from pathlib import Path
from zipfile import ZipFile
from urllib.request import urlretrieve
# Download and unzip the 2018 individual contributions data
url = "https://cg-519a459a-0ea3-42c2-b7bc-fa1143481f74.s3-us-gov-west-1.amazonaws.com/bulk-downloads/2018/indiv18.zip"
zip_path = Path("indiv18.zip")
csv_path = Path("indiv18.csv")
if not zip_path.exists():
urlretrieve(url, zip_path)
if not csv_path.exists():
with ZipFile(zip_path) as zip_file, csv_path.open("w") as csv_file:
for line in zip_file.open("itcont.txt"):
csv_file.write(line.decode())Hi! My name is Nick Crews, and I’m a data engineer that looks at public campaign finance data.
In this post, I’ll walk through how I use Ibis to explore public campaign contribution data from the Federal Election Commission (FEC). We’ll do some loading, cleaning, featurizing, and visualization. There will be filtering, sorting, grouping, and aggregation.
Downloading The Data
Loading the data
Now that we have our raw data in a .csv format, let’s load it into Ibis, using the duckdb backend.
Note that a 4.3 GB .csv would be near the limit of what pandas could handle on my laptop with 16GB of RAM. In pandas, typically every time you perform a transformation on the data, a copy of the data is made. I could only do a few transformations before I ran out of memory.
With Ibis, this problem is solved in two different ways.
First, because they are designed to work with very large datasets, many (all?) SQL backends support out of core operations. The data lives on disk, and are only loaded in a streaming fashion when needed, and then written back to disk as the operation is performed.
Second, unless you explicitly ask for it, Ibis makes use of lazy evaluation. This means that when you ask for a result, the result is not persisted in memory. Only the original source data is persisted. Everything else is derived from this on the fly.
import ibis
from ibis import _
ibis.options.interactive = True
# The raw .csv file doesn't have column names, so we will add them in the next step.
raw = ibis.read_csv(csv_path)
raw┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ C00401224 ┃ A ┃ M6 ┃ P ┃ 201804059101866001 ┃ 24T ┃ IND ┃ STOUFFER, LEIGH ┃ AMSTELVEEN ┃ ZZ ┃ 1187RC ┃ MYSELF ┃ SELF EMPLOYED ┃ 05172017 ┃ 10 ┃ C00458000 ┃ SA11AI_81445687 ┃ 1217152 ┃ column18 ┃ EARMARKED FOR PROGRESSIVE CHANGE CAMPAIGN COMMITTEE (C00458000) ┃ 4050820181544765358 ┃ ┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ string │ string │ string │ string │ int64 │ string │ string │ string │ string │ string │ string │ string │ string │ string │ int64 │ string │ string │ int64 │ string │ string │ int64 │ ├───────────┼────────┼────────┼────────┼────────────────────┼────────┼────────┼───────────────────┼──────────────┼────────┼───────────┼───────────────────┼─────────────────────────┼──────────┼───────┼───────────┼─────────────────┼─────────┼──────────┼─────────────────────────────────────────────────────────────────┼─────────────────────┤ │ C00401224 │ A │ M6 │ P │ 201804059101867748 │ 24T │ IND │ STRAWS, JOYCE │ OCOEE │ FL │ 34761 │ SILVERSEA CRUISES │ RESERVATIONS SUPERVISOR │ 05182017 │ 10 │ C00000935 │ SA11AI_81592336 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544770597 │ │ C00401224 │ A │ M6 │ P │ 201804059101867748 │ 24T │ IND │ STRAWS, JOYCE │ OCOEE │ FL │ 34761 │ SILVERSEA CRUISES │ RESERVATIONS SUPERVISOR │ 05192017 │ 15 │ C00000935 │ SA11AI_81627562 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544770598 │ │ C00401224 │ A │ M6 │ P │ 201804059101865942 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 039020760 │ NONE │ NONE │ 05132017 │ 35 │ C00000935 │ SA11AI_81047921 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544765179 │ │ C00401224 │ A │ M6 │ P │ 201804059101865942 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 039020760 │ NONE │ NONE │ 05152017 │ 35 │ C00000935 │ SA11AI_81209209 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544765180 │ │ C00401224 │ A │ M6 │ P │ 201804059101865942 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 039020760 │ NONE │ NONE │ 05192017 │ 5 │ C00000935 │ SA11AI_81605223 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544765181 │ │ C00401224 │ A │ M6 │ P │ 201804059101865943 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 039020760 │ NONE │ NONE │ 05242017 │ 15 │ C00000935 │ SA11AI_82200022 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544765182 │ │ C00401224 │ A │ M6 │ P │ 201804059101865943 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 03902 │ NOT EMPLOYED │ NOT EMPLOYED │ 05292017 │ 100 │ C00213512 │ SA11AI_82589834 │ 1217152 │ NULL │ EARMARKED FOR NANCY PELOSI FOR CONGRESS (C00213512) │ 4050820181544765184 │ │ C00401224 │ A │ M6 │ P │ 201804059101865944 │ 24T │ IND │ STOTT, JIM │ CAPE NEDDICK │ ME │ 039020760 │ NONE │ NONE │ 05302017 │ 35 │ C00000935 │ SA11AI_82643727 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544765185 │ │ C00401224 │ A │ M6 │ P │ 201804059101867050 │ 24T │ IND │ STRANGE, WINIFRED │ ANNA MSRIA │ FL │ 34216 │ NOT EMPLOYED │ NOT EMPLOYED │ 05162017 │ 25 │ C00000935 │ SA11AI_81325918 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544768505 │ │ C00401224 │ A │ M6 │ P │ 201804059101867051 │ 24T │ IND │ STRANGE, WINIFRED │ ANNA MSRIA │ FL │ 34216 │ NOT EMPLOYED │ NOT EMPLOYED │ 05232017 │ 25 │ C00000935 │ SA11AI_81991189 │ 1217152 │ NULL │ EARMARKED FOR DCCC (C00000935) │ 4050820181544768506 │ │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ … │ └───────────┴────────┴────────┴────────┴────────────────────┴────────┴────────┴───────────────────┴──────────────┴────────┴───────────┴───────────────────┴─────────────────────────┴──────────┴───────┴───────────┴─────────────────┴─────────┴──────────┴─────────────────────────────────────────────────────────────────┴─────────────────────┘
# For a more comprehesive description of the columns and their meaning, see
# https://www.fec.gov/campaign-finance-data/contributions-individuals-file-description/
columns = {
"CMTE_ID": "keep", # Committee ID
"AMNDT_IND": "drop", # Amendment indicator. A = amendment, N = new, T = termination
"RPT_TP": "drop", # Report type (monthly, quarterly, etc)
"TRANSACTION_PGI": "keep", # Primary/general indicator
"IMAGE_NUM": "drop", # Image number
"TRANSACTION_TP": "drop", # Transaction type
"ENTITY_TP": "keep", # Entity type
"NAME": "drop", # Contributor name
"CITY": "keep", # Contributor city
"STATE": "keep", # Contributor state
"ZIP_CODE": "drop", # Contributor zip code
"EMPLOYER": "drop", # Contributor employer
"OCCUPATION": "drop", # Contributor occupation
"TRANSACTION_DT": "keep", # Transaction date
"TRANSACTION_AMT": "keep", # Transaction amount
# Other ID. For individual contributions will be null. For contributions from
# other FEC committees, will be the committee ID of the other committee.
"OTHER_ID": "drop",
"TRAN_ID": "drop", # Transaction ID
"FILE_NUM": "drop", # File number, unique number assigned to each report filed with the FEC
"MEMO_CD": "drop", # Memo code
"MEMO_TEXT": "drop", # Memo text
"SUB_ID": "drop", # Submission ID. Unique number assigned to each transaction.
}
renaming = {old: new for old, new in zip(raw.columns, columns.keys())}
to_keep = [k for k, v in columns.items() if v == "keep"]
kept = raw.relabel(renaming)[to_keep]
kept┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ CMTE_ID ┃ TRANSACTION_PGI ┃ ENTITY_TP ┃ CITY ┃ STATE ┃ TRANSACTION_DT ┃ TRANSACTION_AMT ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ string │ string │ string │ string │ string │ string │ int64 │ ├───────────┼─────────────────┼───────────┼──────────────┼────────┼────────────────┼─────────────────┤ │ C00401224 │ P │ IND │ OCOEE │ FL │ 05182017 │ 10 │ │ C00401224 │ P │ IND │ OCOEE │ FL │ 05192017 │ 15 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05132017 │ 35 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05152017 │ 35 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05192017 │ 5 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05242017 │ 15 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05292017 │ 100 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05302017 │ 35 │ │ C00401224 │ P │ IND │ ANNA MSRIA │ FL │ 05162017 │ 25 │ │ C00401224 │ P │ IND │ ANNA MSRIA │ FL │ 05232017 │ 25 │ │ … │ … │ … │ … │ … │ … │ … │ └───────────┴─────────────────┴───────────┴──────────────┴────────┴────────────────┴─────────────────┘
# 21 million rows
kept.count()┌──────────┐
│ 21730730 │
└──────────┘
Huh, what’s up with those timings? Previewing the head only took a fraction of a second, but finding the number of rows took 10 seconds.
That’s because duckdb is scanning the .csv file on the fly every time we access it. So we only have to read the first few lines to get that preview, but we have to read the whole file to get the number of rows.
Note that this isn’t a feature of Ibis, but a feature of Duckdb. This what I think is one of the strengths of Ibis: Ibis itself doesn’t have to implement any of the optimimizations or features of the backends. Those backends can focus on what they do best, and Ibis can get those things for free.
