Develop Reference

How can I draw EMPTY circels surrounding the data points?

I would like to add to the plot below open circles surrounding each data point and set the diameter proportional to the values of a 3rd variable. Currently, this is what I tried but the circles are filled and cover the data points. Using "facecolors='none'" did not help.
z = df.z # this is the 3rd variable
s = [10*2**n for n in range(len(z))]
ax1 = sns.scatterplot(x='LEF', y='NPQ', hue="Regime", markers=["o",
"^"], s=s, facecolors='none', data=df, ax=ax1)

The following approach loops through the generated dots, and sets their edgecolors to their facecolors. Then the facecolors are set to fully transparent.
import matplotlib.pyplot as plt
import seaborn as sns
tips = sns.load_dataset('tips')
ax = sns.scatterplot(data=tips, x="total_bill", y="tip", hue="day", size="size", sizes=(10, 200))
for dots in ax.collections:
facecolors = dots.get_facecolors()
dots.set_edgecolors(facecolors.copy())
dots.set_facecolors('none')
dots.set_linewidth(2)
plt.show()

Removing legend from mpl parallel coordinates plot?

I have a parallel coordinates plot with lots of data points so I'm trying to use a continuous colour bar to represent that, which I think I have worked out. However, I haven't been able to remove the default key that is put in when creating the plot, which is very long and hinders readability. Is there a way to remove this table to make the graph much easier to read?
This is the code I'm currently using to generate the parallel coordinates plot:
parallel_coordinates(data[[' male_le','
female_le','diet','activity','obese_perc','median_income']],'median_income',colormap = 'rainbow',
alpha = 0.5)
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.rainbow
bounds = [0.00,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N,)
plt.colorbar(mpl.cm.ScalarMappable(norm = norm, cmap=cmap),cax = ax, orientation = 'horizontal',
label = 'normalised median income', alpha = 0.5)
plt.show()
Current Output:
I want my legend to be represented as a color bar, like this:
Any help would be greatly appreciated. Thanks.

You can use ax.legend_.remove() to remove the legend.
The cax parameter of plt.colorbar indicates the subplot where to put the colorbar. If you leave it out, matplotlib will create a new subplot, "stealing" space from the current subplot (subplots are often referenced to by ax in matplotlib). So, here leaving out cax (adding ax=ax isn't necessary, as here ax is the current subplot) will create the desired colorbar.
The code below uses seaborn's penguin dataset to create a standalone example.
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import numpy as np
from pandas.plotting import parallel_coordinates
penguins = sns.load_dataset('penguins')
fig, ax = plt.subplots(figsize=(10, 4))
cmap = plt.get_cmap('rainbow')
bounds = np.arange(penguins['body_mass_g'].min(), penguins['body_mass_g'].max() + 200, 200)
norm = mpl.colors.BoundaryNorm(bounds, 256)
penguins = penguins.dropna(subset=['body_mass_g'])
parallel_coordinates(penguins[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']],
'body_mass_g', colormap=cmap, alpha=0.5, ax=ax)
ax.legend_.remove()
plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax, orientation='horizontal', label='body mass', alpha=0.5)
plt.show()

