Vital Signs

Scripts to create our annual, publicly-available, community-focused datasets; for Baltimore City.

Hi! We are BNIA-JFI.

This package was made to create Vital Signs data.

Check our Github page for more information and tools.

About

• Functions built and used by BNIA for annual Vital Signs data release.
• Made to be shared via IPYNB/ Google Colab notebooks.
• Data may be private and is sometimes public.
• Online documentation and PyPi libraries created from the notebooks.

Included (but not limited to)

• CloseCrawl - Pull MD Courts data.
• TidyAddr - Expertly clean addresses in Baltimore (and beyond). Works Seamlessly with Closecrawl.
• Download ACS - ACS Tutorial. Gives a function and also teaches you how to pull any data for any geography using this API (can aggregate tracts on along a crosswalk).
• Create ACS Statistics - Create pre-made statistics from ACS data. Builds off the ACS Downloader
• VS Indicators - Create other (non ACS) Vital Signs statistics using these pre-made functions.
• convertvssheetforwpupload - For internal developer use when publishing at BNIA

VitalSigns uses functions found in our Dataplay Module and vice-versa.

Usage Instructions

Install the Package

The code is on PyPI so you can install the scripts as a python library using the command:

!pip install VitalSigns dataplay geopandas

{% include important.html content='Contributers should follow the maintanance instructions and will not need to run this line. ' %}

Then...

Import Modules

1. Import the installed module into your code:

from VitalSigns.acsDownload import retrieve_acs_data 

2. use it

retrieve_acs_data(state, county, tract, tableId, year, saveAcs)

Now you could do something like merge it to another dataset!

from dataplay.merge import mergeDatasets
mergeDatasets(left_ds=False, right_ds=False, crosswalk_ds=False,  use_crosswalk = True, left_col=False, right_col=False, crosswalk_left_col = False, crosswalk_right_col = False, merge_how=False, interactive=True)

Getting Help

You can get information on the package by using the help command.

Here we look at the package's modules:

import VitalSigns
help(VitalSigns)

Help on package VitalSigns:

NAME
    VitalSigns

PACKAGE CONTENTS
    BCPSS
    HUD
    _nbdev
    acsDownload
    bidbaltimore
    bpd
    citistat
    cityfinance
    closecrawl
    create
    dhr
    enoch
    fares
    fdic
    indicators
    infousa
    liquor
    mdprop
    rbintel
    tidyaddr
    treebaltimore
    utils

VERSION
    0.0.10

FILE
    /content/drive/My Drive/Software Development Documents/VitalSigns/VitalSigns/__init__.py

Lets take a look at what functions the geoms module provides:

import VitalSigns.acsDownload
help(VitalSigns.acsDownload)

Help on module VitalSigns.acsDownload in VitalSigns:

NAME
    VitalSigns.acsDownload - # AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/03_ACS_Download.ipynb (unless otherwise specified).

FUNCTIONS
    retrieve_acs_data(state, county, tract, tableId, year)

DATA
    __all__ = ['retrieve_acs_data']

FILE
    /content/drive/My Drive/Software Development Documents/VitalSigns/VitalSigns/acsDownload.py

And here we can look at an individual function and what it expects:

import VitalSigns.acsDownload
help(VitalSigns.acsDownload.retrieve_acs_data)

Help on function retrieve_acs_data in module VitalSigns.acsDownload:

retrieve_acs_data(state, county, tract, tableId, year)

Examples

So heres an example:

Import your modules

from VitalSigns.acsDownload import retrieve_acs_data

Read in some data

Define our download parameters.

More information on these parameters can be found in the tutorials!

# Our download function will use Baltimore City's tract, county and state as internal paramters
# Change these values in the cell below using different geographic reference codes will change those parameters
tract = '*'
county = '510'
state = '24'

# Specify the download parameters the function will receieve here
tableId = 'B19001'
year = '17'

And download the Baltimore City ACS data using the imported VitalSigns library.

df = retrieve_acs_data(state, county, tract, tableId, year)
df.head(1)

