http://datacamp-community-prod.s3.amazonaws.com/acfa4325-1d43-4542-8ce4-bea2d287db10

This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning.

PySpark Cheat Sheet: Spark in Python

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This NumPy cheat sheet helps you to tackle the basics of this Python scientific computing library!

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<p>In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators.&nbsp;</p>

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators. 

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https://www.datacamp.com/community/blog/operationalizing-machine-learning-with-mlops

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<p>In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.</p>

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Check out the first official spaCy cheat sheet! A handy two-page reference to the most important concepts and features.

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<p>Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.&nbsp;</p>

<p>For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend&nbsp;to build more plotting integration with Matplotlib as time goes on.</p>

<p>However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you&#39;re learning how to work with Matplotlib. &nbsp;&nbsp;</p>

<p>DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don&#39;t know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.&nbsp;</p>

<p>(By the way, if you want to get started with this Python package, you might want to consider our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">Matplotlib tutorial</a>.)</p>

<p>You&#39;ll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.&nbsp;</p>

<p>Check out the infographic by clicking on the button below:</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/e1a8f39d-71ad-4d13-9a6b-618fe1b8c9e9" target="_blank"><img alt="Python Matplotlib cheat sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838238/Matplotlib_Cheat_Sheet-_Plotting_in_Python_htbtut.png" /></a></p>

<p>With this handy reference, you&#39;ll familiarize yourself in no time with the basics of Matplotlib: you&#39;ll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.</p>

<p>What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the <a href="http://matplotlib.org/gallery.html">Matplotlib Gallery</a>, the <a href="http://matplotlib.org/2.0.0/index.html">documentation</a>&nbsp;and our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">tutorial</a>. &nbsp;&nbsp;</p>

<p>Also, don&#39;t miss out on our other cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-scipy-cheat-sheet/">SciPy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">Numpy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas</a>&nbsp;and the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics</a>.</p>


https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples. 

This CHEAT SHEET introduces you to the basics of MATPLOTLIB that are needed to plot your data with PYTHON. It also includes code samples. 

(Cheat Sheet) Matplotlib: Plotting in Python

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<h2>Conceptos básicos de Python</h2><p>Una introducción a los conceptos básicos de Python. Aprende como utilizar Python de forma interactiva y a tráves de una consola. Crea tus primeras variables y familiarícese con los tipos de datos básicos en Python.</p><h2>Listas de Python</h2><p>Aprende a almacenar, acceder y manipular datos en una lista: el primer paso hacia un trabajo eficiente con gran cantidad de datos.</p><h2>Funciones y paquetes</h2><p>Para aprovechar el código que los desarrolladores de Python han escrito, aprenderás sobre funciones, métodos y paquetes. ¡Esto te ayudará a reducir la cantidad de código que necesitas para resolver problemas!</p><h2>NumPy</h2><p>NumPy es un poderoso paquete de Python para hacer ciencia de datos eficientemente. Aprende como trabajar con arrays de NumPy, una alternativa más rápida y poderosa a las listas, y toma tus primeros pasos en la exploración de datos.</p>

https://www.datacamp.com/community/open-courses/introduccion-a-python

Aprenderás acerca de poderosas maneras de almacenar y manipular datos, así como también geniales herramientas de ciencia de datos para comenzar tus propios análisis. 

Introducción a Python

<h2>Introduction</h2><p>Introduction to data.world</p><h2>Working with datasets</h2><p>Working with data.world datasets</p><h2>Working with multiple datasets</h2><p>Working with multiple datasets</p><h2>Querying with data.world</h2><p>Querying with data.world</p><h2>Wrap up & next steps</h2><p>Setup your own data.world account and apply what you've learned and more!</p>

https://www.datacamp.com/community/open-courses/intro-to-dataworld-in-python

data.world allows data people to discover and share cool data. In this course, you'll learn to use the data.world Python SDK to pull data and metadata into your environment.

Intro to data.world in Python

https://www.datacamp.com/community/tutorials/generate-data-trumania

Discover Python Trumania, a scenario-based random dataset generator library. Learn how to generate a synthetic and random dataset in this step-by-step tutorial. 

Step-by-step tutorial to use Python Trumania a random dataset generator. Learn how to generate synthetic datasets today!

