Welcome to Stat Box рџ‘‹

A low-code python utility for fast statistics collection

Install

pip install stat-box

Usage

Statistics

from stat_box.statistic import StatisticSet, Quantile, 
import pandas as pd

df = pd.DataFrame(
    {"1": {"a": 1, "b": 2}, "2": {"a": 3, "b": 3}, "3": {"a": "1", "b": "d"}}
)
print(StatisticSet({Quantile(i / 100) for i in range(1, 100)}).stat_table(df))

# The same
print(QUANTILE_SET.stat_table(df))

Time series

from stat_box.time_series import TimeSeries, plot, group, rolling_trend, exp1, exp2, linear_trend, diff
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np


# Generate data
data = pd.DataFrame(
    {
        "time": [datetime.now() + relativedelta(days=i) for i in range(365)],
        "value": [
            np.random.randint(-30, 30) + np.random.randint(-i / 7, i / 3 + 1)
            for i in range(365)
        ],
    }
)
# Indexed data
ts = TimeSeries(data)
ts.set_index('time')
plot(ts, title="Indexed data")
# Grouped data
gts = group(ts, "30d")
plot(gts, title="Grouped data")
# Rolling trend
rts = rolling_trend(ts, "30d")
plot(rts, title="Rolling trend")
# EXP_1
alpha = 0.02
e1ts = exp1(ts, alpha)
plot(e1ts, title=f"Exp_1 a trend (alpha = {alpha})")
# EXP_2
alpha = 0.6
beta = 0.9
e2ts = exp2(ts, alpha, beta)
plot(e2ts, title=f"Exp_2 a trend (alpha = {alpha} beta={beta})")
# Linear trend
lts = linear_trend(rts)
plot(lts, title="Linear trend")
# Diff
sdts = diff(rts, "sequential", True)
plot(sdts, title="Sequential diff of rolling data")
edts = diff(rts, "end", True)
plot(edts, title="End diff of rolling data")
ledrs = linear_trend(edts)
plot([edts, ledrs], legend=['edts', 'ledrs'], title="Linear trend of end dif of rolling data")

Author

👤 dmatryus

• Github: @dmatryus.sqrt49

рџ¤ќ Contributing

Contributions, issues and feature requests are welcome!

Feel free to check issues page. PRs are welcome!

Show your support

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рџ“ќ License

Copyright В© 2021 dmatryus.

This project is MIT licensed.

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