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Creating a Dashboard of Graphs in Python

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:

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

matplotlib annotation overlapping y_tick labels on plot

I have tried a number of different things to fix my chart, from zorder on the plots to plt.rcParams.
I feel that this is such a simple problem but I just dont know where I have gone wrong. As you can see the bottom annotation in cyan blue is unreadable and mashed with the y label.
Ideally, the annotation sits over the y label to a point where text inside annotation is readable.
If possible just for the annotation to sit on top and still overlay the y label..something like this
Any help on this would be greatly appreciated.
ax = df.plot(x=df.columns[0], y=df.columns[1], legend=False, zorder=0, linewidth=1)
y1 =df.loc[:, df.columns[2]].tail(1)
y2= df.loc[:, df.columns[1]].tail(1)
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
print(colors)
for var in (y1, y2):
plt.annotate('%0.2f' % var.max(), xy=(1, var.max()), zorder=1, xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
bbox=dict(boxstyle="round", fc=colors[0], ec=colors[0],))
ax2 = ax.twinx()
df.plot(x=df.columns[0], y=df.columns[2], ax=ax2, legend=False, color='#fa8174', zorder=0,linewidth=1)
ax.figure.legend(prop=subtitle_font)
ax.grid(True, color="white",alpha=0.2)
pack = [df.columns[1], df.columns[2], freq[0]]
plt.text(0.01, 0.95,'{0} v {1} - ({2})'.format(df.columns[1], df.columns[2], freq[0]),
horizontalalignment='left',
verticalalignment='center',
transform = ax.transAxes,
zorder=10,
fontproperties=subtitle_font)
ax.text(0.01,0.02,"Sources: FRED, Quandl, #Paul92s",
color="white",fontsize=10,
horizontalalignment='left',
transform = ax.transAxes,
verticalalignment='center',
zorder=20,
fontproperties=subtitle_font)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator())
ax.xaxis.set_minor_locator(matplotlib.dates.MonthLocator((4,7,10)))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
ax.xaxis.set_minor_formatter(ticker.NullFormatter()) # matplotlib.dates.DateFormatter("%m")
plt.setp(ax.get_xticklabels(), rotation=0, ha="center", zorder=-1)
plt.setp(ax2.get_yticklabels(), rotation=0, zorder=-1)
plt.setp(ax.get_yticklabels(), rotation=0, zorder=-1)
plt.gcf().set_size_inches(14,7)
ax.set_xlabel('Data as of; {0}'.format(df['Date'].max().strftime("%B %d, %Y")), fontproperties=subtitle_font)
y1 =df.loc[:, df.columns[2]].tail(1)
y2= df.loc[:, df.columns[1]].tail(1)
for var in (y1, y2):
plt.annotate('%0.2f' % var.max(), xy=(1, var.max()), zorder=1,xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="#fa8174", ec="#fa8174"))
plt.title('{0}'.format("FRED Velocity of M2 Money Stock v Trade Weighted U.S. Dollar Index: Broad"),fontproperties=heading_font)
ax.texts.append(ax.texts.pop())
ax.set_facecolor('#181818')
ax.figure.set_facecolor('#181818')
plt.rcParams['axes.axisbelow'] = True

I don't figure out why zorder doesn't work, but you can directly set the label style of tick labels:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import rand
import matplotlib.patches as mpatches
fig, ax = plt.subplots(1, 1)
ax.plot(rand(100), '^', color='r')
for label in ax.get_xticklabels():
label.set_bbox(dict(facecolor='orange'))
ax1 = ax.twinx()
ax1.plot(rand(100), 'o', color='b')
index_to_add_bbox = [2, 4]
ax1_labels = ax1.get_yticklabels()
for i in index_to_add_bbox:
ax1_labels[i].set_bbox(dict(boxstyle='Circle', facecolor='orange'))
plt.show()

For scatterplot with matplotlib how to include in the legend gradient for dot size and colour?

I create two scatterplots with matplotlib in python with this code, the data for the code is here:
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(arr), vmax=1.05*max(arr))
ax1.scatter(x, y1, s=20, c=arr, cmap='Blues_r', norm=norm, marker='x', label='bla1')
ax3.scatter(x, y2, s=(20*(1.1-arr))**3.5, c=arr, cmap='Reds_r', norm=norm, marker='^', label='bla1')
The created fig. looks like this:
So, the dot size (in ax3) and the dot colour (in ax1 and ax3) are taken from arrays containing floats with all kinds of values in the range [0,1]. My question: How do I create a legend that displays the corresponding y-values for, let's say 5 different dot sizes and 5 different colour nuances?
I would like the legend to look like in the figure below (source here), but with the colour bar and size bar put into a single legend, if possible. Thanks for suggestions and code!

