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Data Visualization Cheat Sheets

<p>Tableau is a business intelligence tool that allows you to effectively report insights through easy-to-use customizable visualizations and dashboards. This cheat sheet is designed to get you started with Tableau and covers creating your first visualization, creating a dashboard, a data story, and more.&nbsp;</p>
<p><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Tableau+Cheat+Sheet.pdf"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://res.cloudinary.com/dyd911kmh/image/upload/v1651236733/tableau_cheat_sheet_5f7309124a.png" alt="" width="500" /></a></p>
<p style="text-align: center;"><a href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Tableau+Cheat+Sheet.pdf"><em><strong>For a printable version of this cheat sheet, click on the image above</strong></em></a></p>
<h2>Why use Tableau?</h2>
<p>While there are many reasons to use Tableau &mdash; here are our top 3 reasons:</p>
<ul>
<li>Easy to use&mdash;no coding involved</li>
<li>Integrates seamlessly with any data source</li>
<li>Fast and can handle large datasets</li>
</ul>
<h2>Tableau Versions</h2>
<p>There are two main versions of Tableau:</p>
<ul>
<li><strong>Tableau Public:</strong> A free version of Tableau that lets you connect to limited data sources, create visualizations and dashboards, and publish dashboards online</li>
<li><strong>Tableau Desktop:</strong> A paid version of tableau which lets you connect to all types of data sources, allows you to save work locally, and has unlimited data sizes</li>
</ul>
<h2>Getting started with Tableau</h2>
<p>When working with Tableau, you will work with Workbooks. Workbooks contain sheets, dashboards, and stories. Similar to Microsoft Excel, a Workbook can contain multiple sheets. A sheet can be any of the following and can be accessed on the bottom left of a workbook.</p>
<ul>
<li><strong>Worksheet</strong>: A worksheet is a single view in a workbook. You can add shelves, cards, legends, visualizations, and more to a worksheet</li>
<li><strong>Dashboard</strong>: A collection of multiple worksheets used to display multiple views simultaneously</li>
<li><strong>Story</strong>: A story is a collection of multiple dashboards and/or sheets that describe a data story</li>
</ul>
<h2>The anatomy of a worksheet</h2>
<h3>The sidebar</h3>
<p>In the sidebar, you&rsquo;ll find useful panes for working with data</p>
<ul>
<li><strong>Data:</strong> The data pane on the left-hand side contains all of the fields in the currently selected data source</li>
<li><strong>Analytics:</strong> The analytics pane on the left-hand side lets you add useful insights like trend lines, error bars, and other useful summaries to visualizations</li>
</ul>
<h3>Tableau data definitions</h3>
<p>When working with data in Tableau, there are multiple definitions to be mindful of</p>
<ul>
<li><strong>Fields:</strong> Fields are all of the different columns or values in a data source or that are calculated in the workbook. They show up in the data pane and can either be dimension or measure fields</li>
<li><strong>Dimensions:</strong> A dimension is a type of field that contains qualitative values (e.g. locations, names, and departments). Dimensions dictate the amount of granularity in visualizations and help reveal nuanced details in the data</li>
<li><strong>Measures: </strong>A measure is a type of field that contains quantitative values (e.g. revenue, costs, and market sizes). When dragged into a view, this data is aggregated, which is determined by the dimensions in the view</li>
<li><strong>Data types:</strong> Every field has a data type which is determined by the type of information it contains. The available data types in Tableau include text, date values, date &amp; time values, numerical values, boolean values, geographical values, and cluster groups</li>
</ul>
<h3>The canvas</h3>
<p>The canvas is where you create your data visualizations</p>
<ul>
<li><strong>Tableau Canvas:</strong> The canvas takes up most of the screen on Tableau and is where you can add visualizations</li>
<li><strong>Rows and columns:</strong> Rows and columns dictate how the data is displayed on the canvas. When dimensions are placed, they create headers for the rows or columns while measures add quantitative values</li>
<li><strong>Marks card:</strong> The marks card allows users to add visual details such as color, size, labels, etc. to rows and columns. This is done by dragging fields from the data pane into the marks card&nbsp;</li>
</ul>
<h2>How to Create a Data Vizualization</h2>
<p class="p1"><strong>Upload a dataset&nbsp;</strong></p>
<ol class="ol1">
<li class="li1">Launch Tableau</li>
<li class="li1">In the <em>Connect</em> section, under <em>To a File</em>, press on the file format of your choice</li>
<li class="li1">For selecting an Excel file, select <em>.xlsx</em> or <em>.xlsx</em></li>
</ol>
<p class="p1"><strong>Creating your first visualization</strong></p>
<ol class="ol1">
<li class="li1">Once your file is uploaded, open a <em>Worksheet</em> and click on the <em>Data</em> pane on the left-hand side</li>
<li class="li1">Drag and drop at least one field into the <em>Columns</em> section, and one field into the <em>Rows</em> section at the top of the canvas</li>
<li class="li1">To add more detail, drag and drop a dimension into the <em>Marks</em> card (e.g. drag a dimension over the color square in the marks card to color visualization components by that dimension)</li>
<li class="li1">To a summary insight like a trendline, click on the <em>Analytics</em> pane and drag the trend line into your visualization</li>
<li class="li1">You can change the type of visualization for your data by clicking on the <em>Show Me button</em> on the top right</li>
</ol>
<h2>Data Visualization Types in Tableau</h2>
<p>Tableau provides a wide range of data visualizations. Here is a list of the most useful visualizations you have in Tableau:</p>
