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Pandas with Visualization & Others

Data Science isn't just about tables. Learn to visualize data directly from Pandas and integrate with SQL.

By TechCoder TeamLast updated: 2026-06-23
In a Nutshell

Data Science isn't just about tables. Learn to visualize data directly from Pandas and integrate with SQL. This hands-on tutorial focuses on practical implementation of pandas with visualization & others concepts.

Module 10: Pandas with Visualization & Other Libraries

Pandas plays nice with everyone. It has built-in plotting capabilities (wrapping Matplotlib) and speaks SQL fluently.

Lesson 21: Plotting with Pandas

You don't always need to import matplotlib.pyplot. Pandas creates plots directly from DataFrames.

  • df.plot(): Line plot (default).
  • df.plot.bar(): Bar chart.
  • df.plot.hist(): Histogram.
  • df.plot.scatter(x='ColA', y='ColB'): Scatter plot.
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Lesson 22: SQL & NumPy Integration

Pandas <-> SQL

  • pd.read_sql(query, connection): Runs a SQL query and returns a DataFrame.
  • df.to_sql("table_name", connection): Writes a DataFrame to a SQL database.

Pandas <-> NumPy

Pandas is built on NumPy.

  • df.values or df.to_numpy(): Converts DataFrame to a NumPy array (matrix).
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Practice: The Hybrid Analyst

Challenge:

  1. Create a dictionary of data.
  2. Convert it to a DataFrame.
  3. Extract the underlying NumPy array.
  4. (Conceptually) Explain what df.plot.hist() would show.

Quiz

Question 1 of 5

Which method creates a scatter plot directly from a DataFrame?

df.scatter()
df.plot.scatter(x='...', y='...')
df.charts.scatter()
plt.scatter(df)

Key Takeaways

✅ df.plot() is the fastest way to see your data.
✅ to_numpy() bridges the gap to Machine Learning.
✅ read_sql allows you to pull data directly from DBs into analysis.