Develop Reference

Adding charts to a matplotlib subgrid

I have the following Matplotlib figure, with 2 charts:
That i created with the following code:
fig = plt.figure(facecolor='#131722',dpi=155, figsize=(8, 4))
ax1 = plt.subplot2grid((1,2), (0,0), facecolor='#131722')
ax2 = plt.subplot2grid((1,2), (0,1), facecolor='#131722')
Now i would like to add two charts, so ax3 and ax4, each needs to be below the two charts, they should have the same width of the two charts but half the height of the two bigger charts. How can i do that? I tried various solutions from here here but i'm struggling to get the expected output

You can achieve this using the gridspec_kw argument of plt.subplots. This allows you to specify the dimensions of the grid (in this case 2x2) and the ratio of the heights:
f, ax = plt.subplots(2, 2 , gridspec_kw={"height_ratios": [2, 1]})
for cAx in ax.flatten():
cAx.set_facecolor('#131722')
f.savefig("test.png", facecolor='#131722')
Alternatively, you can also create a 3x2 grid and specify that the first two subplots need to span two rows:
fig = plt.figure(facecolor='#131722',dpi=155, figsize=(8, 6))
ax1 = plt.subplot2grid((3,2), (0,0), facecolor='#131722', rowspan=2)
ax2 = plt.subplot2grid((3,2), (0,1), facecolor='#131722', rowspan=2)
ax3 = plt.subplot2grid((3,2), (2,0), facecolor='#131722')
ax4 = plt.subplot2grid((3,2), (2,1), facecolor='#131722')

How to manage subplots in Pandas?

My DataFrame is:
df = pd.DataFrame({'A': range(0,-10,-1), 'B': range(10,20), 'C': range(10,30,2)})
and plot:
df[['A','B','C']].plot(subplots=True, sharex=True)
I get one column with 3 subplots, each even height.
How to plot it this way that I have only two subplots where 'A' is in upper one and 'B' and 'C' are in lower and lower graph's height is different than height of graph 'A' (x axis is shared)?

Use subplots with gridspec_kw parmater to setup your grid then use the ax paramter in pandas plot to use those axes defined in your subplots statement:
f, ax = plt.subplots(2,2, gridspec_kw={'height_ratios':[1,2]})
df[['A','B','C']].plot(subplots=True, sharex=True, ax=[ax[0,0],ax[0,1],ax[1,0]])
ax[1,1].set_visible(False)
Output:

For clarity I post my modified code here:
f, ax = plt.subplots(2,1, sharex=True, gridspec_kw={'height_ratios':[1,3]})
f.subplots_adjust(hspace=0)
df[['A','B','C']].plot(subplots=True, ax=[ax[0],ax[1],ax[1]])
That will do it. Thanks.

I was able to do it with .subplot2grid(). Which only creates 3 plots as needed.
ax1 = plt.subplot2grid((3, 2), (0, 0), colspan=1)
ax2 = plt.subplot2grid((3, 2), (0, 1), colspan=1)
ax3 = plt.subplot2grid((3, 2), (1, 0), rowspan=2, sharex=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.plot(df['A'])
ax2.plot(df['B'], color='darkorange')
ax3.plot(df['C'], color='green')
Output:

Using pyplot to create grids of plots

I am new to python and having some difficulties with plotting using pyplot. My goal is to plot a grid of plots in-line (%pylab inline) in Juypter Notebook.
I programmed a function plot_CV which plots cross-validation erorr over the degree of polynomial of some x where across plots the degree of penalization (lambda) is supposed to vary. Ultimately there are 10 elements in lambda and they are controlled by the first argument in plot_CV. So
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax1 = plot_CV(1,CV_ve=CV_ve)
Gives
Now I think I have to use add_subplot to create a grid of plots as in
fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax1 = plot_CV(1,CV_ve=CV_ve)
ax2 = fig.add_subplot(2,2,2)
ax2 = plot_CV(2,CV_ve=CV_ve)
ax3 = fig.add_subplot(2,2,3)
ax3 = plot_CV(3,CV_ve=CV_ve)
ax4 = fig.add_subplot(2,2,4)
ax4 = plot_CV(4,CV_ve=CV_ve)
plt.show()
If I continue this, however, then the plots get smaller and smaller and start to overlap on the x and y labels. Here a picture with a 3 by 3 plot.
Is there a way to space the plots evenly, so that they do not overlap and make better use of the horizontal and vertical in-line space in Jupyter Notebook? To illustrate this point here a screenshot from jupyter:
Final note: I still need to add a title or annotation with the current level of lambda used in plot_CV.
Edit: Using the tight layout as suggested, gives:
Edit 2: Using the fig.set_figheight and fig.set_figwidth I could finally use the full length and heigth available.

The first suggestion to your problem would be taking a look at the "Tight Layout guide" for matplotlib.
They have an example that looks visually very similar to your situation. As well they have examples and suggestions for taking into consideration axis labels and plot titles.
Furthermore you can control the overall figure size by using Figure from the matplotlib.figure class.
Figure(figsize = (x,y))
figsize: x,y (inches)
EDIT:
Here is an example that I pulled from the matplotlib website and added in the:
fig.set_figheight(15)
fig.set_figwidth(15)
example:
import matplotlib.pyplot as plt
plt.rcParams['savefig.facecolor'] = "0.8"
def example_plot(ax, fontsize=12):
ax.plot([1, 2])
ax.locator_params(nbins=3)
ax.set_xlabel('x-label', fontsize=fontsize)
ax.set_ylabel('y-label', fontsize=fontsize)
ax.set_title('Title', fontsize=fontsize)
plt.close('all')
fig = plt.figure()
fig.set_figheight(15)
fig.set_figwidth(15)
ax1 = plt.subplot2grid((3, 3), (0, 0))
ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)
ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
plt.tight_layout()
You can achieve padding of your subplots by using tight_layout this way:
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
That way you can keep your subplots from crowding each other even further.
Have a good one!

