Choropleth maps

import pandas as pd                         # pandas for data management
import geopandas                            # geopandas for maps work
import matplotlib.pyplot as plt             # matplotlib for plotting details

# Load the shape file
world = geopandas.read_file('ne_50m_admin_0_countries.zip')
world.head(2)

world['gdp_cap'] = world['GDP_MD']/world['POP_EST']
world['gdp_cap'].describe()

count    240.000000
mean       0.019169
std        0.030987
min       -0.120000
25%        0.002526
50%        0.007635
75%        0.023559
max        0.200000
Name: gdp_cap, dtype: float64

world[world['NAME']=='United States of America'][['NAME','gdp_cap','GDP_MD','POP_EST']]

world['gdp_cap'] = world['GDP_MD']/world['POP_EST']*1000000
print('US GDP per capita is {0:,.0f}'.format(float(world[world['NAME']=='United States of America']['gdp_cap'])))

US GDP per capita is 65,298

world = world.sort_values('gdp_cap', ascending=False)
world.loc[:, ['NAME', 'gdp_cap']]

world[world['POP_EST']<50000].head(10)

fig, gax = plt.subplots(figsize=(15,5))

world[world['POP_EST']>50000].plot(ax = gax, column='gdp_cap', edgecolor='black', cmap = 'Blues', legend=True)

plt.axis('off')

plt.show()

# import the make_axes_locatable method from matplotlib
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, map_ax = plt.subplots(figsize=(15,10))

# Adapted from https://geopandas.org/en/stable/docs/user_guide/mapping.html

# Create an axis divider object. It allows us to add another axis to the current one (map_ax).
divider = make_axes_locatable(map_ax)

# Add a second axis to the right of the original axes. 
# Make the new axes 3% of the original and add some padding between them.
legend_ax = divider.append_axes('right', size='3%', pad=0.1)


# The plot command works like normal, but: we plot the map on the map_ax (using the ax option)
# and we plot the legend on the legend_ax (using the cax option).
world[world['POP_EST']>50000].plot(
                                   ax = map_ax,                          # give ax the axes for the map
                                   column='gdp_cap',                     # the quantitative column
                                   edgecolor='black',                    # set the edge color
                                   cmap = 'Reds',                        # set the color map
                                   legend=True, cax=legend_ax)           # give cax the legend axes


# Here I ony turn off the axis of the main plot
map_ax.axis('off')

plt.show()

# Read in the counties shape file.
counties = geopandas.read_file('cb_2021_us_county_5m.zip')

# WI fips code is 55. Keep just the WI counties.
wi_counties = counties[counties['STATEFP']=='55'].copy()
wi_counties.head(2)

fig, gax = plt.subplots(figsize=(10,10))
wi_counties.plot(ax=gax, edgecolor='black', color = 'white')
plt.axis('off')
plt.show()

# Read in the counties shape file.
counties = geopandas.read_file('cb_2021_us_county_5m.zip')

# WI fips code is 55. Keep just the WI counties.
wi_counties = counties[counties['STATEFP']=='55'].copy()
wi_counties.head(2)

fig, gax = plt.subplots(figsize=(10,10))
wi_counties.plot(ax=gax, edgecolor='black', color = 'white')
plt.axis('off')
plt.show()

results = pd.read_csv('results.csv', thousands=',')
results.head()

results.dtypes

county    object
total      int64
biden      int64
trump      int64
dtype: object

# First letter cap only
results['county'] = results['county'].str.title()
results['county'] = results['county'].str.strip()
results.head(1)

# Kill any leading or trailing whitespace
wi_counties['NAME']=wi_counties['NAME'].str.title()
wi_counties['NAME']=wi_counties['NAME'].str.strip()

# Merge the two data sets. 
res_w_states = pd.merge(left=wi_counties, right=results, left_on='NAME', right_on='county', how='outer', indicator=True)
res_w_states.head(2)

res_w_states['_merge'].value_counts()

both          72
left_only      0
right_only     0
Name: _merge, dtype: int64

res_w_states['trump_share'] =res_w_states['trump']/res_w_states['total']
res_w_states.head(2)

fig, gax = plt.subplots(figsize = (10,8))

# Plot the counties and pass 'trump_share' as the data to color
res_w_states.plot(ax=gax, edgecolor='black', column='trump_share',  legend=True, cmap='bwr', vmin=0, vmax=1)

plt.axis('off')

plt.show()

fig, gax = plt.subplots(figsize = (10,8))

# Plot the counties and pass 'trump_share' as the data to color
res_w_states.plot(ax=gax, edgecolor='white', column='trump_share',  legend=True, cmap='bwr',
                 legend_kwds={'label':'Republican vote share'}, vmin=0, vmax=1)

plt.axis('off')

plt.show()

cities = geopandas.read_file('cities_4269.zip')
cities.head(1)

fig, gax = plt.subplots(figsize = (10,8))

# Plot the counties and pass 'trump_share' as the data to color
res_w_states.plot(ax=gax, edgecolor='white', column='trump_share',  legend=True, cmap='bwr',
                  legend_kwds={'label':'Republican vote share'}, vmin=0, vmax=1)


cities_for_labels = cities[cities['city'].isin(['Madison', 'Green Bay', 'Milwaukee', 'Kenosha'])]

# Plot the points
cities_for_labels.plot(ax=gax, edgecolor='black', color='white')

# Label the points
for x, y, label in zip(cities_for_labels['geometry'].x, cities_for_labels['geometry'].y, cities_for_labels['city']):
    gax.annotate(label, xy=(x,y), xytext=(3,3), textcoords='offset points')

plt.axis('off')

plt.show()

import matplotlib.patheffects as PathEffects

.annotate(label, xy=(x,y), xytext=(3,3), textcoords='offset points',  
          path_effects=[PathEffects.withStroke(linewidth=3, foreground='white')]
          )

Experiment with different line widths, colors...

import matplotlib.patheffects as PathEffects

fig, gax = plt.subplots(figsize = (10,8))

# Plot the counties and pass 'trump_share' as the data to color
res_w_states.plot(ax=gax, edgecolor='white', column='trump_share',  legend=True, cmap='bwr',
                  legend_kwds={'label':'Republican vote share'}, vmin=0, vmax=1)


cities_for_labels = cities[cities['city'].isin(['Madison', 'Green Bay', 'Milwaukee', 'Kenosha'])]

# Plot the points
cities_for_labels.plot(ax=gax, edgecolor='black', color='white')

# Label the points
for x, y, label in zip(cities_for_labels['geometry'].x, cities_for_labels['geometry'].y, cities_for_labels['city']):
    gax.annotate(label, xy=(x,y), xytext=(3,3), textcoords='offset points',  
                 path_effects=[PathEffects.withStroke(linewidth=5, foreground='white')]
                 )

plt.axis('off')

plt.show()