is an error detected in your code, you switch back to the text editor, correct the error or typos, and re-run the code from the terminal. All of this seems like good practice only at the beginner level when you are writing your first Hello, World!. However, as you move from a beginner to an intermediate level, your code complexity starts to increase, and that is when you need a tool that provides you far more utilities than just letting you write mere text.
When you move up the ladder in your coding journey, you not only have to code but you also need to make sure your code works in all scenarios, which means you also need a testing module. Many times, you have multiple coding and unit test files, and switching from editor to terminal often becomes irritating, confusing, and inefficient. Life is happier and more relaxed when you can write, run, and play with your code from one place.
One that provides you with the capability of not just coding, testing, and running your code from one tool, but also highlighting your syntax, bracket-matching, auto-completing your code, debugging, code suggestions and a lot more, is called Integrated Development Environment or more commonly known as IDE.
For the past couple of years, Jupyter Notebook has been gaining a lot of popularity in terms of coding and debugging. Notebooks have been redefining the concept of an IDE and are adding more and more features into it.
This tutorial is actually written in a Jupyter Notebook. How cool is that?
<h4 id="should-you-ignore-text-editors-">Should you ignore text editors?</h4>
The answer to this is No. Let&#39;s take a closer look at it with the help of the <a href="https://insights.stackoverflow.com/survey/2019#development-environments-and-tools">Stack Overflow Developer Survey 2019 </a> statistics.
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide2_ebdwma.png" />
 <figcaption>(<a href="https://insights.stackoverflow.com/survey/2019#development-environments-and-tools">Source</a>)</figcaption>
</figure>
</center> 

From the above statistics, it isn&#39;t that surprising that even though you have been reading that the integrated development environments are the best compared to text editors. Notepad++ is still in the most popular development environments, according to Stack Overflow.
So based on the above statistics, a lot of text editors can be used as a development environment if you add the correct plugins.
For example, the most popular text editor Notepad++ can also be leveraged as a debugger by installing a plugin called <a href="https://sourceforge.net/projects/npp-plugins/files/DBGP%20Plugin/">DBGP</a> that lets you debug and execute your code.
After looking into it, it makes a lot more sense why a text editor is considered the most popular on the development list. Text editors can be made as powerful as an IDE like PyCharm, Spyder, etc. by adding features on top of it that you might need. Even though the IDEs come with all these features already installed, installing a text editor is far easier, and they are lightweight compared to the IDEs.
Let&#39;s also see which are the most popular Python IDE and text editors. It&#39;s important to note that the below statistics are from December 2018 and should give a good understanding of what is popular today.
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide3_zy6tat.jpg" />
 <figcaption>(<a href="https://www.kdnuggets.com/2018/12/most-popular-python-ide-editor.html">Source</a>) </figcaption>
</figure>
</center> 

Finally, let&#39;s start decoding each IDE one by one and understand their capabilities and why they are the best amongst the rest.
<h2 id="jupyter-notebook">Jupyter Notebook</h2>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide4_b3vkdf.png" />
 <figcaption>(<a href="https://plot.ly/python/ipython-vs-python/">Source</a>) </figcaption>
</figure>
</center> 

Jupyter was introduced in the Year 2014 after its predecessor <code>IPython</code> and from that date it is considered to be a bliss for the coding community. Jupyter stands for Julia, Python, and R, which are the open-source languages for data science. The acronym of Jupyter, however, does not mean that it&#39;s only meant for Julia, Python, and R.
Jupyter Notebook not only provides you the capability to code in one of the above three programming languages, but it also comes with a markdown editor that lets you write <code>HTML</code> code, <code>LaTeX</code>, etc.
It is a web application based server-client structure which is easy-to-use and allows you to create, analyze, and manipulate documents in the form of notebooks. Since it is a web interface, it can integrate many of the existing web libraries for data visualization, including plotly.js.
As shown in the above figure, Jupyter has so many functionalities. You can write a formula using LaTeX, run a Python code, and visualize, for example, a raw audio signal using matplotlib plotting library all in the same notebook.
Jupyter Notebook is not just a mere IDE, but it is widely used as an education tool, for presentations, and even for writing blogs. You can export your notebook from .ipynb format to PDF and HTML files, or you can just export it as a .py file. The user-interface of Jupyter makes it a favorite tool, especially amongst the data science community.
If you want to know more about what all Jupyter has in-stock for you, then do check out <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook">this fantastic article</a> which is very comprehensive, it also takes you through the installation process and a lot more.
If you are only interested in Downloading Jupyter Notebook, then you can follow <a href="https://jupyter.org/install.html">this</a> link.
<h2 id="pycharm">PyCharm</h2>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide5_labenu.jpg" />
 <figcaption>(<a href="https://www.jetbrains.com/pycharm/">Source</a>) </figcaption>
</figure>
</center> 