<ul>
<li><strong>Bar Charts:</strong> Horizontal bars used for comparing specific values across categories (e.g. sales by region)</li>
<li><strong>Stacked Bar Charts:</strong> Used to show categorical data within a bar chart (e.g., sales by region and department)</li>
<li><strong>Side by side chart: </strong>Used to compare values across categories in a bar chart format (e.g., sales by region comparing product types)</li>
<li><strong>Line Chart:</strong> Used for looking at a numeric value over time (e.g., revenue over time)</li>
<li><strong>Scatter Plot:</strong> Used to identify patterns between two continuous variables (e.g., profit vs. sales volume)</li>
<li><strong>Histogram:</strong> Used to show a distribution of data (e.g., Distribution of monthly revenue)</li>
<li><strong>Box-and-Whisper Plot:</strong> Used to compare distributions between categorical variables (e.g., distribution of revenue by region)</li>
<li><strong>Heat map: </strong>Used to visualize data in rows and columns as colors (e.g., revenue by marketing channel)</li>
<li><strong>Highlight table: </strong>Used to show data values with conditional color formatting (e.g., site traffic by marketing channel and year)</li>
<li><strong>Symbol Map:</strong> Used to show geographical data (e.g., Market size opportunity by state)</li>
<li><strong>Treemap:</strong> Used to show hierarchical data (e.g., Show how much revenue subdivisions generate relative to the whole department within an organization)</li>
<li><strong>Dual Combination:</strong> Used to show two visualizations within the same visualization (e.g., profit for a store each month as a bar chart with inventory over time as a line chart)</li>
</ul>
<h2>How to Customize Visualizations in Tableau</h2>
<p>Tableau provides a deep ability to filter, format, aggregate, customize, and highlight specific parts of your data visualizations</p>
<h3>Filtering data with highlights</h3>
<ul>
<li>Once you&rsquo;ve created a visual, click and drag your mouse over the specific portion you want to highlight</li>
<li>Once you let go, you will have the option to <em>Keep Only</em> or <em>Exclude</em> the data</li>
<li>Open the <em>Data</em> pane on the sidebar. Then, you can drag-and-drop a field into the filters card just to the left of the pane</li>
</ul>
<h3>Filtering data with filters</h3>
<ul>
<li>Open the <em>Data</em> pane on the left-hand side</li>
<li>Drag-and-drop a field you want to filter on and add it to the <em>Filters</em> card</li>
<li>Fill out in the modal how you would like your visuals to be filtered on the data</li>
</ul>
<h3>Aggregating data</h3>
<ul>
<li>Right-click on a measure field in the <em>Data</em> pane</li>
<li>Go down to <em>Default</em> properties, <em>Aggregation</em>, and select the aggregation you would like to use&nbsp;</li>
</ul>
<h3>Changing colors</h3>
<ul>
<li>Create a visualization by dragging fields into the <em>Rows</em> and <em>Columns</em> section at the top of the screen</li>
<li>Drag dimensions into the <em>Marks</em> field, specifically into the <em>Color</em> square</li>
<li>To change from the default colors, go to the upper-right corner of the color legend and select <em>Edit Colors</em>. This will bring up a dialog that allows you to select a different palette</li>
</ul>
<h3>Changing fonts</h3>
<ul>
<li>In the <em>Format</em> menu on the top ribbon, press on <em>Select Workbook</em>. This will replace the <em>Data</em> pane and allow you to make formatting decisions for the Workbook</li>
<li>From here, select the font, font size, and color</li>
</ul>
<h2>How to Create Dashboards in Tableau</h2>
<ol>
<li>Launch Tableau</li>
<li>In the <em>Connect</em> section under <em>To A File</em>, press on your desired file type</li>
<li>Select your file</li>
<li>Click the <em>New Sheet</em> at the bottom to create a new sheet</li>
<li>Create a visualization in the sheet by following the steps in the previous sections of this cheat sheet</li>
<li>Repeat steps 4 and 5 until you have created all the visualizations you want to include in your dashboard</li>
<li>Click the <em>New Dashboard</em> at the bottom of the screen</li>
<li>On the left-hand side, you will see all your created sheets. Drag sheets into the dashboard</li>
<li>Adjust the layout of your sheets by dragging and dropping your visualizations</li>
</ol>
<h2>How to Create Stories in Tableau</h2>
<ol>
<li>Click the <em>New Story</em> at the bottom of the screen</li>
<li>Change the size of the story to the desired size in the bottom left-hand corner of the screen under <em>Size</em></li>
<li>Edit the title of the story by renaming the story. To do this, right-click on the story sheet at the bottom and press <em>Rename</em></li>
<li>A story is made of story points, which lets you cycle through different visualizations and dashboards</li>
<li>To begin adding to the story, add a story point from the left-hand side. You can add a blank story point</li>
<li>To add a summary text to the story, click <em>Add a caption</em> and summarize the story point</li>
<li>Add as many story points as you would like to finalize your data story</li>
</ol>
<h2>Learn more about Tableau</h2>
<p>Here are a few extra Tableau resources to get you started:</p>
<ul>
<li><a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals Skills track</a>&nbsp;</li>
<li><a href="https://www.datacamp.com/blog/5-tableau-tips-and-tricks-for-streamlining-your-workflow">5 Tableau Tips and Tricks for Streamlining Your Workflow</a></li>
<li><a href="https://www.datacamp.com/blog/all-about-tableau">All about Tableau</a></li>
</ul>
<p>Check out DataCamp's <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-matplotlib">Introduction to Data Visualization with Matplotlib</a> course.</p>

In this Tableau Cheat Sheet, you'll get the download on creating your first visualization, top visualizations in Tableau, creating dashboards, stories, and more.

