def plot_cloropleth_map(ls,interest,leg,year): mycolors = ['#b35806', '#f1a340', '#fee0b6', '#d8daeb', '#998ec3', '#542788'] mybin = Colorbin(ls, mycolors, proportional=True, decimals=None) state_info = pd.read_csv('chorogrid/databases/usa_states.csv') 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(str(year)+' H1b '+str(interest), font_dict={'font-size': 19}) cg.set_legend(legend_colors, legend_labels, title=leg) #cg = Chorogrid('chorogrid/databases/usa_states.csv', states, colors_by_state) cg.draw_map(spacing_dict={'legend_offset': [-150,-25]}) #cg.draw_squares(spacing_dict={'margin_right': 150}) cg.done(show=False, save_filename = str(year)+' H1b '+str(interest))
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)
dfStateUN = pd.DataFrame(list(dictStateUN.items()),columns=['State', 'Unemployment']) ##Make the Map colors = ['#fff5eb', '#fee6ce', '#fdd0a2', '#fdae6b', '#fd8d3c', '#f16913', '#d94801', '#8c2d04'] chBin = Colorbin(dfStateUN['Unemployment'], colors, proportional=True, decimals=None) chBin.set_decimals(1) chBin.recalc(fenceposts=True) chBin.fenceposts colors_out = chBin.colors_out legend_colors = chBin.colors_in legend_labels = chBin.labels cg = Chorogrid('X:\\Documents\\Research\\Tools\\Python\\chorogrid-master\\chorogrid\\databases\\usa_states.csv', list(dfStateUN['State']), colors_out) cg.set_title('Average Unemployment by State (%)', font_dict={'font-size': 16}) cg.set_legend(legend_colors, legend_labels, title='% Unemployment') cg.draw_map(spacing_dict={'margin_right': 400}) cg.done(show=True) ####### Import the LC Data ####### path = 'D:\\Andrew\\VM_Storage\\LendingClub\\LoanStats3.csv' dfListings = pd.read_csv(path) cat_names = {'Fully Paid': 1, 'Charged Off': 0, 'Current': 1, 'Late (31-120 days)': 0, 'In Grace Period': 1, 'Late (16-30 days)': 0, 'Default': 0, } dfListings['status_cat'] = dfListings['loan_status'].map(cat_names) dfListings = dfListings [~dfListings['status_cat'].isnull()] grpState = dfListings.groupby('addr_state') tsNrLoansState = grpState['status_cat'].size()
chBin = Colorbin(dfStateUN['Unemployment'], colors, proportional=True, decimals=None) chBin.set_decimals(1) chBin.recalc(fenceposts=True) chBin.fenceposts colors_out = chBin.colors_out legend_colors = chBin.colors_in legend_labels = chBin.labels cg = Chorogrid( 'X:\\Documents\\Research\\Tools\\Python\\chorogrid-master\\chorogrid\\databases\\usa_states.csv', list(dfStateUN['State']), colors_out) cg.set_title('Average Unemployment by State (%)', font_dict={'font-size': 16}) cg.set_legend(legend_colors, legend_labels, title='% Unemployment') cg.draw_map(spacing_dict={'margin_right': 400}) cg.done(show=True) ####### Import the LC Data ####### path = 'D:\\Andrew\\VM_Storage\\LendingClub\\LoanStats3.csv' dfListings = pd.read_csv(path) cat_names = { 'Fully Paid': 1, 'Charged Off': 0, 'Current': 1, 'Late (31-120 days)': 0, 'In Grace Period': 1, 'Late (16-30 days)': 0,