Menu
Sub-skill of polars: 4. GroupBy and Aggregations (+1).
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill polars-4-groupby-and-aggregationsThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Showing SKILL.md
Based on SOC occupation classification
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | polars-4-groupby-and-aggregations |
| description | Sub-skill of polars: 4. GroupBy and Aggregations (+1). |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Basic GroupBy:
import polars as pl
df = pl.DataFrame({
"category": ["A", "B", "A", "B", "A", "C"],
"subcategory": ["x", "y", "x", "y", "z", "x"],
"value": [100, 200, 150, 250, 175, 300],
"quantity": [10, 20, 15, 25, 12, 30]
})
# Simple aggregation
result = df.group_by("category").agg([
pl.col("value").sum().alias("total_value"),
pl.col("value").mean().alias("avg_value"),
pl.col("value").min().alias("min_value"),
pl.col("value").max().alias("max_value"),
pl.col("value").std().alias("std_value"),
pl.col("quantity").sum().alias("total_quantity"),
pl.count().alias("count")
])
print(result)
# Multiple group keys
result = df.group_by(["category", "subcategory"]).agg([
pl.col("value").sum(),
pl.count()
])
# Maintain order
result = df.group_by("category", maintain_order=True).agg(
pl.col("value").sum()
)
# Dynamic aggregations
agg_exprs = [
pl.col(c).mean().alias(f"{c}_mean")
for c in ["value", "quantity"]
]
result = df.group_by("category").agg(agg_exprs)
Advanced Aggregations:
# Multiple aggregations on same column
result = df.group_by("category").agg([
pl.col("value").sum().alias("sum"),
pl.col("value").mean().alias("mean"),
pl.col("value").median().alias("median"),
pl.col("value").quantile(0.25).alias("q25"),
pl.col("value").quantile(0.75).alias("q75"),
pl.col("value").var().alias("variance"),
pl.col("value").skew().alias("skewness")
])
# Conditional aggregations
result = df.group_by("category").agg([
pl.col("value").filter(pl.col("quantity") > 15).sum().alias("high_qty_value"),
pl.col("value").filter(pl.col("quantity") <= 15).sum().alias("low_qty_value")
])
# First/last values
result = df.group_by("category").agg([
pl.col("value").first().alias("first_value"),
pl.col("value").last().alias("last_value"),
pl.col("value").head(3).alias("top_3"),
pl.col("value").tail(2).alias("bottom_2")
])
# Unique values
result = df.group_by("category").agg([
pl.col().n_unique().alias(),
pl.col().unique().alias()
])
result = df.group_by().agg([
pl.col().map_elements(
s: s.to_numpy().std(ddof=),
return_dtype=pl.Float64
).alias()
])
Basic Window Functions:
import polars as pl
df = pl.DataFrame({
"date": pl.date_range(date(2025, 1, 1), date(2025, 1, 10), eager=True),
"category": ["A", "B"] * 5,
"value": [100, 110, 105, 115, 108, 120, 112, 125, 118, 130]
})
# Row number within groups
df.with_columns([
pl.col("value").rank().over("category").alias("rank"),
pl.col("value").rank(descending=True).over("category").alias("rank_desc")
])
# Running calculations
df.with_columns([
pl.col("value").cum_sum().over("category").alias("cumsum"),
pl.col("value").cum_max().over("category").alias("cummax"),
pl.col("value").cum_min().over("category").alias("cummin"),
pl.col("value").cum_count().over("category").alias("cumcount")
])
# Lag and lead
df.with_columns([
pl.col("value").shift(1).over("category").alias(),
pl.col().shift(-).over().alias(),
pl.col().shift().over().alias(),
(pl.col() - pl.col().shift().over()).alias()
])
df.with_columns([
pl.col().pct_change().over().alias()
])
Rolling Windows:
# Rolling calculations
df.with_columns([
pl.col("value").rolling_mean(window_size=3).over("category").alias("rolling_mean_3"),
pl.col("value").rolling_sum(window_size=3).over("category").alias("rolling_sum_3"),
pl.col("value").rolling_std(window_size=3).over("category").alias("rolling_std_3"),
pl.col("value").rolling_min(window_size=3).over("category").alias("rolling_min_3"),
pl.col("value").rolling_max(window_size=3).over("category").alias("rolling_max_3")
])
# Time-based rolling windows
df_ts = pl.DataFrame({
"timestamp": pl.datetime_range(
datetime(2025, 1, 1),
datetime(2025, 1, 10),
"1h",
eager=True
),
"value": range(217)
})
df_ts.with_columns([
pl.col("value").rolling_mean_by(
by="timestamp",
window_size="6h"
).alias("rolling_mean_6h"),
pl.col("value").rolling_sum_by(
by="timestamp",
window_size="1d"
).alias("rolling_sum_1d")
])
# Exponential weighted functions
df.with_columns([
pl.col("value").ewm_mean(span=3).alias(),
pl.col().ewm_std(span=).alias()
])