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()

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

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

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

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.