GitLocker: The Coding Marketplace

Description:

pandascharm 0.3.0

pandas-charm is a small Python package for getting character
matrices (alignments) into and out of pandas.
Use this library to make pandas interoperable with
BioPython and DendroPy.
Convert between the following objects:

BioPython MultipleSeqAlignment <-> pandas DataFrame
DendroPy CharacterMatrix <-> pandas DataFrame
“Sequence dictionary” <-> pandas DataFrame

The code has been tested with Python 2.7, 3.5 and 3.6.
Source repository: https://github.com/jmenglund/pandas-charm

Table of contents

Installation
Running the tests
Usage

DendroPy CharacterMatrix to pandas DataFrame
pandas DataFrame to Dendropy CharacterMatrix
BioPython MultipleSeqAlignment to pandas DataFrame
pandas DataFrame to BioPython MultipleSeqAlignment
“Sequence dictionary” to pandas DataFrame
pandas DataFrame to “sequence dictionary”

The name
License
Citing
Author

Installation
For most users, the easiest way is probably to install the latest version
hosted on PyPI:
$ pip install pandas-charm
The project is hosted at https://github.com/jmenglund/pandas-charm and
can also be installed using git:
$ git clone https://github.com/jmenglund/pandas-charm.git
$ cd pandas-charm
$ python setup.py install
You may consider installing pandas-charm and its required Python packages
within a virtual environment in order to avoid cluttering your system’s
Python path. See for example the environment management system
conda or the package
virtualenv.

Running the tests
Testing is carried out with pytest:
$ pytest -v test_pandascharm.py
Test coverage can be calculated with Coverage.py using the following commands:
$ coverage run -m pytest
$ coverage report -m pandascharm.py
The code follow style conventions in PEP8, which can be checked
with pycodestyle:
$ pycodestyle pandascharm.py test_pandascharm.py setup.py

Usage
The following examples show how to use pandas-charm. The examples are
written with Python 3 code, but pandas-charm should work also with
Python 2.7+. You need to install BioPython and/or DendroPy manually
before you start:
$ pip install biopython
$ pip install dendropy

DendroPy CharacterMatrix to pandas DataFrame
>>> import pandas as pd
>>> import pandascharm as pc
>>> import dendropy
>>> dna_string = '3 5\nt1 TCCAA\nt2 TGCAA\nt3 TG-AA\n'
>>> print(dna_string)
3 5
t1 TCCAA
t2 TGCAA
t3 TG-AA

>>> matrix = dendropy.DnaCharacterMatrix.get(
... data=dna_string, schema='phylip')
>>> df = pc.from_charmatrix(matrix)
>>> df
t1 t2 t3
0 T T T
1 C G G
2 C C -
3 A A A
4 A A A
By default, characters are stored as rows and sequences as columns
in the DataFrame. If you want rows to hold sequences, just transpose
the matrix in pandas:
>>> df.transpose()
0 1 2 3 4
t1 T C C A A
t2 T G C A A
t3 T G - A A

pandas DataFrame to Dendropy CharacterMatrix
>>> import pandas as pd
>>> import pandascharm as pc
>>> import dendropy
>>> df = pd.DataFrame({
... 't1': ['T', 'C', 'C', 'A', 'A'],
... 't2': ['T', 'G', 'C', 'A', 'A'],
... 't3': ['T', 'G', '-', 'A', 'A']})
>>> df
t1 t2 t3
0 T T T
1 C G G
2 C C -
3 A A A
4 A A A

>>> matrix = pc.to_charmatrix(df, data_type='dna')
>>> print(matrix.as_string('phylip'))
3 5
t1 TCCAA
t2 TGCAA
t3 TG-AA

BioPython MultipleSeqAlignment to pandas DataFrame
>>> from io import StringIO
>>> import pandas as pd
>>> import pandascharm as pc
>>> from Bio import AlignIO
>>> dna_string = '3 5\nt1 TCCAA\nt2 TGCAA\nt3 TG-AA\n'
>>> f = StringIO(dna_string) # make the string a file-like object
>>> alignment = AlignIO.read(f, 'phylip-relaxed')
>>> print(alignment)
SingleLetterAlphabet() alignment with 3 rows and 5 columns
TCCAA t1
TGCAA t2
TG-AA t3
>>> df = pc.from_bioalignment(alignment)
>>> df
t1 t2 t3
0 T T T
1 C G G
2 C C -
3 A A A
4 A A A

pandas DataFrame to BioPython MultipleSeqAlignment
>>> import pandas as pd
>>> import pandascharm as pc
>>> import Bio
>>> df = pd.DataFrame({
... 't1': ['T', 'C', 'C', 'A', 'A'],
... 't2': ['T', 'G', 'C', 'A', 'A'],
... 't3': ['T', 'G', '-', 'A', 'A']})
>>> df
t1 t2 t3
0 T T T
1 C G G
2 C C -
3 A A A
4 A A A

>>> alignment = pc.to_bioalignment(df, alphabet='generic_dna')
>>> print(alignment)
SingleLetterAlphabet() alignment with 3 rows and 5 columns
TCCAA t1
TGCAA t2
TG-AA t3

“Sequence dictionary” to pandas DataFrame
>>> import pandas as pd
>>> import pandascharm as pc
>>> d = {
... 't1': 'TCCAA',
... 't2': 'TGCAA',
... 't3': 'TG-AA'
... }
>>> df = pc.from_sequence_dict(d)
>>> df
t1 t2 t3
0 T T T
1 C G G
2 C C -
3 A A A
4 A A A

pandas DataFrame to “sequence dictionary”
>>> import pandas as pd
>>> import pandascharm as pc
>>> df = pd.DataFrame({
... 't1': ['T', 'C', 'C', 'A', 'A'],
... 't2': ['T', 'G', 'C', 'A', 'A'],
... 't3': ['T', 'G', '-', 'A', 'A']})
>>> pc.to_sequence_dict(df)
{'t1': 'TCCAA', 't2': 'TGCAA', 't3': 'TG-AA'}

The name
pandas-charm got its name from the pandas library plus an acronym for
CHARacter Matrix.

License
pandas-charm is distributed under the MIT license.

Citing
If you use results produced with this package in a scientific
publication, please just mention the package name in the text and
cite the Zenodo DOI of this project:

Choose your preferred citation style in the “Cite as” section on the Zenodo
page.

Author
Markus Englund, orcid.org/0000-0003-1688-7112