Polars (Python) – Big Data Essentials (100% Practical)

Posted: Thursday, January 8, 2026

Polars (Python) – Big Data Essentials (100% Practical) is a hands-on introduction to working with large datasets efficiently using the Polars library. This course focuses on real-world data processing rather than theory, helping learners understand how to load, clean, transform, and analyze big data at high speed. By using Polars’ lazy execution, parallel processing, and memory-efficient design, students learn how to handle datasets that are too large or too slow for traditional tools like pandas.

Polars Practical Examples

Table of contents

• 1) Install + import
• 2) Read / write (CSV, Parquet, scan_*)
• 3) Inspect + schema + memory
• 4) Select / rename / cast
• 5) Filter (fast patterns)
• 6) with_columns + conditionals
• 7) group_by + aggregations
• 8) Window functions (over)
• 9) Joins (incl. asof)
• 10) Reshape (pivot/melt/explode)
• 11) Strings + dates + time series
• 12) Nulls + dedupe
• 13) Lazy optimization + debugging
• 14) Real pipelines (templates)
• 15) Performance checklist

1) Install + import (always)

Polars runs in Rust and uses vectorized execution. Most operations are “expressions” and are extremely fast.

pip install polars

import polars as pl

# Optional: nicer printing for notebooks/console
pl.Config.set_tbl_rows(12)
pl.Config.set_tbl_cols(12)
pl.Config.set_fmt_str_lengths(120)

2) Read / write (CSV, Parquet) — the big-data way

For large files: prefer scan_csv / scan_parquet (lazy). Use read_* when the dataset comfortably fits in memory.

Read small/medium CSV (eager)

df = pl.read_csv("data.csv")
df.head()

Scan large CSV (lazy) + collect at the end

lf = pl.scan_csv("data.csv")  # LazyFrame (no full read yet)

result = (
    lf
    .select(["id", "department", "salary"])  # projection pushdown
    .filter(pl.col("salary") > 50_000)      # predicate pushdown
    .group_by("department")
    .agg(
        pl.len().alias("rows"),
        pl.col("salary").mean().alias("avg_salary"),
    )
    .sort("avg_salary", descending=True)
    .collect()  # finally execute
)

Schema control (critical for messy CSVs)

schema = {
    "id": pl.Int64,
    "department": pl.Utf8,
    "salary": pl.Float64,
    "date": pl.Utf8,  # parse later (more reliable)
}

lf = pl.scan_csv("data.csv", schema=schema, ignore_errors=True)

Parquet (best for big analytics + storage)

# Read/scan Parquet
dfp = pl.read_parquet("data.parquet")
lfp = pl.scan_parquet("data.parquet")

# Write Parquet (fast + compressed)
df.write_parquet("out.parquet")

Lazy → write without pulling all data into memory

(
    pl.scan_csv("big.csv")
    .select(["id", "department", "salary"])
    .filter(pl.col("salary") > 0)
    .sink_parquet("clean.parquet")  # writes directly
)

3) Inspect data (fast checks that save you hours)

df.shape
df.schema
df.null_count()
df.describe()

df.estimated_size("mb")

lf = pl.scan_csv("big.csv")
lf.fetch(5)  # small sample for debugging (safe)

4) Select / rename / cast (core everyday operations)

df.select(["name", "salary"])

# Regex selection
df.select(pl.col("^sales_.*$"))

df = df.rename({"dept": "department"}).with_columns(
    pl.col("salary").cast(pl.Float64),
    pl.col("id").cast(pl.Int64),
)

5) Filter (fast patterns)

df.filter(pl.col("salary") > 50_000)

df.filter(
    (pl.col("age") > 30) &
    (pl.col("department") == "IT")
)

df.filter(pl.col("department").is_in(["IT", "HR"]))
df.filter(pl.col("salary").is_between(40_000, 120_000))
df.filter(pl.col("name").str.contains("john", literal=False))

6) Create/modify columns + conditionals (ETL essentials)

df = df.with_columns(
    (pl.col("salary") * 0.10).alias("bonus"),
    (pl.col("salary") + pl.col("bonus")).alias("salary_plus_bonus"),
)

df = df.with_columns(
    pl.when(pl.col("salary") >= 100_000).then(pl.lit("high"))
     .when(pl.col("salary") >= 60_000).then(pl.lit("mid"))
     .otherwise(pl.lit("low"))
     .alias("salary_band")
)