PyCharm is an IDE for Python programming developed by the Czech company <a href="https://en.wikipedia.org/wiki/JetBrains">JetBrains</a>, and as the name suggests, it works like a Charm even though it a heavy-weight software. Though it can take time for plot rendering, etc. when compared with other IDEs.
If you have never used JetBrains other IDEs like Java IDE, IntelliJ IDEA, then running your first code successfully can eat up a little bit of your time. Such as setting up its interpreter. PyCharm is, however, especially useful if you have multiple scripts interacting with each other.
PyCharm comes with pre-installed <code>intelligent python assistance</code> that provides a variety of functions like smart code completion, code inspections, on-the-fly error highlighting and quick-fixes, along with automated code refactorings and a plethora of navigation capabilities (<a href="https://www.filehorse.com/download-pycharm/">Source</a>).
It not only lets you code in Python but has extensive support for web development frameworks like Django, Flask, HTML/CSS, Node.js, Pyramid, web2py, etc.
The good thing about PyCharm is that it supports Anaconda and as a result of which all the packages that fall under Anaconda are supported by PyCharm like NumPy, SciPy, MatPlotlib, and so on.
Just like other IDEs, PyCharm has a powerful debugger with a graphical interface. It offers Git integration, has an ssh terminal, and version control. PyCharm IDE is customizable, which allows you to choose between different themes, color schemes, and key-binding.
Additionally, PyCharm lets you add plugins for non-pythonic files, and these plugins take care of the indentation, highlighting of errors and keywords on-the-fly.
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide6_ysbbwz.png" />
 <figcaption>(<a href="https://www.jetbrains.com/pycharm/whatsnew/">Source</a>) </figcaption>
</figure>
</center> 

One of the PyCharm 2019 updates, as shown in the above figure, is editing the source code in your Notebook. This, however, only comes with a Pro edition: as you edit the Notebook, the source code looks like a regular Python file in the PyCharm editor. All the changes made in either PyCharm or the browser are reflected immediately.
Both of PyCharm&#39;s Professional and Community edition can be downloaded from the official JetBrains <a href="https://www.jetbrains.com/pycharm/download/#section=mac">website</a>. It is available for Windows, Linux, and Mac operating systems.
<h2 id="spyder">Spyder</h2>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996941/ide7_l9zsm9.png" />
 <figcaption>(<a href="https://docs.spyder-ide.org">Source</a>) </figcaption>
</figure>
</center> 

Spyder is a lightweight open-source IDE that comes pre-installed with Anaconda distribution and was built mainly for data science practitioners. It may not have an appealing user-interface as PyCharm or Visual Studio, but you should give it a try given the number of functionalities it has to offer!
Because it was built for the data science community, it is integrated with the essential data-centric libraries like NumPy, SciPy, Matplotlib, Pandas, and IPython. The built-in capabilities can be extended further by plugins and APIs.
One of the great features that you would like about Spyder is its <code>Help</code> toolbar, which lets you search a plethora of information related to libraries/modules.
Spyder contains features like a text editor with syntax highlighting, code completion, static code analysis, debugging, and variable exploring. It also has a profiler that recursively determines the run time and number of calls for every function and method called in a file. Moreover, some of the less essential features but still adds value in making Spyder one of the best IDEs are file explorer, history log, internal console, and so on.
Since spyder is open-source, it is free, and since it comes packaged with Anaconda, you will need to download Anaconda from <a href="https://www.anaconda.com">this website</a>. Like other IDEs, Spyder is available for Windows, Mac, and Linux operating systems.
To learn how to install Anaconda, check out the <a href="https://docs.anaconda.com/anaconda/install/">documentation</a>.
<h2 id="visual-studio-code">Visual Studio Code</h2>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1561996940/ide8_zakvks.png" />
 <figcaption>(<a href="https://code.visualstudio.com/docs/getstarted/userinterface">Source</a>) </figcaption>
</figure>
</center> 

Visual Studio (VS) Code is an open-source IDE that was developed by Microsoft four years back, i.e., in the Year 2015. Visual Studio Code has support for all three operating systems; Windows, Linux, and macOS.
VS Code has a lot of built-in features like debugging, embedded Git control and GitHub, syntax highlighting, intelligent code completion, snippets, and code refactoring. It is very much customizable, allowing users to change the theme, keyboard shortcuts, preferences, and install plugins that add additional functionality. It also has a terminal embedded into it.
The fun part about VS Code is that it has a minimalist zen mode, which shows you only the file you&#39;re working on, hiding not just code&#39;s interface, but the rest of your desktop too. This way it keeps you away from distractions and more focussed towards the code you are writing or the error you are debugging for hours and hours.
VS Code also has unit testing support via unit tests, pytest frameworks. It has Linting support which highlights syntactical and stylistic problems in your Python source code. Like PyCharm, VS Code also has a code editing feature for Jupyter environments and notebooks.
Visual Studio Code can be downloaded from <a href="https://code.visualstudio.com">here</a>.
<h2 id="which-is-the-best-ide-">Which is the Best IDE?</h2>
The answer to this is subjective, but it would still be a good idea to start with an IDE that is less convoluted if you are at a beginner level. Once you get a good handle on coding, you should then switch to a better IDE that has a lot of the built-in features that will allow you to code more efficiently.
The above list is a subset of all the IDEs that are out there in the market. It would be best if you test each one of them and find out which one best suits your needs. Some of you might be inclined towards using Jupyter Notebook while some might like PyCharm more, it will be tough to pick one amongst the others.
<h2 id="conclusion">Conclusion</h2>
When it comes to coding, IDEs are no doubt the best; they give you an excellent experience by improving your overall results and the workflow. They help you in coding your ideas and logic much more efficiently and in a neat manner. They have so much to offer starting with writing, running, and debugging your code apart from providing you with a great user-interface with various themes and colors.
If you would like to learn Python, check out the following DataCamp courses:
<ul>
<li><a href="https://www.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a>
</li>
<li><a href="https://www.datacamp.com/courses/intermediate-python-for-data-science">Intermediate Python for Data Science</a>
</li>
</ul>
References:
<ul>
<li><a href="https://www.datacamp.com/community/tutorials/data-science-python-ide">Top 5 Python IDEs For Data Science
</a></li>
</ul>
<ul>
<li><a href="https://www.filehorse.com/download-pycharm/">The free Python IDE for Professional Windows PC Developers
</a></li>
</ul>
Please feel free to ask any questions related to this tutorial in the comments section below.

https://www.datacamp.com/community/tutorials/top-python-ides-for-2019

Curious about which Python environment is best suited for you? In this tutorial, you'll be given an overview of the top Python IDEs used for data science.

