{"id":6994,"date":"2022-10-03T20:00:51","date_gmt":"2022-10-04T01:00:51","guid":{"rendered":"https:\/\/www.sharpsightlabs.com\/?p=6994"},"modified":"2022-10-03T20:25:51","modified_gmt":"2022-10-04T01:25:51","slug":"snslineplot","status":"publish","type":"post","link":"https:\/\/www.sharpsightlabs.com\/blog\/snslineplot\/","title":{"rendered":"How to Use sns.lineplot"},"content":{"rendered":"<p>In this tutorial, I&#8217;ll show you how to use the <code>sns.lineplot<\/code> function to create a Seaborn lineplot.<\/p>\n<p>I&#8217;ll give you a quick introduction to what function does, I&#8217;ll explain how the syntax works, and I&#8217;ll show you some clear examples.<\/p>\n<p>If you need something specific, just click on any of the links below.  Each link will take you to the appropriate section of the tutorial.<\/p>\n<p><strong>Table of Contents:<\/strong><\/p>\n<ul>\n<li><a href=\"#intro-to-sns-lineplot\">Introduction<\/a><\/li>\n<li><a href=\"#sns-lineplot-syntax\">Syntax<\/a><\/li>\n<li><a href=\"#sns-lineplot-examples\">Examples<\/a><\/li>\n<li><a href=\"#sns-lineplot-FAQ\">Frequently Asked Questions<\/a><\/li>\n<\/ul>\n<p>Ok.  Let&#8217;s start with an introduction.<\/p>\n<p><a id = \"intro-to-sns-lineplot\"><\/a><\/p>\n<h2>A quick introduction to sns.lineplot<\/h2>\n<p>So what does the <code>sns.lineplot<\/code> function do?<\/p>\n<p>To put it simply, the Seaborn <code>lineplot()<\/code> function creates <a href=\"https:\/\/en.wikipedia.org\/wiki\/Line_chart\" rel=\"noopener noreferrer\" target=\"_blank\">line<\/a> charts in Python using the Seaborn package.<\/p>\n<p>You can use it to create line charts with a single line, like this:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_one-line.png\" alt=\"A simple line chart with one red line showing time series data.\" width=\"426\" class=\"aligncenter size-full wp-image-5723\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_one-line.png 852w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_one-line-480x373.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 852px, 100vw\" \/><\/p>\n<p>But you can also use it to create line charts with multiple lines.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_multiple-lines.png\" alt=\"An image showing a lineplot that plots two separate lines, one line for different categories.\" width=\"426\" class=\"aligncenter size-full wp-image-5724\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_multiple-lines.png 852w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/simple-seaborn-lineplot_multiple-lines-480x367.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 852px, 100vw\" \/><\/p>\n<p>This is actually much easier to do with Seaborn than in matplotlib.  In fact, Seaborn line charts are easier to create and typically better looking than <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/matplotlib-line-chart\/\" rel=\"noopener noreferrer\" target=\"_blank\">matplotlib line charts<\/a>. So typically, if I need to make a line chart in Python, I use Seaborn.<\/p>\n<p>Ok.  Now that you have a general overview of what the function does, let&#8217;s take a look at the syntax.<\/p>\n<p><a id = \"sns-lineplot-syntax\"><\/a><\/p>\n<h2>The syntax of sns.lineplot<\/h2>\n<p>The syntax of <code>sns.lineplot<\/code> is pretty simple, but there are a few important details that you&#8217;ll need to learn.<\/p>\n<h4>A quick note<\/h4>\n<p>One quick note before we look at the syntax.<\/p>\n<p>Whenever we use a package in our code, like the Seaborn package, we need import it first.<\/p>\n<p>This is important, because <em>how<\/em> we import the package will have an impact on how we call our functions.