Cara menggunakan plot multipolygon python

In this exercise, you'll be using father_son = pd.read_csv('./dataset/father_son.csv', usecols=['fheight', 'sheight']) father_son.head() 2 to plot the father and son height data from the video. The father_son = pd.read_csv('./dataset/father_son.csv', usecols=['fheight', 'sheight']) father_son.head() 3 DataFrame is available in your workspace. In each scatterplot, plot father_son = pd.read_csv('./dataset/father_son.csv', usecols=['fheight', 'sheight']) father_son.head() 4 as x-axis and father_son = pd.read_csv('./dataset/father_son.csv', usecols=['fheight', 'sheight']) father_son.head() 5 as y-axis.

father_son = pd.read_csv('./dataset/father_son.csv', usecols=['fheight', 'sheight']) father_son.head()

fheightsheight065.0485159.77827163.2509463.21404264.9553263.34242365.7525062.79238461.1372364.28113

plt.scatter(father_son.fheight, father_son.sheight, c='darkred', marker='s');

plt.scatter(father_son.fheight, father_son.sheight, c = 'yellow', edgecolor= 'darkblue');

plt.scatter(father_son.fheight, father_son.sheight, c = 'yellow', edgecolor = 'darkblue'); plt.grid(); plt.xlabel('father height (inches)'); plt.ylabel('son height (inches)'); plt.title('Son Height as a Function of Father Height');

Plotting chicken locations

Now you will create a scatterplot that shows where the Nashville chickens are!

chickens = pd.read_csv('./dataset/chickens.csv') # Look at the first few rows of the chickens DataFrame print(chickens.head()) # Plot the locations of all Nashville chicken permits plt.scatter(x=chickens.lng, y=chickens.lat);

Unnamed: 0 permit city state zip district date_issued \ 0 0 288.0 Nashville TN 37211 30.0 11/26/2014 1 1 435.0 Nashville TN 37211 19.0 05/18/2015 2 2 270.0 Nashville TN 37216 7.0 11/20/2014 3 3 249.0 Nashville TN 37216 25.0 12/29/2014 4 4 200.0 Nashville TN 37221 22.0 12/10/2014 lat lng 0 36.029132 -86.724578 1 36.069132 -86.754578 2 36.236134 -86.711204 3 36.246134 -86.751204 4 36.029367 -86.935290

We can see the relative location of the Nashville chickens using longitude for the x-axis and latitude for the y-axis. Next, now you'll up your game by plotting the locations on an actual map!

Geometries and shapefiles

• Shapefiles
  • stores a special type of data known as geometry
    • Point
    • Line
    • Polygon
  • .shp : contains the geometry
  • .dbf : holds attributes for each geometry
  • .shx : links the attributes to the geometry

Creating a GeoDataFrame & examining the geometry

Let's see where service districts are in Nashville.

Note: if you use anaconda on windows, you must be faced in GDAL error. At that case, follow the instruction here

import geopandas as gpd # Read in the services district shapefile and look at the first few rows service_district = gpd.read_file('./dataset/ServiceDistricts.shp') print(service_district.head()) # Print the contents of the service districts geometry in the first row # print(service_district.loc[0, 'geometry'])

OBJECTID AREA_SQ_MI Name \ 0 0 198 Urban Services District 1 4 327 General Services District geometry 0 POLYGON ((1766078.549 711757.270, 1766000.028 ... 1 MULTIPOLYGON (((1800630.485 619627.187, 180060...

The geometry field holds a series of latitude/longitude pairs that define the border of a polygon. Printing a geometry field gives all of the latitude/longitude pairs that make up the polygon boundary.

Plotting shapefile polygons

The next step is to show the map of polygons.

service_district.to_crs(epsg=4326).plot(figsize=(10, 10)); # Plot the Service Districts, color them according to name, and show a legend service_district.to_crs(epsg=4326).plot(column='Name', legend=True, figsize=(10, 10));

Scatterplots over polygons

Plotting points over polygons - part 1

Make a basic plot of the service districts with the chicken locations. The packages needed have already been imported for you. The chickens DataFrame and service_district GeoDataFrame are in your workspace.