from datautils import permute_values
import shapefile
import pandas as pd
from mapdrawer import MapDrawer, ShapeFileIterator
regidx = [2, 4, 11, 5, 6, 8, 16, None, 14, 1, 12, 13, 7, None, 3, 9, 10]
stats_folder = "C:/Users/Glenn/Documents/Stats/Housing/"
dfopen = pd.read_csv(stats_folder + "Bonds Lodged by region.csv", skiprows=0)
dfopen.set_index('Month', inplace=True)
dfclose = pd.read_csv(stats_folder + "Bonds Closed by region.csv", skiprows=0)
dfclose.set_index('Month', inplace=True)
df = dfopen.subtract(dfclose)
df.rename(pd.to_datetime, inplace=True)
df = df.groupby(df.index.year).mean()
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator(shp_folder + "REGC2016_GV_Clipped.shp")
shades = permute_values(regidx, df.loc[2016])
print shades
map1 = MapDrawer(dimensions=(475, 480))
img = map1.draw(shp_iter,
shades,
title="Net New Bonds Lodged (2016)",
legend_header="(New Bonds)",
exclude_regions=[17],
colour_profile=((255, 0, 0), (0, 255, 0)))
img.save("net-bonds-lodged-2016.png", "PNG")
stats_folder = "C:/Users/Glenn/Documents/Stats/Housing/"
df_income = pd.read_csv(
"nzis-jun2015qtr-regional.csv",
skiprows=10,
names=['Region', 'Unused', '2011', '2012', '2013', '2014', '2015'],
nrows=13).drop('Unused', axis=1)
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator(shp_folder + "REGC2016_GV_Clipped.shp")
rentals = permute_values(rental_regions, df_rents.iloc[285, 1:])
income = permute_values(income_regions, df_income['2015'])
shades = []
for ii in range(0, len(rentals)):
if income[ii] == None or rentals[ii] == None:
shades.append(None)
else:
shades.append(income[ii] - (rentals[ii]))
print income
print rentals
print shades
map1 = MapDrawer(dimensions=(475, 480))
img = map1.draw(shp_iter,
shades,
title="Median Income After Rent",
legend_header="($)",
use_divisor=False,
exclude_regions=[17],
colour_profile=((0, 255, 0), (255, 0, 0)))
img.save("income-after-rent.png", "PNG")
from datautils import permute_values
import shapefile
import pandas as pd
from mapdrawer import MapDrawer, ShapeFileIterator
#df = pd.read_csv("DPE389701_population-by-region.csv", skiprows=1)
#df.rename(columns={'Unnamed: 0':'Date'}, inplace=True)
regidx = [2, 4, 11, 5, 6, 8, 16, None, 14, 1, 12, 13, 7, None, 3, 9, 10]
stats_folder = "C:/Users/Glenn/Documents/Stats/Housing/"
df = pd.read_csv(stats_folder + "Active Bonds by region.csv", skiprows=0)
df.set_index('Month', inplace=True)
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator(shp_folder + "REGC2016_GV_Clipped.shp")
shades = permute_values(regidx, df.loc['2016-10-01'])
print shades
map1 = MapDrawer(dimensions=(475, 480))
img = map1.draw(shp_iter,
shades,
title="Active Bonds (Oct 2016)",
legend_header="(Qty)",
exclude_regions=[17],
colour_profile=((255, 0, 0), (0, 255, 0)))
img.save("bonds-active.png", "PNG")
import shapefile
import pandas as pd
from mapdrawer import MapDrawer, ShapeFileIterator
from datautils import permute_values
regions = [1,2,3,4,[5,6],7,8,9,[14,15,16,10],11,12,13]
stats_folder = "C:/Users/Glenn/Documents/Stats/Housing/"
df = pd.read_csv("nzis-jun2015qtr-regional.csv", skiprows=10, names=['Region','Unused', '2011', '2012', '2013', '2014', '2015'],nrows=13).drop('Unused', axis=1)
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator( shp_folder + "REGC2016_GV_Clipped.shp")
shades = permute_values(regions, df['2015'])
print shades
map1 = MapDrawer(dimensions=(475,480))