Python Code to Generate Synthetic & Random Dataset with Trumania

Generating Realistic Random Datasets with Trumania

<center>
<a target=“_blank” href="https://www.datacamp.com/learn/python/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620780469/python_course_banner_hxm8se.png" /></a>
</center><br>
<p>Every value in Python has a data type. Data types are a classification of data that tells the compiler or the interpreter how you want to use the data. The type defines the operations that can be done on the data and the structure in which you want the data to be stored. In data science, you will often need to change the type of your data, so that it becomes easier to use and work with.</p>
<p>This tutorial will tackle some of the important and most frequently used data structures, and you will learn to change their types to suit your need. More specifically, you will learn:</p>
<ul>
<li><a href="#implicit">Implicit and Explicit Data Type Conversion</a></li>
<li><a href="#primitive">Primitive versus Non-primitive Data Structures</a></li>
<li><a href="#intfloat">Integer and Float Conversions</a></li>
<li><a href="#strings">Data Type Conversion with Strings</a></li>
<li><a href="#tuplelist">Conversion to Tuples and Lists</a></li>
<li><a href="#binocthex">Binary, Octal, and Hexadecimal Integers in Python</a></li>
</ul>
<p>Python has many data types. You must have already seen and worked with some of them. You have integers and float to deal with numerical values, boolean (<code>bool</code>) to deal with true/false values and strings to work with alphanumeric characters. You can make use of lists, tuples, dictionary, and sets that are data structures where you can store a collection of values. To learn more about them, be sure to check out DataCamp&#39;s <a href="https://www.datacamp.com/courses/data-types-for-data-science">Data Types for Data Science Course</a>.</p>
<p><a id="implicit"></a></p>
<h2 id="implicit-and-explicit-data-type-conversion">Implicit and Explicit Data Type Conversion</h2>
<p>Data conversion in Python can happen in two ways: either you tell the compiler to convert a data type to some other type explicitly, or the compiler understands this by itself and does it for you. In the former case, you&#39;re performing an explicit data type conversion, whereas, in the latter, you&#39;re doing an implicit data type conversion.</p>
<h3 id="python-implicit-data-type-conversion">Python Implicit Data Type Conversion</h3>
<p>Implicit conversion or <em>coercion</em> is when data type conversion takes place either during compilation or during run time and is handled directly by Python for you. Let&#39;s see an example:</p>
<pre><code class="lang-python">a_int = 1
b_float = 1.0
c_sum = a_int + b_float
print(c_sum)
print(type(c_sum))
</code></pre>
<pre><code>2.0
&lt;class &#39;float&#39;&gt;
</code></pre><p><strong>Tip</strong>: you can use the <code>type()</code> function in Python to check the data type of an object.</p>
<p>In the example, an int value <code>a_int</code> was added to a float value <code>b_float</code>, and the result was automatically converted to a float value <code>c_sum</code> without you having to tell the compiler. This is the implicit data conversion.</p>
<p>Why was the float value not converted to integer instead?</p>
<p>This is due to a broader concept of type promotion in computer science. Simply put, this is a defense mechanism of the compiler that allows you to perform operations whenever possible by converting your data into a different supertype without the loss of information.</p>
<p>That means that the conversion from float to integer is not done because then the compiler will need to remove the fractional part leading to the loss of information.</p>
<h3 id="python-explicit-data-type-conversion">Python Explicit Data Type Conversion</h3>
<p>Explicit conversion also known as <em>type casting</em> is when data type conversion takes place because you clearly defined it in your program. You basically force an expression to be of a specific type. The general form of an explicit data type conversion is as follows:</p>
<pre><code>(required_data_type)(expression)
</code></pre><p><strong>Note</strong>: as you can imagine, with explicit data type conversion, there is a risk of information loss since you&#39;re forcing an expression to be of a specific type.</p>
<p>With all of this in mind, you can dig into some of the commonly used explicit data type conversions...</p>
<p><a id="primitive"></a></p>
<h2 id="primitive-versus-non-primitive-data-structures">Primitive Versus Non-Primitive Data Structures</h2>
<p>Primitive data structures are the building blocks for data manipulation and contain pure, simple values of data. Python has four primitive variable types:</p>
<ul>
<li>Integers</li>
<li>Float</li>
<li>Strings</li>
<li>Boolean</li>
</ul>
<p>Non-primitive data structures don&#39;t just store a value, but rather a collection of values in various formats. In Python, you have the following non-primitive data structures:</p>
<ul>
<li>Lists</li>
<li>Tuples</li>
<li>Dictionary</li>
<li>Sets</li>
</ul>
<p>You can learn more about them with DataCamp&#39;s <a href="https://www.datacamp.com/community/tutorials/data-structures-python">Data Structures in Python Tutorial</a></p>
<p><a id="intfloat"></a></p>
<h2 id="integer-and-float-conversions">Integer and Float Conversions</h2>
<p>Integers and floats are data types that deal with numbers.</p>
<p>To convert the integer to float, use the <code>float()</code> function in Python. Similarly, if you want to convert a float to an integer, you can use the <code>int()</code> function.</p>
<pre><code class="lang-python">a_int = 3
b_int = 2