So, let’s tell duckdb to actually read in the file to its native format so later accesses will be faster. This will be a ~20 seconds that we’ll only have to pay once.
kept = kept.cache()
kept┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ CMTE_ID ┃ TRANSACTION_PGI ┃ ENTITY_TP ┃ CITY ┃ STATE ┃ TRANSACTION_DT ┃ TRANSACTION_AMT ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ string │ string │ string │ string │ string │ string │ int64 │ ├───────────┼─────────────────┼───────────┼──────────────┼────────┼────────────────┼─────────────────┤ │ C00401224 │ P │ IND │ OCOEE │ FL │ 05182017 │ 10 │ │ C00401224 │ P │ IND │ OCOEE │ FL │ 05192017 │ 15 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05132017 │ 35 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05152017 │ 35 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05192017 │ 5 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05242017 │ 15 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05292017 │ 100 │ │ C00401224 │ P │ IND │ CAPE NEDDICK │ ME │ 05302017 │ 35 │ │ C00401224 │ P │ IND │ ANNA MSRIA │ FL │ 05162017 │ 25 │ │ C00401224 │ P │ IND │ ANNA MSRIA │ FL │ 05232017 │ 25 │ │ … │ … │ … │ … │ … │ … │ … │ └───────────┴─────────────────┴───────────┴──────────────┴────────┴────────────────┴─────────────────┘
Look, now accessing it only takes a fraction of a second!
kept.count()┌──────────┐
│ 21730730 │
└──────────┘
Committees Data
The contributions only list an opaque CMTE_ID column. We want to know which actual committee this is. Let’s load the committees table so we can lookup from committee ID to committee name.
def read_committees():
committees_url = "https://cg-519a459a-0ea3-42c2-b7bc-fa1143481f74.s3-us-gov-west-1.amazonaws.com/bulk-downloads/2018/committee_summary_2018.csv"
# This just creates a view, it doesn't actually fetch the data yet
tmp = ibis.read_csv(committees_url)
tmp = tmp["CMTE_ID", "CMTE_NM"]
# The raw table contains multiple rows for each committee id, so lets pick
# an arbitrary row for each committee id as the representative name.
deduped = tmp.group_by("CMTE_ID").agg(CMTE_NM=_.CMTE_NM.arbitrary())
return deduped
comms = read_committees().cache()
comms┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ CMTE_ID ┃ CMTE_NM ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ string │ string │ ├───────────┼────────────────────────────────────────────────────────────────┤ │ C00659441 │ JASON ORTITAY FOR CONGRESS │ │ C00297911 │ TEXAS FORESTRY ASSOCIATION FORESTRY POLITICAL ACTION COMMITTEE │ │ C00340745 │ WADDELL & REED FINANCIAL, INC. POLITICAL ACTION COMMITTEE │ │ C00679217 │ CANTWELL-WARREN VICTORY FUND │ │ C00101204 │ NATIONAL FISHERIES INSTITUTE (FISHPAC) │ │ C00010520 │ MEREDITH CORPORATION EMPLOYEES FUND FOR BETTER GOVERNMENT │ │ C00532788 │ LAFAYETTE COUNTY DEMOCRATIC PARTY │ │ C00128561 │ TOLL BROS. INC. PAC │ │ C00510958 │ WENDYROGERS.ORG │ │ C00665604 │ COMMITTEE TO ELECT BILL EBBEN │ │ … │ … │ └───────────┴────────────────────────────────────────────────────────────────┘
Now add the committee name to the contributions table:
together = kept.left_join(comms, "CMTE_ID").drop("CMTE_ID", "CMTE_ID_right")
together┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ TRANSACTION_PGI ┃ ENTITY_TP ┃ CITY ┃ STATE ┃ TRANSACTION_DT ┃ TRANSACTION_AMT ┃ CMTE_NM ┃ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ string │ string │ string │ string │ string │ int64 │ string │ ├─────────────────┼───────────┼──────────────────┼────────┼────────────────┼─────────────────┼─────────────────────────────────────────────────┤ │ P │ IND │ COHASSET │ MA │ 01312017 │ 230 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ KEY LARGO │ FL │ 01042017 │ 5000 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ LOOKOUT MOUNTAIN │ GA │ 01312017 │ 230 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ NORTH YARMOUTH │ ME │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ ALPHARETTA │ GA │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ FALMOUTH │ ME │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ FALMOUTH │ ME │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ HOLLIS CENTER │ ME │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ FALMOUTH │ ME │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ P │ IND │ ALEXANDRIA │ VA │ 01312017 │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ │ … │ … │ … │ … │ … │ … │ … │ └─────────────────┴───────────┴──────────────────┴────────┴────────────────┴─────────────────┴─────────────────────────────────────────────────┘
Cleaning
First, let’s drop any contributions that don’t have a committee name. There are only 6 of them.
