- pandas
- Plural of a rare, black and white mammal Ailuropoda melanoleuca, commonly, but mistakenly known as the panda
- 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.
- 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
- Futile investment of time and energy in discussion of marginal technical issues.
- Procrastination
Definitions from Wictionary:
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
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.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.