7) group_by + aggregations (your analytics workhorse)

out = df.group_by("department").agg(
    pl.len().alias("rows"),
    pl.col("salary").mean().alias("avg_salary"),
    pl.col("salary").max().alias("max_salary"),
    pl.col("salary").quantile(0.5).alias("median_salary"),
).sort("avg_salary", descending=True)

out = df.group_by(["department", "salary_band"]).agg(
    pl.len().alias("rows"),
    pl.col("salary").sum().alias("total_salary"),
)

8) Window functions (ranking, percent-of-total, running totals)

df2 = df.with_columns(
    pl.col("salary").rank("dense", descending=True).over("department").alias("dept_rank")
)

df2 = df.with_columns(
    (pl.col("salary") / pl.col("salary").sum().over("department")).alias("pct_of_dept_total")
)

9) Joins (data engineering core)

For big joins: keep only needed columns on both sides first (select), and make join keys the right dtype.

df = df1.join(df2, on="id", how="inner")
df = df1.join(df2, on="id", how="left")

# keep rows in df1 that have a match in df2
df_keep = df1.join(df2.select("id"), on="id", how="semi")

# keep rows in df1 that do NOT have a match in df2
df_drop = df1.join(df2.select("id"), on="id", how="anti")

# df_trades: [symbol, ts, price]
# df_quotes: [symbol, ts, bid, ask]
df_asof = df_trades.join_asof(
    df_quotes,
    on="ts",
    by="symbol",
    strategy="backward"
)

10) Reshape data (pivot/melt/explode)

# columns: [date, department, metric]
pivoted = df.pivot(
    values="metric",
    index="date",
    columns="department",
    aggregate_function="sum",
)

long = df.melt(
    id_vars=["id", "date"],
    value_vars=["sales", "returns"],
    variable_name="metric",
    value_name="value",
)

df2 = df.with_columns(
    pl.col("tags").str.split(",").alias("tags")
).explode("tags")

11) Strings + dates + time series

df = df.with_columns(
    pl.col("name").str.strip_chars().str.to_uppercase().alias("name_clean"),
)

df = df.with_columns(
    pl.col("date").str.strptime(pl.Date, "%Y-%m-%d", strict=False).alias("date")
)

# requires a Date/Datetime column
out = df.group_by_dynamic(
    index_column="date",
    every="1d",
    by="department",
).agg(
    pl.col("salary").mean().alias("avg_salary"),
    pl.len().alias("rows"),
)

12) Nulls + duplicates (data quality basics)

df.fill_null(0)
df.drop_nulls()

# Fill with different values per column
df = df.with_columns(
    pl.col("salary").fill_null(0),
    pl.col("department").fill_null("UNKNOWN"),
)

df_unique = df.unique(subset=["id"], keep="first")

13) Lazy optimization + debugging

lf = (
    pl.scan_csv("big.csv")
    .select(["id", "department", "salary", "date"])
    .filter(pl.col("salary") > 0)
)

print(lf.explain(optimized=True))

result = lf.collect(streaming=True)

14) Real pipeline templates

schema = {
    "id": pl.Int64,
    "department": pl.Utf8,
    "salary": pl.Float64,
    "date": pl.Utf8,
}

(
    pl.scan_csv("big.csv", schema=schema, ignore_errors=True)
    .with_columns(
        pl.col("date").str.strptime(pl.Date, "%Y-%m-%d", strict=False).alias("date"),
        pl.col("department").str.strip_chars().str.to_uppercase().alias("department"),
        pl.col("salary").fill_null(0),
    )
    .filter(pl.col("salary") > 0)
    .select(["id", "department", "salary", "date"])
    .sink_parquet("clean.parquet")
)

out = (
    pl.scan_parquet("clean.parquet")
    .group_by_dynamic("date", every="1d", by="department")
    .agg(
        pl.len().alias("rows"),
        pl.col("salary").sum().alias("sum_salary"),
        pl.col("salary").mean().alias("avg_salary"),
    )
    .sort(["date", "department"])
    .collect()
)

out.write_csv("daily_kpis.csv")

15) Performance checklist

• Use lazy: start with scan_csv/scan_parquet, end with collect() or sink_parquet().
• Select early: keep only the columns you need (projection pushdown).
• Filter early: push predicates before join/group_by.
• Prefer Parquet: convert raw CSV to Parquet once, then analyze fast forever.
• Avoid Python loops: use expressions and vectorized operations.
• Debug safely: use lf.fetch(5) and lf.explain(optimized=True).

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