Getting started?
├─ Install Polars → ./references/quickstart.md
├─ Create first DataFrame → ./references/quickstart.md
├─ Understand lazy vs eager → ./references/quickstart.md
├─ Learn expression syntax → ./references/expressions.md
└─ Coming from Pandas → ./references/interop.md

Loading/saving data?
├─ Read CSV file → ./references/io-data.md
├─ Read Parquet (recommended) → ./references/io-data.md
├─ Read JSON/NDJSON → ./references/io-data.md
├─ Read from database → ./references/io-data.md
├─ Read multiple files (glob) → ./references/io-data.md
├─ Write to file → ./references/io-data.md
└─ Larger-than-memory data → ./references/performance.md

Filtering/selecting?
├─ Select columns by name → ./references/dataframes-series.md
├─ Select by pattern/regex → ./references/dataframes-series.md
├─ Select by data type → ./references/dataframes-series.md
├─ Filter rows by condition → ./references/dataframes-series.md
├─ Filter with multiple conditions → ./references/dataframes-series.md
├─ Handle null values → ./references/dataframes-series.md
└─ Add/modify columns → ./references/dataframes-series.md

Aggregating data?
├─ Basic statistics (sum, mean, etc.) → ./references/aggregations-grouping.md
├─ Group by columns → ./references/aggregations-grouping.md
├─ Multiple aggregations → ./references/aggregations-grouping.md
├─ Window functions (over) → ./references/aggregations-grouping.md
├─ Rolling/moving averages → ./references/aggregations-grouping.md
├─ Cumulative operations → ./references/aggregations-grouping.md
└─ Ranking within groups → ./references/aggregations-grouping.md

Combining data?
├─ Join two DataFrames → ./references/joins-concat.md
├─ Left/right/outer join → ./references/joins-concat.md
├─ Anti-join (not in) → ./references/joins-concat.md
├─ Concatenate vertically → ./references/joins-concat.md
├─ Pivot (long to wide) → ./references/joins-concat.md
└─ Unpivot/melt (wide to long) → ./references/joins-concat.md

Performance issues?
├─ Use lazy evaluation → ./references/performance.md
├─ Avoid row iteration → ./references/performance.md
├─ Reduce memory usage → ./references/performance.md
├─ Process large files → ./references/performance.md
├─ Optimize query plan → ./references/performance.md
└─ Common anti-patterns → ./references/performance.md

Having issues?
├─ Type errors → ./references/gotchas.md
├─ Null handling → ./references/gotchas.md
├─ Expression context errors → ./references/gotchas.md
├─ String operations → ./references/strings-datetime-categorical.md
├─ Date parsing issues → ./references/strings-datetime-categorical.md
├─ Performance problems → ./references/gotchas.md
├─ Pandas migration issues → ./references/gotchas.md
├─ Memory errors → ./references/gotchas.md
└─ General troubleshooting → ./references/gotchas.md

import polars as pl
import polars.selectors as cs  # For column selection by type

# Eager: immediate execution
df = pl.read_csv("data.csv")

# Lazy: deferred, optimized execution (preferred for large data)
lf = pl.scan_csv("data.csv")
df = lf.collect()  # Execute when ready

# Select columns
df.select("a", "b")
df.select(pl.col("a"), pl.col("b"))
df.select(pl.all().exclude("id"))

# Filter rows
df.filter(pl.col("a") > 10)
df.filter((pl.col("a") > 10) & (pl.col("b") == "x"))

# Add/modify columns
df.with_columns(
    (pl.col("a") * 2).alias("a_doubled"),
    pl.col("b").str.to_uppercase().alias("b_upper")
)

# Conditional column
df.with_columns(
    pl.when(pl.col("a") > 10)
      .then(pl.lit("high"))
      .otherwise(pl.lit("low"))
      .alias("category")
)

# Group and aggregate
df.group_by("category").agg(
    pl.col("value").sum().alias("total"),
    pl.col("value").mean().alias("average"),
    pl.len().alias("count")
)

# I/O
df = pl.read_csv/parquet/json("file")
lf = pl.scan_csv/parquet/ndjson("file")  # Lazy
df.write_csv/parquet/json("file")

# Selection
df.select("a", "b")
df.select(cs.numeric())  # By type

# Filtering
df.filter(pl.col("a") > 1)

# Aggregation
df.group_by("key").agg(pl.col("val").sum())

# Joining
df1.join(df2, on="key", how="left")

# Sorting
df.sort("col", descending=True)

# Lazy execution
lf.collect()  # Run query
lf.explain()  # Show plan