Source code for taxbrain.utils

Helper functions for the various taxbrain modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as ticker
from collections import defaultdict
from typing import Union, Tuple

import taxcalc as tc
from .typing import ParamToolsAdjustment, TaxcalcReform, PlotColors

[docs]def weighted_sum(df, var, wt="s006"): """ Return the weighted sum of specified variable Parameters ---------- df: Pandas DataFrame data overwhich to compute weighted sum var: str variable name from df for which to computer weighted sum wt: str name of weight variable in df Returns ------- float weighted sum """ return (df[var] * df[wt]).sum()
[docs]def distribution_plot( tb, year: int, figsize: Tuple[Union[int, float], Union[int, float]] = (6, 4), title: str = "default", include_text: bool = False ): """ Create a horizontal bar chart to display the distributional change in after tax income Parameters ---------- tb: TaxBrain object TaxBrain object for analysis year: int year to report distribution for figsize: tuple representing the size of the figure (width, height) in inches title: str title for plot include_text: bool whether to include text for labels Returns ------- fig: Matplotlib.pyplot figure object distribution plot """ def find_percs(data, group): """ Find the percentage of people in the data set that saw their income change by the given percentages """ pop = data["s006"].sum() large_pos_chng = data["s006"][data["pct_change"] > 5].sum() / pop small_pos_chng = data["s006"][(data["pct_change"] <= 5) & (data["pct_change"] > 1)].sum() / pop small_chng = data["s006"][(data["pct_change"] <= 1) & (data["pct_change"] >= -1)].sum() / pop small_neg_change = data["s006"][(data["pct_change"] < -1) & (data["pct_change"] > -5)].sum() / pop large_neg_change = data["s006"][data["pct_change"] < -5].sum() / pop return ( large_pos_chng, small_pos_chng, small_chng, small_neg_change, large_neg_change ) # extract needed data from the TaxBrain object ati_data = pd.DataFrame( {"base": tb.base_data[year]["aftertax_income"], "reform": tb.reform_data[year]["aftertax_income"], "s006": tb.base_data[year]["s006"]} ) ati_data["diff"] = ati_data["reform"] - ati_data["base"] ati_data["pct_change"] = (ati_data["diff"] / ati_data["base"]) * 100 ati_data = ati_data.fillna(0.) # fill in NaNs for graphing # group tupules: (low income, high income, income group name) groups = [ (-9e99, 9e99, "All"), (1e6, 9e99, "$1M or More"), (500000, 1e6, "$500K-1M"), (200000, 500000, "$200K-500K"), (100000, 200000, "$100K-200K"), (75000, 100000, "$75K-100K"), (50000, 75000, "$50K-75K"), (40000, 50000, "$40K-50K"), (30000, 40000, "$30K-40K"), (20000, 30000, "$20K-30K"), (10000, 20000, "$10K-20K"), (-9e99, 10000, "Less than $10K") ] plot_data = defaultdict(list) # traverse list in reverse to get the axis of the plot in correct order for low, high, grp in groups: # find income changes by group sub_data = ati_data[(ati_data["base"] <= high) & (ati_data["base"] > low)] results = find_percs(sub_data, grp) plot_data[grp] = results legend_labels = [ "Increase of > 5%", "Increase 1-5%", "Change < 1%", "Decrease of 1-5%", "Decrease > 5%" ] labels = list(plot_data.keys()) data = np.array(list(plot_data.values())) data_cumsum = data.cumsum(axis=1) category_colors = plt.get_cmap("GnBu")( np.linspace(0.15, 0.85, data.shape[1])) fig, ax = plt.subplots(figsize=figsize) ax.invert_yaxis() ax.set_xlim(0, np.sum(data, axis=1).max()) for i, (colname, color) in enumerate(zip(legend_labels, category_colors)): widths = data[:, i] starts = data_cumsum[:, i] - widths ax.barh(labels, widths, left=starts, height=0.9, label=colname, color=color) if