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#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.
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