# using your data in dataframe df
# create s2
df['s2'] = (20*(1.1-df.arr))**3.5
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, label='arr')
p2 = ax3.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.23, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()
There's no legend for p1 because the size is static.
I think this would be better as two separate plots
I used Customizing Plot Legends: Legend for Size of Points
Separate
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(20, 10))
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, ax=ax1, label='arr')
p2 = ax2.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, ax=ax2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.2, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()

figure with 3X3 subplots having shared x,y axes and labels?

I cannot add create 3x3 subplots with common axes and labels using the following code:
fig = plt.figure(figsize=(24, 12))
ax = fig.add_subplot(111) # The big subplot
fig.text(0.5, 0.04, 'Time (minutes)', ha='center')
fig.text(0.04, 0.5, 'Distance travelled (pixels)', va='center',
rotation='vertical')
# Turn off axis lines and ticks of the big subplot
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off',
left='off', right='off')
ax1 = fig.add_subplot(331)
ax1.errorbar(Time, MD11, MD11err, capsize=3, ls='none',
color='red', elinewidth=0.5, capthick=1)
ax1.scatter(Time, S1, s=20, color = 'grey')
ax1.plot(Time, MD11, color = 'red', label="First 1ug/g
injection")
ax1.plot(Time, S1, color = 'grey', label="Saline")
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.title ('MDMA 1 ug/g ')
ax1.ylabel('First Injection', va='center', rotation='vertical')
ax2 = fig.add_subplot(332)
ax2.errorbar(Time, MD31, MD31err, capsize=5, ls='none',
color='red', elinewidth=1, capthick=2)
ax2.errorbar(Time, S4, S4err, capsize=5, ls='none',
color='gray', elinewidth=1, capthick=2)
ax2.scatter(Time, MD31,s=20, color = 'red')
ax2.scatter(Time, S4,s=20, color = 'grey')
ax2.plot(Time, MD31, color = 'red', label="First 3ug/g
injection")
ax2.plot(Time, S4, color = 'grey', label="Saline")
and so on till ax9. The above code just created the 331 subplot and not the other ones. I have defined all the variables properly. Please help.

python matplotlib plotting many subfigures with the same parameters

My plot is like the following
fig = plt.figure(figsize=(7,3))
ax1 = fig.add_subplot(1,3,1)
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1,3,3)
ax1.scatter(x11, y11, s=50, alpha=0.5, c='orange', marker='o')
ax1.scatter(x12, y12, s=50, alpha=0.5, c='blue', marker='s')
ax2.scatter(x21, y21, s=50, alpha=0.5, c='orange', marker='o')
ax2.scatter(x22, y22, s=50, alpha=0.5, c='blue', marker='s')
ax3.scatter(x31, y31, s=50, alpha=0.5, c='orange', marker='o')
ax3.scatter(x32, y32, s=50, alpha=0.5, c='blue', marker='s')
It seems kinda redundant to set s=50, alpha=0.5 over and over. Is there a way to set them once for all? Also for color and marker, is there a way to write them in one place so it's easier to modify?

You could do this:
fig = plt.figure(figsize=(7,3))
ax1 = fig.add_subplot(1,3,1)
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1,3,3)
xs = [x11, x12, x21, x22, x31, x32]
ys = [y11, y12, y21, y22, y31, y32]
cs = ['orange', 'blue']
ms = 'os'
for j in xrange(len(xs)):
ax1.scatter(xs[j], ys[j], s=50, alpha=0.5, c=cs[j % 2], marker=ms[j % 2])

I like organizing the data and styles, and then using that to organize the plotting. Generating some random data to make a runnable example:
import matplotlib.pyplot as plt
from numpy.random import random
fig, axs = plt.subplots(3, figsize=(7,3)) #axs is an array of axes
orange_styles = {'c':"orange", 'marker':'o'}
blue_styles = {'c':"blue", 'marker':'s'}
pts = []
for i in range(12):
pts.append(random(4))
orange_x = pts[0:3] # organized data is lists of lists
orange_y = pts[3:6]
blue_x = pts[6:10]
blue_y = pts[10:12]
for ax, x, y in zip(axs, orange_x, orange_y): #all the orange cases
ax.scatter(x, y, s=50, alpha=0.5, **orange_styles) # **kwds
for ax, x, y in zip(axs, blue_x, blue_y):
ax.scatter(x, y, s=50, alpha=0.5, **blue_styles)