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A Call to Action: Using Python to Engage with Politicians and Advocate for Change

Published: at 08:15 AM

Note: While I am based in the Philippines, the code examples in this guide focus on US politics and data sources. This is because the client requiring my Python expertise is American, and I tailored the article’s context towards their needs. However, the programming concepts demonstrated are universally applicable across countries and causes. With appropriate data sets and parameters, developers anywhere could use similar techniques to empower political participation and activism within their own governments. The strategies in this guide aim to be a template adaptable to any geographic context.

In today’s digital age, programming skills like Python can be a powerful tool for political activism and driving social change. The intersection of data, technology, and politics has become increasingly crucial. In an era where information flows rapidly, and decisions are often data-driven, Python offers individuals the means to engage more effectively with politicians, participate in the legislative process, and advocate for causes they are passionate about.

Python’s relevance in the realm of political activism extends beyond its reputation as a versatile, beginner-friendly programming language. Its extensive libraries, active community support, and data-processing capabilities provide all the right ingredients to build customized tools for political engagement. As we delve into this comprehensive guide, we will explore various ways in which Python can be harnessed to influence policy, foster civic participation, and shed light on the intricate world of politics.

This guide is intended for developers, data scientists, students, and everyday citizens who wish to leverage Python as a force for change in their communities and beyond. While the code examples may focus on specific political contexts, the programming concepts and techniques demonstrated here are universally applicable. With the appropriate data sets and parameters, developers from any corner of the globe can adapt similar strategies to empower political participation and activism within their own governments.

In the following sections, we will explore hands-on examples and specific techniques for applying Python’s capabilities to civic participation, from analyzing legislative data and automating communication with elected officials to scrutinizing campaign finance and orchestrating events and campaigns. We’ll also delve into the use of machine learning for fact-checking and the pivotal role Python plays in driving voter registration and turnout efforts.

Python, with its unparalleled versatility in processing government records and political data, equips citizens with the tools to monitor political integrity, advocate for change, and amplify their voices. Join us on this journey to discover how Python can be a dynamic force in your quest for political engagement and activism.

An Introduction to Using Python for Civic Engagement

Python is a versatile, beginner-friendly programming language used for tasks like data analysis, machine learning, web development, and more. With its extensive libraries and active community support, Python contains all the right ingredients to build customized tools for political activism.

Some key ways Python can be applied include:

Let’s explore some hands-on examples and specific techniques for applying Python’s capabilities to civic participation.

Analyzing Political Data to Understand Voting Records

One of the most powerful ways to influence policy is to understand a politician’s voting history and the legislation they sponsor. Python makes it possible to gather this data and identify patterns or inconsistencies.

The ProPublica Congress API offers open source data on US legislation and lawmakers, including votes, bills, committees, and members. We can use the requests module to call this API and collect data:

import requests

url = ''
headers = {'X-API-Key': 'YOUR_API_KEY'}

response = requests.get(url, headers=headers)
data = response.json()

for result in data['results'][0]['members']:
    print(result['name'], result['votes_with_party_pct'])

This prints each US Senator’s name and their percentage of votes aligned with the party majority. Adding parameters to the URL can customize the data pull.

For deeper analysis, we can use Pandas to manipulate the acquired data into dataframes and draw insights:

import pandas as pd

df = pd.DataFrame(data['results'][0]['members'])

# Filter to only Republican Senators
republican_senators = df[df['party'] == 'R']

# Get average percentage of votes aligned with party

This reveals trends in party loyalty versus dissent. Further analysis might filter by specific issue areas or count vote flips.

Sharing accessible visualizations of these findings on social media is a great advocacy tactic. Python’s data viz libraries like Matplotlib can create graphs elucidating politicians’ stances:

import matplotlib.pyplot as plt

plt.hist(republican_senators['votes_with_party_pct'], bins=20)
plt.title('Republican Senator Party Loyalty')
plt.xlabel('Percentage Votes Aligned with Party')
plt.ylabel('Number of Senators')

# Save the figure as a PNG

This histogram enables anyone to quickly consume where most Senators fall on the party loyalty spectrum.

With Python’s versatility in processing data from government records, citizens can better monitor politician integrity and shine light on how votes align with campaign promises.

Automating Communication with Elected Officials

Reaching out to local and national representatives is an important civic responsibility. Python’s automation capabilities can help maximize the impact of constituent outreach.