<\/p>\n<p>Among Python data scientists, the common convention is to import Seaborn with the alias <code>sns<\/code>. You can do that with the following code:<\/p>\n<pre>\r\nimport seaborn as sns\r\n<\/pre>\n<p>When we import Seaborn like this, we can use <code>sns<\/code> as a the prefix before the function name.  You&#8217;ll see that just in the next section.<\/p>\n<h3>sns.lineplot syntax<\/h3>\n<p>Ok.  Let&#8217;s look at the syntax.<\/p>\n<p>Assuming that we&#8217;ve imported Seaborn with the alias <code>sns<\/code>, we call the function as <code>sns.lineplot()<\/code>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_syntax-explanation_v2.png\" alt=\"A picture that explains the syntax of the sns.lineplot function, including the &#039;data&#039; parameter, as well as &#039;x&#039; and &#039;y&#039; parameters.\" width=\"572\"  class=\"aligncenter size-full wp-image-5727\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_syntax-explanation_v2.png 1144w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_syntax-explanation_v2-980x528.png 980w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_syntax-explanation_v2-480x258.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) 1144px, 100vw\" \/><\/p>\n<p>Then, inside the parenthesis, there are several parameters that control exactly how the function works.  Let&#8217;s look at those parameters.<\/p>\n<h3>The parameters of sns.lineplot<\/h3>\n<p>The <code>sns.lineplot<\/code> function has about a dozen parameters, but there are only a few that we commonly use.<\/p>\n<p>The commonly used parameters that I think you should know are:<\/p>\n<ul>\n<li><code>data<\/code><\/li>\n<li><code>x<\/code><\/li>\n<li><code>y<\/code><\/li>\n<li><code>hue<\/code><\/li>\n<li><code>color<\/code><\/li>\n<li><code>palette<\/code><\/li>\n<\/ul>\n<p>Let&#8217;s look at these one at a time.<\/p>\n<h6><code>data<\/code><\/h6>\n<p>The <code>data<\/code> parameter enables you to specify the dataframe that contains the data that you want to plot.<\/p>\n<p>Now, to clarify, the argument to this parameter is typically a Pandas dataframe.  That&#8217;s almost always how I use the <code>lineplot()<\/code> function.  Having said that, the <code>data<\/code> parameter <em>will<\/em> accept other types of inputs.  For example, it&#8217;s possible to use <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-array-python\/\" rel=\"noopener noreferrer\" target=\"_blank\">a Numpy array<\/a> or a list of arrays as an argument to this parameter.  <\/p>\n<p>Keep in mind that this is optional.  You don&#8217;t necessarily need to specify a dataframe.  If you don&#8217;t, you can simply pass vectors to the <code>x<\/code> and <code>y<\/code> parameters.<\/p>\n<p>Additionally, there&#8217;s an important point about the syntax.  When you type the name of the dataset as the argument to this parameter, the argument name does <em>not<\/em> need to be inside of quotations.  (This is in contrast to the arguments for <code>x<\/code>, <code>y<\/code>, and <code>hue<\/code>.)<\/p>\n<h6><code>x<\/code><\/h6>\n<p>The <code>x<\/code> parameter enables you to specify the variable that you want to map to the x-axis.<\/p>\n<p>If you set a dataframe with the <code>data=<\/code> parameter, then the argument to the <code>x<\/code> parameter will be a variable name in that dataframe.<\/p>\n<p>Alternatively, if you don&#8217;t use the <code>data<\/code> parameter, you can specify a 1-dimensional Numpy array or a Python list.<\/p>\n<p>When you pass in a dataframe variable as the argument to this parameter, the variable name should be inside of quotation marks.  I&#8217;ll show you examples of this <a href=\"#sns-lineplot-examples\">in the examples section<\/a>.