pandas-charm 0.1.0

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