I have a string "Line2D" appended in the beginning of my matplotlib legend. How to get rid of it? A simple python script that reproduces this problem is as follows:
import numpy as np
import matplotlib.pylab as plt
x=np.linspace(0,1,20)
y=np.sin(2*x)
z=np.cos(2*x)
p1, = plt.plot(x,y, label='sin(x)')
p2, = plt.plot(x,z, label='cos(x)')
plt.legend([p1, p2])
plt.show()
I get a figure in which I want to get rid of the extra string "Line2D" in the legend. I do not have enough reputation to post images. I was using anaconda python if that matters.
Thanks for your help!
If you pass just one list to legend, it has to be the labels you want to show, not the objects whose labels you want to show. It is converting those line objects to strings, which gives Line2D(...).
Since you gave the lines labels when you created them, you don't need to pass anything to legend. Just plt.legend() will automatically use the labels you provided.
You can use plt.legend(handles=[p1, p2]).
Related
I am using bqplot to create a live line graph on jupyter-notebook + VOILA
from bqplot import pyplot as plt2
import datetime
x_values = [] #array of datetimes
y_values = [] #array of 10+ digit numbers
plt2.show()
def functionThatIsCalledRepeatedly(x_val, y_val):
x_values.append(x_val)
y_values.append(y_val)
plt2.plot(x_values, y_values)
Part of the Resulting Plot
My question is, how do I remove the scientific notation from the y-axis. It's a simple task but I have tried a lot of things.
I tried using axes.tick_format property of the graph but I think that only works if you have axes objects which I cannot have because they require the mandatory Scale property which I cannot use because the graph is live and the x and y scales need to be generated/recalibrated while it runs.
I tried changing y_values.append(y_val) to y_values.append("{:.2f}".format(y_val)) but that converts to a string and bqplot doesn't process it as a number so it ends up with negative numbers on top of the 0 sometimes.
I tried converting to a numpy array and then doing np.set_printoptions(suppress=True) which (obviously) didn't work.
Basically tried a lot of things and I think it comes down to some bqplot property that may or may not exist. Have been stuck for a while. Thank you!
You can provide axes options with the tick format you want to the plot method:
plt2.plot(x_values, y_values, axes_options={
y=dict(tick_format='0.2f')
})
You can see examples of this axes_options (using a scatter plot, but that should work the same) in this notebook: https://github.com/bqplot/bqplot/blob/master/examples/Marks/Pyplot/Scatter.ipynb
I'm having a problem that (I think) should have a fairly simple solution. I'm still a relative novice in Python, so apologies if I'm doing something obviously wrong. I'm just trying to create a simple plot with multiple lines, where each line is colored by its own specific, user-defined color. When I run the following code as a test for one of the colors it ends up giving me a blank plot. What am I missing here? Thank you very much!
import numpy as np
import matplotlib.pyplot as plt
from colour import Color
dbz53 = Color('#DD3044')
*a bunch of arrays of data, two of which are called x and mpt1*
fig, ax = plt.subplots()
ax.plot(x, mpt1, color='dbz53', label='53 dBz')
ax.set_yscale('log')
ax.set_xlabel('Diameter (mm)')
ax.set_ylabel('$N(D) (m^-4)$')
ax.set_title('N(D) vs. D')
#ax.legend(loc='upper right')
plt.show()
The statement
ax.plot(x, mpt1, color='dbz53', label='53 dBz')
is wrong with 'dbz53' where python treated it as a string of unknown rgb value.
You can simply put
color='#DD3044'
and it will work.
Or you can try
color=dbz53.get_hex()
without quote if you want to use the colour module you imported.