.dataframe tbody tr th {
    vertical-align: top;
}

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    text-align: right;
}

From there, you can go on to do even greater things using our dataplay library. Like these visuals:

ls

checkpoint.json     docs/     MANIFEST.in  settings.ini
CONTRIBUTING.md     LICENSE   notebooks/   setup.py
docker-compose.yml  Makefile  README.md    VitalSigns/

from VitalSigns.create import createAcsIndicator, mhhi 
mergeUrl = 'https://raw.githubusercontent.com/bniajfi/bniajfi/main/CSA-to-Tract-2010.csv'
merge_left_col = 'tract'
merge_right_col= 'TRACTCE10' 
merge_how = 'outer'

groupBy = 'CSA2010'

method = mhhi
aggMethod = 'sum'
columnsToInclude = []


createAcsIndicator(state, county, tract, year, tableId,
                    mergeUrl, merge_left_col, merge_right_col, merge_how, groupBy,
                    aggMethod, method, columnsToInclude, finalFileName=False).head(1)

Table: B19001, Year: 17 imported.
Index(['TRACTCE10',
       'GEOID10',
       'CSA2010'],
      dtype='object')
Merge file imported
Both are now merged.
Aggregating...
Aggregated
Creating Indicator
Indicator Created

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

from VitalSigns.closecrawl import close_crawl

help(close_crawl)

Help on function close_crawl in module VitalSigns.closecrawl:

close_crawl(case_type, case_year, output, cases='', lower_bound=0, upper_bound=0, debug=False, scrape=True, mine=True, clean=True)
    Main function for Close Crawl.
    
    Args:
        case_type (`str`): type of foreclosure case, options are 'O' and 'C'
        case_year (`str`): year of foreclosure cases
        output (`str`): path of the output CSV file, along with the valid
            extension (.csv)
        lower_bound (`int`, optional): lower bound of range of cases
        upper_bound (`int`, optional): upper bound of range of cases
        debug (`bool`, optional): option for switching between debug mode.
            Default -> True
    
    Returns:
        None

args = {'type': 'C', 'year': '2020', 'output': 'outputfile.csv', 'file': '', 'lower': '1000', 'upper': '1030', 'debug': '0', 'scrape': True, 'mine': True, 'clean': True}
close_crawl(
    case_type=args["type"], case_year=args["year"], output=args["output"],
    cases=args["file"], lower_bound=args["lower"],
    upper_bound=args["upper"], debug=args["debug"],
    scrape=args["scrape"], mine=args["mine"],
    clean=args["clean"]
)

from VitalSigns import tidyaddr

help(tidyaddr)

Help on module VitalSigns.tidyaddr in VitalSigns:

NAME
    VitalSigns.tidyaddr - Create a module object.

DESCRIPTION
    The name must be a string; the optional doc argument can have any type.

FUNCTIONS
    runTidyAddr(filename)

DATA
    __all__ = ['uploadedFilename', 'runTidyAddr']
    uploadedFilename = 'outputfile.csv'

FILE
    /content/drive/My Drive/Software Development Documents/VitalSigns/VitalSigns/tidyaddr.py

output = 'outputfile.csv'
tidyaddr.runTidyAddr( output )