Tableau Cheat Sheet

In this data visualization cheat sheet, you'll learn about the most common data visualizations to employ, when to use them, and their most common use-cases. 

Data Visualization Cheat Sheet

In this Power BI Cheat Sheet, you'll get the download on creating your first visualization, top visualizations in Power BI, using DAX, and more.

Power BI Cheat Sheet

<p>The tidyverse is a powerful collection of R packages that you can use for data science. They are designed to help you to transform and visualize data. All packages within this collection share an underlying philosophy and common APIs.</p>
<p>As you might know, DataCamp recently launched the <a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a> course together with David Robinson, Data Scientist at Stack Overflow.</p>
<p>Now, DataCamp has created a tidyverse cheat sheet for beginners that have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started on discovering this popular collection of packages. You must have already run into packages such as <code>ggplot2</code> and <code>dplyr</code>, so this cheat sheet will definitely come in handy!</p>
<p>It's a quick guide through the basics of manipulating and visualizing your data the powerful tools that the tidyverse has to offer in R!</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/c1fae72f-d2d7-4646-9dce-dd0f8fb5c5e8"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839345/Tidyverse_Cheat_Sheet_For_Beginners_d4lkxp.png" /></a></p>
<p>The tidyverse cheat sheet will guide you through some general information on the tidyverse, and then covers topics such as useful functions, loading in your data, manipulating it with <code>dplyr</code> and lastly, visualize it with <code>ggplot2</code>.</p>
<p>In short, everything that you need to kickstart your data science learning with R!</p>
<p>Do you want to learn more? Also check out and star the <a href="https://www.datacamp.com/courses/exploratory-data-analysis-in-r-case-study">Exploratory Data Analysis in R: Case Study</a> course, also taught by David Robinson, for free now!</p>
<p>Also, don't miss out on our <a href="https://www.datacamp.com/community/tutorials/data-table-cheat-sheet">data.table cheat sheet</a> for data manipulation in R and our <a href="https://www.datacamp.com/community/data-science-cheatsheets">other cheat sheets for data science</a>.</p>
<p>&nbsp;</p>
<h2>What is Tidyverse?&nbsp;&nbsp;</h2>
<p>The tidyverse is a powerful collection&nbsp; of&nbsp; R&nbsp; packages&nbsp; that are actually&nbsp; data tools for transforming and visualizing data. All packages of the tidyverse share an underlying philosophy and common APls.</p>
<p>The core packages are:&nbsp;</p>
<ul>
<li>ggplot2, which implements the grammar of graphics. Y<span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">ou can </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">use it </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">to visualize your data.</span></li>
<li><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">dplyr is </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">a grammar of data You can </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">use it to </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">solve the </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">most </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">common data </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">manipulation </span><span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;">challenges.</span></li>
<li>tidyr helps you to create tidy data or data where each variable is in a column, each observation is a row end each value is a column, each observation is a row end each value is a cell.&nbsp;</li>
<li>readr is a fast and friendly way to read rectangular</li>
<li>purrr enhances R"s functional programming (FP)toolkit by providing a complete and consistent set of tools for working with functions and vectors.&nbsp;</li>
<li>tibble is a modern re-imaginging of the data</li>
<li>stringr provides a cohesive set of functions designed to make working with strings as easy as possible</li>
<li>forcats provide a suite of useful&nbsp; tools that solve common problems with factors.&nbsp;&nbsp;</li>
</ul>
<p><strong>You can install the complete tidyverse with:</strong></p>
<p>&gt; install.packages("tidyverse")</p>
<p><strong>Then, load the core tidyverse and make it available in your current R session by running:</strong></p>
<p>&gt; library(tidyverse)</p>
<p>Note: there are many other tidyverse packages with more specialised usage. They are not &nbsp;loaded automatically with</p>
<p>library(tidyverse),so you'll need to load each one with its own call to library().</p>
<h2><strong>Useful Functions </strong></h2>
<pre class="language-r"><code>&gt; tidyverse_conflicts() #Conflicts between tidyverse and other packages
&gt; tidyverse_deps() #List all tidyverse dependencies
&gt; tidyverse_logo() #Get tidyverse logo, using ASCII or unicode characters
&gt; tidyverse_packages() #List all tidyverse packages
&gt; tidyverse_update() #Update tidyverse packages
</code></pre>
<h2><strong>Loading in the Data&nbsp;</strong></h2>
<pre class="language-r"><code>&gt;	library(datasets) #Load the datasets package
&gt;	library(gapminder) #Load the gapminder package
&gt;	attach(iris) #Attach iris data to the R search path
</code></pre>
<h2><strong>Dplyr&nbsp;</strong></h2>
<h3><strong>Filter&nbsp;</strong></h3>
<p><strong>filter() </strong><strong>allows you </strong><strong>to select </strong><strong>a </strong><strong>subset of rows in </strong><strong>a </strong><strong>data frame.</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;% #Select iris data of species "virginica"
          filter(Species=1"virginica")