Best Python IDEs (Interactive Development Environment)

Top Python IDEs for 2019

<h2 id="topics">Topics</h2>
<ol>
<li><a href="#intro">Introduction</a></li>
</ol>
&nbsp;&nbsp;&nbsp;&nbsp;1.1. <a href="#syntax">Syntax</a>
&nbsp;2. <a href="#DV">Displaying Variables</a>
&nbsp;3. <a href="#E">Expressions</a>
&nbsp;4. <a href="#SC">Special Characters</a>
&nbsp;&nbsp;&nbsp;&nbsp;4.1. <a href="#Q">Quotations</a>
&nbsp;&nbsp;&nbsp;&nbsp;4.2. <a href="#B">Braces</a>
&nbsp;5. <a href="#D">Dictionaries</a>
<a id="intro"></a>
<h2 id="1-introduction">1. Introduction</h2>
What is string formatting?
String formatting is attractively designing your string using formatting techniques provided by the particular programming language. We have different string formatting techniques in Python. We are now going to explore the new f-string formatting technique.
f-string evaluates at runtime of the program. It&#39;s swift compared to the previous methods.
f-string having an easy syntax compared to previous string formatting techniques of Python. We will explore every bit of this formatting using different examples.
<a id='syntax'></a>
<h3 id="1-1-syntax">1.1. Syntax</h3>
Every f-string statement consists of two parts, one is character f or F, and the next one is a string which we want to format. The string will be enclosed in single, double, or triple quotes.
Let&#39;s see the syntax.
<pre><code class="lang-python">## we can also use F, &#39;&#39;, &#39;&#39;&#39;&#39;&#39;, &quot;&quot;&quot;&quot;&quot;&quot;&#39;
f&quot;string&quot;
</code></pre>
In place of string, we have to place our sentence which is going to format.
<a id='DV'></a>
<h2 id="2-displaying-variables">2. Displaying Variables</h2>
We previously use the str.format() method mostly to format the strings. But, the time has changed we have a new method to make our efforts twice as fast.
The variables in the curly { } braces are displayed in the output as a normal print statement. Let&#39;s see an example.
<pre><code class="lang-python">## declaring variables
name = &quot;Datacamp&quot;
type_of_company = &quot;Educational&quot;

## enclose your variable within the {} to display it&#39;s value in the output
print(f&quot;{name} is an {type_of_company} company.&quot;)
</code></pre>
<pre><code>Datacamp is an Educational company.
</code></pre>We have got the values of the variables in the output. See, it&#39;s easy. We can also replace the f with F as well.
<pre><code class="lang-python">## declaring variables
name = &quot;Datacamp&quot;
type_of_company = &quot;Educational&quot;

## enclose your variable within the {} to display it&#39;s value in the output
print(F&quot;{name} is an {type_of_company} company.&quot;)
</code></pre>
<pre><code>Datacamp is an Educational company.
</code></pre>f is either lower or upper it&#39;ll work the same.
<a id='E'></a>
<h2 id="3-expressions">3. Expressions</h2>
What if we can evaluate expressions like arithmetic, function calls, etc., in a string? It&#39;s cool right! Yeah! It&#39;s very, very exciting to use expressions inside a string. f-string allows us to evaluate expressions inside the string.
Just put the expression inside { } to evaluate. f-string evaluates it at runtime of the program. It&#39;s an excellent feature which saves you time and code.
Let&#39;s see examples of the different types of expressions.
<pre><code class="lang-python">## arithmetic expression
print(f&quot;{5 * 5}&quot;)
</code></pre>
<pre><code>25
</code></pre>We can also call functions inside the { }. Let&#39;s define a function greet() and call it in the f-string
<pre><code class="lang-python">def greet(name):
 return &quot;Hello, &quot; + name
</code></pre>
<pre><code class="lang-python">## calling the function using f-string
name = &quot;Datacamp&quot;
print(f&quot;{greet(name)}&quot;)
</code></pre>
<pre><code>Hello, Datacamp
</code></pre>In the same way, we can also call the predefined methods. Let&#39;s see.
<pre><code class="lang-python">## calling the &#39;title&#39; method which makes the first letter of every word upper
string = &quot;datacamp is an educational company.&quot;
print(f&quot;{string.title()}&quot;)
</code></pre>
<pre><code>Datacamp Is An Educational Company.
</code></pre>What else can we do with the f-string? Are we able to display the object? Yes, we can. It&#39;s the same as when we show the output of the variables.
<pre><code class="lang-python">class Sample:

 def __init__(self, name, age):
 self.name = name
 self.age = age

 ## this method will be called when we print the object
 def __str__(self):
 return f&#39;{self.name} is {self.age} years old.&#39;
</code></pre>
<pre><code class="lang-python">john = Sample(&quot;John&quot;, 19)

## it&#39;ll wake up the __str__() method
print(f&quot;{john}&quot;)
</code></pre>
<pre><code>John is 19 years old.
</code></pre><a id='SC'></a>
<h2 id="4-special-characters">4. Special Characters</h2>
What if we want to display special characters like {}, \&#39;, \&quot;?, which are internally used by Python for special meaning. Can we use escape character inside the f-string? Let&#39;s see the answers to these questions.
<a id='Q'></a>
<h3 id="4-1-quotations">4.1. Quotations</h3>
We can use any quotation marks {single or double or triple} in the f-string. We have to use the escape character to print quotation marks. The f-string expression doesn&#39;t allow us to use the backslash. We have to place it outside the { }.
Let&#39;s see it by examples.
<pre><code class="lang-python">name = &quot;Datacamp&quot;

