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# Tablib: format-agnostic tabular dataset library
[](https://jazzband.co/)
[](https://travis-ci.org/jazzband/tablib)
[](https://codecov.io/gh/jazzband/tablib)
_____ ______ ___________ ______
__ /_______ ____ /_ ___ /___(_)___ /_
_ __/_ __ `/__ __ \__ / __ / __ __ \
/ /_ / /_/ / _ /_/ /_ / _ / _ /_/ /
\__/ \__,_/ /_.___/ /_/ /_/ /_.___/
Tablib is a format-agnostic tabular dataset library, written in Python.
Output formats supported:
- Excel (Sets + Books)
- JSON (Sets + Books)
- YAML (Sets + Books)
- Pandas DataFrames (Sets)
- HTML (Sets)
- Jira (Sets)
- TSV (Sets)
- ODS (Sets)
- CSV (Sets)
- DBF (Sets)
Note that tablib *purposefully* excludes XML support. It always will. (Note: This is a
joke. Pull requests are welcome.)
## 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]
```
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).
|
