datafusion-python

name	datafusion-python
description	Use when the user is writing datafusion-python (Apache DataFusion Python bindings) DataFrame or SQL code. Covers imports, data loading, DataFrame operations, expression building, SQL-to-DataFrame mappings, idiomatic patterns, and common pitfalls.

DataFusion Python DataFrame API Guide

What Is DataFusion?

DataFusion is an in-process query engine built on Apache Arrow. It is not a database -- there is no server, no connection string, and no external dependencies. You create a SessionContext, point it at data (Parquet, CSV, JSON, Arrow IPC, Pandas, Polars, or raw Python dicts/lists), and run queries using either SQL or the DataFrame API described below.

All data flows through Apache Arrow. The canonical Python implementation is PyArrow (pyarrow.RecordBatch / pyarrow.Table), but any library that conforms to the Arrow C Data Interface can interoperate with DataFusion.

Core Abstractions

Abstraction	Role	Key import
SessionContext	Entry point. Loads data, runs SQL, produces DataFrames.	from datafusion import SessionContext
DataFrame	Lazy query builder. Each method returns a new DataFrame.	Returned by context methods
Expr	Expression tree node (column ref, literal, function call, ...).	from datafusion import col, lit
functions	290+ built-in scalar, aggregate, and window functions.	from datafusion import functions as F
functions.spark	PySpark-compatible function surface (parameter names match pyspark.sql.functions).	from datafusion.functions import spark

Import Conventions

from datafusion import SessionContext, col, lit
from datafusion import functions as F
from datafusion.functions import spark   # only when porting pyspark code

Data Loading

ctx = SessionContext()

# From files
df = ctx.read_parquet("path/to/data.parquet")
df = ctx.read_csv("path/to/data.csv")
df = ctx.read_json("path/to/data.json")

# From Python objects
df = ctx.from_pydict({"a": [1, 2, 3], "b": ["x", "y", "z"]})
df = ctx.from_pylist([{"a": 1, "b": "x"}, {"a": 2, "b": "y"}])
df = ctx.from_pandas(pandas_df)
df = ctx.from_polars(polars_df)
df = ctx.from_arrow(arrow_table)
df = ctx.read_batch(record_batch)               # one pa.RecordBatch, no named table
df = ctx.read_batches([batch1, batch2])         # several pa.RecordBatch

# From SQL
df = ctx.sql("SELECT a, b FROM my_table WHERE a > 1")

To make a DataFrame queryable by name in SQL, register it first:

ctx.register_parquet("my_table", "path/to/data.parquet")
ctx.register_csv("my_table", "path/to/data.csv")

DataFrame Operations Quick Reference

Every method returns a new DataFrame (immutable/lazy). Chain them fluently.

Projection

df.select("a", "b")                         # preferred: plain names as strings
df.select(col("a"), (col("b") + 1).alias("b_plus_1"))  # use col()/Expr only when you need an expression

df.with_column("new_col", col("a") + lit(10))  # add one column
df.with_columns(
    col("a").alias("x"),
    y=col("b") + lit(1),                    # named keyword form
)

df.drop("unwanted_col")
df.with_column_renamed("old_name", "new_name")

When a column is referenced by name alone, pass the name as a string rather than wrapping it in col(). Reach for col() only when the projection needs arithmetic, aliasing, casting, or another expression operation.

Case sensitivity: both select("Name") and col("Name") lowercase the identifier. For a column whose real name has uppercase letters, embed double quotes inside the string: select('"MyCol"') or col('"MyCol"'). Without the inner quotes the lookup will fail with No field named mycol.

Filtering

df.filter(col("a") > 10)
df.filter(col("a") > 10, col("b") == "x")   # multiple = AND
df.filter("a > 10")                          # SQL expression string

Raw Python values on the right-hand side of a comparison are auto-wrapped into literals by the Expr operators, so prefer col("a") > 10 over col("a") > lit(10). See the Comparisons section and pitfall #2 for the full rule.

Aggregation

# GROUP BY a, compute sum(b) and count(*)
df.aggregate(["a"], [F.sum(col("b")), F.count(col("a"))])

# HAVING equivalent: use the filter keyword on the aggregate function
df.aggregate(
    ["region"],
    [F.sum(col("sales"), filter=col("sales") > 1000).alias("large_sales")],
)

As with select(), group keys can be passed as plain name strings. Reach for col(...) only when the grouping expression needs arithmetic, aliasing, casting, or another expression operation.

