If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.
<a id='t1'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1588351436/t1_p0bram.png" style="width:100%">
</figure>


<h2 id="introduction">Introduction</h2>
Threading allows you to have different parts of your process run concurrently (Source: <a href="https://realpython.com/courses/threading-python/">RealPython</a>). These different parts are usually individual and have a separate unit of execution belonging to the same process. The process is nothing but a running program that has individual units that can be run concurrently. For example, A web-browser could be a process, an application running multiple cameras simultaneously could be a process; a video game is another example of a process.
Inside a process comes the concept of multiple threading or commonly known as multi-threading, where multiple threads work together to achieve a common goal. The most crucial benefit of using threads is that it allows you to run the program in parallel.
Let&#39;s understand the concept of threading with the help of an example. Imagine you have an application which counts the number of cars entering and exiting the mall&#39;s parking. Your apparatus has various cameras that monitor the entry and exit connecting to a central device. Each camera will have an algorithm to monitor the flow of cars, which will belong to the same process or program. However, each camera, along with the algorithm it is being run on, could be part of a separate thread. Not only that, but even the frames being read from the camera and the algorithm predicting on the frames could also be two separate threads.
Another example could be a video game in which the process has to run the tasks in parallel like the graphics, user interaction, and networking (while playing multiplayer games) because it has to be responsive at all times. And to accomplish this, it has to make use of the concept of multi-threading, where each thread would be responsible for running each independent and individual task.
A thread has its flow of execution, which means that the process will have multiple things happening at one time.
It is important to note that each process has at least one thread, and that is called the <code>main thread</code>. If your program doesn&#39;t have any defined thread, then it will at least have the main thread, i.e., the process itself.
If you look at the <a href="#t1">above</a> diagram, there is a process that consists of the main thread consisting of global variables and three different threads t1, t2, and t3. Each thread has its local variables and flow of execution of code. The threads generally share the resources of the process, like the resources available to the main thread will also be shared among the three threads t1, t2, and t3. All the threads will have access to the global variables while having their own local variables.
There are different types of threads:
<ul>
<li>Kernel thread</li>
<li>User thread</li>
<li>Combination of kernel and user thread</li>
</ul>
<h4 id="now-let-s-look-at-some-of-the-advantages-of-having-threading-in-your-program-">Now let&#39;s look at some of the advantages of having threading in your program:</h4>
<ul>
<li>Multi-threading allows the program to speed up the execution provided that it has multiple CPUs.
</li>
<li>It also lets you perform other tasks while the I/O operations are being performed with the help of multiple threads or even main thread along with a single thread. For example, the speed at which the frames from the camera are read and inferred by the algorithm will be handled by different threads. Hence, the algorithm will not have to wait for the frame to be inputted, and the frame reading part will not have to wait for the algorithm execution to complete to be able to read the next frame.
</li>
</ul>
<ul>
<li>Threads within the same process can share the memory and resources of the main thread.</li>
</ul>
<h4 id="challenges-of-threading-">Challenges of Threading:</h4>
<ul>
<li>Remember that Python works based on the CPython implementation, which limits only one thread to be run at a time, hence; threading may not speed up all tasks. And the essential reason behind this is Global Interpreter Lock (GIL).
 If you would like to learn about GIL, then feel free to check out <a href="https://www.datacamp.com/community/tutorials/python-global-interpreter-lock">this</a> tutorial.
</li>
<li>If you are looking for speeding up the CPU intensive task, then threading may not be the best solution for you. In such cases, multi-processing is considered to be useful.
</li>
<li>To execute a program comprising of various threads, it needs to switch quickly across threads; hence, scheduling needs to be taken care of in programs where you have a lot of threads.
</li>
<li>Resource sharing can also be a problem since all the threads share the same resources and memory of the global variables. Hence, operations performed in one thread could cause a memory error for another thread, or the other thread might not get the memory to perform its task.
</li>
</ul>
<h4 id="threading-in-python">Threading in Python</h4>
<ul>
<li>In Python, the threading module is a built-in module which is known as <code>threading</code> and can be directly imported.
</li>
<li>Since almost everything in Python is represented as an object, threading also is an object in Python. A thread is capable of
<ul>
<li>Holding data,</li>
<li>Stored in data structures like dictionaries, lists, sets, etc.</li>
<li>Can be passed as a parameter to a function. 
 </li>
</ul>
</li>
<li>A thread can also be executed as a process.
</li>
<li>A thread in Python can have various states like:
<ul>
<li>Wait,</li>
<li>Locked.</li>
</ul>
</li>
</ul>
Let&#39;s now learn how you can implement threading in Python.
<h4 id="thread-module-in-python3">Thread module in Python3</h4>
Note that Python3 is backward compatible with the <code>thread</code> module, which exists in Python2.7. In Python3, it can be imported as <code>_thread</code> module. So, let&#39;s take an example and understand the <code>_thread</code> module.
<pre><code class="lang-python">import _thread #thread module imported
import time #time module
</code></pre>
Let&#39;s define a function called <code>thread_delay</code>, which will take two parameters as an input, i.e., name of the thread and the delay. Inside this function, you will:
<ul>
<li>Define a counter with zero,</li>
<li>Then you will loop around with a while loop which will run for three times,</li>
<li>Inside the while loop, you will put <code>time.sleep()</code> to add delay, this delay will be useful to understand the execution of the thread. This delay will be in seconds,</li>
<li>Then you will increment your counter by 1,</li>
<li>To see whether your thread execution is going smoothly, you will print the name of the thread and the time at which the thread is executed.</li>
</ul>
<pre><code class="lang-python">def thread_delay(thread_name, delay):
 count = 0
 while count &lt; 3:
 time.sleep(delay)
 count += 1
 print(thread_name, &#39;--------&gt;&#39;, time.time())
</code></pre>
Now to add the thread functionality on the above function or to run the above function in the thread, you would use the <code>start_new_thread</code> method, which is inside the <code>_thread module</code>.
Let&#39;s see the docstring of the <code>start_new_thread</code> method.
<pre><code class="lang-python">?_thread.start_new_thread
</code></pre>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1588360477/thread_start_new_thread_bti5wa.png" style="width:100%">
</figure>



Let&#39;s pass in the function <code>thread_delay</code> without parenthesis and the two arguments, i.e., thread name and the delay (to visualize the thread execution since it is very fast).
<pre><code class="lang-python">_thread.start_new_thread(thread_delay, (&#39;t1&#39;, 1))
_thread.start_new_thread(thread_delay, (&#39;t2&#39;, 3))
</code></pre>
<pre><code>10916



t1 --------&gt; 1588077856.4221454
t1 --------&gt; 1588077857.4418247
t2 --------&gt; 1588077858.422453
t1 --------&gt; 1588077858.4431
t2 --------&gt; 1588077861.4371448
t2 --------&gt; 1588077864.438915
</code></pre>From the above output, you can see that the thread <code>t1</code> starts executing first. Meanwhile, thread <code>t2</code> waits since there is a 3-second delay, and as soon as that delay is finished, thread <code>t2</code> is executed, and also, the delay for thread <code>t1</code> is just 1 second.
Let&#39;s now change the delay of <code>t2</code> to 5 seconds, which would start <code>t2</code> after <code>t1</code> would have finished executing since <code>t1</code> will take only 3 seconds to complete its execution.
<pre><code class="lang-python">_thread.start_new_thread(thread_delay, (&#39;t1&#39;, 1))
_thread.start_new_thread(thread_delay, (&#39;t2&#39;, 5))
</code></pre>
<pre><code>14632



t1 --------&gt; 1588078001.4087512
t1 --------&gt; 1588078002.4272866
t1 --------&gt; 1588078003.432218
t2 --------&gt; 1588078005.4082909
t2 --------&gt; 1588078010.4268317
t2 --------&gt; 1588078015.4283602
</code></pre><h4 id="implementing-threading-using-the-threading-module">Implementing threading using the Threading module</h4>
Let&#39;s use the same example as you used above, but this time you will use the <code>threading</code> module instead of the <code>_thread</code> module.
<pre><code class="lang-python">import threading
import time
</code></pre>
<pre><code class="lang-python">def thread_delay(thread_name, delay):
 count = 0
 while count &lt; 3:
 time.sleep(delay)
 count += 1
 print(thread_name, &#39;--------&gt;&#39;, time.time())
</code></pre>
Inside the <code>threading</code> module is a <code>Thread</code> class, which is a similar spirit to the <code>start_new_thread</code> function of the <code>_thread</code> module.
Let&#39;s look at the docstring of <code>Thread</code> class, which takes in several parameters like group, target (like a function), args, etc.
<pre><code class="lang-python">?threading.Thread
</code></pre>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1588356967/thread1_mcab3p.png" style="width:100%">
</figure>



In the Thread class constructor, you will pass in the target function <code>thread_delay</code> and the arguments of that function.
<pre><code class="lang-python">t1 = threading.Thread(target=thread_delay, args=(&#39;t1&#39;, 1))
t2 = threading.Thread(target=thread_delay, args=(&#39;t2&#39;, 3))
</code></pre>
<a id='t2'></a>
In the threading module, in order to run or execute the thread, you make use of the <code>start()</code> method, which simply responsible for running the thread.
<pre><code class="lang-python">?t1.start
</code></pre>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1588356967/thread2_dq8pej.png" style="width:100%">
</figure>




<pre><code class="lang-python">t1.start()
t2.start()
</code></pre>
<pre><code>t1 --------&gt; 1588081121.7790291
t1 --------&gt; 1588081122.7962046
t2 --------&gt; 1588081123.7789369
t1 --------&gt; 1588081123.7980876
t2 --------&gt; 1588081126.7961557
t2 --------&gt; 1588081129.7995002
</code></pre>You will also use the <code>join</code> method, which means that wait until all the thread execution is complete. So whatever code you have written after the <code>join</code> method will only be executed once these threads have terminated.
<pre><code class="lang-python">?t1.join
</code></pre>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1588356967/thread3_mgzjuo.png" style="width:100%">
</figure>




<pre><code class="lang-python">t1.start()
t2.start()

t1.join()
t2.join()

print(&quot;Thread execution is complete!&quot;)
</code></pre>
<pre><code>t1 --------&gt; 1588081798.7118263
t1 --------&gt; 1588081799.7231226
t2 --------&gt; 1588081800.7129223
t1 --------&gt; 1588081800.730732
t2 --------&gt; 1588081803.7298765
t2 --------&gt; 1588081806.7309563
Thread execution is complete!
</code></pre>Now let&#39;s calculate the volume of cube and square using the threading concept, which is pretty straight forward.
<pre><code class="lang-python">def volume_cube(a):
 print (&quot;Volume of Cube:&quot;, a*a*a)
</code></pre>
<pre><code class="lang-python">def volume_square(a):
 print (&quot;Volume of Square:&quot;, a*a)
</code></pre>
<pre><code class="lang-python">t1 = threading.Thread(target=volume_cube, args=(2))
t2 = threading.Thread(target=volume_square, args=(3))
</code></pre>
<pre><code class="lang-python">t1.start()
t2.start()

t1.join()
t2.join()

print(&quot;Thread execution is complete!&quot;)
</code></pre>
<pre><code>Thread execution is complete!


Exception in thread Thread-9:
Traceback (most recent call last):
 File &quot;C:\Program Files\Anaconda3\lib\threading.py&quot;, line 926, in _bootstrap_inner
 self.run()
 File &quot;C:\Program Files\Anaconda3\lib\threading.py&quot;, line 870, in run
 self._target(*self._args, **self._kwargs)
TypeError: volume_square() argument after * must be an iterable, not int
Exception in thread Thread-8:
Traceback (most recent call last):
 File &quot;C:\Program Files\Anaconda3\lib\threading.py&quot;, line 926, in _bootstrap_inner
 self.run()
 File &quot;C:\Program Files\Anaconda3\lib\threading.py&quot;, line 870, in run
 self._target(*self._args, **self._kwargs)
TypeError: volume_cube() argument after * must be an iterable, not int
</code></pre>Wait; what? As soon as you executed the start method, it resulted in an error. That is because the <code>args</code> parameter expects a tuple, and since the <code>volume_cube</code> and <code>volume_square</code> expects only one parameter, hence, you need to put a comma in the <code>args</code> parameter after specifying the first argument of the functions.
<pre><code class="lang-python">t1 = threading.Thread(target=volume_cube, args=(2,))
t2 = threading.Thread(target=volume_square, args=(3,))
</code></pre>
<pre><code class="lang-python">t1.start()
t2.start()

t1.join()
t2.join()

print(&quot;Thread execution is complete!&quot;)
</code></pre>
<pre><code>Volume of Cube: 8
Volume of Square: 9
Thread execution is complete!
</code></pre>Let&#39;s now learn to use threading as a subclass.
<h4 id="threading-as-a-sub-class">Threading as a sub-class</h4>
In this section, you would learn how to create sub-class from the thread class, which is available inside the threading module, and then create thread instances <code>t1</code> and <code>t2</code> out of that class.
You would use the same <code>thread_delay</code> function here as well.
<pre><code class="lang-python">class DataCampThread(threading.Thread):
 def __init__(self, name, delay):
 threading.Thread.__init__(self)
 self.name = name
 self.delay = delay


 def run(self):
 print(&#39;Starting Thread:&#39;, self.name)
 thread_delay(self.name,self.delay)
 print(&#39;Execution of Thread:&#39;, self.name, &#39;is complete!&#39;)


t1 = DataCampThread(&#39;t1&#39;, 1)
t2 = DataCampThread(&#39;t2&#39;, 3) 

t1.start()
t2.start()

t1.join()
t2.join()

print(&quot;Thread execution is complete!&quot;)
</code></pre>
<pre><code>Starting Thread: t1
Starting Thread: t2
t1 --------&gt; 1588089083.4879806
t1 --------&gt; 1588089084.489222
t1 --------&gt; 1588089085.4909666
Execution of Thread: t1 is complete!
t2 --------&gt; 1588089085.4979634
t2 --------&gt; 1588089088.499365
t2 --------&gt; 1588089091.5012596
Execution of Thread: t2 is complete!
Thread execution is complete!
</code></pre>Now let&#39;s understand the above code step by step:
<ul>
<li>You defined a class as <code>DataCampThread</code>, and in the class argument, you passed in the <code>threading.Thread</code> class. The reason you do that is that you want to create a sub-class from the threading module. Doing this allows you to use all the methods that are available in the <code>threading.Thread</code> class similar to the concept of inheritance.
</li>
<li>Next, you defined the init method of the DataCampThread class and passed in the function along with the delay. The init is a constructor similar to the parameters you passed to the <code>threading.Thread</code> class, here you would be passing to the <code>DataCampThread</code> class.
</li>
<li>Then you initialize the init method of the main threading.Thread class and then equate name and delay.
</li>
<li>Next, you define the <code>run</code> method, recall <a href="#t2">here</a> you defined the <code>start</code> method, which basically underneath the hood calls the <code>run</code> method. Since you have inherited the main class, you are allowed to make changes to the <code>run</code> method. Hence, when you call the <code>start</code> method, whatever modifications you will make in the <code>run</code> method will be reflected now.
</li>
</ul>
<h2 id="conclusion">Conclusion</h2>
Congratulations on finishing the tutorial.
This tutorial was a basic introduction to threading in python. However, threading is a very vast and vital topic, and some concepts might have been left unexplored. Feel free to look at more threading methods like active count, current thread, etc. 
If you would like to learn more, then also check out Python&#39;s <a href="https://docs.python.org/3/library/threading.html">Thread-based Parallelism</a>.
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.