Issue w/ image crossing dateline in imshow & cartopy

I'm trying to plot a square grid of equally-spaced (in lat/lon) data using cartopy, matplotlib, and imshow. The data crosses the dateline, and I've had issues getting a map to work properly.
Here's an example of my issue:
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
lat = np.arange(6000)*0.02 + (-59.99)
lon = np.arange(6000)*0.02 + (85.01)
dat = np.reshape(np.arange(6000*6000),[6000,6000])
tran = ccrs.PlateCarree()
proj = tran
plt.figure(figsize=(8,8))
ax = plt.axes(projection=proj)
print([lon[0],lon[-1],lat[0],lat[-1]])
ax.imshow(dat, extent=[lon[0],lon[-1],lat[0],lat[-1]],transform=tran,interpolation='nearest')
ax.coastlines(resolution='50m', color='black', linewidth=2)
ax.gridlines(crs=proj,draw_labels=True)
plt.show()
tran = ccrs.PlateCarree(central_longitude=180)
proj = tran
plt.figure(figsize=(8,8))
ax = plt.axes(projection=proj)
print([lon[0]-180,lon[-1]-180,lat[0],lat[-1]])
ax.imshow(dat, extent=[lon[0]-180,lon[-1]-180,lat[0],lat[-1]],transform=tran,interpolation='nearest')
ax.coastlines(resolution='50m', color='black', linewidth=2)
ax.gridlines(crs=tran,draw_labels=True)
plt.show()
The first plot yields this image, chopping off at 180E:
The second fixes the map issue, but the grid ticks are now wrong:
I've tried reprojecting, I think (where tran != proj), but it seemingly either hung or was taking too long.
I basically want the bottom image, but with the proper labels. I'm going to have more geolocated data to overplot, so I'd like to do it correctly instead of what seems like a hack right now.

With Cartopy, drawing a map crossing dateline is always challenging. Here is the code that plots the map you want.
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# demo raster data
n1 = 300
m1 = 0.4
lat = np.arange(n1)*m1 + (-59.99)
lon = np.arange(n1)*m1 + (85.01)
dat = np.reshape(np.arange(n1*n1), [n1,n1])
cm_lon=180 # for central meridian
tran = ccrs.PlateCarree(central_longitude = cm_lon)
proj = tran
plt.figure(figsize=(8,8))
ax = plt.axes(projection=proj)
ext = [lon[0]-cm_lon, lon[-1]-cm_lon, lat[0], lat[-1]]
#print(ext)
ax.imshow(dat, extent=ext, \
transform=tran, interpolation='nearest')
ax.coastlines(resolution='110m', color='black', linewidth=0.5, zorder=10)
# this draws grid lines only, must go beyond E/W extents
ax.gridlines(draw_labels=False, xlocs=[80,100,120,140,160,180,-180,-160,-140])
# this draw lables only, exclude those outside E/W extents
ax.gridlines(draw_labels=True, xlocs=[100,120,140,160,180,-160])
plt.show()
The resulting map:

Matplotlib scatter plot - Remove white padding

I'm working with matplotlib to plot a variable in latitude longitude coordinates. The problem is that this image cannot include axes or borders. I have been able to remove axis, but the white padding around my image has to be completely removed (see example images from code below here: http://imgur.com/a/W0vy9) .
I have tried several methods from Google searches, including these StackOverflow methodologies:
Remove padding from matplotlib plotting
How to remove padding/border in a matplotlib subplot (SOLVED)
Matplotlib plots: removing axis, legends and white spaces
but nothing has worked in removing the white space. If you have any advice (even if it is to ditch matplotlib and to try another plotting library instead) I would appreciate it!
Here is a basic form of the code I'm using that shows this behavior:
import numpy as np
import matplotlib
from mpl_toolkits.basemap import Basemap
from scipy import stats
lat = np.random.randint(-60.5, high=60.5, size=257087)
lon = np.random.randint(-179.95, high=180, size=257087)
maxnsz = np.random.randint(12, 60, size=257087)
percRange = np.arange(100,40,-1)
percStr=percRange.astype(str)
val_percentile=np.percentile(maxnsz, percRange, interpolation='nearest')
#Rank all values
all_percentiles=stats.rankdata(maxnsz)/len(maxnsz)
#Figure setup
fig = matplotlib.pyplot.figure(frameon=False, dpi=600)
#Basemap code can go here
x=lon
y=lat
cmap = matplotlib.cm.get_cmap('cool')
h=np.where(all_percentiles >= 0.999)
hl=np.where((all_percentiles < 0.999) & (all_percentiles > 0.90))
mh=np.where((all_percentiles > 0.75) & (all_percentiles < 0.90))
ml=np.where((all_percentiles >= 0.4) & (all_percentiles < 0.75))
l=np.where(all_percentiles < 0.4)
all_percentiles[h]=0
all_percentiles[hl]=0.25
all_percentiles[mh]=0.5
all_percentiles[ml]=0.75
all_percentiles[l]=1
rgba_low=cmap(1)
rgba_ml=cmap(0.75)
rgba_mh=cmap(0.51)
rgba_hl=cmap(0.25)
rgba_high=cmap(0)
matplotlib.pyplot.axis('off')
matplotlib.pyplot.scatter(x[ml],y[ml], c=rgba_ml, s=3, marker=',',edgecolor='none', alpha=0.4)
matplotlib.pyplot.scatter(x[mh],y[mh], c=rgba_mh, s=3, marker='o', edgecolor='none', alpha=0.5)
matplotlib.pyplot.scatter(x[hl],y[hl], c=rgba_hl, s=4, marker='*',edgecolor='none', alpha=0.6)
matplotlib.pyplot.scatter(x[h],y[h], c=rgba_high, s=5, marker='^', edgecolor='none',alpha=0.75)
fig.savefig('/home/usr/code/python/testfig.jpg', bbox_inches=0, nbins=0, transparent="True", pad_inches=0.0)
fig.canvas.draw()