## displaying single quotations
print(f&quot;Hello, \&#39;{name}\&#39;&quot;)

print()

## displaying double quotations
print(f&quot;Hello, \&quot;{name}\&quot;&quot;)
</code></pre>
<pre><code>Hello, &#39;Datacamp&#39;

Hello, &quot;Datacamp&quot;
</code></pre>Let&#39;s see other types of quotations in f-strings.
<pre><code class="lang-python">f&#39;{&quot;Hello, Datacamp&quot;}&#39;
</code></pre>
<pre><code>&#39;Hello, Datacamp&#39;
</code></pre><pre><code class="lang-python">f&quot;{&#39;Hello, Datacamp&#39;}&quot;
</code></pre>
<pre><code>&#39;Hello, Datacamp&#39;
</code></pre><pre><code class="lang-python">f&quot;&quot;&quot;Hello, Datacamp&quot;&quot;&quot;
</code></pre>
<pre><code>&#39;Hello, Datacamp&#39;
</code></pre><pre><code class="lang-python">f&#39;&#39;&#39;Hello, Datacamp&#39;&#39;&#39;
</code></pre>
<pre><code>&#39;Hello, Datacamp&#39;
</code></pre>We can also include { } inside the three quotes as well.
You can&#39;t use the backslashes inside the { } in f-string. You&#39;ll get an error if they are included inside { }.
<pre><code class="lang-python">print(f&quot;{\&quot;Datacamp\&quot;}&quot;)
</code></pre>
<pre><code> File &quot;&lt;ipython-input-60-b9a2502d6274&gt;&quot;, line 1
 print(f&quot;{\&quot;Datacamp\&quot;}&quot;)
 ^
SyntaxError: f-string expression part cannot include a backslash
</code></pre><a id='B'></a>
<h3 id="4-2-braces">4.2. Braces</h3>
We have to use a double set of braces to print the braces using f-string. Let&#39;s see an example.
<pre><code class="lang-python">print(f&quot;{{Datacamp}}&quot;)
</code></pre>
<pre><code>{Datacamp}
</code></pre>If you want to display two sets of braces using f-string, then you have to use four sets of braces.
<pre><code class="lang-python">print(f&quot;{{{{Datacamp}}}}&quot;)
</code></pre>
<pre><code>{{Datacamp}}
</code></pre><a id='D'></a>
<h2 id="5-dictionaries">5. Dictionaries</h2>
We have to be a bit careful while dealing with dictionary keys inside the f-string. You have to use a different quotation to the dictionary key and f-string. You are not permitted to use the same quotations for a dictionary key as if it was an f-string.
<pre><code class="lang-python">person = {&quot;name&quot;: &quot;John&quot;, &quot;age&quot;: 19}
print(f&quot;{person[&#39;name&#39;]} is {person[&#39;age&#39;]} years old.&quot;)
</code></pre>
<pre><code>John is 19 years old.
</code></pre>You are not allowed to use it as follows in the case of dictionaries.
<pre><code class="lang-python">person = {&quot;name&quot;: &quot;John&quot;, &quot;age&quot;: 19}
print(f&#39;{person[&#39;name&#39;]} is {person[&#39;age&#39;]} years old.&#39;)
</code></pre>
<pre><code> File &quot;&lt;ipython-input-65-6849ff0810ae&gt;&quot;, line 2
 print(f&#39;{person[&#39;name&#39;]} is {person[&#39;age&#39;]} years old.&#39;)
 ^
SyntaxError: invalid syntax
</code></pre><h2 id="conclusion">Conclusion</h2>
I hope you learn something new in Python by reading this tutorial. To learn more about strings and previous formatting techniques head over this <a href="https://www.datacamp.com/community/tutorials/python-string-tutorial">tutorial</a> on Datacamp. Also, check out DataCamp&#39;s <a href="https://www.datacamp.com/courses/regular-expressions-in-python">Regular Expressions in Python</a> course.
Happy Coding :)

https://www.datacamp.com/community/tutorials/f-string-formatting-in-python

Learn about the f-string formatting technique in Python 3.6. In this tutorial, you'll see what advantages it offers and go through some example use cases.

Learn about the f-string formatting technique in Python 3.6. Follow this tutorial to see what advantages it offers & go through examples today!

