<h1 id="sql-commands-tutorial-for-data-scientists">SQL Commands Tutorial for Data Scientists</h1>
<p>As a data scientist or data analyst, data is naturally at the heart of the job. There are many sources from which you might gather your data. Often the data lives in a SQL database, in which case an understanding of SQL query commands can be essential in order to perform this role successfully. This article will introduce you to some of the more basic commands, right up to more advanced operations that will serve you well when working as a data analyst or data scientist.</p>
<p>Simple Data Retrieval</p>
<ul>
<li>SELECT FROM</li>
<li>DISTINCT</li>
</ul>
<p>Data Retrieval with Simple Conditions</p>
<ul>
<li>WHERE</li>
<li>ORDER BY</li>
<li>LIMIT</li>
</ul>
<p>Aggregations</p>
<ul>
<li>GROUP BY &amp; COUNT( )</li>
<li>SUM( )</li>
<li>AVG( ) &amp; HAVING</li>
<li>MIN( ) &amp; Alias</li>
</ul>
<p>Joining Tables</p>
<ul>
<li>INNER JOIN (JOIN)</li>
</ul>
<p>Manipulating Data Types</p>
<ul>
<li>CAST( )</li>
<li>ROUND( )</li>
</ul>
<p>Complex Conditions</p>
<ul>
<li>CASE statements</li>
<li>Subqueries</li>
<li>Common Table Expressions (CTEs)</li>
<li>Windows functions: OVER( ) &amp; PARTITION BY</li>
</ul>
<p>In order to understand the commands listed above, we are going to use the open source DVD rental database to show you how these commands can be used. You can find the entity relationship diagram (EDR) <a target="_blank" href="https://www.postgresqltutorial.com/wp-content/uploads/2018/03/printable-postgresql-sample-database-diagram.pdf">here</a>.</p>
<p>For the purpose of this article, we have set up the database locally and completed all queries using <a target="_blank" href="https://www.enterprisedb.com/downloads/postgres-postgresql-downloads">pgAdmin4</a>, so we are using postgreSQL. Many if not all the commands should work in other SQL servers. The <a target="_blank" href="https://www.datacamp.com/community/tutorials/installing-postgresql-windows-macosx">installation of postgreSQL</a> and the setting up of the database itself is outside the scope of this article; we encourage you to check out other resources such as DataCamp’s <a target="_blank" href="https://www.datacamp.com/community/tutorials/beginners-introduction-postgresql?">Beginner&#39;s Guide to PostgreSQL</a> for advice on these processes.</p>
<h1 id="simple-data-retrieval">Simple Data Retrieval</h1>
<h1 id="select-from">SELECT FROM</h1>
<p>The simplest example of retrieval is to get all the content from a specific table in the database.
If we wanted to know all the film categories that are in the ‘Category’ table, you could try inputting this command:</p>
<pre><code>SELECT *
FROM category
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image9_zyfudf.png"></center>


<p>Here we’ve specified the table name after FROM. Since we want all the content in the table, we’ve used * after SELECT to specify that we want everything.</p>
<h1 id="distinct">DISTINCT</h1>
<p>There are cases in which we are only interested in knowing unique values. For example, what if we want to know all the MPAA ratings from the film table? Using <b>DISTINCT</b> allows us to choose unique values alone:</p>
<pre><code>SELECT DISTINCT(rating)
FROM film
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image4_nyxfur.png"></center>

<h1 id="data-retrieval-with-simple-conditions">Data Retrieval with Simple Conditions</h1>
<h1 id="where">WHERE</h1>
<p>There are also situations in which we only want to retrieve specific entries that meet specific conditions. We can provide conditions that have to be met before returning the data using the <b>WHERE</b> clause. In essence, we are filtering entries given the condition. For example, what if we want to know only movies that are rated PG-13?</p>
<pre><code>SELECT title
FROM film
WHERE rating = &#39;PG-13&#39;
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image12_zqzlbh.png"></center>

<p>Additionally, we can figure out the list of movies that have a rental rating of 4.5 or higher:</p>
<pre><code>SELECT title, rental_rate
FROM film
WHERE rental_rate &gt;= 4.5
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image7_fvoyrh.png"></center>

<h1 id="order-by">ORDER BY</h1>
<p>Ordering the returned result is often useful for us to understand the data better. How about if we want the same list above with PG-13 movies, but ordered by the movie length from longest to shortest, and the corresponding movie length? Using <b>ORDER BY</b>, this is easily accomplished:</p>
<pre><code>SELECT title, length
FROM film
WHERE rating = &#39;PG-13&#39;
ORDER BY length DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image10_cklqha.png"></center>

<h1 id="limit">LIMIT</h1>
<p>Additionally, sometimes we are only interested in a limited number of entries. Here, we are actually only interested in the top 10 longest movies that are PG-13, so we can use the <b>LIMIT</b> clause.</p>
<pre><code>SELECT title, length
FROM film
WHERE rating = &#39;PG-13&#39;
ORDER BY length DESC
LIMIT 10
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image8_h5jren.png"></center>

<h1 id="aggregations">Aggregations</h1>
<h1 id="group-by-count-">GROUP BY &amp; COUNT( )</h1>
<p>Aggregations are often used to get a summary of the dataset to gain insights. Aggregations are often used with the <b>GROUP BY</b> clause. For example, if we need to know how many rentals each customer has rented so far, we can count the number of rentals and order the results with the most rentals on top.</p>
<pre><code>SELECT customer_id, COUNT(rental_id)
FROM rental
GROUP BY customer_id
ORDER BY COUNT(rental_id) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image11_agylng.png"></center>

<h1 id="sum-">SUM( )</h1>
<p>How about how much money each customer has spent? We can accomplish this using the SUM aggregator function. Here, we want the top spender on top.</p>
<pre><code>SELECT customer_id, SUM(amount)
FROM payment
GROUP BY customer_id
ORDER BY SUM(amount) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image17_nar8bk.png"></center>

<h1 id="avg-having">AVG( ) &amp; HAVING</h1>
<p>We can add additional conditions using HAVING after you have calculated a grouped aggregation. For example, we want to know the MAAP rating categories that have movies that are longer than 115 minutes on average?</p>
<pre><code>SELECT rating, AVG(length)
FROM film
GROUP BY rating
HAVING AVG(length) &gt; 115
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image5_wzlvp2.png"></center>

<h1 id="min-alias">MIN( ) &amp; Alias</h1>
<p>Grouping is not always necessary using an aggregation. If you want to know the shortest movie length, we can simply use the MIN function. We also want the column to read as ‘shortest_movie_length’, which can be accomplished by assigning an alias using ‘AS’.</p>
<pre><code>SELECT MIN(length) AS shortest_movie_length
FROM film
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image3_kvqpfb.png"></center>

<p>You can also use an alias for table names. This is useful when you are working with long table names, especially when joining tables (see below). We also want to be mindful of what kind of alias we use as it can drastically change the readability of the code .</p>
<p>There are other aggregation functions that are useful, and you can learn more about them in the <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-sql">Introduction to SQL course</a>.</p>
<h1 id="joins">Joins</h1>
<h1 id="inner-join-join-">INNER JOIN (JOIN)</h1>
<p>You have probably noticed that there are only limited things we can do by looking at a single table. We often want to look at the data from multiple tables. This is where we use JOINs. We can join tables based on a common key. For example, if we were looking for films that are not in English:</p>
<pre><code>SELECT film.title, language.name
FROM film
JOIN language
    ON film.language_id = language.language_id
WHERE language.name != &#39;English&#39;
</code></pre><p>(Please note that there are no movies other than those in English in this database. You will get an empty table with this query right above.)</p>
<p>You can also join multiple tables. For example, below we want to know the categories and their corresponding average rating. We’ve also returned values ordered by the average rating, with the highest first.</p>
<pre><code>SELECT category.name, AVG(film.rental_rate) AS average_rating
FROM film
JOIN film_category
    ON film.film_id = film_category.film_id
JOIN category
    ON film_category.category_id = category.category_id
GROUP BY category.name
ORDER BY average_rating DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102296/image6_eemfze.png"></center>

