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178 lines
3.3 KiB
Markdown
178 lines
3.3 KiB
Markdown
# Tablib: format-agnostic tabular dataset library
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[](https://jazzband.co/)
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[](https://travis-ci.org/jazzband/tablib)
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[](https://codecov.io/gh/jazzband/tablib)
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_____ ______ ___________ ______
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__ /_______ ____ /_ ___ /___(_)___ /_
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_ __/_ __ `/__ __ \__ / __ / __ __ \
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/ /_ / /_/ / _ /_/ /_ / _ / _ /_/ /
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\__/ \__,_/ /_.___/ /_/ /_/ /_.___/
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Tablib is a format-agnostic tabular dataset library, written in Python.
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Output formats supported:
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- Excel (Sets + Books)
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- JSON (Sets + Books)
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- YAML (Sets + Books)
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- Pandas DataFrames (Sets)
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- HTML (Sets)
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- Jira (Sets)
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- TSV (Sets)
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- ODS (Sets)
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- CSV (Sets)
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- DBF (Sets)
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Note that tablib *purposefully* excludes XML support. It always will. (Note: This is a
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joke. Pull requests are welcome.)
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## Overview
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`tablib.Dataset()`
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A Dataset is a table of tabular data.
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It may or may not have a header row.
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They can be build and manipulated as raw Python datatypes (Lists of tuples|dictionaries).
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Datasets can be imported from JSON, YAML, DBF, and CSV;
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they can be exported to XLSX, XLS, ODS, JSON, YAML, DBF, CSV, TSV, and HTML.
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`tablib.Databook()`
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A Databook is a set of Datasets.
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The most common form of a Databook is an Excel file with multiple spreadsheets.
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Databooks can be imported from JSON and YAML;
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they can be exported to XLSX, XLS, ODS, JSON, and YAML.
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## Usage
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Populate fresh data files:
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```python
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headers = ('first_name', 'last_name')
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data = [
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('John', 'Adams'),
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('George', 'Washington')
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]
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data = tablib.Dataset(*data, headers=headers)
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```
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Intelligently add new rows:
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```python
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>>> data.append(('Henry', 'Ford'))
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```
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Intelligently add new columns:
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```python
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>>> data.append_col((90, 67, 83), header='age')
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```
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Slice rows:
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```python
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>>> print(data[:2])
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[('John', 'Adams', 90), ('George', 'Washington', 67)]
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```
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Slice columns by header:
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```python
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>>> print(data['first_name'])
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['John', 'George', 'Henry']
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```
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Easily delete rows:
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```python
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>>> del data[1]
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```
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## Exports
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Drumroll please...........
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### JSON!
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```python
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>>> print(data.export('json'))
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[
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{
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"last_name": "Adams",
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"age": 90,
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"first_name": "John"
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},
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{
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"last_name": "Ford",
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"age": 83,
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"first_name": "Henry"
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}
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]
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```
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### YAML!
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```python
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>>> print(data.export('yaml'))
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- {age: 90, first_name: John, last_name: Adams}
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- {age: 83, first_name: Henry, last_name: Ford}
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```
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### CSV...
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```python
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>>> print(data.export('csv'))
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first_name,last_name,age
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John,Adams,90
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Henry,Ford,83
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```
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### EXCEL!
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```python
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>>> with open('people.xls', 'wb') as f:
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... f.write(data.export('xls'))
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```
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### DBF!
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```python
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>>> with open('people.dbf', 'wb') as f:
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... f.write(data.export('dbf'))
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```
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### Pandas DataFrame!
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```python
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>>> print(data.export('df')):
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first_name last_name age
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0 John Adams 90
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1 Henry Ford 83
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```
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It's that easy.
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## Installation
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To install tablib, simply:
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```console
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$ pip install tablib[pandas]
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```
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Make sure to check out [Tablib on PyPI](https://pypi.org/project/tablib/)!
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## Contribute
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Please see the [contributing guide](https://github.com/jazzband/tablib/blob/master/.github/CONTRIBUTING.md).
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