f-string Formatting in Python Tutorial

f-string Formatting in Python

Data can be complex, tedious, and sometimes it can be a difficult task to uncover patterns. Consider a situation where you have some sales data belonging to your company. You wanted to discover a pattern in terms of the spending capacity of the consumers. If you could uncover distinct groups or associations in the data, you could target the different groups accordingly to get maximum sales. The basic idea behind this intuition is called clustering and Tableau has an inherent feature which can automatically cluster similar data points based on certain attributes. Let us explore this functionality of Tableau and see how we can apply the clustering to a real-world data set.
<h2 id="k-means-clustering">K Means Clustering</h2>
Clustering, also known as cluster analysis is an Unsupervised machine learning algorithm that tends to group together similar items, based on a similarity metric.
Tableau uses the K Means clustering algorithm under the hood. K-Means is one of the clustering techniques that split the data into K number of clusters and falls under centroid-based clustering. In K means clustering, for a given number of clusters k, the algorithm splits the dataset into k clusters where every cluster has a centroid which is calculated as the mean value of all the points in that cluster. The data points are then clustered based on their distances from the centroids.
Here are two great tutorials on DataCamp that deal with the concept of clustering in detail:
<ul>
<li><a href="https://www.datacamp.com/community/tutorials/k-means-clustering-python">K-Means Clustering in Python with scikit-learn</a></li>
<li><a href="https://www.datacamp.com/community/tutorials/k-means-clustering-r">K-Means Clustering in R Tutorial</a></li>
</ul>
If you want to learn more about Clustering in Machine Learning, take DataCamp’s <a href="https://www.datacamp.com/courses/unsupervised-learning-in-python">Unsupervised Learning in Python</a> or <a href="https://www.datacamp.com/courses/clustering-methods-with-scipy">Clustering Methods with SciPy</a> course.
The figure below visualizes the K -Means algorithm for a better understanding. Initially, we randomly define 4 centroid points. The K means algorithm then assigns each data point to its nearest cluster (cross). The centroid shifts to a new position as the mean value of all data points change, and the entire process is repeated until we observe no further change in the position of the centroids.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788530/Visualizing_K-Means_algorithm_1_eefbvf.gif" />
<a href="http://tech.nitoyon.com/en/blog/2013/11/07/k-means/">Visualizing K-Means algorithm</a>
<h3 id="the-criterion-for-clustering-in-tableau">The Criterion for Clustering in Tableau</h3>
After having some idea about the clustering methodology, let’s now understand how clustering is accomplished in Tableau. Tableau uses the <a href="https://onlinehelp.tableau.com/v10.0/pro/desktop/en-us/clustering_howitworks.html">Calinski-Harabasz</a> criterion to assess cluster quality. Here is the mathematical interpretation of the Calinski-Harabasz criterion:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_2_gz9zb8.png" />
<a href="https://onlinehelp.tableau.com/v10.0/pro/desktop/en-us/clustering_howitworks.html">Source</a>
&quot;where SSB is the overall between-cluster variance, SSW is the overall within-cluster variance, k is the number of clusters, and N is the number of observations&quot; (<a href="https://onlinehelp.tableau.com/v10.0/pro/desktop/en-us/clustering_howitworks.html">Source</a>).
This ratio gives a value which determines the cohesiveness of the clusters. So the higher this value, clusters are more closely associated having low within-cluster distance and high between-cluster distance.
<h3 id="pre-requisites">Pre-requisites</h3>
This article assumes some familiarity with Tableau. In case you are a complete beginner, it is suggested that you go through the following article on DataCamp which serve as an excellent primer.
<a href="https://www.datacamp.com/community/tutorials/data-visualisation-tableau">Data Visualisation with Tableau</a>
<h3 id="tableau-environment">Tableau Environment</h3>
In this tutorial, we will be working with <a href="https://public.tableau.com/en-us/s/">Tableau Public</a>, which is an absolutely free offering from Tableau. Download the Tableau Public edition from the <a href="https://public.tableau.com/en-us/s/">official website</a>. Follow the installation instructions, and if the following screen appears on clicking the Tableau Icon, you are good to go.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_3_pha7wi.png" />
<h3 id="connecting-to-the-dataset">Connecting to the Dataset</h3>
The dataset that we will be using in this article is called the World Economic Indicators dataset. This dataset includes some of the important indicators driving the economy such as Life expectancy, ease of doing business, population, etc., of the various countries. It has been obtained from the United Nations. The dataset can be accessed from <a href="https://github.com/parulnith/Data-Visualisation-libraries/blob/master/Data%20Visualisation%20with%20Tableau/Cluster%20Analysis%20with%20Tableau/World%20Indicators.xlsx">here</a>.
<ul>
<li>Download the dataset on to your systems.</li>
<li>Import the data into your Tableau workspace from the computer. Use the Data Interpreter, present under Sheets Tab, to rectify and realign the data.</li>
</ul>
<h2 id="formatting-the-data-source">Formatting the Data Source</h2>
Proceed to the worksheet and have a glance over the Measures and Dimensions Tab. There are a lot of features under the Measures tab, which can be clubbed together under a single category. This will also help to better represent all of the data fields.
<ul>
<li>Select Business Tax Rate, Days to Start Business, Ease of Business, Hours to do Tax and Lending Interest &gt; Create Folder.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/Formatting_the_Data_Source_4_w4h3rr.gif" />
<ul>
<li>Name the folder as Business, and now all the above field are included in this particular folder.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_5_os6lh7.png" />
<ul>
<li>Similarly, create folders for Development, Health, and Population. Also, add the following fields, respectively. This is how the Measures Tab looks like after the formatting:</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/Formatting_the_Data_Source_6_mnzvdk.png" />
<h2 id="creating-clusters-from-world-economic-indicators-data">Creating Clusters from World Economic Indicators Data</h2>
