I am just starting to learn data science using python on Data Camp and I noticed something while using the functions in matplotlib.pyplot
import matplotlib.pyplot as plt
year = [1500, 1600, 1700, 1800, 1900, 2000]
pop = [458, 580, 682, 1000, 1650, 6,127]
plt.plot(year, pop)
plt.show() # Here a window opens up and shows the figure for the first time
but when I try to show it again it doesn't..
plt.show() # for the second time.. nothing happens
And I have to retype the line above the show()
to be able to show a figure again
Is this the normal thing or a problem?
Note: I am using the REPL
If you are using Matplotlib from within a script, the function plt. show() is your friend. plt. show() starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.
Matplotlib is used in a IPython shell or a notebook (ex: Kaggle), plt. show() is unnecessary.
In the current versions of the IPython notebook and jupyter notebook, it is not necessary to use the %matplotlib inline function. As, whether you call matplotlib. pyplot. show() function or not, the graph output will be displayed in any case.
The reason your plot is blank is that matplotlib didn't auto-adjust the axis according to the range of your patches. Usually, it will do the auto-adjust jobs with some main plot functions, such as plt. plot(), plt.
Yes, this is normal expected behavior for matplotlib figures.
When you run plt.plot(...)
you create on the one hand the lines
instance of the actual plot:
>>> print( plt.plot(year, pop) )
[<matplotlib.lines.Line2D object at 0x000000000D8FDB00>]
...and on the other hand a Figure
instance, which is set as the 'current figure' and accessible through plt.gcf()
(short for "get current figure"):
>>> print( plt.gcf() )
Figure(432x288)
The lines (as well as other plot elements you might add) are all placed in the current figure. When plt.show()
is called, the current figure is displayed and then emptied (!), which is why a second call of plt.show()
doesn't plot anything.
One way of solving this is to explicitly keep hold of the current Figure
instance and then show it directly with fig.show()
, like this:
plt.plot(year, pop)
fig = plt.gcf() # Grabs the current figure
plt.show() # Shows plot
plt.show() # Does nothing
fig.show() # Shows plot again
fig.show() # Shows plot again...
A more commonly used alternative is to initialize the current figure explicitly in the beginning, prior to any plotting commands.
fig = plt.figure() # Initializes current figure
plt.plot(year, pop) # Adds to current figure
plt.show() # Shows plot
fig.show() # Shows plot again
This is often combined with the specification of some additional parameters for the figure, for example:
fig = plt.figure(figsize=(8,8))
The fig.show()
approach may not work in the context of Jupyter Notebooks and may instead produce the following warning and not show the plot:
C:\redacted\path\lib\site-packages\matplotlib\figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
Fortunately, simply writing fig
at the end of a code cell (instead of fig.show()
) will push the figure to the cell's output and display it anyway. If you need to display it multiple times from within the same code cell, you can achieve the same effect using the display
function:
fig = plt.figure() # Initializes current figure
plt.plot(year, pop) # Adds to current figure
plt.show() # Shows plot
plt.show() # Does nothing
from IPython.display import display
display(fig) # Shows plot again
display(fig) # Shows plot again...
One reason for wanting to show
a figure multiple times is to make a variety of different modifications each time. This can be done using the fig
approach discussed above but for more extensive plot definitions it is often easier to simply wrap the base figure in a function and call it repeatedly.
Example:
def my_plot(year, pop):
plt.plot(year, pop)
plt.xlabel("year")
plt.ylabel("population")
my_plot(year, pop)
plt.show() # Shows plot
my_plot(year, pop)
plt.show() # Shows plot again
my_plot(year, pop)
plt.title("demographics plot")
plt.show() # Shows plot again, this time with title
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