Matplotlib

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Data visualization helps to turn complex data into actionable insights. To make effective and self-explanatory graphs, keep it simple, write labels and legends according to your reader, and choose the right library. Clear data visualization is a valuable tool for data analysis and communication.

Create graphs in Python using matplotlib, a library providing functions for creating various charts and plots. Example: plt.plot(x, y); plt.xlabel("X Axis"); plt.ylabel("Y Axis"); plt.title("Line Plot"); plt.show() to create a simple line plot."

Create graphs in Python using matplotlib, a library providing functions for creating various charts and plots. Example: plt.plot(x, y); plt.xlabel("X Axis"); plt.ylabel("Y Axis"); plt.title("Line Plot"); plt.show() to create a simple line plot.

Use Matplotlib in Python to create data visualizations by installing Matplotlib, importing modules, preparing data, creating plots, customizing plots with labels, titles and legends, and showing the plots using pip install matplotlib, pyplot.plot(), pyplot.xlabel() and pyplot.show().

Your reader, will have no time and no energy to try to understand what's going on your graph. In this article we give you an "to do" and "not to do" examples.

Learn how to do a simple bar plot with Pandas using Python.

Learn how to do a boxplot with Pandas using Python. Using the Pandas DataFrame.boxplot() method.

Learn how to do a pie plot with Pandas using Python using the Pandas.plot() method.