Data Analysis and Visualization in Python for Ecologists: Key Points

Data visualization with Pandas and Matplotlib

Load your required libraries into Python and use common nicknames
Use pandas to load your data –pd.read_csv()– and to explore it –.head(), .tail(), and .info() methods.
The .plot() method on your DataFrame is a good plotting starting point.
Matplotlib allows you to customize every aspect of your plot. Start with the plt.subplots() function to create a figure object and the number of axes (or subplots) you need.
Export plots to a file using the .savefig() method.

pandas DataFrames carry many methods that can help you explore the properties and distribution of data.
Using the help function, reading error messages, and asking for help are all good strategies when things go wrong.
The type of an object determines what kinds of operations you can perform on and with it.
Python evaluates expressions in a line one by one before assigning the final result to a variable.