{"id":12715,"date":"2021-08-05T12:22:00","date_gmt":"2021-08-05T06:52:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/matplotlib-tutorial-for-data-visualisation\/"},"modified":"2024-10-14T23:56:13","modified_gmt":"2024-10-14T18:26:13","slug":"matplotlib-tutorial-for-data-visualisation","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/matplotlib-tutorial-for-data-visualisation\/","title":{"rendered":"Matplotlib in Python: Data Visualization Plots &#038; how to use it"},"content":{"rendered":"\n<p><strong><em>Contributed by: Mr. Sridhar Anchoori<br>LinkedIn profile:&nbsp;<a href=\"https:\/\/www.linkedin.com\/in\/sridhar-anchoori-42156722\/\" rel=\"nofollow\">https:\/\/www.linkedin.com\/in\/sridhar-anchoori-42156722\/<\/a> <\/em><\/strong><\/p>\n\n\n\n<p class=\"has-text-align-center\"><em>\u2018A Picture is Worth more than a thousand words\u2019, similarly in the context of data \u2018A visualisation is worth more than a complex data table or report\u2019.<\/em><gwmw style=\"display:none;\"><\/gwmw><\/p>\n\n\n\n<p>Data Visualisation is one of the critical skills expected from data scientists. Most of the business problems could be understood and addressed using visualisation techniques. Visualisation basically involves Exploratory Data Analysis (EDA) and Graphical Plots. Effective visualisation helps the users to understand the patterns from the data and solve the business problem more effectively. Another advantage of visualisation is to simplify the complex data into an understandable format.<\/p>\n\n\n\n<p>People find it very easy to read an image much easier than text. Visualisation is the best communication platform to analyse and interpret the data. It helps the users to understand vast amounts of information easily. 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Matplotlib has a variety of graphical features and is very easy to understand. This article focuses on different graphical features including syntax.<gwmw style=\"display:none;\"><\/gwmw><\/p>\n\n\n\n\n\n<p>Matplotlib is one of the best visualisation libraries in python for 2 Dimensional datasets. This library is built on NumPy arrays. John Hunter invented this library in the year 2002. It made the plotting simpler and more effective, also made it very easy to generate visualisations. This library supports both static as well as dynamic plotting, and can save images in multiple formats like jpg, png, pdf etc.,&nbsp;<\/p>\n\n\n\n<p>Matplotlib has different types of plots like scatter plot, histogram, bar plot, line plot, pie chart, and many more. The beauty of this library is each part of the plot is customisable including axes, size, titles, colours, legends, markers, size, line etc.<\/p>\n\n\n\n<p>The interface of this library is very similar to that of MATLAB and includes most of the features that are similar to MATLAB. This could be one of the reasons for the name matplotlib as this is been considered as an open-source replacement for MATLAB involves licenses and added cost. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"installing-matplotlib\"><strong>Installing Matplotlib<\/strong><\/h2>\n\n\n\n<p>There are multiple ways to install the <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/python-matplotlib\" target=\"_blank\" rel=\"noreferrer noopener\">python matplotlib<\/a> library. The easiest way to install matplotlib is to download the Anaconda package. Matplotlib is default installed with Anaconda package and does not require any additional steps.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Download anaconda package from the official site of Anaconda<\/li>\n\n\n\n<li>To install matplotlib, go to anaconda prompt and run the following command<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install matplotlib\nor\nconda install matplotlib\n<\/code><\/pre>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Verify whether the matplotlib is properly installed using the following command in Jupyter notebook<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code>import matplotlib\nmatplotlib.