Most aggregate functions accept an optional filter keyword argument. When provided, only rows where the filter expression is true contribute to the aggregate.

Sorting

df.sort("a")                                 # ascending (plain name, preferred)
df.sort(col("a"))                            # ascending via col()
df.sort(col("a").sort(ascending=False))      # descending
df.sort(col("a").sort(nulls_first=False))    # override null placement

df.sort_by("a", "b")                         # ascending-only shortcut

As with select() and aggregate(), bare column references can be passed as plain name strings. A plain expression passed to sort() is already treated as ascending, so reach for col(...).sort(...) only when you need to override a default (descending order or null placement). Writing col("a").sort(ascending=True) is redundant.

For ascending-only sorts with no null-placement override, df.sort_by(...) is a shorter alias for df.sort(...).

Joining

# Equi-join on shared column name
df1.join(df2, on="key")
df1.join(df2, on="key", how="left")

# Different column names
df1.join(df2, left_on="id", right_on="fk_id", how="inner")

# Expression-based join (supports inequality predicates)
df1.join_on(df2, col("a") == col("b"), how="inner")

# Semi join: keep rows from left where a match exists in right (like EXISTS)
df1.join(df2, on="key", how="semi")

# Anti join: keep rows from left where NO match exists in right (like NOT EXISTS)
df1.join(df2, on="key", how="anti")

Join types: "inner", "left", "right", "full", "semi", "anti".

Inner is the default how. Prefer df1.join(df2, on="key") over df1.join(df2, on="key", how="inner") — drop how= unless you need a non-inner join type.

When the two sides' join columns have different native names, use left_on=/right_on= with the original names rather than aliasing one side to match the other — see pitfall #7.

Window Functions

from datafusion import WindowFrame

# Row number partitioned by group, ordered by value
df.window(
    F.row_number(
        partition_by=[col("group")],
        order_by=[col("value")],
    ).alias("rn")
)

# Using a Window object for reuse
from datafusion.expr import Window

win = Window(
    partition_by=[col("group")],
    order_by=[col("value").sort(ascending=True)],
)
df.select(
    col("group"),
    col("value"),
    F.sum(col("value")).over(win).alias("running_total"),
)

# With explicit frame bounds
win = Window(
    partition_by=[col("group")],
    order_by=[col("value").sort(ascending=True)],
    window_frame=WindowFrame("rows", 0, None),  # current row to unbounded following
)

Set Operations

df1.union(df2)                          # UNION ALL (by position)
df1.union(df2, distinct=True)           # UNION DISTINCT
df1.union_by_name(df2)                  # match columns by name, not position
df1.intersect(df2)                      # INTERSECT ALL
df1.intersect(df2, distinct=True)       # INTERSECT (distinct)
df1.except_all(df2)                     # EXCEPT ALL
df1.except_all(df2, distinct=True)      # EXCEPT (distinct)

Limit and Offset

df.limit(10)            # first 10 rows
df.limit(10, offset=20) # skip 20, then take 10

Deduplication

df.distinct()           # remove duplicate rows
df.distinct_on(         # keep first row per group (like DISTINCT ON in Postgres)
    [col("a")],                     # uniqueness columns
    [col("a"), col("b")],           # output columns
    [col("b").sort(ascending=True)], # which row to keep
)

Executing and Collecting Results

DataFrames are lazy until you collect.

df.show()                               # print formatted table to stdout
batches = df.collect()                  # list[pa.RecordBatch]
arr = df.collect_column("col_name")     # pa.Array | pa.ChunkedArray (single column)
table = df.to_arrow_table()             # pa.Table
pandas_df = df.to_pandas()              # pd.DataFrame
polars_df = df.to_polars()              # pl.DataFrame
py_dict = df.to_pydict()                # dict[str, list]
py_list = df.to_pylist()                # list[dict]
count = df.count()                      # int
df = df.cache()                         # materialize in memory, return DataFrame

Date and Timestamp Type Conversion

The Python type returned by to_pydict() / to_pylist() depends on the Arrow column type, and the mapping is inherited from PyArrow:

Arrow type	Python type returned
timestamp(s) / (ms) / (us)	datetime.datetime
timestamp(ns)	pandas.Timestamp
date32 / date64	datetime.date
duration(s) / (ms) / (us)	datetime.timedelta
duration(ns)	pandas.Timedelta

The nanosecond-precision fallback to pandas types is the main surprise: pandas is not a hard dependency of datafusion, but PyArrow reaches for it when datetime.datetime / datetime.timedelta would lose precision (stdlib types only go to microseconds). If you need plain stdlib types, cast to a coarser unit before collecting, e.g. df.select(col("ts").cast(pa.timestamp("us"))).

df.to_pandas() has its own footgun for dates: pandas has no pure-date dtype, so a date32/date64 column comes back as an object column of datetime.date values rather than datetime64[ns]. If downstream code expects a datetime column, cast on the DataFusion side first: col("ship_date").cast(pa.timestamp("ns")).

