I am trying to create a text box within my graph in matplotlib where it gives me the date in which the graph was created.
I have created a text box in the bottom right corner of my graph using the figtext function in matplotlib, but cannot figure out how to incorporate the python datetime function within the text box so it displays the date. Any ideas?
Code and graph below:
#Stacked Bar Char- matplotlib
#Create the general blog and the "subplots" i.e. the bars
f, ax1 = plt.subplots(1, figsize=(8,5), dpi=1000)
# Set the bar width
bar_width = 0.50
# positions of the left bar-boundaries
bar_l = [i+1 for i in range(len(df4['Pandas']))]
# positions of the x-axis ticks (center of the bars as bar labels)
tick_pos = [i+(bar_width/2) for i in bar_l]
#Stack the negative bars by region
#Start bottom at 0, and then use pandas to add bars together
ax1.bar(bar_l, df4['cats1'], width=bar_width, label='cats',color='R', alpha=.4, align='center',
bottom = 0)
ax1.bar(bar_l, df4['dogs1'], width=bar_width,label='dogs',color='#ADD8E6',alpha=.4, fill=True, align='center',
bottom =(df4['cats1']))
ax1.bar(bar_l, df4['zebras1'], width=bar_width, label='zebras',color='#FFA500',alpha=.4, align='center',
bottom = np.array(df4['cats1'])+np.array(df4['dogs1']))
ax1.bar(bar_l, df4['pandas1'], width=bar_width, label='pandas', color='b',alpha=.5, fill=True, align='center',
bottom = np.array(df4['cats1'])+np.array(df4['dogs1'])+np.array(df4['zebras1']))
#Stack the positive bars by region
#Start bottom at 0, and then use pandas to add bars together
ax1.bar(bar_l, df4['cats'], width=bar_width,color='R', alpha=.4, align='center',
bottom = 0)
ax1.bar(bar_l, df4['dogs'], width=bar_width,color='#ADD8E6',alpha=.4, fill=True, align='center',
bottom =(df4['cats']))
ax1.bar(bar_l, df4['zebras'], width=bar_width ,color='#FFA500',alpha=.4, align='center',
bottom = np.array(df4['cats'])+np.array(df4['dogs']))
ax1.bar(bar_l, df4['pandas'], width=bar_width, color='b',alpha=.5, fill=True, align='center',
bottom = np.array(df4['cats'])+np.array(df4['dogs'])+np.array(df4['zebras']))
# set the x ticks with names
plt.xticks(tick_pos, df4['Year'],fontsize=10)
# Set the label and legends
plt.title('Animals on the farm', fontweight='bold')
ax1.set_ylim([-1600,1000])
ax1.set_ylabel("Count",fontsize=12)
ax1.set_xlabel("Year",fontsize=12)
plt.legend(loc='upper left', prop={'size':6})
ax1.axhline(y=0, color='k')
ax1.axvline(x=0, color='k')
plt.figtext(0, 0,"Data as of ", wrap=False,
horizontalalignment='left',verticalalignment ='bottom', fontsize=8)
plt.setp(ax1.get_yticklabels(), rotation='horizontal', fontsize=10)
plt.show()
Graph= Animals on the Farm
Here's an example with today's date:
import datetime as dt
plt.figtext(0, 0,"Data as of {}".format(dt.date.today().strftime('%Y-%m-%d')), wrap=False,
horizontalalignment='left',verticalalignment ='bottom', fontsize=8)
If you have a datetime object, you can use the strftime method to convert it to a string representation using the formatting you want:
from datetime import datetime
plt.figtext(0, 0, 'Date as of ' + datetime.now().strftime('%Y-%m-%d'))
Related
I have created a bar chart and a line chart using two different y-axes for the following dataframe.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'DXC':['T1', 'H1', 'HP', 'T1_or_H1_or_HP'],
'Count': [2485, 5595, 3091, 9933],
'percent':[1.06, 2.39, 1.31, 4.23]})
DXC Count percent
0 T1 2485 1.06
1 H1 5595 2.39
2 HP 3091 1.31
3 T1_or_H1_or_HP 9933 4.23
Using the following code, I can also display values next to each bar in the bar chart. However, I have not been successful thus far in my attempts to also display the label (percent) values for the line plot.
