Develop Reference

How can I adjust the color bar position if there are multiple subplots in a figure?

I want to move my color bar near a specific subplot in my figure, I searched the past questions, nearly all the answer needs the imshow() function, is there any different solution?
this is my current figure
I'll put a few of my codes down there if anyone wondered.
fig,(axe1,axe2)=plt.subplots(2,1,figsize=(40,40))
axe1.barh(count['platform'],count['numbers'],0.8,color=color)
axe2.barh(combine['platform'],combine['score'],color='pink')
fig.colorbar(cm.ScalarMappable(cmap='hsv',norm=col.Normalize(0,1)))

See: https://matplotlib.org/stable/gallery/subplots_axes_and_figures/colorbar_placement.html
Maybe to put next to both axes:
fig,(axe1,axe2)=plt.subplots(2,1,figsize=(40,40), constrained_layout=True)
...
fig.colorbar(cm.ScalarMappable(cmap='hsv',norm=col.Normalize(0,1)), ax=[axe1, axe2])
or just next to axe1:
fig.colorbar(cm.ScalarMappable(cmap='hsv',norm=col.Normalize(0,1)), ax=axe1)

Sns Scatter plot with differet color coding and different markers

I would like to create a Seaborn scatter-plot, using the following dataframe:
df = pd.DataFrame({'A':[1,2,3,4],'B':[2,4,6,8],'C':['y','y','n','n'],'D':[1,1,2,2]})
In my graph A should be the x-variable and B the y-variable. Furthermore I would like to color based on column D. Finally, when C='y' the marker should be open-faced (no facecolor) and when C='n' the marker should have a closed. My original idea was to use the hue and style parameter:
sns.scatterplot(x='A', y='B',
data=df, hue='D',style ='C')
However, I did not manage to obtain the graph I am looking for. Could somebody help me with this? Thank you in advance.

One cannot specify entire marker styles (so 'marker' and 'fillstyle' keys in your case) for matplotlib yet. Have a look on the answer to this post.
So the only thing left for you is to use different markers right away and specify them (as list or dictionary)
sns.scatterplot(data=df, x='A', y='B', hue='D', style='C', markers=['o', 's'])
plt.show()
Apparently, it is very hard to even create non-filled markers in seaborn, as this post explains. The only option is to do some matplotlib-seaborn-hybrid thing... So if you accept to plot things twice onto the same axis (one for a filled marker and one for the unfilled markers), you still have to dig yourself into the quirks of seaborn...

Why are my Matplotlib subplots sized differently?

Hello there!
I am trying to create a figure consisting of a chloropleth map and a bar plot in Matplotlib. To achieve this, i am using the Geopandas library alongside Pandas and Matplotlib. I've run into an interesting problem that i couldn't find any answer for on the internet. Here's the problem:
This link leads to an image that replicates the problem.
As it can be seen on the image above, the map on the top (generated by Geopandas) does not span the same width as the bar chart on the bottom. There is too much whitespace to the left and the right of the figure. I want to get rid of this whitespace and make the map fit horizontally on the space that is allocated to it. I am also leaving a code sample below for those who wish to recreate it:
fig = plt.figure(figsize = (25.60,14.40)) #Here, i am setting the overall figure size
ax_1 = fig.add_subplot(2,1,1) #This will be the map
istanbul_districts.plot(ax = ax_1,
edgecolor = "black",
alpha = 1,
color = "Red") #Istanbul_districts is a GeoDataFrame object.
ax_2 = fig.add_subplot(2,1,2)
labels = list(health.loc[:,"district_eng"].value_counts().sort_values(ascending = False).index)
from numpy import arange
bar_positions = arange(len(labels)) + 1
bar_heights = h_inst_per_district_eng.loc[:,"health_count"].values.astype(int)
ax_2.bar(bar_positions,bar_heights,
width = 0.7,
align = "center",
color = "blue") #This is a generic barplot from Matplotlib
I am leaving a second image that shows the end result of the code snippet above:
This link also leads to an image that replicates the problem.
It can be clearly seen above that the axes of the two subplots do not start and end on the same location. Perhaps that could be the problem? What can be done to make them the same size?
Thanks to all those answer for their time in advance!

Adding an explanation, since you have found one solution.
If you specify matplotlib figure with two axes in a way you did, you get the figure split in half. Both axes are the same. Let's say that the original ratio of the figure is 1:1, your axes will be both 1:2.
This arbitrary ratio is fine for a bar chart, which can be scaled to essentially any ratio. It does not matter much if it is horizontal or vertical (from a plotting perspective, not data-viz).
However, if you want your map to show correct non-distorted shapes, you can't just specify the aspect ratio. That just follows the data. So if you have a map, which bounding box has 1:1 ratio, you can't expect that it will fill the whole 1:2 axis. GeoPandas changes the aspect ratio to follow the map's ratio.
The reason why the first example leaves gaps on side and the "solution" does not is this. Because the leftover space is on top and on the bottom the axis, it is not shown in the solution. Because it is on sides in the issue, it just stays there. If you had your plots next to each other instead of above, it would be vice versa.
Hope it is clearer.

