# 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()
```
---
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