I am creating a violinplot using the following code:
import seaborn as sns
ax = sns.violinplot(data=df[['SoundProduction','SoundForecast','diff']])
ax.set_ylabel("Sound power level [dB(A)]")
It gives me the folowing result:
Is there any way I can plot diff on a second y-axis so that all three series become clearly visible?
Also, is there a way to plot a vertical line in between 2 series? In this case I want a vertical line between SoundForecast and diff once they are plotted on two different axes.
You can achieve this using multiple subplots, which are easily set up using the plt.subplots (see lots more subplot examples).
This allows you to display your distributions on scales that are appropriate, and don't "waste" the display space. Most(all?) of seaborn's plotting functions accept the ax= argument so you can set the axes where the plot will be rendered. The axes also have clear separations between them.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# generate some random distribution data
n = 800 # samples
prod = 95 + 5 * np.random.beta(0.6, 0.5, size=n); # a bimodal distribution
forecast = prod + 3*np.random.randn(n) # forecast is noisy estimate around the "true" production
diff = prod-forecast # should be with mu 0 sigma 3
df = pd.DataFrame(np.array([prod, forecast, diff]).T, columns=['SoundProduction','SoundForecast','diff']);
# set up two subplots, with one wider than the other
fig, ax = plt.subplots(1,2, num=1, gridspec_kw={'width_ratios':[2,1]})
# plot violin distribution estimates separately so the y-scaling makes sense in each group
sns.violinplot(data=df[['SoundProduction','SoundForecast']], ax=ax[0])
sns.violinplot(data=df[['diff']], ax=ax[1])
Related
Is there a way to adjust the axes limits of pairplot(), but not as individual plots? Maybe a setting to produce better axes limits?
I would like to have the plots with a bigger range for the axes. My plots axes allows all the data to be visualized, but it is too 'zoomed in'.
My code is:
import pandas as pd
mport matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
g = sns.pairplot(iris, hue = 'species', diag_kind = 'hist', palette = 'Dark2', plot_kws={"s": 20})
The link for my plot and what I would like to plot to look like is here:
pairplot
To change the subplots, g.map(func, <parameters>) can be used. A small problem is that func needs to accept color as parameter, and plt.margins() gives an error when color is used. Moreover, map uses x and y to indicate the row and column variables. You could write a dummy function that simply calls plt.margin(), for example g.map(lambda *args, **kwargs: plt.margins(x=0.2, y=0.3)).
An alternative is to loop through g.axes.flat and call ax.margins() on each of them. Note that many axes are shared in x and/or y direction. The diagonal is treated differently; for some reason ax.margins needs to be called a second time on the diagonal.
To have the histogram for the different colors stacked instead of overlapping, diag_kws={"multiple": "stack"} can be set.
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
iris = sns.load_dataset('iris')
g = sns.pairplot(iris, hue='species', diag_kind='hist', palette='Dark2',
plot_kws={"s": 20}, diag_kws={"multiple": "stack"})
# g.map(plt.margins, x=0.2, y=0.2) # gives an error
for ax in g.axes.flat:
ax.margins(x=0.2, y=0.2)
for ax in g.diag_axes:
ax.margins(y=0.2)
plt.show()
PS: still another option, is to change the rcParams which will have effect on all the plots created later in the code:
import matplotlib as mpl
mpl.rcParams['axes.xmargin'] = 0.2
mpl.rcParams['axes.ymargin'] = 0.2
I plot boxplots using sns.boxplot and pandas.DataFrame.boxplot in python 3.x.
And I want to ask is it possible to adjust the spacing between boxes in boxplot, so the box of Group_b is farther right to the box of Group_a than in the output figures. Thanks
Codes:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
dict_a = {'value':[1,2,3,7,8,9],'name':['Group_a']*3+['Group_b']*3}
dataframe = pd.DataFrame(dict_a)
sns.boxplot( y="value" , x="name" , data=dataframe )
Output figure:
dataframe.boxplot("value" ,by = "name" )
Output figure 2:
The distance between the two boxes is determined by the x axis limits. For a constant distance in data units between the boxes, what makes them spaced more or less appart is the fraction of this data unit distance compared to the overall data space shown on the axis.
For example, in the seaborn case, the first box sits at x=0, the second at x=1. The difference is 1 unit. The maximal distance between the two boxplots is hence achieved by setting the x axis limits to those exact limits,
ax.set_xlim(0, 1)
Of course this will cut half of each box.
So a more useful value would be ax.set_xlim(0-val, 1+val) with val being somewhere in the range of the width of the boxes.
One needs to mention that pandas uses different units. The first box is at x=1, the second at x=2. Hence one would need something like ax.set_xlim(1-val, 2+val).
