import pandas as pd
import taxcalc as tc
import microdf as mdf
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
Chart options.
mdf.set_plot_style()
## Generate data
base = mdf.calc_df(group_vars=['expanded_income', 'MARS'],
metric_vars=['aftertax_income', 'XTOT'])
base.columns
Define a reform that treats capital gains as ordinary income and sets the top marginal rate to 70%.
CG_REFORM = {
'CG_nodiff': {2019: True},
'II_rt7': {2019: 0.7}
}
reform = mdf.calc_df(reform=CG_REFORM, group_vars=['MARS'], group_n65=True,
metric_vars=['aftertax_income', 'XTOT'])
reform.columns
This example requires `taxcalc` to be installed.
## Setup
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import microdf as mdf
mdf.set_plot_style()
## Prepare data
base = mdf.calc_df(group_vars=['expanded_income', 'MARS'],
metric_vars=['aftertax_income', 'XTOT'])
reform = base.copy(deep=True)
UBI_PP = 10000
reform['ubi'] = reform.XTOT * UBI_PP
reform['aftertax_income'] = reform.aftertax_income + reform.ubi
mdf.add_weighted_metrics(reform, 'aftertax_income')
## Charts
### Change to income percentiles
ax = mdf.quantile_chg_plot(base.aftertax_income, reform.aftertax_income,
base.XTOT_m, reform.XTOT_m)
plt.show()
def test_calc_df():
if not _HAVE_TAXCALC:
pytest.skip("taxcalc is not installed")
df = mdf.calc_df()
# Income measures
## Setup
import numpy as np
import pandas as pd
import taxcalc as tc
import microdf as mdf
tc.__version__
## Load data
Start with a `DataFrame` with `expanded_income` and the variables in `expanded_income` excluded from `tpc_eci`.
df = mdf.calc_df(group_vars=['expanded_income', 'wic_ben', 'housing_ben',
'vet_ben', 'mcare_ben', 'mcaid_ben'],
metric_vars=['XTOT'])
Calculate `tpc_eci`.
df['tpc_eci'] = mdf.tpc_eci(df)
df.head()