&gt;iris%&gt;% #Select iris data of species "virginica" and sepal length greater than 6. 
           filter(Species="virginica",
           Sepal.Length&gt; 6)</code></pre>
<h3><strong>Arrange&nbsp;</strong></h3>
<p><strong>arrange() sorts the observations in a dataset in ascending or descending order based on one of its variables.</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;% #Sort in ascending order of sepal length
          arrange(Sepal.Length)
&gt;iris%&gt;% #Sort in descending order of sepal length
          arrange(desc(Sepal.Length))</code></pre>
<p><strong>Combine multiple dplyr verbs in a row with the pipe operator </strong>%&gt;%:</p>
<pre class="language-r"><code>&gt;iris%&gt;% #Filter for species "virginica" then arrange in descending order of sepal length
          filter(Species="virginica") %&gt;% 
          arrange(desc(Sepal.Length))</code></pre>
<h3><strong>Mutate&nbsp;</strong></h3>
<p><strong>mutate() allows you to update or create new columns of a data frame.</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;% #Change Sepal.Length to be in millimeters
     mutate(Sepal.Length=Sepal.Length*10)
&gt;iris%&gt;% #Create a new column called SLMm
     mutate(SLMm=Sepal.Length*10)</code></pre>
<p><strong>Combine the verbs filter(), arrange(), and mutate():</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;%
     filter(Species="Virginica") %&gt;% 
     mutate(SLMm=Sepal.Length*10) %&gt;% 
     arrange(desc(SLMm))</code></pre>
<h3><strong>Summarize&nbsp;</strong></h3>
<p><strong>summarize() allows you to turn many observations into a </strong><strong>single </strong><strong>data point.</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;% #Summarize to find the median sepal length 
     summarize(medianSL=median(Sepal.Length))

&gt;iris%&gt;% #Filter for virginica then summarize the median sepal Length
     filter(Species="virginica") %&gt;% 
     summarize(medianSL=median(Sepal.Length))</code></pre>
<p><strong>You can also summarize multiple variables at once:</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;%
     filter(Species="virginica") %&gt;% 
     summarize(medianSL=median(Sepal.Length),
          maxSL=max(Sepal.Length))</code></pre>
<p><strong>group_by</strong><strong> </strong><strong>()</strong><strong> </strong><strong>allows</strong><strong> </strong><strong>you</strong><strong> </strong><strong>to</strong><strong> </strong><strong>summarize</strong><strong> </strong><strong>within groups</strong><strong> </strong><strong>instead</strong><strong> </strong><strong>of summarizing the entire dataset:</strong></p>
<pre class="language-r"><code>&gt;iris%&gt;% #Find median and max sepal length of each species 
     group_by(Species) %&gt;% 
     summarize(medianSL=median(Sepal.Length), 
     maxSL=max(Sepal.Length))

&gt;iris%&gt;% #Find median and max petal length of each species with sepal length&gt; 6 
     filter(Sepal.Length&gt;6) %&gt;%
     group_by(Species) %&gt;% 
     summarize(medianPL=median(Petal.Length), 
     maxPL=max(Petal.Length))</code></pre>
<h2><strong>ggplot2</strong></h2>
<h3><strong>Scatter Plot&nbsp;</strong></h3>
<p><strong>Scatter plots allow you to compare two variables within your data. To do this with ggplot2, you use geom_point()</strong></p>
<pre class="language-r"><code>&gt;iris_small f- iris%&gt;%
     filter(Sepal.Length &gt; 5)

&gt;ggplot(iris_small, aes(x=Petal.Length, #Compare petal width and length
     y=Petal.Width)) + geom_point()</code></pre>
<h3><strong>Additional Aesthetics </strong></h3>
<p><strong>Color</strong></p>
<div>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350288/Color_1_981312dad3.png" alt="" /></p>
</div>
<pre class="language-r"><code>&gt;ggplot(iris_small, aes(x=Petal.Length, 
     y=Petal.Width,
     calar=Species)) + 
  geom_point()</code></pre>
<p><strong>Size</strong></p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350288/Size_1_6f5efb4ee5.png" alt="" /></p>
<pre class="language-r"><code>&gt;ggplot(iris_small, aes(x=Petal.Length,
          y=Petal.Width, 
          color=Species, 
          size=Sepal.Length)) +
     geom_point()</code></pre>
<p><strong>Faceting</strong></p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350406/Faceting_1_00cf2d1613.png" alt="" /></p>
<pre class="language-r"><code>&gt;ggplot(iris_small, aes(x=Petal.Length,
          y=Petal.Width)) +
     geom_point()+ 
     facet_wrap(NSpecies)</code></pre>
<h3><strong>&nbsp;Line Plots&nbsp;</strong></h3>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350407/Line_Plots_1_78fc05a798.png" alt="" /></p>
<pre class="language-r"><code>&gt;	by_year &lt;-- gapminder %&gt;% 
          group_by(year) %&gt;%
          summarize(medianGdpPerCap=median(gdpPercap))
&gt;	ggplot(by_year, aes(x=year,
          y=medianGdpPerCap))+
     geom_line()+ 
     expand_limits(y=0)
</code></pre>
<h3>Bar Plots&nbsp;</h3>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350406/bar_plots_1_86e583e6fc.png" alt="" /></p>
<pre class="language-r"><code>&gt; by_species &lt;--iris%&gt;% 
     filter(Sepal.Length&gt;6) %&gt;% 
     group_by(Species) %&gt;% 
     summarize(medianPL=median(Petal.Length))
&gt; ggplot(by_species, aes(x=Species,
          y=medianPL)) +
     geom_col()</code></pre>
<h3>Histograms&nbsp;</h3>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350407/Histograms_1_c4c4ff2be8.png" alt="" /></p>
<pre class="language-r"><code>&gt; ggplot(iris_small, aes(x=Petal.Length))+ 
     geom_histogram()</code></pre>
<h3>Box Plots&nbsp;</h3>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1652350406/Box_plots_1_2fc6d4c0b7.png" alt="" /></p>
<pre class="language-r"><code>&gt; ggplot(iris_small, aes(x=Species,
          y=Sepal.Width))+
     geom_boxplot()
</code></pre>
<p><strong>(Click above to download a printable version or read the online version below)</strong>.</p>
<h2>Learn more about Tidyverse</h2>
<p>Here are a few extra Tidyverse resources to get you started:</p>
<p class="css-9jzl91-CourseShowPage" data-cy="course-title"><a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a></p>
<p class="css-9jzl91-CourseShowPage" data-cy="course-title"><a href="https://www.datacamp.com/courses/working-with-data-in-the-tidyverse">Working with Data in the Tidyverse</a></p>
<p class="css-9jzl91-CourseShowPage" data-cy="course-title"><a href="https://www.datacamp.com/courses/network-analysis-in-the-tidyverse">Network Analysis in the Tidyverse</a></p>
<p class="css-9jzl91-CourseShowPage" data-cy="course-title"><a href="https://www.datacamp.com/courses/machine-learning-in-the-tidyverse">Machine Learning in the Tidyverse</a></p>

