![]() The bar heights are scaled according to the magnitude of the other axis. For example, in the previous instance if its car sales over a year, we would put a number of months on one axis and the sales count on the other axis. The graphs are constructed in order of the frequency. These bar plots can be either horizontal or vertical. Similarly, if you choose Pie Plot, it would be devastating as 12 months stacking in a Pie Plot would be unreadable.īar Plots/Graphs are named so because the plots are created in somewhat rectangular boxes or bars. But instead, if you choose the Line Plot, it would be a little harder to interpret. Proper selection of plots is very essential and this needs to be understood before moving forward with the creation of plots.įor example, to display car sales during the year 2019, you can choose Bar Plots. There are numerous types of plots available in Matplotlib, each has its own usage with certain specific data. pdf figures will have a smaller file size than a. pdf format figure will maintain a high level of resolution at any level of zooming. jpeg figure/.png figure, you will find at a certain level of zooming, the latter figure will become pixelated, whereas the. pdf format is the best way to save your figure because if you try to zoom in a. Therefore the various line styles available are:. Changing the line style:-Sometimes different line styles are required for each line under the same plot for easier visualizations.We can change the marker-size by specifying the amount in the ‘ markersize‘ attribute of the plt.plot() function. The most popularly used markers are mentioned below:. Like color names, there is a specific style of different markers. Changing the marker:-To change the markers on the line, specify the desired type of marker in the ‘ marker‘ attribute of the plt.plot() function.Some of the most commonly used colors are: There are specific color names you can use. Changing the color:-To change the color of the line, just specify the color you want in the ‘ color‘ attribute of the plt.plot() function.We can change the style of the plot by varying the color, marker, marker size, line style, line width. to show the plots in the notebook itself and not displaying the plots in another window, which you know is difficult to refer to. It is a magic command that tries to show the plots ‘inline’ i.e. The second line actually is for users doing visualizations in Jupyter Notebooks.We name it as plt so as not to use matplotlib.pyplot every time we call some methods and hence plt seems faster. Pyplot is just an interface helping us to make easier and better plots. The first line shows how you import the Matplotlib library.It would be best if you download Anaconda and work your code in Jupyter Notebooks. It will download the required packages within seconds and then you are good to go! Go to the terminal window and type pip install matplotlib and press enter. So to start using Matplotlib, you need to import Matplotlib and hence create the environment to perform visualizations. ![]() This article bridges the gap between not knowing anything about Matplotlib and highly efficient to make various kinds of plots within a short period of time. But for a beginner into Data Visualization, it would be difficult to keep up with the example and its code. The website contains lots of examples to create different kinds of plots. The above image is drawn using only Matplotlib. You can also customize the labels, color, thickness of the plot details according to your needs. Using Matplotlib, you can draw lots of cool graphs as per your data like Bar Chart, Scatter Plot, Histograms, Contour Plots, Box Plot, Pie Chart, etc. It is among the first choices to plot graphs for quickly visualizing some data. Matplotlib is a low-level plotting library and is one of the most widely used plotting libraries. Hunter created Matplotlib, a plotting library for Python in 2003. This article was published as a part of the Data Science Blogathon.
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