Python - Stacking two histograms with a scatter plot

Having an example code for a scatter plot along with their histograms
x = np.random.rand(5000,1)
y = np.random.rand(5000,1)
fig = plt.figure(figsize=(7,7))
ax = fig.add_subplot(111)
ax.scatter(x, y, facecolors='none')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
fig1 = plt.figure(figsize=(7,7))
ax1 = fig1.add_subplot(111)
ax1.hist(x, bins=25, fill = None, facecolor='none',
edgecolor='black', linewidth = 1)
fig2 = plt.figure(figsize=(7,7))
ax2 = fig2.add_subplot(111)
ax2.hist(y, bins=25 , fill = None, facecolor='none',
edgecolor='black', linewidth = 1)
What I'm wanting to do is to create this graph with the histograms attached to their respected axis almost like this example
I'm familiar with stacking and merging the x-axis
f, (ax1, ax2, ax3) = plt.subplots(3)
ax1.scatter(x, y)
ax2.hist(x, bins=25, fill = None, facecolor='none',
edgecolor='black', linewidth = 1)
ax3.hist(y, bins=25 , fill = None, facecolor='none',
edgecolor='black', linewidth = 1)
f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
But I have no idea how to attach the histograms to the y axis and x axis like in the picture I posted above, and on top of that, how to vary the size of the graphs (ie make the scatter plot larger and the histograms smaller in comparison)

Seaborn is the way to go for quick statistical plots. But if you want to avoid another dependency you can use subplot2grid to place the subplots and the keywords sharex and sharey to make sure the axes are synchronized.
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(100)
y = np.random.randn(100)
scatter_axes = plt.subplot2grid((3, 3), (1, 0), rowspan=2, colspan=2)
x_hist_axes = plt.subplot2grid((3, 3), (0, 0), colspan=2,
sharex=scatter_axes)
y_hist_axes = plt.subplot2grid((3, 3), (1, 2), rowspan=2,
sharey=scatter_axes)
scatter_axes.plot(x, y, '.')
x_hist_axes.hist(x)
y_hist_axes.hist(y, orientation='horizontal')
You should always look at the matplotlib gallery before asking how to plot something, chances are that it will save you a few keystrokes -- I mean you won't have to ask. There are actually two plots like this in the gallery. Unfortunately the code is old and does not take advantage of subplot2grid, the first one uses rectangles and the second one uses axes_grid, which is a somewhat weird beast. That's why I posted this answer.

I think it's hard to do this solely with matplotlib but you can use seaborn which has jointplot function.
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
x = np.random.rand(1000,1)
y = np.random.rand(1000,1)
data = np.column_stack((x,y))
df = pd.DataFrame(data, columns=["x", "y"])
sns.jointplot(x="x", y="y", data=df);

Using passed axis objects in a matplotlib.pyplot figure?

I am currently attempting to use passed axis object created in function, e.g.:
def drawfig_1():
import matplotlib.pyplot as plt
# Create a figure with one axis (ax1)
fig, ax1 = plt.subplots(figsize=(4,2))
# Plot some data
ax1.plot(range(10))
# Return axis object
return ax1
My question is, how can I use the returned axis object, ax1, in another figure? For example, I would like to use it in this manner:
# Setup plots for analysis
fig2 = plt.figure(figsize=(12, 8))
# Set up 2 axes, one for a pixel map, the other for an image
ax_map = plt.subplot2grid((3, 3), (0, 0), rowspan=3)
ax_image = plt.subplot2grid((3, 3), (0, 1), colspan=2, rowspan=3)
# Plot the image
ax_psf.imshow(image, vmin=0.00000001, vmax=0.000001, cmap=cm.gray)
# Plot the map
???? <----- #I don't know how to display my passed axis here...
I've tried statements such as:
ax_map.axes = ax1
and although my script does not crash, my axis comes up empty. Any help would be appreciated!

You are trying to make a plot first and then put that plot as a subplot in another plot (defined by subplot2grid). Unfortunately, that is not possible. Also see this post: How do I include a matplotlib Figure object as subplot?.
You would have to make the subplot first and pass the axis of the subplot to your drawfig_1() function to plot it. Of course, drawfig_1() will need to be modified. e.g:
def drawfig_1(ax1):
ax1.plot(range(10))
return ax1
# Setup plots for analysis
fig2 = plt.figure(figsize=(12, 8))
# Set up 2 axes, one for a pixel map, the other for an image
ax_map = plt.subplot2grid((3, 3), (0, 0), rowspan=3)
ax_image = plt.subplot2grid((3, 3), (0, 1), colspan=2, rowspan=3)
# Plot the image
ax_image.imshow(image, vmin=0.00000001, vmax=0.000001, cmap=cm.gray)
# Plot the map:
drawfig_1(ax_map)