img = map1.draw(shp_iter, shades, title="Median Income (June 2015)", legend_header="($)", exclude_regions=[17], colour_profile=((0,255,0),(255,0,0)))
img.save("income-regional.png", "PNG")
import shapefile
import pandas as pd
from mapdrawer import MapDrawer
#shp_folder = "C:/Users/Glenn/Documents/Stats/ShapeFiles/"
#shpf = shapefile.Reader(shp_folder + "REGC2016_GV_Full.shp")
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shpf = shapefile.Reader(shp_folder + "REGC2016_GV_Clipped.shp")
fields = shpf.fields
for name in fields:
print name
records = shpf.records()
geom = shpf.shapes()
shades = []
polygons = []
bboxes = []
for fidx, feature in enumerate(geom):
cnt = sx = sy = 0
for x, y in feature.points:
sx += x
sy += y
cnt += 1
print(sx / cnt, sy / cnt)
map = MapDrawer()
map.draw(polygons)
dfpop = pd.read_csv("DPE389701_population-by-region.csv", skiprows=1, nrows=21)
dfpop.rename(columns={
'Unnamed: 0': 'Date',
'New Zealand': 'Population'
},
inplace=True)
dfpop = dfpop.set_index('Date')
dfpop.apply(pd.to_numeric)
popidx = [1, 2, 3, 4, 5, 6, 7, 8, 9, None, 14, 16, 10, 11, 12, 13, None]
popvalues = permute_values(popidx, dfpop.loc['2015'])
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator(shp_folder + "REGC2016_GV_Clipped.shp")
bondvalues = permute_values(regidx, df.loc['2016-10-01'])
shades = []
for ii in range(len(popvalues)):
if bondvalues[ii] and popvalues[ii]:
v = float(popvalues[ii]) / bondvalues[ii]
else:
v = None
shades.append(v)
print shades
map1 = MapDrawer(dimensions=(475, 480))
img = map1.draw(shp_iter,
shades,
title="Population per Net Bonds",
legend_header="(Pop/Bond Ratio)",
exclude_regions=[17],
colour_profile=((255, 0, 0), (0, 255, 0)))
img.save("pop-per-net-bonds.png", "PNG")
#shp_folder = "C:/Users/Glenn/Documents/Stats/ShapeFiles/"
#shpf = shapefile.Reader(shp_folder + "REGC2016_GV_Full.shp")
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shpf = shapefile.Reader(shp_folder + "CB2016_GV_Clipped.shp")
fields = shpf.fields
for name in fields:
print name
records = shpf.records()
geom = shpf.shapes()
shades = []
polygons = []
bboxes = []
for fidx, feature in enumerate(geom):
parts = []
for idx in range(len(feature.parts)-1):
pidx1 = feature.parts[idx]
pidx2 = feature.parts[idx+1]
parts.append(feature.points[pidx1:pidx2-1])
parts.append(feature.points[feature.parts[-1]:])
polygons.append(parts)
bboxes.append(feature.bbox)
map1 = MapDrawer()
img = map1.draw(polygons, shades, draw_legend = False, bboxes=bboxes, title="Community Boards")
img.save("community-boards.png", "PNG")
import shapefile
import pandas as pd
from mapdrawer import MapDrawer, ShapeFileIterator
df = pd.read_csv("DPE389701_population-by-region.csv", skiprows=1)
df.rename(columns={'Unnamed: 0': 'Date'}, inplace=True)
regidx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 11, 0, 12, 0]
nztot = 1569900.0 #df.iloc[19,19].astype(float)
shp_folder = "C:/Users/Glenn/Documents/Stats/2016 Digital Boundaries Generalised Clipped/"
shp_iter = ShapeFileIterator(shp_folder + "REGC2016_GV_Clipped.shp")
shades = []
for idx in range(0, len(regidx)):
if regidx[idx] == 0:
v = 0
else:
v = df.iloc[19, regidx[idx]]
shades.append(v)
map1 = MapDrawer()
img = map1.draw(shp_iter,
shades,
title="Population Distribution of NZ (2015)",
legend_header="(million)",
use_divisor=True)
#map1.number_regions()
#img = map1.draw(polygons, shades, bboxes=bboxes, legend_header="(million)")
img.save("nz-pop.png", "PNG")