Tidy Data¶
__import__('IPython').core.interactiveshell._should_be_async = lambda x: False
__import__('IPython').core.interactiveshell._should_be_async = lambda x: False
> ... a stack of elements is a common abstract data type used in computing. We would not think ‘to add’ two stacks as we would two integers.
>> Jeanette Wing - [Computational thinking and thinking about computing][computational thinking]
[computational thinking]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2696102/
__annotations__
{'computational thinking': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2696102/'}
... a stack of elements is a common abstract data type used in computing. We would not think ‘to add’ two stacks as we would two integers.
Jeanette Wing - Computational thinking and thinking about computing
__annotations__
A modernist style of notebook programming persists where documents are written as if programs are
starting for nothing. Meanwhile, authors of R programming language tend to begin with the assumption
that data exists and so does code. Notebook are a powerful substrate for working with data and
describing the logic behind different permutations.
pidgy was designed to weave projections of tabular into a computational documentation. Specifically,
we are concerned with the DataFrame, a popular tidy data abstraction that serves as a first
class data structure in scientific computing.
A modernist style of notebook programming persists where documents are written as if programs are starting for nothing. Meanwhile, authors of R programming language tend to begin with the assumption that data exists and so does code. Notebook are a powerful substrate for working with data and describing the logic behind different permutations.
pidgy was designed to weave projections of tabular into a computational documentation. Specifically, we are concerned with the DataFrame, a popular tidy data abstraction that serves as a first class data structure in scientific computing.
import pandas as 🐼
🐼
<module 'pandas' from '/home/tonyfast/miniconda3/lib/python3.7/site-packages/pandas/__init__.py'>
import pandas as 🐼
🐼
%matplotlib inline
The figure above illustrates the information in `df`.
A high level numeric project of this data's statistics are:
{{df.describe().to_html()}}
The statistics were created using measurements that look like the following data:
{{df.head(2).to_html()}}
df = 🐼.DataFrame([range(i, i+4) for i in range(10)], columns=list('abcd'))
df.plot()
<AxesSubplot:>
%matplotlib inline
The figure above illustrates the information in df.
A high level numeric project of this data's statistics are:
| a | b | c | d | |
|---|---|---|---|---|
| count | 10.00000 | 10.00000 | 10.00000 | 10.00000 |
| mean | 4.50000 | 5.50000 | 6.50000 | 7.50000 |
| std | 3.02765 | 3.02765 | 3.02765 | 3.02765 |
| min | 0.00000 | 1.00000 | 2.00000 | 3.00000 |
| 25% | 2.25000 | 3.25000 | 4.25000 | 5.25000 |
| 50% | 4.50000 | 5.50000 | 6.50000 | 7.50000 |
| 75% | 6.75000 | 7.75000 | 8.75000 | 9.75000 |
| max | 9.00000 | 10.00000 | 11.00000 | 12.00000 |
The statistics were created using measurements that look like the following data:
| a | b | c | d | |
|---|---|---|---|---|
| 0 | 0 | 1 | 2 | 3 |
| 1 | 1 | 2 | 3 | 4 |
df = 🐼.DataFrame([range(i, i+4) for i in range(10)], columns=list('abcd'))
df.plot()
In technical writing we need to consider existing conventions like:
* Figures above captions
* Table below captions
It still remains to be seen where code canonically fits in reference to figures and tables.
[Why should a table caption be placed above the table?]
[Why should a table caption be placed above the table?]: https://tex.stackexchange.com/questions/3243/why-should-a-table-caption-be-placed-above-the-table
In technical writing we need to consider existing conventions like:
Figures above captions
Table below captions
It still remains to be seen where code canonically fits in reference to figures and tables.
__annotations__
{'Why should a table caption be placed above the table?': 'https://tex.stackexchange.com/questions/3243/why-should-a-table-caption-be-placed-above-the-table',
'computational thinking': 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2696102/'}
__annotations__