Created
October 2, 2024 19:40
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"Godot's funding over time" code
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import pandas as pd | |
import matplotlib.pyplot as plt | |
# I had it as an xlsx but converted to csv for github, hopefully it won't have issues | |
file_path = "~/Godot-Funding.csv" | |
df = pd.read_csv(file_path) | |
# Data "cleaning" | |
df['Year'] = df['Year'].astype(int) | |
df['Month'] = df['Month'].astype(int) | |
df['Day'] = df['Day'].astype(int) | |
# August is completely missing so I dropped the first month | |
df = df[(df['Year'] != 2023) | (df['Month'] != 7)] | |
df['Date'] = pd.to_datetime(df[['Year', 'Month', 'Day']]) | |
df['Donors'] = df['Members'] + df['Sponsors'] | |
### First plot ### | |
fig, ax1 = plt.subplots(figsize=(12, 6)) | |
# Plot Income on the left y-axis | |
color = 'tab:blue' | |
ax1.set_xlabel('Date') | |
ax1.set_ylabel('Income', color=color) | |
ax1.plot(df['Date'], df['Income'], color=color) | |
ax1.tick_params(axis='y', labelcolor=color) | |
# Create a second y-axis for Donors | |
ax2 = ax1.twinx() | |
color = 'tab:red' | |
ax2.set_ylabel('Donors', color=color) | |
ax2.plot(df['Date'], df['Donors'], color=color, linestyle='--') | |
ax2.tick_params(axis='y', labelcolor=color) | |
# "Styling" and "fixes" | |
ax1.set_ylim([0, 65000]) | |
ax2.set_ylim([0, 2000]) | |
ax1.tick_params(axis='x', labelrotation=45) | |
plt.title('Income and Donors') | |
fig.tight_layout() | |
plt.show() | |
### Second plot ### | |
fig, ax1 = plt.subplots(figsize=(12, 6)) | |
# End of month & diff data | |
df_monthly = df.resample('M', on='Date').last() | |
df_monthly['Income_change'] = df_monthly['Income'].diff() | |
df_monthly['Donors_change'] = df_monthly['Donors'].diff() | |
# Calculate bar positions (x-values) | |
bar_width = 0.35 | |
index = range(len(df_monthly)) | |
x_income = [i - bar_width/2 for i in index] | |
x_donors = [i + bar_width/2 for i in index] | |
# Plot Income change on the left y-axis as a bar chart | |
color = 'tab:blue' | |
ax1.set_xlabel('Date') | |
ax1.set_ylabel('Income', color=color) | |
ax1.bar(x_income, df_monthly['Income_change'], width=bar_width, color=color, alpha=0.7, label='Income Change') | |
ax1.tick_params(axis='y', labelcolor=color) | |
# Create a second y-axis for Donors change | |
ax2 = ax1.twinx() | |
color = 'tab:red' | |
ax2.set_ylabel('Donors', color=color) | |
ax2.bar(x_donors, df_monthly['Donors_change'], width=bar_width, color=color, alpha=0.7, label='Donors Change') | |
ax2.tick_params(axis='y', labelcolor=color) | |
# Set y-axis limits to align zeros | |
ax1.set_ylim([-6000, 8000]) | |
ax2.set_ylim([-600, 800]) | |
# Set x-axis ticks to match the original dates | |
plt.xticks(index, df_monthly.index.strftime('%Y-%m'), rotation=45, ha='right') | |
ax1.tick_params(axis='x', labelrotation=45) | |
# Add a title and show the plot | |
plt.title('Monthly Increase/Decrease of Income and Donors') | |
fig.tight_layout() | |
plt.show() |
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Year | Month | Day | Income | Members | Sponsors | |
---|---|---|---|---|---|---|
2023 | 7 | 11 | 19146 | 255 | 6 | |
2023 | 7 | 12 | 19846 | 298 | 6 | |
2023 | 7 | 16 | 23060 | 400 | 8 | |
2023 | 7 | 18 | 23138 | 407 | 8 | |
2023 | 7 | 19 | 23300 | 418 | 8 | |
2023 | 7 | 20 | 25841 | 421 | 9 | |
2023 | 7 | 23 | 25952 | 432 | 9 | |
2023 | 9 | 12 | 25007 | 404 | 9 | |
2023 | 9 | 13 | 27701 | 579 | 9 | |
2023 | 9 | 14 | 30907 | 839 | 9 | |
2023 | 9 | 15 | 32490 | 968 | 9 | |
2023 | 9 | 16 | 33361 | 1067 | 9 | |
2023 | 9 | 17 | 44395 | 1119 | 10 | |
2023 | 9 | 18 | 44395 | 1119 | 10 | |
2023 | 9 | 19 | 46899 | 1294 | 10 | |
