Menu
Sub-skill of polars: 1. DataFrame Creation and I/O.
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-1-dataframe-creation-and-ioThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
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.
Based on SOC occupation classification
Showing SKILL.md
| name | polars-1-dataframe-creation-and-io |
| description | Sub-skill of polars: 1. DataFrame Creation and I/O. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Creating DataFrames:
import polars as pl
import numpy as np
from datetime import datetime, date
# From Python dictionaries
df = pl.DataFrame({
"id": [1, 2, 3, 4, 5],
: [, , , , ],
: [, , , , ],
: [, , , , ],
: [
datetime(, , , , ),
datetime(, , , , ),
datetime(, , , , ),
datetime(, , , , ),
datetime(, , , , ),
]
})
(df)
()
()
np_data = np.random.randn(, )
df_numpy = pl.DataFrame(
np_data,
schema=[, , , , ]
)
records = [
{: , : },
{: , : },
{: , : }
]
df_records = pl.DataFrame(records)
df_typed = pl.DataFrame(
{
: [, , ],
: [, , ],
: [, , ]
},
schema={
: pl.Int32,
: pl.Float64,
: pl.Utf8
}
)
Reading Files:
# CSV files
df = pl.read_csv("data.csv")
# With options
df = pl.read_csv(
"data.csv",
separator=",",
has_header=True,
skip_rows=0,
n_rows=10000, # Read only first N rows
columns=["col1", "col2", "col3"], # Select columns
dtypes={"id": pl.Int64, "value": pl.Float32}, # Specify types
null_values=["NA", "N/A", ""],
ignore_errors=True,
try_parse_dates=True,
encoding="utf8"
)
# Parquet files (recommended for large data)
df = pl.read_parquet("data.parquet")
# Multiple Parquet files with globbing
df = pl.read_parquet("data/*.parquet")
# Parquet with row group filtering
df = pl.read_parquet(
"large_data.parquet",
columns=["id", "value", "date"],
n_rows=100000,
row_count_name="row_nr"
)
# JSON files
df = pl.read_json("data.json")
# JSON Lines (newline-delimited JSON)
df = pl.read_ndjson("data.jsonl")
# Excel files
df = pl.read_excel("data.xlsx", sheet_name="Sheet1")
# Delta Lake
df = pl.read_delta("delta_table/")
# From SQL database using ConnectorX (fast!)
df = pl.read_database(
query="SELECT * FROM sales WHERE date > '2025-01-01'",
connection="postgresql://user:pass@localhost/db"
)
# From URL
df = pl.read_csv("https://example.com/data.csv")
Writing Files:
# CSV
df.write_csv("output.csv")
# Parquet (recommended)
df.write_parquet(
"output.parquet",
compression="zstd", # zstd, lz4, snappy, gzip, brotli
compression_level=3,
statistics=True,
row_group_size=100000
)
# JSON
df.write_json("output.json", row_oriented=True)
df.write_ndjson("output.jsonl")
# Delta Lake
df.write_delta("delta_table/", mode="overwrite")
# IPC/Arrow format (fastest for inter-process communication)
df.write_ipc("output.arrow")