<p>There are many types of joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, OUTER JOIN, CROSS JOIN. Here, we used JOIN, which by default is an INNER JOIN in which you get back entries only if they are present in both tables. If you would like to learn more about the differences between these joins, you can take a look at the <a target="_blank" href="https://www.datacamp.com/courses/joining-data-in-sql">Joining Data in SQL</a> course.</p>
<h1 id="changing-data-types">Changing Data Types</h1>
<h1 id="cast-">CAST( )</h1>
<p>In the example used above where we calculated dollar amounts, you might have noticed that SQL only sees the values as numbers. We can change these to dollar amounts using the CAST function:</p>
<pre><code>SELECT customer_id, CAST(SUM(amount) AS money)
FROM payment
GROUP BY customer_id
ORDER BY SUM(amount) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image2_p13a5u.png"></center>

<p>Money is not the only thing that we can change values into. We can change numbers into floats, text, and even into date and time.</p>
<h1 id="round-">ROUND( )</h1>
<p>Similarly, we can also round numbers. In the example above in which we calculated the average rental rating, there are many decimal points in the calculated values. We can use the ROUND function to round to two decimals:</p>
<pre><code>SELECT category.name, ROUND(AVG(film.rental_rate), 2) AS average_rating
FROM film
JOIN film_category
    ON film.film_id = film_category.film_id
JOIN category
    ON film_category.category_id = category.category_id
GROUP BY category.name
ORDER BY average_rating DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102296/image14_jsv1oa.png"></center>

<h1 id="complex-conditions">Complex Conditions</h1>
<h1 id="case-statement">CASE Statement</h1>
<p>You can imagine that by combining many of the above functions and clauses, you can perform a fairly complex query. However, there are situations in which the basics are not sufficient. For example, if we wanted to add a column with movie-length categories. We can label movies as &#39;Short Film&#39; if less than 50 minutes, &#39;Long Film&#39; if more than 150 minutes, and &#39;Medium Film&#39; for the rest. We can accomplish this by using a CASE statement:</p>
<pre><code>SELECT title, length,
CASE
    WHEN length &lt; 50 THEN &#39;Short Film&#39;
    WHEN length &gt; 150 THEN &#39;Long Film&#39;
    ELSE &#39;Medium Film&#39;
END AS Movie_Length_Category
FROM film
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image16_hkiwbn.png"></center>

<p>As you can see, the result is returned when the specified condition is met. This is similar to ‘if-else’ statements in programming languages.</p>
<h1 id="subqueries">Subqueries</h1>
<p>We may now want to know which movies are the longest in length, and their associated titles. For this, we can use the WHERE clause that we introduced above, just with a more complex condition:</p>
<pre><code>SELECT title, length
FROM film
WHERE length = (SELECT max(length)
                FROM film)
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image15_vuxcbi.png"></center>

<p>Complex queries can happen using subqueries. In this example, we want to know the list of movie titles that have a higher rental rating than the average rental rating of all comedy films:</p>
<pre><code>SELECT title, rental_rate
FROM film
WHERE rental_rate &gt;
(SELECT AVG(film.rental_rate) AS Average_Rating
FROM film
JOIN film_category AS cat_id
    ON film.film_id=cat_id.film_id
JOIN category AS cat
    ON cat_id.category_id=cat.category_id
GROUP BY cat.name, cat_id.category_id
HAVING cat.name = &#39;Comedy&#39;)
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image7_fvoyrh.png"></center>

<p>In this example, we first find the average film rental rating for comedy films. The value found in this query (seen inside the parentheses above) is then used as a condition in the WHERE clause in which we can find the movie titles.</p>
<h1 id="common-table-expressions-ctes-">Common Table Expressions (CTEs)</h1>
<p>In a more complex example, we can use common table expression (CTE) to save a temporary table from which we extract the information needed. For this example, we want to know the average number of rentals each staff processes per week.</p>
<pre><code>WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT staff_id, AVG(rental_numbers)
FROM weekly_rentals
GROUP BY staff_id
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image18_d8ruge.png"></center>

<p>In the above case, we are saving the number of rentals per week in a temporary table called &#39;weekly_rentals&#39; upon which we can then perform another query to get the final result.</p>
<h1 id="window-functions">Window Functions</h1>
<p>Window functions are another set of SQL skills that are important and can take you to the next level. Even though window functions are considered intermediate to advanced level skills, many data analysts and scientists believe them to be essential for their jobs. It is therefore worthwhile for you to spend some time getting familiar with them.</p>
<p>OVER( ) &amp; PARTITION BY
Here, let’s say we want to find out each staff&#39;s weekly number of rentals and the overall total number of rentals, along with the staff ID and the week number. We can reuse the CTE generated in the above example, as shown below:</p>
<pre><code>WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT *, SUM(rental_numbers) OVER() AS total_rentals
FROM weekly_rentals
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image1_zizhng.png"></center>

<p>Having OVER in the code specifies that it’s a window function. Here, we are saying the window function (SUM here) is applied to every row. We can also use the window function along with PARTITION BY to apply the window function at specified ‘partitions’. Let&#39;s say that we now want to figure out the total number of rentals per week instead of the overall number of rentals (in addition to the other columns. Again, in this example below we are using the CTE generated <a target="_blank" href="https://docs.google.com/document/d/1J1UeJEvyURNO-NDlekfvmbSpoGyb9dDL_SyeWt25PXo/edit#heading=h.qr6vhyayaxfv">before</a>.</p>
<pre><code>WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT *, SUM(rental_numbers) OVER(PARTITION BY week) AS total_weekly_rentals
FROM weekly_rentals
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image13_dujtb3.png"></center>

<p>The additional column contains the total weekly rental numbers instead of the running total.</p>
<p>We introduced only a couple of simple examples of window functions here that are important for being a data analyst or scientist in many companies. We encourage you to learn more about these commands using the links provided. You could also check out the <a target="_blank" href="https://www.datacamp.com/courses/intermediate-sql">Intermediate SQL course</a> to learn or brush up your SQL skills. Additionally, you can practice your skills by taking an open source database and trialling these different commands while thinking about relevant questions to answer.</p>


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<p>The&nbsp;data.table R package provides an enhanced version of <code>data.frame</code> that allows you to do <a href="https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping" target="_blank">blazing fast</a> data manipulations. The <code>data.table</code> R package is being used in different fields such as finance and genomics&nbsp;and is especially useful for those of you that are working with large data sets (for example, 1GB to 100GB in RAM).</p>

<p>Although its typical syntax structure is not hard to master, it is unlike other things you might have seen in R. Hence the reason to create this cheat sheet. <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet" target="_blank" title="data.table tutorial">DataCamp</a>&rsquo;s <code>data.table</code> cheat sheet is a quick reference for doing data manipulations in R with the <code>data.table</code> R package and syntax</p>

<p>The cheat sheet will guide you from doing simple data manipulations using <code>data.table</code>&rsquo;s basic <code>i, j, by </code> syntax, to chaining expressions, to using the famous <code>set()</code>-family.</p>

<p>(To practice using the <code>data.table</code> package, try the free first chapter of this <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet">course</a>, which includes exercises in subsetting a <code>data.table</code> and manipulating data with <code>data.table</code>&#39;s&nbsp;<code>by</code>, <code>.N</code>, and <code>j</code> syntax. )</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/008e41e8-7526-4bbc-806b-8c828dfb81d4" target="_blank" title="data table cheat sheet"><img alt="data.table R package tutorial cheat sheet" class="aligncenter wp-image-437 size-medium" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839980/The_data.table_R_Package_Cheat_Sheet_fdbgnt.png" style="width: 800px; height: 551px;" /></a></p>


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<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>
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<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>