__version__\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-to-use-matplotlib\"><strong>How to use Matplotlib<\/strong><\/h2>\n\n\n\n<p>Before using matplotlib, we need to import the package. This can be done using the \u2018import\u2019 method in Jupyter notebook. PyPlot is the graphical module in matplotlib which is mostly used for data visualisation, importing PyPlot is sufficient to work around data visualisation.&nbsp;<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># import matplotlib library as mpl\nimport matplotlib as mpl\n\n#import the pyplot module from matplotlib as plt (short name used for referring the object)\nimport matplotlib.pyplot as plt\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-relation-between-matplotlib-pyplot-and-python\"><strong>The relation between - Matplotlib, Pyplot and Python<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Python Is a very popular programming language, used for web development, mathematics and <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-statistical-analysis\/\">statistical analysis<\/a>. Python works on most of the platforms and is also simple to use.&nbsp;<\/li>\n\n\n\n<li>Python has multiple libraries used for specific purposes, below libraries are mostly used for visualisation and data analysis.\n<ul class=\"wp-block-list\">\n<li>NumPy<\/li>\n\n\n\n<li>Pandas<\/li>\n\n\n\n<li>Matplotlib<\/li>\n\n\n\n<li>Seaborn<\/li>\n\n\n\n<li>Plotly<\/li>\n\n\n\n<li>SciKit-Learn<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>As you observe one of the packages is matplotlib which is developed using python. This library is very widely used for data visualisations.&nbsp;<\/li>\n\n\n\n<li>PyPlot is a module in matplotlib which provides MATLAB like interface. MATLAB is heavily used for statistical analysis in the manufacturing industry. MATLAB is a licensed software and requires a significant amount of money to buy and use, whereas PyPlot is an open-source module and gives similar functionality as MATLAB using python. Just to conclude PyPlot has been seen as a replacement of MATLAB in the context of open source.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"create-a-simple-plot\"><strong>Create a Simple Plot<\/strong><\/h2>\n\n\n\n<p>Here we will be depicting a basic plot using some random numbers generated using NumPy. The simplest way to create a graph is using the \u2018plot()\u2019 method. To generate a basic plot, we need two axes (X) and (Y), and we will generate two random numbers using the \u2018linspace()\u2019 method from Numpy.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># import the NumPy package\nimport numpy as np\n\n# generate random number using NumPy, generate two sets of random numbers and store in x, y\nx = np.linspace(0,50,100)\ny = x * np.linspace(100,150,100)\n\n# Create a basic plot\nplt.plot(x,y)<\/code><\/pre>\n\n\n\n<p># Basic plot is generated as shown below:<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"adding-elements-to-plot\"><strong>Adding Elements to Plot<\/strong><gwmw style=\"display:none;\"><\/gwmw><\/h2>\n\n\n\n<p>The plot generated above does not have all the elements to understand it better. Let\u2019s try to add different elements for the plot for better interpretation. The elements that could be added for the plot includes title, x-Label, y-label, x-limits, y-limits.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># set different elements to the plot generated above\n# Add title using \u2018plt.title\u2019\n# Add x-label using \u2018plt.xlabel\u2019\n# Add y-label using \u2018plt.ylabel\u2019\n# set x-axis limits using \u2018plt.xlim\u2019\n# set y-axis limits using \u2018plt.ylim\u2019\n# Add legend using \u2018plt.legend\u2019<\/code><\/pre>\n\n\n\n<p># Refer chart below, that has the elements added i.e., title, x-label, y-label, x-limits and y-limits<\/p>\n\n\n\n<p>Let\u2019s, add few more elements to the plot like colour, markers, line customisation.