For example, this script emails a customizable message to any number of Senators arguing in support or opposition of a bill:

import smtplib
from email.mime.text import MIMEText
import pandas as pd

# DataFrame of Senator names, state, and contact
senators = pd.read_csv('senators.csv')

def email_senator(name, state, position, email, bill_id):

  message = f'Dear Senator {name},\n\nI am your constituent from {state}. \
  I urge you to {position} Bill {bill_id}, because [INSERT REASONING]. \
  This issue matters deeply to me as [INSERT PERSONAL STORY OR IMPACT].\
  I will be watching your vote closely.\n\nThank you,\n[YOUR NAME]'

  msg = MIMEText(message)
  msg['Subject'] = f'Vote {position} on Bill {bill_id}'
  msg['From'] = '[YOUR EMAIL ADDRESS]'
  msg['To'] = email

  # Send Email
  smtp = smtplib.SMTP('[SMTP SERVER]')

for index, row in senators.iterrows():
   email_senator(row['name'], row['state'], 'support', row['email'], 'HR1234')

This scales constituent outreach exponentially. The script could also randomize components or track response metrics to optimize messaging.

For a grassroots call campaign, Twilio’s Python API sends automated phone calls directing people to contact Representatives about pending votes or issues:

from import Client

# Twilio Credentials
account_sid = 'XXX'
auth_token = 'XXX'
client = Client(account_sid, auth_token)

call_script = "Thank you for your vote on HR 1234. If you have concerns about upcoming legislation, \
please call Senator John Smith's office at 123-456-7890."

call = client.calls.create(


Integrations like the Citizen’s Climate Lobby’s Calling Tool demonstrate real implementations calling representatives on environmental issues.

Python unlocks scalable, streamlined communication channels for coordinating issue-based outreach to all levels of government.

Examining Campaign Finance and Dark Money Influence

Many advocacy groups like OpenSecrets and FollowTheMoney trace how campaign contributions and dark money sway politician stances. Python provides the perfect toolbox for data-driven investigation of money’s political influence.

The FEC API and datasets from watchdog sites enable gathering raw data on fundraising. Python can then process, structure, and analyze donation patterns:

import requests
import pandas as pd
from datetime import datetime

api_key = '[YOUR API KEY]'
url = f'{api_key}&committee_id=[COMMITTEE ID]&two_year_transaction_period=2022'

json_data = requests.get(url).json()

contributions = pd.json_normalize(json_data['results'])

# Filter to contributions over $10,000
large_donations = contributions[contributions['contribution_amount'] > 10000]

# Plot histogram of donation sizes
plt.hist(large_donations['contribution_amount'], bins=100)
plt.xticks([10000, 50000, 100000])
plt.ylim([0, 35])
plt.title("Committee Donation Sizes")
plt.ylabel("Number of Donations")
plt.xlabel("Donation Amount ($)")

Powerful visualizations like this give rapid insight into funding inequalities and imbalances. Website integrations through Flask or Django can share these compelling data stories with a wide audience.

For a full exposé, named entity recognition using Python’s NLTK can trace how bills serve donor interests:

import nltk
from nltk import ne_chunk

bill_text = open('bill_123_text.txt').read()
sentences = nltk.sent_tokenize(bill_text)

for sentence in sentences:
  tagged_words = nltk.pos_tag(nltk.word_tokenize(sentence))

  ne_chunked = ne_chunk(tagged_words)
  for chunk in ne_chunked:
    if hasattr(chunk, 'label'):
      print(chunk.label(), ' '.join(c[0] for c in chunk))

This outputs named entities like organizations, people, and locations referenced in legislation that might signal external influences.

Python gives citizens unmatched power to parse public records, follow the money trail, and directly question elected leaders about conflicts of interest.

Coordinating Events, Protests, and Campaigns

To transform online activism into real world change, Python can build the infrastructure to smoothly operate events, protests, and volunteer-run campaigns.

For example, this Flask web app lets people RSVP for an event and coordinates the volunteer schedule:

from flask import Flask, render_template, request
import sqlite3

app = Flask(__name__)

def homepage():
  return render_template('index.html')

@app.route('/rsvp', methods=['POST'])
def rsvp():
  name = request.form['name']
  email = request.form['email']

  # Store RSVPs into SQLite database
  connection = sqlite3.connect('rsvps.db')
  cursor = connection.cursor()

  insert_query = "INSERT INTO rsvps (name, email) VALUES (?, ?)"
  cursor.execute(insert_query, (name, email))


  return render_template('success.html')

if __name__ == '__main__':

The RSVPs stored in the database can facilitate reminders and day-of logistics. A similar approach builds the backend for volunteer sign-ups across shifts and roles.

Python frameworks like Django offer more robust tools for full-featured organizing platforms with user accounts, events calendars, mailing lists, and content management.