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

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Learn about the different types of docstrings and various docstring formats like Sphinx, Numpy and Pydoc.

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Docstrings in Python

An array is a data structure that can hold multi-dimensional data. In R, the array is objects that can hold two or more than two-dimensional data. For example, in square matrices can contain two rows and two columns and dimension can take five.
Arrays can store the values having only a similar kind of data types. The data can be more than one dimensional, where there are rows and columns and dimensions of some length.
<h2 id="creation-of-an-array-">Creation of an Array:</h2>
The array() function will create an array which takes a vector, which is the numbers and dimension(&#39;dim&#39;) in the argument.
Let&#39;s see an example below where two vectors named &#39;array1&#39; and array2&#39; are created.
<pre><code class="lang-python">vector1 = c (5, 10, 15,20)
vector2 = c (25, 30, 35, 40, 45, 50,55,60)
</code></pre>
You can take two vectors above as an input to an array where the dimension is considered as 4 * 4, and two matrices or dimensional data is created.
<pre><code class="lang-python">final = array (c (array1, array2),dim =c(4,4,3))
print (final)
</code></pre>
The output of the above code is below :
<pre><code>, , 1

 [,1] [,2] [,3] [,4]
[1,] 5 25 45 5
[2,] 10 30 50 10
[3,] 15 35 55 15
[4,] 20 40 60 20

, , 2

 [,1] [,2] [,3] [,4]
[1,] 25 45 5 25
[2,] 30 50 10 30
[3,] 35 55 15 35
[4,] 40 60 20 40

, , 3

 [,1] [,2] [,3] [,4]
[1,] 45 5 25 45
[2,] 50 10 30 50
[3,] 55 15 35 55
[4,] 60 20 40 60
</code></pre>Let&#39;s rename our array to &quot;Arr1&quot; and &quot;Arr2&quot; by using &quot;matrix.names&quot;.Also,the rows name changed to (&quot;row1&quot;,&quot;row2&quot;) and column names will be changed to (&quot;column1&quot;,&quot;column2&quot;,&quot;column3&quot;) respectively.The dimension of the matrix is 2 rows and 3 columns.
<pre><code class="lang-python">array1 = c (9 , 18 )
array2 = c (27,36)
r.names = c (&quot;column1&quot;,&quot;column2&quot;,&quot;column3&quot;)
c.names = c (&quot;row1&quot;,&quot;row2&quot;)
m.names = c (&quot;Arr1&quot;, &quot;Arr2&quot;)

final = array (c (array1,array2), dim=c (2,3,2), dimnames=list (c.names, r.names, m.names))
print(final)
</code></pre>
The output of the above code is below, where there are two rows and three columns. Also, the value in the column gets repeated when once it is finished:
<pre><code>, , Arr1

 column1 column2 column3
row1 9 27 9
row2 18 36 18

, , Arr2

 column1 column2 column3
row1 27 9 27
row2 36 18 36
</code></pre><h2 id="indexing-in-an-array">Indexing in an array</h2>
An array consists of elements in a multi-dimensional manner where each element can be accessed for the operation. The elements can be indexed by using &#39;[]&#39; wherein the array-like matrices consists of rows and columns which can be indexed by:
<code>mat[row, column]</code>
Let&#39;s take an example below where the array contains vector input from 1 to 9.There are only three rows and columns where the value is from 1 to 9 is included.The &#39;c.names)&#39; is the new column name which have vectors (&#39;c1&#39;, &#39;c2&#39;, &#39;c3&#39;) and &#39;r.names&#39; is the new row names (&#39;r1&#39;, &#39;r2&#39;, &#39;r3&#39;) which is also the vector.
<pre><code class="lang-python">a1= c (1,2,3,4)
a2= c (5,6,7,8,9)
r.names = c (&quot;c1&quot;,&quot;c2&quot;,&quot;c3&quot;)
c.names = c (&quot;r1&quot;,&quot;r2&quot;,&quot;r3&quot;)
m.names = c (&quot;first&quot;)

arr = array (c (a1,a2), dim=c (3,3,1), dimnames=list (c.names, r.names, m.names))
print(arr)
</code></pre>
<pre><code>, , first

 c1 c2 c3
r1 1 4 7
r2 2 5 8
r3 3 6 9
</code></pre>The output of the above code is below:
<pre><code> , , first

 c1 c2 c3
r1 1 4 7
r2 2 5 8
r3 3 6 9
</code></pre>You can see the matrices from 1 to 9 are generated with 3 3 dimension(row column) form, and the names of rows and columns are changed.
Let&#39;s see how the elements in the array can be extracted with the following examples.
<ol>
<li>Let&#39;s extract number &#39;7&#39; from the above array &#39;arr&#39;.</li>
</ol>
<pre><code class="lang-python">arr[1,3,1]
</code></pre>
7
The output to the above code is 7.Inside of &#39;arr[1,3,1]&#39; row 1 with column 3 and 1 is the first array &#39;arr&#39;is extracted. 
<ol>
<li>To access 1, &#39;arr[1,1,1]&#39; row 1 with column 1 and 1 is the first array &#39;arr&#39;is extracted.</li>
</ol>
<pre><code class="lang-python">arr[1,1,1]
</code></pre>
1
The output to the above code is 1. 
<ol>
<li>To access multiple values at once, you need to specify the range you want.</li>
</ol>
<pre><code class="lang-python">arr[1:2,1:2,1]
</code></pre>
<div class="output_wrapper"><table>
<thead><tr><th></th><th scope=col>c1</th><th scope=col>c2</th></tr></thead>
<tbody>
 <tr><th scope=row>r1</th><td>1</td><td>4</td></tr>
 <tr><th scope=row>r2</th><td>2</td><td>5</td></tr>
</tbody>
</table></div>



The above code gives the output as below where the value containing 2 rows, 2 columns and 1 is the first array &#39;arr&#39; is extracted:
<pre><code> c1 c2
r1 1 4
r2 2 5
</code></pre><ol>
<li>You can access the entire array &#39;arr&#39; with the following syntax where &#39;arr[ , ,1]&#39; specifies to include all rows and columns each separated by commas, which are indicated by space. The 1 specifies the array &#39;arr&#39; to be extracted.</li>
</ol>
<pre><code class="lang-python">arr[ , ,1]
</code></pre>
<div class="output_wrapper"><table>
<thead><tr><th></th><th scope=col>c1</th><th scope=col>c2</th><th scope=col>c3</th></tr></thead>
<tbody>
 <tr><th scope=row>r1</th><td>1</td><td>4</td><td>7</td></tr>
 <tr><th scope=row>r2</th><td>2</td><td>5</td><td>8</td></tr>
 <tr><th scope=row>r3</th><td>3</td><td>6</td><td>9</td></tr>
</tbody>
</table></div>



The above code gives the output as below where all the elements are present:
<pre><code> c1 c2 c3
r1 1 4 7
r2 2 5 8
r3 3 6 9
</code></pre><ol>
<li>You can get the entire second row by following code where arr[2, ,1] gets the second row with space, and 1 is the &#39;arr&#39; to be extracted.</li>
</ol>
<pre><code class="lang-python">arr[2,,1]
</code></pre>
<dl class=dl-horizontal>
 <dt>c1</dt>
 <dd>2</dd>
 <dt>c2</dt>
 <dd>5</dd>
 <dt>c3</dt>
 <dd>8</dd>
</dl>



The above code gives the output as below and also prints c1,c2,c3.
<pre><code>c1 2
c2 5
c3 8
</code></pre><ol>
<li>You can get the entire second column by following code where arr[,2,1] space with 2 is the second column, and 1 is the &#39;arr&#39; to be extracted.</li>
</ol>
<pre><code class="lang-python">mat[ ,2,1]
</code></pre>
<dl class=dl-horizontal>
 <dt>r1</dt>
 <dd>4</dd>
 <dt>r2</dt>
 <dd>5</dd>
 <dt>r3</dt>
 <dd>6</dd>
</dl>



The above code gives the output as below and also prints r1,r2,r3.
<pre><code>r1 4
r2 5
r3 6
</code></pre><h2 id="matrix-creation-and-addition">Matrix Creation and Addition</h2>
Let&#39;s create a matrix named mat1 and mat2 from &#39;arr[ , ,1]&#39; where the entire rows and columns from &#39;arr&#39; get copied.
<pre><code class="lang-python">mat1 = arr[,,1]
mat2 = arr[,,1]
</code></pre>
<pre><code> c1 c2 c3
r1 2 8 14
r2 4 10 16
r3 6 12 18
</code></pre>Add two matrices &#39;mat1&#39; and &#39;mat2&#39; and store the result in final. The output is also printed.
<pre><code class="lang-python">final &lt;- mat1+mat2
print(final)
</code></pre>
The output below shows that each row and columns of mat1 and mat2 are added to one another.
<pre><code> c1 c2 c3
r1 2 8 14
r2 4 10 16
r3 6 12 18
</code></pre><h2 id="apply-">apply()</h2>
&#39;apply()&#39; is one of the R packages which have several functions that helps to write code in an easier and efficient way. You&#39;ll see the example below where it can be used to calculate the sum of two different arrays.
The syntax for apply() is : 
<code>apply(x, margin, function)</code>
The argument above indicates that: 
x: An array or two-dimensional data as matrices. 
margin: Indicates a function to be applied as margin value to be c(1) for rows, c(2) for columns, and c(1,2) for both rows and columns. 
function: Indicates the R- built-in or user-defined function to be applied over the given data.
Let&#39;s create a vector named &#39;array1&#39; of length three and &#39;array2&#39; of length six with the following code.
<pre><code class="lang-python">array1 = c(5,10,15)
array2 = c(15,20,25,30,35,40)
</code></pre>
You can see below where &#39;array()&#39; accepts two vectors named &#39;array1&#39; and &#39;array2&#39; with a dimension of 3 rows, 3 columns, and 2 matrices are created and stored to &#39;my. Array&#39;.
<pre><code class="lang-python">my.Array &lt;- array(c(array1,array2),dim = c(3,3,2))
print(my.Array)
</code></pre>
<pre><code>, , 1

 [,1] [,2] [,3]
[1,] 5 15 30
[2,] 10 20 35
[3,] 15 25 40

, , 2

 [,1] [,2] [,3]
[1,] 5 15 30
[2,] 10 20 35
[3,] 15 25 40
</code></pre>The above code gives the following output, where two matrices are created with the 3 3 (rows columns).
<pre><code>, , 1

 [,1] [,2] [,3]
[1,] 5 15 30
[2,] 10 20 35
[3,] 15 25 40

, , 2

 [,1] [,2] [,3]
[1,] 5 15 30
[2,] 10 20 35
[3,] 15 25 40
</code></pre>&#39;apply()&#39; is used below to calculate the sum of the two matrices by column-wise.
<pre><code class="lang-python">final &lt;- apply(my.Array, c(2), sum)
print(final)
</code></pre>
<pre><code>[1] 60 120 210
</code></pre>You can see above where c(2) for margin is used where the elements of the matrix in &#39;my. Array&#39; is summed by column-wise to get the following output.
<pre><code>60 120 210
</code></pre>Finally, &#39;apply()&#39; is used below to calculate the sum of the two matrices by row-wise.
<pre><code class="lang-python">final &lt;- apply(my.Array, c(1), sum)
print(final)
</code></pre>
<pre><code>[1] 100 130 160
</code></pre>You can see above where c(1) for margin is used where the elements of the matrix in &#39;my. Array&#39; is summed by row-wise to get the following output.
<pre><code>100 130 160
</code></pre><h2 id="congratulations">Congratulations</h2>
Congratulations, you have made it to the end of this tutorial!
You&#39;ve learned about R&#39;s Array along with its creation, indexing in an array with examples, also with creation and addition of matrices along with the apply() function.
If you would like to learn more about R, take DataCamp&#39;s <a href = "https://www.datacamp.com/learn/r-programming/">Introduction to R course.</a>
References: 
<a href="https://www.tutorialspoint.com/r/r_arrays.htm">R-Arrays</a> 
<a href="https://data-flair.training/blogs/r-array/">R Array Function and Create Array in R</a>

https://www.datacamp.com/community/tutorials/arrays-in-r

Learn about Arrays in R, including indexing with examples, along with the creation and addition of matrices and the apply() function.