# We can do this fearlessly, no .copy() needed, because
# everything in Ibis is immutable. If we did this in pandas,
# we might start modifying the original DataFrame accidentally!
cleaned = together
has_name = cleaned.CMTE_NM.notnull()
cleaned = cleaned[has_name]
has_name.value_counts()┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ NotNull(CMTE_NM) ┃ NotNull(CMTE_NM)_count ┃ ┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ boolean │ int64 │ ├──────────────────┼────────────────────────┤ │ True │ 21730724 │ │ False │ 6 │ └──────────────────┴────────────────────────┘
Let’s look at the ENTITY_TP column. This represents the type of entity that made the contribution:
together.ENTITY_TP.value_counts()┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ ENTITY_TP ┃ ENTITY_TP_count ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ string │ int64 │ ├───────────┼─────────────────┤ │ NULL │ 5289 │ │ CAN │ 13659 │ │ COM │ 867 │ │ IND │ 21687992 │ │ ORG │ 18555 │ │ PAC │ 3621 │ │ PTY │ 49 │ │ CCM │ 698 │ └───────────┴─────────────────┘
We only care about contributions from individuals.
Once we filter on this column, the contents of it are irrelevant, so let’s drop it.
cleaned = together[_.ENTITY_TP == "IND"].drop("ENTITY_TP")It looks like the TRANSACTION_DT column was a raw string like “MMDDYYYY”, so let’s convert that to a proper date type.
from ibis.expr.types import StringValue, DateValue
def mmddyyyy_to_date(val: StringValue) -> DateValue:
return val.cast(str).lpad(8, "0").to_timestamp("%m%d%Y").date()
cleaned = cleaned.mutate(date=mmddyyyy_to_date(_.TRANSACTION_DT)).drop("TRANSACTION_DT")
cleaned┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ TRANSACTION_PGI ┃ CITY ┃ STATE ┃ TRANSACTION_AMT ┃ CMTE_NM ┃ date ┃ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ string │ string │ string │ int64 │ string │ date │ ├─────────────────┼──────────────────┼────────┼─────────────────┼─────────────────────────────────────────────────┼────────────┤ │ P │ COHASSET │ MA │ 230 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ KEY LARGO │ FL │ 5000 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-04 │ │ P │ LOOKOUT MOUNTAIN │ GA │ 230 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ NORTH YARMOUTH │ ME │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ ALPHARETTA │ GA │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ FALMOUTH │ ME │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ FALMOUTH │ ME │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ HOLLIS CENTER │ ME │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ FALMOUTH │ ME │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ P │ ALEXANDRIA │ VA │ 384 │ UNUM GROUP POLITICAL ACTION COMMITTEE (UNUMPAC) │ 2017-01-31 │ │ … │ … │ … │ … │ … │ … │ └─────────────────┴──────────────────┴────────┴─────────────────┴─────────────────────────────────────────────────┴────────────┘
The TRANSACTION_PGI column represents the type (primary, general, etc) of election, and the year. But it seems to be not very consistent:
cleaned.TRANSACTION_PGI.topk(10)┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ ┃ TRANSACTION_PGI ┃ CountStar() ┃ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ │ string │ int64 │ ├─────────────────┼─────────────┤ │ P │ 17013596 │ │ G2018 │ 2095123 │ │ P2018 │ 1677183 │ │ P2020 │ 208501 │ │ O2018 │ 161874 │ │ S2017 │ 124336 │ │ G2017 │ 98401 │ │ P2022 │ 91136 │ │ P2017 │ 61153 │ │ R2017 │ 54281 │ └─────────────────┴─────────────┘
def get_election_type(pgi: StringValue) -> StringValue:
"""Use the first letter of the TRANSACTION_PGI column to determine the election type
If the first letter is not one of the known election stage, then return null.
"""
election_types = {
"P": "primary",
"G": "general",
"O": "other",
"C": "convention",
"R": "runoff",
"S": "special",
"E": "recount",
}
first_letter = pgi[0]
return first_letter.substitute(election_types, else_=ibis.null())
cleaned = cleaned.mutate(election_type=get_election_type(_.TRANSACTION_PGI)).drop(
"TRANSACTION_PGI"
)
cleaned┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ CITY ┃ STATE ┃ TRANSACTION_AMT ┃ CMTE_NM ┃ date ┃ election_type ┃ ┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ string │ string │ int64 │ string │ date │ string │ ├────────────┼────────┼─────────────────┼───────────────────────────┼────────────┼───────────────┤ │ ATLANTA │ GA │ 15 │ NANCY PELOSI FOR CONGRESS │ 2017-06-20 │ primary │ │ AUSTIN │ TX │ 15 │ NANCY PELOSI FOR CONGRESS │ 2017-06-04 │ primary │ │ WASHINGTON │ DC │ 25 │ NANCY PELOSI FOR CONGRESS │ 2017-06-23 │ primary │ │ HONOLULU │ HI │ 10 │ NANCY PELOSI FOR CONGRESS │ 2017-04-20 │ primary │ │ MAMARONECK │ NY │ 110 │ NANCY PELOSI FOR CONGRESS │ 2017-06-02 │ primary │ │ REHOBOTH │ MA │ 10 │ NANCY PELOSI FOR CONGRESS │ 2017-06-01 │ primary │ │ BERKELEY │ CA │ 25 │ NANCY PELOSI FOR CONGRESS │ 2017-06-05 │ primary │ │ BEAUMONT │ TX │ 25 │ NANCY PELOSI FOR CONGRESS │ 2017-04-12 │ primary │ │ CONCORD │ MA │ 200 │ NANCY PELOSI FOR CONGRESS │ 2017-05-05 │ primary │ │ OXNARD │ CA │ 15 │ NANCY PELOSI FOR CONGRESS │ 2017-03-31 │ primary │ │ … │ … │ … │ … │ … │ … │ └────────────┴────────┴─────────────────┴───────────────────────────┴────────────┴───────────────┘
That worked well! There are 0 nulls in the resulting column, so we always were able to determine the election type.
