BasedTab

Usage

Installation

Not on PyPI yet. Install:

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

How to use

Load data

read_csv and save_csv

from basedtab.core import BFrame

bf = BFrame.read_csv(path)
bf.head(2).df

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

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

Groupby

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

┌→─────────────────┐
↓"Dallas"⌷         │
↓          Income  │
│mean 45252.23119  │
│                  │
│"New York City"⌷  │
│          Income  │
│mean 96857.13139  │
│                  │
│"Los Angeles"⌷    │
│          Income  │
│mean 95264.15541  │
│                  │
│"Mountain View"⌷  │
│          Income  │
│mean 135078.4158  │
│                  │
│"Boston"⌷         │
│         Income   │
│mean 91554.5715   │
│                  │
│"Washington D.C."⌷│
│          Income  │
│mean 70991.61281  │
│                  │
│"San Diego"⌷      │
│          Income  │
│mean 100756.2092  │
│                  │
│"Austin"⌷         │
│          Income  │
│mean 90277.51342  │
└~─────────────────┘

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

┌→───────────────────────────┐
↓"Male"⌷                     │
↓             Age      Income│
│mean  44.9126253  95670.2472│
│rmse 46.37557969 98752.06754│
│                            │
│"Female"⌷                   │
│             Age      Income│
│mean 44.99776435 85660.92184│
│rmse 46.46704189 89097.22374│
└~───────────────────────────┘

Rolling

ts = BFrame(dict(price=[5,4.3,4.1,2,10,20,15,25]))
ts

┌→────────────────────────────────┐
│ price:┌→──────────────────────┐ │
│       │5 4.3 4.1 2 10 20 15 25│ │
│       └~──────────────────────┘ │
└∊────────────────────────────────┘

ts.rolling("price", size=3, edge="clamp", mean=MEAN)

┌→───────────────┐
↓0⌷              │
↓           price│
│mean 4.766666667│
│                │
│1⌷              │
│           price│
│mean 4.466666667│
│                │
│2⌷              │
│           price│
│mean 3.466666667│
│                │
│3⌷              │
│           price│
│mean 5.366666667│
│                │
│4⌷              │
│           price│
│mean 10.66666667│
│                │
│5⌷              │
│     price      │
│mean    15      │
│                │
│6⌷              │
│     price      │
│mean    20      │
│                │
│7⌷              │
│           price│
│mean 21.66666667│
└~───────────────┘

ts.rolling("price", size=2, step=2, mean=MEAN, rmse=RMSE)

┌→───────────────┐
↓0⌷              │
↓           price│
│mean        4.65│
│rmse 4.663153439│
│                │
│1⌷              │
│           price│
│mean        3.05│
│rmse 3.225678223│
│                │
│2⌷              │
│          price │
│mean         15 │
│rmse 15.8113883 │
│                │
│3⌷              │
│           price│
│mean          20│
│rmse 20.61552813│
└~───────────────┘

Plot

ts.plot(title="Time Series")

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