Data Filtering in Polars: A Modern Approach to DataFrame Operations

A Beginner’s Guide to Row Filtering with Python and Polars

Python

Polars

Data Analysis

Learn modern data filtering techniques with Polars! This beginner-friendly tutorial covers row filtering, multiple conditions, and lazy evaluation with clear explanations and hands-on exercises.

Author

Alierwai Reng

Published

February 8, 2026

Data Filtering in Polars: A Modern Approach to DataFrame Operations

A Beginner’s Guide to Row Filtering with Python and Polars

Learn modern data filtering techniques with Polars! This beginner-friendly tutorial covers row filtering, multiple conditions, and lazy evaluation with clear explanations and hands-on exercises.

Tested With

polars 1.37.1, Python 3.14.0

Introduction

Welcome to this hands-on Polars filtering tutorial! This guide showcases Polars—a blazingly fast DataFrame library for Python—and introduces essential filtering functions including filter(), is_in(), slice(), and lazy evaluation with .lazy() and .collect().

By the end of this guide, you’ll understand how to:

Filter rows using column expressions and boolean conditions
Select data by position with .slice() and .head()
Apply multiple conditions using logical operators
Work with lists of values using .is_in()
Optimize queries with lazy evaluation
Chain methods for readable, maintainable code

We’ll work through practical, reproducible examples using a small dataset. The techniques you learn are fully transferable to any dataset—from customer data to scientific measurements to business analytics.

Part 1: Environment Setup

Step 1: Load Required Packages

Every Python analysis starts by importing the libraries we need. For this tutorial, we only need Polars!

# Import Polars (convention: use 'pl' alias)
import polars as pl

# Confirmation
print("✅ Polars loaded successfully!")
print(f"📦 Polars version: {pl.__version__}")

✅ Polars loaded successfully!
📦 Polars version: 1.37.1

Package Installation

If you don’t have Polars installed, run this once in your terminal:

uv add "polars>=1.37.1"
pip install polars>=1.37.1

After installation, you only need to import it in each new Python session.

Version note: This tutorial uses Polars 1.37.1. Polars is actively developed, so some features may evolve in future versions.

Part 2: Creating Sample Data

Step 2: Create an Example DataFrame

Before filtering data, we need data to work with! We’ll create a small, reproducible DataFrame.

# Create a DataFrame from a dictionary
df = pl.DataFrame({
    "id": [1, 2, 3, 4, 5, 6],               # Unique identifier
    "int_col": [5, 12, 8, 20, 15, 3],       # Numeric values
    "str_col": ["yes", "no", "yes", "yes", "no", "yes"],  # Text categories
    "group": ["A", "A", "B", "B", "A", "B"] # Grouping variable
})

# Display the result
print(df)

shape: (6, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 1   ┆ 5       ┆ yes     ┆ A     │
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
│ 6   ┆ 3       ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Step 3: Examine Data Types

# Display schema (column names and types)
print(df.schema)

Schema({'id': Int64, 'int_col': Int64, 'str_col': String, 'group': String})

Part 3: Basic Row Filtering

Step 4: Filter with a Single Condition

result = df.filter(pl.col("str_col") == "yes")
print(result)

shape: (4, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 1   ┆ 5       ┆ yes     ┆ A     │
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 6   ┆ 3       ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Step 5: Filter with Numeric Comparisons

result = df.filter(pl.col("int_col") > 10)
print(result)

shape: (3, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
└─────┴─────────┴─────────┴───────┘

Part 4: Position-Based Selection

Step 6: Select Rows by Position

result = df.slice(0, 5)
print(result)

shape: (5, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 1   ┆ 5       ┆ yes     ┆ A     │
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
└─────┴─────────┴─────────┴───────┘

Use .slice(offset, length) for position-based selection, or .head(n) for the first n rows.

Step 7: Using `.head()` and `.tail()`

# First 3 rows
print("First 3 rows:")
print(df.head(3))

print("\nLast 3 rows:")
print(df.tail(3))

First 3 rows:
shape: (3, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 1   ┆ 5       ┆ yes     ┆ A     │
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 3   ┆ 8       ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Last 3 rows:
shape: (3, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
│ 6   ┆ 3       ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Part 5: Multiple Values and Conditions

Step 8: Filter Using Multiple Values

Use .is_in() to filter for membership in a list:

result = df.filter(pl.col("int_col").is_in([8, 20]))
print(result)

shape: (2, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Step 9: Combine Multiple Conditions

Real-world filtering often requires multiple conditions combined with AND or OR logic.