cleaned.election_type.topk(10)┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ ┃ election_type ┃ CountStar() ┃ ┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ │ string │ int64 │ ├───────────────┼─────────────┤ │ primary │ 19061953 │ │ general │ 2216685 │ │ other │ 161965 │ │ special │ 149572 │ │ runoff │ 69637 │ │ convention │ 22453 │ │ recount │ 5063 │ │ NULL │ 664 │ └───────────────┴─────────────┘
About 1/20 of transactions are negative. These could represent refunds, or they could be data entry errors. Let’s drop them to keep it simple.
above_zero = cleaned.TRANSACTION_AMT > 0
cleaned = cleaned[above_zero]
above_zero.value_counts()┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Greater(TRANSACTION_AMT, 0) ┃ Greater(TRANSACTION_AMT, 0)_count ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ boolean │ int64 │ ├─────────────────────────────┼───────────────────────────────────┤ │ True │ 20669809 │ │ False │ 1018183 │ └─────────────────────────────┴───────────────────────────────────┘
Adding Features
Now that the data is cleaned up to a usable format, let’s add some features.
First, it’s useful to categorize donations by size, placing them into buckets of small, medium, large, etc.
edges = [
10,
50,
100,
500,
1000,
5000,
]
labels = [
"<10",
"10-50",
"50-100",
"100-500",
"500-1000",
"1000-5000",
"5000+",
]
def bucketize(vals, edges, str_labels):
# Uses Ibis's .bucket() method to create a categorical column
int_labels = vals.bucket(edges, include_under=True, include_over=True)
# Map the integer labels to the string labels
int_to_str = {str(i): s for i, s in enumerate(str_labels)}
return int_labels.cast(str).substitute(int_to_str)
featured = cleaned.mutate(amount_bucket=bucketize(_.TRANSACTION_AMT, edges, labels))
featured┏━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ CITY ┃ STATE ┃ TRANSACTION_AMT ┃ CMTE_NM ┃ date ┃ election_type ┃ amount_bucket ┃ ┡━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ string │ string │ int64 │ string │ date │ string │ string │ ├──────────────┼────────┼─────────────────┼───────────────────────┼────────────┼───────────────┼───────────────┤ │ REMINGTON │ IN │ 50 │ AMERICA'S LIBERTY PAC │ 2017-05-30 │ primary │ 50-100 │ │ REMINGTON │ IN │ 50 │ AMERICA'S LIBERTY PAC │ 2017-06-05 │ primary │ 50-100 │ │ VANCOUVER │ WA │ 100 │ AMERICA'S LIBERTY PAC │ 2017-06-07 │ primary │ 100-500 │ │ SOLANA BEACH │ CA │ 500 │ AMERICA'S LIBERTY PAC │ 2017-06-26 │ primary │ 500-1000 │ │ HILLSDALE │ MI │ 250 │ AMERICA'S LIBERTY PAC │ 2017-05-15 │ primary │ 100-500 │ │ MIDDLEBURY │ VT │ 500 │ NBT PAC FEDERAL FUND │ 2017-06-05 │ primary │ 500-1000 │ │ WILLISTON │ VT │ 500 │ NBT PAC FEDERAL FUND │ 2017-05-30 │ primary │ 500-1000 │ │ GLENMONT │ NY │ 350 │ NBT PAC FEDERAL FUND │ 2017-06-01 │ primary │ 100-500 │ │ NORWICH │ NY │ 250 │ NBT PAC FEDERAL FUND │ 2017-05-31 │ primary │ 100-500 │ │ CLIFTON PARK │ NY │ 250 │ NBT PAC FEDERAL FUND │ 2017-06-26 │ primary │ 100-500 │ │ … │ … │ … │ … │ … │ … │ … │ └──────────────┴────────┴─────────────────┴───────────────────────┴────────────┴───────────────┴───────────────┘
Analysis
By donation size
One thing we can look at is the donation breakdown by size: - Are most donations small or large? - Where do politicians/committees get most of their money from? Large or small donations?