Clustering helps to uncover the patterns in the dataset. Let’s suppose that you are an analyst with some Tourism company. Due to the increase in life expectancy around the world, there has been a surge in senior tourism. Older people now are more active and are more interested in traveling and seeing the world. Your company is trying to cash in on this phenomenon, and your work is to use the World Indicators sample data to identify the countries where there are enough of the right kind of customers. Clustering is a tool that can help us in identifying such type of countries.
<h3 id="identifying-countries-with-a-high-success-rate-for-senior-tourism">Identifying countries with a high success rate for senior tourism</h3>
Let’s now go through the various steps to perform cluster analysis on the data.
<ul>
<li>Double click <code>Country</code> in the Dimensions under Data pane. Tableau creates a map view with a filled circle representing each country. Change the mark type to Map, on the Marks card,</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_7_ooknke.gif" />
<h3 id="identifying-the-variables-for-clustering">Identifying the variables for clustering</h3>
The next step in clustering is to identify the variables that will be used in the clustering algorithm. In Tableau, the variables are akin to the fields. There is not a single answer to the best variables that will give ideal clusters, but you can experiment with a number of variables to see what gives the desired results. In our case, let’s work with the following fields:
<ul>
<li>Population Urban</li>
</ul>
Urban population is a good indicator of the population density in a country. Higher the density, more business opportunities become available.
<ul>
<li>Population 65+</li>
</ul>
Population greater than 65 signifies senior citizens, which is also our target segment.
<ul>
<li>Life Expectancy Female and Life Expectancy Male</li>
</ul>
Countries with a higher life expectancy signify that people there tend to live longer and will be more interested in traveling.
<ul>
<li>Tourism Per Capita</li>
</ul>
This field doesn’t exist and can be created as a calculated field, using <code>Tourism Outbound</code> and <code>Population Total</code> fields as follows:
<code>Tourism Per Capita = SUM([Tourism Outbound])/SUM([Population Total])</code>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_8_vuvkzr.gif" />
Tourism Outbound represents the money (in US dollars) that people of a country spend annually on international travel. To get the average value, we will need to divide this field by the population of each country.
<h3 id="adding-selected-field-to-the-view">Adding selected field to the view</h3>
Before moving ahead, we need to change the default aggregation from <code>SUM</code> to <code>AVERAGE</code>. Tableau makes it possible to aggregate measures or dimensions, though <a href="https://onlinehelp.tableau.com/current/pro/desktop/en-us/calculations_aggregation.htm#Measures">aggregating</a> measures is more common. Whenever we add a measure to the view, an aggregation is applied to that measure by default. The type of aggregation that needs to be applied depends on the context of the view.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788530/tab-leau_9_pvvkm2.gif" />
Change the Aggregation for all of the selected fields and then drag them on to the Detail on the Marks card as follows:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_10_kqdipi.png" />
<h3 id="clustering">Clustering</h3>
Clustering in Tableau is a simple drag and drop process. The following steps outline the clustering process:
<ul>
<li>Click on the <code>Analytics</code> Pane and drag <code>Cluster</code> onto the view, and the data is clustered by Tableau automatically.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_11_yp6dyd.gif" />
<ul>
<li>Although Tableau can automatically decide the number of clusters to create, we can also control the number of clusters and what variables are used to compute the cluster. Simply drag a field in the box to include it in clustering algorithm or drag it out to exclude it.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_12_fmzmxl.gif" />
<ul>
<li>We shall go with 4 clusters and the default variables for better analysis. Note, some countries did not fall in any cluster and have been marked as not clustered.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_13_dfe3yc.png" />
<ul>
<li>The cluster is created as a new pill and can be seen on the color shelf. Drag this pill on to the Data Pane to be saved as a group.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_14_gavgtm.png" />
So here we have clustered the countries in relation to the chosen measures. But how do we make sense out of these results, and how can we make business decisions based on the clusters? The next section addresses these concerns.
<h2 id="describing-clusters">Describing Clusters</h2>
Click on the Clusters field in the Marks card and click on the Describe Clusters option.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_15_qztzxw.png" />
This displays a document which contains a detailed description of the clusters. There are two tabs in the document:
<h3 id="summary">Summary</h3>
This gives a summary of the results and the average values of each variable for every cluster.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788529/tab-leau_16_pgpgue.png" />
From the above results, we can infer that Cluster 3 has :
<ul>
<li>Highest Life Expectancy for both males and females</li>
<li>Highest Total Tourism Per capita</li>
<li>Highest urban population</li>
</ul>
This means it has a wealthy urban population with a larger life expectancy and hence does seem to be a good market for Senior Tourism Industry. Let us see which countries are included in this cluster.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_17_ga1gw6.gif" />
<h3 id="models">Models</h3>
The models’ tab displays the various statistical value for the average value of all the variables/fields and shows their statistical significance.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562788528/tab-leau_18_afnhla.png" />
Thus as an analyst, you can present this list to the Sales Team so that they can focus on these prospective clients. Clustering has provided us with great insight. From here, you can experiment with different fields, set a threshold for population or Income, etc. There are many ways to cluster the data, but the basic principle stays the same.
<h2 id="conclusion">Conclusion</h2>
In this article, we learned how to perform a cluster analysis of a given dataset in Tableau with a simple drag and drop mechanism. Clustering is a valuable tool and when coupled with Tableau, gives the power of a statistical analysis technique in the hands of analysts.
References
<a href="https://onlinehelp.tableau.com/v10.0/pro/desktop/en-us/clustering_howitworks.html">Tableau Documentation</a>