This tidyverse cheat sheet will guide you through the basics of the tidyverse, and 2 of its core packages: dplyr and ggplot2! 

Tidyverse Cheat Sheet For Beginners

<p>Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn&nbsp;in the fact that it is an&nbsp;interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.&nbsp;</p>
<p>Bokeh is also known for&nbsp;enabling high-performance visual presentation of&nbsp;large data sets in modern web browsers.&nbsp;</p>
<p>For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the&nbsp;wide variety of visualization customization&nbsp;options makes this Python library an indispensable tool for your data science toolbox.</p>
<p>As you might know, DataCamp recently launched the&nbsp;<a href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh/" data-ol-has-click-handler="">Interactive Data Visualization with Bokeh</a>&nbsp;course together with&nbsp;<a href="https://www.continuum.io/people/bryan-van-de-ven" data-ol-has-click-handler="">Bryan Van de Ven</a>, Bokeh core contributor.&nbsp;</p>
<p>Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference&nbsp;or for those who&nbsp;need an extra push to get started.</p>
<p>In short, you'll see that this cheat sheet not&nbsp;only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts.&nbsp;</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/f0c1e06f-53ba-4f3b-aa9f-b196221f55a3" target="_blank" rel="noopener" data-ol-has-click-handler=""><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838040/Python_Data_Visualization-_Bokeh_Cheat_Sheet_cjcnps.png" alt="Python Bokeh Cheat Sheet" /></a></p>
<p>In no time, this Bokeh cheat sheet will make you familiar&nbsp;with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer.&nbsp;</p>
<p>Boost your Python data visualizations now with the help of Bokeh! :)</p>
<p>PS. Check out our&nbsp;<a href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh/" data-ol-has-click-handler="">Interactive Data Visualization with Bokeh</a>&nbsp;course or visit the&nbsp;<a href="http://bokeh.pydata.org/" data-ol-has-click-handler="">Bokeh documentation website</a>. Also,&nbsp;don't miss our&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/" data-ol-has-click-handler="">Pandas cheat sheet</a>&nbsp;or the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/" data-ol-has-click-handler="">Python cheat sheet for data science</a>.&nbsp;</p>
<p>&nbsp;</p>
<h2>Plotting With Bokeh</h2>
<p>The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.</p>
<p>Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1649684986/bokeh_8fc6d5f08f.png" alt="" /></p>
<p class="p1"><span class="s1">The basic</span> <span class="s1">steps</span> <span class="s1">to</span> creating plots with the <span class="s6">bokeh.</span> <span class="s6">plotting</span> interface are:</p>
<ol class="ol1">
<li class="li2"><span class="s11">Prepare some</span> <span class="s11">data</span> <span class="s14">(Python</span> <span class="s14">li</span><span class="s16">sts,</span> NumPy arrays, Pandas Data<span class="s14">Frames</span> and other sequences of values)</li>
<li class="li3">Create a new plot</li>
<li class="li4">Add renderers for your data, with visual customizations</li>
<li class="li5">Specify where to generate the output</li>
<li class="li6">Show or save the results</li>
</ol>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.plotting import figure
&gt;&gt;&gt; from bokeh.io import output_file, show
&gt;&gt;&gt; x = [1, 2, 3, 4, 5] #Step 1
&gt;&gt;&gt; y = [6, 7, 2, 4, 5]
&gt;&gt;&gt; p = figure(title="simple line example", #Step 2
x_axis_label='x',
y_axis_label='y')
&gt;&gt;&gt; p.line(x, y, legend="Temp.", line_width=2) #Step 3
&gt;&gt;&gt; output_file("lines.html") #Step 4
&gt;&gt;&gt; show(p) #Step 5</code></pre>
<h2 class="p1">1. Data&nbsp;</h2>
<p class="p1">Under the hood, your data is converted to Column Data Sources. You can also do this manually:</p>
<pre class="language-python"><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
                     columns= ['mpg', 'cyl',   'hp',   'origin'],
                      index=['Toyota', 'Fiat', 'Volvo'])