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># add color, style, width to line element\nplt.plot(x, y, c = 'r', linestyle = '--', linewidth=2)<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code># add markers to the plot, marker has different elements i.e., style, color, size etc.,\nplt.plot (x, y, marker='*', markersize=3, c=\u2019g\u2019)\n<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># add grid using grid() method\nPlt.grid(True)\n\n# add legend and label\nplt.legend()<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"plots-could-be-customised-at-three-levels\"> Plots could be customised at three levels:<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Colours\n<ul class=\"wp-block-list\">\n<li>b \u2013 blue<\/li>\n\n\n\n<li>c \u2013 cyan<\/li>\n\n\n\n<li>g \u2013 green<\/li>\n\n\n\n<li>k \u2013 black<\/li>\n\n\n\n<li>m \u2013 magenta<\/li>\n\n\n\n<li>r \u2013 red<\/li>\n\n\n\n<li>w \u2013 white<\/li>\n\n\n\n<li>y \u2013 yellow<\/li>\n\n\n\n<li>Can use Hexadecimal, RGB formats<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Line Styles\n<ul class=\"wp-block-list\">\n<li>\u2018-\u2018 : solid line<\/li>\n\n\n\n<li>\u2018- -\u2018: dotted line<\/li>\n\n\n\n<li>\u2018- .\u2019: dash-dot line<\/li>\n\n\n\n<li>\u2018:\u2019 \u2013 dotted line<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Marker Styles\n<ul class=\"wp-block-list\">\n<li>.&nbsp; - point marker<\/li>\n\n\n\n<li>,&nbsp; - Pixel marker<\/li>\n\n\n\n<li>v - Triangle down marker<\/li>\n\n\n\n<li>^ - Triangle up marker<\/li>\n\n\n\n<li>&lt; - Triangle left marker<\/li>\n\n\n\n<li>&gt; - Triangle right marker<\/li>\n\n\n\n<li>1 - Tripod down marker<\/li>\n\n\n\n<li>2 - Tripod up marker<\/li>\n\n\n\n<li>3 - Tripod left marker<\/li>\n\n\n\n<li>4 - Tripod right marker<\/li>\n\n\n\n<li>s - Square marker<\/li>\n\n\n\n<li>p - Pentagon marker<\/li>\n\n\n\n<li>* - Star marker<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Other configurations\n<ul class=\"wp-block-list\">\n<li>color or c<\/li>\n\n\n\n<li>linestyle<\/li>\n\n\n\n<li>linewidth<\/li>\n\n\n\n<li>marker<\/li>\n\n\n\n<li>markeredgewidth<\/li>\n\n\n\n<li>markeredgecolor<\/li>\n\n\n\n<li>markerfacecolor<\/li>\n\n\n\n<li>markersize<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"making-multiple-plots-in-one-figure\"><strong>Making Multiple Plots in One Figure<\/strong><gwmw style=\"display:none;\"><\/gwmw><\/h2>\n\n\n\n<p>There could be some situations where the user may have to show multiple plots in a single figure for comparison purpose. For example, a retailer wants to know the sales trend of two stores for the last 12 months and he would like to see the trend of the two stores in the same figure.<\/p>\n\n\n\n<p>Let\u2019s plot two lines sin(x) and cos(x) in a single figure and add legend to understand which line is what. <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># lets plot two lines Sin(x) and Cos(x)\n# loc is used to set the location of the legend on the plot\n# label is used to represent the label for the line in the legend\n# generate the random number \n\nx= np.arange(0,1500,100)\nplt.plot(np.sin(x),label='sin function x')\nplt.plot(np.cos(x),label='cos functon x')\nplt.legend(loc='upper right')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># To show the multiple plots in separate figure instead of a single figure, use plt.show() statement before the next plot statement as shown below\nx= np.linspace(0,100,50)\nplt.plot(x,'r',label='simple x')\nplt.show()\nplt.plot(x*x,'g',label='two times x')\nplt.show()\nplt.legend(loc='upper right')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"create-subplots\"><strong>Create Subplots<\/strong><gwmw style=\"display:none;\"><gwmw style=\"display:none;\"><\/gwmw><\/gwmw><\/h2>\n\n\n\n<p>There could be some situations where we should show multiple plots in a single figure to show the complete storyline while presenting to stakeholders. This can be achieved with the use of subplot in matplotlib library. For example, a retail store has 6 stores and the manager would like to see the daily sales of all the 6 stores in a single window to compare. This can be visualised using subplots by representing the charts in rows and columns.