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<p>54% of the respondents from the&nbsp;<a href="http://www.oreilly.com/data/free/2016-data-science-salary-survey.csp">O&#39;Reilly Data Science Salary Survey</a>&nbsp;indicated that they used Python as a data science tool. Nobody can deny that Python has been on the rise in the data science industry&nbsp;and it certainly seems that it&#39;s here to stay.</p>

<p>This rise in popularity in the industry, the long gone infancy of Python packages for data analysis, the low and gradual learning curve and the fact that it is a&nbsp;fully fledged programming language are only a couple of reasons that make Python an exceptional tool for data science.</p>

<p>Although Python is a very readable language, you might still be able to use some help.</p>

<p>That&#39;s enough reason for DataCamp to make a Python cheat sheet for data science, especially&nbsp;for&nbsp;beginners. It&nbsp;can serve as a quick reference for those of you who are just beginning their&nbsp;data science journey or it can serve as a guide to make it easier to learn about and use Python.</p>

<p>This cheat sheet is free additional material that complements DataCamp&#39;s&nbsp;<a href="https://www.datacamp.com/courses/intro-to-python-for-data-science/">Intro to Python for Data Science course</a>, where you learn by doing.</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/0eff0330-e87d-4c34-88d5-73e80cb955f2" target="_blank"><img alt="Python For Data Science Cheat Sheet For Beginners" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620781328/Python_For_Data_Science_-_A_Cheat_Sheet_For_Beginners_igzrny.png" /></a></p>

<p><strong>(Above is the printable version of this cheat sheet)</strong></p>

<br>

<p>This Python cheat sheet will guide you through variables and data types, Strings, Lists, to eventually land at the fundamental package for scientific computing with Python, Numpy.</p>

<div id="scoped-content">
<style type="text/css">:target:before {
content:"";
display:block;
height:150px;
margin:-150px 0 0;
}

h3 {font-weight:normal;
}
h4 {
	font-weight: lighter;
}
table {
    width: 100%;
    table-layout: fixed;
}
td {color: black; font-size: 16px; padding-top: 2px }
th {
    height: 50px;
}
th, td {
		padding: 1.5px;
    text-align: left;}
tr:hover {background-color: #f5f5f5}
</style>
<h2>Install Python</h2>
<a href="https://www.continuum.io/downloads">Download Anaconda</a>

<h2>Libraries</h2>

<h3>Import libraries</h3>

<table>
	<tbody>
		<tr>
			<td><code>import numpy</code></td>
		</tr>
		<tr>
			<td><code>import numpy as np</code></td>
		</tr>
	</tbody>
</table>

<h3>Selective import</h3>

<table>
	<tbody>
		<tr>
			<td><code>from math import pi</code></td>
		</tr>
	</tbody>
</table>

<h2>Asking for Help</h2>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; help(str)</code></td>
		</tr>
	</tbody>
</table>

<h2>Variables and Data Types</h2>

<h3>Variable Assignment</h3>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; x=5</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x</code></td>
		</tr>
		<tr>
			<td><code>5</code></td>
		</tr>
	</tbody>
</table>

<h3>Calculations With Variables</h3>

<table>
	<tbody>
		<tr>
			<td>Sum of two variables</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x+2</code></td>
		</tr>
		<tr>
			<td><code>7</code></td>
		</tr>
		<tr>
			<td>Subtraction of two variables</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x-2</code></td>
		</tr>
		<tr>
			<td><code>3</code></td>
		</tr>
		<tr>
			<td>Multiplication of two variables</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x*2 </code></td>
		</tr>
		<tr>
			<td><code>10</code></td>
		</tr>
		<tr>
			<td>Exponentiation of a variable</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x**2</code></td>
		</tr>
		<tr>
			<td><code>25</code></td>
		</tr>
		<tr>
			<td>Remainder of a variable</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x%2</code></td>
		</tr>
		<tr>
			<td><code>1</code></td>
		</tr>
		<tr>
			<td>Division of a variable</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; x/float(2)</code></td>
		</tr>
		<tr>
			<td><code>2.5</code></td>
		</tr>
	</tbody>
</table>

<h3>Types and Type Conversion</h3>

<table>
	<tbody>
		<tr>
			<td>Variables to strings</td>
		</tr>
		<tr>
			<td><code>str()</code></td>
		</tr>
		<tr>
			<td><code>&#39;5&#39;, &#39;3.45&#39;, &#39;True&#39;</code></td>
		</tr>
		<tr>
			<td>Variables to integers</td>
		</tr>
		<tr>
			<td><code>int()</code></td>
		</tr>
		<tr>
			<td><code>5, 3, 1</code></td>
		</tr>
		<tr>
			<td>Variables to floats</td>
		</tr>
		<tr>
			<td><code>float()</code></td>
		</tr>
		<tr>
			<td><code>5.0, 1.0</code></td>
		</tr>
		<tr>
			<td>Variables to booleans</td>
		</tr>
		<tr>
			<td><code>bool()</code></td>
		</tr>
		<tr>
			<td><code>True, True, True</code></td>
		</tr>
	</tbody>
</table>

<h2>Strings</h2>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_string = &#39;thisStringIsAwesome&#39;</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string</code></td>
		</tr>
		<tr>
			<td><code>&#39;thisStringIsAwesome&#39;</code></td>
		</tr>
	</tbody>
</table>

<h3>String Operations</h3>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_string * 2</code></td>
		</tr>
		<tr>
			<td><code>&#39;thisStringIsAwesomethisStringIsAwesome&#39;</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string + &#39;Innit&#39;</code></td>
		</tr>
		<tr>
			<td><code>&#39;thisStringIsAwesomeInnit&#39;</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; &#39;m&#39; in my_string</code></td>
		</tr>
		<tr>
			<td><code>&#39;True&#39;</code></td>
		</tr>
	</tbody>
</table>

<h3>Selecting String Characters</h3>



<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_string[3]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string[4:9]</code></td>
		</tr>
	</tbody>
</table>

<h3>String Methods</h3>

<table>
	<tbody>
		<tr>
			<td>String to uppercase</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string.upper()</code></td>
		</tr>
		<tr>
			<td>String to lowercase</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string.lower()</code></td>
		</tr>
		<tr>
			<td>Count String elements</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string.count(&#39;w&#39;)</code></td>
		</tr>
		<tr>
			<td>Replace String elements</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string.replace(&#39;e&#39;, &#39;i&#39;)</code></td>
		</tr>
		<tr>
			<td>Strip whitespace from ends</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_string.strip()</code></td>
		</tr>
	</tbody>
</table>

<h2>Lists</h2>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; a = &#39;is&#39;</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; b = &#39;nice&#39;</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list = [&#39;my&#39;, &#39;list&#39;, a, b]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list2 = [[4,5,6,7], [3,4,5,6]]</code></td>
		</tr>
	</tbody>
</table>

<h3>Selecting List Elements</h3>


Subset

<table>
	<tbody>
		<tr>
			<td>Select item at index 1</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[1]</code></td>
		</tr>
		<tr>
			<td>Select 3rd last item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[-3]</code></td>
		</tr>
	</tbody>
</table>

<h4>Slice</h4>

<table>
	<tbody>
		<tr>
			<td>Select items at index 1 and 2</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[1:3]</code></td>
		</tr>
		<tr>
			<td>Select items after index 0</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[1:]</code></td>
		</tr>
		<tr>
			<td>Select items before index 3</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[:3]</code></td>
		</tr>
		<tr>
			<td>Copy my_list</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list[:]</code></td>
		</tr>
	</tbody>
</table>

<h4>Subset Lists of Lists</h4>

<table>
	<tbody>
		<tr>
			<td>my_list[list][itemOfList]</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list2[1][0]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list2[1][:2]</code></td>
		</tr>
	</tbody>
</table>

