From 9d2f7d69991a45fa60e495f3bfa58d47dbaebc68 Mon Sep 17 00:00:00 2001
From: Claude Paroz <claude@2xlibre.net>
Date: Fri, 8 Nov 2019 17:04:26 +0100
Subject: Point README to the documentation

---
 README.md | 141 +-------------------------------------------------------------
 1 file changed, 2 insertions(+), 139 deletions(-)

diff --git a/README.md b/README.md
index b527e84..43e6937 100644
--- a/README.md
+++ b/README.md
@@ -29,149 +29,12 @@ Output formats supported:
 Note that tablib *purposefully* excludes XML support. It always will. (Note: This is a
 joke. Pull requests are welcome.)
 
+Tablib documentation is graciously hosted on https://tablib.readthedocs.io
 
-## Overview
-
-`tablib.Dataset()`
-
-A Dataset is a table of tabular data.
-It may or may not have a header row.
-They can be build and manipulated as raw Python datatypes (Lists of tuples|dictionaries).
-Datasets can be imported from JSON, YAML, DBF, and CSV;
-they can be exported to XLSX, XLS, ODS, JSON, YAML, DBF, CSV, TSV, and HTML.
-
-`tablib.Databook()`
-
-A Databook is a set of Datasets.
-The most common form of a Databook is an Excel file with multiple spreadsheets.
-Databooks can be imported from JSON and YAML;
-they can be exported to XLSX, XLS, ODS, JSON, and YAML.
-
-
-## Usage
-
-Populate fresh data files:
-
-```python
-headers = ('first_name', 'last_name')
-
-data = [
-    ('John', 'Adams'),
-    ('George', 'Washington')
-]
-
-data = tablib.Dataset(*data, headers=headers)
-```
-
-Intelligently add new rows:
-
-```python
->>> data.append(('Henry', 'Ford'))
-```
-
-Intelligently add new columns:
-
-```python
->>> data.append_col((90, 67, 83), header='age')
-```
-
-Slice rows:
-
-```python
->>> print(data[:2])
-[('John', 'Adams', 90), ('George', 'Washington', 67)]
-```
-
-Slice columns by header:
-
-```python
->>> print(data['first_name'])
-['John', 'George', 'Henry']
-```
-
-Easily delete rows:
-
-```python
->>> del data[1]
-```
-
-
-## Exports
-
-Drumroll please...........
-
-### JSON!
-
-```python
->>> print(data.export('json'))
-[
-  {
-    "last_name": "Adams",
-    "age": 90,
-    "first_name": "John"
-  },
-  {
-    "last_name": "Ford",
-    "age": 83,
-    "first_name": "Henry"
-  }
-]
-```
-
-### YAML!
-
-```python
->>> print(data.export('yaml'))
-- {age: 90, first_name: John, last_name: Adams}
-- {age: 83, first_name: Henry, last_name: Ford}
-```
-
-### CSV...
-
-```python
->>> print(data.export('csv'))
-first_name,last_name,age
-John,Adams,90
-Henry,Ford,83
-```
-
-### EXCEL!
-
-```python
->>> with open('people.xls', 'wb') as f:
-...     f.write(data.export('xls'))
-```
-
-### DBF!
-
-```python
->>> with open('people.dbf', 'wb') as f:
-...     f.write(data.export('dbf'))
-```
-
-### Pandas DataFrame!
-
-```python
->>> print(data.export('df')):
-      first_name last_name  age
-0       John     Adams   90
-1      Henry      Ford   83
-```
-
-It's that easy.
-
-
-## Installation
-
-To install tablib, simply:
-
-```console
-$ pip install tablib[pandas]
-```
+It is also available in the ``docs`` directory of the source distribution.
 
 Make sure to check out [Tablib on PyPI](https://pypi.org/project/tablib/)!
 
-
 ## Contribute
 
 Please see the [contributing guide](https://github.com/jazzband/tablib/blob/master/.github/CONTRIBUTING.md).
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