2023 | 9 | 20 | 47236 | 1322 | 10 | |
2023 | 9 | 21 | 49578 | 1391 | 10 | |
2023 | 9 | 23 | 50265 | 1437 | 11 | |
2023 | 9 | 24 | 50432 | 1471 | 11 | |
2023 | 9 | 26 | 49698 | 1500 | 10 | |
2023 | 9 | 28 | 49872 | 1522 | 10 | |
2023 | 9 | 29 | 49948 | 1536 | 10 | |
2023 | 9 | 30 | 50096 | 1551 | 10 | |
2023 | 10 | 1 | 50192 | 1561 | 10 | |
2023 | 10 | 2 | 50304 | 1568 | 10 | |
2023 | 10 | 3 | 50323 | 1569 | 10 | |
2023 | 10 | 4 | 50356 | 1572 | 10 | |
2023 | 10 | 5 | 50410 | 1581 | 10 | |
2023 | 10 | 6 | 50532 | 1583 | 10 | |
2023 | 10 | 7 | 50603 | 1586 | 10 | |
2023 | 10 | 8 | 50543 | 1578 | 10 | |
2023 | 10 | 9 | 51771 | 1595 | 11 | |
2023 | 10 | 10 | 51794 | 1598 | 11 | |
2023 | 10 | 11 | 51828 | 1598 | 11 | |
2023 | 10 | 12 | 51214 | 1579 | 11 | |
2023 | 10 | 13 | 51247 | 1574 | 11 | |
2023 | 10 | 14 | 50534 | 1566 | 13 | |
2023 | 10 | 15 | 49727 | 1505 | 13 | |
2023 | 10 | 16 | 50126 | 1551 | 13 | |
2023 | 10 | 17 | 49706 | 1543 | 13 | |
2023 | 10 | 18 | 49636 | 1550 | 13 | |
2023 | 10 | 19 | 49656 | 1547 | 13 | |
2023 | 10 | 20 | 49833 | 1544 | 13 | |
2023 | 10 | 21 | 49482 | 1545 | 13 | |
2023 | 10 | 22 | 49423 | 1538 | 13 | |
2023 | 10 | 23 | 49252 | 1531 | 13 | |
2023 | 10 | 24 | 50372 | 1542 | 14 | |
2023 | 10 | 25 | 50349 | 1541 | 14 | |
2023 | 10 | 26 | 50377 | 1542 | 14 | |
2023 | 10 | 27 | 50404 | 1547 | 14 | |
2023 | 10 | 28 | 50512 | 1549 | 14 | |
2023 | 10 | 29 | 50485 | 1547 | 14 | |
2023 | 10 | 30 | 50305 | 1530 | 14 | |
2023 | 10 | 31 | 50254 | 1517 | 14 | |
2023 | 11 | 1 | 50295 | 1522 | 14 | |
2023 | 11 | 2 | 50425 | 1542 | 14 | |
2023 | 11 | 3 | 50430 | 1542 | 14 | |
2023 | 11 | 4 | 50706 | 1542 | 15 | |
2023 | 11 | 9 | 50709 | 1550 | 15 | |
2023 | 11 | 27 | 55048 | 1511 | 16 | |
2023 | 12 | 3 | 55409 | 1548 | 16 | |
2023 | 12 | 6 | 55417 | 1550 | 16 | |
2023 | 12 | 11 | 55479 | 1555 | 16 | |
2023 | 12 | 19 | 55312 | 1523 | 16 | |
2024 | 1 | 2 | 54754 | 1507 | 16 | |
2024 | 1 | 4 | 54744 | 1507 | 16 | |
2024 | 1 | 8 | 54824 | 1501 | 16 | |
2024 | 1 | 11 | 53005 | 1337 | 17 | |
2024 | 1 | 19 | 53817 | 1425 | 17 | |
2024 | 1 | 21 | 54540 | 1447 | 17 | |
2024 | 1 | 27 | 54855 | 1469 | 17 | |
2024 | 2 | 4 | 54923 | 1478 | 17 | |
2024 | 2 | 5 | 54576 | 1474 | 17 | |
2024 | 2 | 6 | 54625 | 1470 | 17 | |
2024 | 2 | 7 | 54856 | 1476 | 17 | |
2024 | 2 | 8 | 54906 | 1482 | 17 | |
2024 | 3 | 7 | 53493 | 1470 | 18 | |
2024 | 3 | 24 | 54117 | 1434 | 18 | |
2024 | 3 | 26 | 54076 | 1432 | 18 | |
2024 | 4 | 9 | 53832 | 1427 | 19 | |
2024 | 4 | 16 | 52939 | 1347 | 19 | |
2024 | 4 | 18 | 52775 | 1360 | 19 | |
2024 | 5 | 3 | 54027 | 1443 | 17 | |
2024 | 5 | 4 | 54032 | 1444 | 17 | |
2024 | 5 | 16 | 54292 | 1449 | 17 | |
2024 | 5 | 23 | 54817 | 1454 | 18 | |
2024 | 5 | 30 | 54757 | 1444 | 18 | |
2024 | 5 | 31 | 51531 | 1446 | 16 | |
2024 | 6 | 14 | 52026 | 1455 | 17 | |
2024 | 6 | 22 | 51951 | 1454 | 17 | |
2024 | 6 | 24 | 52006 | 1457 | 17 | |
2024 | 6 | 29 | 51936 | 1450 | 17 | |
2024 | 7 | 5 | 52336 | 1442 | 18 | |
2024 | 7 | 9 | 52321 | 1441 | 18 | |
2024 | 7 | 11 | 52436 | 1451 | 18 | |
2024 | 7 | 12 | 52466 | 1456 | 18 | |
2024 | 7 | 13 | 52451 | 1457 | 18 | |
2024 | 7 | 15 | 52511 | 1461 | 18 | |
2024 | 7 | 16 | 52536 | 1462 | 18 | |
2024 | 8 | 15 | 52666 | 1474 | 18 | |
2024 | 8 | 22 | 59015 | 1486 | 19 | |
2024 | 8 | 28 | 59715 | 1494 | 19 | |
2024 | 8 | 31 | 59725 | 1492 | 19 | |
2024 | 9 | 15 | 59418 | 1494 | 19 | |
2024 | 9 | 17 | 59538 | 1495 | 19 | |
2024 | 9 | 19 | 59483 | 1491 | 19 | |
2024 | 9 | 28 | 54052 | 1483 | 18 | |
2024 | 9 | 29 | 54032 | 1481 | 18 | |
2024 | 9 | 30 | 54302 | 1484 | 18 | |
2024 | 10 | 1 | 56002 | 1533 | 18 | |
2024 | 10 | 2 | 56397 | 1550 | 18 |
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