https://www.datacamp.com/community/tutorials/cluster-analysis-in-tableau

Learn how to easily cluster your data in Tableau with its built-in clustering tool.

Cluster Analysis in Tableau

Exploratory data analysis is the backbone of any data science project. Before proceeding with the model creation and training the dataset on any machine learning algorithm, it is essential that the dataset is explored to get an overview of what’s in it.
Consider the famous Superstore dataset. The data is that of a United States Superstore and contains information about products, sales, profits, and so on that you can use to identify key areas for improvement within this fictitious company. The first step would be to import the dataset and explore it. You can download the data from <a href="https://github.com/parulnith/Data-Visualisation-libraries/blob/master/Data%20Visualisation%20with%20Tableau/Quadrant%20Analysis%20in%C2%A0Tableau/Sample-Superstore%20.xls">here</a>.
<h3 id="python-for-eda">Python for EDA</h3>
Let’s use Python to import the dataset and explore it. If you want to learn more on how to view and clean data in Python, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/cleaning-data-in-python">Cleaning Data in Python</a> course.
<pre><code>import pandas as pd 
import numpy as np
superstore_data = pd.read_excel(&#39;Sample-Superstore .xls&#39;,header = 3) 
superstore_data.head()
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_1_bvtlof.png" />
The dataframe shows the first five rows of the dataset containing different attributes. We can also see if there are any missing values and the different columns:
<pre><code>superstore_data.info()
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_2_saf6jq.png" />
The information above shows that there are no null values and a total of 9994 records. Visualization is also an important part of EDA since it gives a better perspective. We could create a scatter plot between Sales and Profit, but what if there was a way to further modify the existing scatter plot to give us more information? One such way of visualizing information is through a Quadrant Chart. Let’s understand more about it in the next few sections and create one of our own.
<h2 id="quadrant-analysis-chart">Quadrant Analysis Chart</h2>
If you are a part of the Data Science ecosystem, you must have heard about <a href="https://www.gartner.com/imagesrv/research/methodologies/magic_quad_faq.pdf">Gartner’s Magic Quadrant</a>(MQ). These MQs are a series of reports containing the market research and analysis of several technology companies. As a matter of fact, they are one of the most anticipated and awaited reports in this space. Here is the <a href="https://www.qlik.com/us/gartner-magic-quadrant-business-intelligence">2019 Magic Quadrant</a> for Analytics and Business Intelligence Platforms which shows that Tableau and PowerBI are leading the way.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_3_nlqpq4.png" />
A Quadrant chart is technically a scatter plot that is divided into four equal sections or quadrants, hence the name. A Quadrant Analysis chart helps to represent the data in separate quadrants so that it becomes easy to locate which data points are similar and which are not, based on certain characteristics.
<h2 id="creating-quadrant-charts-in-tableau">Creating Quadrant Charts in Tableau</h2>
<a href="https://www.tableau.com/">Tableau</a> is a great data analytics and visualization tool used widely in the industry today. Tableau’s ease of use comes from the fact that it has a drag and drop interface. As a result, it makes it very easy and intuitive to create a plethora of plots and quadrant charts is one of them.
<h4 id="pre-requisites">Pre-requisites</h4>
This tutorial assumes some familiarity with Tableau, its properties, and how it is used to do the analysis. In case you are a complete beginner, it is suggested that you go through the following article, which serves as an excellent introduction to Tableau:
<a href="https://www.datacamp.com/community/tutorials/data-visualisation-tableau">Data Visualization with Tableau</a>
<h2 id="tableau-environment">Tableau Environment</h2>
In this tutorial, we will be working with <a href="https://public.tableau.com/en-us/s/">Tableau Public</a>, which is an absolutely free offering from Tableau. Download the Tableau Public edition from the <a href="https://public.tableau.com/en-us/s/">official website</a>. Follow the installation instructions, and if the following screen appears on clicking the Tableau Icon, you are good to go.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_4_okwlmj.png" />
<h2 id="connecting-to-data-source">Connecting to Data Source</h2>
We’ll use the same Superstore dataset that had been described earlier. In case you haven’t download the dataset, you can find it <a href="https://github.com/parulnith/Data-Visualisation-libraries/blob/master/Data%20Visualisation%20with%20Tableau/Quadrant%20Analysis%20in%C2%A0Tableau/Sample-Superstore%20.xls">here</a>.
<ul>
<li>Import the Data into Tableau workspace from the computer.</li>
<li>Under the Sheets Tab, three sheets will become visible, namely Orders, People, and Returns. However, we will focus only on the Orders data. Double click on <code>Orders</code> Sheet, and it opens up just like a spreadsheet.</li>
<li>We observe that the first three rows of data look a bit different and is not in the desired format. We shall use Data Interpreter, also present under Sheets Tab, to rectify this. By clicking on it, we get a nicely formatted sheet.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_5_dwiexm.gif" />
<h2 id="creating-a-scatter-plot">Creating a Scatter Plot</h2>
Let’s say we want to see how Discount is related to Profits of the Superstore, and which products are more profitable than the other. The first step is to create a simple scatterplot between Discount and Profit Ratio. <code>Profit Ratio</code> gives a better sense than the field Profit alone, and since it doesn’t exist in the original database, we shall create one.
<ul>
<li>Open up a new worksheet and create a new calculated field called <code>Profit Ratio</code> by using the formula mentioned below.
<code>sum([Profit])/sum([Sales])</code>
</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_6_bmhuqm.gif" />
<ul>
<li>Drag <code>Discount</code> to Columns and <code>Profit Ratio</code> to Rows. Finally, drag the <code>Sub Category</code> on to the Colors Shelf. Convert the Measure of Discount to Average from Sum. You get a scatter plot which is colored by the different products.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_7_x3vjjj.png" />
<h2 id="creating-parameters">Creating Parameters</h2>
Now that we have a basic scatter plot, it’s time to create the two parameters which will act as the basis on which the Quadrants will be calculated. In our case, we shall create a <code>Discount Parameter</code> and a <code>Profit Ratio Parameter</code>, which will be derived from the Discount and Profit Ratio Fields, respectively.