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\n# subplots are used to create multiple plots in a single figure\n# let\u2019s create a single subplot first following by adding more subplots\nx = np.random.rand(50)\ny = np.sin(x*2)\n\n#need to create an empty figure with an axis as below, figure and axis are two separate objects in matplotlib\nfig, ax = plt.subplots()\n\n#add the charts to the plot\nax.plot(y)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Let\u2019s add multiple plots using subplots() function\n# Give the required number of plots as an argument in subplots(), below function creates 2 subplots\nfig, axs = plt.subplots(2)\n\n#create data\nx=np.linspace(0,100,10)\n\n# assign the data to the plot using axs\naxs&#091;0].plot(x, np.sin(x**2))\naxs&#091;1].plot(x, np.cos(x**2))\n\n# add a title to the subplot figure\nfig.suptitle('Vertically stacked subplots')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Create horizontal subplots\n# Give two arguments rows and columns in the subplot() function\n# subplot() gives two dimensional array with 2*2 matrix\n# need to provide ax also similar 2*2 matrix as below\n\nfig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n\n# add the data to the plots\nax1.plot(x, x**2)\nax2.plot(x, x**3)\nax3.plot(x, np.sin(x**2))\nax4.plot(x, np.cos(x**2))\n\n# add title\nfig.suptitle('Horizontal plots')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># another simple way of creating multiple subplots as below, using axs\nfig, axs = plt.subplots(2, 2)\n\n# add the data referring to row and column\naxs&#091;0,0].plot(x, x**2,'g')\naxs&#091;0,1].plot(x, x**3,'r')\naxs&#091;1,0].plot(x, np.sin(x**2),'b')\naxs&#091;1,1].plot(x, np.cos(x**2),'k')\n\n# add title\nfig.suptitle('matrix sub plots')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"figure-object\"><strong>Figure Object<\/strong><\/h2>\n\n\n\n<p>Matplotlib is an object-oriented library and has objects, calluses and methods. Figure is also one of the classes from the object \u2018figure\u2019. The object figure is a container for showing the plots and is instantiated by calling figure() function.<\/p>\n\n\n\n<p>\u2018plt.figure()\u2019 is used to create the empty figure object in matplotlib. Figure has the following additional parameters.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Figsize \u2013 (width, height) in inches<\/li>\n\n\n\n<li>Dpi \u2013 used for dots per inch (this can be adjusted for print quality)<\/li>\n\n\n\n<li>facecolor<\/li>\n\n\n\n<li>edgecolor<\/li>\n\n\n\n<li>linewidth<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code># let\u2019s create a figure object\n# change the size of the figure is \u2018figsize = (a,b)\u2019 a is width and \u2018b\u2019 is height in inches\n# create a figure object and name it as fig\n\nfig = plt.figure(figsize=(4,3))\n\n# create a sample data\nX = np.array(&#091;1,2,3,4,5,6,8,9,10])\nY = X**2\n\n# plot the figure\nplt.plot(X,Y)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># let\u2019s change the figure size and also add additional parameters like facecolor, edgecolor, linewidth\n\nfig = plt.figure(figsize=(10,3),facecolor='y',edgecolor='r',linewidth=5)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"axes-object\"><strong>Axes Object<\/strong><\/h2>\n\n\n\n<p>Axes is the region of the chart with data, we can add the axes to the figure using the \u2018add_axes()\u2019 method.&nbsp; This method requires the following four parameters i.e., left, bottom, width, and height<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Left \u2013 position of axes from left of figure<\/li>\n\n\n\n<li>bottom \u2013 position of axes from the bottom of figure<\/li>\n\n\n\n<li>width \u2013 width of the chart<\/li>\n\n\n\n<li>height \u2013 height of the chart<\/li>\n<\/ul>\n\n\n\n<p>Other parameters that can be used for the axes object are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Set title using \u2018ax.set_title()\u2019<\/li>\n\n\n\n<li>Set x-label using \u2018ax.set_xlabel()\u2019<\/li>\n\n\n\n<li>Set y-label using \u2018ax.set_ylabel()\u2019<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code># lets add axes using add_axes() method\n# create a sample data\ny = &#091;1, 5, 10, 15, 20,30]\nx1 = &#091;1, 10, 20, 30, 45, 55]\nx2 = &#091;1, 32, 45, 80, 90, 122]\n# create the figure\nfig = plt.figure()\n# add the axes\nax = fig.add_axes(&#091;0,0,2,1])\nl1 = ax.plot(x1,y,'ys-') \nl2 = ax.plot(x2,y,'go--')\n\n# add additional parameters\nax.legend(labels = ('line 1', 'line 2'), loc = 'lower right') \nax.set_title(\"usage of add axes function\")\nax.set_xlabel('x-axix')\nax.set_ylabel('y-axis')\nplt.show()<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"different-types-of-matplotlib-plots\"><strong>Different Types of Matplotlib Plots<\/strong><\/h2>\n\n\n\n<p>Matplotlib has a wide variety of plot formats, few of them include bar chart, line chart, pie chart, scatter chart, bubble chart, waterfall chart, circular area chart, stacked bar chart etc., We will be going through most of these charts in this document with some examples. There are some elements that are common for each plot that can be customised like axis, color etc., and there could be some elements that are specific to the respective chart.