(Tutorial) Arrays in R

Arrays in R

A string is a character that is made of one character or contains a collection of characters. It is enclosed inside single quotes(&#39;This is a string&#39;) or inside the double quotes(&quot;This is also a string&quot;). But in the internal representation of R strings are represented in double-quotes.
In this tutorial, you will learn about strings in R, and we&#39;ll cover the following topics. 
<ol>
<li>First, you will look into Strings in R and Rule for String in R.</li>
<li>After that, you&#39;ll dive the Concatenation of String in R with other essential properties of String.</li>
<li>Also, Extracting and Replacing a Character String is explained in detail.</li>
<li>Finally, Formatting String, which might be any vector input converted to the well-formatted format.</li>
</ol>
<h2 id="rule-for-string-in-r">Rule for String in R</h2>
<ol>
<li>The string that starts with a single quote needs to end with a single quote. However, you can put double quotes, and through the Escape Sequence(&#39;\&#39;) single quote can also become a part of the string.</li>
 For example: &#39;cars&#39;, &#39;merry&quot;s&#39; , &#39;merry\&#39;s&#39; are valid string in R. 
<li>The string that starts with double-quote needs to end with a double quote. However, you can put single quotes, and through the Escape Sequence(&#39;\&#39;), double-quote can also become a part of the string.</li>
 For example: &quot;cars&quot; , &quot;merry&#39;s&quot;, &quot;merry\&quot;s&quot; are also valid string in R.

</ol>

<h2 id="concatenation-of-string">Concatenation of String</h2>
Concatenation of String is making the strings to join or merge. 
The syntax for concatenating strings in R is done by: 
<code>paste(..., sep = &quot;&quot;,collapse = NULL)</code> 
<code>paste0(..., collapse = NULL)</code>
The argument above in paste states that: 
<ol>
<li>&#39;...&#39;- Indicates one or more characters or objects which converts into character vectors.</li>
<li>&#39;sep&#39;- Indicates a separation character</li>
<li>&#39;collapse&#39;- Indicates an optional separation character.</li>
</ol>
For example, let&#39;s consider the example below where the variables contain the character string:
<code>
my.var1 = &quot;I&quot;
my.var2 = &quot;eat&quot;
my.var3 = &quot;rice&quot;
print(paste(my.var1,my.var2,my.var3))
</code>
The above code gives a single string as below: 
<pre><code>&quot;I eat rice&quot;
</code></pre>For example, let&#39;s consider the example below where the variables contain the vector converted into a single string:
<code>
my.var1 = c(&#39;Book&#39;,&#39;Copy&#39;)
my.var2 = c(&#39;Pen&#39;,&#39;Chair&#39;)
print(paste(my.var1,my.var2,sep=&quot; &quot;,collapse=&quot;-&quot;))
</code>
The above code gives the following output: 
<pre><code>&quot;Book Pen-Copy Chair&quot;
</code></pre>Here, the output contains a &#39;-&#39; between Pen and Copy due to the use of a collapse, which makes the separation between two different vectors. In contrast, the sep includes a space(&#39; &#39;), where individual items are separated by it.
Let&#39;s consider the example containing paste0() 
paste0() takes an argument as sep =,&#39;&#39; i.e. without space and use for its efficiency.
<pre><code>state.name &lt;- paste0(&#39;Mary&#39;,&#39;l&#39;,&#39;and&#39;)
print(state.name)
</code></pre>The output of the above code is below where all characters are merged to be one, and there is no separate space through sep. 
<pre><code>&quot;Maryland&quot;
</code></pre><h2 id="finding-length">Finding Length</h2>
nchar(char,type, allowNA,keepNA) - Finding the total number of characters in a given vector. 
nzchar(char)- Returns TRUE if the x is non-empty and FALSE if it contains any string. 
The argument above in paste states that: 
<ol>
<li>char- Indicates a character vector.</li>
<li>type- Indicates a one of the three types, i.e, &quot;width&quot;,&quot;chars&quot;,&quot;bytes&quot;.</li>
<li>allowNA- Indicates a Logical default value as FALSE and incase the result cannot be calculated will not be an error but returns NA.</li>
<li>keepNA- Indicates a Logical default value as FALSE where it will map the missing value to 2 and nzchar() will give TRUE, but both will map to TRUE as well in the case of the missing values.
 </li>
</ol>
In the example below to determine the length of a character vector.
<code>
my.char = nchar(&quot;hello&quot;)
print(my.char) </code>
The above example returns &#39;5&#39;, as the output because there are five characters present in the vector.
For the example of using nzchar() let&#39;s look at the code below:
<code>
new.char = nzchar(&quot;monkey&quot;)
print(new.char)
</code>
The above code gives the logical value &#39;TRUE&#39; because the &#39;new.char&#39; is not a empty string.
<code>
my.char = nzchar(&quot;&quot;)
print(my.char)
</code>
The above code gives the logical value &#39;FALSE&#39; because the &#39;my.char&#39; is a empty string.
<h2 id="changing-to-upper-case-and-lower-case">Changing to Upper Case and Lower Case</h2>
toupper(char) - Changes all the characters present to uppercase. 
tolower(char) - Changes all the characters present to lowercase.
Let&#39;s look at the example for changing into uppercase.
<code>
 my.var = toupper(&quot;I&#39;m changed to upper case&quot;)
 print(my.var)
</code>
The above code gives the output as below where each character present in the variable &#39;my.var&#39; changed into uppercase. 
<pre><code>&quot;I&#39;M CHANGED TO UPPER CASE&quot;
</code></pre>Let&#39;s look at the example for changing into lowercase.
<code>
 my.var = tolower(&quot;I&#39;m changed to lower case&quot;)
 print(my.var)
</code>
The above code gives the output as below where each character present in the variable &#39;my.var&#39; changed into lowercase. 
<pre><code>&quot;&quot;i&#39;m changed to lower case&quot;&quot;
</code></pre><h2 id="extracting-and-replacing-a-character-string">Extracting and Replacing a Character String</h2>
substr(char, start, stop): Extracting and Replacing of a character string. 
The argument above states that:
<ol>
<li>char- Indicates a character string.</li>
<li>start - Indicates an integer that specifies the corresponding starting value to be returned.</li>
<li>stop - Indicates an integer that specifies the corresponding stopping value to be returned.</li>
</ol>
Let&#39;s see another example where the characters get extracted.
<pre><code>final.value &lt;- substring(&#39;Remaining&#39;, 3, 9)
print(final.value)
</code></pre>In the above code,&#39;Remaining&#39; is a string where the first index is &#39;R&#39;, which is indexed as integer 1. The last character is g with its index as integer 9.
The above code shows the output below where starting value as integer 3, which has the corresponding character as &#39; m &#39; in &#39;Remaining&#39;.Similarly, other characters &#39;ainin&#39; up to integer 9, which is inclusive with the corresponding character as &#39;g&#39;, gets printed out. 
<pre><code>&quot;maining&quot;
</code></pre>Let&#39;s see another example where the characters get replaced.
<pre><code>y = &quot;Remaining&quot;
substring(y, 3, 5) &lt;- &quot;der&quot;
print(y)
</code></pre>The above code shows the output where the starting integer value from 3 to 5 with corresponding character value in y gets replaced by &#39;der&#39;. 
<pre><code>&quot;Rederning&quot;
</code></pre>substring(char, first, last = 1000000L): Extracting or Replacing of a character string. 
The argument above states that:
<ol>
<li>char- Indicates a character string.</li>
<li>first - Indicates an integer which specifies the corresponding first value to be returned.</li>
<li>last - Indicates an integer which specifies the corresponding last value to be returned and default value to 1000000L.</li>
</ol>
<pre><code>my.value &lt;- substring(&#39;Remaining&#39;, 3, 8)
print(my.value)
</code></pre>The above code shows the output below where starting value as integer 3, which has the corresponding character as &#39; m &#39; in &#39;Remaining&#39;.Similarly, other characters up to integer 8, which is inclusive with the corresponding character as &#39;n&#39; get printed out. 
<pre><code>&quot;mainin&quot;
</code></pre>Let&#39;s see another example where the characters get replaced.
<pre><code>y = &quot;newcharacter&quot;
substring(y, 6,9) &lt;- &quot;???&quot;
print(y)
</code></pre>The above code shows the output where the starting integer value from 6 to 9 with corresponding character value in y gets replaced by &#39;???&#39;. 
<pre><code>&quot;newch???cter&quot;
</code></pre><h2 id="formatting-string">Formatting String</h2>
format(char,width,scientific,justify,nsmall,digits): It converts a vector of integer and character to a commonly used format. 
The argument above states that:
<ol>
<li>char - can be a vector of integers or characters.</li>
<li>width - indicates a minimum width of string produced.</li>
<li>scientific - default as FALSE but use TRUE for scientific notation.</li>
<li>justify - default to none but can display strings to &quot;right&quot;, &quot;left&quot;, and &quot;center&quot;.</li>
<li>nsmall - indicates the total number after the decimal place</li>
<li>digits - indicates the total number before and after decimal place.</li>
</ol>
Let&#39;s take an example for formatting string for scientific notation. 
<pre><code>my.var &lt;- format(c(2, 19.267), scientific = TRUE)
print(my.var)
</code></pre>The above code shows the output, which is expressed in scientific or exponential notation
<pre><code>&quot;2.0000e+00&quot; &quot;1.9267e+01&quot;
</code></pre>The output &quot;1.9267e+01&quot; = 1.9267 x 10(to the 1 power) = 1.9267x10 = 19.267.
Let&#39;s take an example of formatting using the nsmall.
<pre><code>my.var &lt;- format(92.56656577, nsmall = 6)
print(my.var)
</code></pre>The above code gives the output as below where the 6 numbers after decimal place are included. 
<pre><code>&quot;92.566565&quot;
</code></pre>Let&#39;s take an example of formatting using the digits.
<pre><code>my.var &lt;- format(102.848793834, digits = 7)
print(my.var)
</code></pre>The above code gives the output as below where the number before starting decimal place are included up to the number after the decimal place. 
<pre><code>&quot;102.8488&quot;
</code></pre>Seven numbers are included as the output where the last digit needs to be 7, but due to the preceding number, i.e., 9 is greater than 7, it gets increased to 8. In simple words, round-off takes place.
Let&#39;s take an example for formatting using the right justify.
<pre><code>my.var &lt;- format(&#39;Running&#39;, justify = &#39;r&#39;,width = 9)
print(my.var)
</code></pre>The above code gives the output as below where the character &#39;Running&#39; is justified to the right with some space of width. 
<pre><code>&quot; Running&quot;
</code></pre>Let&#39;s take an example of formatting using the justify character in the center.
<pre><code>my.var &lt;- format(&#39;Running&#39;, justify = &#39;c&#39;,width = 12)
print(my.var)
</code></pre>The above code gives the output as below where the character &#39;Running&#39; is justified to center with some space of width between right and left. 
<pre><code>&quot; Running &quot;
</code></pre><h2 id="congratulations">Congratulations</h2>
Congratulations, you have made it to the end of this tutorial!
You&#39;ve learned about R&#39;s Strings: its rules, concatenation, along with essential properties of the string, Extracting and Replacing a Character String, and formatting a string.
If you would like to learn more about R, take DataCamp&#39;s <a href = "https://www.datacamp.com/learn/r-programming/">Introduction to R course.</a>

https://www.datacamp.com/community/tutorials/strings-in-r

Learn about R's Strings: its rules, concatenation, along with essential properties of the string, extracting and replacing a character string, and formatting a string.

(Tutorial) Strings in R

Strings in R

If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/intermediate-python">Intermediate Python</a> course.
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585947048/doc1_mph0hj.png" style="width:100%">
 <figcaption><center><a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Python&#39;s Pandas Library Documentation using Sphinx</a </center></figcaption>
</figure>

<h3 id="relevance-of-documenting-your-project">Relevance of Documenting your Project</h3>
Documentation is an essential part of any project you work on, irrespective of the programming language you use. A project having an application consisting of various API&#39;s up and running being used by many users but without documentation would be deemed incomplete. For a second, imagine yourself as a developer, how would you feel if you wanted to replicate a project or use some aspect of a project that does not have any documentation? I am sure you would have a hard time integrating it with your architecture.
Better documentation will make your project more successful because you know that when you share the project or the software with the world, you would like the world to use it, especially when it&#39;s an open-source project the goal would be even more. You would also want the community to contribute to your project and make it better.
The well-known author of Python programming language quotes that <code>Code is more often read than written.</code> This quote should highlight the importance documentation has on your code or project being implemented by others.
Imagine yourself as an employee of XYZ company, you are serving your notice period, and your manager would like you to transfer that project to your colleague. You might give him a KT (knowledge transfer), but what if your colleague fails to execute one of the codes from the project successfully? There could be several reasons as to why the code doesn&#39;t work, maybe the binaries your code runs on doesn&#39;t match with the binaries of the current OS.
<h4 id="what-exactly-is-a-documentation-">What exactly is a Documentation?</h4>
Documentation has several components attached to it. It needs to be well-structured around these components and adhere to those components to be considered proper documentation.
On an abstract level, the components are:
<ul>
<li>Making sure the codebase of your project is well commented.
</li>
<li>It follows Python&#39;s PEP-8 coding standards.
</li>
<li>Concrete tutorials about how the project was built, especially when its an open-source project developed for a learning-oriented purpose.
</li>
<li>A guide on how to install the necessary packages, modules used to build the software, a technical specification sheet if the project also includes any hardware. For example, how to install anaconda, TensorFlow, Keras, etc.
</li>
<li>Several discussions on the project&#39;s progress at each time step that could have led to the successful implementation of the software.
</li>
<li>Reference material that provides a technical description of the technology stack used during the development phase.
</li>
<li>The architectural design of the project or the software solution.
</li>
</ul>
These points are just some of the components that could exist in a well-structured and a perfect looking document. It is important to keep all these components distinct, which would also make it easier to maintain the documentation in the future.
An example of comprehensive documentation that has most of the components we discussed would be similar to the one as shown below:
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585947048/doc2_hnrhge.png" style="width:200%">
 <figcaption><center><a href="https://docs.djangoproject.com/en/3.0/">Django&#39;s Documentation</a </center></figcaption>
</figure>

A lot of people often get confused between <code>commenting</code> &amp; <code>documenting</code> and consider them similar. Commenting is used to describe your code to the user, maintainer, and even for your self as a future reference. Commenting only works at the code-level and can be categorized as a subset of documentation. Comments help guide the reader to:
<ul>
<li>understand your code,</li>
<li>make it self-explanatory, and</li>
<li>understand its purpose and design.</li>
</ul>
It is important to remember that since Python follows the PEP-8 coding standards, even comments have to adhere to those standards. The official Python documentation states that flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters.
To check whether your code adheres to the PEP-8 standards, you can use the <code>pylint</code> module of Python. This module can be used to modify the character limit for comments and all other lines of code.
Let&#39;s see a couple of examples.
<ul>
<li>Importing module description</li>
</ul>
<pre><code class="lang-python">import tensorflow as tf
#imports tensorflow as tf. Tensorflow is an n-dimensional matrix.
#just like a 1-D vector, 2-D array, 3-D array etc.
</code></pre>
<ul>
<li>Variable definition description</li>
</ul>
<pre><code class="lang-python">n_classes = 10 # MNIST total classes (0-9 digits)
</code></pre>
For a more in-depth understanding of commenting and its do&#39;s &amp; don&#39;ts, check out <a href="https://blog.codinghorror.com/when-good-comments-go-bad/">this</a> helpful post.
Let&#39;s now learn how docstrings can help in documenting your project codebase.
<h3 id="docstrings-for-documenting-python-code">Docstrings for Documenting Python Code</h3>
Python Docstring is the documentation string that is string literal, and it occurs in the class, module, function, or method definition, and is written as a first statement. Docstrings are accessible from the doc attribute <code>(__doc__)</code> for any of the Python objects, and also with the built-in <code>help()</code> function can come in handy.
Also, Docstrings are great for understanding the functionality of the larger part of the code, i.e., the general purpose of any class, module, or function. In contrast, the comments are used for code, statement, and expressions, which tend to be small. They are a descriptive text written by a programmer mainly for themselves to know what the line of code or expression does and also for the developer who wishes to contribute to that project. It is an essential part that documenting your code is going to serve well enough for writing clean code and well-written programs. Though already mentioned, there are no standards and rules for doing so.
There are two forms of writing a Docstring: one-line Docstrings and multi-line Docstrings. These are the documentation that is used by Data Scientists/programmers in their projects.
<ul>
<li>The <code>one-line</code> Docstrings are the Docstrings, which fits all in one line. You can use one of the quotes, i.e., triple single or triple-double quotes, opening quotes, and closing quotes need to be the same. In the one-line Docstrings, closing quotes are in the same line as with the opening quotes. Also, the standard convention is to use the triple-double quotes.</li>
</ul>
<pre><code class="lang-python">def square(a):
 &#39;&#39;&#39;Returned argument a is squared.&#39;&#39;&#39;
 return a**a

print (square.__doc__)


help(square)
</code></pre>
<pre><code>Returned argument a is squared.
Help on function square in module __main__:

square(a)
 Returned argument a is squared.
</code></pre><ul>
<li><code>Multi-line</code> Docstrings also contains the same string literals line as in One-line Docstrings, but it is followed by a single blank along with the descriptive text.</li>
</ul>
<pre><code class="lang-python">def some_function(argument1):
 &quot;&quot;&quot;Summary or Description of the Function

 Parameters:
 argument1 (int): Description of arg1

 Returns:
 int:Returning value

 &quot;&quot;&quot;

 return argument1

print(some_function.__doc__)
</code></pre>
<pre><code>Summary or Description of the Function

 Parameters:
 argument1 (int): Description of arg1

 Returns:
 int:Returning value
</code></pre><figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585947048/doc3_hajmrg.png" style="width:200%">
 <figcaption><center><a href="https://www.datacamp.com/community/tutorials/docstrings-python">Renowned Docstring Formats</a </center></figcaption>
</figure>

From the above table, let&#39;s pick <code>Pydoc</code> as one of the docstring formats and explore it a bit.
As you learned that docstrings are accessible through the built-in Python <code>__doc__</code> attribute and the <code>help()</code> function. You could also make use of the built-in module known as <code>Pydoc</code>, which is very different in terms of the features &amp; functionalities it possesses when compared to the doc attribute and the help function.
Pydoc is a tool that would come handy when you want to share the code with your colleagues or make it open-source, in which case you would be targeting a much wider audience. It could generate web pages from your Python documentation and can also launch a web server.
Let&#39;s see how it works.
The easiest and convenient way to run the Pydoc module is to run it as a script. To run it inside a jupyter lab cell, you would make use of the exclamation mark (!) character.
<pre><code class="lang-python">!python -m pydoc
</code></pre>
<pre><code>pydoc - the Python documentation tool

pydoc &lt;name&gt; ...
 Show text documentation on something. &lt;name&gt; may be the name of a
 Python keyword, topic, function, module, or package, or a dotted
 reference to a class or function within a module or module in a
 package. If &lt;name&gt; contains a &#39;\&#39;, it is used as the path to a
 Python source file to document. If name is &#39;keywords&#39;, &#39;topics&#39;,
 or &#39;modules&#39;, a listing of these things is displayed.

pydoc -k &lt;keyword&gt;
 Search for a keyword in the synopsis lines of all available modules.

pydoc -n &lt;hostname&gt;
 Start an HTTP server with the given hostname (default: localhost).

pydoc -p &lt;port&gt;
 Start an HTTP server on the given port on the local machine. Port
 number 0 can be used to get an arbitrary unused port.

pydoc -b
 Start an HTTP server on an arbitrary unused port and open a Web browser
 to interactively browse documentation. This option can be used in
 combination with -n and/or -p.

pydoc -w &lt;name&gt; ...
 Write out the HTML documentation for a module to a file in the current
 directory. If &lt;name&gt; contains a &#39;\&#39;, it is treated as a filename; if
 it names a directory, documentation is written for all the contents.
</code></pre>If you look at the above output, the very first use of Pydoc is to show text documentation on a function, module, class, etc. so let&#39;s see how you can leverage that better than the help function.
<pre><code class="lang-python">!python -m pydoc glob
</code></pre>
<pre><code>Help on module glob:

NAME
 glob - Filename globbing utility.

MODULE REFERENCE
 https://docs.python.org/3.7/library/glob

 The following documentation is automatically generated from the Python
 source files. It may be incomplete, incorrect or include features that
 are considered implementation detail and may vary between Python
 implementations. When in doubt, consult the module reference at the
 location listed above.

FUNCTIONS
 escape(pathname)
 Escape all special characters.

 glob(pathname, *, recursive=False)
 Return a list of paths matching a pathname pattern.

 The pattern may contain simple shell-style wildcards a la
 fnmatch. However, unlike fnmatch, filenames starting with a
 dot are special cases that are not matched by &#39;*&#39; and &#39;?&#39;
 patterns.

 If recursive is true, the pattern &#39;**&#39; will match any files and
 zero or more directories and subdirectories.

 iglob(pathname, *, recursive=False)
 Return an iterator which yields the paths matching a pathname pattern.

 The pattern may contain simple shell-style wildcards a la
 fnmatch. However, unlike fnmatch, filenames starting with a
 dot are special cases that are not matched by &#39;*&#39; and &#39;?&#39;
 patterns.

 If recursive is true, the pattern &#39;**&#39; will match any files and
 zero or more directories and subdirectories.

DATA
 __all__ = [&#39;glob&#39;, &#39;iglob&#39;, &#39;escape&#39;]

FILE
 c:\users\hda3kor\.conda\envs\test\lib\glob.py
</code></pre>Now, let&#39;s extract the <code>glob</code> documentation using the help function.
<pre><code class="lang-python">help(glob)
</code></pre>
<pre><code>---------------------------------------------------------------------------

NameError Traceback (most recent call last)

&lt;ipython-input-13-6f504109e3a2&gt; in &lt;module&gt;
----&gt; 1 help(glob)


NameError: name &#39;glob&#39; is not defined
</code></pre>Well, as you can see, it throws a name error as glob is not defined. So for you to use the help function for extracting the documentation, you need first to import that module, which is not the case in Pydoc.
Let&#39;s explore the most interesting feature of the Pydoc module, i.e., running Pydoc as a web service.
To do this, you would simply run the Pydoc as a script but with a <code>-b</code> argument which will start an HTTP server on an arbitrary unused port and open a Web browser to browse documentation interactively. This is helpful, especially when you have various other services running on your system, and you do not remember which port would be in an idle state.
<pre><code class="lang-python">!python -m pydoc -b
</code></pre>
<pre><code>^C
</code></pre>The moment you run the above cell, a new window will open on an arbitrary port number, and the web browser will look similar to the one shown below.
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585947048/doc4_tg8rmb.png" style="width:200%">
</figure>

Let&#39;s look at the documentation of the <code>h5py</code> module, which is a file format used to store weights of neural network architecture.
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585947048/doc5_zgficw.png" style="width:200%">
</figure>

<h4 id="essentials-while-documenting-python-projects">Essentials while Documenting Python Projects</h4>
Irrespective of the goal, vision, and project purpose, the documentation of every project remains more or less the same. The project could fall under the following categories:
<ul>
<li>Private ( personal ) Project: Could be for portfolio building or working as a freelancer maintaining a GitHub repository.
</li>
<li>Collaborative ( team) Projects: Could be a project being run at your organization or working on a Kaggle competition.
</li>
<li>Open-source Projects: Open Source projects are mainly focused on being shared with a broad audience. It expects collaboration, contributions, and maintainability of the codebase and documentation in a longer run.
</li>
</ul>
Even though the above three categories of projects have different visions, the documentation template could be shared across all kinds of projects.
Let&#39;s say you are working on an open-source project and are required to create a GitHub repository for it which should have detailed documentation updated regularly, following are the essential points you need to keep in mind:
<ul>
<li>Requirements file: A lot of time, authors tend to forget this, but the requirements file is very important. It helps the users to reproduce your code quickly. It is usually a text file that contains all the packages, modules along with their respective versions that were used in the project. Requirements can even be mentioned in the Readme file, but having it separately is always better since the user can just run that file with a <code>pip</code> command, and all the dependencies are then installed on their respective system.
</li>
<li>Readme: Readme file is usually a markdown file format, which also serves as the backbone for many projects. A summary of the project, its features, and its purpose with a nice logo. It shall include instructions for installing or operating the project. Additionally, add any significant changes since the previous version. Testing scripts or like a quick tour on running their code successfully within the Readme gives more confidence to the user to pursue your project. It could also highlight the potential problems the users could face.
</li>
<li>How to Collaborate: It is important, especially when you are working on open-source projects. This should include how new collaborators could contribute to the project. This includes developing new features, fixing known bugs, adding documentation, adding new tests, or reporting issues. The collaborators could even release a v2.0 of the same project and take the project to greater heights.
</li>
<li>License: A plaintext file that describes the license your project is using. Especially crucial for open-source projects, licenses like Boost, Apache, MIT, etc. This informs the user whether the project is free to be used commercially or up to what extent.
</li>
<li>Tasks assignment: If you are working on a shared project like Kaggle, you could define the tasks each member is assigned, and the progress level of the task. This helps keep track of the overall progress made in the project.
</li>
<li>Framework Re-usability: This plays a vital role in a shared project like Kaggle wherein your teammates could reuse what you might have built, which could save a lot of the time. For example, data preprocessing pipeline, data cross-validation script, etc.
</li>
</ul>
A highly recommended documentation that is very well structured and could potentially be a perfect example of how an open-source project shall look like then do check out <a href="https://github.com/huggingface/transformers">huggingface</a> transformers GitHub repository.
<h1 id="conclusion">Conclusion</h1>
Congratulations on finishing the tutorial.
One good exercise for you all would be to explore the Pydoc module a bit more and other Python docstring formats like Epydoc and Google docstrings and find out how they differ from each other.
Please feel free to ask any questions related to this tutorial in the comments section below.
References:
<ul>
<li><a href="https://www.datacamp.com/community/tutorials/docstrings-python">Docstrings in Python</a></li>
</ul>
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/intermediate-python">Intermediate Python</a> course.

https://www.datacamp.com/community/tutorials/documenting-python-code

Learn why there is a need for documenting code and best practices to do it. Further, learn to leverage the potential of the Pydoc module for documenting purposes.

Learn why there is a need for DOCUMENTING code and best practices to do it. Further, learn to leverage the potential of the PYDOC module for documenting purposes.

(Tutorial) How to Document Python Code

How to Document Python Code

<h2 id="variables">Variables</h2>
Variables are the identifier or the named space in the memory, which are stored and can be referenced and manipulated later in the program. R is a Dynamically Typed and Interpreted language where Type Checking of the variable and other objects is done at the run time. It also means that the Interpreter of R doesn&#39;t force the programmer to explicitly declare the &#39;data-type&#39; of the variable before its usage.
<h2 id="rule-variable-in-r">Rule Variable in R</h2>
<ol>
<li>The variable name must start with letter and can contain number,letter,underscore(&#39;&#39;) and period(&#39;.&#39;).</li>
 Example: variableName1,new.variable, 
 <ul>
<li>Underscore(&#39;&#39;) at the beginning of the variable name are not allowed.</li>
 Example: &#39;_my_var&#39; is not a valid variable name.
<li>Period(&#39;.&#39;) at the beginning of the variable name are allowed but should not be followed by a number. It is preferable in R to use &#39;.&#39; which helps to separate the different words for the identifier.</li>
 Example: &#39;.myvar&#39; is a valid variable name. However, &#39;.1myvar&#39; is not a valid variable name because the period followed by a number is not valid.