# Filter: int_col > 10 AND str_col == "yes"
result = df.filter(
    (pl.col("int_col") > 10) & (pl.col("str_col") == "yes")
)

print(result)

shape: (1, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 4   ┆ 20      ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Common Pitfall: Forgetting Parentheses

When combining conditions, each condition must be wrapped in parentheses.

Wrong:

# This will cause an error!
df.filter(pl.col("int_col") > 10 & pl.col("str_col") == "yes")

Correct:

# Wrap each condition in ()
df.filter((pl.col("int_col") > 10) & (pl.col("str_col") == "yes"))

This is due to Python’s operator precedence—parentheses ensure the comparison happens before the logical operation!

Step 10: OR Logic

# Filter: group == "A" OR int_col > 15
result = df.filter(
    (pl.col("group") == "A") | (pl.col("int_col") > 15)
)

print(result)

shape: (4, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 1   ┆ 5       ┆ yes     ┆ A     │
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
└─────┴─────────┴─────────┴───────┘

Logical Operators Reference

& — AND (both conditions must be True)
| — OR (at least one condition must be True)
~ — NOT (inverts the condition)

Example of NOT:

# Keep rows where str_col is NOT "yes"
df.filter(~(pl.col("str_col") == "yes"))

Part 6: Method Chaining and Readability

Step 11: Chain Multiple Filters

result = (
    df
    .filter(pl.col("str_col") == "yes")
    .filter(pl.col("int_col") > 10)
)

print(result)

shape: (1, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 4   ┆ 20      ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘

Chaining filters is more readable for complex logic; Polars optimizes both chained and combined conditions equally.

Step 12: Combine Filtering and Selection

result = (
    df
    .filter(pl.col("str_col") == "yes")
    .select(["id", "int_col"])
)

print(result)

shape: (4, 2)
┌─────┬─────────┐
│ id  ┆ int_col │
│ --- ┆ ---     │
│ i64 ┆ i64     │
╞═════╪═════════╡
│ 1   ┆ 5       │
│ 3   ┆ 8       │
│ 4   ┆ 20      │
│ 6   ┆ 3       │
└─────┴─────────┘

Part 7: Working with Python Variables

Step 13: Dynamic Filtering with Variables

Polars lets you use Python variables directly in filter expressions—no special syntax needed:

target_values = [8, 20]
min_threshold = 10

result1 = df.filter(pl.col("int_col").is_in(target_values))
result2 = df.filter(pl.col("int_col") > min_threshold)

print(result1)
print(result2)

shape: (2, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
└─────┴─────────┴─────────┴───────┘
shape: (3, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 4   ┆ 20      ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
└─────┴─────────┴─────────┴───────┘

Part 8: Lazy Evaluation

Step 14: Introduction to Lazy Mode

Lazy evaluation lets Polars plan and optimize your entire query before execution:

lazy_result = (
    df.lazy()
    .filter(pl.col("int_col") > 10)
    .filter(pl.col("str_col") == "yes")
    .select(["id", "int_col"])
)

result = lazy_result.collect()
print(result)

shape: (1, 2)
┌─────┬─────────┐
│ id  ┆ int_col │
│ --- ┆ ---     │
│ i64 ┆ i64     │
╞═════╪═════════╡
│ 4   ┆ 20      │
└─────┴─────────┘

Use lazy evaluation for complex queries, large datasets, and production pipelines. Call .collect() to execute the optimized plan.

