I'm trying to do a basic line graph here, but I can't seem to figure out how to adjust my x axis.
And here is the error I get when I try adjusting my range.
from pylab import *
plot ( range(0,11),[9,4,5,2,3,5,7,12,2,3],'.-',label='sample1' )
plot ( range(0,11),[12,5,33,2,4,5,3,3,22,10],'o-',label='sample2' )
xlabel('x axis')
ylabel('y axis')
title('my sample graphs')
legend(('sample1','sample2'))
savefig("sampleg.png",dpi=(640/8))
show()
File "C:\Python26\lib\site-packages\matplotlib\axes.py", line 228, in _xy_from_xy
raise ValueError("x and y must have same first dimension")
ValueError: x and y must have same first dimension
I want my range to be a list of strings: ["12/1/2007","12/1/2008", "12/1/2009","12/1/2010"]
Any suggestions?
Honestly, I found the code online and was trying to rewrite it to properly understand it. I think I'm going to start from scratch so that I know what I'm doing but I need help on where to start.
I posted another question which explains what I want to do here:
Using PyLab to create a 2D graph from two separate lists
range(0,11) should be range(0,10).
In addition to Steve's observation: If your points are always some y-value at the same consecutive integer x's, matplotlib makes the range even implicit.
plot([9,4,5,2,3,5,7,12,2,3],'.-',label='sample1')
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 can use DataFrameGroupBy.boxplot(...) to create a boxplot in the following way:
In [15]: df = pd.DataFrame({"gene_length":[100,100,100,200,200,200,300,300,300],
...: "gene_id":[1,1,1,2,2,2,3,3,3],
...: "density":[0.4,1.1,1.2,1.9,2.0,2.5,2.2,3.0,3.3],
...: "cohort":["USA","EUR","FIJ","USA","EUR","FIJ","USA","EUR","FIJ"]})
In [17]: df.groupby("cohort").boxplot(column="density",by="gene_id")
In [18]: plt.show()
This produces the following image:
This is exactly what I want, except instead of making three subplots, I want all the plots to be in one plot (with different colors for USA, EUR, and FIJ). I've tried
In [17]: df.groupby("cohort").boxplot(column="density",subplots=False,by="gene_id")
but it produces the error
KeyError: 'gene_id'
I think the problem has something to do with the fact that by="gene_id" is a keyword sent to the matplotlib boxplot method. If someone has a better way of producing the plot I am after, perhaps by using DataFrame.boxplot(?) instead, please respond here. Thanks so much!
To use the pure pandas functions, I think you should not GroupBy before calling boxplot, but instead, request to group by certain columns in the call to boxplot on the DataFrame itself:
df.boxplot(column='density',by=['gene_id','cohort'])
To get a better-looking result, you might want to consider using the Seaborn library. It is designed to help precisely with this sort of tasks:
sns.boxplot(data=df,x='gene_id',y='density',hue='cohort')
EDIT to take into account comment below
If you want to have each of your cohort boxplots stacked/superimposed for each gene_id, it's a bit more complicated (plus you might end up with quite an ugly output). You cannot do this using Seaborn, AFAIK, but you could with pandas directly, by using the position= parameter to boxplot (see doc). The catch it to generate the correct sequence of positions to place the boxplots where you want them, but you'll have to fix the tick labels and the legend yourself.
pos = [i for i in range(len(df.gene_id.unique())) for _ in range(len(df.cohort.unique()))]
df.boxplot(column='density',by=['gene_id','cohort'],positions=pos)
An alternative would be to use seaborn.swarmplot instead of using boxplot. A swarmplot plots every point instead of the synthetic representation of boxplots, but you can use the parameter split=False to get the points colored by cohort but stacked on top of each other for each gene_id.
sns.swarmplot(data=df,x='gene_id',y='density',hue='cohort', split=False)
Without knowing the actual content of your dataframe (number of points per gene and per cohort, and how separate they are in each cohort), it's hard to say which solution would be the most appropriate.
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'm new to Python and MatPlotlib.
This is my first posting to Stackoverflow - I've been unable to find the answer elsewhere and would be grateful for your help.
I'm using Windows XP, with Enthought Canopy v1.1.1 (32 bit).