For maximum reach, Python site scrapers can identify reporters and influencers receptive to the cause. Automated tweeting then targets these users with relevant messages:

from twitter_scraper import get_tweets
import tweepy
import time

# Create API object
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth, wait_on_rate_limit=True)

for tweet in get_tweets('NYTimes OR WashPost OR CNN politics'):
  if 'climate change' in tweet.text:
    print('Relevant tweet found!')
    # Tweet content at reporter who authored the tweet
    api.update_status('@' + tweet.user + ' ' + customized_message, tweet.tweetId)
    time.sleep(60*5) # Delay to respect Twitter limits

This capitalizes on news hooks and conversations to broadcast the cause.

Python offers nearly endless possibilities for organizing smooth, data-driven advocacy campaigns.

Detecting Fake News and Misinformation with Machine Learning

In an era of rampant misinformation, Python’s machine learning capabilities can help automate fact checking and source verification. This preserves honest political discourse.

A simple starting point uses Python’s spaCy library for natural language processing to detect biased or misleading language:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "New research proves climate change is a hoax propagated by the left-wing media."

doc = nlp(text)

for token in doc:
  print(token.text, token.pos_, token.dep_)

# Detect biased nouns like 'hoax'
for token in doc:
  if token.pos_ == 'NOUN' and token.dep_ == 'attr':
    print("Bias noun detected:", token.text)

This basic linguistic analysis flags subjective or emotionally charged terms that hint at misinformation.

For robust fake news classification, we can build a machine learning model on a training dataset containing text samples labeled as true or false:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
import pandas as pd

# Load labeled dataset of true and fake news
df = pd.read_csv('news_dataset.csv')

vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(df['text'])

clf = PassiveAggressiveClassifier(), df['label'])

# Test the model
example = ["New data confirms climate change is accelerating faster than predicted"]
vectors_example = vectorizer.transform(example)

prediction = clf.predict(vectors_example)

# Outputs ['True']

Integrating a classifier like this into a web app allows users to instantly check political claims. The model can continue to learn over time, leading to more robust classifications.

Python’s machine learning prowess equips ordinary people to cut through political spin and lies.

Turning Out Voters with Targeted Registration and Messaging

Running voter registration drives and get-out-the-vote efforts are impactful ways Python can fuel participation in democracy.

For example, this script scrapes your local election office webpage to collect voter eligibility requirements:

from bs4 import BeautifulSoup
import requests

url = ''
page = requests.get(url)

soup = BeautifulSoup(page.content, 'html.parser')

reqs = soup.find(id='voter-requirements')

The parsed prerequisites help educate potential voters and simplify registration form filling.

To manage voter data, the Voting Information Project API integrates with Python to request voter files, validate registration status, and look up polling places:

import requests

api_key = '[YOUR API KEY]'
state = 'TX'
county = 'Travis'

registration_url = f'{state}&district-divisions={county}'
registration_data = requests.get(registration_url, headers={'Accept': 'application/json', 'x-api-key': api_key}).json()

for election in registration_data['elections']:
  print(election['title'], election['date'])

# Query voter info
voter_url = f"{api_key}&state={state}&county={county}"
voter_info = requests.get(voter_url).json()

Loaded into a Pandas DataFrame, this data fuels detailed demographic analysis to target underrepresented groups.

For direct voter outreach, the Twilio API from earlier allows sending personalized SMS reminder messages:

from import Client
import pandas as pd

# Load voter DataFrame
voters = pd.read_csv('voters.csv')

client = Client(account_sid, auth_token)

for index, row in voters[voters['party']=='Democrat'].iterrows():
  message = client.messages.create(
    body = f"This is Alex from the [ORGANIZATION]. Don't forget to vote on November 5th!",
    from_ = '[TWILIO PHONE NUMBER]',
    to = row['phone']

Targeted outreach and messaging drives participation from apathetic or marginalized audiences. Python data skills make inclusion, organizing, and mobilizing easier than ever.

Putting Python to Work for Causes You Care About

From analyzing legislation, to coordinating volunteers, to exposing shady donors, to mobilizing voters, Python is a versatile tool for an active, engaged democracy.

Here are some recommended ways to get started:

With this guide’s example code as a foundation, developers, analysts, students, and concerned citizens alike can start leveraging Python’s capabilities for civic engagement and grassroots activism. The limit is your imagination.

Python lowers the barriers to organizing people, gathering data, and building solutions. Whether analyzing unfair gerrymandered districts, creating police accountability databases, developing apps to report pollution violations, or any other worthy cause - Python is an essential tool to give citizens a greater voice.

Democracy is not a spectator sport. It is the responsibility of an informed public to boldly participate and shape society for the better. Python provides tangible technical skills to channel people’s energy and passion into effective action.

So whatever your issue, whatever your cause, consider how Python can enhance your impact. The future of our nation depends on everyday people standing up and demanding progress. Python helps turn that collective demand into meaningful, data-driven change. Our shared fates are closely intertwined, and we all must join in working towards a just, equitable, and prosperous society. Python is ready to serve as your trusty ally in that critical mission.