include_text: # add text label xcenters = starts + widths / 2 r, g, b, _ = color text_color = "white" if r * g * b < 0.5 else "darkgrey" for y, (x, c) in enumerate(zip(xcenters, widths)): ax.text(x, y, f"{c * 100:.1f}%", ha="center", va="center", color=text_color) ax.legend(bbox_to_anchor=(1, 1), loc="upper left", fontsize="small") ax.set_xlabel("Portion of Bin", fontweight="bold") ax.set_ylabel("Expanded Income Bin", fontweight="bold") ax.get_xaxis().set_major_formatter( mpl.ticker.FuncFormatter(lambda x, p: format(f'{int(x * 100)}%')) ) if title == "default": title = f"Percentage Change In After Tax Income - {year}" ax.set_title(title) ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.tick_params(axis="y", which="both", length=0, pad=15) return fig
[docs]def differences_plot( tb, tax_type: str, figsize: Tuple[Union[int, float], Union[int, float]] = (6, 4), title: str = "default" ): """ Create a bar chart that shows the change in total liability for a given tax Parameters ---------- tb: TaxBrain object TaxBrain object for analysis tax_type: str tax for which to show the change in liability options: 'income', 'payroll', 'combined' figsize: tuple representing the size of the figure (width, height) in inches title: str title for plot Returns ------- fig: Matplotlib.pyplot figure object differences plot """ def axis_formatter(x, p): if x >= 0: return f"${x * 1e-9:,.2f}b" else: return f"-${x * 1e-9:,.2f}b" acceptable_taxes = ["income", "payroll", "combined"] msg = f"tax_type must be one of the following: {acceptable_taxes}" assert tax_type in acceptable_taxes, msg # find change in each tax variable tax_vars = ["iitax", "payrolltax", "combined"] agg_base = tb.multi_var_table(tax_vars, "base") agg_reform = tb.multi_var_table(tax_vars, "reform") agg_diff = agg_reform - agg_base # transpose agg_diff to make plotting easier plot_data = agg_diff.transpose() tax_var = tax_vars[acceptable_taxes.index(tax_type)] plot_data["color"] = np.where(plot_data[tax_var] < 0, "red", "blue") fig, ax = plt.subplots(figsize=figsize) ax.grid(True, axis='y', alpha=0.55) ax.set_axisbelow(True) plot_data.index, plot_data["combined"], alpha=0.55, color=plot_data["color"] ) if title == "default": title = f"Change in Aggregate {tax_type.title()} Tax Liability" ax.set_title(title) ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.get_yaxis().set_major_formatter( mpl.ticker.FuncFormatter(axis_formatter) ) ax.xaxis.set_ticks(list(plot_data.index)) ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter(useOffset=False)) return fig
[docs]def update_policy( policy_obj: tc.Policy, reform: Union[TaxcalcReform, ParamToolsAdjustment], **kwargs ): """ Convenience method that updates the Policy object with the reform dict using the appropriate method, given the reform format. Parameters ---------- policy_obj: Tax-Calculator Policy class object Policy object for tax parameterization used for analysis reform: str or dict parameters for tax policy Returns ------- None modifies the Policy object """ if is_paramtools_format(reform): policy_obj.adjust(reform, **kwargs) else: policy_obj.implement_reform(reform, **kwargs)
[docs]def is_paramtools_format(reform: Union[TaxcalcReform, ParamToolsAdjustment]): """ Check first item in reform to determine if it is using the ParamTools adjustment or the Tax-Calculator reform format. If first item is a dict, then it is likely be a Tax-Calculator reform: { param: {2020: 1000} } Otherwise, it is likely to be a ParamTools format. Parameters ---------- reform: str or dict parameters for tax policy Returns ------- bool True if reform is likely to be in ParamTools format """ for param, data in reform.items(): if isinstance(data, dict): return False # taxcalc reform else: # Not doing a specific check to see if the value is a list # since it could be a list or just a scalar value. return True