fig=plt.figure()
#AX: bar chart
ax=df["Count"].plot(kind="bar", color="orange")
ax.set_ylabel("Counts")
ax.set_xlabel("")
ax.set_ylim(0,20000)
for tick in ax.get_xticklabels():
tick.set_rotation(0)
#AX2: Create secondary y-axis with same x-axis as above for plotting percent values
ax2=ax.twinx()
ax2.plot(ax.get_xticks(),df["percent"], color="red", linewidth=4, marker = "o")
ax2.grid(False)
ax2.set_ylabel("Percent", color = "red")
ax2.set_ylim(0,4.5)
ax2.tick_params(labelcolor="red", axis='y')
def add_value_labels(ax, spacing=5):
for i in ax.patches:
y_value = i.get_height()
x_value = i.get_x() + i.get_width() / 2
space = spacing
va = 'bottom'
# Use Y value as label and format number with no decimal place
label = "{:.0f}".format(y_value)
# Create annotation
ax.annotate(label,(x_value, y_value), xytext=(0, space), textcoords="offset points", ha='center', va=va)
add_value_labels(ax)
plt.show()
Can somebody suggest how to display labels for both bar plot and line plot in the same figure?
Here is a modified function that will achieve the required task. The trick is to extract the x and y values based on the type of the chart you have. For a line chart, you can use ax.lines[0] and then get_xdata and get_ydata
def add_value_labels(ax, typ, spacing=5):
space = spacing
va = 'bottom'
if typ == 'bar':
for i in ax.patches:
y_value = i.get_height()
x_value = i.get_x() + i.get_width() / 2
label = "{:.0f}".format(y_value)
ax.annotate(label,(x_value, y_value), xytext=(0, space),
textcoords="offset points", ha='center', va=va)
if typ == 'line':
line = ax.lines[0]
for x_value, y_value in zip(line.get_xdata(), line.get_ydata()):
label = "{:.2f}".format(y_value)
ax.annotate(label,(x_value, y_value), xytext=(0, space),
textcoords="offset points", ha='center', va=va)
add_value_labels(ax, typ='bar')
add_value_labels(ax2, typ='line')
From matplotlib v3.4.0 it's easier to use matplotlib.pyplot.bar_label, as explained in this answer.
The OP has many extraneous steps, which can be removed by using the yticks, secondary_y, and ylabel parameters for pandas.DataFrame.plot
pandas.DataFrame.itertuples can be used to annotate the line with matplotlib.axes.Axes.annotate because .Index corresponds to the x-axis locations and .percent is the correct y value for ax2.
See How to add hovering annotations to a plot for additional options to annotate the line.
See How to change the color of the axis, ticks and labels for a plot to easily change colors of various aspects of the figure.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# create the bar plot for the Count column and specify the yticks
ax = df.Count.plot(kind='bar', color='tab:orange', rot=0, yticks=range(0, 20001, 2500), figsize=(9, 5), ylabel='Counts')
# add bar labels
ax.bar_label(ax.containers[0])
# add the line plot for the percent column and specify the yticks and secondary_y
ax2 = df.percent.plot(marker='.', yticks=np.arange(0, 5, 0.5), secondary_y=True, ax=ax, ylabel='Percent')
# annotate the line by iterating through each row with itertuples
for row in df.itertuples():
ax2.annotate(text=row.percent, xy=(row.Index, row.percent))
Can someone please help me plot x axis labels in percentages given the following code of my horizontal bar chart?
Finding it difficult to find as I want a more simplistic chart without x axis labels and ticks.