Hello again!
swatchai's comment set me up on the right track and i found the culprit. Simply adjusting the figsize to a value like (19,19) fixed the problem. I'd still be happy if anyone can explain exactly why this happens.
Here's what it looks like when the figsize is a square (19,19):
Thanks for your efforts!

Changing the marker on the same set of data

I have a set of data that comes from two different sources, and I have multiple sets graphed together. So essentially 6 scatterplots with error bars (all different colors), and each scatterplot has two sources.
Basically I want the blue scatterplot to have two different markers, 'o' and's'. I currently have done this by plotting each point individually with a loop and checking to see if the source is 1 or 2. If it is 1 it plots a 's' if the source is 2 then it plots a 'o'.
However this method does not really allow for having a legend. (Data1, Data2,...Data6)
Is there a better way of doing this?
EDIT:
I want a cleaner method for this, something along the lines of
x=[1,2,3]
y=[4,5,6]
m=['o','s','^']
plt.scatter(x,y,marker=m)
But this returns an error Unrecognized marker style

A more pythonic way (but still a loop) might be something like
x=[1,2,3]
y=[4,5,6]
l=['data1','data2','data3']
m=['ob','sb','^b']
f,a = plt.subplots(1,1)
[a.plot(*data, label=lab) for data,lab in zip(zip(x,y,m),l)]
plt.legend(loc='lower right')
plt.xlim(0,4)
plt.ylim(3,7);
But I guess this is not the most efficient way if you have lots of datapoints.
If you want to use scatter try something like
m=['o','s','^']
f,a = plt.subplots(1,1)
[a.scatter(*data, marker=m1, label=l1) for data,m1,l1 in zip(zip(x,y),m,l)]
I'm pretty sure, there is also a possibility to apply ** and dicts here.
UPDATE:
Instead of looping over the plot command the ability of matplotlib's plot function to read an arbitrary number of x,y,fmt groups, see docs.
x=np.random.random((3,6))
y=np.random.random((3,6))
l=['data1','data2','data3']
m=['ob','sb','^b']
plt.plot(*[i[j] for i in zip(x,y,m) for j in range(3)])
plt.legend(l,loc='lower right')

Calling plot in a loop is fine. You just need to keep the list of lines returned by plot and use fig.legend to create a legend for the whole figure. See http://matplotlib.org/examples/pylab_examples/figlegend_demo.html

Seconded to #tcaswell 's comments, .scatter() returns collections.PathCollection, which provides a fast way of plotting a large number of identical shaped objects. You can use a loop to plot the data as many scatter plots (and many different datasets) but in my opinion it looses all the speed benefit provided by .scatter().
With these being said, it is however not true that the dots have to be identical in a scatter plot. You can have different linewidth, edgecolor and many other things. But the dots have to be the same shape. See this example, assigning different colors (and only plot one dataset):
>>> sc=plt.scatter(x, y, label='test')
>>> sc.set_color(['r','g','b'])
>>> plt.legend()
See details in http://matplotlib.org/api/collections_api.html.

These were all alright, but not really what I was looking for. The problem was how I parsed through my data and how I could add a legend in the wouldn't mess that up. Since I did a for-loop and plotted each point individually based on if it was measured at Observation location 1 or 2 whenever I made a legend it would plot over 50 legend entries. So I plotted my data as full sets (Invisibly and with no change in symbols) then again in color with the varying symbols. This worked better. Thanks though

Make more than one chart in same IPython Notebook cell

I have started my IPython Notebook with
ipython notebook --pylab inline
This is my code in one cell
df['korisnika'].plot()
df['osiguranika'].plot()
This is working fine, it will draw two lines, but on the same chart.
I would like to draw each line on a separate chart.
And it would be great if the charts would be next to each other, not one after the other.
I know that I can put the second line in the next cell, and then I would get two charts. But I would like the charts close to each other, because they represent the same logical unit.

You can also call the show() function after each plot.
e.g
plt.plot(a)
plt.show()
plt.plot(b)
plt.show()

Make the multiple axes first and pass them to the Pandas plot function, like:
fig, axs = plt.subplots(1,2)
df['korisnika'].plot(ax=axs[0])
df['osiguranika'].plot(ax=axs[1])
It still gives you 1 figure, but with two different plots next to each other.

Something like this:
import matplotlib.pyplot as plt
... code for plot 1 ...
plt.show()
... code for plot 2...
plt.show()
Note that this will also work if you are using the seaborn package for plotting:
import matplotlib.pyplot as plt
import seaborn as sns
sns.barplot(... code for plot 1 ...) # plot 1
plt.show()
sns.barplot(... code for plot 2 ...) # plot 2
plt.show()

Another way, for variety. Although this is somewhat less flexible than the others. Unfortunately, the graphs appear one above the other, rather than side-by-side, which you did request in your original question. But it is very concise.
df.plot(subplots=True)
If the dataframe has more than the two series, and you only want to plot those two, you'll need to replace df with df[['korisnika','osiguranika']].

I don't know if this is new functionality, but this will plot on separate figures:
df.plot(y='korisnika')
df.plot(y='osiguranika')
while this will plot on the same figure: (just like the code in the op)
df.plot(y=['korisnika','osiguranika'])
I found this question because I was using the former method and wanted them to plot on the same figure, so your question was actually my answer.