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"bar-graph\"><span style=\"text-decoration: underline;\">Bar Graph<\/span><gwmw style=\"display:none;\"><\/gwmw><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"overview\"><strong>Overview:&nbsp;<\/strong><\/h4>\n\n\n\n<p>Bar graph represents the data using bars either in Horizontal or Vertical directions. Bar graphs are used to show two or more values and typically the x-axis should be categorical data. The length of the bar is proportional to the counts of the categorical variable on x-axis.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"function\"><strong>Function:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The function used to show bar graph is \u2018plt.bar()\u2019<\/li>\n\n\n\n<li>The bar() function expects two lists of values one on x-coordinate and another on y-coordinate<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"customisations\"><strong>Customisations:<\/strong><\/h4>\n\n\n\n<p>plt.bar() function has the following specific arguments that can be used for configuring the plot.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Width, Color, edge colour, line width, tick_label, align, bottom,&nbsp;<\/li>\n\n\n\n<li>Error Bars \u2013 xerr, yerr<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"example\"><strong>Example:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code># lets create a simple bar chart\n# x-axis is shows the subject and y -axis shows the markers in each subject\n\nsubject = &#091;'maths','english','science','social','computer']\nmarks =&#091;70,80,50,30,78]\nplt.bar(subject,marks)\nplt.show()<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#let\u2019s do some customizations\n#width \u2013 shows the bar width and default value is 0.8\n#color \u2013 shows the bar color\n#bottom \u2013 value from where the y \u2013 axis starts in the chart i.e., the lowest value on y-axis shown\n#align \u2013 to move the position of x-label, has two options \u2018edge\u2019 or \u2018center\u2019\n#edgecolor \u2013 used to color the borders of the bar\n#linewidth \u2013 used to adjust the width of the line around the bar\n#tick_label \u2013 to set the customized labels for the x-axis\n\nplt.bar(subject,marks,color ='g',width = 0.5,bottom=10,align ='center',edgecolor='r',linewidth=2,tick_label=subject)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># errors bars could be added to represent the error values referring to an array value\n# here in this example we used standard deviation to show as error bars\nplt.bar(subject,marks,color ='g',yerr=np.std(marks))<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># to plot horizontal bar plot use plt.barh() function\nplt.barh(subject,marks,color ='g',xerr=np.std(marks))<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"pie-chart\"><span style=\"text-decoration: underline;\">Pie Chart:<\/span><gwmw style=\"display:none;\"><\/gwmw><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"overview\"><strong>Overview:&nbsp;<\/strong><\/h4>\n\n\n\n<p>Pie charts display the proportion of each value against the total sum of values. This chart requires a single series to display. The values on the pie chart shows the percentage contribution in terms of a pie called <strong><em>Wedge\/Widget<\/em><\/strong>. The angle of the wedge\/widget is calculated based on the proportion of values. This visualisation is best when we are trying to compare different segments within the total values. For example, a sales manager wants to know the contribution of type of payments in a month i.e., paid through cash, credit card, debit card, PayPal, any other online apps.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"function\"><strong>Function:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The function used for pie chart is \u2018plt.pie()\u2019<\/li>\n\n\n\n<li>To draw a pie chart, we need only one list of values, each wedge is calculated as proportion converted into angle.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"customisations\"><strong>Customisations:<\/strong><\/h4>\n\n\n\n<p>plt.pie() function has the following specific arguments that can be used for configuring the plot.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>labels \u2013 used to show the widget categories<\/li>\n\n\n\n<li>explode \u2013 used to pull out the widget\/wedge slice<\/li>\n\n\n\n<li>autopct \u2013 used to show the % of contributions for the widgets<\/li>\n\n\n\n<li>Set_aspect \u2013 used to&nbsp;&nbsp;<\/li>\n\n\n\n<li>shadow \u2013 to show the shadow for a slice<\/li>\n\n\n\n<li>colours \u2013 to set the custom colours for the wedges<\/li>\n\n\n\n<li>startangle \u2013 to set the&nbsp;angles of the wedges<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"example\"><strong>Example:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code># Let\u2019s create a simple pie plot\n# Assume that we have a data on number of tickets resolved in a month\n# the manager would like to know the individual contribution in terms of tickets closed in the week\n# data \nTickets_Closed = &#091;10, 20, 8, 35, 30, 25]\nAgents = &#091;'Raj', 'Ramesh', 'Krishna', 'Arun', 'Virag', 'Mahesh']\n\n# create pie chart\nplt.pie(Tickets_Closed, labels = Agents)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#Let\u2019s add additional parameters to pie plot\n#explode \u2013 to move one of the wedges of the plot\n#autopct \u2013 to add the contribution %\n\nexplode = &#091;0.2,0.1,0,0.1,0,0]\nplt.pie(Tickets_Closed, labels = Agents, explode=explode, autopct='%1.1f%%' )<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"scatter-plot\"><span style=\"text-decoration: underline;\">Scatter Plot<\/span><gwmw style=\"display:none;\"><\/gwmw><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"overview\"><strong>Overview:&nbsp;<\/strong><\/h4>\n\n\n\n<p>Scatterplot is used to visualise the relationship between two columns\/series of data. The graph displays the collection of data points without connecting. The chart needs two variables, one variable shows X-position and the second variable shows Y-position. Scatterplot is used to represent the association between variables and mostly advised to use before regression. Scatterplot helps in understanding the following information across the two columns<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Any relationship exists between the two columns<\/li>\n\n\n\n<li>+ ve Relationship<\/li>\n\n\n\n<li>Or -Ve relationship<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"function\"><strong>Function:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The function used for the scatter plot is \u2018plt.scatter()\u2019<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"customizations\"><strong>Customizations:<\/strong><\/h4>\n\n\n\n<p>plt.scatter() function has the following specific arguments that can be used for configuring the plot.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>size \u2013 to manage the size of the points<\/li>\n\n\n\n<li>color \u2013 to set the color of the points<\/li>\n\n\n\n<li>marker \u2013 type of marker&nbsp;<\/li>\n\n\n\n<li>alpha \u2013 transparency of point<\/li>\n\n\n\n<li>norm \u2013 to normalize the data (scaling between 0 to 1)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"example\"><strong>Example:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code># let's create a  simple scatter plot\n# generate the data with random numbers\nx = np.random.randn(1000)\ny = np.random.randn(1000)\nplt.scatter(x,y)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># as you observe there is no correlation exists between x and y\n# let\u2019s try to add additional parameters\n# size \u2013 to manage the size of the points\n#color \u2013 to set the color of the points\n#marker \u2013 type of marker \n#alpha \u2013 transparency of point\n\nsize = 150*np.random.randn(1000)\ncolors = 100*np.random.randn(1000)\nplt.scatter(x, y, s=size, c = colors, marker ='*', alpha=0.7)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"histogram\"><span