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seaborn barplot: vary color with x and hue

My dataset contains information on the short-term and long-term effects of a decision support model. I would like to plot this in a barplot, with 4 bars:
model on, short term
model on, long term
model off, short term
model on, long term
here is some sample code:
df = pd.DataFrame(columns=["model", "time", "value"])
df["model"] = ["on"]*2 + ["off"]*2
df["time"] = ["short", "long"] * 2
df["value"] = [1, 10, 2, 4]
sns.barplot(data=df, x="model", hue="time", y="value")
plt.show()
it looks like this:
There are many other related figures and they have established color conventions. Model on/off is encoded in the hue of the color. Longterm vs shortterm is encoded in the saturation of the color. So let's assume that I have given variables with color values. How can I assign each individual bar in the barplot an individual color?
The docs for seaborn.barplot only show color, which specifies one color for all elements and palette which only gives different hue values different colors.

Seaborn gives you convenience to do simple plots, but if you are trying to depart from the options that it gives you, it is usually simpler to use straight matplotlib functions instead:
plt.bar(x='model',height='value',data=df.loc[df.time=='short'], width=-0.4, align='edge', color=['C0','C1'])
plt.bar(x='model',height='value',data=df.loc[df.time=='long'], width=0.4, align='edge', color=['C2','C3'])

The existing answer shows a nice way on how to arrange barplots with pyplot.
Unfortunately, my code relies heavily on other seaborn functionality, such as error bars, etc. So I would prefer to be able to keep the seaborn barplot functionality and just specify my own colors.
It is possible to iterate over the bars in a seaborn barplot as matplotlib patches. That allows setting a color, hatch, etc: Is it possible to add hatches to each individual bar in seaborn.barplot?
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
df = pd.DataFrame(columns=["model", "time", "value"])
df["model"] = ["on"]*2 + ["off"]*2
df["time"] = ["short", "long"] * 2
df["value"] = [1, 10, 2, 4]
fig, ax = plt.subplots()
bar = sns.barplot(data=df, x="model", hue="time", y="value", edgecolor="white")
colors = ["red", "green", "blue", "black"]
# Loop over the bars
for i,thisbar in enumerate(bar.patches):
# Set a different hatch for each bar
thisbar.set_color(colors[i])
thisbar.set_edgecolor("white")
However, if you do this, it will not update the legend. You can use the following code to create a custom legend. It is complicated, because I need multiple color patches for every legend entry. This is apparently quite complicated to do: Python Matplotlib Multi-color Legend Entry
# add custom legend
ax.get_legend().remove()
legend_pos = np.array([1, 10])
patch_size = np.array([0.05, 0.3])
patch_offset = np.array([0.06, 0])
r2 = mpatches.Rectangle(legend_pos, *patch_size, fill=True, color='red')
r3 = mpatches.Rectangle(legend_pos + patch_offset, *patch_size, fill=True, color='blue')
ax.add_patch(r2)
ax.add_patch(r3)
ax.annotate('Foo', legend_pos + 3* patch_offset - [0, 0.1], fontsize='x-large')
plt.show()

Matplotlib: Plot on double y-axis plot misaligned

I'm trying to plot two datasets into one plot with matplotlib. One of the two plots is misaligned by 1 on the x-axis.
This MWE pretty much sums up the problem. What do I have to adjust to bring the box-plot further to the left?
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
titles = ["nlnd", "nlmd", "nlhd", "mlnd", "mlmd", "mlhd", "hlnd", "hlmd", "hlhd"]
plotData = pd.DataFrame(np.random.rand(25, 9), columns=titles)
failureRates = pd.DataFrame(np.random.rand(9, 1), index=titles)
color = {'boxes': 'DarkGreen', 'whiskers': 'DarkOrange', 'medians': 'DarkBlue',
'caps': 'Gray'}
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
plotData.plot.box(ax=ax1, color=color, sym='+')
failureRates.plot(ax=ax2, color='b', legend=False)
ax1.set_ylabel('Seconds')
ax2.set_ylabel('Failure Rate in %')
plt.xlim(-0.7, 8.7)
ax1.set_xticks(range(len(titles)))
ax1.set_xticklabels(titles)
fig.tight_layout()
fig.show()
Actual result. Note that its only 8 box-plots instead of 9 and that they're starting at index 1.

