#MATPLOTLIB SCATTER PLOT HOW TO#

We could have plotted the same two line plots above by calling the plot() function twice, illustrating that we can paint any number of charts onto the canvas. Here we pass it two sets of x,y pairs, each with their own color. NumPy is your best option for data science work because of its rich set of features. Even without doing so, Matplotlib converts arrays to NumPy arrays internally. Here we use np.array() to create a NumPy array. Leave off the dashes and the color becomes the point market, which can be a triangle (вЂњvвЂќ), circle (вЂњoвЂќ), etc. If you put dashes (вЂњвЂ“вЂњ) after the color name, then it draws a line between each point, i.e., makes a line chart, rather than plotting points, i.e., a scatter plot. If you only give plot() one value, it assumes that is the y coordinate. *args and **kargs lets you pass values to other objects, which we illustrate below. The format is plt.plot(x,y,colorOptions, *args, **kargs). You can feed any number of arguments into the plot() function. This is because plot() can either draw a line or make a scatter plot. We use plot(), we could also have used scatter(). The two arrays must be the same size since the numbers plotted picked off the array in pairs: (1,2), (2,2), (3,3), (4,4).

This way, NumPy and Matplotlib will be imported, which you need to install using pip. If you are using a virtual Python environment you will need to source that environment (e.g., source pењ“4/bin/activate) just like youвЂ™re running Python as a regular user. After all, you canвЂ™t graph from the Python shell, as that is not a graphical environment. Use the right-hand menu to navigate.) Install ZeppelinДЇirst, download and install Zeppelin, a graphical Python interpreter which weвЂ™ve previously discussed. (This article is part of our Data Visualization Guide.

#MATPLOTLIB SCATTER PLOT HOW TO#

In this article, weвЂ™ll explain how to get started with Matplotlib scatter and line plots.

Matplotlib scatter plot