<\/p>\n<h6><code>y<\/code><\/h6>\n<p>The <code>y<\/code> parameter enables you to specify the variable that you want to map to the y-axis.<\/p>\n<p>Much like the <code>x<\/code> parameter, the argument can be a dataframe variable name or a Numpy array or list.  <\/p>\n<p>You&#8217;ll pass in a variable name if you set a dataframe with the <code>data<\/code> parameter.  In that case, the argument to the <code>y<\/code> parameter will be a variable name in that dataframe.  If you pass in a variable name like this, the variable name should be inside quotation marks.<\/p>\n<p>Alternatively, if you don&#8217;t use the <code>data<\/code> parameter, you can specify a 1-dimensional Numpy array or a Python list.<\/p>\n<h6><code>color<\/code><\/h6>\n<p>You can use the <code>color<\/code> parameter to change the color of the line, if you have a line chart with only one line.<\/p>\n<p>If you have a line chart with multiple different lines, you should probably use the <code>hue<\/code> parameter instead.  (You&#8217;ll see examples of both of these <a href=\"#sns-lineplot-examples\">in the examples section<\/a>.)<\/p>\n<p>Appropriate arguments to the <code>color<\/code> parameter include <a href=\"https:\/\/matplotlib.org\/3.1.0\/gallery\/color\/named_colors.html\" rel=\"noopener noreferrer\" target=\"_blank\">Python named colors<\/a>, but hexidecimal colors will also work.<\/p>\n<h6><code>hue<\/code><\/h6>\n<p>The <code>hue<\/code> parameter effectively enables you create a multi-line line chart.<\/p>\n<p>It does this by allowing you to map a categorical variable to the color of the lines.<\/p>\n<p>So effectively, if you have a categorical variable with multiple categories, you can use this parameter to create a line chart with multiple lines.  Each line will have a different color.<\/p>\n<p>I&#8217;ll show you an example of this in <a href=\"#example-3\">example 3<\/a>.<\/p>\n<h6><code>palette<\/code><\/h6>\n<p>You can use the <code>palette<\/code> parameter to change the color of the lines for a multi-line line chart.<\/p>\n<p>Remember: in the section above about the <code>hue<\/code> parameter, I noted that you can create a multi-line lineplot by mapping a categorical variable to <code>hue<\/code>.  If you do that, the <code>lineplot()<\/code> function will create a line chart with multiple lines, and each line will be a different color (you&#8217;ll see this in <a href=\"#example-3\">example 3<\/a>).<\/p>\n<p>When you create a multi-line linechart, Seaborn will set the different line colors to default values, according to a default color palette.  Additionally, you create a multi-line linechart, you will <em>not<\/em> be able to change the color of the lines with the <code>color<\/code> parameter. Remember that the <code>color<\/code> parameter only works for single-line linecharts.<\/p>\n<p>So to change the colors of a multi-line line chart, you need to use the <code>palette<\/code> parameter.<\/p>\n<p>Python has several <a href=\"https:\/\/matplotlib.org\/tutorials\/colors\/colormaps.html\" rel=\"noopener noreferrer\" target=\"_blank\">qualitative color palettes<\/a>.  The ones I recommend for line charts are:<\/p>\n<ul>\n<li><code>hls<\/code><\/li>\n<li><code>Dark2<\/code><\/li>\n<li><code>Set1<\/code><\/li>\n<li><code>tab10<\/code><\/li>\n<\/ul>\n<p>I&#8217;ll show you an example of how to use the <code>palette<\/code> parameter in <a href=\"#example-4\">example 4<\/a>.<\/p>\n<p><a id = \"sns-lineplot-examples\"><\/a><\/p>\n<h2>Examples: How to make Line Charts in Python using Seaborn<\/h2>\n<p>Ok.  Now that we&#8217;ve looked at the syntax, let&#8217;s take a look at some examples of how to create line charts with the Seaborn lineplot function.<\/p>\n<p>I&#8217;ll show you several different examples.  <\/p>\n<p>The links below will take you to a specific example, if you need to build a specific kind of line chart.<\/p>\n<p><strong>Examples:<\/strong><\/p>\n<ul>\n<li><a href=\"#example-1\">Create a Simple Line Chart with One line<\/a><\/li>\n<li><a href=\"#example-2\">Change the Color of the Line<\/a><\/li>\n<li><a href=\"#example-3\">Create a Line Chart with Multiple Lines<\/a><\/li>\n<li><a href=\"#example-4\">Change the Colors of a Multi-line Line Chart<\/a><\/li>\n<\/ul>\n<p>Having said that, there is some code that you&#8217;ll need to run before the examples will work.<\/p>\n<h4>Run this code first<\/h4>\n<p>Before you run the examples, you&#8217;ll need to import Seaborn and download the dataset that we&#8217;ll be working with.<\/p>\n<h5>Import Pacakges<\/h5>\n<p>First, we need to import the Seaborn package and also import Pandas.<\/p>\n<p>You can do that by running the following code:<\/p>\n<pre>\r\nimport pandas as pd\r\nimport seaborn as sns\r\nsns.set()\r\n<\/pre>\n<p>As I explained earlier in <a href=\"#sns-lineplot-syntax\">the syntax section<\/a>, when we import Seaborn this way, we can use the prefix <code>sns<\/code> when we call our Seaborn functions.<\/p>\n<p>Here, I&#8217;ve also used the <code>sns.set()<\/code> function, which sets the plot formatting of our charts.<\/p>\n<p><a id = \"get-data\"><\/a><\/p>\n<h5>Get dataset<\/h5>\n<p>Next, we need to get the data that we&#8217;re going to visualize.<\/p>\n<p>The data that we&#8217;ll be working with is time-series stock data for two companies: Amazon and Google.<\/p>\n<p>To get his data, you need to run the Pandas <code>read_csv()<\/code> function, which will retrieve and import a CSV file of the data that I&#8217;ve stored on this website.<\/p>\n<p>You can download and import the data by running this code:<\/p>\n<pre>\r\nstocks = pd.read_csv(\"https:\/\/www.sharpsightlabs.com\/datasets\/amzn_goog_2000-01-01_to_2020-12-05.csv\")\r\nstocks.date = pd.to_datetime(stocks.date)\r\n<\/pre>\n<p>Let&#8217;s print out the stocks dataframe so you can take a look:<\/p>\n<pre>\r\nprint(stocks)\r\n<\/pre>\n<p>OUT:<\/p>\n<pre>\r\n           date         open         high          low        close     adj_close    volume stock \r\n0    2000-01-03    81.500000    89.562500    79.046875    89.375000     89.375000  16117600  amzn  \r\n1    2000-01-04    85.375000    91.500000    81.750000    81.937500     81.937500  17487400  amzn  \r\n2    2000-01-05    70.500000    75.125000    68.000000    69.750000     69.750000  38457400  amzn  \r\n3    2000-01-06    71.312500    72.687500    64.000000    65.562500     65.562500  18752000  amzn  \r\n4    2000-01-07    67.000000    70.500000    66.187500    69.562500     69.562500  10505400  amzn  \r\n...         ...          ...          ...          ...          ...           ...       ...   ...  \r\n9364 2020-11-30  1781.183960  1788.064941  1755.000000  1760.739990   1760.739990   1823800  goog  \r\n9365 2020-12-01  1774.369995  1824.829956  1769.369995  1798.099976   1798.099976   1739000  goog  \r\n9366 2020-12-02  1798.099976  1835.650024  1789.473022  1827.949951   1827.949951   1222000  goog  \r\n9367 2020-12-03  1824.010010  1847.199951  1822.650024  1826.770020   1826.770020   1227300  goog  \r\n9368 2020-12-04  1824.520020  1833.160034  1816.989990  1827.989990   1827.989990   1376100  goog \r\n<\/pre>\n<p>If you look carefully, you&#8217;ll see that the data has several variables related to stock information, including <code>date<\/code>, the <code>close<\/code> price, and the ticker symbol (the <code>stock<\/code> variable).