&gt;&gt;&gt; from bokeh.models import ColumnOataSource
&gt;&gt;&gt; cds_df = ColumnOataSource(df)</code></pre>
<h2>2. Plotting&nbsp;</h2>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.plotting import figure
&gt;&gt;&gt;p1= figure(plot_width=300, tools='pan,box_zoom')
&gt;&gt;&gt; p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
&gt;&gt;&gt; p3 = figure()</code></pre>
<h2>3. Renderers &amp; Visual Customizations&nbsp;</h2>
<h3>Glyphs&nbsp;</h3>
<p>Scatter Markers&nbsp;<br /><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646753503/bokeh_scattermarkers_xzd87y.png" alt="Bokeh Scatter Markers " width="300" height="147" /></p>
<pre class="language-python"><code>&gt;&gt;&gt; p1.circle(np.array([1,2,3]), np.array([3,2,1]), fill_color='white')
&gt;&gt;&gt; p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)
</code></pre>
<p>Line Glyphs&nbsp;</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646753774/Line_Glyphs_kqaudq.png" alt="Bokeh Line Glyphs " width="300" height="147" /></p>
<pre class="language-python"><code>&gt;&gt;&gt; pl.line([1,2,3,4], [3,4,5,6], line_width=2)
&gt;&gt;&gt; p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),
pd.DataFrame([[3,4,5],[3,2,1]]),
color="blue")
</code></pre>
<h3>Customized Glyphs</h3>
<p>Selection and Non-Selection Glyphs&nbsp;</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646754133/Selection_Glyphs_ek1vkm.png" alt="Selection Glyphs " width="300" height="304" /></p>
<pre class="language-python"><code>&gt;&gt;&gt; p = figure(tools='box_select')
&gt;&gt;&gt; p. circle ('mpg', 'cyl', source=cds_df,
selection_color='red',
nonselection_alpha=0.1)
</code></pre>
<p>Hover Glyphs</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646754164/hover_glyphs_om7fio.png" alt="Hover Glyphs" width="300" height="284" /></p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.models import HoverTool
&gt;&gt;&gt;hover= HoverTool(tooltips=None, mode='vline')
&gt;&gt;&gt; p3.add_tools(hover)
</code></pre>
<p>Color Mapping&nbsp;</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1646754124/Colormapping_hkepbk.png" alt="Bokeh Colormapping Glyphs " width="300" height="300" /></p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.models import CategoricalColorMapper
&gt;&gt;&gt; color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
&gt;&gt;&gt;  p3. circle ('mpg', 'cyl', source=cds_df,
            color=dict(field='origin',
                 transform=color_mapper), legend='Origin')
</code></pre>
<h2>4. Output &amp; Export&nbsp;</h2>
<h3>Notebook</h3>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.io import output_notebook, show
&gt;&gt;&gt; output_notebook()
</code></pre>
<h3>HTML&nbsp;</h3>
<p>Standalone HTML&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.embed import file_html
&gt;&gt;&gt; from bokeh.resources import CON
&gt;&gt;&gt; html = file_html(p, CON, "my_plot")

&gt;&gt;&gt; from  bokeh.io  import  output_file,  show
&gt;&gt;&gt; output_file('my_bar_chart.html',  mode='cdn')</code></pre>
<p>Components</p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.embed import components
&gt;&gt;&gt; script, div= components(p)</code></pre>
<h3>PNG</h3>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.io import export_png
&gt;&gt;&gt; export_png(p, filename="plot.png")</code></pre>
<h3>SVG&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.io import export_svgs
&gt;&gt;&gt; p. output_backend = "svg"
&gt;&gt;&gt; export_svgs(p,filename="plot.svg")</code></pre>
<h3>Legend Location&nbsp;</h3>
<p>Inside Plot Area&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; p.legend.location = 'bottom left'
</code></pre>
<p>Outside Plot Area&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.models import Legend
&gt;&gt;&gt; r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
&gt;&gt;&gt; r2 = p2.line([1,2,3,4], [3,4,5,6])
&gt;&gt;&gt; legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
&gt;&gt;&gt; p.add_layout(legend, 'right')</code></pre>
<h3>Legend Background &amp; Border&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; p.legend. border_line_color = "navy"
&gt;&gt;&gt; p.legend.background_fill_color = "white"</code></pre>
<h3>Legend Orientation&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; p.legend.orientation = "horizontal"
&gt;&gt;&gt; p.legend.orientation = "vertical"</code></pre>
<h3>Rows &amp; Columns Layout</h3>
<p>Rows</p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.layouts import row
&gt;&gt;&gt;layout= row(p1,p2,p3)
</code></pre>
<p>Columns</p>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.layouts import columns
&gt;&gt;&gt;layout= column(p1,p2,p3)</code></pre>
<p>Nesting Rows &amp; Columns&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt;layout= row(column(p1,p2), p3)
</code></pre>
<h3>Grid Layout&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.layouts import gridplot
&gt;&gt;&gt; rowl = [p1,p2]
&gt;&gt;&gt; row2 = [p3]
&gt;&gt;&gt; layout = gridplot([[p1, p2],[p3]])</code></pre>
<h3>Tabbed Layout&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; from bokeh.models.widgets import Panel, Tabs
&gt;&gt;&gt; tab1 = Panel(child=p1, title="tab1")
&gt;&gt;&gt; tab2 = Panel(child=p2, title="tab2")
&gt;&gt;&gt; layout = Tabs(tabs=[tab1, tab2])</code></pre>
<h3>Linked Plots</h3>
<p>Linked Axes&nbsp;</p>
<pre class="language-python"><code>Linked Axes
&gt;&gt;&gt; p2.x_range = p1.x_range
&gt;&gt;&gt; p2.y_range = p1.y_range</code></pre>
<p>Linked Brushing&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; p4 = figure(plot_width = 100, tools='box_select,lasso_select')
&gt;&gt;&gt; p4.circle('mpg', 'cyl' , source=cds_df)
&gt;&gt;&gt; p5 = figure(plot_width = 200, tools='box_select,lasso_select')
&gt;&gt;&gt; p5.circle('mpg', 'hp', source=cds df)
&gt;&gt;&gt;layout= row(p4,p5)</code></pre>
<h2>5. Show or Save Your Plots&nbsp;&nbsp;</h2>
<pre class="language-python"><code>&gt;&gt;&gt; show(p1)
&gt;&gt;&gt; show(layout)
&gt;&gt;&gt; save(p1)</code></pre>
<p>&nbsp;</p>
<p>If you are interested in learning more, check out DataCamp&rsquo;s <a href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh" data-ol-has-click-handler="">Interactive Data Visualization with Bokeh</a>&nbsp;course!&nbsp;</p>