<h3>Lists Operations</h3>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_list + my_list</code></td>
		</tr>
		<tr>
			<td><code>[&#39;my&#39;, &#39;list&#39;, &#39;is&#39;, &#39;nice&#39;, &#39;my&#39;, &#39;list&#39;, &#39;is&#39;, &#39;nice&#39;]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list * 2</code></td>
		</tr>
		<tr>
			<td><code>[&#39;my&#39;, &#39;list&#39;, &#39;is&#39;, &#39;nice&#39;, &#39;my&#39;, &#39;list&#39;, &#39;is&#39;, &#39;nice&#39;]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list2 &gt; 4</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; True</code></td>
		</tr>
	</tbody>
</table>

<h3>List Methods</h3>

<table>
	<tbody>
		<tr>
			<td>Get the index of an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.index(a)</code></td>
		</tr>
		<tr>
			<td>Count an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.count(a)</code></td>
		</tr>
		<tr>
			<td>Append an item at a time</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.append(&#39;!&#39;)</code></td>
		</tr>
		<tr>
			<td>Remove an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.remove(&#39;!&#39;)</code></td>
		</tr>
		<tr>
			<td>Remove an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; del(my_list[0:1])</code></td>
		</tr>
		<tr>
			<td>Reverse the list</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.reverse()</code></td>
		</tr>
		<tr>
			<td>Append an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.extend(&#39;!&#39;)</code></td>
		</tr>
		<tr>
			<td>Remove an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.pop(-1)</code></td>
		</tr>
		<tr>
			<td>Insert an item</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.insert(0,&#39;!&#39;)</code></td>
		</tr>
		<tr>
			<td>Sort the list</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_list.sort()</code></td>
		</tr>
	</tbody>
</table>

<h2>Numpy Arrays</h2>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_list = [1, 2, 3, 4]</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array = np.array(my_list)</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_2darray = np.array([[1,2,3],[4,5,6]])</code></td>
		</tr>
	</tbody>
</table>

<h3>Selecting Numpy Array Elements</h3>



<h4>Subset</h4>

<table>
	<tbody>
		<tr>
			<td>Select item at index 1</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array[1]</code></td>
		</tr>
		<tr>
			<td><code>2</code></td>
		</tr>
	</tbody>
</table>

<h4>Slice</h4>

<table>
	<tbody>
		<tr>
			<td>Select items at index 0 and 1</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array[0:2]</code></td>
		</tr>
		<tr>
			<td><code>array([1, 2])</code></td>
		</tr>
	</tbody>
</table>

<h4>Subset 2D Numpy arrays</h4>

<table>
	<tbody>
		<tr>
			<td>my_2darray[rows, columns]</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_2darray[:,0]</code></td>
		</tr>
		<tr>
			<td><code>array([1, 4])</code></td>
		</tr>
	</tbody>
</table>

<h4>Numpy Array Operations</h4>

<table>
	<tbody>
		<tr>
			<td><code>&gt;&gt;&gt; my_array &gt; 3</code></td>
		</tr>
		<tr>
			<td><code>array([False, False, False, True], dtype=bool)</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array * 2</code></td>
		</tr>
		<tr>
			<td><code>array([2, 4, 6, 8])</code></td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array + np.array([5, 6, 7, 8])</code></td>
		</tr>
		<tr>
			<td><code>array([6, 8, 10, 12])])</code></td>
		</tr>
	</tbody>
</table>

<h4>Numpy Array Functions</h4>

<table>
	<tbody>
		<tr>
			<td>Get the dimensions of the array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array.shape</code></td>
		</tr>
		<tr>
			<td>Append items to an array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.append(other_array)</code></td>
		</tr>
		<tr>
			<td>Insert items in an array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.insert(my_array, 1, 5)</code></td>
		</tr>
		<tr>
			<td>Delete items in an array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.delete(my_array,[1])</code></td>
		</tr>
		<tr>
			<td>Mean of the array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.mean(my_array)</code></td>
		</tr>
		<tr>
			<td>Median of the array</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.median(my_array)</code></td>
		</tr>
		<tr>
			<td>Correlation coefficient</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; my_array.corrcoef()</code></td>
		</tr>
		<tr>
			<td>Standard deviation</td>
		</tr>
		<tr>
			<td><code>&gt;&gt;&gt; np.std(my_array)</code></td>
		</tr>
	</tbody>
</table>

<h2>To download this cheat sheet, click below</h2>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/0eff0330-e87d-4c34-88d5-73e80cb955f2" target="_blank"><img alt="Python For Data Science Cheat Sheet For Beginners" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1620781328/Python_For_Data_Science_-_A_Cheat_Sheet_For_Beginners_igzrny.png" /></a></p>

<p>If you&#39;re interested in more cheat sheets, check out our <a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh cheat sheet</a> for data visualization in Python and our&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas cheat sheet</a> for data manipulation in Python.&nbsp;</p>

<p>Do you want to learn more? Complete the <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science/">Intro to Python for Data Science course</a>&nbsp;today!&nbsp;</p>
</div>


https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics

This handy one-page reference presents the Python basics that you need to do data science