<\/p>\n<p>Let&#8217;s quickly check the unique values of the <code>stock<\/code> variable <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/pandas-unique\/\" rel=\"noopener noreferrer\" target=\"_blank\">using the Pandas unique function<\/a>:<\/p>\n<pre>\r\npd.unique(stocks.stock)\r\n<\/pre>\n<p>OUT:<\/p>\n<pre>\r\narray(['amzn', 'goog'], dtype=object)\r\n<\/pre>\n<p>As you can see, the <code>stock<\/code> variable has two unique values: <code>'amzn'<\/code> and <code>'goog'<\/code>.<\/p>\n<p>With these variables and the records in this dataframe, we&#8217;ll be able to create a few different types of line charts of stock price over time.<\/p>\n<h6>Create data subset of Amazon data<\/h6>\n<p>We&#8217;ll also create a subset that contains only Amazon data.<\/p>\n<p>To create this dataframe, we&#8217;re going to take the <code>stock<\/code> dataset and <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/pandas-query\/\" rel=\"noopener noreferrer\" target=\"_blank\">subset the rows the dataframe<\/a> using the Pandas <code>query()<\/code> method.<\/p>\n<pre>\r\namazon_stock = stocks.query(\"stock == 'amzn'\")\r\n<\/pre>\n<p>If you print this out and do some data inspection to <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/pandas-unique\/\" rel=\"noopener noreferrer\" target=\"_blank\">check the unique values<\/a> of the <code>stock<\/code> variable, you&#8217;ll see that amazon_stock <\/p>\n<p><a id = \"example-1\"><\/a><\/p>\n<h3>EXAMPLE 1: Create a Simple Line Chart with One line<\/h3>\n<p>First, let&#8217;s start out by creating a simple line chart.<\/p>\n<p>Here, we&#8217;re going to plot the <code>amazon_stock<\/code> dataframe to show the Amazon stock price over time.<\/p>\n<p>Let&#8217;s run the code and take a look:<\/p>\n<pre>\r\nsns.lineplot(data = amazon_stock, x = 'date', y = 'close')\r\n<\/pre>\n<p>OUT:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_amzn-stock-price.png\" alt=\"A line chart made in Python of Amazon stock price over time.\" width=\"397\" height=\"265\" class=\"aligncenter size-full wp-image-5728\" \/><\/p>\n<h6>Explanation<\/h6>\n<p>So what happened here?<\/p>\n<p>We used the Seaborn <code>lineplot()<\/code> function to create a line chart of Amazon stock price over time.<\/p>\n<p>Let&#8217;s dig in to the syntax though.<\/p>\n<p>We called the function with the syntax <code>sns.lineplot()<\/code>. <\/p>\n<p>We indicated that we want to plot data in the <code>amazon_stock<\/code> dataframe with the code <code>data = amazon_stock<\/code>.  Remember that the <code>amazon_stock<\/code> dataframe contains only Amazon stock data.<\/p>\n<p>Then we mapped the <code>date<\/code> variable to the x-axis with the code <code>x = 'date'<\/code> and mapped <code>close<\/code> to the y-axis with <code>y = 'close'<\/code>.  (Remember, <code>close<\/code> is the closing price of the stock, on any given day.)<\/p>\n<p>The output then is a line chart of the Amazon stock closing price by date.<\/p>\n<p><a id = \"example-2\"><\/a><\/p>\n<h3>EXAMPLE 2: Change the Color of the Line<\/h3>\n<p>Next, let&#8217;s change the color of the line.<\/p>\n<p>As you saw in the previous example, when we create a single-line line chart, the default line color is &#8216;blue&#8217; (unless you&#8217;ve changed your default <code>matplotlib<\/code>\/<code>Seaborn<\/code> settings.)<\/p>\n<p>In many cases, the default blue color will be fine.<\/p>\n<p>But in some other cases, you may want to change the line color.<\/p>\n<p>This is very easy to do.<\/p>\n<p>To change the line color of a lineplot with a single line, we can use the <code>color<\/code> parameter.<\/p>\n<p>Let&#8217;s take a look:<\/p>\n<pre>\r\nsns.lineplot(data = amazon_stock\r\n            ,x = 'date'\r\n            ,y = 'close'\r\n            ,color = 'red'\r\n            )\r\n<\/pre>\n<p>OUT:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_amzn-stock_red-color.png\" alt=\"An image of a Seaborn line chart, where the line color has been changed to red.