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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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