[docs]def lorenz_data(tb, year: int, var: str = "aftertax_income"): """ Pull data used for the lorenz curve plot Parameters ---------- tb: TaxBrain class object TaxBrain object for analysis year: int year of data to use var: str name of the variable to use Returns ------- final_data: Pandas DataFrame DataFrame with Lorenz curve for baseline and reform """ data = pd.DataFrame({ "base": tb.base_data[year][var], "reform": tb.reform_data[year][var], "wt": tb.base_data[year]["s006"] }) data["wt_base"] = data["base"] * data["wt"] data["wt_reform"] = data["reform"] * data["wt"] data.sort_values("base", inplace=True) data["cwt"] = data["wt"].cumsum() data['percentile'] = data["cwt"] / data["wt"].sum() # each bin has 1% of the population _bins = np.arange(0, 1.01, step=0.01) data["bin"] = pd.cut(data['percentile'], bins=_bins) gdf = data.groupby("bin") base = gdf["wt_base"].sum() base = np.where(base < 0, 0, base) reform = gdf["wt_reform"].sum() reform = np.where(reform < 0, 0, reform) final_data = pd.DataFrame({ "Base": base.cumsum() / data["wt_base"].sum(), "Reform": reform.cumsum() / data["wt_reform"].sum(), "Population": gdf["wt"].sum().cumsum() / data["wt"].sum() }) return final_data
[docs]def lorenz_curve( tb, year: int, var: str = "aftertax_income", figsize: Tuple[Union[int, float], Union[int, float]] = (6, 4), xlabel: str = "Cummulative Percentage of Tax Units", ylabel: str = "Cummulative Percentage of Income", base_color: PlotColors = "blue", base_linestyle: str = "-", reform_color: PlotColors = "red", reform_linestyle: str = "--", dpi: Union[int, float] = 100 ): """ Generate a Lorenz Curve Parameters ---------- tb: TaxBrain class object TaxBrain object for analysis year: int year of data you want to use for the lorenz curve var: str name of the variable to use figsize: tuple representing the size of the figure (width, height) in inches xlabel: str x axis label ylabel: str y axis label base_color: str color used for the base line base_linestyle: str linestyle for the base line reform_color: str color used for the reform line reform_linestyle: str linestyle for the reform line dpi: int dots per inch in the figure image Returns ------- None """ plot_data = lorenz_data(tb, year, var) fig, ax = plt.subplots(figsize=figsize) ax.plot([0, 1], [0, 1], c="black", alpha=0.5) # 45 degree line ax.plot( plot_data["Population"], plot_data["Base"], c=base_color, linestyle=base_linestyle, label="Base" ) ax.plot( plot_data["Population"], plot_data["Reform"], c=reform_color, linestyle=reform_linestyle, label="Reform" ) ax.legend(loc="upper left") ax.set_xlabel(xlabel, fontweight="bold") ax.set_ylabel(ylabel, fontweight="bold") ax.set_xlim(0, 1) ax.set_ylim(0, 1) return fig
[docs]def volcano_plot( tb, year: int, y_var: str = "expanded_income", x_var: str = "combined", min_y: Union[int, float] = 0.01, max_y: Union[int, float] = 9e99, log_scale: bool = True, increase_color: PlotColors = "#F15FE4", decrease_color: PlotColors = "#41D6C2", dotsize: Union[int, float] = .75, alpha: float = 0.5, figsize: Tuple[Union[int, float], Union[int, float]] = (6, 4), dpi: Union[int, float] = 100, xlabel: str = "Change in Tax Liability", ylabel: str = "Expanded Income" ): """ Create a volcano plot to show change in tax tax liability Parameters ---------- tb: TaxBrain class object TaxBrain object for analysis year: int year for the plot min_y: float minimum amount for the y