Python For Data Science - A Cheat Sheet For Beginners

<p>Python is the most popular programming language today and is widely used across verticals from software and web development to game development, IoT and more. More importantly, it’s become the de facto programming language for data science and machine learning. This blog post will outline why and when to use Python, how to install it, basic Python syntax and functionality, an introduction to simple data analysis, and more.</p>
<h2 id="when-and-why-to-use-python">When and why to use Python</h2>
<p>Python is a highly versatile language with many use cases across verticals. Just like many programming languages, Python is open source and free to use, and contains a large library of tools and packages (more on packages later!) that simplify working with a variety of tasks. Here’s are some verticals where Python is useful:</p>
<h3 id="desktop-and-mobile-software-development">Desktop and Mobile Software Development</h3>
<p>Whether building simple software applications like calculators, or document editors and social networks. Python offers a plethora of tools to prototype, develop, and run desktop or mobile applications.</p>
<h3 id="web-development">Web Development</h3>
<p>Did you know that web applications like Uber, Netflix, and Spotify <a href="https://yourstory.com/mystory/top-twelve-python-applications-real-world-know">were built with Python</a>? There are a variety of web frameworks on Python that simplify website development.</p>
<h3 id="game-development">Game Development</h3>
<p>This may come as a surprise, but games such as The Sims 4 and Civilization IV were built with Python. Many Python libraries exist that streamline prototyping and development of games.</p>
<h3 id="internet-of-things-iot-">Internet of Things (IoT)</h3>
<p>The rise of embedded systems and the internet of things (IoT) has catalyzed innovation and business process improvement across domains. Python offers a set of packages that make it easy to design, create, and deploy an IoT device on <a href="https://www.arduino.cc/">Arduino</a>, <a href="https://www.raspberrypi.org/">Raspberry Pi</a>, and other <a href="https://www.home-assistant.io/integrations/">IoT devices</a> such as Alexa and Google Home.</p>
<h3 id="data-science-and-machine-learning">Data Science and Machine Learning</h3>
<p>Python’s rise in popularity can be attributed to its rich set of packages and tools for data science and machine learning. Python can be used across the data science workflow, from exploratory data analysis, data pre-processing, model building and deployment, and finally to results interpretation and communication. Since Python is used across the data science workflow, it is used by a variety of data roles from data analysts, data scientists, data engineers, machine learning engineers, and more.</p>
<h2 id="how-to-install-python">How to install Python</h2>
<p>Python can be installed in a variety of avenues, depending on if you have a Mac or Windows, or if you want to install it with Anaconda. Since Windows and Mac are installed similarly, the following instructions can apply to both operating systems. If you want a deeper look into installing Python for data science, <a href="https://www.datacamp.com/community/tutorials/python-developer-set-up">check out this article</a>.</p>
<h3 id="mac-and-windows">Mac and Windows</h3>
<p>Go to the Python website, which will send you to the downloads page as seen in the screenshot below. Choose Windows or Mac and click on the download button to access the installer package.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python1_j4xkeo.png" /></center><br></p>
<h3 id="anaconda">Anaconda</h3>
<p>One of the more convenient ways to install Python for data science is by installing an <a href="https://www.anaconda.com/products/individual">Anaconda</a> distribution, which also installs all relevant data science packages. Follow the link above to install it on either Mac, Windows, or Linux. For a deep dive on installing Anaconda, check out these blog posts for <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">MacOs</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a>.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617985265/DLanaconda_x9jrol.png" /></center><br></p>
<h2 id="how-to-get-started-with-python-for-data-science">How to get started with Python for data science</h2>
<p>In this section, we will cover how to open Python on Mac and Windows, how to launch the terminal, the basics of Python like print statements and operators, variables and assigning values, data types, packages and how to install them, as well as the popular data science library <code>pandas</code> and how to install it.</p>
<h3 id="1-launching-python">1. Launching Python</h3>
<h4 id="launching-terminal">Launching Terminal</h4>
<p>To launch Python, you will first need to launch your terminal. For both Mac and Windows, you can simply search it on your computer. On Mac, the easiest way is to do a Spotlight Search, which is the magnifying glass icon on the upper right corner of your desktop.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python2_uasjil.png" /></center><br></p>
<p>Similarly, for Windows, you will search for the command prompt through the start menu, as described in a DataCamp article <a href="https://www.datacamp.com/community/tutorials/running-a-python-script">How to Run Python Scripts</a>. You can also launch the new Windows Terminal after installing and searching for it as mentioned on the Windows blog post <a href="https://devblogs.microsoft.com/commandline/introducing-windows-terminal/">Introducing Windows Terminal</a>. After you launch it, here&#39;s what you will see:</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python3_pv1jvj.png" /></center><br></p>
<h4 id="how-to-open-python-on-mac-windows">How to open Python on Mac + Windows</h4>
<p>After Python installation is complete, you can launch your terminal by simply typing the word ‘python’. This will start up Python and show you the current version you have installed. The three arrows point to the right means you are ready to code in Python!</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python4_gajrzb.png" /></center><br></p>
<h3 id="2-basic-python-syntax">2. Basic Python Syntax</h3>
<p>In the following syntax commands below, you will type the command and then hit <code>enter</code> or <code>return</code> on your keyboard to see the results.</p>
<h4 id="print-statements">Print statements</h4>
<p>Next, you can start your first command in Python. We will start off with the <code>print()</code> statement, which will be typing the word print, followed by parenthesis. The output will be typed within those same parentheses and enclosed by quotes.</p>
<pre><code class="lang-python">print(&quot;Hello, World&quot;)
</code></pre>
<pre><code>Hello, World
</code></pre><h4 id="operators">Operators</h4>
<p>Then, you can perform operations, like +, -, and *.  As you can see, the + adds the two numbers together, the - subtracts the second number from the first number, the * multiples both numbers together.</p>
<pre><code class="lang-python">5 + 5
</code></pre>
<pre><code>10
</code></pre><pre><code class="lang-python">5 - 5
</code></pre>
<pre><code>0
</code></pre><pre><code class="lang-python">5 * 5
</code></pre>
<pre><code>25
</code></pre><h4 id="booleans">Booleans</h4>
<p>The next command is to compare two values. In the case below, we will look at comparing the same two numbers, five and five. The command is asking if 5 is greater than 5, which of course, it is not, so the returned output is <code>False</code>. For booleans, we expect the output to be either true or false.</p>
<pre><code class="lang-python">5 &gt; 5
</code></pre>
<pre><code>False
</code></pre><h4 id="creating-variables-and-assigning-them">Creating variables and assigning them</h4>
<p>In this section, we see that you can assign a number to the name <code>variable_1</code>, and then return it by calling that same <code>variable_1</code> to get the value that you had assigned it:</p>
<pre><code class="lang-python">variable_1 = 100
</code></pre>
<pre><code class="lang-python">variable_1
</code></pre>
<pre><code class="lang-python">100
</code></pre>
<h4 id="lists">Lists</h4>
<pre><code class="lang-python">my_list = [&#39;potato&#39;, 3, 4.02]
</code></pre>
<pre><code class="lang-python">my_list[0]
</code></pre>
<pre><code>‘potato’
