Rewrite intros to focus on data frames

README.md

@@ -1,31 +1,16 @@
-Python Classes for Data Manipulation
-====================================
+Simple, Light-Weight Data Frames for Python
+===========================================
 
-[![Test](https://github.com/otsaloma/dataiter/workflows/Test/badge.svg)](https://github.com/otsaloma/dataiter/actions)
-[![Documentation Status](https://readthedocs.org/projects/dataiter/badge/?version=latest)](https://dataiter.readthedocs.io/en/latest/?badge=latest)
-[![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter/)
+[![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter)
 [![Downloads](https://pepy.tech/badge/dataiter/month)](https://pepy.tech/project/dataiter)
 
-Dataiter currently includes the following classes.
-
-**`DataFrame`** is a class for tabular data similar to R's `data.frame`
-or `pandas.DataFrame`. It is under the hood a dictionary of NumPy arrays
-and thus capable of fast vectorized operations. You can consider this to
-be a light-weight alternative to Pandas with a simple and consistent
-API. Performance-wise Dataiter relies on NumPy and Numba and is likely
-to be at best comparable to Pandas.
-
-**`ListOfDicts`** is a class useful for manipulating data from JSON
-APIs. It provides functionality similar to libraries such as
-Underscore.js, with manipulation functions that iterate over the data
-and return a shallow modified copy of the original. `attd.AttributeDict`
-is used to provide convenient access to dictionary keys.
-
-**`GeoJSON`** is a simple wrapper class that allows reading a GeoJSON
-file into a `DataFrame` and writing a data frame to a GeoJSON file. Any
-operations on the data are thus done with methods provided by the data
-frame class. Geometry is read as-is into the "geometry" column, but no
-special geometric operations are currently supported.
+Dataiter's **`DataFrame`** is a class for tabular data similar to R's
+`data.frame`, implementing all common operations to manipulate data. It
+is under the hood a dictionary of NumPy arrays and thus capable of fast
+vectorized operations. You can consider it to be a light-weight
+alternative to Pandas with a simple and consistent API. Performance-wise
+Dataiter relies on NumPy and Numba and is likely to be at best
+comparable to Pandas.
 
 ## Installation
 
@@ -41,19 +26,18 @@ pip install -U numba
 ```
 
 Dataiter optionally uses **Numba** to speed up certain operations. If
-you have Numba installed and importing it succeeds, Dataiter will use it
-automatically. It's currently not a hard dependency, so you need to
-install it separately.
+you have Numba installed, Dataiter will use it automatically. It's
+currently not a hard dependency, so you need to install it separately.
 
 ## Documentation
 
 https://dataiter.readthedocs.io/
 
 If you're familiar with either dplyr (R) or Pandas (Python), the
 comparison table in the documentation will give you a quick overview of
-the differences and similarities.
+the differences and similarities in common operations.
 
-https://dataiter.readthedocs.io/en/latest/comparison.html
+https://dataiter.readthedocs.io/en/stable/comparison.html
 
 ## Development
 

doc/index.rst

@@ -1,35 +1,17 @@
 Dataiter Documentation
 ======================
 
-Dataiter is a Python package of classes for data manipulation. Dataiter
-is intended for practical data science and data engineering work with a
-focus on providing a simple and consistent API for common operations.
-Currently included are the following classes.
-
-:class:`.DataFrame`
-   A class for tabular data similar to R's ``data.frame`` or
-   ``pandas.DataFrame``. It is under the hood a dictionary of NumPy
-   arrays and thus capable of fast vectorized operations. You can
-   consider this to be a light-weight alternative to Pandas with a
-   simple and consistent API. Performance-wise Dataiter relies on NumPy
-   and Numba and is likely to be at best comparable to Pandas.
-
-:class:`.ListOfDicts`
-   A class useful for manipulating data from JSON APIs. It provides
-   functionality similar to libraries such as Underscore.js, with
-   manipulation functions that iterate over the data and return a
-   shallow modified copy of the original. ``attd.AttributeDict`` is used
-   to provide convenient access to dictionary keys.
-
-:class:`.GeoJSON`
-   A simple wrapper class that allows reading a GeoJSON file into a
-   :class:`.DataFrame` and writing a data frame to a GeoJSON file. Any
-   operations on the data are thus done with methods provided by the
-   data frame class. Geometry is read as-is into the "geometry" column,
-   but no special geometric operations are currently supported.
-
-.. warning:: Dataiter is still evolving and the API is subject to
-             breaking changes.
+Dataiter's :class:`.DataFrame` is a class for tabular data similar to R's
+``data.frame``, implementing all common operations to manipulate data. It is
+under the hood a dictionary of NumPy arrays and thus capable of fast vectorized
+operations. You can consider it to be a light-weight alternative to Pandas with
+a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba
+and is likely to be at best comparable to Pandas.
+
+Additionally Dataiter includes :class:`.ListOfDicts`, a class for manipulating
+hierarchical data, such as from JSON APIs or document databases, and
+:class:`.GeoJSON`, a class for manipulating data from GeoJSON files in a data
+frame.
 
 .. toctree::
    :maxdepth: 1

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "dataiter"
 dynamic = ["version"]
-description = "Classes for data manipulation"
+description = "Simple, light-weight data frames for Python"
 readme = "README.md"
 license = "MIT"
 requires-python = ">=3.9.0"