Streaming Results

Prefer streaming over collect() when the result is too large to materialize in memory, when you want to start processing before the query finishes, or when you may break out of the loop early. execute_stream() pulls one RecordBatch at a time from the execution plan rather than buffering the whole result up front.

# Single-partition stream; batch is a datafusion.RecordBatch
stream = df.execute_stream()
for batch in stream:
    process(batch.to_pyarrow())         # convert to pa.RecordBatch if needed

# DataFrame is iterable directly (delegates to execute_stream)
for batch in df:
    process(batch.to_pyarrow())

# One stream per partition, for parallel consumption
for stream in df.execute_stream_partitioned():
    for batch in stream:
        process(batch.to_pyarrow())

Async iteration is also supported via async for batch in df: ... (or df.execute_stream()), which is useful when batches are interleaved with other I/O.

Caching Intermediate Results

df.cache() materializes a DataFrame as an in-memory table and returns a new DataFrame backed by it. Reach for it when the same intermediate result feeds multiple downstream queries — without cache(), each branch re-executes the full upstream plan (re-reading files, recomputing filters/aggregates).

base = (
    ctx.read_parquet("orders.parquet")
    .filter(col("status") == "shipped")
    .cache()                                # materialize once, reuse below
)
by_region = base.aggregate(["region"], [F.sum(col("amount")).alias("total")])
by_customer = base.aggregate(["customer"], [F.sum(col("amount")).alias("total")])

Skip cache() for single-use DataFrames — the lazy plan is already optimal.

The cached table is owned by the DataFrame returned from cache() (and any DataFrames chained from it). To free the memory, drop every reference — let them go out of scope, or del base; del by_region; del by_customer.

Writing Results

df.write_parquet("output.parquet")
df.write_csv("output.csv")
df.write_json("output.json")

You can also pass a directory path (e.g., "output/") to write a multi-file partitioned output.

Expression Building

Column References and Literals

col("column_name")              # reference a column
lit(42)                          # integer literal
lit("hello")                     # string literal
lit(3.14)                        # float literal
lit(pa.scalar(value))            # PyArrow scalar (preserves Arrow type)

lit() accepts PyArrow scalars directly -- prefer this over converting Arrow data to Python and back when working with values extracted from query results.

Arithmetic

col("price") * col("quantity")            # multiplication
col("a") + lit(1)                          # addition
col("a") - col("b")                        # subtraction
col("a") / lit(2)                          # division
col("a") % lit(3)                          # modulo

Date Arithmetic

Date32 and Date64 columns both require Interval types for arithmetic, not Duration. Use PyArrow's month_day_nano_interval type, which takes a (months, days, nanos) tuple:

import pyarrow as pa

# Subtract 90 days from a date column
col("ship_date") - lit(pa.scalar((0, 90, 0), type=pa.month_day_nano_interval()))

# Subtract 3 months
col("ship_date") - lit(pa.scalar((3, 0, 0), type=pa.month_day_nano_interval()))

Important: lit(datetime.timedelta(days=90)) creates a Duration(µs) literal, which is not compatible with Date32/Date64 arithmetic (Duration(ms) and Duration(ns) are rejected too). Always use pa.month_day_nano_interval() for date operations.

Timestamps behave differently: Timestamp columns do accept Duration, so col("ts") - lit(datetime.timedelta(days=1)) works. The interval-only rule applies specifically to date columns.

Comparisons

col("a") > 10
col("a") >= 10
col("a") < 10
col("a") <= 10
col("a") == "x"
col("a") != "x"
col("a") == None                           # same as col("a").is_null()
col("a") != None                           # same as col("a").is_not_null()

Comparison operators auto-wrap the right-hand Python value into a literal, so writing col("a") > lit(10) is redundant. Drop the lit() in comparisons. Reach for lit() only when auto-wrapping does not apply — see pitfall #2.

