I am looking for a way to clean-up the ticks in my pyplot scatter plot.
To create a scatter plot from a Pandas dataset column with strings as elements, I followed the example in [2] - and got me a nice scatter plot:
input are 10k data points where the X axis has only ~200 unique 'names', that got matched to scalars for plotting. Obviously, plotting all the 10k ticks on the x axis is a bit clocked. So, I am looking for a way, to print each unique tick only once and not for each data point?
My code looks like:
fig2 = plt.figure()
WNsUniques, WNs = numpy.unique(taskDataFrame['modificationhost'], return_inverse=True)
scatterWNs = fig2.add_subplot(111)
scatterWNs.scatter(WNs, taskDataFrame['cpuconsumptiontime'])
scatterWNs.set(xticks=range(len(WNsUniques)), xticklabels=WNsUniques)
plt.xticks(rotation='vertical')
plt.savefig("%s_WNs-CPUTime_scatter.%s" % (dfName,"pdf"))
actually, I was hoping that setting the plot x ticks to the unique names should be sufficient - but apparently not? Probably it is something easy, but how do I reduce the ticks for my subplot to unique once (should they not already be uniqueified as returned by numpy.unique?)?
Maybe someone has an idea for me?
Cheers ans thanks,
Thomas
You can use the set_xticks method to accomplish this. Note that 200 axis ticks with labels are still quite a lot to force on a small plot like this, and this is what you might already be seeing with the above code. Without complete code to play with, I can't say for sure.
Additionally, what is the size of WNsUniques? That can easily be used to check if your call to unique is doing what you think.
Related
There is a similar question here, however I fail to adapt the provided solutions to my case.
I want to have a jointplot with kind=hex while removing the marginal plot of the x-axis as it contains no information. In the linked question the suggestion is to use JointGrid directly, however Seaborn then seems to to be unable to draw the hexbin plot.
joint_kws = dict(gridsize=70)
g = sns.jointplot(data=all_data, x="Minute of Hour", y="Frequency", kind="hex", joint_kws=joint_kws)
plt.ylim([49.9, 50.1])
plt.xlim([0, 60])
g.ax_joint.axvline(x=30,ymin=49, ymax=51)
plt.show()
plt.close()
How to remove the margin plot over the x-axis?
Why is the vertical line not drawn?
Also is there a way to exchange the right margin to a plot which more clearly resembles the density?
edit: Here is a sample of the dataset (33kB). Read it with pd.read_pickle("./data.pickle")
I've been fiddling with an analog problem (using a scatterplot instead of the hexbin). In the end, the solution to your first point is awkwardly simple. Just add this line :
g.ax_marg_x.remove()
Regarding your second point, I've no clue as to why no line is plotted. But a workaround seems to be to use vlines instead :
g.ax_joint.vlines(x=30, ymin=49, ymax=51)
Concerning your last point, I'm afraid I haven't understood it. If you mean increasing/reducing the margin between the subplots, you can use the space argument stated in the doc.
I am creating a series of boxplots in order to compare different cancer types with each other (based on 5 categories). For plotting I use seaborn/matplotlib. It works fine for most of the cancer types (see image right) however in some the x axis collapses slightly (see image left) or strongly (see image middle)
https://i.imgur.com/dxLR4B4.png
Looking into the code how seaborn plots a box/violin plot https://github.com/mwaskom/seaborn/blob/36964d7ffba3683de2117d25f224f8ebef015298/seaborn/categorical.py (line 961)
violin_data = remove_na(group_data[hue_mask])
I realized that this happens when there are too many nans
Is there any possibility to prevent this collapsing by code only
I do not want to modify my dataframe (replace the nans by zero)
Below you find my code:
boxp_df=pd.read_csv(pf_in,sep="\t",skip_blank_lines=False)
fig, ax = plt.subplots(figsize=(10, 10))
sns.violinplot(data=boxp_df, ax=ax)
plt.xticks(rotation=-45)
plt.ylabel("label")
plt.tight_layout()
plt.savefig(pf_out)
The output is a per cancer type differently sized plot
(depending on if there is any category completely nan)
I am expecting each plot to be in the same width.
Update
trying to use the order parameter as suggested leads to the following output:
https://i.imgur.com/uSm13Qw.png
Maybe this toy example helps ?
|Cat1|Cat2|Cat3|Cat4|Cat5
|3.93| |0.52| |6.01
|3.34| |0.89| |2.89
|3.39| |1.96| |4.63
|1.59| |3.66| |3.75
|2.73| |0.39| |2.87
|0.08| |1.25| |-0.27
Update
Apparently, the problem is not the data but the length of the title
https://github.com/matplotlib/matplotlib/issues/4413
Therefore I would close the question
#Diziet should I delete it or does my issue might help other ones?
