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Alex Spinov
Alex Spinov

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7 Python Libraries That Replaced My Paid Subscriptions

#beginners #python #discuss #productivity

I used to pay for 7 different SaaS tools. Then I discovered that Python libraries do the same thing for free.

Here's every subscription I cancelled and what replaced it.

1. Pandas → Replaced Excel ($14/mo)

I was paying for Microsoft 365 mainly for Excel. Pandas does everything Excel does, but programmable.

import pandas as pd

# Read any format
df = pd.read_csv("sales.csv")  # or .xlsx, .json, .sql, .html

# Pivot tables
pivot = df.pivot_table(values="revenue", index="month", columns="product", aggfunc="sum")

# VLOOKUP equivalent
merged = pd.merge(orders, customers, on="customer_id")
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Saved: $168/year

2. BeautifulSoup + Requests → Replaced ScrapingBee ($49/mo) 

import requests
from bs4 import BeautifulSoup

resp = requests.get("https://example.com")
soup = BeautifulSoup(resp.text, "html.parser")
prices = [el.text for el in soup.select(".price")]
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For 90% of scraping tasks, you don't need a paid service.

Saved: $588/year

3. Matplotlib + Seaborn → Replaced Tableau ($75/mo) 

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme()
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.lineplot(data=df, x="date", y="revenue", ax=axes[0])
sns.barplot(data=df, x="product", y="sales", ax=axes[1])
plt.savefig("dashboard.png", dpi=150)
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Saved: $900/year

4. Scikit-learn → Replaced DataRobot ($400/mo) 

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2%}")
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Saved: $4,800/year

5. Schedule + APScheduler → Replaced Zapier ($20/mo) 

import schedule
import time

def check_prices():
    # Your automation logic here
    pass

schedule.every(1).hour.do(check_prices)
schedule.every().monday.at("09:00").do(send_report)

while True:
    schedule.run_pending()
    time.sleep(60)
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Saved: $240/year

6. NLTK + spaCy → Replaced MonkeyLearn ($200/mo) 

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for ent in doc.ents:
    print(f"{ent.text} → {ent.label_}")
# Apple → ORG | U.K. → GPE | $1 billion → MONEY
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Saved: $2,400/year

7. SEC EDGAR API → Replaced Yahoo Finance Premium ($50/mo) 

import requests

headers = {"User-Agent": "MyApp (email@example.com)"}
url = "https://data.sec.gov/api/xbrl/companyfacts/CIK0001318605.json"
data = requests.get(url, headers=headers).json()
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📖 Full SEC EDGAR tutorial

Saved: $600/year

Total Savings: $9,696/year

Paid Tool Cost Python Replacement Cost
Excel (M365) $168/yr Pandas $0
ScrapingBee $588/yr BeautifulSoup $0
Tableau $900/yr Matplotlib + Seaborn $0
DataRobot $4,800/yr Scikit-learn $0
Zapier $240/yr Schedule $0
MonkeyLearn $2,400/yr spaCy + NLTK $0
Yahoo Finance $600/yr SEC EDGAR API $0
Total $9,696/yr $0

Obviously there's a time investment to learn these libraries. But once you do, you never go back.

What paid tool have you replaced with a free alternative?

I'm always looking for more swaps.

Follow for more developer tools and money-saving Python tips.

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