</ul>
<li>Reserved words or keywords are not allowed to be defined as a variable name.</li>
<li>Special characters such as &#39;#&#39;, &#39;&amp;&#39;, etc., along with White space (tabs, space) are not allowed in a variable name.</li></ol>

<h2 id="variable-assignment">Variable Assignment</h2>
Variables in R can be assigned in one of three ways. 
<ol>
<li>Assignment Operator: &quot;=&quot; used to assign the value.The following example contains 20 as value which is stored in the variable &#39;first.variable&#39;
Example: <code>first.variable = 20</code> 
</li>
<li>&#39;&lt;-&#39; Operator: The following example contains the New Program as the character which gets assigned to &#39;second.variable&#39;. 
Example: <code>second.variable &lt;- &quot;New Program&quot;</code> 
</li>
<li>&#39;-&gt;&#39; Operator: The following example contains 565 as the integer which gets assigned to &#39;third.variable&#39;. 
Example: <code>565 -&gt; third.variable</code></li>
</ol>
<h2 id="variable-type">Variable Type</h2>
The type of variable in R can be determined by class(),typeof() and mode() 
<ol>
<li>&#39;class()&#39;- will give the high-level type of an object, which is to say from the perspective of Object-Oriented Programming in R.Depending upon whether it is a vector or any other data structure, the return type will be different.
<ul>

<li>If the type of object is the vector, then one of the following data types is chosen.
<ul>
 <li>Integer</li>
 <li>Numeric</li>
 <li>Character</li>
 <li>Logical</li>
</ul>
For example, the integer type and numeric type will be returned below. 
 <code>my.var1 = 8L</code> 
 Returns:&#39;integer&#39; 
<code>my.var2 = 8.5</code> 
Returns:&#39;numeric&#39; </li> 
<li>However, with the objects like matrix(),data.frame(),list() and array() the respective type is returned.
 For example, the list type will be returned as &quot;list&quot; below. 
 <code>my.var3 = list(6,9,5)</code> 
 Returns:&#39;list&#39; 
</li>



</li>
</ol>
<ol>
<li>typeof()- The return values are low-level, which matches the internal type of object in R.It is mostly used by R programmer to determine the type of an object. 
For Example, some of the internal types of objects in R are shown below: 
<code>typeof(8+6i)</code> 
Returns: complex 
<code>typeof(factor(c(4,5,6)))</code> 
Returns: integer</li>
</ol>
You can explore the <a href="https://renenyffenegger.ch/notes/development/languages/R/functions/typeof">Object internal type with typeof()</a> to find the different internal type of an object. 
3.mode()- It also returns the values which are the same and closely related to &#39;typeof().&#39; However, there are some differences between &#39;typeof()&#39; and mode() on the following types.
<ul>
 <li>type &quot;symbol&quot; in typeof() is &quot;name&quot; in mode()</li>
 For Example: <code>typeof(as.name(&#39;foo&#39;))</code>Returns &#39;symbol&#39; whereas <code>mode(as.name(&#39;foo&#39;))</code> Returns &#39;name&#39;. 
 <li>type &quot;language&quot; in typeof() is returned as &quot;(&quot; or &quot;call&quot; in mode()</li>
 For Example: <code>typeof(y~x)</code>Returns &#39;language&#39; whereas <code>mode(45L)</code> Returns &#39;call&#39;. 
 <li>type &quot;integer&quot; and &quot;double&quot; in typeof() is returned as &quot;numeric&quot; in mode()</li>
 For Example: <code>typeof(45L)</code>Returns &#39;integer&#39; whereas <code>mode(45L)</code> Returns &#39;numeric&#39;. 
 <li>type &quot;special&quot; and &quot;builtin&quot; in typeof() is returned as &quot;function&quot; in mode()</li>
 For Example: <code>typeof(return)</code>Returns &#39;special&#39; whereas <code>mode(45L)</code> Returns &#39;function&#39;. </ul>





<h2 id="removing-variable">Removing Variable</h2>
The variable in R can easily be removed or deleted by using &#39;rm()&#39;. 
For example, the output of the code below is 8.
<pre><code>my.var = 8
cat(my.var)
</code></pre>The variable is removed by using &#39;rm(my.var)&#39;.
<pre><code>rm(my.var)
</code></pre>You can check whether the variable is removed or not through the help of &#39;cat(my.var)&#39;
<pre><code>cat(my.var)
</code></pre>The above code will result in the following error: 
Error in cat(my.var): object &#39;my.var&#39; not found
<h2 id="congratulations">Congratulations</h2>
Congratulations, you have made it to the end of this tutorial!
In this tutorial, you have covered R&#39;s variables, rules for defining variables, different types of assignment of the variable along with different kinds of checking the variable types, and removal of the variable.
If you would like to learn more about R, take DataCamp&#39;s <a href = "https://www.datacamp.com/learn/r-programming/">Introduction to R course.</a>

https://www.datacamp.com/community/tutorials/variables-in-r

In this tutorial, you'll learn all about R variables including how to define variables, remove variables, and much more.

(Tutorial) Definitive Guide: Variables in R

Definitive Guide: Variables in R

If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.
A global interpreter lock (GIL) is a mechanism to apply a global lock on an interpreter. It is used in computer-language interpreters to synchronize and manage the execution of threads so that only one native thread (scheduled by the operating system) can execute at a time.
In a scenario where you have multiple threads, what can happen is that both the thread might try to acquire the memory at the same time, and as a result of which they would overwrite the data in the memory. Hence, arises a need to have a mechanism that could help prevent this phenomenon. 
Some popular interpreters that have GIL are CPython and Ruby MRI. As most of you would know that Python is an interpreted language, it has various distributions like CPython, Jython, IronPython. Out of these, GIL is supported only in CPython, and it is also the most widely used implementation of Python. CPython has been developed in both C and Python language primarily to support and work with applications that have a lot of C language underneath the hood.
Even if your processor has multiple cores, a global interpreter will allow only one thread to be executed at a time. This is because, when a thread starts running, it acquires the global interpreter lock. When it waits for any I/O operation ( reading/writing data from/to disk ) or a CPU bound operation ( vector/matrix multiplication ), it releases the lock so that other threads of that process can run. Hence, it prevents you from running the other threads at the same time.
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364509/gil_g47wzi.png" />
</figure>
</center> 

Let&#39;s take a moment and understand the above diagram. As you can see, there is a <code>factorial</code> function and two threads 1 and 2, thread 1 is in the locked state while thread 2 is in a wait state. This means that only one of the threads is able to access the function. Now, let&#39;s assume that the <code>factorial</code> function takes 2 seconds to complete. Then, in an ideal case, both the threads should be able to finish the execution in 2 seconds. However, this is not the case in Python, both the threads will run serially and not parallel to each other.
The threads 1 and 2 calling the <code>factorial</code> function may take twice as much time as a single thread calling the function twice. This also tells you that the memory manager governed by the interpreter is not <code>thread-safe</code>, which means that multiple threads fail to access the same shared data simultaneously.
Hence, GIL (Source: <a href="https://www.dabeaz.com/python/UnderstandingGIL.pdf">Understanding the Python GIL</a>):
<ul>
<li>Limits the threading operation.
</li>
<li>Parallel execution is restricted.
</li>
<li>The GIL ensures that only one thread runs in the interpreter at once.
</li>
<li>It helps in simplifying various low-level details like memory management.
</li>
</ul>
<h4 id="with-gil-cooperative-computing-or-coordinated-multitasking-is-achieved-instead-of-parallel-computing-">With GIL cooperative computing or coordinated multitasking is achieved instead of parallel computing.</h4>
<a id='table'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364510/gil2_kgyuui.png" style="width:100%">
 <figcaption><center><a href="https://speakerdeck.com/dabeaz/understanding-the-python-gil?slide=10">Source</a </center></figcaption>
</figure>


As it can be observed from the above diagram, there are three threads initially the thread 1 is running and it has acquired the GIL, and when an I/O operation like a read, write, etc. is done, thread 1 releases the GIL and it is then acquired by thread 2. This cycle keeps on going, and GIL keeps changing threads alternatively till the threads have completed the execution of the program. Remember that if any of the threads that do not have the lock and have not completed the execution, they will then be in a waiting state.
<a id='gil1'></a>
<h5 id="gil-in-python2-vs-python3">GIL in Python2 vs. Python3</h5>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364509/gil3_asst6i.png" />
</figure>
</center>

Global Interpreter Lock in Python 2.7 works differently when compared to Python3. In a pure CPU-bound operation, the thread continues to run since there is no I/O operation in which case the other threads will be in an idle or a wait state, which is not what you would want. To solve this issue, Python2 has terminology known as <code>ticks</code>. The global interpreter lock performs a check to monitor the state of thread-like wait state, I/O operation, or whether it is being run. However, GIL does not keep monitoring the threads at every instance or time, rather it uses the concept of <code>ticks</code>, and it checks the threads at every 100 ticks.
A tick is a byte-code instruction, and when the 100 byte-code instructions are completed, the GIL checks whether the threads are running or in a waiting state. It is important to remember that a tick is not related to time since it is a byte-code instruction. Each tick might take a longer or shorter time to run compared to the other.
<a id='table'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364510/gil4_id7go7.png" style="width:100%">
 <figcaption><center><a href="https://speakerdeck.com/dabeaz/understanding-the-python-gil?slide=12">Source</a </center></figcaption>
</figure>



This periodic check of monitory the threads after every 100 ticks is essential, especially in CPU bound operations since they do not have any I/O operations. Note that you can modify the tick counter using the built-in <code>sys</code> module of Python.
<a id='table'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364510/gil5_qju0lo.png" style="width:100%">
 <figcaption><center><a href="https://speakerdeck.com/dabeaz/understanding-the-python-gil?slide=11">Source</a </center></figcaption>
</figure>

Let&#39;s discuss some of the demerits of the above approach:
Now, if you look at <a href="#gil1">this</a> diagram, you may notice that the threads are not running parallel to each other but in a serial order, also known as cooperative computing governed by the global interpreter lock. Also, the threads have to wait for longer durations. For example, the thread T3 had to wait for both the threads T1 &amp; T2 to release the GIL. Hence, the threads starve for the lock and the compute.
Another demerit is that there is a battle between the threads as to which thread will acquire the GIL. Initially, it might be the case that based on the priority T1 would have got the GIL, then T2, and so on. However, when T3 releases GIL, it will send a notification to all other threads, in which case all other threads will keep fighting to acquire the GIL. Having said that, you could potentially overcome this by setting the priorities of each thread.
<h4 id="in-python3-you-have-fixed-time-allocated-for-each-thread-i-e-5ms-of-execution-time-this-prevents-the-threads-from-starving-for-resources-since-the-waiting-time-is-equal-for-all-the-threads-the-threads-will-wait-to-see-if-the-gil-gets-released-by-thread-1-on-purpose-because-of-i-o-or-sleep-operation-if-not-it-will-force-it-to-release-after-5ms-hence-there-will-not-be-any-starvation-of-cpu-time-since-every-thread-will-get-an-equal-amount-of-time-and-this-will-eliminate-the-challenge-of-acquiring-the-gil-">In Python3, you have fixed-time allocated for each thread, i.e., 5ms of execution time. This prevents the threads from starving for resources since the waiting time is equal for all the threads. The threads will wait to see if the GIL gets released by Thread 1 on purpose because of I/O or sleep operation. If not, it will force it to release after 5ms. Hence, there will not be any starvation of CPU time since every thread will get an equal amount of time, and this will eliminate the challenge of acquiring the GIL.</h4>
<a id='table'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585364510/gil8_kcolyd.png" style="width:100%">
 <figcaption><center></center></figcaption>
</figure>

However, it is important to note that the new implementation of GIL gives more priority to CPU-bound jobs as compared to the I/O operations. In a case where a thread has released the GIL, and two threads are waiting, GIL will be acquired by the thread which has a requirement of running a CPU-bound job as compared to the other thread.
<h1 id="conclusion">Conclusion</h1>
Congratulations on finishing the tutorial.
This tutorial covered an advanced topic, and the purpose was to give you all a theoretical overview of how exactly GIL works in Python. Since this tutorial did not cover any coding aspects of GIL and threads, one good exercise for you all would be to experiment on your own by creating multiple threads and a method that performs an I/O intensive task and finally analyzes how the GIL is switched between each thread.
Please feel free to ask any questions related to this tutorial in the comments section below.
References:
<ul>
<li><a href="https://www.dabeaz.com/python/UnderstandingGIL.pdf">Understanding the Python GIL</a></li>
</ul>
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.

https://www.datacamp.com/community/tutorials/python-global-interpreter-lock

Learn what Global Interpreter Lock is, how it works, and why you should use it.

(Tutorial) Python Global Interpreter Lock

Python Global Interpreter Lock

If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.
<a id='log1'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584715062/logging1_sjhzzn.png" style="width:100%">
 <figcaption><center>Levels of Logging</center></figcaption>
</figure>

In general, logging is a way of keeping a log generated from a computer program. A log file usually has a set of records of events that occur in your code ( for example, a python code ) or an operating system. These events or messages are written to a log file, a message, or an event can also be a communication between different users.
It usually depends on the developers on how they would like to generate logs. Maybe they would like to log every operation of an application they developed, log only when an error occurs, or monitor essential operations done by a user and log them. An essential operation could be to check the application or the server was accessed by which end-user or IP address, and in case it was an unsuccessful attempt, the developer then could seek help from the logs and provide better insights than a mere stack trace.
A nice example would be when you try to do an installation on Linux as a sudo user when you are not a sudo user. You see a message &quot;Xyz is not in the sudoers file. This incident will be reported&quot;, which is written in a log file.
The good news is that Python comes with a built-in logging module, and you can use it off the shelf without any installation.
<h4 id="how-logging-is-different-from-a-print-function-">How logging is different from a Print Function?</h4>
Logging is similar in spirit to a <code>Print Function</code>. However, a Print function lacks a lot of information that might be useful to a developer. Logging, on the other hand, can log the timestamps and line number at which the error occurred. It can log errors or any information to files, sockets, etc., and it offers you five types of severity based on which you can differentiate your logging.
Five Levels of Logging as shown <a href="#log1">here</a>, are:
<ul>
<li>Debug (10): Useful for diagnosing issues in the code.
</li>
<li>Info (20): It can act as an acknowledgment that there are no bugs in the code. One good use-case of Info level logging is the progress of training a machine learning model.
</li>
<li>Warning (30): Indicative of a problem that could occur in the future. For example, a warning of a module that might be discontinued in the future or low-ram warning.
</li>
<li>Error (40): A serious bug in the code, could be a syntax error, out of memory error, exceptions.
</li>
<li>Critical (50): An error due to which the program might stop functioning or might exit abruptly.
</li>
</ul>
Enough of theory, right?
Let&#39;s now understand how you can implement logging in a Python code.
But before that, let&#39;s see the logging attribute table, you would be using some of the below logging attributes in today&#39;s tutorial.
<a id='table'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584715063/logging5_ugwysb.png" style="width:100%">
 <figcaption><center><a href="https://docs.python.org/3/library/logging.html">Logging Attributes</a </center></figcaption>
</figure>


<pre><code class="lang-python">import logging
</code></pre>
Now you will call the <code>basicConfig()</code> method, which helps you in creating a basic configuration for the logging system to work smoothly. 
 