\" width=\"397\" height=\"265\" class=\"aligncenter size-full wp-image-5729\" \/><\/p>\n<h6>Explanation<\/h6>\n<p>This is pretty easy to understand, but let me explain.<\/p>\n<p>The code for this line chart is almost exactly the same as the code for the basic line chart that we saw <a href=\"#example-1\">in example 1<\/a>.<\/p>\n<p>The major difference is that we used the <code>color<\/code> parameter to change the color of the line.  Specifically, we changed it to <code>red<\/code> with the code <code>color = 'red'<\/code>.  Notice that the name of the color is enclosed inside quotation marks.  <\/p>\n<p>Keep in mind that <a href=\"https:\/\/matplotlib.org\/3.1.0\/gallery\/color\/named_colors.html\" rel=\"noopener noreferrer\" target=\"_blank\">Python has many other valid colors<\/a> that you can use.  Play around a little bit and find some colors that you like.<\/p>\n<p>One last note about this: you can use this for line charts with a single line, but it won&#8217;t work for multi-line line charts.<\/p>\n<p>That being said, let&#8217;s look at how to create a multi-line line chart.<\/p>\n<p><a id = \"example-3\"><\/a><\/p>\n<h3>EXAMPLE 3: Create a Line Chart with Multiple Lines<\/h3>\n<p>Here, we&#8217;ll create a line chart with multiple lines.  Each line will be a different color.<\/p>\n<p>In this example, we&#8217;re going to plot data from the <code>stocks<\/code> dataframe. Remember that we <a href=\"#get-data\">created this dataframe<\/a> near the beginning of the examples section.  Also remember that this dataframe contains data for two stocks: Amazon and Google.<\/p>\n<p>To plot this data, we&#8217;ll use the sns.lineplot() function in a way that&#8217;s very similar to <a href=\"#example-1\">example 1<\/a> and <a href=\"#example-2\">example 2<\/a>.<\/p>\n<p>We&#8217;re setting <code>data = stocks<\/code> to indicate that we&#8217;re plotting the <code>stocks<\/code> dataframe. And we&#8217;re setting <code>x = 'date'<\/code> and <code>y = 'close'<\/code> to map the appropriate variables to the x and y axes.<\/p>\n<p>But to create a chart with multiple lines, we&#8217;ll use the <code>hue<\/code> parameter.<\/p>\n<p>Let&#8217;s take a look:<\/p>\n<pre>\r\nsns.lineplot(data = stocks\r\n            ,x = 'date'\r\n            ,y = 'close'\r\n            ,hue = 'stock'\r\n            )\r\n<\/pre>\n<p>OUT:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_amzn-goog-stock-price.png\" alt=\"A line chart that shows lines for two different stocks: Amazon in blue and Google in orange.\" width=\"397\" height=\"265\" class=\"aligncenter size-full wp-image-5730\" \/><\/p>\n<h6>Explanation<\/h6>\n<p>This is very similar to previous examples.<\/p>\n<p>The main difference is that we&#8217;re using the <code>hue<\/code> parameter to create a line chart with multiple lines that have different colors.<\/p>\n<p>Specifically, we set <code>hue = 'stock'<\/code>. <\/p>\n<p>Remember that the <code>stocks<\/code> dataframe contains time series data for stock ticker symbols for the stocks.  <\/p>\n<p>The <code>stock<\/code> variable is a categorical variable in that dataframe that records the ticker symbol of the stock record.   If we check the unique values for this variable, you&#8217;ll see that there are only two values: <code>amzn<\/code> and <code>goog<\/code>. These are the stock ticker symbols for Amazon and Google respectively.<\/p>\n<p>When we want to create a line chart with multiple lines, we can map a categorical variable to hue.  If we do this, <code>sns.lineplot()<\/code> will create a separate line for each category of our categorical variable.