A handy cheat sheet to interactive plotting and statistical charts with Bokeh.

Python Data Visualization: Bokeh Cheat Sheet

<p>Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.&nbsp;</p>
<p>For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend&nbsp;to build more plotting integration with Matplotlib as time goes on.</p>
<p>However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib. &nbsp;&nbsp;</p>
<p>DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.&nbsp;</p>
<p>(By the way, if you want to get started with this Python package, you might want to consider our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">Matplotlib tutorial</a>.)</p>
<p>You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.&nbsp;</p>
<p>Check out the infographic by clicking on the button below:</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/e1a8f39d-71ad-4d13-9a6b-618fe1b8c9e9" target="_blank" rel="noopener"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838238/Matplotlib_Cheat_Sheet-_Plotting_in_Python_htbtut.png" alt="Python Matplotlib cheat sheet" /></a></p>
<p>With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.</p>
<p>What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the <a href="http://matplotlib.org/gallery.html">Matplotlib Gallery</a>, the <a href="http://matplotlib.org/2.0.0/index.html">documentation</a>&nbsp;and our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">tutorial</a>. &nbsp;&nbsp;</p>
<p>Also, don't miss out on our other cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-scipy-cheat-sheet/">SciPy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">Numpy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas</a>&nbsp;and the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics</a>.</p>
<p>&nbsp;</p>
<p><span style="font-size: 18pt;"><strong>Matplotlib</strong></span>&nbsp;</p>
<p>Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.</p>
<h2>Prepare the Data&nbsp;</h2>
<h3>1D Data&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; x = np.linspace(0, 10, 100)
&gt;&gt;&gt; y = np.cos(x)
&gt;&gt;&gt; z = np.sin(x)</code></pre>
<h3>2D Data or Images&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; data = 2 * np.random.random((10, 10))
&gt;&gt;&gt; data2 = 3 * np.random.random((10, 10))
&gt;&gt;&gt; Y, X = np.mgrid[-3:3:100j, -3:3:100j]
&gt;&gt;&gt; U = 1 X** 2 + Y
&gt;&gt;&gt; V = 1 + X Y**2
&gt;&gt;&gt; from matplotlib.cbook import get_sample_data
&gt;&gt;&gt; img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))</code></pre>
<h2>Create Plot</h2>
<pre class="language-python"><code>&gt;&gt;&gt; import matplotlib.pyplot as plt</code></pre>
<h3>Figure&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; fig = plt.figure()
&gt;&gt;&gt; fig2 = plt.figure(figsize=plt.figaspect(2.0))</code></pre>
<h3>Axes&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; fig.add_axes()
&gt;&gt;&gt; ax1 = fig.add_subplot(221) #row-col-num
&gt;&gt;&gt; ax3 = fig.add_subplot(212)
&gt;&gt;&gt; fig3, axes = plt.subplots(nrows=2,ncols=2)
&gt;&gt;&gt; fig4, axes2 = plt.subplots(ncols=3)</code></pre>
<h2>Save Plot&nbsp;</h2>
<pre class="language-python"><code>&gt;&gt;&gt; plt.savefig('foo.png') #Save figures
&gt;&gt;&gt; plt.savefig('foo.png',  transparent=True) #Save transparent figures</code></pre>
<h2>Show Plot</h2>
<pre class="language-python"><code>&gt;&gt;&gt; plt.show()</code></pre>
<h2>Plotting Routines&nbsp;</h2>
<h2>1D Data&nbsp;</h2>
<pre class="language-python"><code>&gt;&gt;&gt; fig, ax = plt.subplots()
&gt;&gt;&gt; lines = ax.plot(x,y) #Draw points with lines or markers connecting them
&gt;&gt;&gt; ax.scatter(x,y) #Draw unconnected points, scaled or colored
&gt;&gt;&gt; axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
&gt;&gt;&gt; axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
&gt;&gt;&gt; axes[1,1].axhline(0.45) #Draw a horizontal line across axes
&gt;&gt;&gt; axes[0,1].axvline(0.65) #Draw a vertical line across axes
&gt;&gt;&gt; ax.fill(x,y,color='blue') #Draw filled polygons
&gt;&gt;&gt; ax.fill_between(x,y,color='yellow') #Fill between y values and 0</code></pre>
<h3>2D Data&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; fig, ax = plt.subplots()
&gt;&gt;&gt; im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
      vmin=-2,
      vmax=2)
&gt;&gt;&gt; axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
&gt;&gt;&gt; axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
