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python-3.xmatplotlibplotdata-visualizationseaborn

Display seaborn plots at some point later in code

Let's say at some point in my code, I have following two graphs: i.e. graph_p_changes and graph_p_contrib 

line_grapgh_p_changes = df_p_change[['year','interest accrued', 'trade debts', 'other financial assets']].melt('year', var_name='variables',  value_name='p_changes')
graph_p_changes = sns.factorplot(x="year", y="p_changes", hue='variables', data=line_grapgh_p_changes, height=5, aspect=2)
graph_p_changes.set(xlabel='year', ylabel='percentage change in self value across the years')

line_grapgh_p_contrib = df_p_contrib[['year','interest accrued', 'trade debts', 'other financial assets']].melt('year', var_name='variables',  value_name='p_changes')
graph_p_contrib = sns.factorplot(x="year", y="p_changes", hue='variables', data=line_grapgh_p_contrib, height=5, aspect=2)
graph_p_contrib.set(xlabel='year', ylabel='percentage chnage in contribution to total value')

Now at some point later in my code, I just want to display one of the above two graphs. But when I do plt.show(), it displays both of the above graphs in my jupyter notebook. How can I display only one graph at any point in my code.

Solution

  • You'll want to refer to the assigned variable for each plot and then add .fig after that to redisplay it in a Jupyter notebook cell.

    Specifically, in your case you'd reference graph_p_changes.fig or graph_p_contrib.fig in a cell and execute that cell to see an individual plot again.

    This is similar to how you can show Seaborn's ClusterGrids again, see here. Because the title of your question said 'seaborn plots', I'll add for sake of completeness, this doesn't hold for plots like Seaborn's line plot (lineplot) or bar plot (barplot) , that produce AxesSubplot objects. There you use .figure, for example ax.figure to recall most of the examples listed on Seaborn's lineplot documentation. (The suggestion of ax.figure assumes you added assigning the plot to a variable named ax, for example using code like ax = sns.lineplot(....)

    Example catplots with code

    This is using example code from here and seaborn's catplot documentation (see below) to make two plots. If this code was in one cell and then that cell was run, you'd see two plots in the output below it.

    import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

titanic = sns.load_dataset("titanic")
exercise = sns.load_dataset("exercise")
g = sns.catplot("alive", col="deck", 
                col_wrap=3, data=titanic[titanic.deck.notnull()], 
                kind="count", height=2.5, aspect=.8)

another_plot = sns.catplot(x="time", y="pulse", hue="kind", data=exercise)

    Later, each can be displayed again individually as output of other cells with g.fig or another_plot.fig, depending on which plot you want to show.

    Additionaly, I'll suggest to improve your long-term code viability, you may want to move on to using catplot in your plotting calls as that is what factorplot is now called in seaborn. See here where it says "factorplot still exists and will pass its arguments through to catplot() with a warning. It may be removed eventually, but the transition will be as gradual as possible."

    UPDATE:

    OP commented that what was desired was code allowing interspersed stdout/stderr output with plots at precise points among that stream and not just at the end.

    For some reason, Seaborn plots (even simple line plots) don't seem to get 'captured' correctly with io.capture_output(), and so I had to use the %%capture cell magic command in the producing cell and combine the output in a separate cell. However, Plotly plots I tried based on example code are captured by io.capture_output() and allow facile intermixing all in the same cell. This is all illustrated in an example notebook available here; it is best viewed in static form here at nbviewer because nbviewer renders the Plotly plots while GitHub doesn't. The top of that notebook includes a link where you can launch an active Jupyter session where it will run.

    Update related to this UPDATE:
    In an insightful answer to 'seaborn stop figure from being visualized', ffrosch suggests you "can temporarily disable/enable the inline creation with plt.ioff() and plt.ion()." This may offer yet another way to fine-tune when Seabor plots show among the output and/or offer another way to constrain ouput since %%capture cell magic worked yet io.capture_output() did not. (I have yet to try this.)