style=\"text-decoration: underline;\">Histogram<\/span><gwmw style=\"display:none;\"><\/gwmw><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"overview\"><strong>Overview:&nbsp;<\/strong><\/h4>\n\n\n\n<p>Histogram is used to understand the distribution of the data. It is an estimate of the probability distribution of continuous data. It is similar to bar graph as discussed above but this is used to represent the distribution of a continuous variable whereas bar graph is used for discrete variable. Every distribution is characterised by four different elements including<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Center of the distribution<\/li>\n\n\n\n<li>Spread of the distribution<\/li>\n\n\n\n<li>Shape of the distribution<\/li>\n\n\n\n<li>Peak of the distribution<\/li>\n<\/ul>\n\n\n\n<p>Histogram requires two elements x-axis shown using bins and y-axis shown with the frequency of the values in each of the bins form the data set. Every bin has a range with minimum and maximum values.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"function\"><strong>Function:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The function used for scatter plot is \u2018plt.hist()\u2019<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"customisations\"><strong>Customisations:<\/strong><\/h4>\n\n\n\n<p>plt.hist() function has the following specific arguments that can be used for configuring the plot.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>bins \u2013 number of bins<\/li>\n\n\n\n<li>color<\/li>\n\n\n\n<li>edgecolor<\/li>\n\n\n\n<li>alpha \u2013 transparency of the color<\/li>\n\n\n\n<li>normed&nbsp;<\/li>\n\n\n\n<li>xlim \u2013 to set the x-limits<\/li>\n\n\n\n<li>ylim \u2013 to set the y-limits<\/li>\n\n\n\n<li>xticks, yticks<\/li>\n\n\n\n<li>facecolor, edgecolor, density<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"example\"><strong>Example:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code># let\u2019s generate random numbers and use the random numbers to generate histogram\ndata = np.random.randn(1000)\nplt.hist(data)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># let\u2019s add additional parameters\n# facecolor\n# alpha\n# edgecolor\n# bins\n\ndata = np.random.randn(1000)\nplt.hist(data, facecolor ='y',linewidth=2,edgecolor='k', bins=30, alpha=0.6)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># lets create multiple histograms in a single plot\n# Create random data\nhist1 = np.random.normal(25,10,1000)\nhist2 = np.random.normal(200,5,1000)\n\n#plot the histogram\nplt.hist(hist1,facecolor = 'yellow',alpha = 0.5, edgecolor ='b',bins=50)\nplt.hist(hist2,facecolor = 'orange',alpha = 0.8, edgecolor ='b',bins=30)<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"saving-plot\"><strong>Saving Plot<\/strong><gwmw style=\"display:none;\"><\/gwmw><\/h2>\n\n\n\n<p>Saving plot as an image using \u2018savefig()\u2019 function in matplotlib. The plot can be saved in multiple formats like .png, .jpeg, .pdf and many other supporting formats.<br><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># let's create a figure and save it as image\nitems = &#091;5,10,20,25,30,40]\nx = np.arange(6)\nfig = plt.figure()\nax = plt.subplot(111)\nax.plot(x, y, label='items')\nplt.title('Saving as Image')\nax.legend()\nfig.savefig('saveimage.png')<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p>Image is saved with a filename as \u2018saveimage.png\u2019.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#To display the image again, use the following package and commands\nimport matplotlib.image as mpimg\nimage = mpimg.imread(\"saveimage.png\")\nplt.imshow(image)\nplt.show()\n<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"matplotlib-tutorial-faqs\"><strong>Matplotlib Tutorial FAQs<\/strong><\/h2>\n\n\n\n<p><strong>Q: What is Matplotlib used for?<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> Matplotlib is a plotting library meant especially for Python programming language as well as for NumPy, which is its numerical mathematics extension. An object-oriented API is provided by it to embed plots into applications by using general-purpose GUI toolkits such as wxPython, Tkinter, Qt, or GTK.<\/p>\n\n\n\n<p><strong>Q: How do I learn Matplotlib?