Sorry for not including the line below in the code example:
ax.set_title("VERY LONG TITLE", fontsize=20)
It's hard to be sure without data to test it with, but I think you can pass the names of your categories/cancers to the order= parameter. This forces seaborn to use/display those, even if they are empty.
for instance:
tips = sns.load_dataset("tips")
ax = sns.violinplot(x="day", y="total_bill", data=tips, order=['Thur','Fri','Sat','Freedom Day','Sun','Durin\'s Day'])
I am working with a semi-large data set of approx 100,000 records. When I plot a df column as a line with the code below the plot takes approx 2 seconds.
with plt.style.context('ggplot'):
plt.figure(3,figsize=(16,12))
plt.subplot(411)
df_pca_std['PC1_resid'].plot(title ="PC1 Residual", color='r')
#If I change the plot to a bar (no other change)
df_X_std['PC1_resid'].plot(**kind='bar'**, title ="PC1 Residual", color='r')
it takes 112 seconds and the render changes like this (jumbled x axis):
I have suppressed the axis and changed the style but neither helped. Anyone have ideas how to better render and take less time? The data being plotted is being checked for mean reversion and is better displayed as bar plot.
Not the best charts visually but at least it renders. Plotted 2.1 million bars in 14.2 secs.
import pygal
bar_chart = pygal.Bar()
bar_chart.add('PC1_residuals',df_X_std['PC1_resid'])
bar_chart.render_to_file('bar_chart.svg')
One possible solution: I do not actually need to plot bars but can use the very fast line plot and the 'fill_between' attribute to color the plot from zero to the line. The effect is similar to plotting all the bars in a fraction of the time.
Use pydatetime method of DatetimeIndex to convert Date (the df index) to an array of datetime.datetime's that can be used by matplotlib then change the plot.
plotDates = mpl.date2num(df.index.to_pydatetime())
plt.fill_between(plotDates,0,df_pca_std['PC1_resid'], alpha=0.5)
I'm using pandas to work with a data set and am tring to use a simple line plot with error bars to show the end results. It's all working great except that the plot looks funny.
By default, it will put my 2 data groups at the far left and right of the plot, which obscures the error bar to the point that it's not useful (the error bars in this case are key to intpretation so I want them plainly visible).
Now, I fix that problem by setting xlim to open up some space on either end of the x axis so that the error bars are plainly visible, but then I have an offset from where the x labels are to where the actual x data is.
Here is a simplified example that shows the problem:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df6 = pd.DataFrame( [-0.07,0.08] , index = ['A','B'])
df6.plot(kind='line', linewidth=2, yerr = [ [0.1,0.1],[0.1,0.1 ] ], elinewidth=2,ecolor='green')
plt.xlim(-0.2,1.2) # Make some room at ends to see error bars
plt.show()
I tried to include a plot (image) showing the problem but I cannot post images yet, having just joined up and do not have anough points yet to post images.
What I want to know is: How do I shift these labels over one tick to the right?
Thanks in advance.
Well, it turns out I found a solution, which I will jsut post here in case anyone else has this same issue in the future.
Basically, it all seems to work better in the case of a line plot if you just specify both the labels and the ticks in the same place at the same time. At least that was helpful for me. It sort of forces you to keep the length of those two lists the same, which seems to make the assignment between ticks and labels more well behaved (simple 1:1 in this case).
So I coudl fix my problem by including something like this:
plt.xticks([0, 1], ['A','B'] )
right after the xlim statement in code from original question. Now the A and B align perfectly with the place where the data is plotted, not offset from it.
Using above solution it works, but is less good-looking since now the x grid is very coarse (this is purely and aesthetic consideration). I could fix that by using a different xtick statement like:
plt.xticks([-0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0], ['','A','','','','','B',''])
This gives me nice looking grid and the data where I need it, but of course is very contrived-looking here. In the actual program I'd find a way to make that less clunky.
Hope that is of some help to fellow seekers....
I have a problem with Matplotlib's subplots. I do not know the number of subplots I want to plot beforehand, but I know that I want them in two rows. so I cannot use
plt.subplot(212)
because I don't know the number that I should provide.
It should look like this:
Right now, I plot all the plots into a folder and put them together with illustrator, but there has to be a better way with Matplotlib. I can provide my code if I was unclear somewhere.
My understanding is that you only know the number of plots at runtime and hence are struggling with the shorthand syntax, e.g.:
plt.subplot(121)
Thankfully, to save you having to do some awkward math to figure out this number programatically, there is another interface which allows you to use the form:
plt.subplot(n_cols, n_rows, plot_num)
So in your case, given you want n plots, you can do:
n_plots = 5 # (or however many you programatically figure out you need)
n_cols = 2
n_rows = (n_plots + 1) // n_cols
for plot_num in range(n_plots):
ax = plt.subplot(n_cols, n_rows, plot_num)
# ... do some plotting
Alternatively, there is also a slightly more pythonic interface which you may wish to be aware of:
fig, subplots = plt.subplots(n_cols, n_rows)
for ax in subplots:
# ... do some plotting
(Notice that this was subplots() not the plain subplot()). Although I must admit, I have never used this latter interface.
HTH