![]() ![]() Plt.plot(x, x, label='linear',linewidth=3) We can change the dimensions of the graph using the figsize argument in plt.figure(). Plt.grid(color='red', alpha=0.2, linewidth=2) Plt.plot(months,salesC,linewidth=2,marker='o') Plt.plot(months,salesB,linewidth=2,marker='o') Plt.plot(months,salesA,linewidth=2,marker='o') Linestyle: To change the line style of the grid lines. Linewidth: To alter the thickness of the grid lines. A few common attributes we can use are:Ĭolor: To change the color of the grid lines.Īlpha: To change the visibility of the grid lines. This is the default grid that gets added if we don’t use any customization. Let’s add one to the Monthly Sales Comparison Plot: plt.plot(months,salesA,linewidth=2) The plt.grid() function is used to add a grid to the plots. The values can be ‘upper left’, ‘upper right’, ‘lower left’, and ‘lower right’ of the corresponding graph. Loc is used to specify the location of the legend index. When plotting multiple lines in a graph, legends are used to describe the different elements using (). Here’s our sample data to show the monthly sales of a company: In Matplotlib, we do this using xlabel() and ylabel(). Most times, it’s necessary to add texts or labels to the axes of the graphs to help viewers understand what the plot is actually about. Markerfacecolor is used to change the color of the marker to highlight it more, and markeredgecolor is used to change the borders: plt.plot(x, marker='o', markersize=10, markeredgecolor='black', We can change the size of the markers using the argument markersize. Here’s how they can be viewed, along with a few examples: Like linestyle, there’s a long list of selections of linemarkers. Markers are used to highlight points on the graph. Linewidth is used to change the thickness of the plot: plt.plot(x,linestyle='dashdot',color='green',linewidth=5) Let’s try out a few linestyles and some other arguments: plt.plot(x,linestyle=':',color='red') Here’s a list of all the available options: import matplotlib Matplotlib offers a variety of linestyles that can be customized using the ls or linestyle argument in the plot(). Let’s plot a simple line graph using sample data. Customizing plots using Matplotlib Line styles png images of the plot directly into the IPython Notebook. The %matplotlib inline command is used to embed static. Or, by running this command in cmd: conda install -c conda-forge matplotlib ![]() Matplotlib can installed directly from Jupyter Notebook by running the command: !pip install matplotlib Image source: Matplotlib Data visualization using Matplotlib Installation and loading It offers a variety of plots like Line, Scatter, Bar, Histogram, Box, etc. It is the go-to Python library for graphs and visualizations. They take different approaches to resolving the main challenge in representing categorical data with a scatter plot, which is that all of the points belonging to one category would fall on the same position along the axis corresponding to the categorical variable.Matplotlib was created by John Hunter during his post-doctoral research in neurobiology and released in 2003. ![]() There are actually two different categorical scatter plots in seaborn. The default representation of the data in catplot() uses a scatterplot. Remember that this function is a higher-level interface each of the functions above, so we’ll reference them when we show each kind of plot, keeping the more verbose kind-specific API documentation at hand. In this tutorial, we’ll mostly focus on the figure-level interface, catplot(). The unified API makes it easy to switch between different kinds and see your data from several perspectives. When deciding which to use, you’ll have to think about the question that you want to answer. These families represent the data using different levels of granularity. Stripplot() (with kind="strip" the default) It’s helpful to think of the different categorical plot kinds as belonging to three different families, which we’ll discuss in detail below. There are a number of axes-level functions for plotting categorical data in different ways and a figure-level interface, catplot(), that gives unified higher-level access to them. Similar to the relationship between relplot() and either scatterplot() or lineplot(), there are two ways to make these plots. In seaborn, there are several different ways to visualize a relationship involving categorical data. ![]() If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialized approach to visualization. In the examples, we focused on cases where the main relationship was between two numerical variables. In the relational plot tutorial we saw how to use different visual representations to show the relationship between multiple variables in a dataset. ![]()
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