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 data xaxis = [ 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 histogram x = [ 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 :
Matplotlib.pyplot.savefig() in Python
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.