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Advanced Techniques

Master advanced PXL patterns using the available functions and complex filtering expressions.

Available PXL Functions

Section titled “Available PXL Functions”

PXL provides six core functions for data transformation and text analysis:

Data Functions

Section titled “Data Functions”
  • filter(): Filter data based on conditions
  • ntile(): Create distribution tiles/quantiles

Text Analysis Functions

Section titled “Text Analysis Functions”
  • extract(): Extract specific information from text
  • classify(): Classify text into categories
  • score(): Create sentiment scores between two poles
  • word_count(): Count words in text columns

Advanced Filter Patterns

Section titled “Advanced Filter Patterns”

Numeric Comparisons

Section titled “Numeric Comparisons”
filter(revenue >= 1000)
filter(age > 25 and age < 65)
filter(score != 0)
filter(rating == 5)

String Comparisons

Section titled “String Comparisons”
filter(customer_type is "premium")
filter(status is not "cancelled")
filter(product_name like "iPhone")
filter(description not like "old")
filter(email != "")

Sequence Matching

Section titled “Sequence Matching”
filter(region in ("US", "Canada", "Mexico"))
filter(status not in ("cancelled", "refunded"))
filter(category in (1, 2, 3, 4))

Temporal Filtering

Section titled “Temporal Filtering”
filter(order_date after "2024-01-01")
filter(created_at before "2024-12-31")
filter(delivery_date between "2024-01-01" and "2024-03-31")

Complex Boolean Logic

Section titled “Complex Boolean Logic”
filter(revenue > 1000 and customer_type is "premium")
filter(status is "active" or last_login after "2024-01-01")
filter(age >= 18 and (country is "US" or country is "CA"))

Text Analysis Functions

Section titled “Text Analysis Functions”

Extract Information

Section titled “Extract Information”
extract("email addresses" from customer_notes)
extract("product names" from review_text)
extract("dollar amounts" from transaction_descriptions)

Classify Text

Section titled “Classify Text”
classify(feedback_text into (
  "positive": "happy, satisfied, great experience",
  "negative": "frustrated, disappointed, poor service",
  "neutral": "factual, informational, no strong emotion"
))


classify(support_tickets into (
  "billing",
  "technical",
  "feature_request",
  "complaint"
))

Score Sentiment

Section titled “Score Sentiment”
score(review_text from "terrible" to "amazing")
score(employee_feedback from "dissatisfied" to "highly_engaged")
score(product_reviews from "hate" to "love")

Count Words

Section titled “Count Words”
word_count(product_description)
word_count(customer_comments)

Data Distribution

Section titled “Data Distribution”

Create Quantiles

Section titled “Create Quantiles”
ntile(revenue, 4)    // Create quartiles
ntile(age, 10)       // Create deciles
ntile(score, 100)    // Create percentiles

Chaining Functions

Section titled “Chaining Functions”

Functions can be chained using the -> operator:

filter(customer_type is "premium") ->
extract("product mentions" from feedback_text)


filter(revenue > 10000) ->
score(satisfaction_survey from "very_dissatisfied" to "very_satisfied")


filter(order_date after "2024-01-01") ->
classify(support_tickets into ("billing", "technical", "shipping"))

Advanced Pattern Examples

Section titled “Advanced Pattern Examples”

Customer Segmentation Analysis

Section titled “Customer Segmentation Analysis”
// High-value customers with recent activity
filter(lifetime_value > 5000 and last_purchase after "2024-01-01")


// Premium customers with complaints
filter(tier is "premium") ->
classify(feedback into ("complaint", "praise", "suggestion", "question"))

Product Analysis

Section titled “Product Analysis”
// Recent product reviews
filter(review_date after "2024-06-01") ->
score(review_text from "disappointed" to "delighted")


// Extract features mentioned in feedback
filter(product_category in ("electronics", "software")) ->
extract("specific features mentioned" from customer_feedback)

Time-Based Analysis

Section titled “Time-Based Analysis”
// Weekend vs weekday analysis
filter(order_date between "2024-01-01" and "2024-12-31") ->
ntile(order_total, 5)


// Seasonal pattern analysis
filter(created_at between "2024-12-01" and "2024-12-31") ->
extract("holiday mentions" from customer_messages)

Syntax Rules

Section titled “Syntax Rules”

Identifiers

Section titled “Identifiers”
  • Column names: customer_id, order_date, revenue
  • Special identifier: $ (refers to current dataset)

Literals

Section titled “Literals”
  • Strings: Must be quoted with double quotes "premium"
  • Numbers: Support integers and decimals 1000, 99.99, -5
  • Dates: ISO format "2024-01-01" or with time "2024-01-01T10:30:00"
  • Booleans: true, false (case insensitive)
  • Null: null

Operators

Section titled “Operators”
  • Comparison: >, <, >=, <=, ==, !=
  • String: is, is not, like, not like
  • Set: in, not in, contains
  • Temporal: before, after, between
  • Boolean: and, or
  • Chaining: -> (connects function outputs)

Parentheses and Grouping

Section titled “Parentheses and Grouping”
  • Function parameters: filter(condition)
  • Boolean grouping: (condition1 or condition2) and condition3
  • Lists: ("value1", "value2", "value3")

Error Handling

Section titled “Error Handling”

Common syntax errors to avoid:

// ❌ Incorrect - missing quotes around strings
filter(status is premium)


// ✅ Correct - strings must be quoted
filter(status is "premium")


// ❌ Incorrect - wrong date format
filter(date after "01/01/2024")


// ✅ Correct - use ISO date format
filter(date after "2024-01-01")


// ❌ Incorrect - missing parentheses
filter revenue > 1000


// ✅ Correct - function calls need parentheses
filter(revenue > 1000)

What’s Next?

Section titled “What’s Next?”

Function Reference

Explore detailed examples and parameters for each PXL function.

Function Reference

Spreadsheet Interface

Learn how to use PXL within the spreadsheet interface for interactive data transformation.

Spreadsheet Guide