It allows you to set the following parameters:
<ul>
<li>the severity of the logging: the five levels of logging.</li>
<li>log the events into a file.</li>
<li>log only the current logs by overwriting the old ones using the filemode.</li>
<li>file format in which logs are stored. </li>
</ul>
Remember that the <code>basicConfig()</code> method is optional, and you might want to skip it.
<pre><code class="lang-python">logging.basicConfig()
</code></pre>
Let&#39;s now build the five levels of severity.
<pre><code class="lang-python">logging.debug(&quot;A Debug Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.info(&quot;A Info Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.warning(&quot;A Warning Logging Message&quot;)
</code></pre>
<pre><code>WARNING:root:A Warning Logging Message
</code></pre><pre><code class="lang-python">logging.error(&quot;An Error Logging Message&quot;)
</code></pre>
<pre><code>ERROR:root:An Error Logging Message
</code></pre><pre><code class="lang-python">logging.critical(&quot;A Critical Logging Message&quot;)
</code></pre>
<pre><code>CRITICAL:root:A Critical Logging Message
</code></pre>As you can observe from the above outputs, only the ones which have severity level greater than 20 were successfully logged since that is the default configuration of logging in Python.
However, as discussed above, you can modify the severity levels using the <code>basicConfig()</code> method.
Here you would change the severity level to 20, which means logging levels having severity greater than 10 will be logged.
Note: Kindly restart the kernel before running the below cells to see the correct output.
<pre><code class="lang-python">import logging
</code></pre>
<pre><code class="lang-python">logging.basicConfig(level=logging.INFO)
</code></pre>
<pre><code class="lang-python">logging.debug(&quot;A Debug Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.info(&quot;A Info Logging Message&quot;)
</code></pre>
<pre><code>INFO:root:A Info Logging Message
</code></pre><pre><code class="lang-python">logging.warning(&quot;A Warning Logging Message&quot;)
</code></pre>
<pre><code>WARNING:root:A Warning Logging Message
</code></pre><pre><code class="lang-python">logging.error(&quot;An Error Logging Message&quot;)
</code></pre>
<pre><code>ERROR:root:An Error Logging Message
</code></pre><pre><code class="lang-python">logging.critical(&quot;A Critical Logging Message&quot;)
</code></pre>
<pre><code>CRITICAL:root:A Critical Logging Message
</code></pre>Let&#39;s now log the above outputs in a log file. To achieve this, you would use the <code>basicConfig()</code> method.
<pre><code class="lang-python">import logging
</code></pre>
<pre><code class="lang-python">logging.basicConfig(level = logging.INFO, filename = &#39;datacamp.log&#39;)
</code></pre>
<pre><code class="lang-python">logging.debug(&quot;A Debug Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.info(&quot;A Info Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.warning(&quot;A Warning Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.error(&quot;An Error Logging Message&quot;)
</code></pre>
<pre><code class="lang-python">logging.critical(&quot;A Critical Logging Message&quot;)
</code></pre>
<a id='log1'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584715063/logging2_ovbrzd.png" style="width:100%">
</figure>

As you can see, now the logs are being generated in a log file <code>datacamp.log</code>. Upon running the above cells multiple times, you would notice that logs will be added in the log file that many times. By default, the file is in <code>append</code> mode.
<a id='log1'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584715063/logging3_crzayi.png" style="width:100%">
</figure>

You can make use of the <code>filemode</code> keyword to prevent the log file from being written with multiple copies of logs i.e., append mode. The filemode can be changed to <code>write</code> mode, which will overwrite the previous logs and only save the current ones. Since the filemode is set to <code>w</code>, this means that the log file will be opened in <code>write mode</code> each time <code>basicConfig()</code> is run, which will ultimately overwrite the file.
The file mode functionality in logging is similar to a standard file handling that Python offers.
<pre><code class="lang-python">import logging
</code></pre>
<pre><code class="lang-python">logging.basicConfig(level = logging.INFO, filename = &#39;datacamp.log&#39;, filemode = &#39;w&#39;)
</code></pre>
<pre><code class="lang-python">logging.debug(&quot;A Debug Logging Message&quot;)
logging.info(&quot;A Info Logging Message&quot;)
logging.warning(&quot;A Warning Logging Message&quot;)
logging.error(&quot;An Error Logging Message&quot;)
logging.critical(&quot;A Critical Logging Message&quot;)
</code></pre>
Let&#39;s run the above lines of code multiple times. You will notice that this time only the current log entries will be saved, and the previous ones will be overwritten, as shown below.
<a id='log1'></a>
<figure>
 <img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584715063/logging4_eara4d.png" style="width:100%">
</figure>

Let&#39;s look at some more logging attributes like date, time, line number at which warning or error was generated. Well, as you might agree by now that all these are advantages over a simple Print function and is far more capable than a mere Print function.
In order for you to accomplish this, you will pass the logging attributes <code>asctime</code> and <code>lineno</code>. Also you will pass the <code>message</code> attribute, which will be a placeholder for the message you would like to display for the logs. All these attributes will be passed to the <code>format</code> method. 
<pre><code class="lang-python">import logging
</code></pre>
<pre><code class="lang-python">logging.basicConfig(format=&#39;Date-Time : %(asctime)s : Line No. : %(lineno)d - %(message)s&#39;, \
 level = logging.DEBUG)
</code></pre>
<pre><code class="lang-python">logging.debug(&quot;A Debug Logging Message&quot;)
logging.info(&quot;A Info Logging Message&quot;)
logging.warning(&quot;A Warning Logging Message&quot;)
logging.error(&quot;An Error Logging Message&quot;)
logging.critical(&quot;A Critical Logging Message&quot;)
</code></pre>
<pre><code>Date-Time : 2020-03-18 23:58:12,429 : Line No. : 1 - A Debug Logging Message
Date-Time : 2020-03-18 23:58:12,436 : Line No. : 2 - A Info Logging Message
Date-Time : 2020-03-18 23:58:12,437 : Line No. : 3 - A Warning Logging Message
Date-Time : 2020-03-18 23:58:12,440 : Line No. : 4 - An Error Logging Message
Date-Time : 2020-03-18 23:58:12,441 : Line No. : 5 - A Critical Logging Message
</code></pre>Let&#39;s look at the definitions of each attribute straight from this <a href="#table">table</a>.
<ul>
<li><code>asctime</code>: Human-readable time when the LogRecord was generated. By default, this is of the form <code>2020-03-18 22:24:42,670</code> (the numbers after the comma are millisecond portion of the time). Since this attribute is a string, hence, you use <code>s</code> keyword after it.
</li>
<li><code>lineno</code>: Source line number where the logging call was called. Since this attribute is an integer, hence, you use <code>d</code> keyword after it.
</li>
<li><code>message</code>: The logged message, computed as msg % args. This is set when Formatter.format() is invoked. If you do not specify this attribute while using the format() parameter, the messages you wish to log will not be generated. Similarly, for a message attribute, you use <code>s</code> since its a string.
</li>
<li><code>funcName</code>: This will output the module or the function name in which the log is generated. It is useful when you have a lot of functions in your code, and you want to segregate the logs based on the function or method they belong to.
</li>
</ul>
Let&#39;s take a simple example and see how you can plug in the logging debug function in place of the print function.
<pre><code class="lang-python">import logging

logging.basicConfig(format=&#39;%(asctime)s :: %(levelname)s :: %(funcName)s :: %(lineno)d \
:: %(message)s&#39;, level = logging.INFO)

def addition(x, y):
 add = x + y
 return add


def subtract(x, y):
 sub = x - y
 return sub


def multiply(x, y):
 mul = x * y
 return mul


def divide(x, y):
 div = x / y
 return div

def exponent(x, y):
 exp = x ** y
 return exp


num1 = 20
num2 = 2

def main():
 add_result = addition(num1, num2)
 logging.info(&#39;Add: {} + {} = {}&#39;.format(num1, num2, add_result))

 sub_result = subtract(num1, num2)
 logging.info(&#39;Sub: {} - {} = {}&#39;.format(num1, num2, sub_result))

 mul_result = multiply(num1, num2)
 logging.info(&#39;Mul: {} * {} = {}&#39;.format(num1, num2, mul_result))

 div_result = divide(num1, num2)
 logging.info(&#39;Div: {} / {} = {}&#39;.format(num1, num2, div_result))

 exp_result = exponent(num1, num2)
 logging.info(&#39;Exp: {} ** {} = {}&#39;.format(num1, num2, exp_result))


main()
</code></pre>
<pre><code>2020-03-19 00:01:27,372 :: INFO :: main :: 35 :: Add: 20 + 2 = 22
2020-03-19 00:01:27,376 :: INFO :: main :: 38 :: Sub: 20 - 2 = 18
2020-03-19 00:01:27,377 :: INFO :: main :: 41 :: Mul: 20 * 2 = 40
2020-03-19 00:01:27,378 :: INFO :: main :: 44 :: Div: 20 / 2 = 10.0
2020-03-19 00:01:27,379 :: INFO :: main :: 47 :: Exp: 20 ** 2 = 400
</code></pre><h1 id="conclusion">Conclusion</h1>
Congratulations on finishing the tutorial.
You might want to try out other logging attributes and understand how they can be leveraged in your code.
Please feel free to ask any questions related to this tutorial in the comments section below.
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.

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

Learn about the fundamentals of Logging in Python.

(Tutorial) Logging in Python

Logging in Python

If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.
Dictionary is a built-in Python Data Structure that is mutable. It is similar in spirit to List, Set, and Tuples. However, it is not indexed by a sequence of numbers but indexed based on <code>keys</code> and can be understood as associative arrays. On an abstract level, it consists of a <code>key</code> with an associated <code>value</code>. In Python, the Dictionary represents the implementation of a <code>hash-table</code>.
<h4 id="you-might-wonder-what-are-keys-">You might wonder what are keys?</h4>
Well, as shown in the figure below, keys are immutable ( which cannot be changed ) data types that can be either strings or numbers. However, a key can not be a mutable data type, for example, a list. Keys are unique within a dictionary and can not be duplicated inside a dictionary, in case if it is used more than once then subsequent entries will overwrite the previous value.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1584113961/dictionary2_obxo0j.jpg"></center>

<code>Key</code> connects with the <code>value</code>, hence, creating a map-like structure. If you for a second, remove keys from the picture, all you are left with is a data structure containing a sequence of numbers. Dictionaries, therefore, hold a <code>key: value</code> pair at each position.
A dictionary is represented by a pair of curly braces <code>{}</code> in which enclosed are the <code>key: value</code> pairs separated by a comma.
Let&#39;s look at the syntax of a dictionary:
dictionary = {&quot;key_1&quot;: &quot;value_1&quot;, &quot;key_2&quot;: &quot;value_2&quot;, &quot;key_3&quot;: &quot;value_3&quot;}#
<h3 id="unique-keys">Unique Keys</h3>
Since now you know that <code>keys</code> in a dictionary have to be unique, let&#39;s understand it with the help of an example.
<pre><code class="lang-python">dictionary_unique = {&quot;a&quot;: &quot;alpha&quot;, &quot;o&quot;: &quot;omega&quot;, &quot;g&quot;: &quot;gamma&quot;}
</code></pre>
Let&#39;s print this out.
<pre><code class="lang-python">print(dictionary_unique)
</code></pre>
<pre><code>{&#39;a&#39;: &#39;alpha&#39;, &#39;o&#39;: &#39;omega&#39;, &#39;g&#39;: &#39;gamma&#39;}
</code></pre>Great, so until now, everything looks fine. You were able to print your first dictionary output. Now, let&#39;s repeat the key <code>g</code> with a new value and see what happens.
<pre><code class="lang-python">dictionary_unique = {&quot;a&quot;: &quot;alpha&quot;, &quot;o&quot;: &quot;omega&quot;, &quot;g&quot;: &quot;gamma&quot;, &quot;g&quot;: &quot;beta&quot;}
</code></pre>
<pre><code class="lang-python">print(dictionary_unique)
</code></pre>
<pre><code>{&#39;a&#39;: &#39;alpha&#39;, &#39;o&#39;: &#39;omega&#39;, &#39;g&#39;: &#39;beta&#39;}
</code></pre>As expected, the key <code>g</code> previous value <code>gamma</code> was overwritten by value <code>beta</code>.
<h3 id="immutable-keys">Immutable Keys</h3>
Now let&#39;s see what happens when you try to define the <code>key</code> as a mutable data type.
<pre><code class="lang-python">dictionary_immutable = {[&quot;a&quot;,&quot;b&quot;,&quot;c&quot;]: &quot;alpha&quot;, &quot;o&quot;: &quot;omega&quot;, &quot;g&quot;: &quot;gamma&quot;, &quot;g&quot;: &quot;beta&quot;}
</code></pre>
<pre><code>---------------------------------------------------------------------------

TypeError Traceback (most recent call last)

&lt;ipython-input-16-78a55d50cd65&gt; in &lt;module&gt;
----&gt; 1 dictionary_immutable = {[&quot;a&quot;,&quot;b&quot;,&quot;c&quot;]: &quot;alpha&quot;, &quot;o&quot;: &quot;omega&quot;, &quot;g&quot;: &quot;gamma&quot;, &quot;g&quot;: &quot;beta&quot;}