# Explicit type conversion from int to float
c_float_sum = float(a_int + b_int)
print(c_float_sum)
</code></pre>
<pre><code>5.0
</code></pre><pre><code class="lang-python">a_float = 3.3
b_float = 2.0

# Explicit type conversion from float to int
c_int_sum = int(a_float + b_float)
print(c_int_sum)

c_float_sum = a_float + b_float
print(c_float_sum)
</code></pre>
<pre><code>5
5.3
</code></pre><p><a id="strings"></a></p>
<h2 id="data-type-conversion-with-strings">Data Type Conversion with Strings</h2>
<p>A string is a collection of one or more characters (letters, numbers, symbols). You may need to convert strings to numbers or numbers to strings fairly often. Check out how you can do this using the <code>str()</code> function:</p>
<pre><code class="lang-python">price_cake = 15
price_cookie = 6
total = price_cake + price_cookie
print(&quot;The total is: &quot; + total  + &quot;$&quot;)
</code></pre>
<pre><code>---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

&lt;ipython-input-12-54bd76b9b4bd&gt; in &lt;module&gt;()
      2 price_cookie = 6
      3 total = price_cake + price_cookie
----&gt; 4 print(&quot;The total is: &quot; + total  + &quot;$&quot;)


TypeError: Can&#39;t convert &#39;int&#39; object to str implicitly
</code></pre><p>The example above gives a <code>TypeError</code>, informing that the compiler cannot implicitly convert an integer value to a string.</p>
<p>It might seem intuitive to you what the program should do here. However, the compiler might not always be sure, and that&#39;s why it provides a mechanism with the explicit type casting so that you can clearly state what you want. Let&#39;s see the same example with type casting:</p>
<pre><code class="lang-python">price_cake = 15
price_cookie = 6
total = price_cake + price_cookie
print(&quot;The total is: &quot; + str(total)  + &quot;$&quot;)
</code></pre>
<pre><code>The total is: 21$
</code></pre><p>It works the same way when you convert float to string values.</p>
<p>In Python, you can also convert strings to integer and float values whenever possible. Let&#39;s see what this means:</p>
<pre><code class="lang-python">price_cake = &#39;15&#39;
price_cookie = &#39;6&#39;

# String concatenation
total = price_cake + price_cookie
print(&quot;The total is: &quot; + total + &quot;$&quot;)

# Explicit type conversion to integer
total = int(price_cake) + int(price_cookie)
print(&quot;The total is: &quot; + str(total)  + &quot;$&quot;)
</code></pre>
<pre><code>The total is: 156$
The total is: 21$
</code></pre><p>Lets break down the code.</p>
<p><code>price_cake</code> and <code>price_cookie</code> are initially strings. Then, you need to find the total, which means they have to be first converted to their corresponding integer values. Else, the compiler will assume the operation that you want is a string concatenation rather than a numerical addition. You then need to put this value into the final display string and consequently need to convert the total to a string so as to concatenate it with the rest of the display message.</p>
<p>Hopefully, this example helps you to see the importance of data type conversions. Even though this is a very small example of data type conversion, you can already see how useful it can be.</p>
<p><strong>Note</strong>: did you notice the &quot;whenever possible&quot; when trying to convert a string to integers or float? This is because it is not always possible to convert strings to numbers and apply numerical operations on them. The compiler is aware of this and will, therefore, give you an error when you try to do so. Check out the example below:</p>
<pre><code class="lang-python">price_cake = &#39;fifteen&#39;
price_cookie = &#39;six&#39;
total = int(price_cake) + int(price_cookie)
</code></pre>
<pre><code>---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

