Post #5: Shapes and Colors

A short description of the post.

Now that we have our Data downloaded as a zip file, we’ll need to dig into it to access what we want. The file types we want are the .shp, .shx, and .dbf. We need each in order to have a complete shapefile.

First we need a few packages that allow us to work with zipfile

import zipfile
from io import BytesIO
import io
from google.colab import files
import re, os
from urllib.request import urlopen

### Put Zip in Memory
f = zipfile.ZipFile(["file_name.zip", 'r')

### Extract .shp, .shx and .dbf file from zip into doc folder
listOfFileNames = f.namelist()
# Iterate over the file names
for fileName in listOfFileNames:
    # Check filename endswith csv
    if fileName.endswith('.shp') or fileName.endswith('.shx') or fileName.endswith('.dbf'):
        # Extract a single file from zip
         f.extract(fileName)

This code has taken our zip folder, extracted our 3 winners and placed them into a doc folder. Now we can work towards visualizing a field. We will be accomplishing this using the geopandas package. There are plenty of other visualization packages out there that may make the mapp look nicer, but our purpose is to make sure that the data actually works and can create an accurate map.

First, we’re goint to import geopandas, declare a link to our files, read the file, then view the dataframe

### Print Table Version of .shp
import geopandas as gpd
import geoplot
df_link = '/content/doc/BJ Christensen-Michaud-304-Potatoes.shp'
df = gpd.read_file(df_link)
df.head()

Try a basic plot

### Basic Field Plot
df.plot()

I don’t love the look, but we’re on the rigth track. Let’s try to add some color.

import contextily as ctx
df = df.set_crs(epsg=4326)
df = df.to_crs(epsg=4326)

sections = df.dissolve(by='SECTIONID', aggfunc='sum')

sections.plot(column = 'NetYld', scheme='quantiles', cmap='viridis');

And there we have it! While the plot is anything beautiful, the real win comes from the fact that we were able to access real John Deere Field Operation data and display it in a way that could potentially help a farmer make revenue altering decisions. The possibilities for future analysis is as endless as the supply with our newfound API connection.