<\/p>\n<p>So when we set <code>hue = 'stock'<\/code>, Seaborn created two different lines: one line for <code>amzn<\/code> and one line for <code>goog<\/code>. <\/p>\n<p>Notice that both lines have different colors &#8230; they have different &#8220;hues.&#8221;<\/p>\n<p>Keep in mind that this is a relatively simple example with 2 lines, but if you have data with more than 2 categories, you can create a line chart with more lines.<\/p>\n<p><a id = \"example-4\"><\/a><\/p>\n<h3>EXAMPLE 4: Change the Colors of a Multi-line Line Chart<\/h3>\n<p>Finally, let&#8217;s modify the multi-line chart that we created in <a href=\"#example-3\">example 3<\/a> by changing the colors of the lines.<\/p>\n<p>To do this, we&#8217;ll use the <code>palette<\/code> parameter.<\/p>\n<p>Let&#8217;s run the code and then I&#8217;ll explain.<\/p>\n<pre>\r\nsns.lineplot(data = stocks\r\n            ,x = 'date'\r\n            ,y = 'close'\r\n            ,hue = 'stock'\r\n            ,palette = 'hls'\r\n            )\r\n<\/pre>\n<p>OUT:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2021\/02\/seaborn-lineplot_amzn-goog_hsl-palette.png\" alt=\"A Python line chart made with Seaborn, where we&#039;ve changed the line colors by changing the color palette.\" width=\"397\" height=\"265\" class=\"aligncenter size-full wp-image-5731\" \/><\/p>\n<h6>Explanation<\/h6>\n<p>So what happened here?<\/p>\n<p>In this example, we modified the line colors by changing the color palette.  To do this, we set the <code>palette<\/code> parameter to <code>palette = 'hls'<\/code>. &#8216;hls&#8217; is the name of a Python color palette.<\/p>\n<p>When we did this, it changed the line colors to colors that are in the &#8216;hls&#8217; palette.<\/p>\n<p>Python actually has a <a href=\"https:\/\/matplotlib.org\/tutorials\/colors\/colormaps.html\" rel=\"noopener noreferrer\" target=\"_blank\">variety of color palettes<\/a>.  When you&#8217;re creating line charts, where each line represents a different category, I recommend that you use a &#8220;qualitative&#8221; color palette.<\/p>\n<p>Python has a variety of qualitative color palettes, but the ones I recommend for line charts are:<\/p>\n<ul>\n<li><code>hls<\/code><\/li>\n<li><code>Dark2<\/code><\/li>\n<li><code>Set1<\/code><\/li>\n<li><code>tab10<\/code><\/li>\n<\/ul>\n<p>Try these out and see which ones you like.<\/p>\n<p>Again though, Python has a variety of <a href=\"https:\/\/matplotlib.org\/tutorials\/colors\/colormaps.html\" rel=\"noopener noreferrer\" target=\"_blank\">qualitative palettes and other color palettes<\/a>, so check them out and try them in your plots.<\/p>\n<p><a id = \"sns-lineplot-FAQ\"><\/a><\/p>\n<h2>Frequently asked questions about Seaborn Lineplots<\/h2>\n<p>Ok.  Now that we&#8217;ve looked at some examples of Seaborn line charts, let me address a common question.<\/p>\n<p><strong>Frequently asked questions:<\/strong><\/p>\n<ul>\n<li><a href=\"#KEYWORD-FAQ-1\">Is a Seaborn line chart better than a Matplotlib line chart?<\/li>\n<\/ul>\n<p><a id = \"KEYWORD-FAQ-1\"><\/a><\/p>\n<h3>Is a Seaborn line chart better than a Matplotlib line chart?<\/h3>\n<p>One common question among Python data scientists is about whether they should use Seaborn or Matplotlib to create their charts.<\/p>\n<p>The short answer is: I strongly prefer Seaborn.<\/p>\n<p>The code to create a line chart with Seaborn is simpler compared to the code for <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/matplotlib-line-chart\/\" rel=\"noopener noreferrer\" target=\"_blank\">a Matplotlib line chart<\/a>.<\/p>\n<p>Seaborn is easier to use when you&#8217;re working with dataframes (matplotlib isn&#8217;t really designed to work on dataframes).