andersonfrailey

andersonfrailey

andersonfrailey

import turtle
import random


def draw_s(t, size=100, start=(0, 0), rel_gap=2):
    """
    Draw the 90's S using turtle.
    Parameters
    ----------
    t: turtle object

andersonfrailey

# list to hold plot objects
plot_list = []

# loop through each year, creating a plot
for year in range(2018, 2028):
    # advance calculators a year
    calc.advance_to_year(year)
    calc.calc_all()
    calc_base.advance_to_year(year)
    calc_base.calc_all()

andersonfrailey

def plot(cds_list, year):
    def ticker():
        """
        function used to create the y-axis labels in the plot
        """
        if tick == 25:
            return 'All Taxpayers'
        elif tick == 21:
            return '$1M or more'
        elif tick == 18.75:

andersonfrailey

def find_perc(data, bin_name):
    # find percentatge for each type of effect
    sum_wt = data.s006.sum()
    # tax liability decrease of > $500
    large_cut = tc.zsum(data.s006[data['change'] < -500]) / float(sum_wt)
    # tax liability decrease of $100-500
    small_cut = tc.zsum(data.s006[(data['change'] >= -500) & (data['change'] <= -100)]) / float(sum_wt)
    # tax liability change of less than $100
    no_change = tc.zsum(data.s006[(data['change'] > -100) & (data['change'] < 100)]) / float(sum_wt)
    # tax liability increase of $100-500

andersonfrailey

rec = tc.Records(data='../Tax-Calculator/puf.csv')
pol = tc.Policy()
calc_base = tc.Calculator(records=rec, policy=pol)
calc_base.advance_to_year(2018)
calc_base.calc_all()

records = tc.Records(data='../Tax-Calculator/puf.csv')
policy = tc.Policy()
calc = tc.Calculator(records=records, policy=policy)

andersonfrailey

from bokeh.plotting import figure
from bokeh.io import show, output_notebook
from bokeh.models import ColumnDataSource, Legend, FuncTickFormatter, NumeralTickFormatter
from bokeh.layouts import column
import taxcalc as tc
import pandas as pd
import numpy as np
import copy
output_notebook()

andersonfrailey

"""
This script retreaves information on homeless services facilities from
http://opendata.dc.gov/datasets/homeless-service-facilities
"""
import requests
import pandas as pd

api_url = 'https://maps2.dcgis.dc.gov/dcgis/rest/services/DCGIS_DATA/Public_Service_WebMercator/MapServer/6/query?where=1%3D1&outFields=*&outSR=4326&f=json'
raw_data = requests.get(api_url)