AAindex (aaindex1/2/3) to clean pandas DataFrames
Project description
PyAAindex
pyaaindex provides cleaned pandas DataFrames from AAindex (aaindex1, aaindex2, aaindex3).
Install
pip install pyaaindex
Quick Start
from pyaaindex.api import get_features, to_frame
# Fetch features by ID. The package automatically resolves across aaindex1, 2, and 3.
data = get_features(["ARGP820101", "ALTS910101"])
# data['idx1'] -> single pandas.DataFrame for aaindex1 features
# data['idx2'] -> dict formatted for aaindex2 pair matrices
# data['idx3'] -> dict formatted for aaindex3 pair matrices
print(data['idx1'])
# A C D E F ...
# ARGP... 0.61 1.07 0.46 0.47 2.02
# Pair matrices are returned as `{feature_name: {aa1: [values]}}` where [values] are sorted alphabetically based on aa2.
print(data['idx2']['ALTS910101']['A'])
# [3.0, -3.0, 0.0, ...]
# Convert JSON array matrices to pandas DataFrames
df_dict = to_frame(data['idx2'])
print(df_dict['ALTS910101'])
# Returns a full DataFrame where rows=aa1, columns=aa2
Output Shapes
idx1 (single amino-acid index)
The idx1 key returns a single pandas DataFrame.
- Index: Canonical amino acids (
A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y). - Columns: The calculated feature weights.
idx2 and idx3 (pair data)
The idx2 and idx3 keys return a Python dictionary (JSON-friendly).
- Structure:
{feature_name: {amino_acid_1: [values]}} - Sorting: The
[values]are strictly aligned in alphabetical order byaa2.
To manipulate idx2 or idx3 as Pandas figures, pass the dictionary payload into to_frame():
# Convert dict -> Dict[str, pd.DataFrame]
frames = to_frame(data['idx2'])
df = frames['ALTS910101']
This utility dynamically restores the alphabetical columns and assigns the index for instant usability.
Acknowledgments
- https://www.genome.jp/aaindex/
- Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., and Kanehisa, M.; AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36, D202-D205 (2008). [PMID:17998252]
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