So in a figure where three vertical subplots have been added with add_subplot, how can I select let's say the middle one?
Right now I do this list comprehension:
[r[0] for r in sorted([[ax, ax.get_geometry()[2]] for ax in self.figure.get_axes()], key=itemgetter(1))]
where I can simply select the index I want, with the corresponding axes. Is there a more straightforward way of doing this?
From the matplotlib documentation:
If the figure already has a subplot with key (args, kwargs) then it will simply make that subplot current and return it.
Here's an example:
import matplotlib.pyplot as plt
fig = plt.figure()
for vplot in [1,2,3]:
ax = fig.add_subplot(3,1,vplot)
ax.plot(range(10),range(10))
ax_again = fig.add_subplot(3,1,2)
ax_again.annotate("The middle one",xy=(7,5),xytext=(7,5))
plt.show()
The middle plot is called again so that it can be annotated.
What if I set the background with my original call, do I need to set it again when I get the subplot the second time?
Yes. The arguments and keywords for the original call are used to make a unique identifier. So for the figure to generate this unique identifier again, you need to pass the same arguments (grid definition, position) and keywords again. For example:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(2,1,1,axisbg='red')
ax.plot(range(10),range(10))
ax = fig.add_subplot(2,1,2)
ax.plot(range(10),range(10))
ax_again = fig.add_subplot(2,1,1,axisbg='red')
ax_again.annotate("The top one",xy=(7,5),xytext=(7,5))
plt.show()
What if I use ax_again.change_geometry() ?
You would think change_geometry, e.g. from a 312 to a 422, would change how you use add_subplot, but it doesn't. There appears to be a bug or undefined behavior when you call change_geometry. The unique key that was original generated using the arguments and keywords, to the first add_subplot call, does not get updated. Therefore, if you want to get an axis back with an add_subplot call, you need to call add_subplot with the original arguments and keywords. For more info, follow this issue report:
https://github.com/matplotlib/matplotlib/issues/429
My guess for now is that if you change any property of the subplot after generating it with add_subplot call, the unique will not be adjusted. So just use the original arguments and keywords, and hopefully this will work out.
Related
Hi the following code represents the first 10 integers' cubes.
The scatter method works fine, the plot method shifts everything one to the left.
The axis looks correct to me.
I tried to figure it out but I don't know where I'm going wrong.
Thank you .
import matplotlib.pyplot as plt
n_values = range(1,11,1)
n_cubes = [n**3 for n in n_values]
fig, ax = plt.subplots()
ax.plot(n_cubes)
ax.scatter(n_values, n_cubes, c=n_cubes, cmap=plt.cm.Reds, s=20)
ax.axis([1, 12, 0, 1100])
print(n_cubes, n_values)
plt.style.use('seaborn')
plt.show()
If you call ax.plot() with only one argument, it will make its own x-axis values. In python, these start with zero. So, all is shifted.
So, you need to call the function like this:
ax.plot(n_values, n_cubes)
I'm trying to be able to control the colour of an individual data point using a corresponding rgb tuple. I've tried looping through the data set and plotting individual data points however I get the same effect as the code I have below; all that happens is it refuses to produce a graph.
This is an example of the data type I'm working with
Any tips?
import matplotlib.pyplot as plt
y=[(0.200,0.1100,0.520)]
for i in range(4):
y.append(y)
plt.plot([1,2,3,4], [3,4,5,2],c=y)
plt.show()
One problem is that you are appending the list to the new list. Instead, try appending the tuple to the list. Moreover, you need to use scatter plot for the color argument which contains rgb tuple for each point. However, in oyur case, I see only a single color for all the scatter points.
tup=(0.200,0.1100,0.520)
y = []
for i in range(4):
y.append(tup)
plt.scatter([1,2,3,4], [3,4,5,2], c=y)
A rather short version to your code is using a list comprehension
tup=(0.200,0.1100,0.520)
y = [tup for _ in range(4)]
plt.scatter([1,2,3,4], [3,4,5,2], c=y)
My data-frame contains the following column headers: subject, Group, MASQ_GDA, MASQ_AA, MASQ_GDD, MASQ_AD
I was successfully able to plot one of them using a bar plot with the following specifications:
bar_plot = sns.barplot(x="Group", y='MASQ_GDA', units="subject", ci = 68, hue="Group", data=demo_masq)
However, I am attempting to create several of such bar plot side by side. Might anyone know how I can accomplish this, for each plot to contain the remaining 3 variables (MASQ_AA, MASQ_GDD, MASQ_AD). Here is an example of what I am trying to achieve.
