def plot_states(states, values, title="", legend="", colors=DEFAULT_COLORS, complements=DEFAULT_COMPLEMENTS, font={}, spacing={}, decimals=1, shape="hex", quantile=False): states = [_get_state(state) for state in states] states, values = zip(*[(state, value) for state, value in zip(states, values) if state in STATE_ABBREVS.values()]) if quantile: values = pd.qcut(values, 6, [-20, -10, -1, 1, 10, 20]) # Colors cbin = Colorbin(values, colors, proportional=True, decimals=None) cbin.set_decimals(decimals) cbin.recalc(fenceposts=True) cbin.calc_complements(0.0, *complements) # Choropleth cg = Chorogrid(STATE_FILEPATH, states, cbin.colors_out) cg.set_title(title, font_dict=font) if quantile: labels = _quantile_labels(len(colors)) else: labels = cbin.labels cg.set_legend(cbin.colors_in, labels, title=legend) # Draw draw_fn = { "hex": cg.draw_hex, "square": cg.draw_squares, "multihex": cg.draw_multihex, "multisquare": cg.draw_multisquare, "map": cg.draw_map }[shape] draw_fn(spacing_dict=spacing) return cg.done(show=True)
def plot_counties(fips, values, title="", legend="", colors=DEFAULT_COLORS, complements=DEFAULT_COMPLEMENTS, font={}, spacing={}, decimals=1, quantile=False, statelines=False): fips, values = zip(*[(fip, value) for fip, value in zip(fips, values) if fip in COUNTY_IDS]) if quantile: values = pd.qcut(values, 6, [-20, -10, -1, 1, 10, 20]) # Colors cbin = Colorbin(values, colors, proportional=True, decimals=None) cbin.set_decimals(decimals) cbin.recalc(fenceposts=True) cbin.calc_complements(0.0, *complements) # Choropleth cg = Chorogrid(COUNTY_FILEPATH, fips, cbin.colors_out, id_column="fips_integer") cg.set_title(title, font_dict=font) if quantile: labels = _quantile_labels(len(colors)) else: labels = cbin.labels cg.set_legend(cbin.colors_in, labels, title=legend) # Draw cg.draw_map(spacing_dict=spacing) if statelines: with open(STATELINES_FILEPATH, 'r') as f: statelines = f.read() cg.add_svg(statelines) return cg.done(show=True)
listings['Year'] = listings.dates_object.map(lambda x: x.year) ## data visualization -------------------------------------------------------------------------------------- # 1.1 number of loans issued by states, normalized by state population - choropleth map counts = listings.groupby(['BorrowerState']).ListingAmountFunded.count() population.index = population['state'] df = population.join(counts, how = 'right') df['percentage'] = df['ListingAmountFunded']/df['population']*10000 mycolors = ['#e0e9f0', '#c1d4e2', '#a3bed4', '#84a9c6','#6694b8', '#517693', '#3d586e'] mybin = Colorbin(df['percentage'], mycolors, proportional=True, decimals=None) mybin.set_decimals(1) mybin.recalc(fenceposts=True) mybin.fenceposts mybin.calc_complements(0.5, '#e0e0e0', '#101010') states = list(df.index) colors_by_state = mybin.colors_out font_colors_by_state = mybin.complements legend_colors = mybin.colors_in legend_labels = mybin.labels cg = Chorogrid('chorogrid/databases/usa_states.csv', states, colors_by_state) cg.set_title('Number of P2P Loans Normalized by State Population', font_dict={'font-size': 16}) cg.set_legend(legend_colors, legend_labels, title='# of Loans scaled by population') cg.draw_hex(spacing_dict={'margin_right': 210}, font_colors=font_colors_by_state) cg.done(show=True) # 1.2 number of loans issued by states - choropleth map df = listings.groupby(['BorrowerState']).ListingAmountFunded.count() mycolors = ['#e0e9f0', '#c1d4e2', '#a3bed4', '#84a9c6','#6694b8', '#517693', '#3d586e']