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Python is an interpreted high-level general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant indentation.

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Been playing around a bit with PANDAS and ArcGIS Feature Servers for Tax Data

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Been playing around a bit with PANDAS and ArcGIS Feature Servers for Tax Data … 📜

For example, to find the most valuable parcels in Albany County.

from arcgis.features import FeatureLayer
lyr_url = 'https://gisservices.its.ny.gov/arcgis/rest/services/NYS_Tax_Parcel_Centroid_Points/MapServer/0'
layer = FeatureLayer(lyr_url)
query_result1 = layer.query(where="COUNTY_NAME='Albany' AND FULL_MARKET_VAL > 100000000", 
                                    out_fields='PARCEL_ADDR,CITYTOWN_NAME,FULL_MARKET_VAL,OWNER_TYPE', out_sr='4326')

df=query_result1.sdf.sort_values(by='FULL_MARKET_VAL', ascending=False)
df['Full Market Value'] = df['FULL_MARKET_VAL'].map('${:,.0f}'.format)

df
  OBJECTID PARCEL_ADDR CITYTOWN_NAME FULL_MARKET_VAL OWNER_TYPE SHAPE Full Market Value
11 26652 64 Eagle St Albany 1204254925 2 {“x”: -73.75980312511581, “y”: 42.650469918250… $1,204,254,925
3 9150 1200 Washington Ave Albany 886298715 2 {“x”: -73.81092293494828, “y”: 42.679257168282… $886,298,715
4 10208 1400 Washington Ave Albany 642398287 2 {“x”: -73.82369286130952, “y”: 42.685845700657… $642,398,287
0 885 251 Fuller Rd Albany 440042827 2 {“x”: -73.83559002316825, “y”: 42.690208093507… $440,042,827
5 18164 632 New Scotland Ave Albany 377568201 8 {“x”: -73.80381341626146, “y”: 42.655758957669… $377,568,201
1 906 141 Fuller Rd Albany 321199143 2 {“x”: -73.83323986150171, “y”: 42.693189748928… $321,199,143
19 108087 See Card 1067 Watervliet 280898876 1 {“x”: -73.70670724174552, “y”: 42.719628647232… $280,898,876
15 65380 737 Alb Shaker Rd Colonie 263916100 3 {“x”: -73.80365248218001, “y”: 42.747956678125… $263,916,100
9 21923 304 Madison Ave Albany 234265418 2 {“x”: -73.76227373289564, “y”: 42.648000674457… $234,265,418
2 907 201 Fuller Rd Albany 203426124 2 {“x”: -73.83362605353057, “y”: 42.692609131686… $203,426,124
16 69999 515 Loudon Rd Colonie 166065600 8 {“x”: -73.74958475282632, “y”: 42.719321807666… $166,065,600
7 20592 47 New Scotland Ave Albany 162276338 8 {“x”: -73.77597163421673, “y”: 42.653565689693… $162,276,338
6 20574 132 S Lake Ave Albany 146296360 2 {“x”: -73.77970918544908, “y”: 42.654390366929… $146,296,360
8 20597 113 Holland Ave Albany 143498501 2 {“x”: -73.77306688593143, “y”: 42.650762742870… $143,498,501
17 78203 Mannsville Colonie 142570400 1 {“x”: -73.71245452369443, “y”: 42.718124477080… $142,570,400
18 95509 1 Crossgates Mall Rd Guilderland 130554700 8 {“x”: -73.84702700595471, “y”: 42.687699053797… $130,554,700
10 24521 86 S Swan St Albany 128436403 2 {“x”: -73.75980563770365, “y”: 42.653931892804… $128,436,403
13 46883 1916 US 9W Coeymans 110000000 8 {“x”: -73.83388475575597, “y”: 42.488730743021… $110,000,000
12 35152 380 River Rd Bethlehem 105263158 8 {“x”: -73.76445503554325, “y”: 42.595925419330… $105,263,158
14 65097 15 Wolf Rd Colonie 101967213 8 {“x”: -73.81423716588279, “y”: 42.709939498581… $101,967,213

Not afraid to use Python 🐍

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Not afraid to use Python 🐍

I use R for a lot of projects as it has superior libraries for working with Census data, maps and spatial data. Tigris makes it easy to call and process all kinds of census lines.

But sometimes projects are best done in Python. I recently had a project where I needed run a regular expression to extract text from a PDF and put it in an excel file. Python was easy to do it in a few lines of code while I think it would have been harder to do in R.

I don’t really buy into all this doctoraire about you should use the same tool for every project. Use what makes sense, what you are best at, what executes quickly and produces the best results.