In the plot command, you could enter Hex colours. A much more simple way to beautify your plot would be to simply use matplotlib styles. For instance, before any plot function, just write
plt.style.use('ggplot')
Consider the following code
import matplotlib.pyplot as plt
import numpy as np
time=np.arange(-100,100,01)
val =np.sin(time/10.)
time=-1.0*time
plt.figure()
plt.plot(time,val)
plt.xlim([70,-70])
plt.savefig('test.pdf')
when I open the pdf in inkscape, I can select (with F2) the entire data, it's just invisible outside of the specified xlim interval:
The problem seems to be the line
time=-1.0*time
If I omit this line, everything works perfectly.. no idea why this is. I often need such transformations because I deal with paleo-climate data which are sometimes given in year B.C. and year A.D., respectively.
The problem I see with this behavior is that someone could in principle get the data outside the range which I want to show.
Has someone a clue how to solve this problem (except for an slice of the arrays before plotting)?
I use matplotlib 1.1.1rc2
You can mask your array when plotting according to the limits you choose. Yes, this also requires changes to the code, but maybe not as extensive as you might fear. Here's an updated version of your example:
import matplotlib.pyplot as plt
import numpy as np
time=np.arange(-100,100,01)
val =np.sin(time/10.)
time=-1.0*time
plt.figure()
# store the x-limites in variables for easy multi-use
XMIN = -70.0
XMAX = 70.0
plt.plot(np.ma.masked_outside(time,XMIN,XMAX),val)
plt.xlim([XMIN,XMAX])
plt.savefig('test.pdf')
The key change is using np.ma.masked_outside for your x-axis value (note: the order of XMIN and XMAX in the mask-command is not important).
That way, you don't have to change the array time if you wanted to use other parts of it later on.
When I checked with inkscape, no data outside of the plot was highlighted.
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.
I am a new user to the python & matplotlib, this might be a simple question but I searched the internet for hours and couldn't find a solution for this.
I am plotting precipitation data from which is in the NetCDF format. What I find weird is that, the data doesn't have any negative values in it.(I checked that many times,just to make sure). But the value in the colorbar starts with a negative value (like -0.0000312 etc). It doesnt make sense because I dont do any manipulations to the data, other that just selecting a part of the data from the big file and plotting it.
So my code doesn't much to it. Here is the code:
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
cd progs
f=Dataset('V21_GPCP.1979-2009.nc')
lats=f.variables['lat'][:]
lons=f.variables['lon'][:]
prec=f.variables['PREC'][:]
la=lats[31:52]
lo=lons[18:83]
pre=prec[0,31:52,18:83]
m = Basemap(width=06.e6,height=05.e6,projection='gnom',lat_0=15.,lon_0=80.)
x, y = m(*np.meshgrid(lo,la))
m.drawcoastlines()
m.drawmapboundary(fill_color='lightblue')
m.drawparallels(np.arange(-90.,120.,5.),labels=[1,0,0,0])
m.drawmeridians(np.arange(0.,420.,5.),labels=[0,0,0,1])
cs=m.contourf(x,y,pre,50,cmap=plt.cm.jet)
plt.colorbar()
The output that I got for that code was a beautiful plot, with the colorbar starting from the value -0.00001893, and the rest are positive values, and I believe are correct. Its just the minimum value thats bugging me.
I would like to know:
Is there anything wrong in my code? cos I know that the data is right.
Is there a way to manually change the value to 0?
Is it right for the values in the colorbar to change everytime we run the code, cos for the same data, the next time I run the code, the values go like this " -0.00001893, 2.00000000, 4.00000000, 6.00000000 etc"
I want to customize them to "0.0, 2.0, 4.0, 6.0 etc"
Thanks,
Vaishu
Yes, you can manually format everything about the colorbar. See this:
import matplotlib.colors as mc
import matplotlib.pyplot as plt
plt.imshow(X, norm=mc.Normalize(vmin=0))
plt.colorbar(ticks=[0,2,4,6], format='%0.2f')
Many plotting functions including imshow, contourf, and others include a norm argument that takes a Normalize object. You can set the vmin or vmax attributes of that object to adjust the corresponding values of the colorbar.
colorbar takes the ticks and format arguments to adjust which ticks to display and how to display them.