I am using the pandas plot facilities, to plot a bar plot:
spy_price_data.iloc[40:,1].plot(kind='bar')
The bar data is plotted correctly, but the figure contains weird artefacts in the form of additional horizontal bars below the actual figure:
What could be the problem here?
The 'weird artefacts' are your ticklabels. You can even (almost) read them at the end:
The last value seems to say something like 2018-08-19 20:00:00.
To make the plot more readable, take a look at the answer from ImportanceOfBeingErnest to the question Matplotlib: How to increase space between tickmarks (or reduce number of tickmarks)?
Related
I am trying to plot time series data in a kind of "climate stripes plot" using the package Altair.
The problem is that I do not know how to change the range in the legend to standardise all my plots with the same colour range and numbers in the legend. At the moment, each time I plot something the legend adapts to the range of the data.
I think the problem is with the "domain" property, maybe is not in the correct place ?
Thank you for your help :)
This is the code for the plot :
chart=alt.Chart(source).mark_rect().encode(
x=('day:O'),
y='subasins:N',
color=alt.Color('90%:Q',legend=alt.Legend(title='CH4'), bin=alt.Bin(maxbins=20),
scale=alt.Scale(scheme='blueorange'),domain=[1830,2000])
).properties(width=100).facet(column=alt.Column('month'))
chart.show()
Plots that I get now with different scales in the legend
You're using the right approach with domain, it just needs to be put inside alt.Scale:
scale=alt.Scale(scheme='blueorange', domain=[1830, 2000])
When you're using a bin transform, one way to ensure the scale is consistent is to specify the bin extent:
bin=alt.Bin(maxbins=20, extent=[1830, 2000])
I produced a histogram which looks something like this:
Code that I used to produce this plot:
sns.countplot(table.column_name)
As you can notice, the entire histogram gets clustered at the left end due to uneven distribution of data.
How do I zoom in at the left end?
One way that I tried and it gave me a marginally better result was :
plt.xlim(0,25)
Is there a better way to do this?
Looks like the data would be better viewed on a logarithmic scale. On your matplotlib plot axis, ax, use:
ax.set_yscale('log')
I'm creating a plot with factorplot and then trying to add a subplot on top of each box. How can I get the x-axis locations of each individual box in the factor plot to put another line on top?
Maybe there's a way to get all the x-axis values of each box plot on the axes?
Here's my basic factor plot:
I want to add 1 subplot (the circle) in the middle of each box plot. However, I cannot figure out how to get the x-value of each box to properly space the points.
I see a lot of code for positions and offsets in the seaborn source that lays these out. However, I'm wondering if there is a more straight-forward method to get this information or at least approximate it.
As per #mwaskom's comments, you can use sns.stripplot() (and now also sns.swarmplot()) to include your data points with a data summary plot such as a box or violinplot.
The bug is documented here:
Matplotlib errorbar not centered on marker
and here:
https://github.com/matplotlib/matplotlib/issues/3400
Basically, the markers are plotted off by 1 pixel all the time.. You can even see this on Matplotlib's own tutorial page if you look closely at the second plot: http://matplotlib.org/1.2.1/examples/pylab_examples/errorbar_demo.html
This is very frustrating as I cannot produce publication-quality plots from matplotlib, and I'm very surprised this has not been fixed.
In any case, I have too much time and code invested into matplotlib to switch to a different package. So my question is how would you go about making a workaround? I suppose one solution is to plot the markers 1 pixel to the left/right from the errorbars. I don't know how to do this. I figured out how to get the display coordinates of my plot points, but how can I make an interactive plot that preserves the 1-pixel offset? I can plot them with 1-pixel offsets, but then you can't zoom or manipulate the plot.
It seems like the Matplotlib team have fixed the issue when calling savefig() using .svg or .pdf, but for .png I've found that you can circumvent this issue by using an odd number for the error line thickness. Using the first example on the Matplotlib tutorial, if we use
plt.errorbar(x, y, yerr=0.4, marker='X', markersize=15)
then the bars are offset like this:
However if we use a line width of 3
plt.errorbar(x, y, yerr=0.4, marker='X', markersize=15, elinewidth=3)
then the bars are centred like this:
This isn't a perfect solution, but it does the job if you don't mind having slightly thicker lines.
I have the following graph, consisting of several lines:
Now, I would like to label all the lines in the plot. However, using legend() crams all the labels together in a box, which makes the plot somewhat difficult to interpret. What I'd like to to instead is to use inline labels. My ideal output would use labels like the following matplotlib contour plot, but with text labels for lines instead of numbers:
I haven't been able to find out how to do this in the matplotlib documentation. Is there a way to achieve this? If not, what other software could I use to generate this type of plot?
May I suggest another solution to your problem. Since in your case legend overlaps the charts you might just want to move the legend outside of the plot.
Method do move legend outside of plot is described here:
Moving matplotlib legend outside of the axis makes it cutoff by the figure box