Boolean Logic

Important: Python's and, or, not keywords do NOT work with Expr objects. You must use the bitwise operators:

(col("a") > 1) & (col("b") < 10)   # AND
(col("a") > 1) | (col("b") < 10)   # OR
~(col("a") > 1)                    # NOT

Always wrap each comparison in parentheses when combining with &, |, ~ because Python's operator precedence for bitwise operators is different from logical operators.

Null Handling

col("a").is_null()
col("a").is_not_null()
col("a").fill_null(lit(0))          # replace NULL with a value (single expression)
F.coalesce(col("a"), col("b"))     # first non-null value
F.nullif(col("a"), lit(0))         # return NULL if a == 0

To fill nulls across the whole DataFrame (optionally limited to a subset of columns), use the DataFrame-level method:

df.fill_null(0)                     # every column
df.fill_null(0, subset=["a", "b"])  # only these columns

CASE / WHEN

# Simple CASE (matching on a single expression)
status_label = (
    F.case(col("status"))
    .when(lit("A"), lit("Active"))
    .when(lit("I"), lit("Inactive"))
    .otherwise(lit("Unknown"))
)

# Searched CASE (each branch has its own predicate)
severity = (
    F.when(col("value") > 100, lit("high"))
    .when(col("value") > 50, lit("medium"))
    .otherwise(lit("low"))
)

Casting

import pyarrow as pa

col("a").cast(pa.float64())
col("a").cast(pa.utf8())
col("a").cast(pa.date32())

col("a").try_cast(pa.int32())        # like cast(), but yields NULL on failure instead of erroring

To cast several columns at once at the DataFrame level, pass a mapping to df.cast(...):

df.cast({"a": pa.float64(), "b": pa.int32()})

Aliasing

(col("a") + col("b")).alias("total")

BETWEEN and IN

col("a").between(1, 10)                                 # 1 <= a <= 10 (bounds auto-wrap)
F.in_list(col("a"), [lit(1), lit(2), lit(3)])           # a IN (1, 2, 3)
F.in_list(col("a"), [lit(1), lit(2)], negated=True)     # a NOT IN (1, 2)

Struct and Array Access

col("struct_col")["field_name"]     # access struct field
col("array_col")[0]                  # access array element (0-indexed)
col("array_col")[1:3]                # array slice (0-indexed)

Lambda Functions

Some array functions take a lambda function that runs once per element. Pass a Python lambda directly — its parameter names become the lambda parameters and its return value becomes the body:

F.array_transform(col("a"), lambda v: v * 2)    # map: [1,2,3] -> [2,4,6]
F.array_filter(col("a"), lambda v: v > 2)        # filter: [1,2,3] -> [3]
F.array_any_match(col("a"), lambda v: v > 3)     # predicate: any element > 3

For explicit parameter names, build the lambda by hand:

F.array_transform(col("a"), F.lambda_(["v"], F.lambda_var("v") * lit(2)))

SQL-to-DataFrame Reference

SQL	DataFrame API
SELECT a, b	df.select("a", "b")
SELECT a, b + 1 AS c	df.select(col("a"), (col("b") + lit(1)).alias("c"))
SELECT *, a + 1 AS c	df.with_column("c", col("a") + lit(1))
WHERE a > 10	df.filter(col("a") > 10)
GROUP BY a with SUM(b)	df.aggregate(["a"], [F.sum(col("b"))])
SUM(b) FILTER (WHERE b > 100)	F.sum(col("b"), filter=col("b") > 100)
ORDER BY a DESC	df.sort(col("a").sort(ascending=False))
LIMIT 10 OFFSET 5	df.limit(10, offset=5)
DISTINCT	df.distinct()
a INNER JOIN b ON a.id = b.id	a.join(b, on="id")
a LEFT JOIN b ON a.id = b.fk	a.join(b, left_on="id", right_on="fk", how="left")
WHERE EXISTS (SELECT ...)	a.join(b, on="key", how="semi")
WHERE NOT EXISTS (SELECT ...)	a.join(b, on="key", how="anti")
UNION ALL	df1.union(df2)
UNION (distinct)	df1.union(df2, distinct=True)
INTERSECT ALL	df1.intersect(df2)
INTERSECT (distinct)	df1.intersect(df2, distinct=True)
EXCEPT ALL	df1.except_all(df2)