TypeError: unhashable type: &#39;list&#39;
</code></pre>From the above output, you can observe that defining the first key of the dictionary as a list results in a <code>TypeError</code> since dictionary keys must be immutable types and list is a mutable type.
However, there is a workaround to it, which is, replacing the list to a tuple, since a tuple is an immutable data type.
<pre><code class="lang-python">dictionary_immutable = {(&quot;a&quot;,&quot;b&quot;,&quot;c&quot;): &quot;alpha&quot;, &quot;o&quot;: &quot;omega&quot;, &quot;g&quot;: &quot;gamma&quot;, &quot;g&quot;: &quot;beta&quot;}
</code></pre>
<pre><code class="lang-python">dictionary_immutable
</code></pre>
<pre><code>{(&#39;a&#39;, &#39;b&#39;, &#39;c&#39;): &#39;alpha&#39;, &#39;o&#39;: &#39;omega&#39;, &#39;g&#39;: &#39;beta&#39;}
</code></pre><h3 id="accessing-keys-and-values">Accessing Keys and Values</h3>
Since you now know how to create dictionaries, let&#39;s learn to access the keys and the values from the dictionary. To access the key value pair, you would use the <code>.items()</code> method, which will return a list of <code>dict_items</code> in the form of a key, value tuple pairs.
<pre><code class="lang-python">dictionary_unique.items()
</code></pre>
<pre><code>dict_items([(&#39;a&#39;, &#39;alpha&#39;), (&#39;o&#39;, &#39;omega&#39;), (&#39;g&#39;, &#39;beta&#39;)])
</code></pre>To access keys and values separately, you could use a for loop on the dictionary or the <code>.keys()</code> and <code>.values()</code> method.
<pre><code class="lang-python">for key, value in dictionary_unique.items(): #accessing keys
 print(key,end=&#39;,&#39;)
</code></pre>
<pre><code>a,o,g,
</code></pre><pre><code class="lang-python">for key, value in dictionary_unique.items(): #accessing values
 print(value,end=&#39;,&#39;)
</code></pre>
<pre><code>alpha,omega,beta,
</code></pre><pre><code class="lang-python">dictionary_unique.keys() #accessing keys without for loop
</code></pre>
<pre><code>dict_keys([&#39;a&#39;, &#39;o&#39;, &#39;g&#39;])
</code></pre><pre><code class="lang-python">dictionary_unique.values() #accessing values without for loop
</code></pre>
<pre><code>dict_values([&#39;alpha&#39;, &#39;omega&#39;, &#39;beta&#39;])
</code></pre>You could even access a value by specifying a key as a parameter to the dictionary.
<pre><code class="lang-python">dictionary_unique[&#39;a&#39;]
</code></pre>
<pre><code>&#39;alpha&#39;
</code></pre><pre><code class="lang-python">dictionary_unique[&#39;g&#39;]
</code></pre>
<pre><code>&#39;beta&#39;
</code></pre><h3 id="nested-dictionary">Nested Dictionary</h3>
Creating a nested dictionary is quite simple. In the nested dictionary, you pass in a dictionary inside a dictionary or to put it simply; a dictionary is passed as a value to a key of the main dictionary.
Here you will create a <code>datacamp</code> dictionary inside a nested dictionary <code>dictionary_nested</code>.
<pre><code class="lang-python">dictionary_nested = {&quot;datacamp&quot;:{&quot;Deep Learning&quot;: &quot;Python&quot;, &quot;Machine Learning&quot;: &quot;Pandas&quot;},&quot;linkedin&quot;:&quot;jobs&quot;,&quot;nvidia&quot;:&quot;hardware&quot;}
</code></pre>
<pre><code class="lang-python">dictionary_nested
</code></pre>
<pre><code>{&#39;datacamp&#39;: {&#39;Deep Learning&#39;: &#39;Python&#39;, &#39;Machine Learning&#39;: &#39;Pandas&#39;},
 &#39;linkedin&#39;: &#39;jobs&#39;,
 &#39;nvidia&#39;: &#39;hardware&#39;}
</code></pre><pre><code class="lang-python">dictionary_nested[&#39;datacamp&#39;][&#39;Deep Learning&#39;]
</code></pre>
<pre><code>&#39;Python&#39;
</code></pre><pre><code class="lang-python">dictionary_nested[&#39;datacamp&#39;][&#39;Machine Learning&#39;]
</code></pre>
<pre><code>&#39;Pandas&#39;
</code></pre><pre><code class="lang-python">dictionary_nested[&#39;linkedin&#39;]
</code></pre>
<pre><code>&#39;jobs&#39;
</code></pre><pre><code class="lang-python">dictionary_nested[&#39;nvidia&#39;]
</code></pre>
<pre><code>&#39;hardware&#39;
</code></pre><h3 id="dictionary-comprehension">Dictionary Comprehension</h3>
Dictionary comprehensions can be used to create dictionaries from arbitrary key and value expressions. It is a simple and concise way of creating dictionaries and is often faster than the usual <code>for</code> loop implementations.
<pre><code class="lang-python">import time
t1 = time.time()
dict_comprehension = {i: i**3 for i in range(200000)}
print(time.time() - t1)
</code></pre>
<pre><code>0.0897526741027832
</code></pre>Let&#39;s print the first ten key: value pairs from the <code>dict_comprehension</code> dictionary. To achieve this, you will import <code>islice</code> from the <code>itertools</code> built-in package and specify <code>n</code> as a number of key value pairs you want to extract.
<pre><code class="lang-python">from itertools import islice

comp_10 = list(islice(dict_comprehension.items(),10))
print(comp_10)
</code></pre>
<pre><code>[(0, 0), (1, 1), (2, 8), (3, 27), (4, 64), (5, 125), (6, 216), (7, 343), (8, 512), (9, 729)]
</code></pre><pre><code class="lang-python">import time
t1 = time.time()
dict_comprehension = dict()
for i in range(200000):
 dict_comprehension[i+1] = i**3
print(time.time() - t1)
</code></pre>
<pre><code>0.10853934288024902
</code></pre>As you can see from the above two implementations of a dictionary, dictionary comprehension with a minimal margin, is still the winner in terms of the time it takes to run. You would notice that as you keep increasing the <code>range</code> parameter, the difference in the time will also increase.
<h3 id="word-frequency">Word Frequency</h3>
From a collection of written texts, in this case, a string of text, also known as <code>corpus</code>, lets us create a word frequency with the help of a dictionary.
<pre><code class="lang-python">corpus = &#39;learn all about the Python Dictionary and its potential. \
 You would also learn to create word frequency using the Dictionary&#39;
</code></pre>
<pre><code class="lang-python">word_freq = dict()
corpus_word = str(corpus).split()
for word in range(len(corpus_ref)):
 if corpus_word[word] not in word_freq:
 word_freq[corpus_word[word]] = 1

 else:
 word_freq[corpus_word[word]] += 1
</code></pre>
<pre><code class="lang-python">word_freq
</code></pre>
<pre><code>{&#39;learn&#39;: 2,
 &#39;all&#39;: 1,
 &#39;about&#39;: 1,
 &#39;the&#39;: 2,
 &#39;Python&#39;: 1,
 &#39;Dictionary&#39;: 2,
 &#39;and&#39;: 1,
 &#39;its&#39;: 1,
 &#39;potential.&#39;: 1,
 &#39;You&#39;: 1,
 &#39;would&#39;: 1,
 &#39;also&#39;: 1,
 &#39;to&#39;: 1,
 &#39;create&#39;: 1,
 &#39;word&#39;: 1,
 &#39;frequency&#39;: 1,
 &#39;using&#39;: 1}
</code></pre>Great! So as you can observe from the above output, you were able to get a word count or word frequency from a string of text with the help of a Python Dictionary.
<h1 id="conclusion">Conclusion</h1>
Congratulations on finishing the tutorial.
Please feel free to ask any questions related to this tutorial in the comments section below.
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.

https://www.datacamp.com/community/tutorials/dictionary-python

Learn all about the Python Dictionary and its potential. You will also learn how to create word frequency using the Dictionary.

Learn all about the PYTHON Dictionary and its potential. You will also learn how to create word frequency using the DICTIONARY.

(Tutorial) Python Dictionaries: The Definitive Guide

Python Dictionaries: The Definitive Guide

<center>
<a target=“_blank” href="https://www.datacamp.com/learn/r/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604679611/BANNER_mrsctv.png" /></a>
</center> 
R is one of the main languages used for data science today. As such, it is natural that any beginner may want to know how to get started with this powerful language regardless of the operating system running on a computer. Thus, this tutorial will address this by covering the installation process of R on Windows 10, Mac OSX, and Ubuntu Linux.
Furthermore, it will also go over the installation of RStudio, which is an IDE (Integrated Development Environment) that makes R easier to use as well as how to install R packages such as dplyr or ggplot2.
<h2 id="installing-r-on-windows-10">Installing R on Windows 10</h2>
Installing R on Windows 10 is very straightforward. The easiest way is to install it through CRAN, which stands for The Comprehensive R Archive Network. Just visit the <a href="https://cran.r-project.org/">CRAN downloads</a> page and follow the links as shown in the video below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946484/ezgif.com-resize_zzbzbg.gif" /></center>

Once the download is finished, you will obtain a file named &quot;R-3.6.3-win.exe&quot; or similar depending on the version of R that you download. The links shown in the video above will take you to the most recent version. To finish installing R on your computer, all that is left to do is to run the .exe file. Most of the time, you will likely want to go with the defaults, so click the button &#39;Next&#39; until the process is complete, as shown in the video below. Note that, even though I do not do so, you can add desktop or quick start shortcuts during the process.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946566/ezgif.com-resize_1_pnz4tu.gif" /></center>

<h3 id="installing-rstudio">Installing RStudio</h3>
Once R is installed, you can proceed to install the RStudio IDE to have a much-improved environment to work in your R scripts. It includes a console that supports direct code execution and tools for plotting and keeping track of your variables in the workspace, among other features. The installation process is very straightforward, as well. Simply go to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and follow the video below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946691/ezgif.com-video-to-gif_6_pz7f09_x4spfr.gif" /></center>

Once the download is complete, you will get a file named &quot;RStudio-1.2.5033.exe&quot; or similar. Again this will be dependent on the version. To complete the installation, it is as easy as before. Just run the previously mentioned .exe file with the default settings by clicking &#39;Next&#39;, and wait until the installation finishes. Bear in mind that RStudio requires that R is installed beforehand.
<h3 id="installing-packages-in-r">Installing Packages in R</h3>
Now you have base R installed on your system and a nice IDE to begin your R programming journey. However, base R is rather limited in the things that it can do, which is why we have R packages such as dplyr for enhanced data-wrangling capabilities or ggplot2 for improved data visualizations. There are two simple ways to install R packages using RStudio. The first is to execute the following line of code in the console:
<code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code>
The second is shown in the video below. It is an easy-to-use graphical interface built into RStudio from which you can search and download any R package available on CRAN.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946948/ezgif.com-resize_3_usjbvk.gif" /></center>

<h2 id="installing-r-on-mac-osx">Installing R on Mac OSX</h2>
 Installing R on Mac OS is similar to Windows. Once again, The easiest way is to install it through CRAN by going to the <a href="https://cran.r-project.org/">CRAN downloads</a> page and following the links as shown in the video below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583947086/ezgif.com-resize_4_pmdxfv.gif" /></center>

The next step is to click on the &quot;R-3.6.2.pkg&quot; (or newer version) file to begin the installation. You can leave the default options as is just like for Windows.
<h3 id="installing-rstudio-and-r-packages">Installing RStudio and R packages</h3>
This process is essentially the same as in Windows. To download RStudio, go to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and get the .dmg for Mac OS, as shown in the image below. Remember to keep default installation options.
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583354177/Img1_a2bfpu.png" /></center>

Once you open RStudio, installing packages is the same as with Windows. You can use either <code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code> in the console or go ahead and use the graphical interface shown in the video under the installing packages in R subsection of this tutorial.
<h2 id="installing-r-on-ubuntu-19-04-18-04-16-04">Installing R on Ubuntu 19.04/18.04/16.04</h2>
Installing R on Ubuntu maybe a little bit more tricky for those unused to working in the command line. However, it is perhaps just as easy as with Windows or Mac OS. Before you start, make sure to have root access in order to use sudo.
As it is common, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands:
<code>sudo apt update</code>
<code>sudo apt -y upgrade</code>
After that, all that you have to do is run the following in the command line to install base R.
<code>sudo apt -y install r-base</code>
<h3 id="installing-rstudio-and-r-packages">Installing RStudio and R Packages</h3>
Once base R is installed, you can go ahead and install RStudio. For that we are going to head over again to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and download the .deb file for our Ubuntu version as shown in the image below:
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583354178/Img2_q3uwjt.png" /></center>

Once you have the .deb file, all that is left is to navigate to your downloads folder using <code>cd Downloads</code> in the command line and then run the following command to begin the installation process:
<code>sudo dpkg -i rstudio-1.2.5033-amd64.deb</code>
You may encounter some dependency problems that may cause your first try to install RStudio to fail, but this has an easy fix. Just run the following command and try again:
<code>sudo apt -f install</code>
When the process finishes, you will have an RStudio shortcut in your Ubuntu app list, but you will also be able to start RStudio by typing rstudio in the command line.
Once you open RStudio, installing packages can be done in the exact same manner as with Windows or Mac OS. Either by typing <code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code> in the console or using the graphical interface shown in the video under the installing packages in R subsection of this tutorial
<h2 id="conclusion">Conclusion</h2>
I hope that this tutorial will help those of you eager to dive into the world of R programming regardless of your operating system choice. If you are looking to start learning R as such after installing it, please refer to the <a href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a> course, which will guide you through the basics of R programming. Keep learning; the sky is the limit.

https://www.datacamp.com/community/tutorials/installing-R-windows-mac-ubuntu

This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself.