<h4 id="discount-parameter">Discount Parameter</h4>
Click on the Discount field &gt; Create &gt; Parameter and fill in the values as shown in the figure below. Set the Current Value to the point of the data of your choice (in this example, 0.2).
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_8_wwihdr.png" />
<h4 id="profit-ratio-parameter">Profit Ratio Parameter</h4>
Perform the same steps as above using the Profit Ratio field.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_9_qwt7pv.png" />
A new heading called Parameters is now visible in the worksheet where both of our calculated parameters appear.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794718/data_viz_10_rcgtsw.png" />
<h2 id="adding-reference-lines">Adding Reference Lines</h2>
A Reference line in Tableau is simply a vertical or horizontal line in a graph that represents a point of reference. For instance, a reference line drawn on a Sales chart can easily differentiate between high and low sales points. We shall be creating two reference lines, one for each axis.
<ul>
<li>Right click on the <code>Profit Ratio axis</code> (y) and select Add Reference Line.</li>
<li>Set the Reference line Value to the <code>Profit Ratio</code> Parameter.</li>
<li>Leave the other fields with their default values and click OK.</li>
<li>Right click on the <code>Avg Discount</code> axis (x) and select Add Reference Line.</li>
<li>Set the Reference line Value to the <code>Avg Discount</code> Parameter.</li>
<li>Leave the other fields with their default values and click OK.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794719/data_viz_11_ayxspi.gif" />
<h2 id="assigning-a-color-to-every-quadrant">Assigning a color to every Quadrant</h2>
We now have our four quadrants, but the data points in every quadrant are randomly colored, which doesn’t help us in visualizing which data point falls in which quadrant. We can write a simple calculation that assigns a color to a data point based on their quadrant i.e., UPPER RIGHT, UPPER LEFT, BOTTOM RIGHT, or BOTTOM LEFT.
<ul>
<li>Click Analysis &gt; Create calculated field and name the field as Quadrant Color.</li>
<li>Enter the formula as mentioned below:
<pre><code>IF AVG([Discount]) &gt;= [Discount Parameter] 
AND [Profit Ratio] &gt; [Profit Ratio Parameter] THEN &#39;UPPER RIGHT&#39; 
ELSEIF AVG([Discount]) &lt; [Discount Parameter] 
AND [Profit Ratio] &gt; [Profit Ratio Parameter] THEN &#39;UPPER LEFT&#39; 
ELSEIF AVG([Discount]) &gt;[Discount Parameter] 
AND [Profit Ratio] &lt; [Profit Ratio Parameter] THEN &#39;BOTTOM RIGHT&#39; 
ELSE &#39;BOTTOM LEFT&#39; 
END
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794719/data_viz_12_n0sac9.gif" />
</li>
<li>The data points currently have been colored by the Sub-category. However, we want to color them with their respective quadrants. Simply drag the <code>Sub-Category</code> field on to the Detail card and <code>Quadrant Color</code> field on to the color. Instantly all data points are now colored with respect to the quadrant in which they fall. Change the <code>Shape</code> to circle to get filled circles and adjust the size and tooltip preferences accordingly.
</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794719/data_viz_13_eveuza.gif" />
So here we have a Quadrant Analysis of the Discount and Profit Ratio of an American Superstore. It is pretty evident from the graph that products which lie in the <code>Bottom Right</code> are the least profit-making products while the ones lying in the <code>Upper Left</code> are the most profitable ones.
<h2 id="creating-dynamic-quadrant-chart">Creating Dynamic Quadrant Chart</h2>
The quadrants in the above chart are fixed and can only be changed once we change the values of the linked Reference lines. What if we want to experiment with different thresholds to see how the products change their quadrants in real time? This is actually possible and is referred to as a dynamic quadrant chart and is achieved through the <code>Actions.</code>
<h4 id="actions">Actions</h4>
Actions is a feature in Tableau that lets us add interactivity to the data. With the help of this feature, we can interact with our visualization by either selecting, clicking or hovering on them. Actions can perform the following tasks in a visualization:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794719/data_viz_14_i8jvth.png" />
Let’s now set up a worksheet action with the following steps:
<ul>
<li>Goto Worksheet&gt;Actions and add <code>Change Parameter</code> action. This is because we want to create an action that changes the parameters accordingly.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794720/data_viz_15_ld2m0q.png" />
<ul>
<li>Next, we will name the action as <code>Update Discount Parameter</code> and select the corresponding <code>Target Parameter</code> and <code>Value</code>, which in this case would be <code>Discount Parameter</code> and <code>AVG(Discount)</code> respectively.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794721/data_viz-16_zmhbnx.gif" />
<ul>
<li>Similarly, create another action called <code>Update Profit Ratio Parameter</code>.</li>
</ul>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794721/data_viz_17_zbuvdk.gif" />
Now, as you click on a data point, it becomes the center of the Quadrant. Thus what you now get is a dynamic Quadrant chart, and you can easily see how changing the values of Profit Ratio, and Discount changes the position of products in the quadrant.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1562794721/data_viz_18_wo0uda.gif" />
<h3 id="conclusion">Conclusion</h3>
In this article, we learned how to convert a scatter plot to an interactive Quadrant Chart. It typically helps to have a general overview of data and understand how similar and dissimilar the objects are when working with a Quadrant chart. From here, you can proceed with the normal data analysis either in R or Python to further do some sales forecasting for coming months and analyze the data to identify important features in the dataset that actually drive sales.

https://www.datacamp.com/community/tutorials/quadrant-analysis-in-tableau

Learn how to analyze data in the form of a dynamic quadrant chart in Tableau.

Quadrant Analysis in Tableau

https://www.datacamp.com/community/tutorials/moving-averages-in-pandas

Learn how you can capture trends and make sense out of time series data with the help of a moving or rolling average.

Follow our step by step tutorial and learn how to capture trends. Use time series data to calculate a moving average or exponential moving average today!

Pandas & Numpy Moving Average & Exponential Moving Average Tutorial

Moving Averages in pandas

https://www.datacamp.com/community/tutorials/implementing-neural-style-transfer-using-tensorflow

In this tutorial, you'll learn how to implement power applications like Prisma using TensorFlow 2.0.

community

datacamp

Data Science Tutorials