</code></pre><p>A list is a data structure in which values can be stored and accessed. The neat thing about lists is that they do not all need to be the same data type. Below, we can see that we have a str, int, and a float, respectively. In order to access the items in the list so that they appear in the output, you can type your list name followed by brackets with the place of the item you want. For example, we append [0] to our listname to return the 1st element in our list as seen below.</p>
<p>For a further exploration into Python lists, see the article <a href="https://www.datacamp.com/community/tutorials/python-list-index">Python List Index()</a>.</p>
<h4 id="data-types">Data types</h4>
<p>Python values can have a variety of forms, including ones like <code>int</code>, <code>float</code>, and <code>str</code>. For example, <code>int</code> represents integers, <code>float</code> represents decimal numbers, and <code>str</code> represents “strings”, or more generally text.</p>
<p>Using the <code>type()</code> function, we will be able to check the type of our values. In this case, we’re looking at three values, including 10, 10.06, and the word ‘tutorial’. The first is an integer, while the second is a float, and the last is a string. Below, we can see the command and its respected data type output.</p>
<pre><code class="lang-python">type(10)
</code></pre>
<pre><code>&lt;class &#39;int&#39;&gt;
</code></pre><pre><code class="lang-python">type(10.06)
</code></pre>
<pre><code>&lt;class &#39;float&#39;&gt;
</code></pre><pre><code class="lang-python">type(&#39;tutorial&#39;)
</code></pre>
<pre><code>&lt;class &#39;str&#39;&gt;
</code></pre><p>For a more in-depth look at data types and how to convert them, you can read the following blog, <a href="https://www.datacamp.com/community/tutorials/python-data-type-conversion">Python Data Type Conversion Tutorial</a>.</p>
<h4 id="packages-and-installation">Packages and installation</h4>
<p><a href="https://docs.python.org/3/installing/index.html">Packages</a> are powerful tools that have preserved functionalities. For example, <code>pandas</code> is a package that lets you work with tabular data in Python—more on that below. Think of them as apps you download on Python that provide a specific functionality. You will want to exit out of Python in your terminal by typing Ctrl + D, and then typing the following command below (for Mac users). Here we’re installing a sample package called SomePackage just to illustrate the installation.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984300/python10_uwyf92.png" /></center><br></p>
<h4 id="installing-pandas-and-jupyter-notebook">Installing pandas and Jupyter Notebook</h4>
<p><a href="https://www.datacamp.com/community/tutorials/pandas">pandas</a> is perhaps the most used data science library in Python. It allows you to work with tabular data in Python and perform a variety of operations. In some sense, you can consider it as the Excel or spreadsheets of Python. The <a href="https://pandas.pydata.org/">developers of pandas</a> describe it as “a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.”</p>
<p>Jupyter notebook is a web application that interprets code, saves text, and displays visualizations. Both Jupyter and pandas are available to launch if you downloaded Anaconda, or you can install them by running the following commands in terminal:</p>
<pre><code class="lang-python">pip install pandas
</code></pre>
<pre><code class="lang-python">pip install notebooks
</code></pre>
<h3 id="3-basic-data-science-with-python">3. Basic data science with Python</h3>
<p>In this section, we’ll cover how to import a popular Python package while assigning it an alias, how to read in a CSV file, how to show your data in something called a DataFrame, and how to visualize a column by using a certain plotting function. We have already learned how to install pandas above, and because Jupyter Notebook is easy to use and works across several computer types, we will use that web application to display the following data science functions with pandas.</p>
<p>You can launch a Jupyter notebook by simply typing <code>jupyter notebook</code> in your terminal.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617990202/jupyter_hello_world2_su37ik.png" /></center><br></p>
<h4 id="importing-packages-with-an-alias">Importing packages with an alias</h4>
<p>Before we begin working with pandas, we need to import it in our environment. Similar to launching an app after installing it, importing a package is simple. We can import pandas and assign an alias to it that is shorter than the original name so that it is easier to work with this package. We will assign pandas as the letters <code>pd</code> instead for shorthand notation—this change is purely optional.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<h4 id="importing-your-data-by-reading-in-a-csv-file">Importing your data by reading in a CSV file</h4>
<p>After you have imported pandas as <code>pd</code>, you can use <code>pd</code> to read in your data, which can be a CSV file. This function is to enclose the CSV file path in quotes, within the parentheses of <code>pd.read_csv()</code>. You can then store your CSV to a dataframe with the = operation. You will now be able to print out your dataframe, but you will only want to see the first rows instead of all of it. The data, in this case, is from past year’s stock performance with the fields date, open, high, low, close, volume, and name, all of which describe characteristics of daily popular stocks. However, it is important to note that you can use any CSV file here to illustrate importing data.</p>
<h4 id="dataframe-head-function">Dataframe head function</h4>
<p>Showing the first few rows of your data is important to check to see if you have imported the correct CSV, as well as seeing if data looks good in general. The way to do this method is by calling your dataframe and attaching the <code>.head()</code> method to the end of it. This function will return the first 5 rows of your data by default. If you want to show more, you can put that value in the parentheses of <code>.head()</code>.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<pre><code class="lang-python">stocks_df = pd.read.csv(&#39;/documents/data/stock_data.csv&#39;)
</code></pre>
<pre><code class="lang-python">stocks_df.head()
</code></pre>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.35_PM_jtlhgz.png" /></center><br></p>
<h4 id="visualize-column-with-the-plot-function">Visualize column with the plot function</h4>
<p>The <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html">.plot()</a> method  is useful for displaying your dataframe’s columns. In this example, I am showing the x-axis as the “date” column, and the “close” column amount as the y-axis, visualized by a line chart.</p>
<pre><code class="lang-python">stocks_df.plot(x=&quot;date&quot;, y=&quot;close&quot;)
</code></pre>
<pre><code>&lt;AxesSubplot:xlabel=&#39;date&#39;&gt;
</code></pre><p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.20_PM_zkb0am.png" /></center><br></p>
<h3 id="4-suggestions-for-practical-applications-">4. Suggestions for practical applications:</h3>
<p>This overview of Python for data science has only just scratched the surface. There are plenty of practical applications that you can employ with Python in data science across several industries, including customer churn, sales forecasting, image classification, product classification, sentiment analysis, credit card fraud detection, and recommendation systems. DataCamp’s <a href="https://www.datacamp.com/community/tutorials/recommender-systems-python">Beginner Tutorial: Recommender Systems in Python</a> demonstrates how to successfully recommend movies to people based on a variety of factors with the use of machine learning. Another popular data science use case in the financial industry is credit card fraud detection, which is explained in <a href="https://www.datacamp.com/community/news/credit-card-fraud-detection-using-machine-learning-algorithm-8dyh3fefvrb">Credit Card Fraud Detection Using Machine Learning Algorithm</a>. As you can see, not only are there plenty of specific data science use cases, but there are also industry specific use cases.</p>
<p>For a more in-depth introduction to Python, check out DataCamp’s <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python</a> course, or find out more about <a href="https://www.datacamp.com/community/blog/how-to-learn-python">learning Python on our blog</a>.</p>


