Matplotlib is highly useful visualization library in Python. It is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. Visualization plays a very important role as it helps us to understand huge chunks of data and extract knowledge.
Matplotlib.pyplot.savefig()
As the name suggests savefig() method is used to save the figure created after plotting data. The figure created can be saved to our local machines by using this method.
Syntax: savefig(fname, dpi=None, facecolor=’w’, edgecolor=’w’, orientation=’portrait’, papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None, metadata=None)
Parameters:
| PARAMETERS | DESCRIPTION |
|---|---|
| fname | Filename .png for image, .pdf for pdf format. File location can also be specified here. |
| dpi | Number of dots per inch.(picture quality) |
| papertype | Paper type could be “a0 to a10”, “executive”, “b0 to b10”, “letter”, “legal”, “ledger”. |
| format | File format such as .png, .pdf. |
| facecolor and edgecolor | Default as White. |
| bbox_inches | Set it as “tight” for proper fit of the saved figure. |
| pad_inches | Padding around the saved figure. |
| transparent | Makes background of the picture transparent. |
| Orientation | Landscape or Portrait. |
Example 1:
# importing required modules import matplotlib.pyplot as plt  # creating plotting dataxaxis =[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]yaxis =[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  # plotting plt.plot(xaxis, yaxis)plt.xlabel("X")plt.ylabel("Y")  # saving the file.Make sure you # use savefig() before show().plt.savefig("squares.png")  plt.show() |
Output :
Example 2:
# importing the modules import matplotlib.pyplot as plt    # creating data and plotting a histogramx =[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]plt.hist(x)  # saving the figure.plt.savefig("squares1.png",            bbox_inches ="tight",            pad_inches = 1,            transparent = True,            facecolor ="g",            edgecolor ='w',            orientation ='landscape')  plt.show() |
Output :