&lt;ipython-input-25-80591b8accda&gt; in &lt;module&gt;()
      1 price_cake = &#39;fifteen&#39;
      2 price_cookie = &#39;six&#39;
----&gt; 3 total = int(price_cake) + int(price_cookie)


ValueError: invalid literal for int() with base 10: &#39;fifteen&#39;
</code></pre><p><a id="tuplelist"></a></p>
<h2 id="type-conversion-to-tuples-and-lists">Type Conversion to Tuples and Lists</h2>
<p>Just like with integers and floats, you can also convert lists to tuples and tuples to lists.</p>
<p>Remember what tuple and lists are? Lists and Tuples in Python are used to store a collection of homogeneous items. The difference between tuples and list is that tuples are immutable, which means once defined you cannot delete, add or edit any values inside it.</p>
<p>Why would you convert lists to tuples?</p>
<p>That&#39;s because tuples are immutable data type and allows substantial optimization to the programs that you create.</p>
<p>And why would you convert tuples to lists?</p>
<p>Maybe you want to make changes to the initial tuple. Thus, you can convert them to lists and then make the change, then convert them back to tuples.</p>
<p>You can use the <code>tuple()</code> function to return a tuple version of the value passed to it and similarly the <code>list()</code> function to convert to a list:</p>
<pre><code class="lang-python">a_tuple = (1,2,3,4,5,6,7,8,9,10)
b_list = [1,2,3,4,5]

print(tuple(b_list))
print(list(a_tuple))
</code></pre>
<pre><code>(1, 2, 3, 4, 5)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
</code></pre><p>You can also convert a string into a list or a tuple.</p>
<pre><code class="lang-python">dessert = &#39;Cake&#39;

# Convert the characters in a string to individual items in a tuple
print(tuple(dessert))

# Convert a string into a list
dessert_list = list(dessert)
dessert_list.append(&#39;s&#39;)
print(dessert_list)
</code></pre>
<pre><code>(&#39;C&#39;, &#39;a&#39;, &#39;k&#39;, &#39;e&#39;)
[&#39;C&#39;, &#39;a&#39;, &#39;k&#39;, &#39;e&#39;, &#39;s&#39;]
</code></pre><p><a id="binocthex"></a></p>
<h2 id="binary-octal-and-hexadecimal-integers-in-python">Binary, Octal, and Hexadecimal Integers in Python</h2>
<p>The number systems refer to the number of symbols or characters used to represent any numerical value. The number system that you typically use every day is called <em>decimal</em>. In the decimal system, you use ten different symbols: 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. With these ten symbols, you can represent any quantity. Binary, hexadecimal, and octal refer to different number systems.</p>
<p>When you run out of symbols, you go to the next digit placement. In the decimal system, to represent one higher than 9, you use 10 meaning one unit of ten and zero units of one. However, it is different in other number systems. For example, when you consider a binary system which only uses two symbols: 0 and 1, when you run out of symbols, you need to go to the next digit placement. So this is how you will count in binary: 0, 1, 10, 11, 100, 101 and so on.</p>
<p>Let&#39;s check out some of the number systems in more detail in the next sections.</p>
<p><a id="binary"></a></p>
<h3 id="convert-to-binary-number">Convert to Binary Number</h3>
<p>Binary integers are the number represented with base two. Which means in the binary number system, there are only two symbols used to represent numbers: 0 and 1. When you count up from zero in binary, you run out of symbols more quickly: 0, 1, ???</p>
<p>Furthermore, there are no more symbols left. You do not go to the digit 2 because 2 doesn&#39;t exist in binary. Instead, you use a special combination of 1s and 0s. In a binary system, 1000 is equal to 8 in decimal. In binary, you use powers of two, which means 8 is basically: (1<em>(2^3)) + (0</em>(2^2)) + (0<em>(2^1)) + (0</em>(2^0))= 8. The position of the 1 and 0 defines the power to which 2 is to be raised to.</p>
<p>Let&#39;s see this with a more complex example to make it clear:</p>
<pre><code>Binary Number = 1001111
Decimal value = (1*(2^6)) + (0*(2^5)) + (0*(2^4)) + (1*(2^3)) + (1*(2^2)) + (1*(2^1)) + (1*(2^0))
              = 79
</code></pre><p>In Python, you can simply use the <code>bin()</code> function to convert from a decimal value to its corresponding binary value.</p>
<p>And similarly, the <code>int()</code> function to convert a binary to its decimal value. The <code>int()</code> function takes as second argument the base of the number to be converted, which is 2 in case of binary numbers.</p>
<pre><code class="lang-python">a = 79