I want to plot a dotted-style linear regression line through a scatter plot of data, where both x and y arrays contain random floating point data.
The dots in the resulting dotted line are not distributed evenly along the regression line, and are "smeared together" in the middle of the red line, making it look messy (see upper plot resulting from attached minimal example code).
This does not seem to occur if the items in the array of x values are evenly distributed (lower plot).
I'm therefore guessing that this is an issue with how MatplotLib renders dotted lines, or with how Canopy interfaces Python with Matplotlib.
Please could you tell me a workaround which will make the dots on the dotted line type appear evenly distributed; even if both x and y data are non-evenly distributed; whilst still using Canopy and Matplotlib?
(As a general point, I'm always keen to improve my coding skills - if any code in my example can be written more neatly or concisely, I'd be grateful for your expertise).
Many thanks in anticipation
Dave
(UK)
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
#generate data
x1=10 * np.random.random_sample((40))
x2=np.linspace(0,10,40)
y=5 * np.random.random_sample((40))
slope, intercept, r_value, p_value, std_err = stats.linregress(x1,y)
line = (slope*x1)+intercept
plt.figure(1)
plt.subplot(211)
plt.scatter(x1,y,color='blue', marker='o')
plt.plot(x1,line,'r:',label="Regression Line")
plt.legend(loc='upper right')
slope, intercept, r_value, p_value, std_err = stats.linregress(x2,y)
line = (slope*x2)+intercept
plt.subplot(212)
plt.scatter(x2,y,color='blue', marker='o')
plt.plot(x2,line,'r:',label="Regression Line")
plt.legend(loc='upper right')
plt.show()
Welcome to SO.
You have already identified the problem yourself, but seem a bit surprised that a random x-array results in the line be 'cluttered'. But you draw a dotted line repeatedly over the same location, so it seems like the normal behavior to me that it gets smeared at places where there are multiple dotted lines on top of each other.
If you don't want that, you can sort your array and use that to calculate the regression line and plot it. Since its a linear regression, just using the min and max values would also work.
x1_sorted = np.sort(x1)
line = (slope * x1_sorted) + intercept
or
x1_extremes = np.array([x1.min(),x1.max()])
line = (slope * x1_extremes) + intercept
The last should be faster if x1 becomes very large.
With regard to your last comment. In your example you use whats called the 'state-machine' environment for plotting. It means that specified commands are applied to the active figure and the active axes (subplots).
You can also consider the OO approach where you get figure and axes objects. This means you can access any figure or axes at any time, not just the active one. Its useful when passing an axes to a function for example.
In your example both would work equally well and it would be more a matter of taste.
A small example:
# create a figure with 2 subplots (2 rows, 1 column)
fig, axs = plt.subplots(2,1)
# plot in the first subplots
axs[0].scatter(x1,y,color='blue', marker='o')
axs[0].plot(x1,line,'r:',label="Regression Line")
# plot in the second
axs[1].plot()
etc...
I'm plotting a timeseries in pandas using matplotlib and I'm trying to color a plot look like this.
I have the times for the A-F points. I've tried to get the position of them in the plot using
gcf().canvas.mpl_connect('button_press_event', debug_print_onclick_event)
and ended up with x positions being around 22'395'850 (not even close to unixtime :S)
My code basically looks like this:
plot = data.plot(legend=False) #where data is the timeseries (pandas.DataFrame).
plot.add_patch(
plt.Rectangle(
(0,22395760),
60,
45,
facecolor='green',
edgecolor='green'
)
)
plt.draw()
plt.show()
But nothings of the patch shows up.
Also tested to use time directly, it actually ran but no patch was rendered.
plt.Rectangle(
(0,datetime_D),
60,
4*pandas.datetools.Minutes(15),
facecolor='green',
edgecolor='green'
)
What is the underlying type? How should I position things in time in matplotlib? Any uglyhack working is appreciated.
You seem to have swapped x and y as first argument of Rectangle((x,y), ...).
Rectangle((22395760, 0), ...)
Instead of using a patch, plot.axvspan() seems a better match for what you want to do.
plt.gca().axvspan(date,date+2*pandas.datetools.Minute(15),facecolor='green',edge color='green',alpha=0.3)