Step 15: Viewing the Query Plan

Use .explain() to see how Polars optimizes your lazy query without executing it:

lazy_query = (
    df.lazy()
    .filter(pl.col("group") == "B")
    .filter(pl.col("int_col") > 5)
    .select(["id", "int_col"])
)

print(lazy_query.explain())

simple π 2/3 ["id", "int_col"]
  FILTER [([(col("group")) == (String(B))]) & ([(col("int_col")) > (5)])] FROM
    DF ["id", "int_col", "str_col", "group"]; PROJECT["id", "int_col", "group"] 3/4 COLUMNS

Part 9: Additional Filtering Methods

Step 16: Range Filtering with `.is_between()`

result = df.filter(pl.col("int_col").is_between(8, 15))
print(result)

shape: (3, 4)
┌─────┬─────────┬─────────┬───────┐
│ id  ┆ int_col ┆ str_col ┆ group │
│ --- ┆ ---     ┆ ---     ┆ ---   │
│ i64 ┆ i64     ┆ str     ┆ str   │
╞═════╪═════════╪═════════╪═══════╡
│ 2   ┆ 12      ┆ no      ┆ A     │
│ 3   ┆ 8       ┆ yes     ┆ B     │
│ 5   ┆ 15      ┆ no      ┆ A     │
└─────┴─────────┴─────────┴───────┘

.is_between() is inclusive by default; use closed="none" for exclusive bounds.

Step 17: Handling Null Values

# Create DataFrame with some null values
df_with_nulls = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value": [10, None, 30, None]
})

print("Original data:")
print(df_with_nulls)

# Filter: Keep only non-null values
print("\nNon-null rows:")
print(df_with_nulls.filter(pl.col("value").is_not_null()))

# Filter: Keep only null values
print("\nNull rows:")
print(df_with_nulls.filter(pl.col("value").is_null()))

Original data:
shape: (4, 2)
┌─────┬───────┐
│ id  ┆ value │
│ --- ┆ ---   │
│ i64 ┆ i64   │
╞═════╪═══════╡
│ 1   ┆ 10    │
│ 2   ┆ null  │
│ 3   ┆ 30    │
│ 4   ┆ null  │
└─────┴───────┘

Non-null rows:
shape: (2, 2)
┌─────┬───────┐
│ id  ┆ value │
│ --- ┆ ---   │
│ i64 ┆ i64   │
╞═════╪═══════╡
│ 1   ┆ 10    │
│ 3   ┆ 30    │
└─────┴───────┘

Null rows:
shape: (2, 2)
┌─────┬───────┐
│ id  ┆ value │
│ --- ┆ ---   │
│ i64 ┆ i64   │
╞═════╪═══════╡
│ 2   ┆ null  │
│ 4   ┆ null  │
└─────┴───────┘

Student Exercises

Reinforce your learning with hands-on practice using a separate dataset.

How to Use This Section

Attempt each exercise before checking expected results
Run your code and inspect the output
Compare your results with the expected output
Refine your code for clarity and correctness
Share solutions in the PyStatR+ Learning Community on Facebook: @PyStatRPlus-Learning-Community

Learning deepens when you explain your thinking and learn from others!

Exercise DataFrame

exercise_df = pl.DataFrame({
    "student_id": [101, 102, 103, 104, 105, 106],
    "score": [55, 78, 62, 91, 84, 47],
    "passed": ["no", "yes", "no", "yes", "yes", "no"],
    "cohort": ["A", "A", "B", "B", "A", "B"]
})

print(exercise_df)

shape: (6, 4)
┌────────────┬───────┬────────┬────────┐
│ student_id ┆ score ┆ passed ┆ cohort │
│ ---        ┆ ---   ┆ ---    ┆ ---    │
│ i64        ┆ i64   ┆ str    ┆ str    │
╞════════════╪═══════╪════════╪════════╡
│ 101        ┆ 55    ┆ no     ┆ A      │
│ 102        ┆ 78    ┆ yes    ┆ A      │
│ 103        ┆ 62    ┆ no     ┆ B      │
│ 104        ┆ 91    ┆ yes    ┆ B      │
│ 105        ┆ 84    ┆ yes    ┆ A      │
│ 106        ┆ 47    ┆ no     ┆ B      │
└────────────┴───────┴────────┴────────┘

Exercise 1 — Filter Students Who Passed

Goal: Keep only rows where passed == "yes".

# Your code here:

Expected result (student_id): [102, 104, 105]

Solution

exercise_df.filter(pl.col("passed") == "yes")

Alternative (with column selection):

exercise_df.filter(pl.col("passed") == "yes").select("student_id")

Exercise 2 — Select the First 4 Students by Position

Goal: Get the first 4 rows using position-based selection.