# Base 2(binary)
bin_a = bin(a)
print(bin_a)
print(int(bin_a, 2)) #Base 2(binary)
</code></pre>
<pre><code>0b1001111
79
</code></pre><p><a id="octal"></a></p>
<h3 id="conversion-to-octal">Conversion to Octal</h3>
<p>Octal is another number system with fewer symbols to use than the conventional decimal number system. Octal is base eight, which means that eight symbols are used to represent all the quantities. They are 0, 1, 2, 3, 4, 5, 6 and 7. After 7 is 10, since 8 doesn&#39;t exist.</p>
<p>Just like you used powers of two in binary to determine the value of a number, you will use powers of 8 since this is base eight.</p>
<p>In a binary system, 10 is equal to 8 in octal. Let&#39;s break it down: (1<em>(8^1)) + (0</em>(8^0))= 8.</p>
<p>A more complex example:</p>
<pre><code>Octal Number = 117
Decimal value = (1*(8^2)) + (1*(8^1)) + (7*(8^0))
              = 79
</code></pre><p>In Python, you can use the <code>oct()</code> function to convert from a decimal value to its corresponding octal value. Alternatively, you can also use the <code>int()</code> function along with the correct base which is 8 for the octal number system.</p>
<pre><code class="lang-python">a = 79

# Base 8(octal)
oct_a = oct(a)
print(oct_a)
print(int(oct_a, 8))
</code></pre>
<pre><code>0o117
79
</code></pre><p><a id="hexa"></a></p>
<h3 id="type-conversion-to-hexadecimal">Type conversion to Hexadecimal</h3>
<p>Hexadecimal is a base 16 number system. Unlike binary and octal, hexadecimal has six additional symbols that it used beyond the numbers found in the decimal number system.</p>
<p>But what comes after 9?</p>
<p>Once additional symbols are needed beyond the normal numerical values, letters are to be used. So in hexadecimal, the total list of symbols used is: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E and F.</p>
<p>Using the same example as earlier:</p>
<pre><code>Hexadecimal Number = 4F
Decimal value = (4*(16^1)) + (F*(16^0))
              = 79
</code></pre><p>In Python, you can use the <code>hex()</code> function to convert from a decimal value to its corresponding hexadecimal value, or the <code>int()</code> function with base 16 for the hexadecimal number system.</p>
<pre><code class="lang-python">a = 79

# Base 16(hexadecimal)
hex_a = hex(a)
print(hex_a)
print(int(hex_a, 16))
</code></pre>
<pre><code>0x4f
79
</code></pre><h2 id="you-made-it-">You Made It!</h2>
<p>Congrats! You learned about data type conversions in Python primarily using built-in methods. This will definitely help you work around various data types, providing you with more flexibility when writing your programs.</p>
<p>DataCamp has an excellent course on <a href="https://www.datacamp.com/courses/intermediate-python-for-data-science">Intermediate Python for Data Science</a> where you can learn how to plot your data using matplotlib, work with dictionaries and the famous pandas DataFrame. You will also see how you can control the flow of your program with loops. There is also a case study at the end of the course where you will get to apply all that you have learned and put your knowledge to work! The first chapter is free, so go ahead and try it out!</p>


https://www.datacamp.com/community/tutorials/python-data-type-conversion

In this Python tutorial, you'll tackle implicit and explicit data type conversion of primitive and non-primitive data structures with the help of code examples!

Understand a variety of data type conversions in Python. Learn about primitive & non-primitive data structures with the help of code examples today!

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Operationalizing Machine Learning with MLOps (with Alessya Visnjic)

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Operationalizing Machine Learning with MLOps

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