If you look in the documentation for sns.barplot(), you will see that the function accepts a parameter ax= allowing you to tell seaborn which Axes object to use to plot the result
ax : matplotlib Axes, optional
Axes object to draw the plot onto, otherwise uses the current Axes.
Therefore, the simple way to obtain the desired output is to create the Axes beforehand, and then calling sns.barplot() with the corresponding ax parameter
fig, axs = plt.subplots(1,4) # create 4 subplots on 1 row
for ax,col in zip(axs,["MASQ_GDA", "MASQ_AA", "MASQ_GDD", "MASQ_AD"]):
sns.barplot(x="Group", y=col, units="subject", ci = 68, hue="Group", data=demo_masq, ax=ax) # <- notice ax= argument
Another option, and maybe an option that is more in line with the philosophy of seaborn is to use a FacetGrid. This would allow you to automatically create the required number of subplots depending on the number of categories in your dataset. However, it requires to reshape your dataframe so that the content of your MASQ_* columns are on a single column, with a new column showing what category each value corresponds to.
I have a question that is basically the same as a question back from 2014 (see here). However, my script still throws an error.
Here is what I do: I have a pandas dataframe with a few columns. I plot a simple boxplot comparison.
g = sns.boxplot(x='categories', y='oxygen', hue='target', data=df)
g.set_xticklabels(rotation=30)
The graph looks like this:
I'd like to rotate the x-labels by 30 degrees. Hence I use g.set_xticklabels(rotation=30). However, I get the following error:
set_xticklabels() missing 1 required positional argument: 'labels'
I don't know how to pass the matplotlib labels argument to seaborns sns.boxplot. Any ideas?
The question you link to uses a factorplot. A factorplot returns its own class which has a method called set_xticklabels(rotation). This is different from the set_xticklabels method of the matplotlib Axes.
In the linked question's answers there are also other options which you may use
ax = sns.boxplot(x='categories', y='oxygen', hue='target', data=df)
ax.set_xticklabels(ax.get_xticklabels(),rotation=30)
or
ax = sns.boxplot(x='categories', y='oxygen', hue='target', data=df)
plt.setp(ax.get_xticklabels(), rotation=45)
If you do not need to reset labels: ax.tick_params(axis='x', labelrotation=90)
I have a dataset with 80 variables. I am interested in creating a function that will automate the creation of a 20 X 4 GridSpec in Matplotlib. Each subplot would either contain a histogram or a barplot for each of the 80 variables in the data. As a first step, I successfully created two functions (I call them 'counts' and 'histogram') that contain the layout of the plot that I want. Both of them work when tested on individual variables. As a next step, I attempted to create a function that would take the column names, loop through a conditional to test whether the data type is an object or otherwise and call the right function based on the datatype as a new subplot. Here is the code that I have so far:
Creates list of coordinates we will need for subplot specification:
A = np.arange(21)
B = np.arange(4)
coords = []
for i in A:
for j in B:
coords.append([A[i], B[j]])
#Create the gridspec and layout the figure
import matplotlib.gridspec as gridspec
fig = plt.figure(figsize=(12,6))
gs = gridspec.GridSpec(2,4)
#Function that relies on what we've done above:
def grid(cols=['MSZoning', 'LotFrontage', 'LotArea', 'Street', 'Alley']):
for i in cols:
for vals in coords:
if str(train[i].dtype) == 'object':
plt.subplot('gs'+str(vals))
counts(cols)
else:
plt.subplot('gs'+str(vals))
histogram(cols)
When attempted, this code returns an error:
ValueError: Single argument to subplot must be a 3-digit integer
For purposes of helping you visualize, what I am hoping to achieve, I attach the screen shot below, which was produced by the line by line coding (with my created helper functions) I am trying to avoid:
Can anyone help me figure out where I am going wrong? I would appreciate any advice. Thank you!
The line plt.subplot('gs'+str(vals)) cannot work; which is also what the error tells you.
As can be seen from the matplotlib GridSpec tutorial, it needs to be
ax = plt.subplot(gs[0, 0])
So in your case you may use the values from the list as
ax = plt.subplot(gs[vals[0], vals[1]])
Mind that you also need to make sure that the coords list must have the n*m elements, if the gridspec is defined as gs = gridspec.GridSpec(n,m).