<\/p>\n<p>Seaborn plots are typically better looking when you use the default settings.<\/p>\n<p>And importantly, for line charts, it&#8217;s easier to create a multi-line line chart with Seaborn than with matplotlib.<\/p>\n<p>Overall, for data visualization generally, and for line charts specifically, I strongly recommend that you use Seaborn instead of matplotlib.<\/p>\n<h5>Leave your other questions in the comments below<\/h5>\n<p>Do you still have questions about the Seaborn lineplot function?<\/p>\n<p>If so, just leave your questions in the comments section below.<\/p>\n<h2>If you want to master Seaborn, join our course<\/h2>\n<p>In this blog post, I&#8217;ve shown you how to use the Seaborn lineplot function to create line charts in Python. But if you need to visualize your Python data, there&#8217;s a lot more to learn.<\/p>\n<p>That said, if you&#8217;re serious about learning and mastering data visualization in Python, you should join our premium online course, <a href=\"https:\/\/www.sharpsightlabs.com\/waitlist-seaborn-mastery\/\" rel=\"noopener noreferrer\" target=\"_blank\">Seaborn Mastery<\/a>.<\/p>\n<p>Seaborn Mastery is our online course that will teach you everything you need to know about data visualization with the Seaborn package.<\/p>\n<p>Inside the course, you&#8217;ll learn:<\/p>\n<ul>\n<li>how to create essential plots like bar charts, line charts, scatterplots, and more<\/li>\n<li>techniques for creating multivariate data visualization<\/li>\n<li>how to add titles and annotations to your plots<\/li>\n<li>how to &#8220;tell stories with data&#8221;<\/li>\n<li>learn &#8220;how to think&#8221; about visualization<\/li>\n<li>and much more &#8230;<\/li>\n<\/ul>\n<p>Additionally, when you enroll, you&#8217;ll get access to our unique practice system that will enable you to memorize all of the syntax you learn.  If you practice like we show you, you&#8217;ll memorize Seaborn syntax and become &#8220;fluent&#8221; in writing data visualization.<\/p>\n<p>You can find out more here:<\/p>\n<p style=\"text-align: center;\">\n<a href=\"https:\/\/www.sharpsightlabs.com\/waitlist-seaborn-mastery\/\" target=\"_blank\" class=\"myButton\" rel=\"noopener noreferrer\">Learn More About Seaborn Mastery<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this tutorial, I&#8217;ll show you how to use the sns.lineplot function to create a Seaborn lineplot. I&#8217;ll give you a quick introduction to what function does, I&#8217;ll explain how the syntax works, and I&#8217;ll show you some clear examples. If you need something specific, just click on any of the links below. Each link &#8230; <a title=\"How to Use sns.lineplot\" class=\"read-more\" href=\"https:\/\/www.sharpsightlabs.com\/blog\/snslineplot\/\" aria-label=\"Read more about How to Use sns.lineplot\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":5722,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[58,24,74],"tags":[],"publishpress_future_action":{"enabled":false,"date":"2024-05-04 08:04:28","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category"},"_links":{"self":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/6994"}],"collection":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/comments?post=6994"}],"version-history":[{"count":0,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/6994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media\/5722"}],"wp:attachment":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media?parent=6994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/categories?post=6994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/tags?post=6994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}