#data #visualization 
# Data Visualization and Presentation

## How to represent data scientifically

Remember PCC:
1. Purpose
2. Composition
3. Color

## Plotting with Matplotlib
### Simple plot
You've probably seen the `matplotlib` package being imported at the top of the scripts. Matplotlib allows us to create static, animated and interactive visualizations in Python. It can even create publication quality plots.

Initialize
```python
import numpy as np
import matplotlib.pyplot as plt
```
Prepare data
```python
x = np.linspace(0,10*np.pi,1000)
y = np.sin(x)
```
Render
```python
fig, ax = plt.subplots()
ax.plot(X,Y)
plt.show()
```


### Customizing plots 
subplots, twin axis, labels, annotations

Colormaps and figure aesthetics.




### Other types of plots
- `scatter`
- `bar`
- `imshow`
- `contourf`
- `pie`
- `hist`
- `errorbar`
- `boxplot`

## Plotting different types of data



## Plotting for reports and publication quality graphs
Now that you've 


### Saving figures
save formats
	figure size
	bitmap vs vector format




## Problem:
Using pandas to plot spectroscopy data from raw data


## Problem:
Create a muli-panel figure showing raw data, fitted curve and residuals. Format with consistent style, legend, and color scheme for publication-ready quality.