A utility library that provides a consistent interface for reading tabular data.
Project description
# tabulator-py
[](https://travis-ci.org/datapackages/tabulator-py)
[](https://coveralls.io/r/datapackages/tabulator-py?branch=master)
A utility library that provides a consistent interface for reading tabular data.
## Getting Started
### Installation
To get started (under development):
```
$ pip install tabulator
```
### Simple interface
Fast access to the table with `topen` (stands for `table open`) function:
```python
from tabulator import topen, processors
with topen('path.csv', with_headers=True) as table:
for row in table:
print(row)
print(row.get('header'))
```
For the most use cases `topen` function is enough. It takes the
`source` argument:
```
<scheme>://path/to/file.<format>
```
and uses corresponding `Loader` and `Parser` to open and start to iterate
over the table. Also user can pass `scheme` and `format` explicitly
as function arguments. The last `topen` argument is `encoding` - user can force Tabulator
to use encoding of choice to open the table.
Read more about `topen` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/topen.py).
Function `topen` returns `Table` instance. We use context manager
to call `table.open()` on enter and `table.close()` when we exit:
- table can be iterated like file-like object returning row by row
- table can be read row by bow using `readrow` method (it returns row tuple)
- table can be read into memory using `read` function (return list or row tuples)
with `limit` of output rows as parameter.
- headers can be accessed via `headers` property
- table pointer can be set to start via `reset` method.
Read more about `Table` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/table.py).
In the example above we use `processors.Headers` to extract headers
from the table (via `with_headers=True` shortcut). Processors is a powerfull
Tabulator concept. Parsed data goes thru pipeline of processors to be updated before
returning as table row.
Read more about `Processor` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/processors/api.py).
Read a processors tutorial - [tutorial](https://github.com/datapackages/tabulator-py/blob/master/docs/processors.md).
### Advanced interface
To get full control over the process you can use more parameters.
Below all parts of Tabulator are presented:
```python
from tabulator import topen, processors, loaders, parsers
table = topen('path.csv',
loader_options={'encondig': 'utf-8'},
parser_options={'delimeter': ',', quotechar: '|'},
loader_class=loaders.File,
parser_class=parsers.CSV,
iterator_class=CustomIterator,
table_class=CustomTable)
table.add_processor(processors.Headers(skip=1))
headers = table.headers
contents = table.read(limit=10)
print(headers, contents)
table.close()
```
Also `Table` class can be instantiated by user (see documentation).
But there is no difference between it and `topen` call with extended
list of parameters except `topen` also calls the `table.open()` method.
## Design Overview
Tabulator uses modular architecture to be fully extensible and flexible.
It uses loosely coupled modules like `Loader`, `Parser` and `Processor`
to provide clear data flow.
## Documentation
API documentation is presented as docstrings:
- High-level:
- [topen](https://github.com/datapackages/tabulator-py/blob/master/tabulator/topen.py)
- Core elements:
- [Row](https://github.com/datapackages/tabulator-py/blob/master/tabulator/row.py)
- [Table](https://github.com/datapackages/tabulator-py/blob/master/tabulator/table.py)
- [Iterator](https://github.com/datapackages/tabulator-py/blob/master/tabulator/iterator.py)
- Plugin elements:
- [Loader API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/loaders/api.py)
- [Parser API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/parsers/api.py)
- [Processor API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/processors/api.py)
## Contributing
Please read the contribution guideline:
[How to Contribute](CONTRIBUTING.md)
Thanks!
[](https://travis-ci.org/datapackages/tabulator-py)
[](https://coveralls.io/r/datapackages/tabulator-py?branch=master)
A utility library that provides a consistent interface for reading tabular data.
## Getting Started
### Installation
To get started (under development):
```
$ pip install tabulator
```
### Simple interface
Fast access to the table with `topen` (stands for `table open`) function:
```python
from tabulator import topen, processors
with topen('path.csv', with_headers=True) as table:
for row in table:
print(row)
print(row.get('header'))
```
For the most use cases `topen` function is enough. It takes the
`source` argument:
```
<scheme>://path/to/file.<format>
```
and uses corresponding `Loader` and `Parser` to open and start to iterate
over the table. Also user can pass `scheme` and `format` explicitly
as function arguments. The last `topen` argument is `encoding` - user can force Tabulator
to use encoding of choice to open the table.
Read more about `topen` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/topen.py).
Function `topen` returns `Table` instance. We use context manager
to call `table.open()` on enter and `table.close()` when we exit:
- table can be iterated like file-like object returning row by row
- table can be read row by bow using `readrow` method (it returns row tuple)
- table can be read into memory using `read` function (return list or row tuples)
with `limit` of output rows as parameter.
- headers can be accessed via `headers` property
- table pointer can be set to start via `reset` method.
Read more about `Table` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/table.py).
In the example above we use `processors.Headers` to extract headers
from the table (via `with_headers=True` shortcut). Processors is a powerfull
Tabulator concept. Parsed data goes thru pipeline of processors to be updated before
returning as table row.
Read more about `Processor` - [documentation](https://github.com/datapackages/tabulator-py/blob/master/tabulator/processors/api.py).
Read a processors tutorial - [tutorial](https://github.com/datapackages/tabulator-py/blob/master/docs/processors.md).
### Advanced interface
To get full control over the process you can use more parameters.
Below all parts of Tabulator are presented:
```python
from tabulator import topen, processors, loaders, parsers
table = topen('path.csv',
loader_options={'encondig': 'utf-8'},
parser_options={'delimeter': ',', quotechar: '|'},
loader_class=loaders.File,
parser_class=parsers.CSV,
iterator_class=CustomIterator,
table_class=CustomTable)
table.add_processor(processors.Headers(skip=1))
headers = table.headers
contents = table.read(limit=10)
print(headers, contents)
table.close()
```
Also `Table` class can be instantiated by user (see documentation).
But there is no difference between it and `topen` call with extended
list of parameters except `topen` also calls the `table.open()` method.
## Design Overview
Tabulator uses modular architecture to be fully extensible and flexible.
It uses loosely coupled modules like `Loader`, `Parser` and `Processor`
to provide clear data flow.
## Documentation
API documentation is presented as docstrings:
- High-level:
- [topen](https://github.com/datapackages/tabulator-py/blob/master/tabulator/topen.py)
- Core elements:
- [Row](https://github.com/datapackages/tabulator-py/blob/master/tabulator/row.py)
- [Table](https://github.com/datapackages/tabulator-py/blob/master/tabulator/table.py)
- [Iterator](https://github.com/datapackages/tabulator-py/blob/master/tabulator/iterator.py)
- Plugin elements:
- [Loader API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/loaders/api.py)
- [Parser API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/parsers/api.py)
- [Processor API](https://github.com/datapackages/tabulator-py/blob/master/tabulator/processors/api.py)
## Contributing
Please read the contribution guideline:
[How to Contribute](CONTRIBUTING.md)
Thanks!
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