VitalSigns 0.0.6

Python Scripts for BNIA-JFIs Vital Signs Data

Vital Signs

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

Hi! We are BNIA-JFI.

This is BNIA-JFI's principal repository for creating Vital Signs Indicators.

Included

• IPYNB/ Google Colab notebooks with indicator creation notes and scripts.
• Online documentation and PyPi libraries created from the notebooks.

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.

Install

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

!pip install VitalSigns geopandas

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

Their modules will be retrieved from the VitalSigns-GDrive repo they have mounted into their Colabs Enviornment.

Then...

Examples

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)

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

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
    0.0.5

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

help(VitalSigns.acsDownload)

Help on module VitalSigns.acsDownload in VitalSigns:

NAME
    VitalSigns.acsDownload - # AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/90_ACS_Explore_and_Download.ipynb (unless otherwise specified).

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

DATA
    __all__ = ['retrieve_acs_data']
    __warningregistry__ = {'version': 352, ('Passing a negative integer is...

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

help(VitalSigns.acsDownload.retrieve_acs_data)

Help on function retrieve_acs_data in module VitalSigns.acsDownload:

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

Here an example:

from VitalSigns.acsDownload import retrieve_acs_data

# 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'
saveAcs = False

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

Number of Columns 17

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

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

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

</style>

	B19001_001E_Total	B19001_002E_Total_Less_than_$10,000	B19001_003E_Total_$10,000_to_$14,999	B19001_004E_Total_$15,000_to_$19,999	B19001_005E_Total_$20,000_to_$24,999	B19001_006E_Total_$25,000_to_$29,999	B19001_007E_Total_$30,000_to_$34,999	B19001_008E_Total_$35,000_to_$39,999	B19001_009E_Total_$40,000_to_$44,999	B19001_010E_Total_$45,000_to_$49,999	B19001_011E_Total_$50,000_to_$59,999	B19001_012E_Total_$60,000_to_$74,999	B19001_013E_Total_$75,000_to_$99,999	B19001_014E_Total_$100,000_to_$124,999	B19001_015E_Total_$125,000_to_$149,999	B19001_016E_Total_$150,000_to_$199,999	B19001_017E_Total_$200,000_or_more	state	county	tract
NAME
Census Tract 2710.02	1510	209	73	94	97	110	119	97	65	36	149	168	106	66	44	50	27	24	510	271002
Census Tract 2604.02	1134	146	29	73	80	41	91	49	75	81	170	57	162	63	11	6	0	24	510	260402
Census Tract 2712	2276	69	43	41	22	46	67	0	30	30	80	146	321	216	139	261	765	24	510	271200
Census Tract 2804.04	961	111	108	61	42	56	37	73	30	31	106	119	74	23	27	24	39	24	510	280404
Census Tract 901	1669	158	124	72	48	108	68	121	137	99	109	191	160	141	28	88	17	24	510	90100

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

Have Fun!

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