[Horizontal Bar Chart][1]
# Plot the figure size
plt.figure(figsize= (8,6))
# New variable and plot the question of the data frame in a normalized in a horizontal bar chat.
ax1 = df[q1].value_counts(normalize=True).sort_values().plot(kind="barh", color='#fd6a02', width=0.75, zorder=2)
# Draw vague vertical axis lines and set lines to the back of the order
vals = ax1.get_xticks()
for tick in vals:
ax1.axvline(x=tick, linestyle='dashed', alpha=0.4, color = '#d3d3d3', zorder=1)
# Tot3als to produce a composition ratio
total_percent = df[q1].value_counts(normalize=True) *100
# Remove borders
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.spines['bottom'].set_visible(False)
# Set the title of the graph inline with the Y axis labels.
ax1.set_title(q1, weight='bold', size=14, loc = 'left', pad=20, x = -0.16)
# ax.text(x,y,text,color)
for i,val in enumerate(total):
ax1.text(val - 1.5, i, str("{:.2%}".format(total_percent), color="w", fontsize=10, zorder=3)
# Create axis labels
plt.xlabel("Ratio of Responses", labelpad=20, weight='bold', size=12)
Each time I get a EOF error. Can someone help?
It's not based on your code, but I'll customize the answer from the official reference.
The point is achieved with ax.text(), which is a looping process.
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.rcdefaults()
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
ax.barh(y_pos, performance, align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')
# Totals to produce a composition ratio
total = sum(performance)
# ax.text(x,y,text,color)
for i,val in enumerate(performance):
ax.text(val - 1.5, i, str("{:.2%}".format(val/total)), color="w", fontsize=10)
plt.show()
Hi I have the following code. The code is in a for loop, and it makes over 300 plots.
sns.set(style='white', palette='cubehelix', font='sans-serif')
fig, axs = plt.subplots(2, 3, dpi =200);
fig.subplots_adjust(hspace=0.5, wspace=1)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
#tmppath = 'path/{0}'.format(key);
##
sns.countplot(y='Ethnicity', data=value, orient='h', ax=axs[0,0]);
sns.despine(top=True, right=True, left=True, bottom=True,offset=True)
sns.countplot(y='Program Ratio', data=value,orient='v',ax=axs[1,0]);
sns.despine(offset=True)
sns.countplot(y='Site', data = value, ax=axs[0,1]);
sns.despine(offset=True)
sns.countplot(y='HOUSING_STATUS', data = value, ax = axs[1,1])
sns.despine(offset=True)
sns.countplot(y='Alt. Assessment', data = value, ax = axs[0,2])
sns.despine(offset=True)
pth = os.path.join(tmppath, '{0}'.format(key))
for p in axs.patches:
ax.text(p.get_x() + p.get_width()/2., p.get_width(), '%d' %
int(p.get_width()),
fontsize=12, color='red', ha='center', va='bottom')
#plt.tight_layout(pad=2.0, w_pad=1.0, h_pad=2.0);
plt.set_title('{0}'.format(key)+'Summary')
sns.despine()
axs[0,0].set_xticklabels('','Ethnicity')
axs[1,0].set_axis_labels('','Program Ratio')
axs[0,1].set_axis_labels('','Students by Site')
axs[1,1].set_axis_labels('','Housing Status')
axs[0,2].set_axis_labels('','Alt Assessment')
fig.tight_layout()
fig.subplots_adjust(top=0.88)
fig.suptitle('{0}'.format(key)+' Summary')
plt.suptitle('{0}'.format(key)+' Summary')
plt.savefig("path/{0}/{1}.pdf".format(key,key), bbox_inches = 'tight');
plt.clf()
plt.suptitle('{0} Summary'.format(key))
plt.savefig("path/{0}/{1}.pdf".format(key,key), bbox_inches = 'tight');
plt.clf()
I've checked out the links below ( and more):
Remove xticks in a matplotlib plot?
https://datascience.stackexchange.com/questions/48035/how-to-show-percentage-text-next-to-the-horizontal-bars-in-matplotlib
When I try the method from the second link. I end up with graphs like so
Without that the graph looks something like so
I want to get rid of the words count and the ticks on each subplot xaxis.
#ImportanceOfBeingErnest
Thanks, I followed your advice and this post.
