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How should I use axes.margins method?

fig, ax=plt.subplots(len(dagecat), 1, figsize=(15, 9*len(dagecat)))
for i, age in enumerate(list(dagecat.keys())[:9]):
dagecat[age].fillna(0).plot(y='ds', use_index=True, ax=ax[i], kind='bar', color='k', width=-0.4, align='edge') # black bars
dagecat[age].fillna(0).plot(y='dns', use_index=True, ax=ax[i], kind='bar',
width=0.4, color=(0.827, 0.827, 0.827, 1), align='edge') # gray bars
ax[i].margins(x=0.5)
for j, p in enumerate(ax[i].patches):
if j < 20:
ax[i].text(x=p.get_x()+0.5*p.get_width(), y=p.get_height()+0.01*dagecat[age]['dns'].max(), s=f"{p.get_height()}", fontsize=12, ha='center')
else:
ax[i].text(x=p.get_x()+0.5*p.get_width(), y=p.get_height()+0.01*dagecat[age]['dns'].max(), s=f"{p.get_height()}", fontsize=12, ha='center')
The graph at the top is one of the subplot drawn by the code above.
Since the first black bar is completely stuck to y axis, I used ax[i].margins method to make some space but it didn't work.
Am I using margins method in a wrong way? Or is there any problem in the other part of my code?

y-axis range is not logical (matplotlib python)

I just specify the x and y axis limitations but the numbers' order is wrong. how can I fix this?
here is my code:
fig, ax = plt.subplots(figsize=(20,10))
ax.plot(df.finish_price, label="Stock Values", color = 'blue')
plt.ylabel("Price", color='b')
# Generate a new Axes instance, on the twin-X axes (same position)
ax2 = ax.twinx()
ax2.plot(df.sentiment, label= 'Sentiment', color='green')
ax2.tick_params(axis='y', labelcolor='green')
plt.ylim(bottom = -1)
plt.ylim(top=1)
plt.xlabel("Days")
plt.ylabel("Sentiment", color='g')
fig.legend()
plt.show()
and here is the result:
as you can see the numbers' order on the right y-axis is wrong.

Adding count plot totals and removing specific labels

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

Python/Matplotlib - How to put text in the corner of equal aspect figure

I would like to put text in the right bottom corner of equal aspect figure.
I set the position relative to the figure by ax.transAxes,
but I have to define the relative coordinate value manually depending on height scales of each figures.
What would be a good way to know axes height scale and the correct text position within the script?
ax = plt.subplot(2,1,1)
ax.plot([1,2,3],[1,2,3])
ax.set_aspect('equal')
ax.text(1,-0.15, 'text', transform=ax.transAxes, ha='right', fontsize=16)
print ax.get_position().height
ax = plt.subplot(2,1,2)
ax.plot([10,20,30],[1,2,3])
ax.set_aspect('equal')
ax.text(1,-0.15, 'text', transform=ax.transAxes, ha='right', fontsize=16)
print ax.get_position().height

Use annotate.
In fact, I hardly ever use text. Even when I want to place things in data coordinates, I usually want to offset it by some fixed distance in points, which is much easier with annotate.
As a quick example:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, subplot_kw=dict(aspect=1))
axes[0].plot(range(1, 4))
axes[1].plot(range(10, 40, 10), range(1, 4))
for ax in axes:
ax.annotate('Test', xy=(1, 0), xycoords='axes fraction', fontsize=16,
horizontalalignment='right', verticalalignment='bottom')
plt.show()
If you'd like it slightly offset from the corner, you can specify an offset through the xytext kwarg (and textcoords to control how the values of xytext are interpreted). I'm also using the ha and va abbreviations for horizontalalignment and verticalalignment here:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, subplot_kw=dict(aspect=1))
axes[0].plot(range(1, 4))
axes[1].plot(range(10, 40, 10), range(1, 4))
for ax in axes:
ax.annotate('Test', xy=(1, 0), xycoords='axes fraction', fontsize=16,
xytext=(-5, 5), textcoords='offset points',
ha='right', va='bottom')
plt.show()
If you're trying to place it below the axes, you can use the offset to place it a set distance below in points:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, subplot_kw=dict(aspect=1))
axes[0].plot(range(1, 4))
axes[1].plot(range(10, 40, 10), range(1, 4))
for ax in axes:
ax.annotate('Test', xy=(1, 0), xycoords='axes fraction', fontsize=16,
xytext=(0, -15), textcoords='offset points',
ha='right', va='top')
plt.show()
Also have a look at the Matplotlib annotation guide for more information.

How to position and align a matplotlib figure legend?