We also will compare performance of Ibis vs pandas during this groupby.
def summary_by(table, by):
return table.group_by(by).agg(
n_donations=_.count(),
total_amount=_.TRANSACTION_AMT.sum(),
mean_amount=_.TRANSACTION_AMT.mean(),
median_amount=_.TRANSACTION_AMT.approx_median(),
)
def summary_by_pandas(df, by):
return df.groupby(by, as_index=False).agg(
n_donations=("election_type", "count"),
total_amount=("TRANSACTION_AMT", "sum"),
mean_amount=("TRANSACTION_AMT", "mean"),
median_amount=("TRANSACTION_AMT", "median"),
)
# persist the input data so the following timings of the group_by are accurate.
subset = featured["election_type", "amount_bucket", "TRANSACTION_AMT"]
subset = subset.cache()
pandas_subset = subset.execute()Let’s take a look at what we are actually computing:
by_type_and_bucket = summary_by(subset, ["election_type", "amount_bucket"])
by_type_and_bucket┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ election_type ┃ amount_bucket ┃ n_donations ┃ total_amount ┃ mean_amount ┃ median_amount ┃ ┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ string │ string │ int64 │ int64 │ float64 │ int64 │ ├───────────────┼───────────────┼─────────────┼──────────────┼──────────────┼───────────────┤ │ primary │ 500-1000 │ 634677 │ 334630687 │ 527.245649 │ 500 │ │ general │ 5000+ │ 3125 │ 44496373 │ 14238.839360 │ 7537 │ │ special │ 500-1000 │ 7811 │ 4003293 │ 512.519908 │ 500 │ │ runoff │ 100-500 │ 18193 │ 3088289 │ 169.751498 │ 100 │ │ convention │ 500-1000 │ 1824 │ 945321 │ 518.268092 │ 500 │ │ general │ <10 │ 115873 │ 536742 │ 4.632158 │ 5 │ │ general │ 50-100 │ 304363 │ 16184312 │ 53.174374 │ 50 │ │ general │ 1000-5000 │ 246101 │ 460025242 │ 1869.253851 │ 1978 │ │ general │ 10-50 │ 660787 │ 14411588 │ 21.809733 │ 25 │ │ other │ 500-1000 │ 119 │ 62535 │ 525.504202 │ 500 │ │ … │ … │ … │ … │ … │ … │ └───────────────┴───────────────┴─────────────┴──────────────┴──────────────┴───────────────┘
OK, now let’s do our timings.
One interesting thing to pay attention to here is the execution time for the following groupby. Before, we could get away with lazy execution: because we only wanted to preview the first few rows, we only had to compute the first few rows, so all our previews were very fast.
But now, as soon as we do a groupby, we have to actually go through the whole dataset in order to compute the aggregate per group. So this is going to be slower. BUT, duckdb is still quite fast. It only takes milliseconds to groupby-agg all 20 million rows!
%timeit summary_by(subset, ["election_type", "amount_bucket"]).execute() # .execute() so we actually fetch the data161 ms ± 4.75 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)Now let’s try the same thing in pandas:
%timeit summary_by_pandas(pandas_subset, ["election_type", "amount_bucket"])2.19 s ± 6.54 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)It takes about 4 seconds, which is about 10 times slower than duckdb. At this scale, it again doesn’t matter, but you could imagine with a dataset much larger than this, it would matter.
Let’s also think about memory usage:
pandas_subset.memory_usage(deep=True).sum() / 1e9 # GB2.782586667The source dataframe is couple gigabytes, so probably during the groupby, the peak memory usage is going to be a bit higher than this. You could use a profiler such as FIL if you wanted an exact number, I was too lazy to use that here.
Again, this works on my laptop at this dataset size, but much larger than this and I’d start having problems. Duckdb on the other hand is designed around working out of core so it should scale to datasets into the hundreds of gigabytes, much larger than your computer’s RAM.
Back to analysis
OK, let’s plot the result of that groupby.
Surprise! (Or maybe not…) Most donations are small. But most of the money comes from donations larger than $1000.
Well if that’s the case, why do politicians spend so much time soliciting small donations? One explanation is that they can use the number of donations as a marketing pitch, to show how popular they are, and thus how viable of a candidate they are.
This also might explain whose interests are being served by our politicians.
import altair as alt
# Do some bookkeeping so the buckets are displayed smallest to largest on the charts
bucket_col = alt.Column("amount_bucket:N", sort=labels)
n_by_bucket = (
alt.Chart(by_type_and_bucket.execute())
.mark_bar()
.encode(
x=bucket_col,
y="n_donations:Q",
color="election_type:N",
)
)
total_by_bucket = (
alt.Chart(by_type_and_bucket.execute())
.mark_bar()
.encode(
x=bucket_col,
y="total_amount:Q",
color="election_type:N",
)
)
n_by_bucket | total_by_bucketBy election stage
Let’s look at how donations break down by election stage. Do people donate differently for primary elections vs general elections?