Here is what is a compact version of what I ended up with
sns.set(style='white', palette=sns.palplot(sns.color_palette(ui)), font='sans-serif')
plt.figure(figsize=(20,20))
fig, axs2 = plt.subplots(2, 3, dpi =300);
fig.subplots_adjust(top=.8)
fig.subplots_adjust(hspace=1, wspace=1.5)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
sns.countplot(y='column',palette = ui,order = df.value_counts().index, data=df,
orient='h', ax=axs2[0,0]);
axs2[0,0].set_xlabel('')
axs2[0,0].set_xticks([])
axs2[0,0].set_ylabel('')
axs2[0,0].set_title('label',size = 'small')
axs2[0,0].tick_params(axis='y', which='major', labelsize=8)
sns.despine(top=True, right=True, left=True, bottom=True,offset=True)
for p in axs2[0,0].patches:
axs2[0,0].annotate(int(p.get_width()),((p.get_x() + p.get_width()), p.get_y()), xytext=(15, -10), fontsize=8,color='#000000',textcoords='offset points'
,horizontalalignment='center')
fig.suptitle('{0}#{1}'.format(dur,key)+' Summary', va = 'top', ha= 'center') #size = 'small')
props = dict(boxstyle='square', facecolor='white', alpha=0.5)
fig.text(0.85, 0.925, dt.date.today().strftime("%b %d, %Y"), fontsize=9, verticalalignment='top', bbox=props)
fig.text(0.15, 0.925, 'No. of stuff'+ str(len(value['column'].unique())),fontsize = 10, va = 'top', ha = 'center')
plt.savefig("path/{0}/{1} # {2}.pdf".format(dur,dur,key), bbox_inches = 'tight');
plt.clf()
plt.close('all')
Excuse the black marks, didn't want to show the info
This chart almost looks good but is probably not the way to model this in matplotlib. How to have two horizontal bars that extend to the left and right of vertical line at an x-point to show the change of the two datasets eg SDR from 0.7 to 0.25.
Currently i patch things together with '$-$' markers which make misaligned legends and i am not able to place properly. If i change the figsize the markers start misaligning from the vertical bar at their x-point, eg SDR.
How to model this kind of chart proberly?
layer0 = np.random.random(10)
fig, ax = plt.subplots(1,1, figsize=(15/2,1.5*2.5),)
ind = np.arange(10, dtype=np.float64)*1#cordx
ax.plot(ind[0::2]+0.05, layer0[0::2]-0.04, ls='None', marker='$-$', markersize=40)
ax.plot(ind[1::2]-0.15, layer0[1::2]-0.04, ls='None', marker='$-$', markersize=40)
ax.set_ylim(0,1.05)
ax.set_yticks(np.arange(0, 1.1, step=0.1))
ax.set_xticks(ind[0::2]+0.5)
ax.set_xticklabels( ('SDR', 'SSR', 'SCR', 'RCR', 'GUR') )
plt.grid(b=True)
plt.grid(color='black', which='major', axis='y', linestyle='--', lw=0.2)
plt.show()
Alternatively, you can use a horizontal bar chart barh which is more intuitive in this case. Here the key parameter is left which will shift your horizontal bar charts to left/right.
Following is a complete answer:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(2)
layer0 = np.random.random(10)
fig, ax = plt.subplots(1,1, figsize=(15/2,1.5*2.5),)
n = 10
width = 0.5
ind = np.arange(n, dtype=np.float64)*1#cordx
ax.barh(layer0[0::2], [width]*int(n/2), height=0.01, left = ind[0::2])
ax.barh(layer0[1::2], [width]*int(n/2), height=0.01, left = ind[0::2]+width)
ax.set_ylim(0,1.05)
ax.set_yticks(np.arange(0, 1.1, step=0.1))
ax.set_xticks(ind[0::2]+0.5)
ax.set_xticklabels( ('SDR', 'SSR', 'SCR', 'RCR', 'GUR') )
plt.grid(b=True)
plt.grid(color='black', which='major', axis='y', linestyle='--', lw=0.2)
plt.show()
up until now i havent thought of bar charts with bottom offset, which seems to be ok:
layer0 = np.random.random(10)
fig, ax = plt.subplots(1,1, figsize=(15/1.3,1.5*2.5),)# sharey=True)
ind = np.arange(10, dtype=np.float64)*1#cordx
height=0.03
width=0.8
ax.bar(ind[0::2]-width/2, height, width=width, bottom=layer0[0::2]-height)
ax.bar(ind[0::2]+width/2, height, width=width, bottom=layer0[1::2]-height)
ax.set_ylim(-0.,1.05)
plt.grid(color='black', which='major', axis='x', linestyle='-', lw=0.8)
I am trying to create a horizontal bar graph with a table in matplotlib. I am almost there, but fail to align the table with the graph. What I got so far:
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
# Example data
appsol = ['llolLl', 'nnM', 'lllld bbbblnl', 'x2x', 'foobar', 'EXZ', 'Flups', 'Flaps', 'Foobar Barfooment', 'ABC', 'FABAS', 'common', 'AQT', 'Faberjak', 'simsalsa', 'LESS', 'Wermut']
y_pos = np.arange(len(appsol)) - .3
y_pos_2 = np.arange(len(appsol)) - .1
y_pos_3 = np.arange(len(appsol)) + .1
y_pos_4 = np.arange(len(appsol)) + .3
num_tickets = [4, 4,3,2,6,7,8,1,4,4,3,2,6,7,8,1,9]
num_tickets_2 = [7,6,5,4,3,4,2,1,2,4,1,0,3,0,2,1,0]
num_tickets_3 = [1,2,1,1,1,2,2,3,1,1,2,1,3,1,1,2,3]
num_tickets_4 = [8,7,6,2,13,6,8,9,7,6,5,4,3,6,8,9,12]
bar_width = .2
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111)
# correct yticks
plt.yticks(y_pos_2, appsol)
plt.barh(y_pos, num_tickets, bar_width, align='center', alpha=0.4, color='r')
plt.barh(y_pos_2, num_tickets_2, bar_width, align='center', alpha=0.4, color='b')
plt.barh(y_pos_3, num_tickets_3, bar_width, align='center', alpha=0.4, color='y')
plt.barh(y_pos_4, num_tickets_4, bar_width, align='center', alpha=0.4, color='g')
plt.yticks(y_pos, appsol)
plt.xlabel('Numbers')
plt.title('Horizontal Bar Chart with table')
# Table
empty_labels = ['' for a in appsol ]
plt.yticks(y_pos_2, empty_labels)
plt.tick_params(\
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
left='off', # ticks along the bottom edge are off
right='off', # ticks along the top edge are off
labelbottom='off')
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.4, bottom=0.2)
cell_text = []
i = len(num_tickets) - 1
for j in num_tickets:
cell_text.append([num_tickets[i], num_tickets_2[i], num_tickets_3[i], num_tickets_4[i]])
i -= 1
row_lables = appsol
column_labels = ['So Huge\nMice', 'Elephants', 'Reptiles', 'Germs']
the_table = ax.table(cellText=cell_text,
rowLabels=row_lables,
colLabels=column_labels,
loc='left')
the_table.set_fontsize(15)
the_table.scale(.5,5.0)
plt.savefig('barh_graph.png')
plt.close('all')
This produces almost what I want, except that the table rows are not aligned with the bars of the graph. So I need a way to either move the graph down half a table row, or the table up half a table row. How can I do this?
I think its easier if you create two subplots and add the table to the left subplot, instead of deriving a new subplot from the single subplot you have now. If you add the table to an existing subplot, you can use the bbox to stretch it from 0 to 1 (so fully) in the y-direction. Since the table has a header, setting the ylim of the right plot to (0, n_items), will make both align properly, and adding a slight offset because the bars are also given an offset of 0.4 (offset of the outer bar + half a barwidth). This should work automatically if the number of elements changes.
bar_width = .2
fig, axs = plt.subplots(1,2, figsize=(12,6))
fig.subplots_adjust(wspace=0, top=1, right=1, left=0, bottom=0)
axs[1].barh(y_pos[::-1], num_tickets[::-1], bar_width, align='center', alpha=0.4, color='r')
axs[1].barh(y_pos_2[::-1], num_tickets_2[::-1], bar_width, align='center', alpha=0.4, color='b')
axs[1].barh(y_pos_3[::-1], num_tickets_3[::-1], bar_width, align='center', alpha=0.4, color='y')
axs[1].barh(y_pos_4[::-1], num_tickets_4[::-1], bar_width, align='center', alpha=0.4, color='g')
axs[1].set_yticks([])
axs[1].set_xlabel('Numbers')
axs[1].set_title('Horizontal Bar Chart with table')
axs[1].set_ylim(0 - .4, (len(appsol)) + .4)
cell_text = list(zip(num_tickets, num_tickets_2, num_tickets_3, num_tickets_4))
row_lables = appsol
column_labels = ['So Huge\nMice', 'Elephants', 'Reptiles', 'Germs']
axs[0].axis('off')
the_table = axs[0].table(cellText=cell_text,
rowLabels=row_lables,
colLabels=column_labels,
bbox=[0.4, 0.0, 0.6, 1.0])
the_table.set_fontsize(15)
plt.savefig('barh_graph.png')
plt.close('all')
If you look closely you might notice that the bars are ranging from the top of the table to the bottom, you could tweak them a bit to make them start at the center of the upper and lower cell of the table.
