nojazz 0.0.3

A collection of one-off functions I use for various tasks as a Data Scientist-- if it ain't broke, don't fix it.

nojazz

A collection of one-off functions I use for various tasks as a Data Scientist-- if it ain't broke, don't fix it.

Name inspired by my favorite Pearls Before Swine comic

Disclaimer: I also like jazz

Installation

nojazz is hosted on PyPI and can be installed with a simple

pip install nojazz

Features

As mentioned above, I finally sat down to make this package after noticing that I was copying a lot of the same helper methods from project to project. To that end, nojazz is hardly comprehensive (nor necessarily related, submodule to submodule) but has a few, small "figured it out once and packaged it for reuse" utilities for:

sqlite3

My first and biggest re-use culprit. nojazz provides a simple context manager to allow that handles all of the connection/commit/closing of sqlite database functions.

with connect_to_db('test.db') as cursor:
    cursor.execute('some sql')

Note, this will generate a nojazz_conf.py at the root of your project when calling. The library uses this to read the directory location for all of these database connections (i.e. on another drive, perhaps)

pandas

There are all kinds of tricks to this library. For now, nojazz provides the following helper methods

fill_time_series_nulls

For a given dataset with rows: agents and columns: observations, there are applications where want to consider NULL values as 0, but only after the user has shown up for the first time. This function does this, at the row-level and returns a copy of the original DataFrame. For example

   0    1     2     3     4
0  1   NaN   NaN    1    NaN
1 NaN  NaN   1     NaN    1

transforms to

   0    1     2     3     4
0  1    0     0     1    NaN
1 NaN  NaN    1     0     1

realign_nonnull_data

Similar to fill_time_series_nulls, this function is used when you want to line all of your records up to the same starting period. Like so

    0     1     2    3
0  NaN   NaN   NaN   1
1  NaN   NaN    1    1
2  NaN    1     1    1

transforms to

    0     1     2    3
0   1    NaN   NaN   NaN
1   1     1    NaN   NaN
2   1     1     1    NaN

Note, this likely means needing to rename the columns to something more appropriate. Furthermore, we maintained the shape of the original DataFrame by padding all extra fields with NaN