Let’s ignore everything but primary and general elections, since they are the most common, and arguably the most important.
gb2 = by_type_and_bucket[_.election_type.isin(("primary", "general"))]
n_donations_per_election_type = _.n_donations.sum().over(group_by="election_type")
frac = _.n_donations / n_donations_per_election_type
gb2 = gb2.mutate(frac_n_donations_per_election_type=frac)
gb2┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ election_type ┃ amount_bucket ┃ n_donations ┃ total_amount ┃ mean_amount ┃ median_amount ┃ frac_n_donations_per_election_type ┃ ┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ string │ string │ int64 │ int64 │ float64 │ int64 │ float64 │ ├───────────────┼───────────────┼─────────────┼──────────────┼──────────────┼───────────────┼────────────────────────────────────┤ │ general │ <10 │ 115873 │ 536742 │ 4.632158 │ 5 │ 0.052544 │ │ general │ 50-100 │ 304363 │ 16184312 │ 53.174374 │ 50 │ 0.138017 │ │ general │ 1000-5000 │ 246101 │ 460025242 │ 1869.253851 │ 1961 │ 0.111598 │ │ general │ 10-50 │ 660787 │ 14411588 │ 21.809733 │ 25 │ 0.299642 │ │ general │ 100-500 │ 700821 │ 123174568 │ 175.757530 │ 150 │ 0.317796 │ │ general │ 500-1000 │ 174182 │ 91015697 │ 522.532162 │ 500 │ 0.078985 │ │ general │ 5000+ │ 3125 │ 44496373 │ 14238.839360 │ 7601 │ 0.001417 │ │ primary │ 5000+ │ 44085 │ 1558371116 │ 35349.237065 │ 10000 │ 0.002422 │ │ primary │ 100-500 │ 3636287 │ 637353634 │ 175.275943 │ 150 │ 0.199765 │ │ primary │ 500-1000 │ 634677 │ 334630687 │ 527.245649 │ 500 │ 0.034867 │ │ … │ … │ … │ … │ … │ … │ … │ └───────────────┴───────────────┴─────────────┴──────────────┴──────────────┴───────────────┴────────────────────────────────────┘
It looks like primary elections get a larger proportion of small donations.
alt.Chart(gb2.execute()).mark_bar().encode(
x="election_type:O",
y="frac_n_donations_per_election_type:Q",
color=bucket_col,
)By recipient
Let’s look at the top players. Who gets the most donations?
Far and away it is ActBlue, which acts as a conduit for donations to Democratic interests.
Beto O’Rourke is the top individual politician, hats off to him!
by_recip = summary_by(featured, "CMTE_NM")
by_recip┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ CMTE_NM ┃ n_donations ┃ total_amount ┃ mean_amount ┃ median_amount ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ string │ int64 │ int64 │ float64 │ int64 │ ├──────────────────────────────────────────────────────────────────┼─────────────┼──────────────┼─────────────┼───────────────┤ │ INDIANA DENTAL PAC │ 111 │ 62236 │ 560.684685 │ 410 │ │ BEAM SUNTORY INC POLITICAL ACTION COMMITTEE │ 407 │ 64806 │ 159.228501 │ 65 │ │ AMEDISYS, INC. POLITICAL ACTION COMMITTEE │ 132 │ 25000 │ 189.393939 │ 75 │ │ PIEDMONT TRIAD ANESTHESIA P A FEDERAL PAC │ 132 │ 90375 │ 684.659091 │ 600 │ │ AHOLD DELHAIZE USA, INC POLITICAL ACTION COMMITTEE │ 369 │ 48062 │ 130.249322 │ 100 │ │ DIMITRI FOR CONGRESS │ 87 │ 34719 │ 399.068966 │ 250 │ │ RELX INC. POLITICAL ACTION COMMITTEE │ 5491 │ 306908 │ 55.892916 │ 34 │ │ MAKING INVESTMENTS MAJORITY INSURED PAC │ 14 │ 30600 │ 2185.714286 │ 1000 │ │ AMERICAN ACADEMY OF OTOLARYNGOLOGY-HEAD AND NECK SURGERY ENT PAC │ 765 │ 285756 │ 373.537255 │ 365 │ │ MIMI WALTERS VICTORY FUND │ 840 │ 2514824 │ 2993.838095 │ 2506 │ │ … │ … │ … │ … │ … │ └──────────────────────────────────────────────────────────────────┴─────────────┴──────────────┴─────────────┴───────────────┘
top_recip = by_recip.order_by(ibis.desc("n_donations")).head(10)
alt.Chart(top_recip.execute()).mark_bar().encode(
x=alt.X("CMTE_NM:O", sort="-y"),
y="n_donations:Q",
)By Location
Where are the largest donations coming from?