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

Get a step-by-step guide on how to install Python and use it for basic data science functions.

Python Tutorial for Beginners | DataCamp

Python Tutorial for Beginners

https://www.datacamp.com/community/tutorials/pandas

Are you ready to begin your pandas journey? Here’s a step-by-step guide on how to get started.

pandas Tutorial for Beginners

https://www.datacamp.com/community/tutorials/sql

Are you ready to begin your SQL journey? Here’s a step-by-step guide on how to get started.

SQL Tutorial for Beginners | DataCamp

SQL Tutorial for Beginners

<h2 id="what-is-a-jupyter-notebook-">What is a Jupyter Notebook?</h2>
<p>&#39;Jupyter&#39; is an acronym meaning Julia, Python, and R. These programming languages were the original use case for Jupyter. Now though, Jupyter Notebook supports many other languages (Source: <a href="https://medium.com/@blessedmarcel1/how-to-install-jupyter-notebook-on-mac-using-homebrew-528c39fd530f">How to install Jupyter Notebook on Mac using Homebrew</a>).</p>
<p>The main components of the whole environment are, on the one hand, the notebooks themselves and the application. On the other hand, you also have a notebook kernel and a notebook dashboard.</p>
<p>&quot;&#39;notebook&#39; denotes documents that contain both code and rich text elements, such as figures, links, equations, etc. Because of the mix of code and text elements, these documents are the ideal place to bring together an analysis description and its results. They also can be executed to perform the data analysis in real-time&quot; —<a href="https://medium.com/@blessedmarcel1/how-to-install-jupyter-notebook-on-mac-using-homebrew-528c39fd530f">How to install Jupyter Notebook on Mac using Homebrew</a>.</p>
<p>Let&#39;s look at these components in more detail.</p>
<h3 id="what-is-the-jupyter-notebook-app-">What is the Jupyter Notebook App?</h3>
<p>As a server-client application, the Jupyter Notebook App allows you to edit and run your notebooks via a web browser. The application can be executed on a PC without Internet access, or it can be installed on a remote server, where you can access it through the Internet.</p>
<h2 id="advantages-of-a-jupyter-notebook">Advantages of a Jupyter Notebook</h2>
<ul>
<li><p>Notebook has the ability to re-run individual code snippets, and it provides you the flexibility of modifying them before re-running.</p>
</li>
<li><p>You can deploy a Jupyter Notebook on a remote server and access it from your local web browser.</p>
</li>
<li><p>You can insert notes and documentation to your code in a Jupyter Notebook in various formats like markdown, latex, and HTML.</p>
</li>
<li><p>Headings in a Jupyter Notebook are also supported, which is very useful when you want to provide heading &amp; sub-heading to signify various important parts of your codebase. It helps improve the readers&#39; experience.</p>
</li>
<li><p>Use a Jupyter Notebook as cells.</p>
</li>
<li><p>Ability to share a Jupyter Notebook in various formats like <code>.py</code>, <code>.html</code>, <code>.md</code>, <code>.pdf</code>, etc.</p>
</li>
<li><p>For <em>machine learning</em> folks, Jupyter Notebook is very helpful since it allows for Data Visualization. Jupyter Notebook supports visualizations and enables you to render graphs and charts. These are generated from codes with the help of modules like Matplotlib, Plotly, or Bokeh.</p>
</li>
</ul>
<h2 id="how-to-install-the-notebook-using-pip-">How to Install the Notebook Using <code>pip</code></h2>
<p>If you don&#39;t want to install Anaconda, you just have to make sure that you have the latest version of <code>pip</code>. If you have installed Python, you will typically already have it.</p>
<p>What you do need to do is upgrade <code>pip</code> but before, that let&#39;s install and create a virtual environment:</p>
<pre><code class="lang-python"># On OS X or Linux
pip3 install virtualenv
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup1_woihqi.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Now let&#39;s create a virtual environment <code>jupyter</code> and activate it!</p>
<pre><code class="lang-python">virtualenv jupyter
source jupyter/bin/activate
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup2_a5yndv.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Let&#39;s upgrade <code>pip</code> now, but first, let&#39;s check the already installed <code>pip</code> version.</p>
<pre><code class="lang-python">pip3 --version
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465230/jup3_nnffd7.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>From the above output, you can see that we have <code>pip</code> version <code>20.2.1</code>.</p>
<p>Let&#39;s upgrade that:</p>
<pre><code class="lang-python"># On Windows
python -m pip install -U pip setuptools
# On OS X or Linux
pip3 install -U pip setuptools
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup4_qgtyl2.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Once you have <code>pip</code>, you can just run the below command to install a Jupyter Notebook, which would take a while to install.</p>
<pre><code class="lang-python">pip3 install jupyter
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup5_rgihme.png" alt="Installing Jupyter Notebook">
</figure>
</center>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup6_cztm0d.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Great! So you are all set to run and start using Jupyter Notebook but before that, let&#39;s also look at how you can install jupyter using Anaconda.</p>
<h2 id="how-to-install-jupyter-notebook-using-anaconda">How to Install Jupyter Notebook Using Anaconda</h2>
<p>One of the requirements here is Python, Python 3.3, or greater since Python 2.7 has reached the end of its life (EOL) on January 1st, 2020. The general recommendation is that you use the Anaconda distribution to install both Python and the Jupyter Notebook.</p>
<p>Jupyter Notebook in Anaconda comes pre-installed, meaning that you do not have to install it explicitly. All you need to install is Anaconda, and Jupyter Notebook would already be present inside it.</p>
<p>Anaconda&#39;s advantage is that you have access to over 720 packages that can easily be installed with Anaconda&#39;s conda, a package, dependency, and environment manager. You can follow the instructions for the installation of Anaconda here for <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Mac</a> or <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a>.</p>
<p>Is something not clear? You can always read up on the Jupyter installation instructions <a href="https://jupyter.readthedocs.io/en/latest/install.html">here</a>.</p>
<p>If you need more information about installing packages in Python, you can go to <a href="https://packaging.python.org/installing/">this page</a>.</p>
<h2 id="getting-started-with-jupyter-notebook">Getting Started With Jupyter Notebook</h2>
<p>Now that you have learned all about what Jupyter Notebooks are, their benefits, and how to install them on your machines, let&#39;s put them to some practical use!</p>
<h3 id="launching-jupyter-notebook">Launching Jupyter Notebook</h3>
<p>To launch your Jupyter notebook, simply type the below command!</p>
<pre><code class="lang-python">jupyter notebook
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup7_hsg0fi.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Once you do this, the Jupyter notebook should open up the below tab for you inside your web browser:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup9_rv1uc2.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Note that the Jupyter Notebook will open using the below URL:</p>
<p><a href="http://localhost:8888/tree">http://localhost:8888/tree</a></p>
<p>However, the port number <code>8888</code> might change if a process is using it or a second Jupyter Notebook session runs in the background.</p>
<p>In some cases, the Jupyter Notebook might not open up on its own, in which case you can simply copy one of the URLs that are generated when you run the command <code>jupyter notebook</code>, which in our case is:</p>
<p><a href="http://localhost:8889/?token=e41173caeda552cdcab1bcc775e2463ac510b8847f73604b">http://localhost:8889/?token=e41173caeda552cdcab1bcc775e2463ac510b8847f73604b</a></p>
<p>Now, let&#39;s open a Python3 Notebook inside Jupyter by clicking the <code>New</code> tab and select <code>Python3</code>:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup10_mthrq0.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>And finally, we have our very first Python3 Jupyter Notebook:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup11_pfmhbo.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="adding-heading-and-notes-in-jupyter-notebook">Adding Heading and Notes in Jupyter Notebook</h3>
<p>In the drop-down menu shown below, you have four options:</p>
<ul>
<li><strong>Code</strong> – In the code cell, you write the Python code.</li>
</ul>
<ul>
<li><strong>Markdown</strong> – In this cell type, you can add headings, notes, documentation, etc.</li>
</ul>
<ul>
<li><strong>Raw NBConvert</strong> – This cell type can be used to render different code formats into HTML or LaTeX. This information is stored in the notebook metadata and converted appropriately.</li>
</ul>
<p>Let&#39;s look at the <code>markdown</code> example for adding headings and bullet points:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup12_gki9jz.png" alt="Installing Jupyter Notebook">
</figure>
</center>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup13_njmhvx.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="sharing-a-jupyter-notebook">Sharing a Jupyter Notebook</h3>
<p>This is one of the great and cool features of a Jupyter Notebook.</p>
<p>Go to the <code>Files</code> menu, and you’ll see a <code>Download As</code> option where you can download the Jupyter Notebook in almost nine different ways. The most commonly used is either a <code>.ipynb</code> or <code>.py</code> file:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup14_v2gzvf.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="installing-a-python-module-in-jupyter-notebook">Installing a Python Module in Jupyter Notebook</h3>
<p>Installing a Python module inside Jupyter Notebook is as easy as installing it from the terminal. All you need to do is use an <code>!</code>(exclamation) mark at the start of the command, as shown below:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup15_prcdrd.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="writing-a-python-function-in-jupyter-notebook">Writing a Python Function in Jupyter Notebook</h3>
<p>Let&#39;s write a Python function that generates random integers between 0 and 100 five times, and each time the size of the vector increments by one.</p>
<p>First, let&#39;s import <code>numpy</code>.</p>
<pre><code class="lang-python">import numpy as np
</code></pre>
<p>Next, the <a href="https://www.datacamp.com/community/tutorials/numpy-random"><code>random</code> function</a> which takes two inputs, <code>low</code> and <code>size</code> of the array, and finally returns the <code>num</code>.</p>
<pre><code class="lang-python">def random(low, size):
    num = np.random.randint(low=low ,size=size)
    return num
</code></pre>
<pre><code class="lang-python">for i in range(5):
    print(random(100, i+1))
</code></pre>
<pre><code>[80]
[19 89]
[66 72 77]
[41 55 82 10]
[95 90 53 99 46]
</code></pre><h1 id="conclusion">Conclusion</h1>
<p>In this Jupyter Notebook tutorial, you were introduced to its advantages. You further learned how to install Jupyter Notebook using <code>pip</code> and Anaconda. Finally, we covered a few practical use-cases like how to make headings, write python code, and how to share a Jupyter Notebook.</p>
<p>To learn more about Jupyter Notebook, please check out this complete comprehensive tutorial <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook#WhatIs">Jupyter Notebook Tutorial: The Definitive Guide</a>.</p>


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

Follow download, setup, & install steps for Jupyter Notebook using Pip & Anaconda in this tutorial. Get up &running with Jupyter Notebook today!

How to Download & Setup Install Jupyter Notebook using Pip & Anaconda

Installing Jupyter Notebook

<p>SQL Server is a relational database management system. One of the key principles of the relational database is that data is stored across multiple tables.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_564/v1603485855/join1_nitybm.png" /></center>