# Your code here:

Expected result (student_id): [101, 102, 103, 104]

Solution

# Method 1: Using .slice()
exercise_df.slice(0, 4)

# Method 2: Using .head()
exercise_df.head(4)

Exercise 3 — Filter Students with Specific Scores

Goal: Keep rows where score is either 62 or 91.

# Your code here:

Expected result (student_id): [103, 104]

Solution

exercise_df.filter(pl.col("score").is_in([62, 91]))

Exercise 4 — Multiple Conditions with AND

Goal: Find students who passed AND scored above 80.

# Your code here:

Expected result (student_id): [104, 105]

Solution

# Method 1: Single filter with &
exercise_df.filter(
    (pl.col("score") > 80) & (pl.col("passed") == "yes")
)

# Method 2: Chained filters
exercise_df.filter(
    pl.col("score") > 80
).filter(
    pl.col("passed") == "yes"
)

Exercise 5 — Use Python Variables in Filters

Goal: Store scores [62, 91] in a variable, then filter using .is_in().

# Your code here:

Expected result (student_id): [103, 104]

Solution

target_scores = [62, 91]
exercise_df.filter(pl.col("score").is_in(target_scores))

Exercise 6 — Filter and Select Specific Columns

Goal: Get only student_id and score for students who passed.

# Your code here:

Expected result:

shape: (3, 2)
┌────────────┬───────┐
│ student_id ┆ score │
│ ---        ┆ ---   │
│ i64        ┆ i64   │
╞════════════╪═══════╡
│ 102        ┆ 78    │
│ 104        ┆ 91    │
│ 105        ┆ 84    │
└────────────┴───────┘

Solution

exercise_df.filter(
    pl.col("passed") == "yes"
).select(["student_id", "score"])

Mini Challenge — Combine Cohort and Pass Status

Goal: Find students in cohort B who passed.

Example Solutions

# Method 1: Single filter with &
exercise_df.filter(
    (pl.col("cohort") == "B") & (pl.col("passed") == "yes")
)

# Method 2: Chained filters (more readable)
exercise_df.filter(
    pl.col("cohort") == "B"
).filter(
    pl.col("passed") == "yes"
)

# Method 3: With lazy evaluation
result = (
    exercise_df.lazy()
    .filter(pl.col("cohort") == "B")
    .filter(pl.col("passed") == "yes")
    .collect()
)
print(result)

Expected output:

shape: (1, 4)
┌────────────┬───────┬────────┬────────┐
│ student_id ┆ score ┆ passed ┆ cohort │
│ ---        ┆ ---   ┆ ---    ┆ ---    │
│ i64        ┆ i64   ┆ str    ┆ str    │
╞════════════╪═══════╪════════╪════════╡
│ 104        ┆ 91    ┆ yes    ┆ B      │
└────────────┴───────┴────────┴────────┘

Bonus Challenge — Lazy Evaluation Practice

Goal: Use lazy evaluation to find students who: - Scored between 60 and 85 (inclusive) - Are in cohort A or B - Return only student_id and score

Solution

result = (
    exercise_df.lazy()
    .filter(pl.col("score").is_between(60, 85))
    .filter(pl.col("cohort").is_in(["A", "B"]))
    .select(["student_id", "score"])
    .collect()
)
print(result)

Expected output:

shape: (3, 2)
┌────────────┬───────┐
│ student_id ┆ score │
│ ---        ┆ ---   │
│ i64        ┆ i64   │
╞════════════╪═══════╡
│ 102        ┆ 78    │
│ 103        ┆ 62    │
│ 105        ┆ 84    │
└────────────┴───────┘

Conclusion

Congratulations! You’ve completed a comprehensive introduction to data filtering with Polars in Python.

What You’ve Learned

Core Filtering Skills: ✅ Filtering rows with .filter() and pl.col() ✅ Position-based selection with .slice(), .head(), and .tail() ✅ Filtering multiple values with .is_in() ✅ Combining conditions using & (AND) and | (OR)

Advanced Techniques: ✅ Method chaining for readable code ✅ Using Python variables in filters ✅ Lazy evaluation with .lazy() and .collect() ✅ Range filtering with .is_between() ✅ Null value handling with .is_null() and .is_not_null()

Best Practices: ✅ Using pl.col() for column expressions ✅ Wrapping conditions in parentheses when combining ✅ Choosing between chained filters vs. combined conditions ✅ Understanding when lazy evaluation provides benefits

Next Steps for Learning

Beginner: 1. Practice filtering with your own datasets 2. Experiment with different comparison operators (>, <, !=) 3. Try combining 3 or more conditions

Intermediate: 4. Learn aggregations with .group_by() and .agg() 5. Explore window functions using .over() 6. Study joins with .join()

Advanced: 7. Master lazy evaluation for large datasets 8. Explore the .str namespace for text operations 9. Learn the .dt namespace for date/time operations 10. Contribute to the Polars community!