f2 = featured.mutate(loc=_.CITY + ", " + _.STATE).drop("CITY", "STATE")
by_loc = summary_by(f2, "loc")
# Drop the places with a small number of donations so we're
# resistant to outliers for the mean
by_loc = by_loc[_.n_donations > 1000]
by_loc┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ loc ┃ n_donations ┃ total_amount ┃ mean_amount ┃ median_amount ┃ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ string │ int64 │ int64 │ float64 │ int64 │ ├─────────────────┼─────────────┼──────────────┼─────────────┼───────────────┤ │ NAZARETH, PA │ 1460 │ 138710 │ 95.006849 │ 38 │ │ FULSHEAR, TX │ 1504 │ 346778 │ 230.570479 │ 50 │ │ GLOUCESTER, MA │ 4956 │ 563331 │ 113.666465 │ 25 │ │ NORMAN, OK │ 6195 │ 945333 │ 152.596126 │ 35 │ │ OAK PARK, IL │ 12017 │ 3413138 │ 284.025797 │ 39 │ │ AUSTIN, TX │ 189865 │ 33315922 │ 175.471635 │ 38 │ │ MIAMI BEACH, FL │ 12825 │ 10598453 │ 826.390097 │ 100 │ │ SAN ANTONIO, TX │ 140529 │ 18925978 │ 134.676672 │ 35 │ │ HAMBURG, NY │ 2322 │ 170254 │ 73.322136 │ 8 │ │ PITTSBURGH, PA │ 74208 │ 14358578 │ 193.490971 │ 42 │ │ … │ … │ … │ … │ … │ └─────────────────┴─────────────┴──────────────┴─────────────┴───────────────┘
def top_by(col):
top = by_loc.order_by(ibis.desc(col)).head(10)
return (
alt.Chart(top.execute())
.mark_bar()
.encode(
x=alt.X('loc:O', sort="-y"),
y=col,
)
)
top_by("n_donations") | top_by("total_amount") | top_by("mean_amount") | top_by(
"median_amount"
)By month
When do the donations come in?
by_month = summary_by(featured, _.date.month().name("month_int"))
# Sorta hacky, .substritute doesn't work to change dtypes (yet?)
# so we cast to string and then do our mapping
month_map = {
"1": "Jan",
"2": "Feb",
"3": "Mar",
"4": "Apr",
"5": "May",
"6": "Jun",
"7": "Jul",
"8": "Aug",
"9": "Sep",
"10": "Oct",
"11": "Nov",
"12": "Dec",
}
by_month = by_month.mutate(month_str=_.month_int.cast(str).substitute(month_map))
by_month┏━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓ ┃ month_int ┃ n_donations ┃ total_amount ┃ mean_amount ┃ median_amount ┃ month_str ┃ ┡━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩ │ int32 │ int64 │ int64 │ float64 │ int64 │ string │ ├───────────┼─────────────┼──────────────┼─────────────┼───────────────┼───────────┤ │ NULL │ 1514 │ 250297 │ 165.321664 │ 100 │ NULL │ │ 1 │ 348979 │ 174837854 │ 500.998209 │ 124 │ Jan │ │ 2 │ 581646 │ 255997655 │ 440.126219 │ 100 │ Feb │ │ 3 │ 1042577 │ 430906797 │ 413.309326 │ 81 │ Mar │ │ 4 │ 1088244 │ 299252692 │ 274.986760 │ 50 │ Apr │ │ 5 │ 1374247 │ 387317192 │ 281.839576 │ 48 │ May │ │ 6 │ 1667285 │ 465305247 │ 279.079610 │ 44 │ Jun │ │ 7 │ 1607053 │ 320528605 │ 199.451172 │ 35 │ Jul │ │ 8 │ 2023466 │ 473544182 │ 234.026261 │ 35 │ Aug │ │ 9 │ 2583847 │ 697888624 │ 270.096729 │ 38 │ Sep │ │ … │ … │ … │ … │ … │ … │ └───────────┴─────────────┴──────────────┴─────────────┴───────────────┴───────────┘
months_in_order = list(month_map.values())
alt.Chart(by_month.execute()).mark_bar().encode(
x=alt.X("month_str:O", sort=months_in_order),
y="n_donations:Q",
)Conclusion
Thanks for following along! I hope you’ve learned something about Ibis, and maybe even about campaign finance.
Ibis is a great tool for exploring data. I now find myself reaching for it when in the past I would have reached for pandas.
Some of the highlights for me:
- Fast, lazy execution, a great display format, and good type hinting/editor support for a great REPL experience.
- Very well thought-out API and semantics (e.g.
isinstance(val, NumericValue)?? That’s beautiful!) - Fast and fairly complete string support, since I work with a lot of text data.
- Extremely responsive maintainers. Sometimes I’ve submitted multiple feature requests and bug reports in a single day, and a PR has been merged by the next day.
- Escape hatch to SQL. I didn’t have to use that here, but if something isn’t supported, you can always fall back to SQL.
Check out The Ibis Website for more information.