I found a workaround: I adjust the y-boundaries of the graph:
ax.set_ylim([-.5,17.5])
Of course these numbers only work with these data dimensions.
So still, if someone has a better soltution I would like to see it.
For completness, here is the enhanced version (also changed font size and added a legend):
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
# set font and size
font = {'family' : 'Bitstream Vera Sans',
'size' : 18}
matplotlib.rc('font', **font)
# Example data
appsol = ['llolLl', 'nnM', 'lllld bbbblnl', 'x2x', 'foobar', 'EXZ', 'Flups', 'Flaps', 'Foobar Barfooment', 'ABC', 'FABAS', 'common', 'AQT', 'Faberjak', 'simsalsa', 'LESS', 'Wermut']
y_pos = np.arange(len(appsol)) - .3
y_pos_2 = np.arange(len(appsol)) - .1
y_pos_3 = np.arange(len(appsol)) + .1
y_pos_4 = np.arange(len(appsol)) + .3
num_tickets = [4, 4,3,2,6,7,8,1,4,4,3,2,6,7,8,1,9]
num_tickets_2 = [7,6,5,4,3,4,2,1,2,4,1,0,3,0,2,1,0]
num_tickets_3 = [1,2,1,1,1,2,2,3,1,1,2,1,3,1,1,2,3]
num_tickets_4 = [8,7,6,2,13,6,8,9,7,6,5,4,3,6,8,9,12]
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111)
# correct yticks
plt.yticks(y_pos_2, appsol)
# this aligns table and graph!!!
ax.set_ylim([-.5,17.5])
labels = ['So Huge Mice', 'Elephants', 'Reptiles', 'Germs']
bar_width = .2
plt.barh(y_pos, num_tickets, bar_width, align='center', alpha=0.4, color='r', label=labels[0])
plt.barh(y_pos_2, num_tickets_2, bar_width, align='center', alpha=0.4, color='b', label=labels[1])
plt.barh(y_pos_3, num_tickets_3, bar_width, align='center', alpha=0.4, color='y', label=labels[2])
plt.barh(y_pos_4, num_tickets_4, bar_width, align='center', alpha=0.4, color='g', label=labels[3])
plt.yticks(y_pos, appsol)
plt.xlabel('Numbers')
plt.title('Horizontal Bar Chart with table')
# Legend
plt.legend(loc='lower center', shadow=True)
num_plots = 4
x_legend = 0.3
y_legend = -0.1
ax.legend(loc='lower center',
bbox_to_anchor=(x_legend, y_legend),
ncol=num_plots, # we want the legend on just one line
shadow=True)
# Table
empty_labels = ['' for a in appsol ]
plt.yticks(y_pos_2, empty_labels)
plt.tick_params(\
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
left='off', # ticks along the bottom edge are off
right='off', # ticks along the top edge are off
labelbottom='off')
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.4, bottom=0.1)
cell_text = []
i = len(num_tickets) - 1
for j in num_tickets:
cell_text.append([num_tickets[i], num_tickets_2[i], num_tickets_3[i], num_tickets_4[i]])
i -= 1
row_lables = appsol
column_labels = ['So Huge\nMice', 'Elephants', 'Reptiles', 'Germs']
the_table = ax.table(cellText=cell_text,
rowLabels=row_lables,
colLabels=column_labels,
loc='left')
the_table.set_fontsize(18)
the_table.scale(.5,5.34)
plt.savefig('barh_graph.png')
plt.close('all')
And this is what it looks like: