I have been trying to find some answers, but most of them don't include a table, or they solve the problem generally and I get in trouble trying to find a workaround with the table I created as I managed to put the table through an empty axis. But now decreasing the right-axis size (as the table gets accommodated to the axis size) and increasing the left two-axis size is becoming a daunting task.
I have this code:
fig = plt.figure(figsize=(18,5))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(223)
ax3 = fig.add_subplot(122)
ax3.axis('off')
data = pd.DataFrame({'metrics': ['MSLE train', 'msle_test', 'asdsad'],
'values': [0.43, 0.52, 0.54]})
ax3.table(cellText=data.values, colLabels=data.columns, loc='center')
fig.suptitle(f'Train MSLE: {msle_train}, Test MSLE: {msle_test}')
ax1 = y_data.plot(label='Original data', ax=ax1, c='blue')
ax1 = y_pred_train.plot(ax=ax1, c='orange')
ax1 = y_pred_test.plot(ax=ax1, c='orange', linestyle='--')
ax1.legend()
ax2 = error_train.plot(label='Train error', ax=ax2)
ax2 = error_test.plot(label='Test error', ax=ax2, linestyle='--')
ax2.legend()
plt.show()
That returns this plot:
I'm looking to increase the horizontal size of the two left plots, something near the red mark:
Any suggestions?
You can use gridspec.
It even works with a vertical centered right hand side and a table:
import matplotlib.pyplot as plt
from matplotlib import gridspec
import pandas as pd
data = pd.DataFrame({'metrics': ['MSLE train', 'msle_test', 'asdsad'],
'values': [0.43, 0.52, 0.54]})
fig = plt.figure(figsize=(18,5))
gs = gridspec.GridSpec(4, 2, width_ratios=[3,1])
ax1 = fig.add_subplot(gs[0:2,:-1])
ax1.set_title('ax1')
ax2 = fig.add_subplot(gs[2:4,:-1])
ax2.set_title('ax2')
ax3 = fig.add_subplot(gs[1:3,1])
ax3.set_axis_off()
ax3.table(cellText=data.values, colLabels=data.columns, loc='center')
fig.tight_layout()
plt.show()
Notes:
Horizontal alignment is set with the ratio of width_ratios=[3,1]
fig.tight_layout() is helpfull to automatically align the spacing between the plots.
Vertical centering is achieved with a little workaround by having initially a larger vertical grid than required (no. of vertical plots) and distributing the plots and table accordingly (see e.g. gs[2:4).
The titles were just added for visual orientation.
ax3.set_axis_off() is required to suppress the plot frame at the table position - without it you'll get:
Related
I have created a plot using matplotlib and seaborn in python. The code is given below:
fig = plt.figure(figsize=(15, 8), facecolor='#e4e5e9', dpi=100,)
spec = fig.add_gridspec(ncols=6, nrows=2)
ax0 = fig.add_subplot(spec[0, :4])
sns.countplot(x='Min. Exp.',data=df, color='#657ffb', **{'edgecolor':'black'})
plt.ylabel("Job Count", fontsize=11)
plt.xlabel('Minimum Experience', fontsize=11)
plt.xlim(0,20)
plt.ylim(0,2400)
ax1 = fig.add_subplot(spec[1, :4])
sns.countplot(x='Max. Exp.',data=df, color='#bad2ef', **{'edgecolor':'black'})
plt.ylabel("Job Count", fontsize=11)
plt.xlabel('Maximum Experience', fontsize=11)
plt.ylim(0,2400)
# plt.xlim(0,20)
ax2 = fig.add_subplot(spec[:, 4])
sns.boxplot(y=df['Min. Exp.'], color='#657ffb')
plt.xlabel('Minimum Experience',labelpad=5, **{'fontsize':10})
plt.ylim(0,31)
plt.ylabel('')
ax3 = fig.add_subplot(spec[:, 5])
sns.boxplot(y=df['Max. Exp.'], color='#bad2ef')
plt.xlabel('Maximum Experience',labelpad=5, **{'fontsize':10})
plt.ylabel('')
plt.ylim(0,31)
for ax in [ax0,ax1,ax2,ax3]:
ax.patch.set_alpha(0.0)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
fig.suptitle('Job Experience Demanded in Data Scientist Roles in India', fontsize=17, y=1)
plt.savefig('job_exp2.jpg')
plt.show()
And the output is:
job_exp2.jpg
But the title is too close to the top edge. (The y parameter in fig.suptitle() can only adjust space below the title). How can I adjust the title position slightly downwards or add some space above the title (so that it is not too close to the edge)?
use plt.savefig('job_exp2.jpg',bbox_inches='tight')
or try using plt.title('Title', x=0.5, y=0.9) or whatever fits in your figure.
can also try plt.title('Title', pad=value)
I have two plots that I generated from my data:
Here the second plot shows the distribution of results from the first one.
What I want is to plot them side-by-side so you could see both the data and the distribution on the same plot. And I want plots to share y-axis as well.
I tried to do the following:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(40, 15), sharey=True)
ax1 = sns.lineplot(plotting_df.index, plotting_df.error, color=('#e65400'), lw=2, label='random forest residual error')
ax1 = sns.lineplot(plotting_df.index, plotting_df.val, color=('#9b9b9b'), lw=1, label='current model residual error')
ax1 = sns.lineplot(plotting_df.index, 0, color=('#2293e3'), lw=1)
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Residual Fe bias', fontsize=16)
ax1.set_title('Models residual error comparison', fontsize=20, fontweight='bold')
sns.despine(ax=ax1, top=True, bottom=True, right=True)
ax2 = sns.distplot(results_df.error, hist=True, color=('#e65400'), bins=81,
label='Random forest model', vertical=True)
ax2 = sns.distplot(plotting_df.val, hist=True, color=('#9b9b9b'),
bins=81, label='Rolling averages model', vertical=True)
ax2.set_title('Error distribution comparison between models', fontsize=20, fontweight='bold')
sns.despine(ax=ax2, top=True, right=True)
fig.savefig("blabla.png", format='png')
But when I do run it I get strange results - the first chart is in the second column, whereas I wanted it on the left and the second chart is completely blank. Not sure what I did wrong here.
Both lineplot and distplot accept a matplotlib axes object as an argument, which tells it which axes to plot onto. If no axes is passed into it, then the plot is placed onto the current axes.
You create a figure and 2 axes using :
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(40, 15), sharey=True)
Therefore, ax2 will be the current axes. So your distplot is being plotted on top of your lineplot, both in ax2.
You need to pass the axes into the seaborn plotting functions.
sns.lineplot(..., ax=ax1)
sns.distplot(..., ax=ax2)
Currently, I have the first y axis (probability) of my subplots aligned. However, I am attempting to get the secondary y axis (sample size) of the subplots aligned. I've tried to simply set the y-axis limit, but this solution isn't very generalizable.
Here is my code:
attacks = 5
crit_rate = .5
idealdata = fullMatrix(attacks, crit_rate)
crit_rate = ("crit_%.0f" % (crit_rate*100))
actualdata = trueDataM(attacks, crit_rate)
[enter image description here][1]
fig, axs = plt.subplots(attacks+1, sharex=True, sharey=True)
axs2 = [ax.twinx() for ax in axs]
fig.text(0.5, 0.04, 'State', ha='center')
fig.text(0.04, 0.5, 'Probability', va='center', rotation='vertical')
fig.text(.95, .5, 'Sample Size', va='center', rotation='vertical')
fig.text(.45, .9, 'Ideal vs. Actual Critical Strike Rate', va='center')
cmap = plt.get_cmap('rainbow')
samplesize = datasample(attacks, 'crit_50')
fig.set_size_inches(18.5, 10.5)
for i in range(attacks+1):
axs[i].plot(idealdata[i], color=cmap(i/attacks), marker='o', lw=3)
axs[i].plot(actualdata[i], 'gray', marker='o', lw=3, ls='--')
axs2[i].bar(range(len(samplesize[i])), samplesize[i], width=.1, color=cmap(i/attacks), alpha = .6)
plt.show()
https://i.stack.imgur.com/HKJlE.png
Without data to confirm my assumptions it's hard to tell if this will be correct.
You are not making any attempt to scale the left y-axes so that data must all have the same range. To ensure the right y-axes all have the same scale/limits you need to determine the range (max and min) of the (all) data being plotted on those axes then apply that to all of them.
It isn't clear whether samplesize is a Numpy ndarray or a lists of lists, I'm also assuming that it is a 2-d structure with range(attacks+1) rows. Since you are making bar charts on the second y-axes you only need to find the largest height in all the data.
# for a list of lists
biggest = max(max(row) for row in samplesize)
# or
biggest = max(map(max,samplesize))
# for an ndarray
biggest = samplesize.max()
Then apply that scale to all the right y-axes before they are shown
for ax in axs2:
ax.set_ylim(top=biggest)
If you determine biggest prior to the plot loop you can just add a line to that loop:
for i in range(attacks+1):
...
axs2[i].set_ylim(top=biggest)
You'll find plenty of related SO Q&A'a searching with the terms: matplotlib subplots same y scale, matplotlib subplots y axis limits or something similar.