(Tutorial) How to Install R on Windows, Mac OS X, and Ubuntu

How to Install R on Windows, Mac OS X, and Ubuntu

If you would like to learn Tableau, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a> course.
<h3 id="connecting-to-the-dataset">Connecting to the Dataset</h3>
The dataset has been taken from the Airbnb <a href="http://insideairbnb.com/get-the-data.html">website</a>. We will use the listings.csv file of New York City, NY (2019), which describes the listing activity and its other metrics. All information about hosts, geographical availability, and price has been included to make exciting visualizations.
To connect to your file, go to <code>Connect -&gt; Text file -&gt; Choose file path -&gt; Open</code>
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src = "https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348142/1_u6ymas.png" /></a></center>

A quick glance over the data will look something like this:
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src = "https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348142/2_y8mo8a.png" /></a></center>

<h3 id="what-are-measure-names-and-measure-values-">What are Measure Names and Measure Values?</h3>
As soon as you load the Excel file and open a new worksheet in Tableau, it will automatically categorize all the column headings of your file into one of the below categories (as seen in the Data pane):
<ul>
<li>Dimensions</li>
<li>Measures</li>
</ul>
Notice that Tableau mistakenly classifies <code>Reviews Per Month</code> into <code>Dimensions</code>. Correct this by dragging the field into the <code>Measures</code> pane, as we want to use this field as a continuous numerical variable.
Apart from the column names, the Date pane will always contain a number of fields that do not come from your original data, two of which are Measure Values and Measure Names. Tableau automatically creates these fields so that you can build certain types of views that involve multiple measures. [1]
This article is going to focus on how we can use these multiple measures.
<a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src = "https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348142/3_ibkimd.png" /></a>
<ul>
<li>The Measure Values field always appears at the bottom of the Measures area in the Data pane and contains all the measures in your data, collected into a single field with continuous values. [2]
</li>
<li>The Measure Names field always appears at the bottom of the Dimensions area in the Data pane and contains the names of all measures in your data, collected into a single field with discrete values. [3]
</li>
</ul>
 [1][2][3] <a href="https://help.tableau.com/current/pro/desktop/en-us/datafields_understanddatawindow_meavalues.htm">Official Tableau documentation</a>
<h3 id="tablulating-measure-names-and-measure-values">Tablulating Measure Names and Measure Values</h3>
Let&#39;s start by first dragging the Measure Names field in the view. Next, drag the Measure Values field directly into the view or under the Text section in the Marks card. You will see a table with variable names against default aggregations applied to the corresponding numerical variables.
You may remove some measures values (which you do not want to be included) by dragging them out of the Measure Values card.
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583349004/ezgif.com-video-to-gif_3_pewghr.gif" /></a></center>



We know that <code>Dimensions</code> are types of categories, and <code>Measures</code> are the numerical values that we want to examine across such categories. So, let&#39;s see how we can tabulate multiple dimensions across multiple measures.
We would like to cross-tabulate multiple dimensions with multiple measures. Begin by simply dragging two-dimension fields, say <code>Neighbourhood Group</code> into the Rows tab and <code>Room Type</code> into the Columns tab. You will see a nice 5x3 table containing 5 neighbourhood groups and 3 room types.
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348142/5_htnjjl.png" /></a></center>

Now suppose you want to show both the <code>Price</code> and the <code>Number of Reviews</code> for each <code>Neighbourhood Group</code> and <code>Room Type</code>. First drag <code>Price</code> into the view. Next, when you add <code>Number of Reviews</code> to the text table (by dragging it and dropping it in the view), the measures are combined, and the <code>Measure Values</code> field is added to Marks Text Card. Notice that the <code>Measure Names</code> field is automatically added to the Rows shelf.
It is vital to use <code>Measure Names</code> whenever dealing with more than one measure value.
<h4 id="aggregate-functions">Aggregate functions</h4>
Similar to the aggregate functions (SUM, AVG, MEAN, COUNT) that we have in MySQL, PostgreSQL, etc., Tableau also provides a number of aggregate functions for its <code>Measures</code>.
An aggregate function performs a calculation on a set of values and returns a single value.
Here, since we would like to see the average price (and not the total price of all rooms) of each room type in different neighborhoods, we change the (default) aggregate function of <code>Price</code> from <code>SUM()</code> to <code>AVG()</code> using the Measure Values card.
<h3 id="tabulating-multiple-measure-names-and-measure-values">Tabulating Multiple Measure Names and Measure Values</h3>
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348470/ezgif.com-video-to-gif_sfxbyz.gif" /></a></center>



Looks good! But there are too many numbers to comprehend. To get better insights, let&#39;s go ahead and make a graphical representation of the same data.
Select the Horizontal Bars graph using the Show Me button on the top right corner of Tableau. You will see a bar for the <code>AVG(PRICE)</code> and <code>SUM(NUMBER OF REVIEWS)</code> for each Neighborhood Group and Room Type.
Make it neater by color-coding the bars according to the <code>Neighborhood Group</code>. Tableau automatically creates and displays a legend for the same. Lastly, swap the rows and columns to make the graph more intuitive and understandable.
The graph now clearly shows the pricing and popularity (number of reviews) trends for every locality and room type.
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348610/ezgif.com-video-to-gif_1_cke5b0.gif" /></a></center>


It is always a good idea to use a geographical map when dealing with places, latitudes and longitudes. Below are the steps on how you can convert your same data into a beautiful geographical visualization.
<h3 id="visualization-using-map">Visualization using Map</h3>
We will again use the <code>Color</code> mark to classify and color each (latitude, longitude) pair into its
associated neighborhood group.
Similarly, we will use the <code>Size</code> mark to indicate the range of prices of the Airbnb
properties. Larger circles denote higher prices, and smaller circles denote lower prices.
Both the color and size legends can be found on the right.
<center><a href="https://www.datacamp.com/courses/introduction-to-tableau"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583348824/ezgif.com-video-to-gif_2_bbgkbx.gif" /></a></center>



<h3 id="conclusion">Conclusion</h3>
In this article, we saw how Tableau automatically segregates your data fields into <code>Dimensions</code> and <code>Measures</code>. In case it misclassifies any column, we can easily drag-and-drop the field to its correct category.
We also cross-tabulated multiple dimensions with multiple measures using different visualizations - Text Table, Bar Graph, and Map.
We explored how we can use the <code>Color</code> and <code>Size</code> options in the Marks card. In addition, we experimented with some aggregate functions like <code>SUM()</code> and <code>AVG()</code> to draw meaningful inferences.
If you would like to learn more about Tableau, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a> course.

https://www.datacamp.com/community/tutorials/tableau-visualizations-with-measure-names-and-measure-values

Learn about different categories of variables in Tableau and how they can be leveraged to analyze data through insightful visualizations.

Learn about different categories of variables in TABLEAU and how they can be leveraged to analyze DATA through insightful VISUALIZATIONS.

(Tutorial) Creating Tableau Visualizations Using Airbnb Dataset

Creating Tableau Visualizations Using Airbnb Dataset

If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://learn.datacamp.com/courses/introduction-to-data-science-in-python">Introduction to Data Science in Python</a> course.
<a href="https://www.datacamp.com/learn/python/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583330891/python_3_qgciu1.png"></a>
In today&#39;s tutorial, you will be learning about a built-in Python function called <code>range()</code> function. It is a very popular and widely used function in Python, especially when you are working with predominantly <code>for</code> loops and sometimes with <code>while</code> loops. It returns a sequence of numbers and is immutable (whose value is fixed). The range function takes one or at most three arguments, namely the start and a stop value along with a step size.
Range function was introduced only in Python3, while in Python2, a similar function <code>xrange()</code> was used, and it used to return a generator object and consumed less memory. The <code>range()</code> function, on the other hand, returns a list or sequence of numbers and consumes more memory than <code>xrange()</code>.
Since the <code>range()</code> function only stores the start, stop, and step values, it consumes less amount of memory irrespective of the range it represents when compared to a list or tuple.
The <code>range()</code> function can be represented in three different ways, or you can think of them as three range parameters:
<ul>
<li>range(stop_value) : This by default considers the starting point as zero.</li>
</ul>
<ul>
<li>range(start_value, stop_value) : This generates the sequence based on the start and stop value.</li>
</ul>
<ul>
<li>range(start_value, stop_value, step_size): It generates the sequence by incrementing the start value using the step size until it reaches the stop value.</li>
</ul>
<center>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583346610/range2_kpyiuy.png" />
</center> 

Let&#39;s first check the type of the <code>range()</code> function.
<pre><code class="lang-python">type(range(100))
</code></pre>
<pre><code>range
</code></pre>Let&#39;s start with a simple example of printing a sequence of ten numbers, which will cover your first range parameter.
<ul>
<li>To achieve this, you will be just passing in the stop value. Since Python works on zero-based indexing, hence, the sequence will start with zero and stop at the specified number, i.e., $n-1$, where $n$ is the specified number in the range function.</li>
</ul>
<pre><code class="lang-python">range(10) #it should return a lower and an upper bound value.
</code></pre>
<pre><code>range(0, 10)
</code></pre><pre><code class="lang-python">for seq in range(10):
 print(seq)
</code></pre>
<pre><code>0
1
2
3
4
5
6
7
8
9
</code></pre>As expected, the above cell returns a sequence of numbers starting with $0$ and ending at $9$.
You could also use the range function as an argument to a list in which case it would result in a list of numbers with a length equal to the stop value as shown below:
<pre><code class="lang-python">list(range(10))
</code></pre>
<pre><code>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
</code></pre><pre><code class="lang-python">len(list(range(10)))
</code></pre>
<pre><code>10
</code></pre><ul>
<li>Next, let&#39;s look at the second way of working with the range function. Here you will specify both start and the stop value.</li>
</ul>
<pre><code class="lang-python">range(5,10)
</code></pre>
<pre><code>range(5, 10)
</code></pre><pre><code class="lang-python">for seq in range(5,10):
 print(seq)
</code></pre>
<pre><code>5
6
7
8
9
</code></pre>Similarly, you can use the <code>range</code> function to print the negative integer values as well.
<pre><code class="lang-python">for seq in range(-5,0):
 print(seq)
</code></pre>
<pre><code>-5
-4
-3
-2
-1
</code></pre><pre><code class="lang-python">list(range(10,20))
</code></pre>
<pre><code>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
</code></pre><ul>
<li>Let&#39;s now add the third parameter, i.e., the step size to the range function, and find out how it affects the output. You will specify the start point as 50, the end/stop value as 1000 with a step size of 100. The below range function should output a sequence starting from 50 incrementing with a step of 100.</li>
</ul>
<pre><code class="lang-python">range(50,1000,100)
</code></pre>
<pre><code>range(50, 1000, 100)
</code></pre>You will notice that it will print all even numbers.
<pre><code class="lang-python">for seq in range(50,1000,100):
 print(seq)
</code></pre>
<pre><code>50
150
250
350
450
550
650
750
850
950
</code></pre>It is important to note that the <code>range()</code> function can only work when the specified value is an integer or a whole number. It does not support the float data type and the string data type. However, you can pass in both positive and negative integer values to it.
Let&#39;s see what happens when you try to pass float values.
<pre><code class="lang-python">for seq in range(0.2,2.4):
 print(seq)
</code></pre>
<pre><code>---------------------------------------------------------------------------

TypeError Traceback (most recent call last)

&lt;ipython-input-32-4d2f304928d0&gt; in &lt;module&gt;
----&gt; 1 for seq in range(0.2,2.4):
 2 print(seq)


TypeError: &#39;float&#39; object cannot be interpreted as an integer
</code></pre><ul>
<li>You would have scratched your head at least once while trying to reverse a linked list of integer values in C language. However, in python, it can be achieved with the range function with just interchanging the start and stop along with adding a negative step size.
 Isn&#39;t that so simple? Let&#39;s find out!
</li>
</ul>
<pre><code class="lang-python">for seq in range(100,10,-10):
 print(seq)
</code></pre>
<pre><code>100
90
80
70
60
50
40
30
20
</code></pre>Say you have a list of integer values, and you would like to find the sum of the list, but using the <code>range()</code> function. Let&#39;s find out it can be done.
First, you will define the list consisting of integer values. Then initialize a counter in which you will store the value each time you iterate over the list and also add the current list value with the old count value.
To access the elements from the list, you will apply the range function on the <code>length</code> of the list and then access the list elements bypassing the index <code>i</code> which will start from zero and stop at the length of the list.
<pre><code class="lang-python">list1 = [2,4,6,8,10,12,14,16,18,20]
count = 0

for i in range(len(list1)):
 count = count + list1[i]
 print(count)
print(&#39;sum of the list:&#39;, count)
</code></pre>
<pre><code>2
6
12
20
30
42
56
72
90
110
sum of the list: 110
</code></pre>You could also concatenate two or more range functions using the <code>itertools</code> package class called <code>chain</code>. And not just the <code>range</code> function, you could even concatenate list, tuples, etc. Remember that <code>chain</code> method returns a generator object, and to access the elements from that generator object, you can either use a <code>for</code> loop or use <code>list</code> and pass the generator object as an argument to it.
<pre><code class="lang-python">from itertools import chain

a1 = range(10,0,-2)
a2 = range(30,20,-2)
a3 = range(50,40,-2)

final = chain(a1,a2,a3)

print(final) #generator object
</code></pre>
<pre><code>&lt;itertools.chain object at 0x107155490&gt;
</code></pre><pre><code class="lang-python">print(list(final))
</code></pre>
<pre><code>[10, 8, 6, 4, 2, 30, 28, 26, 24, 22, 50, 48, 46, 44, 42]
</code></pre>You can apply equality comparisons between range functions. Given two range functions, if they represent the same sequence of values, then they are considered to be equal. Having said that, two equal range functions don&#39;t need to have the same start, stop, and step attributes.
Let&#39;s understand it with an example.
<pre><code class="lang-python">list(range(0, 10, 3))
</code></pre>
<pre><code>[0, 3, 6, 9]
</code></pre><pre><code class="lang-python">list(range(0, 11, 3))
</code></pre>
<pre><code>[0, 3, 6, 9]
</code></pre><pre><code class="lang-python">range(0, 10, 3) == range(0, 11, 3)
</code></pre>
<pre><code>True
</code></pre><pre><code class="lang-python">range(0, 10, 3) == range(0, 11, 2)
</code></pre>
<pre><code>False
</code></pre>As you can observe from the above outputs, even though the parameters of the range function are different, they are still considered to be equal since the sequence of both the functions is the same. While in the second example, changing the step size makes the comparison False.
<h2 id="conclusion">Conclusion</h2>
Congratulations on finishing the tutorial.
You might want to tinker around a bit with the Range function and find out a way to customize it for accepting data types other than just integers.
Please feel free to ask any questions related to this tutorial in the comments section below.
If you are just getting started in Python and would like to learn more, take DataCamp&#39;s <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Data Science in Python</a> course.

https://www.datacamp.com/community/tutorials/python-range-function

Learn about the Python Range() function and its capabilities with the help of examples.

Learn about the PYTHON Range() function and its CAPABILITIES with the help of examples.

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