pandasbikeshed

pandas

1. Plural of a rare, black and white mammal Ailuropoda melanoleuca, commonly, but mistakenly known as the panda
2. Plural of (now rare without qualifying word) the red panda, a small raccoon-like animal, Ailurus fulgens of northeast Asia with reddish fur and a long, ringed tail.
3. a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

bikeshed

Structure to store small vehicles such as bicycles

bikesheddding

1. Futile investment of time and energy in discussion of marginal technical issues.
2. Procrastination

Definitions from Wictionary:

Installation

As of now I do not recommend the installation in a production environment.

Therefore cloning the environment and using pip install --editable is recommended for your testing endavours into the pandas-bikeshed

If you simply want to give it a go in your virtual environment run:

pip install git+https://github.com/sholderbach/pandasbikeshed.git@master

Features

Fast and easy conditional indexing into pd.DataFrames and pd.Series using the pandasbikeshed.fancyfilter utilities.

Just import the small accessor me via:

from pandasbikeshed.fancyfilter import me

now you can do:

my_long_dataset_name.loc[me.cost > 500,['name', 'product_id']]

instead of e.g.:

my_long_dataset_name.loc[my_long_dataset_name.cost > 500,['name', 'product_id']]

As you don't have to store an intermediate DataFrame or boolean masks seperately it is very useful for method chaining pipelines.

e.g.:

df = ... # Some tidy table with order, customer and shipment information
query_customers = [...] # Some customers we want to query
count_unique = lambda x: x.nunique()
analysis = (df.loc[(me.customer.isin(query_customers)) & (me.order == 'active')]
            .groupby('shipment')
            .agg(items=('id', 'count'),
                cost=('item_prize', 'sum'),
                num_customers=('customer', count_unique),
                num_destinations=('city', count_unique))
            .loc[me.num_customers > 1]
            .sort_values(by='cost'))

Currently implemented are the standard python comparison operators (<, <=, ==, !=, >=, >) .isin (to select all entries that are present in a list passed to .isin) and logical chaining with &, | and ^ as well as convenience functions for .isna and a np.isfinite like check.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.