The problem is that all the solutions given at Matplotlib plots: removing axis, legends and white spaces are actually meant to work with imshow.
So, the following clearly works
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.set_aspect('auto')
plt.show()
and produces
But here, you are using scatter. Adding a scatter plot
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.scatter([2,3,4,1], [2,3,4,8], c="r", s=2500)
ax.set_aspect('auto')
plt.show()
produces
Scatter has the particularity that matplotlib tries to make all points visible by default, which means that the axes limits are set such that all scatter points are visible as a whole.
To overcome this, we need to specifically set the axes limits:
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.scatter([2,3,4,1], [2,3,4,8], c="r", s=2500)
ax.set_xlim([1,4])
ax.set_ylim([2,8])
ax.set_aspect('auto')
plt.show()
such that we will get the desired behaviour.

matplotlib colorbar for scatter

I'm working with data that has the data has 3 plotting parameters: x,y,c. How do you create a custom color value for a scatter plot?
Extending this example I'm trying to do:
import matplotlib
import matplotlib.pyplot as plt
cm = matplotlib.cm.get_cmap('RdYlBu')
colors=[cm(1.*i/20) for i in range(20)]
xy = range(20)
plt.subplot(111)
colorlist=[colors[x/2] for x in xy] #actually some other non-linear relationship
plt.scatter(xy, xy, c=colorlist, s=35, vmin=0, vmax=20)
plt.colorbar()
plt.show()
but the result is TypeError: You must first set_array for mappable

From the matplotlib docs on scatter 1:
cmap is only used if c is an array of floats
So colorlist needs to be a list of floats rather than a list of tuples as you have it now.
plt.colorbar() wants a mappable object, like the CircleCollection that plt.scatter() returns.
vmin and vmax can then control the limits of your colorbar. Things outside vmin/vmax get the colors of the endpoints.
How does this work for you?
import matplotlib.pyplot as plt
cm = plt.cm.get_cmap('RdYlBu')
xy = range(20)
z = xy
sc = plt.scatter(xy, xy, c=z, vmin=0, vmax=20, s=35, cmap=cm)
plt.colorbar(sc)
plt.show()

Here is the OOP way of adding a colorbar:
fig, ax = plt.subplots()
im = ax.scatter(x, y, c=c)
fig.colorbar(im, ax=ax)

If you're looking to scatter by two variables and color by the third, Altair can be a great choice.
Creating the dataset
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(40*np.random.randn(10, 3), columns=['A', 'B','C'])
Altair plot
from altair import *
Chart(df).mark_circle().encode(x='A',y='B', color='C').configure_cell(width=200, height=150)
Plot