elections 0.0.2

There's a bit of an air of mystery around this project...

Easy access to (US 2020, for now) election statistics.

How to use

import pandas as pd
from elections import President2020TimeSeries, Races2020, Election2020RawJson

President's race stats

s = President2020TimeSeries()
len(s)

51

s is a dictionary-like interface to the presidential race.

It's keys are the states

print(*s)

alabama alaska arizona arkansas california colorado connecticut delaware district-of-columbia florida georgia hawaii idaho illinois indiana iowa kansas kentucky louisiana maine maryland massachusetts michigan minnesota mississippi missouri montana nebraska nevada new-hampshire new-jersey new-mexico new-york north-carolina north-dakota ohio oklahoma oregon pennsylvania rhode-island south-carolina south-dakota tennessee texas utah vermont virginia washington west-virginia wisconsin wyoming

It's values are dataframes containing the stats.

state = 'georgia'
df = s[state]
df

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	votes	eevp	eevp_source	trumpd	bidenj
timestamp
2020-11-04T09:23:03Z	0	0	edison	0.000	0.000
2020-11-04T00:14:11Z	408	0	edison	0.674	0.326
2020-11-04T00:15:51Z	127106	2	edison	0.370	0.618
2020-11-04T00:19:55Z	173638	3	edison	0.431	0.557
2020-11-04T00:21:57Z	174006	3	edison	0.432	0.557
...	...	...	...	...	...
2020-11-06T23:14:40Z	4969860	99	edison	0.493	0.494
2020-11-06T23:17:43Z	4969869	99	edison	0.493	0.494
2020-11-06T23:36:39Z	4969873	99	edison	0.493	0.494
2020-11-06T23:41:44Z	4969880	99	edison	0.493	0.494
2020-11-06T23:45:40Z	4970093	99	edison	0.493	0.494

456 rows × 5 columns

df['bidenj'].plot(figsize=(16, 6), grid=True, title=state);

Other races

But that's not the only race going on here.

s = Races2020()
len(s)

51

data = s['new-york']   # by the way, you can tab-complete this if you're working in a jupyter notebook
print(type(data))
print(f"{len(data)} items... Here are the first 5:")
list(data)[:5]

<class 'py2store.base.Store'>
242 items... Here are the first 5:





['president-general-2020-11-03',
 'house-general-district-001-2020-11-03',
 'house-general-district-002-2020-11-03',
 'house-general-district-003-2020-11-03',
 'house-general-district-004-2020-11-03']

So we see that now, instead of just getting the president's race, we get... 242 races (one of which is the president's race).

What you need to know, as well, is that President2020TimeSeries just gave you one of the many datas available for the race (the 'timeseries' one), extracted and formated for your convenience, since it's probably the main information you're here for.

But there are other associated (raw) datas you may or may not be interested in. Here's what you got:

data['president-general-2020-11-03'].keys()  # you can tab complete here as well (you're welcome!)

dict_keys(['race_id', 'race_slug', 'url', 'state_page_url', 'ap_polls_page', 'edison_exit_polls_page', 'race_type', 'election_type', 'election_date', 'runoff', 'race_name', 'office', 'officeid', 'race_rating', 'seat', 'seat_name', 'state_id', 'state_slug', 'state_name', 'state_nyt_abbrev', 'state_shape', 'party_id', 'uncontested', 'report', 'result', 'result_source', 'gain', 'lost_seat', 'votes', 'electoral_votes', 'absentee_votes', 'absentee_counties', 'absentee_count_progress', 'absentee_outstanding', 'absentee_max_ballots', 'provisional_outstanding', 'provisional_count_progress', 'poll_display', 'poll_countdown_display', 'poll_waiting_display', 'poll_time', 'poll_time_short', 'precincts_reporting', 'precincts_total', 'reporting_display', 'reporting_value', 'eevp', 'tot_exp_vote', 'eevp_source', 'eevp_value', 'eevp_display', 'county_data_source', 'incumbent_party', 'no_forecast', 'last_updated', 'candidates', 'has_incumbent', 'leader_margin_value', 'leader_margin_votes', 'leader_margin_display', 'leader_margin_name_display', 'leader_party_id', 'counties', 'votes2016', 'margin2016', 'clinton2016', 'trump2016', 'votes2012', 'margin2012', 'expectations_text', 'expectations_text_short', 'absentee_ballot_deadline', 'absentee_postmark_deadline', 'update_sentences', 'race_diff', 'winnerCalledTimestamp', 'timeseries'])

t = data['president-general-2020-11-03']
print(t['trump2016'], t['clinton2016'])

2819534 4556124

Election2020RawJson

But if you want even more, and even more raw than the above, we can give that to you.

With Election2020RawJson you get access to the original full json.

raw_jsons = Election2020RawJson()
json_data = raw_jsons['california']
json_data.keys()

dict_keys(['data', 'meta'])

json_data['meta']

{'version': 10403,
 'track': '2020-11-03',
 'timestamp': '2020-11-06T23:52:57.623Z'}

json_data['data'].keys()

dict_keys(['races', 'party_control', 'liveUpdates'])

pd.DataFrame(json_data['data']['party_control']).set_index('race_type').T

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

race_type	house	president	senate
state_id
needed_for_control	218	270	50
total	435	538	100
no_election	{}	{}	{'democrat': 35, 'republican': 30, 'other': 0}
winner	False	False	False
parties	{'democrat': {'party_id': 'democrat', 'name_di...	{'democrat': {'party_id': 'democrat', 'name_di...	{'democrat': {'party_id': 'democrat', 'name_di...
winnerCalledTimestamp	None	None	None

pd.DataFrame(json_data['data']['liveUpdates'])

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	id	author	author_title_or_location	text	link_url	link_text	linked_state_1	linked_state_2	linked_state_3	image_url	...	call_type	race_id	winner	party_id	candidate_last_name	candidate_name_display	candidate_id	race_call_party_winner	state_name	link
0	333	Nate Cohn	in New York	New ballots from Clark County (that’s Las Vega...			NV				...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	332	Nate Cohn	in New York	The latest Arizona ballot releases aren’t look...			AZ				...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	331	Nick Corasaniti	in Philadelphia	There are still 102,000 mail ballots to be cou...			PA				...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	330	Dave Philipps	in Las Vegas	Biden nets 2,520 votes in the Las Vegas area, ...			NV				...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	329	Katie Glueck	in Wilmington, Del.	I’m told Biden spent the day watching election...							...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
444	5	Nate Cohn	in New York	Tonight, the needle will be back — sort of. We...	https://www.nytimes.com/2020/11/02/upshot/need...	Learn more about the needle					...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
445	4	Sheera Frenkel	in Silicon Valley	Times tech reporters will be monitoring for mi...							...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
446	3	Michael Barbaro	in New York	From 4 p.m. to 8 p.m. Eastern time, we’ll be t...	https://nytimes.com/thedaily	Listen here					...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
447	2	Trip Gabriel	in Butler County, Pa.	A look at Trump and the G.O.P.’s closing strat...	https://www.nytimes.com/2020/11/02/us/politics...						...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
448	1	Shane Goldmacher	in New Hope, Pa.	Here’s our recap of the final day of campaigni...	https://www.nytimes.com/2020/11/02/us/politics...						...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

449 rows × 31 columns