Update README.md

README.md

@@ -1,72 +1,3 @@
-# owid-repack-py
+This library has been folded into OWID's ETL monorepo.
 
-![build status](https://github.com/owid/owid-repack-py/actions/workflows/python-package.yml/badge.svg)
-[![PyPI version](https://badge.fury.io/py/owid-repack.svg)](https://badge.fury.io/py/owid-repack)
-![](https://img.shields.io/badge/python-3.8|3.9|3.10|3.11-blue.svg)
-
-_Pack Pandas DataFrames into smaller, more memory efficient types._
-
-## Overview
-
-When you load data into Pandas, it will use standard types by default:
-
-- `object` for strings
-- `int64` for integers
-- `float64` for floating point numbers
-
-However, for many datasets there is a much more compact representation that Pandas could be using for that data. Using a more compact representation leads to lower memory usage, and smaller binary files on disk when using formats such as Feather and Parquet.
-
-This library does just one thing: it shrinks your data frames to use smaller types.
-
-## Installing
-
-`pip install owid-repack`
-
-## Usage
-
-The `owid.repack` module exposes two methods, `repack_series()` and `repack_frame()`.
-
-`repack_series()` will detect the smallest type that can accurately fit the existing data in the series.
-
-```ipython
-In [1]: from owid import repack
-
-In [2]: pd.Series([1, 2, 3])
-Out[2]:
-0    1
-1    2
-2    3
-dtype: int64
-
-In [3]: repack.repack_series(pd.Series([1.5, 2, 3]))
-Out[3]:
-0    1.5
-1    2.0
-2    3.0
-dtype: float32
-
-In [4]: repack.repack_series(pd.Series([1, None, 3]))
-Out[4]:
-0       1
-1    <NA>
-2       3
-dtype: UInt8
-
-In [5]: repack.repack_series(pd.Series([-1, None, 3]))
-Out[5]:
-0      -1
-1    <NA>
-2       3
-dtype: Int8
-```
-
-The `repack_frame()` method simply does this across every column in your DataFrame, returning a new DataFrame.
-
-## Releases
-
-- `0.1.2`:
-    - Shrink columns with all NaNs to Int8
-- `0.1.1`:
-    - Fix Python support in package metadata to support 3.8.1 onwards
-- `0.1.0`:
-  - Migrate first version from `owid-catalog-py` repo
+Find it here: https://github.com/owid/etl/tree/master/lib/repack