On top of each other as well as other chart elements. The plot resizes to show these annotations.Įach note shows the coordinates at which it was added and these notes can be placed Knowing that the chart is in (0,0) to (1,1). This demonstrates that annotations can be added outside the chart
#How to install matplotlib 2.0.2 code#
The following code adds fifty notes at random locations from (-1.0, -1.0) Space is added to the right of the chart to allow for the notes to be displayed.Īdd random notes inside and outside the chart Is where the text in the annotation starts and the text is left-justified. The notes are added at the 4 corners of the chart and the default is that this Sample line chart with notes at four corners annotate ( 'Note added at (1,1)', xy = ( 1, 1 ), xycoords = 'axes fraction' ) 10 11 fig. annotate ( 'Note added at (1,0)', xy = ( 1, 0 ), xycoords = 'axes fraction' ) 9 ax. annotate ( 'Note added at (0,1)', xy = ( 0, 1 ), xycoords = 'axes fraction' ) 8 ax. annotate ( 'Note added at (0,0)', xy = ( 0, 0 ), xycoords = 'axes fraction' ) 7 ax. tick_params ( labelsize = 14 ) 4 5 # Add a four notes 6 ax. Useful when adding annotations to data points.ġ. The default for the xy values is to use the units of the chart and this is very Use annotate to add a note to the chart with the addition of the following code. Sample line chart with three sets of ten random samples tick_params ( labelsize = 14 ) 30 31 plt. set_visible ( False ) 24 25 # Don't show any ticks on any spine 26 ax. set_ylabel ( 'Y label', fontsize = 16 ) 20 21 # Hide the right and top spines 22 ax. set_xlabel ( 'X label', fontsize = 16 ) 19 ax.
#How to install matplotlib 2.0.2 install#
cool ( 0.3 ), marker = 'o', markersize = 8 ) 15 ax. Matplotlib for C++ pip3 install matplotlib or pip for Python 2 Includes and Linking The header matplotlibcpp.h depends on the Python header, Python.h, the corresponding Python library libpython, and on numpy/arrayobject.h. set_title ( "Sample Line Chart", fontsize = 18, fontweight = 'bold' ) 13 14 ax. subplots ( figsize = ( 10, 7 ), facecolor = plt. With a typical installation of matplotlib, such as from a binary installer or a linux distribution package, a good default backend will already be set, allowing both interactive work and plotting from scripts, with output to the screen and/or to a file, so at least initially you will not need to use any of the methods given above. seed ( 42 ) # seed used to keep the same random points on repeated runs 4 val1 = sorted () 5 val2 = sorted () 6 val3 = sorted () 7 x_val = list ( range ( samples )) 8 9 # Create a line chart 10 fig, ax = plt. 1 # Create the data 2 samples = 10 3 random.