The issue is a mismatch between how box() and plot() work - box() starts at x-position 1 and plot() depends on the index of the dataframe (which defaults to starting at 0). There are only 8 plots because the 9th is being cut off since you specify plt.xlim(-0.7, 8.7). There are several easy ways to fix this, as #Sheldore's answer indicates, you can explicitly set the positions for the boxplot. Another way you can do this is to change the indexing of the failureRates dataframe to start at 1 in construction of the dataframe, i.e.
failureRates = pd.DataFrame(np.random.rand(9, 1), index=range(1, len(titles)+1))
note that you need not specify the xticks or the xlim for the question MCVE, but you may need to for your complete code.

You can specify the positions on the x-axis where you want to have the box plots. Since you have 9 boxes, use the following which generates the figure below
plotData.plot.box(ax=ax1, color=color, sym='+', positions=range(9))

How to change the positions of subplot titles and axis labels in Seaborn FacetGrid?

I am trying to plot a polar plot using Seaborn's facetGrid, similar to what is detailed on seaborn's gallery
I am using the following code:
sns.set(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1.25)
# Set up a grid of axes with a polar projection
g = sns.FacetGrid(df_total, col="Construct", hue="Run", col_wrap=5, subplot_kws=dict(projection='polar'), size=5, sharex=False, sharey=False, despine=False)
# Draw a scatterplot onto each axes in the grid
g.map(plt.plot, 'Rad', ''y axis label', marker=".", ms=3, ls='None').set_titles("{col_name}")
plt.savefig('./image.pdf')
Which with my data gives the following:
I want to keep this organisation of 5 plots per line.
The problem is that the title of each subplot overlap with the values of the ticks, same for the y axis label.
Is there a way to prevent this behaviour? Can I somehow shift the titles slightly above their current position and can I shift the y axis labels slightly on the left of their current position?
Many thanks in advance!
EDIT:
This is not a duplicate of this SO as the problem was that the title of one subplot overlapped with the axis label of another subplot.
Here my problem is that the title of one subplot overlaps with the ticks label of the same subplot and similarly the axis label overlaps with the ticks label of the same subplot.
I also would like to add that I do not care that they overlap on my jupyter notebook (as it as been created with it), however I want the final saved image with no overlap, so perhaps there is something I need to do to save the image in a slightly different format to avoid that, but I don't know what (I am only using plt.savefig to save it).
EDIT 2: If someone would like to reproduce the problem here is a minimal example:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
sns.set(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1.5)
# Generate an example radial datast
r = np.linspace(0, 10000, num=100)
df = pd.DataFrame({'label': r, 'slow': r, 'medium-slow': 1 * r, 'medium': 2 * r, 'medium-fast': 3 * r, 'fast': 4 * r})
# Convert the dataframe to long-form or "tidy" format
df = pd.melt(df, id_vars=['label'], var_name='speed', value_name='theta')
# Set up a grid of axes with a polar projection
g = sns.FacetGrid(df, col="speed", hue="speed",
subplot_kws=dict(projection='polar'), size=4.5, col_wrap=5,
sharex=False, sharey=False, despine=False)
# Draw a scatterplot onto each axes in the grid
g.map(plt.scatter, "theta", "label")
plt.savefig('./image.png')
plt.show()
Which gives the following image in which the titles are not as bad as in my original problem (but still some overlap) and the label on the left hand side overlap completely.

In order to move the title a bit higher you can set at new position,
ax.title.set_position([.5, 1.1])
In order to move the ylabel a little further left, you can add some padding
ax.yaxis.labelpad = 25
To do this for the axes of the facetgrid, you'd do:
for ax in g.axes:
ax.title.set_position([.5, 1.1])
ax.yaxis.labelpad = 25

The answer provided by ImportanceOfBeingErnest in this SO question may help.