I have a figure with two subplots as 2 rows and 1 column. I can add a nice looking figure legend with
fig.legend((l1, l2), ['2011', '2012'], loc="lower center",
ncol=2, fancybox=True, shadow=True, prop={'size':'small'})
However, this legend is positioned at the center of the figure and not below the center of the axes as I would like to have it. Now, I can obtain my axes coordinates with
axbox = ax[1].get_position()
and in theory I should be able to position the legend by specifying the loc keyword with a tuple:
fig.legend(..., loc=(axbox.x0+0.5*axbox.width, axbox.y0-0.08), ...)
This works, except that the legend is left aligned so that loc specifies the left edge/corner of the legend box and not the center. I searched for keywords such as align, horizontalalignment, etc., but couldn't find any. I also tried to obtain the "legend position", but legend doesn't have a *get_position()* method. I read about *bbox_to_anchor* but cannot make sense of it when applied to a figure legend. This seems to be made for axes legends.
Or: should I use a shifted axes legend instead? But then, why are there figure legends in the first place? And somehow it must be possible to "center align" a figure legend, because loc="lower center" does it too.
Thanks for any help,
Martin

In this case, you can either use axes for figure legend methods. In either case, bbox_to_anchor is the key. As you've already noticed bbox_to_anchor specifies a tuple of coordinates (or a box) to place the legend at. When you're using bbox_to_anchor think of the location kwarg as controlling the horizontal and vertical alignment.
The difference is just whether the tuple of coordinates is interpreted as axes or figure coordinates.
As an example of using a figure legend:
import numpy as np
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
# The key to the position is bbox_to_anchor: Place it at x=0.5, y=0.5
# in figure coordinates.
# "center" is basically saying center horizontal alignment and
# center vertical alignment in this case
fig.legend([line1, line2], ['yep', 'nope'], bbox_to_anchor=[0.5, 0.5],
loc='center', ncol=2)
plt.show()
As an example of using the axes method, try something like this:
import numpy as np
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
# The key to the position is bbox_to_anchor: Place it at x=0.5, y=0
# in axes coordinates.
# "upper center" is basically saying center horizontal alignment and
# top vertical alignment in this case
ax1.legend([line1, line2], ['yep', 'nope'], bbox_to_anchor=[0.5, 0],
loc='upper center', ncol=2, borderaxespad=0.25)
plt.show()

This is a very good question and the accepted answer indicates the key (i.e. loc denotes alignment and bbox_to_anchor denotes position). I have also tried some codes and would like to stress the importance of bbox_transform property that may sometimes needs to be explicitly specified to achieve desired effects. Below I will show you my findings on fig.legend. ax.legend should be very similar as loc and bbox_to_anchor works the same way.
When using the default setting, we will have the following.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6,4), sharex=True)
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
fig.legend([line1, line2], ['yep', 'nope'], loc='lower center', ncol=2)
This is basically satisfactory. But it could be easily found that the legend overlays with the x-axis ticklabels of ax2. This is the problem that will become even severe when figsize and/or dpi of the figure changes, see the following.
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6,12), sharex=True, facecolor='w', gridspec_kw={'hspace':0.01})
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
fig.legend([line1, line2], ['yep', 'nope'], loc='lower center', ncol=2)
So you see there are big gaps between ax2 and the legend. That's not what we want. Like the questioner, we would like to manually control the location of the legend. First, I will use the 2-number style of bbox_to_anchor like the answer did.
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6,12), sharex=True, facecolor='w', gridspec_kw={'hspace':0.01})
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
axbox = ax2.get_position()
# to place center point of the legend specified by loc at the position specified by bbox_to_anchor.
fig.legend([line1, line2], ['yep', 'nope'], loc='center', ncol=2,
bbox_to_anchor=[axbox.x0+0.5*axbox.width, axbox.y0-0.05])
Almost there! But it is totally wrong as the center of the legend is not at the center of what we really mean! The key to solving this is that we need to explicitly inform the bbox_transform as fig.transFigure. By default None, the Axes' transAxes transform will be used. This is understandable as most of the time we will use ax.legend().
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6,12), sharex=True, facecolor='w', gridspec_kw={'hspace':0.01})
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
axbox = ax2.get_position()
# to place center point of the legend specified by loc at the position specified by bbox_to_anchor!
fig.legend([line1, line2], ['yep', 'nope'], loc='center', ncol=2,
bbox_to_anchor=[axbox.x0+0.5*axbox.width, axbox.y0-0.05], bbox_transform=fig.transFigure)
As an alternative, we can also use a 4-number style bbox_to_anchor for loc. This is essentially specify a real box for the legend and loc really denotes alignment! The default bbox_to_anchor should just be [0,0,1,1], meaning the entire figure box! The four numbers represent x0,y0,width,height, respectively. It is very similar to specifying a cax for a shared colorbar! Hence you can easily change the y0 just a little bit lower than axbox.y0 and adjust loc accordingly.
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6,12), sharex=True, facecolor='w', gridspec_kw={'hspace':0.01})
x = np.linspace(0, np.pi, 100)
line1, = ax1.plot(x, np.cos(3*x), color='red')
line2, = ax2.plot(x, np.sin(4*x), color='green')
axbox = ax2.get_position()
# to place center point specified by loc at the position specified by bbox_to_anchor!
fig.legend([line1, line2], ['yep', 'nope'], loc='lower center', ncol=2,
bbox_to_anchor=[0, axbox.y0-0.05,1,1], bbox_transform=fig.transFigure)