> For the complete documentation index, see [llms.txt](https://igb.mit.edu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://igb.mit.edu/mini-courses/python/data-processing-with-python/matplotlib/basic-plotting.md).
# Basic Plotting
### Getting Ready
#### Import packages
```
import numpy as np
```
```
from numpy.random import randn
```
```
import matplotlib.pyplot as plt
```
```
import pandas as pd
```
```
from pandas import Series, DataFrame,date_range
```
```
from matplotlib import cm
```
#### Read in data
```
dat=pd.read_csv("C:\\Users\duan\Desktop\PythonDataProcessingVisualization\metaDataMean.txt", sep='\s+')
```
```
dat
```
### Line Plot
```
%matplotlib inline
```
```
plt.figure()
dat.samplemeans.plot()
```
```
plt.figure()
dat.samplemeans.plot(secondary_y=True, style='g')
```
For list of named colors see [here](https://matplotlib.org/stable/gallery/color/named_colors.html)
Change plotting style and add legend
```
plt.figure();dat.samplemeans.plot(style='k--', label='sample means'); plt.legend()
```
### Bar Plot
```
plt.figure();
dat.samplemeans.plot(kind='bar')
```
\
add a horizontal line
```
plt.figure();
dat.samplemeans.plot(kind='bar'); plt.axhline(10,linewidth=4, color='r')
```
plot horizontal bars
```
plt.figure();
dat.samplemeans.plot(kind='barh',color="pink")
```
```
plt.figure();
dat.samplemeans.plot(kind='barh',color = [cm.rainbow(i) for i in np.linspace(0, 1, len(dat.samplemeans))])
```
### Histogram Plot
```
plt.figure();
dat.samplemeans.hist()
```
### Box Plot
```
plt.figure();
bp = dat.boxplot()
```
Define a title
```
plt.figure();
bp=dat.boxplot()
bp.set_title('My box plot')
```
Boxplot by celltype
```
plt.figure();
bp = dat.boxplot(by='genotype')
```
Better layout by excluding the automatic title
```
plt.figure();
bp = dat.boxplot(by='genotype')
plt.suptitle('') # that's what you're after
plt.show()
```
More sophisticated plotting can better reveal the trend
```
plt.figure();
bp = dat.boxplot(column=['samplemeans'], by=['celltype','genotype'])
title_boxplot = 'expression means'
plt.title( title_boxplot )
plt.suptitle('')
plt.show()
```