&gt;&gt;&gt; CS = plt.contour(Y,X,U) #Plot contours
&gt;&gt;&gt; axes2[2].contourf(data1) #Plot filled contours
&gt;&gt;&gt; axes2[2]= ax.clabel(CS) #Label a contour plot</code></pre>
<h3>Vector Fields&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
&gt;&gt;&gt; axes[1,1].quiver(y,z) #Plot a 2D field of arrows
&gt;&gt;&gt; axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows</code></pre>
<h3>Data Distributions&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; ax1.hist(y) #Plot a histogram
&gt;&gt;&gt; ax3.boxplot(y) #Make a box and whisker plot
&gt;&gt;&gt; ax3.violinplot(z)  #Make a violin plot</code></pre>
<h2>Plot Anatomy &amp; Workflow&nbsp;</h2>
<h3>Plot Anatomy&nbsp;</h3>
<p>&nbsp;y-axis &nbsp; &nbsp; &nbsp;</p>
<p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1649685886/graph_dc34a358f7.png" alt="" /></p>
<p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;x-axis&nbsp;</p>
<h3>Workflow&nbsp;</h3>
<p>The basic steps to creating plots with matplotlib are:<br /><br />1 Prepare Data <br />2 Create Plot <br />3 Plot <br />4 Customized Plot <br />5 Save Plot <br />6 Show Plot</p>
<pre class="language-python"><code>&gt;&gt;&gt; import matplotlib.pyplot as plt
&gt;&gt;&gt; x = [1,2,3,4]  #Step 1
&gt;&gt;&gt; y = [10,20,25,30] 
&gt;&gt;&gt; fig = plt.figure() #Step 2
&gt;&gt;&gt; ax = fig.add_subplot(111) #Step 3
&gt;&gt;&gt; ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
&gt;&gt;&gt; ax.scatter([2,4,6],
          [5,15,25],
          color= 'darkgreen',
          marker= '^' )
&gt;&gt;&gt; ax.set_xlim(1, 6.5)
&gt;&gt;&gt; plt.savefig('foo.png' ) #Step 5
&gt;&gt;&gt; plt.show() #Step 6</code></pre>
<h2>Close and Clear&nbsp;</h2>
<pre class="language-python"><code>&gt;&gt;&gt; plt.cla()  #Clear an axis
&gt;&gt;&gt; plt.clf(). #Clear the entire figure
&gt;&gt;&gt; plt.close(). #Close a window</code></pre>
<h2>Plotting Customize Plot&nbsp;</h2>
<h3>Colors, Color Bars &amp; Color Maps&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; plt.plot(x, x, x, x**2, x, x** 3)
&gt;&gt;&gt; ax.plot(x, y, alpha = 0.4)
&gt;&gt;&gt; ax.plot(x, y, c= 'k')
&gt;&gt;&gt; fig.colorbar(im, orientation= 'horizontal')
&gt;&gt;&gt; im = ax.imshow(img,
            cmap= 'seismic' )</code></pre>
<h3>Markers&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; fig, ax = plt.subplots()
&gt;&gt;&gt; ax.scatter(x,y,marker= ".")
&gt;&gt;&gt; ax.plot(x,y,marker= "o")</code></pre>
<h3>Linestyles&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; plt.plot(x,y,linewidth=4.0)
&gt;&gt;&gt; plt.plot(x,y,ls= 'solid') 
&gt;&gt;&gt; plt.plot(x,y,ls= '--') 
&gt;&gt;&gt; plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
&gt;&gt;&gt; plt.setp(lines,color= 'r',linewidth=4.0)</code></pre>
<h3>Text &amp; Annotations&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; ax.text(1,
           -2.1, 
           'Example Graph', 
            style= 'italic' )
&gt;&gt;&gt; ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "-&gt;", 
connectionstyle="arc3"),)</code></pre>
<h3>Mathtext&nbsp;</h3>
<pre class="language-python"><code>&gt;&gt;&gt; plt.title(r '$sigma_i=15$', fontsize=20)</code></pre>
<h3>Limits, Legends and Layouts&nbsp;</h3>
<p>Limits &amp; Autoscaling&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; ax.margins(x=0.0,y=0.1) #Add padding to a plot
&gt;&gt;&gt; ax.axis('equal')  #Set the aspect ratio of the plot to 1
&gt;&gt;&gt; ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
&gt;&gt;&gt; ax.set_xlim(0,10.5) #Set limits for x-axis</code></pre>
<p>Legends&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
&gt;&gt;&gt; ax.legend(loc= 'best')  #No overlapping plot elements</code></pre>
<p>Ticks&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
&gt;&gt;&gt; ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 
              length=10)</code></pre>
<p>Subplot Spacing&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
             hspace=0.3,
             left=0.125,
             right=0.9,
             top=0.9,
             bottom=0.1)
&gt;&gt;&gt; fig.tight_layout() #Fit subplot(s) in to the figure area</code></pre>
<p>Axis Spines&nbsp;</p>
<pre class="language-python"><code>&gt;&gt;&gt; ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
&gt;&gt;&gt; ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward</code></pre>

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