Advanced Plotting

Getting Ready

dat2=pd.read_csv("C:\\Users\duan\Desktop\PythonDataProcessingVisualization\meanByClass.txt", sep='\s+')

dat2

Explore a fake gene expression data modified from iris.csv

rpkm=pd.read_csv("C:\\Users\duan\Desktop\PythonDataProcessingVisualization\\fakeExpressionDat.csv")

rpkm

Advanced Line Plot

plt.figure(); dat2.plot(); plt.legend(loc='best')

Get rid of the legend

dat2.plot(legend=False)

Separate the features

dat2.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')

Plotting on a Secondary Y-axis

plt.figure()
dat2.WtTypeA.plot(color="b")
dat2.WtTypeB.plot(color="turquoise")
dat2.KOTypeA.plot(color="r")
dat2.KOTypeB.plot(color="pink")
dat2.replicate.plot(secondary_y=True, style='g')

Plot a subset of columns

plt.figure()
dat2.WtTypeA.plot(color="b")
dat2.WtTypeB.plot(color="turquoise")
dat2.KOTypeA.plot(color="r")
dat2.KOTypeB.plot(color="pink")

Selective Plotting on Secondary Y-axis

plt.figure()
dat3=dat2.drop(['replicate'], axis = 1)
ax = dat3.plot(secondary_y=['wtTypeA', 'KOTypeA'])
ax.set_ylabel('TypeB scale')
ax.right_ax.set_ylabel('TypeA scale')

Targeting different subplots by passing an ax argument

fig, axes = plt.subplots(nrows=2, ncols=2)
dat2['WtTypeA'].plot(ax=axes[0,0]); axes[0,0].set_title('WtTypeA')
dat2['KOTypeA'].plot(ax=axes[0,1]); axes[0,1].set_title('KOTypeA')
dat2['WtTypeB'].plot(ax=axes[1,0]); axes[1,0].set_title('WtTypeB')
dat2['KOTypeB'].plot(ax=axes[1,1]); axes[1,1].set_title('KOTypeB')

Adjusting spacing between subplots

fig, axes = plt.subplots(nrows=2, ncols=2)
dat2['WtTypeA'].plot(ax=axes[0,0]); axes[0,0].set_title('WtTypeA')
dat2['KOTypeA'].plot(ax=axes[0,1]); axes[0,1].set_title('KOTypeA')
dat2['WtTypeB'].plot(ax=axes[1,0]); axes[1,0].set_title('WtTypeB')
dat2['KOTypeB'].plot(ax=axes[1,1]); axes[1,1].set_title('KOTypeB')
plt.subplots_adjust(left=0.1,
                    bottom=0.1,
                    right=0.9,
                    top=0.9,
                    wspace=0.4,
                    hspace=0.4)

Advanced Bar Plots

Looking at one replicate a time

plt.figure();
dat2.iloc[1].plot(kind='bar'); plt.axhline(0, color='k')

Looking at all replicates at the same time

plt.figure();
dat2.plot(kind='bar'); plt.axhline(0, color='k')

plt.figure();
dat2.plot(kind='bar', colormap='Greens')

stacked boxes

dat3.plot(kind='bar', stacked=True);

Advanced Histogram

plt.figure()
dat.hist(by="genotype", figsize=(6, 4),bins=20)

Scatter Plot

from pandas.plotting import scatter_matrix
rpkm=pd.read_csv("C:\\Users\duan\Desktop\IntroductionToMatplotlib\\fakeExpressionDat.csv")
rpkm

scatter_matrix(rpkm, alpha=0.9, figsize=(6, 6), diagonal='kde')

Parallel Coordinates

Parallel coordinates is a plotting technique for plotting multivariate data. It allows one to see clusters in data and to estimate other statistics visually. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together

from pandas.plotting import parallel_coordinates
plt.figure()
parallel_coordinates(rpkm, 'pathway')

from pandas.plotting import parallel_coordinates
plt.figure()
parallel_coordinates(rpkm, 'pathway',colormap='gist_rainbow')

from pandas.plotting import parallel_coordinates
plt.figure()
parallel_coordinates(rpkm, 'pathway',colormap='spring')

from pandas.plotting import parallel_coordinates
plt.figure()
parallel_coordinates(rpkm, 'pathway',colormap='autumn')

Andrews Curves

Andrews Curves are smoothed versions of Parallel Coordinates

from pandas.plotting import andrews_curves

plt.figure()
andrews_curves(rpkm, 'pathway')
plt.show()

A potential issue when plotting a large number of columns is that it can be difficult to distinguish some series due to repetition in the default colors. To remedy this, we can either loop through different colors using rainbow() function. Or DataFrame plotting supports the use of the colormap= argument, which accepts either a Matplotlib colormap or a string that is a name of a colormap registered with Matplotlib

plt.figure()
andrews_curves(rpkm, 'pathway',color = [cm.rainbow(i) for i in np.linspace(0, 1, 3)])
plt.show()

plt.figure()
andrews_curves(rpkm, 'pathway',colormap='jet')
plt.show()

plt.figure()
andrews_curves(rpkm, 'pathway',colormap="winter")
plt.show()

RadViz

from pandas.plotting import radviz
plt.figure()
radviz(rpkm, 'pathway')
plt.show()

from pandas.plotting import radviz
plt.figure()
radviz(rpkm, 'pathway',colormap="Set1")
plt.show()