def __init__(self, files):
"""
==Parameters==
|files| : entered in as a list
"""
self.self = self
self.files = files
cpi.update()
def __init__(self):
self.self = self
self.files = [
'/Users/jenniferpaige/code/DSI/getit/TTS_10-Dec-2019_p1.csv',
'/Users/jenniferpaige/code/DSI/getit/TTS_10-Dec-2019_p2.csv'
]
cpi.update()
def inflation_price_adjustment(price, base_year, target_year):
if cpi.LATEST_YEAR < target_year:
logger.info("Updating CPI data")
cpi.update()
if cpi.LATEST_YEAR < target_year:
raise ValueError(
f"CPI data are only available through {cpi.LATEST_YEAR}. Your target year is "
f"{target_year}"
)
price = price * cpi.inflate(1, base_year, to=target_year)
return price
def _normalize_census_data(df: pd.DataFrame) -> pd.DataFrame:
# Adjust for inflation
cpi.update()
df['home_value'] = df.apply(
lambda x: cpi.inflate(x['home_value'], x['year']), axis=1)
df['household_income'] = df.apply(
lambda x: cpi.inflate(x['household_income'], x['year']), axis=1)
# Normalize USD amounts
grouped = df.groupby('geoid')
for col in ('household_income', 'home_value'):
df[f'relative_{col}'] = grouped[col].transform(lambda s: s / s.iloc[0])
# Normalize population composition
population = df['population']
for col in df.columns:
if col.startswith('pop_'):
df[col] /= population
return df
import pandas as pd
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import plotly.graph_objects as go
import cpi
cpi.update()
from app import app
dis = pd.read_csv('Assets/Discretionary Outlays - Sheet1.csv')
# Remove newline char from columns
dis.columns = [col.replace("\n", "") for col in dis.columns]
# Set index to years
dis = dis.set_index('Year')
mand = pd.read_csv('Assets/Mandatory Outlays - Sheet1.csv')
# Remove newline char from columns
mand.columns = [col.replace("\n", "") for col in mand.columns]
# Set index to years
mand = mand.set_index('Year')
# Fill n.a.
mand['Medicarea'] = mand['Medicarea'].replace('n.a.', 0)
new_dis = dis.drop(columns=['Total'])
new_mand = mand.drop(
columns=['Total', 'Memorandum: Major Health Care Programs (Net)c'])
spend = new_dis.join(new_mand, how='inner')