Resources: - Official Polars Documentation — Comprehensive guides and API reference - Polars User Guide — In-depth tutorials and concepts - Modern Polars — Community-driven cookbook - Polars GitHub — Source code and issue tracking

Alier Reng

Founder, Lead Educator & Creative Director at PyStatR+

Alier Reng is a Data Scientist, Educator, and Founder of PyStatR+, a platform advancing open and practical data science education. His work blends analytics, philosophy, and storytelling to make complex ideas human and empowering. Knowledge is freedom. Data is truth's language — ethics and transparency, its grammar.

Editor’s Note

This tutorial reflects a deliberate editorial balance between approachability and technical depth. While Polars offers many advanced features (including streaming mode, custom expressions, and plugin systems), this guide emphasizes the core filtering operations that analysts encounter daily.

The decision to introduce lazy evaluation in a beginner tutorial deserves explanation: although lazy mode is technically “advanced,” understanding its existence early helps learners grasp Polars’ performance advantages and builds good habits. We present lazy evaluation with clear examples and explanations, making it accessible rather than intimidating.

By introducing pl.col() expressions from the start, learners develop an intuition for Polars’ expression API—the foundation for all advanced operations. This approach aligns with the PyStatR+ Charter by emphasizing clarity, honesty, and accessibility without unnecessary complexity.

Acknowledgements

This lesson is part of the broader PyStatR+ Learning Platform, developed with gratitude to mentors, learners, and the open-source community that continually advances the Python data science ecosystem. Special thanks to the Polars development team for creating a library that combines performance with elegance, making data analysis faster and more enjoyable for everyone.

References

Polars Documentation — Official documentation and API reference
Polars User Guide — Comprehensive tutorials
Polars GitHub Repository — Source code and development
Apache Arrow — The columnar format underlying Polars

PyStatR+ — Learning Simplified. Communication Amplified. 🚀

Data Filtering in Polars: A Modern Approach to DataFrame Operations

A Beginner’s Guide to Row Filtering with Python and Polars

Introduction

Part 1: Environment Setup

Step 1: Load Required Packages

Part 2: Creating Sample Data

Step 2: Create an Example DataFrame

Step 3: Examine Data Types

Part 3: Basic Row Filtering

Step 4: Filter with a Single Condition

Step 5: Filter with Numeric Comparisons

Part 4: Position-Based Selection

Step 6: Select Rows by Position

Step 7: Using .head() and .tail()

Part 5: Multiple Values and Conditions

Step 8: Filter Using Multiple Values

Step 9: Combine Multiple Conditions

Step 10: OR Logic

Part 6: Method Chaining and Readability

Step 11: Chain Multiple Filters

Step 12: Combine Filtering and Selection

Part 7: Working with Python Variables

Step 13: Dynamic Filtering with Variables

Part 8: Lazy Evaluation

Step 14: Introduction to Lazy Mode

Step 15: Viewing the Query Plan

Part 9: Additional Filtering Methods

Step 16: Range Filtering with .is_between()

Step 17: Handling Null Values

Student Exercises

Exercise DataFrame

Exercise 1 — Filter Students Who Passed

Exercise 2 — Select the First 4 Students by Position

Exercise 3 — Filter Students with Specific Scores

Exercise 4 — Multiple Conditions with AND

Exercise 5 — Use Python Variables in Filters

Exercise 6 — Filter and Select Specific Columns

Mini Challenge — Combine Cohort and Pass Status

Bonus Challenge — Lazy Evaluation Practice

Conclusion

Alier Reng

Editor’s Note

Acknowledgements

References

Join the Conversation

Step 7: Using `.head()` and `.tail()`

Step 16: Range Filtering with `.is_between()`