![]() 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. The numbers assigned to fig were arrived at with a hit-and-trial method to achieve the best looking plot.Matplotlib was created by John Hunter during his post-doctoral research in neurobiology and released in 2003. Note: we have used parameters cex to decrease the size of labels and mai to define margins. For example, the whole plot area would be c(0, 1, 0, 1) with (x1, y1) = (0, 0) being the lower-left corner and (x2, y2) = (1, 1) being the upper-right corner. We need to provide the coordinates in a normalized form as c(x1, x2, y1, y2). The graphical parameter fig lets us control the location of a figure precisely in a plot. ![]() Note that only the ordering of the subplot is different. Same plot with the change par(mfcol = c(2, 2)) would look as follows. # Plot 2: Horizontal boxplot for Temperatureīoxplot(Ozone, horizontal=TRUE) Subplot using mfrow in R programming # create a new plotting window and set the plotting area into a 2*2 array # extracting Temperature and Ozone data from the airquality dataset The only difference between the two is that, mfrow fills in the subplot region row wise while mfcol fills it column wise. This same phenomenon can be achieved with the graphical parameter mfcol. Pie(max.temp, main = "Piechart", radius = 1, labels = names(max.temp)) Two subplots side by side in R programming # create a new plotting window and set the plotting area into a 1*2 arrayīarplot(max.temp, main = "Barplot", names.arg = names(max.temp)) For example, if we need to plot two graphs side by side, we would have m=1 and n=2. It takes in a vector of form c(m, n) which divides the given plot into m*n array of subplots. Graphical parameter mfrow can be used to specify the number of subplot we need. Here we will focus on those which help us in creating subplots. You will see a long list of parameters and to know what each does you can check the help section ?par. For example, you can look at all the parameters and their value by calling the function without any argument. The par() function helps us in setting or inquiring about these parameters. ![]() R programming has a lot of graphical parameters which control the way our graphs are displayed. We can put multiple graphs in a single plot by setting some graphical parameters with the help of par() function. The most common way to create multiple graphs is using the par() function to set graphical parameters. Sometimes we need to put two or more graphs in a single plot.
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