Common Pitfalls

1. Boolean operators: Use &, |, ~ -- not Python's and, or, not. Always parenthesize: (col("a") > 1) & (col("b") < 2).

2. Wrapping scalars with lit(): Prefer raw Python values on the right-hand side of comparisons — col("a") > 10, col("name") == "Alice" — because the Expr comparison operators auto-wrap them. Writing col("a") > lit(10) is redundant. Reserve lit() for places where auto-wrapping does not apply:

   • standalone scalars passed into function calls: F.coalesce(col("a"), lit(0)), not F.coalesce(col("a"), 0)
   • arithmetic between two literals with no column involved: lit(1) - col("discount") is fine, but lit(1) - lit(2) needs both
   • values that must carry a specific Arrow type, via lit(pa.scalar(...))
   • .when(...), .otherwise(...), F.nullif(...), F.in_list(...) and similar method/function arguments (note: .between(...) auto-wraps its bounds, so col("a").between(1, 10) needs no lit())

3. Column name quoting: Column names are normalized to lowercase by default in both select("...") and col("..."). To reference a column with uppercase letters, use double quotes inside the string: select('"MyColumn"') or col('"MyColumn"').

4. DataFrames are immutable: Every method returns a new DataFrame. You must capture the return value:

   df = df.filter(col("a") > 1)   # correct
   df.filter(col("a") > 1)         # WRONG -- result is discarded
   

5. Window frame defaults: When using in a window, the default frame is . For a full partition frame, set .

Idiomatic Patterns

Fluent Chaining

result = (
    ctx.read_parquet("data.parquet")
    .filter(col("year") >= 2020)
    .select(col("region"), col("sales"))
    .aggregate(["region"], [F.sum(col("sales")).alias("total")])
    .sort(col("total").sort(ascending=False))
    .limit(10)
)
result.show()

Using Variables as CTEs

Instead of SQL CTEs (WITH ... AS), assign intermediate DataFrames to variables:

base = ctx.read_parquet("orders.parquet").filter(col("status") == "shipped")
by_region = base.aggregate(["region"], [F.sum(col("amount")).alias("total")])
top_regions = by_region.filter(col("total") > 10000)

Reusing Expressions as Variables

Just like DataFrames, expressions (Expr) can be stored in variables and used anywhere an Expr is expected. This is useful for building up complex expressions or reusing a computed value across multiple operations:

# Build an expression and reuse it
disc_price = col("price") * (lit(1) - col("discount"))
df = df.select(
    col("id"),
    disc_price.alias("disc_price"),
    (disc_price * (lit(1) + col("tax"))).alias("total"),
)

# Use a collected scalar as an expression
max_val = result_df.collect_column("max_price")[0]   # PyArrow scalar
cutoff = lit(max_val) - lit(pa.scalar((0, 90, 0), type=pa.month_day_nano_interval()))
df = df.filter(col("ship_date") <= cutoff)           # cutoff is already an Expr

Important: Do not wrap an Expr in lit(). lit() is for converting Python/PyArrow values into expressions. If a value is already an Expr, use it directly.

Window Functions for Scalar Subqueries

Where SQL uses a correlated scalar subquery, the idiomatic DataFrame approach is a window function:

-- SQL scalar subquery
SELECT *, (SELECT SUM(b) FROM t WHERE t.group = s.group) AS group_total FROM s

# DataFrame: window function
win = Window(partition_by=[col("group")])
df = df.with_column("group_total", F.sum(col("b")).over(win))

Semi/Anti Joins for EXISTS / NOT EXISTS

-- SQL: WHERE EXISTS (SELECT 1 FROM other WHERE other.key = main.key)
-- DataFrame:
result = main.join(other, on="key", how="semi")

-- SQL: WHERE NOT EXISTS (SELECT 1 FROM other WHERE other.key = main.key)
-- DataFrame:
result = main.join(other, on="key", how="anti")

Computed Columns

# Add computed columns while keeping all originals
df = df.with_column("full_name", F.concat(col("first"), lit(" "), col("last")))
df = df.with_column("discounted", col("price") * lit(0.9))

Available Functions (Categorized)

The functions module (imported as F) provides 290+ functions. Key categories:

Aggregate: sum, avg, min, max, count, count_star, median, stddev, stddev_pop, var_samp, var_pop, corr, covar, approx_distinct, approx_median, approx_percentile_cont, array_agg, string_agg, first_value, last_value, bit_and, bit_or, bit_xor, bool_and, bool_or, grouping, regr_* (9 regression functions)

