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Analyze Funding Histories

Analyze the funding history of your favorite projects. New to OSO? Check out our Getting Started guide to set up your API access.

This tutorial shows how to quickly lookup the funding history of your favorite projects, and then explore funding from specific sources (e.g. Gitcoin Grants, Open Collective, etc.)

Getting Started​

Before running any analysis, you'll need to set up your environment:

Start your Python notebook with the following:

import os
import pandas as pd
from pyoso import Client

OSO_API_KEY = os.environ['OSO_API_KEY']
client = Client(api_key=OSO_API_KEY)

For more details on setting up Python notebooks, see our guide on writing Python notebooks.

Basic Funding Analysis​

Project Funding History​

Get the complete funding history for a specific project:

query = """
SELECT
  time,
  event_source,
  from_project_name as funder,
  amount,
  grant_pool_name
FROM oss_funding_v0
WHERE to_project_name = 'uniswap'
ORDER BY time DESC
"""
df = client.to_pandas(query)

# Optional: Display total funding by source
print("Funding by source:")
print(df.groupby('event_source')['amount'].sum())

Top Funded Projects​

Find the projects that have received the most funding:

query = """
SELECT
  to_project_name,
  COUNT(DISTINCT event_source) as funding_sources,
  COUNT(*) as number_of_grants,
  SUM(amount) as total_funding
FROM oss_funding_v0
GROUP BY to_project_name
HAVING total_funding > 0
ORDER BY total_funding DESC
LIMIT 20
"""
df = client.to_pandas(query)

# Optional: Create a bar chart
import plotly.express as px

fig = px.bar(df, 
    x='to_project_name', 
    y='total_funding',
    title='Top Funded Projects'
)
fig.show()

Advanced Analysis​

Funding vs Development Activity​

Compare funding with development metrics to understand the relationship between financial support and project activity:

query = """
WITH project_funding AS (
  SELECT
    to_project_name,
    SUM(amount) as total_funding,
    COUNT(DISTINCT event_source) as funding_sources
  FROM oss_funding_v0
  GROUP BY to_project_name
)
SELECT
  f.to_project_name,
  f.total_funding,
  f.funding_sources,
  m.active_developer_count_6_months,
  m.commit_count_6_months,
  m.opened_issue_count_6_months,
  m.star_count,
  m.fork_count
FROM project_funding f
JOIN code_metrics_by_project_v1 m
  ON f.to_project_name = m.project_name
WHERE f.total_funding > 0
ORDER BY f.total_funding DESC
"""
df = client.to_pandas(query)

# Optional: Create a scatter plot
import plotly.express as px

fig = px.scatter(df, 
    x='total_funding', 
    y='commit_count_6_months',
    size='active_developer_count_6_months',
    hover_data=['to_project_name', 'funding_sources'],
    title='Funding vs Development Activity'
)
fig.show()

Funding Source Analysis​

Analyze the distribution and impact of different funding sources:

query = """
SELECT
  event_source,
  grant_pool_name,
  COUNT(DISTINCT to_project_name) as projects_funded,
  COUNT(*) as number_of_grants,
  SUM(amount) as total_funding,
  AVG(amount) as average_grant
FROM oss_funding_v0
GROUP BY event_source, grant_pool_name
HAVING total_funding > 0
ORDER BY total_funding DESC
"""
df = client.to_pandas(query)

# Optional: Create a treemap visualization
import plotly.express as px

fig = px.treemap(df, 
    path=[px.Constant('All Sources'), 'event_source', 'grant_pool_name'],
    values='total_funding',
    title='Funding Distribution by Source'
)
fig.show()

Social Network Funding Analysis​

Analyze funding patterns within social networks by combining funding data with social graph data:

query = """
SELECT DISTINCT
  donations.donor_address,
  users.user_source_id as fid,
  users.user_name as username,
  donations.project_name,
  amount_in_usd,
  timestamp
FROM gitcoin.all_donations as donations
JOIN artifacts_by_user_v1 as users
  ON lower(donations.donor_address) = users.artifact_name
WHERE
  user_source = 'FARCASTER'
  AND users.user_source_id IN (
    WITH max_date AS (
      SELECT max(date) as last_date
      FROM karma3.localtrust
    )
    SELECT CAST(j as string) as fid
    FROM karma3.localtrust
    WHERE i = 5650  -- Replace with your FID to see your network
    ORDER BY v DESC
    LIMIT 150
  )
ORDER BY amount_in_usd DESC
"""
df = client.to_pandas(query)

# Optional: Create a network visualization
import networkx as nx
import plotly.graph_objects as go

# Create a graph of donors and projects
G = nx.Graph()
for _, row in df.iterrows():
    G.add_edge(row['username'], row['project_name'], weight=row['amount_in_usd'])

# Create a spring layout
pos = nx.spring_layout(G)

# Create edges trace
edge_x = []
edge_y = []
for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_x.extend([x0, x1, None])
    edge_y.extend([y0, y1, None])

edges_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.5, color='#888'),
    hoverinfo='none',
    mode='lines')

# Create nodes trace
node_x = []
node_y = []
node_text = []
for node in G.nodes():
    x, y = pos[node]
    node_x.append(x)
    node_y.append(y)
    node_text.append(node)

nodes_trace = go.Scatter(
    x=node_x, y=node_y,
    mode='markers+text',
    hoverinfo='text',
    text=node_text,
    textposition="top center",
    marker=dict(size=10)
)

# Create the figure
fig = go.Figure(data=[edges_trace, nodes_trace],
    layout=go.Layout(
        showlegend=False,
        hovermode='closest',
        margin=dict(b=0,l=0,r=0,t=0),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
    )
)
fig.show()

For more examples of funding analysis, check out our Insights Repo.