variable to be included in the plot max_y: float maximum amount for the y variable to be included in the plot y_var: str variable on the y axis x_var: str variable on the x axis log_scale: bool whether the y-axis should use a log scale. If this is true, min_inc must be >= 0 increase_color: str color to use for dots when x increases decrease_color: str color to use for dots when x decrease dotsize: int size of the dots in the scatter plot alpha: float attribute for transparency of the dots figsize: tuple the figure size of the plot (width, height) in inches dpi: int dots per inch in the figure xlabel: str label on the x axis ylabel: str label on the y axis Returns ------- fig: Matplotlib.pyplot figure object volcano plot figure """ def log_axis(x, pos): """ Converts y-axis log values """ return f"${np.exp(x):,.0f}" def axis_formatter(x, pos): if x >= 0: return f"${x:,.0f}" else: return f"-${abs(x):,.0f}" if log_scale and min_y < 0: msg = "`min_y` must be >= 0 when `log_scale` is true" raise ValueError(msg) _y = tb.base_data[year][y_var] _x_change = tb.reform_data[year][x_var] - tb.base_data[year][x_var] mask = np.logical_and(_y >= min_y, _y <= max_y) y = _y[mask] x_change = _x_change[mask] colors = [increase_color if x >= 0 else decrease_color for x in x_change] xformatter = ticker.FuncFormatter(axis_formatter) yformatter = ticker.FuncFormatter(axis_formatter) if log_scale: yformatter = ticker.FuncFormatter(log_axis) y = np.log(y) fig, ax = plt.subplots(figsize=figsize) ax.scatter(x_change, y, c=colors, s=dotsize, alpha=alpha) ax.axvline(0, color='black', alpha=0.5) ax.grid(True, linestyle="--") ax.xaxis.set_major_formatter(xformatter) ax.xaxis.set_tick_params(rotation=25) ax.yaxis.set_major_formatter(yformatter) ax.set_xlabel(xlabel, fontweight="bold") ax.set_ylabel(ylabel, fontweight="bold") return fig
def revenue_plot( tb, tax_vars: list = ["iitax", "payrolltax", "combined"], figsize: Tuple[Union[int, float], Union[int, float]] = (6, 4) ): """Plot the changes in tax revenue from a given reform Parameters ---------- tb : TaxBrain class object TaxBrain object for analysis tax_vars: list List of tax varaibles to include on the graph """ def axis_formatter(x, p): if x >= 0: return f"${x * 1e-9:,.2f}" else: return f"-${x * 1e-9:,.2f}" assert tax_vars, "`tax_vars` must contain at least one tax variable" for var in tax_vars: if var not in ["iitax", "payrolltax", "combined"]: msg = ( f"`{var}` is invalid. Valid tax variables are " "`iitax`, `payrolltax`, `combined`" ) raise ValueError(msg) label_map = { "iitax": "Income", "payrolltax": "Payroll", "combined": "Combined" } color_map = { "Income: Base": "#12719e", "Income: Reform": "#73bfe2", "Payroll: Base": "#408941", "Payroll: Reform": "#98cf90", "Combined: Base": "#a4201d", "Combined: Reform": "#e9807d" } base_data = tb.multi_var_table(tax_vars, "base", include_total=False) reform_data = tb.multi_var_table(tax_vars, "reform", include_total=False) fig, ax = plt.subplots(figsize=figsize) years = base_data.columns for tax in tax_vars: base_label = f"{label_map[tax]}: Base" reform_label = f"{label_map[tax]}: Reform" ax.plot( years, base_data.loc[tax], label=base_label, color=color_map[base_label] ) ax.plot( years, reform_data.loc[tax], label=reform_label, color=color_map[reform_label] ) ax.legend(loc='upper right', bbox_to_anchor=(1.40, 1), title="Tax Type") ax.set_ylabel("Tax Liability (Billions)") ax.set_title("Tax Liability by Year") # remove plot borders ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # convert y axis to billions ax.get_yaxis().set_major_formatter( mpl.ticker.FuncFormatter(axis_formatter) ) return fig