<p>We will need to be able to join tables together in order to extract the data we need. We use <code>primary</code> and <code>foreign</code> keys to join tables.</p>
<h2 id="primary-key">Primary Key</h2>
<p>A primary key is a column that is used to uniquely identify each row in a table. This uniqueness can be achieved by using a sequential integer as an identity column. Or sometimes, existing columns naturally contain unique values, and they can be used.</p>
<p>In the below example, we can see the first few rows from the <code>artist</code> table. It has two columns, <code>artist_id</code>, and <code>name</code>. The <code>artist_id</code> column acts as a primary key for this table, it is an integer column, and each value is different.</p>
<pre><code>+-----------+-------------------+
| artist_id | name              |
|-----------+-------------------|
| 1         | AC/DC             |
| 2         | Accept            |
| 3         | Aerosmith         |
| 4         | Alanis Morissette |
| 5         | Alice In Chains   |
+-----------+-------------------+
</code></pre><p>Now let&#39;s look at the <code>album</code> table. Can you spot the primary key? Yes, it&#39;s the <code>album_id</code> column. It&#39;s common for the primary key to be named &quot;table-name underscore id&quot;. But, you&#39;ll have noticed there is also an <code>artist_id</code> column. That also consists of integers and has the same name as the <code>artist_id</code> column in the artist table. What does this mean?</p>
<pre><code>+----------+-------------------------+-----------+
| album_id | title                   | artist_id |  
|----------+-------------------------+-----------|
| 1        | For Those About To Rock | 1         |
| 2        | Balls to the Wall       | 2         |
| 3        | Restless and Wild       | 2         |
| 4        | Let There Be Rock       | 1         |
| 5        | Big Ones                | 3         |
+----------+-------------------------+-----------+
</code></pre><h2 id="foreign-keys">Foreign Keys</h2>
<p>Well, from the previous output, it is clear that the <code>artist_id</code> in the artist table acts as a <code>Foreign_key</code> to the album table. The <code>FOREIGN KEY</code> constraint is used to prevent actions that would destroy links between tables.</p>
<ul>
<li><code>artist</code> table</li>
</ul>
<pre><code>+-----------+-------------------+
| artist_id | name              |
|-----------+-------------------|
| 1         | AC/DC             |
| 2         | Accept            |
| 3         | Aerosmith         |
| 4         | Alanis Morissette |
| 5         | Alice In Chains   |
+-----------+-------------------+
</code></pre><ul>
<li><code>album</code> table</li>
</ul>
<pre><code>+----------+-------------------------+-----------+
| album_id | title                   | artist_id |  
|----------+-------------------------+-----------|
| 1        | For Those About To Rock | 1         |
| 2        | Balls to the Wall       | 2         |
| 3        | Restless and Wild       | 2         |
| 4        | Let There Be Rock       | 1         |
| 5        | Big Ones                | 3         |
+----------+-------------------------+-----------+
</code></pre><p>This means that when the <code>artist_id</code> in the album table matches the <code>artist_id</code> in the artist table, those rows can be linked as a <code>JOIN</code>.</p>
<h2 id="joining-album-and-artist">Joining Album and Artist</h2>
<p>Rows 1 and 4 of the artist table have an <code>artist_id</code> of 1. The <code>artist_id</code> 1 in the artist table is AC/DC. So, when we join the two tables together, we return the album details from the album table, and the corresponding artist details from the artist table - joined using the <code>artist_id</code> field, which is common to both tables.</p>
<p>This is known as an <code>inner</code> join.</p>
<pre><code class="lang-SQL">SELECT
  album_id,
  title,
  album.artist_id,
  name AS artist_name
FROM album
INNER JOIN artist ON artist.artist_id = album.artist_id
WHERE album.artist_id = 1;
</code></pre>
<pre><code>+----------+-------------------------+-----------+-------------+
| album_id | title                   | artist_id | artist_name |
|----------+---------------------------------------+-----------|
| 1        | For Those About To Rock | 1         | AC/DC       |
| 4        | Let There Be Rock       | 1         | AC/DC       |
+----------+-------------------------+-----------+-------------|
</code></pre><p>We joined the album and artist tables by matching the <code>artist_id</code> from the artist table to the <code>artist_id</code> in the album table.</p>
<p>When selecting columns with the same name from different tables, you must fully qualify the column name. Otherwise, SQL Server will not know which table to <code>SELECT</code> the column from.</p>
<p>To fully qualify a column, you have to prefix it with the relevant table name, followed by a period.</p>
<h3 id="inner-join-syntax">Inner Join Syntax</h3>
<p>Here&#39;s a generic example to remind you of the syntax. When we select the relevant columns from the main table, <code>table_A</code> in this case, then select any additional column we want to join to, which in this case is <code>table_B</code>. Then we specify the join using the keywords <code>INNER JOIN</code> and <code>ON</code>, providing the necessary key columns from both the tables.</p>
<pre><code class="lang-sql">SELECT
    table_A.columnX,
    table_A.columnY,
    table_B.columnZ,
FROM table_A
INNER JOIN table_B ON table_B.foreign_key = table_A.primary_key;
</code></pre>
<h2 id="not-using-a-where-clause">Not Using a <code>WHERE</code> Clause</h2>
<p>In this example, we don&#39;t specify a <code>WHERE</code> clause, so we return ALL combinations of ALL matches between the artist and album tables, again, based on the <code>artist_id</code> column, which is common to both.</p>
<pre><code class="lang-SQL">SELECT
  album_id,
  title,
  album.artist_id,
  name AS artist_name
FROM album
INNER JOIN artist on artist.artist_id = album.artist_id;
</code></pre>
<pre><code>+----------+---------------------------------------+-----------+
| album_id | title                   | artist_id | artist_name |
|----------+---------------------------------------+-----------|
| 1        | For Those About To Rock | 1         | AC/DC       |
| 4        | Let There Be Rock       | 1         | AC/DC       |
| 2        | Balls To The Wall       | 2         | Accept      |
| 3        | Restless and Wild       | 2         | Accept      |
+----------+---------------------------------------+-----------+
</code></pre><h2 id="multiple-inner-join">Multiple Inner Join</h2>
<p>You can also join more than two tables using <code>INNER JOINS</code>. You simply provide an additional <code>INNER JOIN</code> for each combination of tables. In our code below, we join tables A and B, and also B and C.</p>
<pre><code class="lang-SQL">SELECT
  table_A.columnX,
  table_A.columnY,
  table_B.columnZ table_C columnW
FROM table_A
INNER JOIN table_B ON table_B.foreign_key = table_A.primary_key
INNER JOIN table_C ON table_C.foreign_key = table_B.primary_key;
</code></pre>
<p>To learn more about joining tables, please see this video from our course <a href="https://campus.datacamp.com/courses/introduction-to-sql-server/joining-tables?ex=1">Introduction to SQL Server</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/DRDFtejKZs8" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/introduction-to-sql-server">Introduction to SQL Server</a> course by John MacKintosh.</p>


https://www.datacamp.com/community/tutorials/sql-joins

(Tutorial) Joins in SQL

Joins in SQL

<p>SQL or structured query language is the native language for interacting with databases and is designed for exactly this purpose. It is a language of databases. A database models real-life entities like professors and universities by storing them in tables. Each table contains data from a single entity type. This reduces redundancy by storing entities only once. For example, there only needs to be one row of data containing a certain company&#39;s details. Lastly, a database can be used to model the relationship between entities.</p>
<p>If you would like to know how to learn the basics of SQL or become an expert, check out these blog posts:</p>
<ul>
<li><a href="https://www.datacamp.com/community/blog/how-to-learn-sql">How to Learn SQL</a></li>
<li><a href="https://www.datacamp.com/community/blog/blog-how-to-become-a-sql-expert">How to Become a SQL Expert</a></li>
</ul>
<h2 id="querying-databases">Querying Databases</h2>
<p>While SQL can be used to create and modify databases, this tutorial&#39;s focus will be on querying databases. A query is a request for data from a database table (or combination of tables). Querying is an essential skill for a data scientist since the data you need for your analyses will often live in databases.</p>
<p>In SQL, you can select data from a table using a <code>SELECT</code> statement. For example, the following query selects the <code>name</code> column from the <code>people</code> table:</p>
<pre><code class="lang-sql">SELECT name
FROM people;
</code></pre>
<p>In this query, <code>SELECT</code> and <code>FROM</code> are called keywords. In SQL, keywords are not case-sensitive, which means you can write the same query as:</p>
<pre><code class="lang-sql">select name
from people;
</code></pre>
<p>That said, it&#39;s good practice to make SQL keywords uppercase to distinguish them from other parts of your query, like column and table names.</p>
<p>It&#39;s also good practice to include a semicolon at the end of your query. This tells SQL where the end of your query is!</p>
<h2 id="sql-order-of-execution">SQL Order of Execution</h2>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603483591/SQL_order_of_execution_1_xd76fb_feygfm.png" /></center>

<p>Note: Your query will always need a SELECT and a FROM statement (to identify which columns you want returned from which table)—the others are optional.</p>
<h2 id="example-of-select">Example of SELECT</h2>
<p>In the following example, you will SELECT the <code>title</code> column from the <code>films</code> table.</p>
<pre><code class="lang-sql">SELECT title
FROM films;
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best/v1603483189/whatsql4_knglmg.png" /></center>

<p><a href="https://campus.datacamp.com/courses/introduction-to-sql/selecting-columns?ex=7">Try it for yourself</a>.</p>
<p>This content is taken from DataCamp’s <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course by Nick Carchedi.</p>


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