The following would add a slider to the plot to see the effect of different values.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
dict_a = {'value':[1,2,3,7,8,9],'name':['Group_a']*3+['Group_b']*3}
dataframe = pd.DataFrame(dict_a)
fig, (ax, ax2, ax3) = plt.subplots(nrows=3,
gridspec_kw=dict(height_ratios=[4,4,1], hspace=1))
sns.boxplot( y="value" , x="name" , data=dataframe, width=0.1, ax=ax)
dataframe.boxplot("value", by = "name", ax=ax2)
from matplotlib.widgets import Slider
slider = Slider(ax3, "", valmin=0, valmax=3)
def update(val):
ax.set_xlim(-val, 1+val)
ax2.set_xlim(1-val, 2+val)
slider.on_changed(update)
plt.show()
The default color of the seasonal decomposition graphs is a light blue.
1. How can you change the colors so that each of the lines is a different color?
2. If each plot can not have a separate color, how would I change all of the colors to say red?
I've tried adding arguments to decomposition.plot(color = 'red')
and searching the documentation for clues.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# I want 7 days of 24 hours with 60 minutes each
periods = 7 * 24 * 60
tidx = pd.date_range('2016-07-01', periods=periods, freq='D')
np.random.seed([3,1415])
# This will pick a number of normally distributed random numbers
# where the number is specified by periods
data = np.random.randn(periods)
ts = pd.Series(data=data, index=tidx, name='TimeSeries')
decomposition = sm.tsa.seasonal_decompose(ts, model ='additive')
fig = decomposition.plot()
plt.show()
A decomposition plot in which each graph is a different color.
The decomposition object in the code you posted uses pandas in the plotting method. I don't see a way of passing colors directly to the plot method, and it doesn't take **kwargs.
A work around would be to call the pandas plotting code directly on the object:
fig, axes = plt.subplots(4, 1, sharex=True)
decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')
I am having trouble using the pyplot.hist function to plot 2 histograms on the same figure. For each binning interval, I want the 2 bars to be centered between the bins (Python 3.6 user). To illustrate, here is an example:
import numpy as np
from matplotlib import pyplot as plt
bin_width=1
A=10*np.random.random(100)
B=10*np.random.random(100)
bins=np.arange(0,np.round(max(A.max(),B.max())/bin_width)*bin_width+2*bin_width,bin_width)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(A,bins,color='Orange',alpha=0.8,rwidth=0.4,align='mid',label='A')
ax.hist(B,bins,color='Orange',alpha=0.8,rwidth=0.4,align='mid',label='B')
ax.legend()
ax.set_ylabel('Count')
I get this:
Histogram_1
A and B series are overlapping, which is not good. Knowing there are only 3 option for 'align', (centered on left bin, middle of 2 bins, centered on right bin), i see no other options than modifying the bins, by adding:
bins-=0.25*bin_width
Before plotting A, and adding:
bins+=0.5*bin_width
Before plotting B. That gives me: Histogram
That's better! However, I had to modify the binning, so it is not the same for A and B.
I searched for a simple way to use the same bins, and then shift the 1st and 2nd plot so they are correctly displayed in the binning intervals, but I didn't find it. Any advice?
I hope I explained my problem clearly.
As previously was mentioned in the above comment you do not need a hist plot function. Use numpy histogram function and plot it results with bar function of matplotlib.
According to bins count and count of data types you can calculate bin width. Ticks you may adjust with xticks method:
import numpy as np
import matplotlib.pylab as plt
A=10*np.random.random(100)
B=10*np.random.random(100)
bins=20
# calculate heights and bins for both lists
ahist, abins = np.histogram(A, bins)
bhist, bbins = np.histogram(B, abins)
fig = plt.figure()
ax = fig.add_subplot(111)
# calc bin width for two lists
w = (bbins[1] - bbins[0])/3.
# plot bars
ax.bar(abins[:-1]-w/2.,ahist,width=w,color='r')
ax.bar(bbins[:-1]+w/2.,bhist,width=w,color='orange')
# adjsut xticks
plt.xticks(abins[:-1], np.arange(bins))
plt.show()
I have three dataframes and I plot the KDE using seaborn module in python. The issue is that these plots try to make the area under the curve 1 (which is how they are intended to perform), so the height in the plots are normalized ones. But is there any way to show the actual values instead of the normalized ones. Also is there any way I can find out the point of intersection for the curves?
Note: I do not want to use the curve_fit method of scipy as I am not sure about the distribution I will get for each dataframe, it can be multimodal also.
import seaborn as sns
plt.figure()
sns.distplot(data_1['gap'],kde=True,hist=False,label='1')
sns.distplot(data_2['gap'],kde=True,hist=False,label='2')
sns.distplot(data_3['gap'],kde=True,hist=False,label='3')
plt.legend(loc='best')
plt.show()
Output for the code is attached in the link as I can't post images.plot_link
You can just grab the line and rescale its y-values with set_data:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# create some data
n = 1000
x = np.random.rand(n)
# plot stuff
fig, ax = plt.subplots(1,1)
ax = sns.distplot(x, kde=True, hist=False, ax=ax)
# find the line and rescale y-values
children = ax.get_children()
for child in children:
if isinstance(child, matplotlib.lines.Line2D):
x, y = child.get_data()
y *= n
child.set_data(x,y)
# update y-limits (not done automatically)
ax.set_ylim(y.min(), y.max())
fig.canvas.draw()