Window: row_number, rank, dense_rank, percent_rank, cume_dist, ntile, lag, lead, first_value, last_value, nth_value

String: length, lower, upper, trim, ltrim, rtrim, lpad, rpad, starts_with, ends_with, contains, substr, substring, replace, reverse, repeat, split_part, concat, concat_ws, initcap, ascii, chr, left, right, strpos, translate, overlay, levenshtein

F.substr(str, start) takes only two arguments and returns the tail of the string from start onward — passing a third length argument raises TypeError: substr() takes 2 positional arguments but 3 were given. For the SQL-style 3-arg form (SUBSTRING(str FROM start FOR length)), use F.substring(col("s"), lit(start), lit(length)). For a fixed-length prefix, F.left(col("s"), lit(n)) is cleanest.

# WRONG — substr does not accept a length argument
F.substr(col("c_phone"), lit(1), lit(2))
# CORRECT
F.substring(col("c_phone"), lit(1), lit(2))   # explicit length
F.left(col("c_phone"), lit(2))                # prefix shortcut

Math: abs, ceil, floor, round, trunc, sqrt, cbrt, exp, ln, log, log2, log10, pow, signum, pi, random, factorial, gcd, lcm, greatest, least, sin/cos/tan and inverse/hyperbolic variants

Date/Time: now, today, current_date, current_time, current_timestamp, date_part, date_trunc, date_bin, extract, to_timestamp, to_timestamp_millis, to_timestamp_micros, to_timestamp_nanos, to_timestamp_seconds, to_unixtime, from_unixtime, make_date, make_time, to_date, to_time, to_local_time, date_format

Conditional: case, when, coalesce, nullif, ifnull, nvl, nvl2

Array/List: array, make_array, array_agg, array_length, array_element, array_slice, array_append, array_prepend, array_concat, array_contains, array_has, array_has_all, array_has_any, array_position, array_remove, array_distinct, array_sort, array_reverse, flatten, array_to_string, array_intersect, array_union, array_except, generate_series (Most array_* functions also have list_* aliases.)

Struct/Map: struct, named_struct, get_field, make_map, map_keys, map_values, map_entries, map_extract

Regex: regexp_like, regexp_match, regexp_replace, regexp_count, regexp_instr

Hash: md5, sha224, sha256, sha384, sha512, digest

Type: arrow_typeof, arrow_cast, arrow_try_cast, arrow_field, arrow_metadata, cast_to_type, with_metadata

Note: cast_to_type(value, type_ref, *, try_cast=False) is the single Python entry point for both upstream cast_to_type and try_cast_to_type; pass try_cast=True for the variant that returns NULL on failure.

Other: in_list, order_by, alias, col, encode, decode, to_hex, to_char, uuid, version, bit_length, octet_length

Spark-Compatible Functions

A separate datafusion.functions.spark namespace mirrors the pyspark.sql.functions API for callers porting code from PySpark.

from datafusion.functions import spark

Use it for DataFrame work; for SQL, register the Spark UDFs first:

ctx = SessionContext()
ctx.enable_spark_functions()                  # makes Spark UDFs visible to SQL
ctx.sql("SELECT sha2('hello', 256)").show()

Coverage spans aggregate, array, bitmap, bitwise, datetime, hash, JSON, map, math, string, URL, and conditional categories. The authoritative list of what is currently exposed is the __all__ in python/datafusion/functions/spark.py:

python -c "from datafusion.functions import spark; print(sorted(spark.__all__))"

When you need to know whether a specific pyspark function is available, check __all__ rather than this skill — the list there moves with the code; any enumeration here would drift.

Semantic divergences vs the default namespace. Functions that exist in both functions and functions.spark may behave differently:

Function	Default functions	functions.spark
concat	NULL inputs treated as empty	NULL inputs propagate to NULL
round	HALF_EVEN (banker's)	HALF_UP
trunc	Numeric truncation	Date truncation

Pick the namespace whose semantics match your intent — both stay imported side by side; enable_spark_functions() only affects SQL.

Parameter names match pyspark exactly. The spark namespace uses pyspark parameter names (col, str, numBits, partToExtract, ...) so you can paste pyspark code and keep keyword arguments working. The default namespace keeps DataFusion's parameter names.