Explore and download data from Census APIs
Project description
CenPy
=======
An interface to explore and query the US Census API and return Pandas
Dataframes. Ideally, this package is intended for exploratory data analysis
and draws inspiration from sqlalchemy-like interfaces and `acs.R`.
A [demo notebook is available](http://nbviewer.ipython.org/github/ljwolf/cenpy/blob/master/demo.ipynb).
Installation
------
This package depends on [Pandas](https://pandas.pydata.org) and [requests](https://docs.python-requests.org/en/latest). You can install `cenpy` and other dependencies using `pip`:
```pip install cenpy```
If you do not have `pip`, simply copy the module somewhere in your python path.
Usage
------
Once done, importing `cenpy` will provide the `explorer` and `base` modules. To
create a connection:
```
cxn = cenpy.base.Connection('2010sf1')
```
Check the variables required and geographies supported:
```
cxn.variables #is a pandas dataframe containing query-able vbls
cxn.geographies #is a pandas dataframe containing query-able geographies
```
Note that some geographies (like tract) have higher-level requirements that
you'll have to specify for the query to work.
The structure of the query function maps to the Census API's use of `get`,
`for`, and `in`. The main arguments for the query function are `cols`,
`geo_unit` and `geo_filter`, and map backwards to those predicates,
respectively. If more predicates are required for the search, they can be added
as keyword arguments at the end of the query.
The `cols` argument must be a list of columns to retrieve from the dataset.
Then, you must specify the `geo_unit` and `geo_filter`, which provide *what*
the unit of aggregation should be and *where* the units should be. `geo_unit`
must be a string containing the unit of analysis and an identifier. For
instance, if you want all counties in Arizona, you specify `geo_unit = 'county:*'`
and `geo_filter` = {'state','04'}.
ToDo:
------
- [ ] Provide Joins to geodata the TIGER files REST api
=======
An interface to explore and query the US Census API and return Pandas
Dataframes. Ideally, this package is intended for exploratory data analysis
and draws inspiration from sqlalchemy-like interfaces and `acs.R`.
A [demo notebook is available](http://nbviewer.ipython.org/github/ljwolf/cenpy/blob/master/demo.ipynb).
Installation
------
This package depends on [Pandas](https://pandas.pydata.org) and [requests](https://docs.python-requests.org/en/latest). You can install `cenpy` and other dependencies using `pip`:
```pip install cenpy```
If you do not have `pip`, simply copy the module somewhere in your python path.
Usage
------
Once done, importing `cenpy` will provide the `explorer` and `base` modules. To
create a connection:
```
cxn = cenpy.base.Connection('2010sf1')
```
Check the variables required and geographies supported:
```
cxn.variables #is a pandas dataframe containing query-able vbls
cxn.geographies #is a pandas dataframe containing query-able geographies
```
Note that some geographies (like tract) have higher-level requirements that
you'll have to specify for the query to work.
The structure of the query function maps to the Census API's use of `get`,
`for`, and `in`. The main arguments for the query function are `cols`,
`geo_unit` and `geo_filter`, and map backwards to those predicates,
respectively. If more predicates are required for the search, they can be added
as keyword arguments at the end of the query.
The `cols` argument must be a list of columns to retrieve from the dataset.
Then, you must specify the `geo_unit` and `geo_filter`, which provide *what*
the unit of aggregation should be and *where* the units should be. `geo_unit`
must be a string containing the unit of analysis and an identifier. For
instance, if you want all counties in Arizona, you specify `geo_unit = 'county:*'`
and `geo_filter` = {'state','04'}.
ToDo:
------
- [ ] Provide Joins to geodata the TIGER files REST api
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