If I have a function (get_plot()) from a library which plots and returns an object of type "matplotlib.axes._subplots.AxesSubplot". How can I generate 4 of these plots and add them to a matplotlib subplot grid?
df = pd.DataFrame({'x': [1,2,3,4,5]})
def get_plot(data):
return df.plot(data)
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0][0].plt(get_plot(df))
axes[0][1].plt(get_plot(df))
axes[1][0].plt(get_plot(df))
axes[1][1].plt(get_plot(df))
throws an error:
AttributeError: 'AxesSubplot' object has no attribute 'plt'
Note: the function get_plot() comes from a library which I do not want to modify.
Related
I haven't been able to find a solution to this.. Say I define some plotting function so that I don't have to copy-paste tons of code every time I make similar plots...
What I'd like to do is use this function to create a few different plots individually and then put them together as subplots into one figure. Is this even possible? I've tried the following but it just returns blanks:
import numpy as np
import matplotlib.pyplot as plt
# function to make boxplots
def make_boxplots(box_data):
fig, ax = plt.subplots()
box = ax.boxplot(box_data)
#plt.show()
return ax
# make some data:
data_1 = np.random.normal(0,1,500)
data_2 = np.random.normal(0,1.1,500)
# plot it
box1 = make_boxplots(box_data=data_1)
box2 = make_boxplots(box_data=data_2)
plt.close('all')
fig, ax = plt.subplots(2)
ax[0] = box1
ax[1] = box2
plt.show()
I tend to use the following template
def plot_something(data, ax=None, **kwargs):
ax = ax or plt.gca()
# Do some cool data transformations...
return ax.boxplot(data, **kwargs)
Then you can experiment with your plotting function by simply calling plot_something(my_data) and you can specify which axes to use like so.
fig, (ax1, ax2) = plt.subplots(2)
plot_something(data1, ax1, color='blue')
plot_something(data2, ax2, color='red')
plt.show() # This should NOT be called inside plot_something()
Adding the kwargs allows you to pass in arbitrary parameters to the plotting function such as labels, line styles, or colours.
The line ax = ax or plt.gca() uses the axes you have specified or gets the current axes from matplotlib (which may be new axes if you haven't created any yet).
I am a bit lost on the best approach to add labels to my markers with a seaborn relplot. I see in the matplotlib documentation that there is a axes.text() method that looks to be the right approach, but it doesn't appear that this method exists. Does seaborn behave differently than matplotlib in this sense? What would the right approach be?
Error:
AttributeError: 'numpy.ndarray' object has no attribute 'text'
Code:
line_minutes_asleep = sns.relplot(
x = "sleep_date",
y = "minutes_asleep",
kind = "line",
data = df,
height=10, # make the plot 5 units high
aspect=3
)
x = df.sleep_date
y = df.minutes_asleep
names = df.minutes_asleep
print(line_minutes_asleep.axes.text())
relplot returns a FacetGrid, which is a figure containing several subplots. The property .axes of a FacetGrid is a 2D ndarray of Axes objects. Therefore, you need to use FacetGrid.axes[i,j] to get a reference to the subplot.
If you want to write something in the first subplot (axes[0,0]), at the position x,y=(20,5), you would need to do:
import seaborn as sns
sns.set(style="ticks")
tips = sns.load_dataset("tips")
g = sns.relplot(x="total_bill", y="tip", hue="day", data=tips)
g.axes[0,0].text(20,5,"this is a text")
The image below shows, what i want, 3 different plots in one execution but using a function
enter image description here
enter image description here
I used the following code:
def box_hist_plot(data):
sns.set()
ax, fig = plt.subplots(1,3, figsize=(20,5))
sns.boxplot(x=data, linewidth=2.5, ax=fig[0])
plt.hist(x=data, bins=50, density=True, ax = fig[1])
sns.violinplot(x = data, ax=fig[2])
and i got this error:
inner() got multiple values for argument 'ax'
Besides the fact that you should not call a Figure object ax and an array of Axes object fig, your problem comes from the line plt.hist(...,ax=...). plt.hist() should not take an ax= parameter, but is meant to act on the "current" axes. If you want to specify which Axes you want to plot, you should use Axes.hist().
def box_hist_plot(data):
sns.set()
fig, axs = plt.subplots(1,3, figsize=(20,5))
sns.boxplot(x=data, linewidth=2.5, ax=axs[0])
axs[1].hist(x=data, bins=50, density=True)
sns.violinplot(x = data, ax=axs[2])
all, I use the such code to plot the images
import matplotlib.pyplot as plt
plt.imshow(array,cmap='jet')
plt.show()
however, now I want to get the handle (im) of im=plt.imshow(array,cmap='jet')
How can I get the handle of im if I ignore the handle in the second step.
You can call the method get_images() on the ax.
Example code:
ax = plt.gca()
ax.imshow(array)
ax.get_images() # returns a list of AxesImage objects if any.
You can retrieve all the children present on the axes and filter on the object type.
Like:
ax = plt.gca()
imgs = [obj for obj in ax.get_children() if isinstance(obj, mpl.image.AxesImage)]
If you have only one AxesImage on the axes it returns a list containing one object, for example:
[<matplotlib.image.AxesImage at 0x7be6400>]
I've managed to make a set of subplots using hist2d and ImageGrid with the code below:
from mpl_toolkits.axes_grid1 import ImageGrid
fig = figure(figsize(20, 60))
grid = ImageGrid(fig, 111, nrows_ncols=(1, 3), axes_pad=0.25)
for soa, ax in zip(soalist, grid):
# grab my data from pandas DataFrame...
samps = allsubs[allsubs['soa'] == soa]
x, y = samps['x'], samps['y']
# calls hist2d and returns the Image returned by hist2d
img = gazemap(x, y, ax, std=True, mean=True)
ax.set_title("{0} ms".format(soa * 1000))
# attempt to show a colorbar for that image
grid.cbar_axes[-1].colorbar(img)
show() # threw this in for good measure, but doesn't help!
I get no explicit error (which is good, because I passed an Image to colorbar), but my colorbar does not appear. What gives?
Okay, I fixed it!
All I had to do was pass the cbar_mode and cbar_location kwargs to ImageGrid!