Is the Pandas: tabular data lesson free?

The interactive Pandas: tabular data lesson is part of Pro (₹299/month). The first 5 lessons of the track are free — no account or card needed.

How long does the Pandas: tabular data lesson take?

About 12 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Pandas: tabular data lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Pandas: tabular data lesson?

Advanced level. Assumes comfort with classes, functions, and standard library. Not for first-time Python learners.

Pandas: tabular data

Advanced
12 min
Fundamentals
advanced · pandas · data
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Lesson 38 of 43

In this track

You’ll be able to

Load CSV and Excel files into pandas DataFrames
Filter rows and select columns with boolean indexing
Group data with .groupby() and aggregate with .sum(), .mean()
Handle missing values with .dropna() and .fillna()

Why this matters

Pandas is the lingua franca of data engineering, ML feature pipelines, and analyst-facing ETL — every Airflow DAG and Jupyter notebook eventually touches a DataFrame. Staff engineers avoid .apply() and .iterrows() in favor of vectorized column ops, groupby().agg(), and merge with explicit how= and validate= to catch join-cardinality bugs early.

Common pitfalls

Chained assignment like df[df.x > 0]['y'] = 1 — triggers SettingWithCopyWarning; use .loc[mask, 'y'] = 1.
Merging without validate='one_to_one' — silent row explosions from duplicate keys ruin downstream counts.
Storing strings as object dtype — 10× more memory than category for low-cardinality columns like brand or region.

Advanced · 12 minNumPy: arrays at speedVectorized math in Python. Auto-installs NumPy in your browser on first run.Advanced · 12 minSeries & DataFrame: the two shapes of pandasThe two objects you'll use every day — a labeled column (Series) and a labeled table (DataFrame) — plus loc/iloc selection.Advanced · 14 minCleaning messy dataReal datasets arrive dirty: missing cells, wrong dtypes, duplicate rows. Fix them with isna/fillna/dropna, to_numeric, rename and drop_duplicates.

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You’ll be able to

Load CSV and Excel files into pandas DataFrames
Filter rows and select columns with boolean indexing
Group data with .groupby() and aggregate with .sum(), .mean()
Handle missing values with .dropna() and .fillna()

Why this matters

Common pitfalls

Chained assignment like df[df.x > 0]['y'] = 1 — triggers SettingWithCopyWarning; use .loc[mask, 'y'] = 1.
Merging without validate='one_to_one' — silent row explosions from duplicate keys ruin downstream counts.
Storing strings as object dtype — 10× more memory than category for low-cardinality columns like brand or region.

You’ll be able to

Why this matters

Common pitfalls

Related lessons

You’ll be able to

Why this matters

Common pitfalls

Related lessons