pandas-charm 0.1.1

A small Python library for getting character matrices (alignments) into and out of pandas

pandas-charm is a small Python package (or library) for getting character matrices (alignments) into and out of pandas. The intention of the package is to make pandas interoperable with other scientific packages that can be used for working with character matrices, like for example BioPython and Dendropy.

With pandas-charm, it is currently possible to convert between the following objects:

• BioPython MultipleSeqAlignment <-> pandas DataFrame

• DendroPy CharacterMatrix <-> pandas DataFrame

Source repository: https://github.com/jmenglund/pandas-charm

The name

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

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 be installed using git:

$ git clone https://github.com/jmenglund/pandas-charm.git
$ cd pandas-charm
$ python setup.py install

Running tests

After installing the pandas-charm, you may want to check that everything works as expected. Below is an example of how to run the tests with pytest. The packages BioPython, DendroPy, pytest, coverage, and pytest-cov need to be installed.

$ cd pandas-charm
$ py.test -v --cov-report term-missing --cov pandascharm.py

Usage

Below are a few examples on 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

Converting a DendroPy CharacterMatrix to a 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_from_string(
...     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

As seen above, characters are stored as rows and sequences as columns in the DataFrame. If you want rows to hold sequences, it is easy to 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

Converting a pandas DataFrame to a 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, type='dna')
>>> print(matrix.as_string('phylip'))
3 5
t1  TCCAA
t2  TGCAA
t3  TG-AA

Converting a BioPython MultipleSeqAlignment to a 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

Converting a pandas DataFrame to a 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

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:

You can select a citation style from the dropdown menu in the “Cite as” section on the Zenodo page.