redframes: General Purpose Data Manipulation Library

[amotl]

About

redframes (rectangular data frames) is a general purpose data manipulation library that prioritizes syntax, simplicity, and speed (to a solution). Importantly, the library is fully interoperable with pandas, compatible with scikit-learn, and works great with matplotlib.

Synopsis

import redframes as rf

df = rf.DataFrame({
    'bear': ['Brown bear', 'Polar bear', 'Asian black bear', 'American black bear', 'Sun bear', 'Sloth bear', 'Spectacled bear', 'Giant panda'],
    'genus': ['Ursus', 'Ursus', 'Ursus', 'Ursus', 'Helarctos', 'Melursus', 'Tremarctos', 'Ailuropoda'],
    'weight (male, lbs)': ['300-860', '880-1320', '220-440', '125-500', '60-150', '175-310', '220-340', '190-275'],
    'weight (female, lbs)': ['205-455', '330-550', '110-275', '90-300', '45-90', '120-210', '140-180', '155-220']
})

df
    .rename({"weight (male, lbs)": "male", "weight (female, lbs)": "female"})
    .gather(["male", "female"], into=("sex", "weight"))
    .split("weight", into=["min", "max"], sep="-")
    .gather(["min", "max"], into=("stat", "weight"))
    .mutate({"weight": lambda row: float(row["weight"])})
    .group(["genus", "sex"])
    .rollup({"weight": ("weight", rf.stat.mean)})
    .spread("sex", using="weight")
    .mutate({"dimorphism": lambda row: round(row["male"] / row["female"], 2)})
    .drop(["male", "female"])
    .sort("dimorphism", descending=True)

# | genus      |   dimorphism |
# |:-----------|-------------:|
# | Ursus      |         2.01 |
# | Tremarctos |         1.75 |
# | Helarctos  |         1.56 |
# | Melursus   |         1.47 |
# | Ailuropoda |         1.24 |

Resources

• https://github.com/maxhumber/redframes