dataplay 0.0.27

Code Tutorials

Data Handling Handbook

The one stop shop to learn about data intake, processing, and visualization.

The Dataplay Handbook uses techniques covered in the Datalabs Guidebook.

Install

The code is on PyPI so you can just run:

pip install dataplay geopandas

From the terminal to install the code and its dependencies

How to use

1. Import the installed module into your code:

from dataplay.acsDownload import retrieve_acs_data 

2. use it

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

Heres another one

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)

Examples

Import your modules

%%capture 
from dataplay.acsDownload import retrieve_acs_data
from dataplay.merge import mergeDatasets

import pandas as pd

from dataplay.geoms import readInGeometryData
from dataplay.geoms import workWithGeometryData

Read in some data

Define our download parameters.

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

tract = '*'
county = '510'
state = '24'
tableId = 'B19001'
year = '17'
saveAcs = False

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

Number of Columns 17

#hide_input
df.head()

<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	...	state	county	tract
NAME
Census Tract 1901	796	237	76	...	24	510	190100
Census Tract 1902	695	63	87	...	24	510	190200
Census Tract 2201	2208	137	229	...	24	510	220100
Census Tract 2303	632	3	20	...	24	510	230300
Census Tract 2502.07	836	102	28	...	24	510	250207

5 rows × 20 columns

Here we can import and display a dataset

#hide
# This dataset is taken from the public database provided by BNIAJFI hosted by Esri / ArcGIS
# BNIA ArcGIS Homepage: https://data-bniajfi.opendata.arcgis.com/
csa_gdf_url = "https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Hhchpov/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson"
csa_gdf = readInGeometryData(url=csa_gdf_url, porg=False, geom='geometry', lat=False, lng=False, revgeocode=False,  save=False, in_crs=2248, out_crs=False)
csa_gdf.plot(column='hhchpov18')

RECIEVED url: https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Hhchpov/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson, 
 porg: g, 
 geom: geometry, 
 lat: False, 
 lng: False, 
 revgeocode: False, 
 in_crs: 2248, 
 out_crs: 2248





<matplotlib.axes._subplots.AxesSubplot at 0x7f8840335048>

Now in this example we will load in a bunch of coorinates

geoloom_gdf_url = "https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson"
geoloom_gdf = readInGeometryData(url=geoloom_gdf_url, porg=False, geom='geometry', lat=False, lng=False, revgeocode=False,  save=False, in_crs=4326, out_crs=False)
geoloom_gdf = geoloom_gdf.dropna(subset=['geometry']) 

RECIEVED url: https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson, 
 porg: g, 
 geom: geometry, 
 lat: False, 
 lng: False, 
 revgeocode: False, 
 in_crs: 4326, 
 out_crs: 4326

And then use the dataset we retrieved just before it to attach community labels on which the points sit

geoloom_w_csas = workWithGeometryData(method='pinp', df=geoloom_gdf, polys=csa_gdf, ptsCoordCol='geometry', polygonsCoordCol='geometry', polyColorCol='hhchpov18', polygonsLabel='CSA2010', pntsClr='red', polysClr='white')

Total Points:  61.0
Total Points in Polygons:  50
Prcnt Points in Polygons:  0.819672131147541


/usr/local/lib/python3.6/dist-packages/dataplay/geoms.py:112: FutureWarning:     You are passing non-geometry data to the GeoSeries constructor. Currently,
    it falls back to returning a pandas Series. But in the future, we will start
    to raise a TypeError instead.
  polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)

Lets map it after inspecting it a bit!

type(geoloom_w_csas)

geopandas.geodataframe.GeoDataFrame

geoloom_w_csas.plot(column='pointsinpolygon')

<matplotlib.axes._subplots.AxesSubplot at 0x7f88403b8710>

Legal

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License

Copyright © 2019 BNIA-JFI

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.