statbasket 1.0.0

A small statistics package for data science students and enthusiasts

Stat Basket

Stat Basket is a small statistics package intended for use with student projects or small datasets (those containing <1 million data points). It is implemented with pure python, so no external dependencies are required.

Installation

Use the package manager pip to install stat-basket.

pip install statbasket

Usage

The statbasket package includes two classes, StatBasket and StatMe.

StatBasket

Using the StatBasket class generates all statistical data on initialization, and individual statistics can be accessed via the StatBasket object attributes:

from statbasket import StatBasket

data = (13, 26, 41, 35, 12)
# Perform all calculations and store in attributes
basket = StatBasket(data, first_data_name="my_data")

print(basket.n)
print(basket.mean)

Output:

5
25.4

A string summary of the statistics can be generated using the describe() method:

print(basket.describe())

Output:

________________________________________________
|==============================================|
|           DESCRIPTION OF my_data             |
|==============================================|
|----------General Sample Statistics-----------|
|==============================================|
|      Size of Sample (n)             5        |
|     Minimum Value (min)             12       |
|     Maximum Value (max)             41       |
|==============================================|
|---------Measures of Central Tendency---------|
|==============================================|
|         Mean (mean)                25.4      |
|       Median (median)              26.0      |
|         Mode (mode)             multimodal   |
|            Range                   29.0      |
|       Skewness (skew)             0.034      |
|==============================================|
|------------Measures of Variation-------------|
|==============================================|
|        Variance (var)             167.3      |
|  Standard Deviation (stdev)       12.934     |
|    Standard Error (sterr)         5.784      |
|  Coeff. of Variation (cov)        0.509      |
|==============================================|
|--------Confidence Interval Statistics--------|
|==============================================|
|    Confidence Level (cl)           0.95      |
|      alpha, two-tailed            0.025      |
|   t-score (score_critical)        2.776      |
|    Margin of Error (moe)          16.058     |
| CI (mean - moe, mean + moe)  [9.342, 41.458] |
------------------------------------------------

StatMe

Alternatively, if you want to perform calculations on-the-fly, without performing the entire batch of calculations at once, you can use the StatMe class of methods:

from statbasket import StatMe

data = (13, 26, 41, 35, 12)
# Perform single operations on data
mean = StatMe.get_mean(data)
ci = StatMe.get_ci(data, cl=0.99)

print(mean)
print(ci)

Output:

25.4
(-1.2316627975047787, 52.03166279750478)

View the individual class docstrings for a full list of available attributes and methods:

from statbasket import StatMe, StatBasket
help(StatMe)
help(StatBasket)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

License

MIT