You are able to display the legend quite easily using the following command: plt.legend() Scatter plot in Python with Seabornįor completeness, we are including a simple example that leverages the Seaborn library (also built on Matplotlib). Next, we used the pyplot scatter function to draw a. This is a simple python scatter plot example where we declared two lists of random numeric values. Draw scatter plot matplotlib how to#Plt.title('Scatter example with custom markers') Adding a legend to the chart How to draw a scatter plot in Matplotlib The basic syntax to draw matplotlib pyplot scatter plot is x: list of arguments that represents the X-axis. We can easily modify the marker style and size of our plots. Plt.ylabel('Cost') Change the marker type and size Plt.title('Simple scatter with Matplotlib') Matplotlib offers a rich set of capabilities to create static charts. The required positional arguments supplied to ax.scatter() are two. my_(x='Duration', y='Cost', title= 'Simple scatter with Pandas', label= ).legend( bbox_to_anchor= (1.02, 1)) Rendering a Plot with Matplotlib Scatter plots of (x,y) point pairs are created with Matplotlibs ax.scatter() method. Note the usage of the bbox_to_anchor parameter to offset the legend from the chart. We used the label parameter to define the legend text. My_(x='Duration', y='Cost', title= 'Simple scatter with Pandas', c='green') Displaying the scatter legend in Pandas We can easily change the color of our scatter points. Draw scatter plot matplotlib code#Here’s our chart: Changing the plot colors The following code block draws a scatter plot that depicts the relation between the demand and price: import matplotlib.pyplot as plt x1 214, 5, 91, 81, 122, 16, 218, 22 x2 12, 125, 149, 198, 22, 26, 28, 32 plt.scatter (x1, x2) Set X and Y axis labels plt.xlabel ('Demand') plt.ylabel ('Price') Display the graph plt. my_(x='Duration', y='Cost', title= 'Simple scatter with Pandas') Once we have our DataFrame, we can invoke the ot() method to render the scatter using the built-in plotting capabilities of Pandas. My_data = pd.om_dict() Drawing a chart with Pandas We’ll define the x and y variables as well as create a DataFrame. Python scatter plots example – a step-by-step guide Importing libraries import matplotlib.pyplot as plt It’s important to note that the Pandas plotting capabilities are a subset from those available in Matplotlib, a powerful Data Visualization library, which we have covered in other tutorials. In this Data Visualization tutorial we’ll learn how to quickly render and customize custom charts using Python and the Pandas library.
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