pip install fredapi -q

import pandas as pd import matplotlib.pyplot as plt import numpy as np import plotly.express as px import os plt.style.use('fivethirtyeight') pd.set_option('max_columns', 500) color_pal = plt.rcParams['axes.prop_cycle'].by_key()['color'] from fredapi import Fred fred_key = os.environ["FRED"]

fred = Fred(api_key = fred_key)

sp_search = fred.search('S&P', order_by = 'popularity')

sp_search.head()

sp500 = fred.get_series(series_id = 'SP500') sp500.plot(figsize = (10,5), title = 'S&P 500', lw = 2)

uemp_results = fred.search('unemployment')

unrate = fred.get_series('UNRATE') unrate.plot()

uemp_df = fred.search('unemployment rate state', filter=('frequency', 'Monthly')) uemp_df

uemp_df = uemp_df.query('seasonal_adjustment == "Seasonally Adjusted" and units == "Percent" and observation_start == "1976-01-01 00:00:00"') uemp_df

uemp_df = uemp_df.loc[uemp_df['title'].str.contains('Unemployment Rate')] uemp_df

all_results = [] for myid in uemp_df.index: results = fred.get_series(myid) results = results.to_frame(name=myid) all_results.append(results)

uemp_states = pd.concat(all_results, axis=1).drop(['CSOUUR', 'CMWRUR', 'CWSTUR', 'CNERUR', 'CNEWUR'], axis=1)

uemp_states = uemp_states.dropna()

#plot states uemployment rate px.line(uemp_states)

ax= uemp_states.loc[uemp_states.index == '2020-05-01'].T.sort_values('2020-05-01').plot(kind='barh', figsize=(8,12), width=0.7, edgecolor='black', title='Unemployment rate by state, april May 2020') ax.legend().remove() plt.show()

id_to_state = uemp_df['title'].str.replace('Unemployment Rate in ','').to_dict()

uemp_states.columns = [id_to_state[c] for c in uemp_states.columns]

part_df = fred.search('participation rate by state', filter=('frequency', 'Monthly')) part_df = part_df.query('seasonal_adjustment == "Seasonally Adjusted" and units == "Percent" and observation_start == "1976-01-01 00:00:00"') part_df

part_id_to_state = part_df['title'].str.replace('Labor Force Participation Rate for ', '').to_dict()

all_results = [] for myid in part_df.index: results = fred.get_series(myid) results = results.to_frame(name=myid) all_results.append(results) time.sleep(0.1) # Don't request too fast and get blocked part_states = pd.concat(all_results, axis=1) part_states.columns = [part_id_to_state[c] for c in part_states.columns]

part_states

fig, axs = plt.subplots(10, 5, figsize=(30, 30), sharex=True) axs = axs.flatten() i = 0 for state in uemp_states.columns: if state in ["District Of Columbia","Puerto Rico"]: continue ax2 = axs[i].twinx() uemp_states.query('index >= 2020 and index < 2022')[state] \ .plot(ax=axs[i], label='Unemployment') part_states.query('index >= 2020 and index < 2022')[state] \ .plot(ax=ax2, label='Participation', color=color_pal[1]) ax2.grid(False) axs[i].set_title(state) i += 1 plt.tight_layout() plt.show()

#Fix DC column uemp_states = uemp_states.rename(columns={'the District of Columbia' : 'District of Columbia'})

state = 'California' fig, ax = plt.subplots(figsize=(10, 5), sharex=True) ax2 = ax.twinx() uemp_states2 = uemp_states.asfreq('MS') l1 = uemp_states2.query('index >= 2020 and index < 2022')[state] \ .plot(ax=ax, label='Unemployment') l2 = part_states.dropna().query('index >= 2020 and index < 2022')[state] \ .plot(ax=ax2, label='Participation', color=color_pal[1]) ax2.grid(False) ax.set_title(state) fig.legend(labels=['Unemployment','Participation']) plt.show()