censusgeocode | Python wrapper for the US Census Geocoder | Wrapper library
kandi X-RAY | censusgeocode Summary
kandi X-RAY | censusgeocode Summary
Python wrapper for the US Census Geocoder
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Top functions reviewed by kandi - BETA
- Assign data to a batch file
- Post address batch
- Parse the results of a batch query
- Construct the URL for the given searchtype
- Get oneline records for a given address
- Fetch data from Census API
censusgeocode Key Features
censusgeocode Examples and Code Snippets
import censusgeocode as cg
cg.coordinates(x=-76, y=41)
cg.onelineaddress('1600 Pennsylvania Avenue, Washington, DC')
cg.address('1600 Pennsylvania Avenue', city='Washington', state='DC', zip='20006')
cg.addressbatch('data/addresses.csv')
cg.oneline
censusgeocode '100 Fifth Avenue, New York, NY'
-73.992195,40.73797
censusgeocode '1600 Pennsylvania Avenue, Washington DC'
-77.03535,38.898754
censusgeocode 'Hollywood & Vine, LA, CA'
-118.32668,34.101624
unique id, street address, state, city
from censusgeocode import CensusGeocode
cg = CensusGeocode(benchmark='Public_AR_Current', vintage='Census2020_Current')
cg.onelineaddress(foobar)
Community Discussions
Trending Discussions on censusgeocode
QUESTION
I am relatively new to Python, coming from a Stata background, and am still struggling with some core Python concepts. For example, i'm currently working on a small program that hits the US Census Bureau API to geocode some addresses and my instinct is to loop over my csv files, feed them into the API call, and then name the output(s) sequentially using the iterator. E.g.
...ANSWER
Answered 2021-Mar-10 at 14:16Maybe you coud create one main DataFrame and add to it an extra field representing i
, Then, while looping over your CSV files, you could load the data as a new DataFrame, add the i-field to every line and append the read data to your main DataFrame.
p.s. in k = cg.addressbatch('dta/batchfiles/split_test ' + str(i) + '.csv')
I would recommend the use os.path.join() for this purpose
QUESTION
I'm trying to build a small program that uses the censusgeo
package to interact with the US Census Bureau API address batch facility. The API has a limit of 10,000 addresses in any single call but my dataframe has approx. 3 million rows. As such I want to split the dataframe into N parts, each comprising roughly 10,000 rows, and then feed each one into the API call, extract the output and append it all together.
I found this stackoverflow post which has been quite helpful in giving me a function to split my df. It doesn't return dataframes though (e.g. they don't show up if I run %who_ls DataFrame
) and I don't know how to call the outputs individually in order to then feed them into an API call.
This is the function i'm using to split the dataframe:
...ANSWER
Answered 2021-Mar-10 at 12:50I think i've found a solution to my question. If I assign the function call to an object, I can then access the different chunks it creates using standard indexing notation. E.g.
QUESTION
I am trying to do a simple geocode operation that I have been able to do many times in the past. This simple code no longer works, because it returns this output. {'States': [], 'Counties': [], '2020 Census Blocks': [], 'Census Tracts': []}. What could possibly explain this? I have run this code many times in the past and I have never seen this before.
...ANSWER
Answered 2021-Feb-24 at 02:01x
is the longitude and y
is the latitude. You need to swap them, like this:
QUESTION
Suppose I have a list of api keys I am downloading from the census data
Example:
...ANSWER
Answered 2020-Jan-10 at 17:23It looks like you're already chunking the variable_lists here:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install censusgeocode
You can use censusgeocode like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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