How to use seaborn pointplot and violinplot in the same figure? (change xticks and marker of pointplot)

I am trying to create violinplots that shows confidence intervals for the mean. I thought an easy way to do this would be to plot a pointplot on top of the violinplot, but this is not working since they seem to be using different indices for the xaxis as in this example:
import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset("titanic")
titanic.dropna(inplace=True)
fig, (ax1,ax2,ax3) = plt.subplots(1,3, sharey=True, figsize=(12,4))
#ax1
sns.pointplot("who", "age", data=titanic, join=False,n_boot=10, ax=ax1)
#ax2
sns.violinplot(titanic.age, groupby=titanic.who, ax=ax2)
#ax3
sns.pointplot("who", "age", data=titanic, join=False, n_boot=10, ax=ax3)
sns.violinplot(titanic.age, groupby=titanic.who, ax=ax3)
ax3.set_xlim([-0.5,4])
print(ax1.get_xticks(), ax2.get_xticks())
gives: [0 1 2] [1 2 3]
Why are these plots not assigning the same xtick numbers to the 'who'-variable and is there any way I can change this?
I also wonder if there is anyway I can change the marker for pointplot, because as you can see in the figure, the point is so big so that it covers the entire confidence interval. I would like just a horizontal line if possible.

I'm posting my final solution here. The reason I wanted to do this kind of plot to begin with, was to display information about the distribution shape, shift in means, and outliers in the same figure. With mwaskom's pointers and some other tweaks I finally got what I was looking for.
The left hand figure is there as a comparison with all data points plotted as lines and the right hand one is my final figure. The thick grey line in the middle of the violin is the bootstrapped 99% confidence interval of the mean, which is the white horizontal line, both from pointplot. The three dotted lines are the standard 25th, 50th and 75th percentile and the lines outside that are the caps of the whiskers of a boxplot I plotted on top of the violin plot. Individual data points are plotted as lines beyond this points since my data usually has a few extreme ones that I need to remove manually like the two points in the violin below.
For now, I am going to to continue making histograms and boxplots in addition to these enhanced violins, but I hope to find that all the information is accurately captured in the violinplot and that I can start and rely on it as my main initial data exploration plot. Here is the final code to produce the plots in case someone else finds them useful (or finds something that can be improved). Lots of tweaking to the boxplot.
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
#change the linewidth which to get a thicker confidence interval line
mpl.rc("lines", linewidth=3)
df = sns.load_dataset("titanic")
df.dropna(inplace=True)
x = 'who'
y = 'age'
fig, (ax1,ax2) = plt.subplots(1,2, sharey=True, figsize=(12,6))
#Left hand plot
sns.violinplot(df[y], groupby=df[x], ax=ax1, inner='stick')
#Right hand plot
sns.violinplot(df[y], groupby=df[x], ax=ax2, positions=0)
sns.pointplot(df[x],df[y], join=False, ci=99, n_boot=1000, ax=ax2, color=[0.3,0.3,0.3], markers=' ')
df.boxplot(y, by=x, sym='_', ax=ax2, showbox=False, showmeans=True, whiskerprops={'linewidth':0},
medianprops={'linewidth':0}, flierprops={'markeredgecolor':'k', 'markeredgewidth':1},
meanprops={'marker':'_', 'color':'w', 'markersize':6, 'markeredgewidth':1.5},
capprops={'linewidth':1, 'color':[0.3,0.3,0.3]}, positions=[0,1,2])
#One could argue that this is not beautiful
labels = [item.get_text() + '\nn=' + str(df.groupby(x).size().loc[item.get_text()]) for item in ax2.get_xticklabels()]
ax2.set_xticklabels(labels)
#Clean up
fig.suptitle('')
ax2.set_title('')
fig.set_facecolor('w')
Edit: Added 'n='

violinplot takes a positions argument that you can use to put the violins somewhere else (they currently just inherit the default matplotlib boxplot positions).
pointplot takes a markers argument that you can use to change how the point estimate is rendered.

Is there a function to make scatterplot matrices in matplotlib?

Example of scatterplot matrix
Is there such a function in matplotlib.pyplot?

