# cepan
[](https://pypi.org/project/cepan/#history)
[](https://pypi.org/project/cepan/)
[](https://github.com/kanga333/cepan/actions/workflows/test.yml)
[](https://github.com/kanga333/cepan/actions/workflows/lint.yml)
[](http://mypy-lang.org/)
[](https://github.com/psf/black)
[](https://opensource.org/licenses/MIT)
Retrieves data from aws cost explore as a pandas dataframe.
Main features
- Support for input with type hints
- Retrieving results as pandas.Dataframe
## Installation
```
pip install cepan
```
## Usage
```python
from datetime import datetime
import cepan as ce
df = ce.get_cost_and_usage(
time_period=ce.TimePeriod(
start=datetime(2020, 1, 1),
end=datetime(2020, 1, 2),
),
granularity="DAILY",
filter=ce.And(
[
ce.Dimensions(
"SERVICE",
["Amazon Simple Storage Service", "AmazonCloudWatch"],
),
ce.Tags("Stack", ["Production"]),
]
),
metrics=["BLENDED_COST"],
group_by=ce.GroupBy(
dimensions=["SERVICE", "USAGE_TYPE"],
),
)
print(df)
```
All paginated results will be returned as a Dataframe.
```
Time SERVICE BlendedCost
0 2020-01-01 Amazon Simple Storage Service 100.000000
1 2020-01-01 AmazonCloudWatch 10.000000
```
### List of currently supported APIs
- get_dimension_values
- get_tags
- get_cost_and_usage
### Alias of aws service name
Normally, the Cost Explorer API requires complex and long names to filter by service name.
For example, if you only need the value of an EC2 instance, you would need to specify `Amazon Elastic Compute Cloud - Compute`.
```python
df = ce.get_cost_and_usage(
time_period=ce.TimePeriod(
start=datetime(2020, 1, 1),
end=datetime(2020, 1, 2),
),
granularity="DAILY",
filter=ce.Dimensions(
"SERVICE",
["Amazon Elastic Compute Cloud - Compute"],
),
group_by=ce.GroupBy(
dimensions=["SERVICE", "USAGE_TYPE"],
),
)
```
cepan supports aliases with short service names.
If you only need the value of the EC2 instance, you can specify it with `EC2`.
```python
df = ce.get_cost_and_usage(
time_period=ce.TimePeriod(
start=datetime(2020, 1, 1),
end=datetime(2020, 1, 2),
),
granularity="DAILY",
filter=ce.Dimensions(
"SERVICE",
["EC2"],
),
group_by=ce.GroupBy(
dimensions=["SERVICE", "USAGE_TYPE"],
),
)
```
Correspondence table of aliases is shown in [service_alias.tsv](service_alias.tsv).
You can also run the `show_service_alias` method to get the table.
```python
print(ce.show_service_alias())
```
## License
MIT License