&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> There are several resources available on the internet to learn Matplotlib. However, if you want to choose any one, you can consider Great Learning. You can get a tutorial on Matplotlib and learn about it.<\/p>\n\n\n\n<p><strong>Q: How do you plot a line in Python?&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> You can use Matplotlib's pyplot library to plot a line in Python. To plot a line, you first have to import Matplotib. After the import is done, including the line %matplotlib inline by using a Jupyter notebook.<\/p>\n\n\n\n<p><strong>Q: When should I use Matplotlib?<\/strong><\/p>\n\n\n\n<p><strong>A: <\/strong>Matplotlib is used by some people interactively from the python shell, and the plotting windows pop up as they type commands. While Jupyter notebooks are run by some people to draw inline plots for a fast and quick data analysis. Some people embed Matplotlib into graphical user interfaces such as PyQt or PyGObject to create rich applications.<\/p>\n\n\n\n<p><strong>Q: Is Matplotlib difficult to learn?&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> To learn Matplotlib, you have to thoroughly know Python. It is not at all difficult to learn Matplotlib but may take around 5-10 weeks, including basic Python syntax, object-oriented programming, variables, data types, loops, and functions.<\/p>\n\n\n\n<p><strong>Q: How do you install Matplotlib?&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> Installation of Matplotlib depends on the distribution of Python that is installed in your computer. You can install Matplotlib with Anaconda Prompt and pip. To install with Anaconda Prompt, you have to open it, and type &gt; conda install matplotlib. To install Matplotlib with pip, you need to open a terminal window and type $ pip install matplotlib.<\/p>\n\n\n\n<p><strong>Q: What is the difference between Matplotlib and Seaborn?&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> Matplotlib is a Python library that is used for plotting graphs through other libraries such as Numpy and Pandas.&nbsp; Seaborn is also a Python library that is used for plotting graphs through Pandas, Matplotlib, and Numpy.<\/p>\n\n\n\n<p><strong>Q: Is MATLAB a Matplotlib?<\/strong><\/p>\n\n\n\n<p><strong>A:<\/strong> Matplotlib is a Python visualization library; however, Matlab is a programming language as well as a numerical computing environment. Matplotlib can also be called a data plotting library in Python. MATLAB is a totally different programming language than Python.<\/p>\n\n\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is Matplotlib used for?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Matplotlib is a plotting library meant especially for Python programming language as well as for NumPy, which is its numerical mathematics extension. An object-oriented API is provided by it to embed plots into applications by using general-purpose GUI toolkits such as wxPython, Tkinter, Qt, or GTK.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How do I learn Matplotlib?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"here are several resources available on the internet to learn Matplotlib. However, if you want to choose any one, you can consider Great Learning. 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MATLAB is a totally different programming language than Python.\"\n    }\n  }]\n}\n<\/script>\n\n\n\n<p>This brings us to the end of this Matplotlib tutorial. If you wish to learn more about Python, upskill with <a href=\"https:\/\/www.mygreatlearning.com\/pg-program-artificial-intelligence-course\" target=\"_blank\" rel=\"noreferrer noopener\">Great Learning's PG program in Artificial Intelligence and Machine Learning.<\/a> <\/p>\n\n\n\n<p><br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Contributed by: Mr. Sridhar AnchooriLinkedIn profile:&nbsp;https:\/\/www.linkedin.com\/in\/sridhar-anchoori-42156722\/ \u2018A Picture is Worth more than a thousand words\u2019, similarly in the context of data \u2018A visualisation is worth more than a complex data table or report\u2019. Data Visualisation is one of the critical skills expected from data scientists. 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