Here is a toy example:
from matplotlib import pyplot as plt
import numpy as np
lines = np.random.randint(0,200,(5,10))
bars = [np.random.randint(0,np.random.randint(0,10000),10) for _ in (0,0,0,0,0,)]
fig, axs = plt.subplots(lines.shape[0], sharex=True, sharey=True)
axs2 = [ax.twinx() for ax in axs]
#xs = np.arange(lines.shape[1])
xs = np.arange(1,11)
biggest = max(map(max,bars))
for ax,ax2,line,row in zip(axs,axs2,lines,bars):
bars = ax2.bar(xs,row)
ax.plot(line)
ax2.set_ylim(top=biggest)
plt.show()
plt.close()
I'm trying to remove the white space from the plot that I created:
As it is possible to see, there a big white spot on the right and also on the bottom, how to fix it? Here is my script:
fig = plt.figure(figsize=(7,7))
ax1 = plt.subplot2grid((4,3), (0,0),)
ax2 = plt.subplot2grid((4,3), (1,0),)
ax3 = plt.subplot2grid((4,3), (0,1),)
ax4 = plt.subplot2grid((4,3), (1,1),)
data = self.dframe[i]
tes = print_data(data, self.issues, self.color, self.type_user)
tes.print_top(data=data, top=10, ax=ax1, typegraph="hbar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax2, typegraph="prod_bar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax3, typegraph="reg_hbar", problem=self.issues[i], tone=self.color[i])
tes.print_top(data=data, top=10, ax=ax4, typegraph=self.type_user, problem=self.issues[i], tone=self.color[i])
problem = self.issues[i]
plt.tight_layout()
name = problem + str('.PNG')
plt.close(fig)
fig.savefig(name)
You are creating too many subplots!
If we look at this line:
ax1 = plt.subplot2grid((4,3), (0,0),)
We can see the first argument given to subplot2grid are the dimensions of the subplot grid to be made, in this case 4 rows, and 3 columns. You are then plotting in the subplots in the top left of your figure (the second argument given) which leaves a lot of space that's not used.
So to solve this, reduce the number of subplots by using:
ax1 = plt.subplot2grid((2,2), (0,0),)
Full example:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(25)
fig = plt.figure(figsize=(7,7))
ax1 = plt.subplot2grid((2,2), (0,0),)
ax2 = plt.subplot2grid((2,2), (1,0),)
ax3 = plt.subplot2grid((2,2), (0,1),)
ax4 = plt.subplot2grid((2,2), (1,1),)
ax1.plot(data)
ax2.plot(data)
ax3.plot(data)
ax4.plot(data)
plt.show()
Giving:
you can use
plt.subplots_adjust(left=0.09, bottom=0.07, right=0.98, top=0.97, wspace=0.2 , hspace=0.17 ) to adjust the window.
But the issue is that a lot of the space in your plot is empty
maybe you should change
plt.subplot2grid((4,3)... to plt.subplot2grid((2,2)
I've seen a couple examples but the plots are constructed differently and I don't see how to make the syntax work. Here's my code:
pdf_file = PdfPages(sys.argv[1].split('.')[0] + "_graphs.pdf")
for i in range(0, len(list_of_data)):
biorep = int(list_of_figure_key[i].split('.')[1])
construct = int(list_of_figure_key[i].split('.')[0].split('_')[1])
plot(time, list_of_data[i], color=color_dict[construct], linestyle=linestyle_dict[biorep], label=list_of_figure_key[i] )
xlabel('time (hours)', fontsize=9)
ylabel(ReadType, fontsize=9)
xlim(min(time),max(time))
legend(fontsize=8, loc='center left', bbox_to_anchor=(1, .5))
pdf_file.savefig()
It produces a beautiful figure but the legend is much too long and goes off the edge of the page. I'd like to shrink the plot on the x-axis so the legend will fit as a 2-column legend.
Figure can be seen here: http://i.imgur.com/mvgzIhj.jpg
Thanks in advance!
You can make a two-column legend using the ncol legend attribute. You can shrink the width of the plot by drawing the axis on the plot and fixing its size:
from matplotlib import pyplot as plt
fig = plt.figure() # initialize figure
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # add axis
To make this work with your code, something like this should work:
# import pyplot
from matplotlib import pyplot as plt
# set up filename to save it
pdf_file = PdfPages(sys.argv[1].split('.')[0] + "_graphs.pdf")
# set up axis object
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# plot your data
for i in range(0, len(list_of_data)):
biorep = int(list_of_figure_key[i].split('.')[1])
construct = int(list_of_figure_key[i].split('.')[0].split('_')[1])
ax.plot(time, list_of_data[i], color=color_dict[construct],
linestyle=linestyle_dict[biorep], label=list_of_figure_key[i] )
# modify axis limits and legend
ax.set_xlabel('time (hours)', fontsize=9)
ax.set_ylabel(ReadType, fontsize=9)
ax.set_xlim(min(time),max(time))
ax.legend(fontsize=8, loc='upper left', bbox_to_anchor=(1, .5), ncol=2)
# save final figure
plt.savefig(pdf_file)
In your code, you were remaking the legend, the limits and the legend at each iteration of the for-loop, as well as saving and then overwriting the pdf image. This isn't necessary -- you can just do it once at the end.
For more legend tips, this post is handy. This one is also helpful.