outputfile.csv
 ~~~~~~~~~~~ Getting TIDYADDR ~~~~~~~~~~~ 
 ~~~~~~~~~~~ Installing TIDYADDR ~~~~~~~~~~~
 ~~~~~~~~~~~ Running TIDYADDR ~~~~~~~~~~~
cd tidyaddr-js && node tidyaddr.js clean-csv outputfile.csv outputfile_tidyaddred.csv

! cd tidyaddr-js && ls

dev	 LICENSE	 outputfile_tidyaddred.csv  README.md	 todo.todo
init.js  outputfile.csv  package.json		    tidyaddr.js

Have Fun!

ACS Scripts

These scripts will download and clean ACS data for Baltimore and then construct indicators from the data.

AcsDownload.ipynb Mount notebook to google drive Read in ACS Meta Data from XLSX and the crosswalk data from a csv Add path to python script that performs download function Enter a year and Run the download function for every record in XLSX sheet For each dataset, remove columnID’s then save it as Raw Then, Append Community Names using a crosswalk and save again in as Clean

AcsIndicators.ipynb Mount notebook to google drive Read in ACS Meta Data from XLSX Prepare the Compare Historic Data

• For Each Indicator
• Grab its Meta Data
• If the Indicator is valid for the year, and uses ACS Data, and method exists
• retrieve the Python ACS indicator
• Put Baltimore City at the bottom of the list
• Write the results back into the XL dataframe
• Save the Dataset
• drop columns with any empty values
• Save the Data xlsx Do comparison to historic year if exists. Write xlsx.

MISC

This section is not definite but provides a good idea of how our scripts are made.

Basic Indicator Creation Outline

• ? Count = 1
• Create the num and denom
• filter num denom
• ? sum/ median = ungrouped.median
• group by csa
• ? bcity = median or sum
• perform the calculation
• compare years

Miscellaneous things I should have for every notebook

• Module/filenames need to be fixed.
• RB Intel has the best prelim analysis script. The others are messed up a bit?
• include links indicator Esri and Bnia pages details on category, name, description, years
• Don’t drop columns at end, but keep selected at beginning.
• Merge on CSA for ordering
• Bcity Median gets calculated before aggregation. Appended after
• Add Years in header. Use denom and numerator as var names.
• Code to compare past years

FOR CONTRIBUTERS

Dev Instructions

From a local copy of the git repo: 0. Clone the repo local onto GDrive

• Via Direct-DL&Drive-Upload or Colab/Terminal/Git
• git clone https://github.com/BNIA/VitalSigns.git

1. Update the the IPYNB

• From the GDrive VitalSigns folder via Colabs

2. Build the new libraries from these NBs

• Using this index.ipynb
• • Mount the Colabs Enviornment (and navigate to) the GDrive VitalSigns folder
• • run !nbdev_build_lib to build .py modules.

3. Test the Library/ Modules

• Using the same runtime as step 2's index.ipynb.
• • Do not install the module from PyPi (if published) and then...
• • Import your module ( from your VitalSigns/VitalSigns)
• • If everything runs properly, go to step 5.

4. Edit modules directly

• Within the same runtime as step 2/3's index.ipynb...
• • Locate the VitalSigns/VitalSigns using the colab file nav
• • double-click the .py modules in the file nav to open them in an in-browser editor
• Make changes and return to step 3 with the following caveat:
• • Use the hot module reloading to ensure updates are auto-re-imported
• • %load_ext autoreload %autoreload 2
• Then when finished, persist the changes from the .py modules back to the .ipynb docs
• • via !nbdev_update_lib and !relimport2name

5. Create Docs, Push to Github, and Publish to PyPI

• All done via nbdev
• Find more notes I made on that here: dataplay > nbdev notes
• !nbdev_build_docs --force_all True --mk_readme True
• !git commit -m ...
• %%capture ! pip install twine
• !nbdev_bump_version
• ! make pypi

# https://nbdev.fast.ai/tutorial.html#Set-up-prerequisites
# settings.ini > requirements = fastcore>=1.0.5 torchvision<0.7
# https://nbdev.fast.ai/tutorial.html#View-docs-locally
# console_scripts = nbdev_build_lib=nbdev.cli:nbdev_build_lib
# https://nbdev.fast.ai/search

Dev Scripts

! nbdev_build_lib

ls

checkpoint.json     docs/         Makefile     package-lock.json  setup.py
CONTRIBUTING.md     index_files/  MANIFEST.in  README.md          VitalSigns/
docker-compose.yml  LICENSE       notebooks/   settings.ini

# !git mv index.md README.md -f

fatal: bad source, source=index.md, destination=README.md

!nbdev_update_lib

# ! git remote remove origin

# ! git remote remove origin

# !git remote add origin https://github.com/BNIA/VitalSigns.git

!git remote -v

origin	https://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@github.com/BNIA/VitalSigns.git (fetch)
origin	https://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@github.com/BNIA/VitalSigns.git (push)

# step 3
project = 'VitalSigns'
token = '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' 
username = 'BNIA'
print(f'https://{token}@github.com/{username}/{project}.git')
! git remote remove origin 
! git remote add origin 'https://{token}@github.com/{username}/{project}.git'
#! git push -u origin main 
!git push 'https://{token}@github.com/{username}/{project}.git' 

https://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@github.com/BNIA/VitalSigns.git
Counting objects: 17, done.
Delta compression using up to 2 threads.
Compressing objects: 100% (17/17), done.
Writing objects: 100% (17/17), 6.24 KiB | 1.04 MiB/s, done.
Total 17 (delta 13), reused 0 (delta 0)
remote: Resolving deltas: 100% (13/13), completed with 3 local objects.
To https://github.com/BNIA/VitalSigns.git
   9b8fcc9..bdcbbdb  main -> main

! git push -u ORIGIN main #-f

fatal: 'ORIGIN' does not appear to be a git repository
fatal: Could not read from remote repository.

Please make sure you have the correct access rights
and the repository exists.