BasedTab

Based Tabular Data

Usage

Installation

For development:

git clone https://github.com/AnswerDotAI/basedtab.git
pip install -e .

Install latest from the GitHub repository:

$ pip install git+https://github.com/AnswerDotAI/basedtab.git

or from conda

$ conda install -c AnswerDotAI basedtab

or from pypi

$ pip install basedtab

How to use

Load data

read_csv and save_csv

from basedtab.core import *
from nbdev.config import get_config

data_path = str(get_config().config_path/'data/toy.csv')
bf = BFrame.read_csv(data_path)
bf.head(3).df

	Number	City	Gender	Age	Income	Illness
0	1	Dallas	Male	41	40367.0	No
1	2	Dallas	Male	54	45084.0	No
2	3	Dallas	Male	42	52483.0	No

Groupby

from basedpl import add, tally, square
MEAN = add.reduce_first / tally
bf.groupby("City", "Income", mean=MEAN).df.astype(int)

		Income
Dallas	mean	45252
New York City	mean	96857
Los Angeles	mean	95264
Mountain View	mean	135078
Boston	mean	91554
Washington D.C.	mean	70991
San Diego	mean	100756
Austin	mean	90277

RMSE = MEAN.under(square)
bf.head(100).groupby("Gender", ["Age", "Income"], mean=MEAN, rmse=RMSE)

┌→───────────────────────────┐
↓"Male"⌷                     │
↓             Age      Income│
│mean 44.57407407 48301.42593│
│rmse 45.66970793 49632.10639│
│                            │
│"Female"⌷                   │
│             Age      Income│
│mean 45.30434783 39739.36957│
│rmse 46.73468128 40738.65853│
└~───────────────────────────┘

Rolling

ts = BFrame(dict(price=[5,4.3,4.1,2,10,20,15,25]))
ts.rolling("price", size=2, step=2, mean=MEAN).df

		price
0	mean	4.65
1	mean	3.05
2	mean	15.00
3	mean	20.00

Plot

ts.plot(title="Time Series")

bf["Income"].hist(title="Income Distribution")