For those who do not want to define their own functions, there is a great data analysis libarary in Python, called Pandas, where one can find the scatter_matrix() method:
from pandas.plotting import scatter_matrix
df = pd.DataFrame(np.random.randn(1000, 4), columns = ['a', 'b', 'c', 'd'])
scatter_matrix(df, alpha = 0.2, figsize = (6, 6), diagonal = 'kde')

Generally speaking, matplotlib doesn't usually contain plotting functions that operate on more than one axes object (subplot, in this case). The expectation is that you'd write a simple function to string things together however you'd like.
I'm not quite sure what your data looks like, but it's quite simple to just build a function to do this from scratch. If you're always going to be working with structured or rec arrays, then you can simplify this a touch. (i.e. There's always a name associated with each data series, so you can omit having to specify names.)
As an example:
import itertools
import numpy as np
import matplotlib.pyplot as plt
def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()
def scatterplot_matrix(data, names, **kwargs):
"""Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# Set up ticks only on one side for the "edge" subplots...
if ax.is_first_col():
ax.yaxis.set_ticks_position('left')
if ax.is_last_col():
ax.yaxis.set_ticks_position('right')
if ax.is_first_row():
ax.xaxis.set_ticks_position('top')
if ax.is_last_row():
ax.xaxis.set_ticks_position('bottom')
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[x], data[y], **kwargs)
# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
return fig
main()

You can also use Seaborn's pairplot function:
import seaborn as sns
sns.set()
df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")

Thanks for sharing your code! You figured out all the hard stuff for us. As I was working with it, I noticed a few little things that didn't look quite right.
[FIX #1] The axis tics weren't lining up like I would expect (i.e., in your example above, you should be able to draw a vertical and horizontal line through any point across all plots and the lines should cross through the corresponding point in the other plots, but as it sits now this doesn't occur.
[FIX #2] If you have an odd number of variables you are plotting with, the bottom right corner axes doesn't pull the correct xtics or ytics. It just leaves it as the default 0..1 ticks.
Not a fix, but I made it optional to explicitly input names, so that it puts a default xi for variable i in the diagonal positions.
Below you'll find an updated version of your code that addresses these two points, otherwise preserving the beauty of your code.
import itertools
import numpy as np
import matplotlib.pyplot as plt
def scatterplot_matrix(data, names=[], **kwargs):
"""
Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid.
"""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
fig.subplots_adjust(hspace=0.0, wspace=0.0)
for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# Set up ticks only on one side for the "edge" subplots...
if ax.is_first_col():
ax.yaxis.set_ticks_position('left')
if ax.is_last_col():
ax.yaxis.set_ticks_position('right')
if ax.is_first_row():
ax.xaxis.set_ticks_position('top')
if ax.is_last_row():
ax.xaxis.set_ticks_position('bottom')
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
# FIX #1: this needed to be changed from ...(data[x], data[y],...)
axes[x,y].plot(data[y], data[x], **kwargs)
# Label the diagonal subplots...
if not names:
names = ['x'+str(i) for i in range(numvars)]
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
# FIX #2: if numvars is odd, the bottom right corner plot doesn't have the
# correct axes limits, so we pull them from other axes
if numvars%2:
xlimits = axes[0,-1].get_xlim()
ylimits = axes[-1,0].get_ylim()
axes[-1,-1].set_xlim(xlimits)
axes[-1,-1].set_ylim(ylimits)
return fig
if __name__=='__main__':
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()
Thanks again for sharing this with us. I have used it many times! Oh, and I re-arranged the main() part of the code so that it can be a formal example code or not get called if it is being imported into another piece of code.

While reading the question I expected to see an answer including rpy. I think this is a nice option taking advantage of two beautiful languages. So here it is:
import rpy
import numpy as np
def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
mpg = data[0,:]
disp = data[1,:]
drat = data[2,:]
wt = data[3,:]
rpy.set_default_mode(rpy.NO_CONVERSION)
R_data = rpy.r.data_frame(mpg=mpg,disp=disp,drat=drat,wt=wt)
# Figure saved as eps
rpy.r.postscript('pairsPlot.eps')
rpy.r.pairs(R_data,
main="Simple Scatterplot Matrix Via RPy")
rpy.r.dev_off()
# Figure saved as png
rpy.r.png('pairsPlot.png')
rpy.r.pairs(R_data,
main="Simple Scatterplot Matrix Via RPy")
rpy.r.dev_off()
rpy.set_default_mode(rpy.BASIC_CONVERSION)
if __name__ == '__main__': main()
I can't post an image to show the result :( sorry!