# Notebook conversion example

A quick example of how the conversion tool can convert an Jupyter notebook into markdown files and associated images. I demonstrate how common items like code blocks and inline images work well, while other features (namely Latex equations) do not.

### Quick overview

##### Things that work well:
• Text in Markdown cells
• Code blocks
• Tables (even better with my CSS code)
• Inline plots (best when using my new_post shortcut)
• YAML as first line (if cell type is Raw NBConvert)
##### Things that don’t work so well:
• LaTeX equations
##### Things my new_post function does:
• Convert to markdown
• Fix inline plot image addresses
• Move post and images to blog directory
• Add and commit new post files to git
• push changes to server
##### Things I’d like to work better:
• Remove previous entries with same name (after prompt?)
• Differentiation between Input and Result cells
• Remove the 42 rows × 23 columns output after tables (or attach it to the table better)
• Fix LaTeX (perhaps by using different markdown interpreter in Jekyll)
• Fix manual image issue
import pandas as pd
%matplotlib inline
print df.shape
df

(42, 23)

Unnamed: 0 142.24 144.78 147.32 149.86 152.4 154.94 157.48 160.02 162.56 ... 172.72 175.26 177.8 180.34 182.88 185.42 187.96 190.5 193.04 195.58
0 9.4 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 9.5 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 9.6 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
3 9.7 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 9.8 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
5 9.9 0 0 1 0 1 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
6 10.0 1 0 0 1 2 0 2 0 0 ... 0 0 0 0 0 0 0 0 0 0
7 10.1 0 0 0 1 3 1 0 1 1 ... 0 0 0 0 0 0 0 0 0 0
8 10.2 0 0 2 2 2 1 0 2 0 ... 0 0 0 0 0 0 0 0 0 0
9 10.3 0 1 1 3 2 2 3 5 0 ... 0 0 0 0 0 0 0 0 0 0
10 10.4 0 0 1 1 2 3 3 4 3 ... 0 0 0 0 0 0 0 0 0 0
11 10.5 0 0 0 1 3 7 6 4 3 ... 0 1 0 0 0 0 0 0 0 0
12 10.6 0 0 0 1 4 5 9 14 6 ... 0 1 0 0 0 0 0 0 0 0
13 10.7 0 0 1 2 4 9 14 16 15 ... 2 0 0 0 0 0 0 0 0 0
14 10.8 0 0 0 2 5 6 14 27 10 ... 1 0 0 0 0 0 0 0 0 0
15 10.9 0 0 0 0 2 6 14 24 27 ... 1 0 0 0 0 0 0 0 0 0
16 11.0 0 0 0 2 6 12 15 31 37 ... 6 0 0 0 0 0 0 0 0 0
17 11.1 0 0 0 3 3 12 22 26 24 ... 4 1 0 0 0 0 0 0 0 0
18 11.2 0 0 0 3 2 7 21 30 38 ... 4 1 0 0 0 0 0 0 0 1
19 11.3 0 0 0 1 0 5 10 24 26 ... 7 2 0 0 0 0 0 0 0 0
20 11.4 0 0 0 0 3 4 9 29 56 ... 10 11 0 0 0 0 0 0 0 0
21 11.5 0 0 0 0 0 5 11 17 33 ... 25 11 2 0 0 0 0 0 0 0
22 11.6 0 0 0 0 2 1 4 13 37 ... 27 12 2 2 0 1 0 0 0 0
23 11.7 0 0 0 0 0 2 9 17 30 ... 24 9 9 2 0 0 0 0 0 0
24 11.8 0 0 0 0 1 0 2 11 15 ... 29 10 5 1 0 0 0 0 0 0
25 11.9 0 0 0 0 1 1 2 12 10 ... 19 10 9 3 1 0 0 0 0 0
26 12.0 0 0 0 0 0 0 1 4 8 ... 22 16 8 2 2 0 0 0 0 0
27 12.1 0 0 0 0 0 0 0 2 4 ... 15 27 10 4 1 0 0 0 0 0
28 12.2 0 0 0 0 0 0 1 2 5 ... 16 11 8 1 1 0 0 0 0 0
29 12.3 0 0 0 0 0 0 0 0 4 ... 20 23 6 5 0 0 0 0 0 0
30 12.4 0 0 0 0 0 0 1 1 1 ... 4 7 7 1 0 0 1 0 0 0
31 12.5 0 0 0 0 0 0 0 1 0 ... 11 8 6 8 0 2 0 0 0 0
32 12.6 0 0 0 0 0 0 0 0 0 ... 5 7 8 6 3 1 1 0 0 0
33 12.7 0 0 0 0 0 0 0 0 0 ... 5 5 8 2 2 0 0 0 0 0
34 12.8 0 0 0 0 0 0 0 0 0 ... 3 1 8 5 3 1 1 0 0 0
35 12.9 0 0 0 0 0 0 0 0 0 ... 2 2 0 1 1 0 0 0 0 0
36 13.0 0 0 0 0 0 0 0 0 0 ... 1 0 1 0 2 1 0 0 0 0
37 13.1 0 0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 0 0 0
38 13.2 0 0 0 0 0 0 0 0 0 ... 0 1 0 3 0 0 0 0 0 0
39 13.3 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 1 0 1 0 0 0
40 13.4 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
41 13.5 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 1 0 0 0 0

42 rows × 23 columns

Make a plot with:

• column 3
• column 5

Details about the data available here.

df.plot(y=[3,5]);


### Equations are not so great…

$\alpha = \frac{d}{dx}e^{-x}$