To get further acquainted with Matplotlib functions, let us plot a multiline plot with axes, labels, title, and legend configured in one single snippet.
In this example, we take real-world data from the World Bank on agriculture. As the world population continues to grow, food security continues to be an important global issue. Let us have a look at the production data of a few major crops in the recent decade by plotting a multiline plot with the following code:
Data source: https://data.oecd.org/agroutput/crop-production.htm OECD (2017), Crop production (indicator). doi: 10.1787/49a4e677-en (Accessed on 25 December 2017)
# Import relevant modules import pandas as pd import matplotlib.pyplot as plt # Import dataset crop_prod = pd.read_csv('OECD-THND_TONNES.txt',delimiter=' ') years = crop_prod[crop_prod['Crop']=='SOYBEAN']['Year'] rice = crop_prod[crop_prod['Crop']=='RICE']['Value'] wheat = crop_prod[crop_prod['Crop']=='WHEAT']['Value'] maize = crop_prod[crop_prod['Crop']=='MAIZE']['Value'] soybean = crop_prod[crop_prod['Crop']=='SOYBEAN']['Value'] # Plot the data series plt.plot(years, rice, label='Rice') plt.plot(years, wheat, label='Wheat') plt.plot(years, maize, label='Maize') plt.plot(years, soybean, label='Soybean') # Label the x- and y-axes plt.xlabel('Year',size=12,fontweight='semibold') plt.ylabel('Thousand tonnes',size=12,fontweight='semibold') # Add the title and legend plt.title('Total OECD crop production in 1995-2016', size=14, fontweight='semibold') plt.legend() # Show the figure plt.show()
From the resultant plot, we can observe the production of maize > wheat > soybean > rice, a generally growing trend of crop production and a steady growth of soybean production:
