Creating a Dashboard of Graphs in Python-CodePudding

using matplotlib in Jupyter Notebook, I have already written code to produce a number of different graphs (example code below used to create some of my graphs). I would like to put all of these graphs into a dashboard. Is there a preferred method for my instance where I already have the graph produced and just want to plug it in to a dashboard template?

It seems like there are a number of different options (bokeh and plotly).

# multiple line plots
plt.plot( 'Week', 'ESI_1_2_3', data=SZ_ED_TOT, marker='o', color='blue', linewidth=2)
plt.plot( 'Week', 'ESI_4_5', data=SZ_ED_TOT, marker='o', color='red', linewidth=2)
plt.ylabel('Patient Count')
plt.xlabel('Week')
plt.xticks(rotation = 90)
plt.title('SZ Acuity Counts')
plt.gcf().set_size_inches(20, 8)
plt.legend()
plt.show()

# multiple line plots
plt.plot( 'Week', '%_1_2_3', data=UMC_ED_TOT, marker='o', color='blue', linewidth=2)
plt.plot( 'Week', '%_4_5', data=UMC_ED_TOT, marker='o', color='red', linewidth=2)
plt.ylabel('% of Patients')
plt.xlabel('Week')
plt.xticks(rotation = 90)
plt.title('UMC Acuity % Of Total Patients')
plt.gcf().set_size_inches(20, 8)
plt.legend()
plt.show()

#dual Y axis graph
ax = UMC_Cobmined.plot.bar(x='Week', y='Total', rot=90,color=(0.2, 0.4, 0.6, 0.6))
ax.set_xlabel('Week')
ax.set_ylabel('Total Volume')

ax2 = ax.twinx()

ax2.plot(ax.get_xticks(),
   UMC_Cobmined[['%_ESI_3','%_ESI_4','%_ESI_5']].values,
   linestyle='-',
   marker='o', linewidth=2.0)
ax2.set_ylabel('% LWBS By Acuity')
ax2.legend(["%_ESI_3", "%_ESI_4","%_ESI_5"]);

plt.title('UMC LWBS Acuity % by Volume')
plt.gcf().set_size_inches(20, 8)
plt.show()

Ideal example of what I would like my dashboard to look like:

CodePudding user response：

You can achieve this by using a grid in matplotlib:

import matplotlib.pytplot as plt

fig = plt.figure()
grid = fig.add_gridspec(NUMBER_OF_GRID_COLUMNS, NUMBER_OF_GRID_ROWS)

and then add each plot to an axis:

ax1 = fig.add_subplot(grid[0, 0])
# add plot to ax1 
ax1.plot(x,y)
ax1.title("Plot on ax1")

ax2 = fig.add_subplot(grid[1,0])
# add plot to ax2
ax2.plot(x,y)
ax2.title("Plot on ax2")

plt.show()

and so on