pldatacli 0.1.3

A data analysis cli tool using polars lazyframes

pldatacli

A simple command-line tool for quick CSV data analysis using Polars, with lazy execution for efficiency.

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Tech Stack

• Polars – fast DataFrame engine with lazy execution for efficient data processing
• Typer – modern CLI framework for building command-line interfaces
• Rich – beautiful terminal rendering for clean table output

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PyPi Repository

Check the Repository on PyPI - https://pypi.org/project/pldatacli/

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Installation

• Option 1: Directly with pip

pip install pldatacli

• Option2 : with uv package manager (Requires uv to be installed)

uv tool install pldatacli

Usage

Basic query command

pldatacli query FILE [OPTIONS]

Example file:

SampleSuperstore.csv

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Filter rows

Single filter:

pldatacli query SampleSuperstore.csv \
  --filter "State=Texas"

Multiple filters:

pldatacli query SampleSuperstore.csv \
  --filter "State=Texas" \
  --filter "Category=Furniture"

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Group by columns

Single column:

pldatacli query SampleSuperstore.csv \
  --groupby Region

Multiple columns:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --groupby Category

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Aggregations

Single aggregation:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=sum"

Multiple aggregations:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=sum,mean"

Multiple columns with aggregations:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --groupby Category \
  --agg "Sales=sum,mean" \
  --agg "Profit=sum"

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Sorting

Single sort:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=sum" \
  --sort "Profit_sum:desc"

Multiple sorts:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=sum" \
  --sort "Region:asc" \
  --sort "Profit_sum:desc"

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Rounding results

Round float columns to 2 digits:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=mean" \
  --round 2

Custom rounding:

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --agg "Profit=mean" \
  --round 4

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Limiting rows

Head:

pldatacli query SampleSuperstore.csv \
  --head 5

Tail:

pldatacli query SampleSuperstore.csv \
  --tail 10

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Full query example

pldatacli query SampleSuperstore.csv \
  --groupby Region \
  --groupby Category \
  --agg "Profit=sum,mean" \
  --sort "Profit_sum:desc" \
  --head 5 \
  --round 2

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Schema inspection

Get columns, dtypes, and null counts without processing the full dataset:

pldatacli schema SampleSuperstore.csv

Example output:

LazyFrame Schema
┏━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ Column       ┃ Dtype   ┃ Nulls ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ Ship Mode    │ String  │     0 │
│ Segment      │ String  │     0 │
│ Country      │ String  │     0 │
│ City         │ String  │     0 │
│ State        │ String  │     0 │
│ Postal Code  │ Int64   │     0 │
│ Region       │ String  │     0 │
│ Category     │ String  │     0 │
│ Sub-Category │ String  │     0 │
│ Sales        │ Float64 │     0 │
│ Quantity     │ Int64   │     0 │
│ Discount     │ Float64 │     0 │
│ Profit       │ Float64 │     0 │
└──────────────┴─────────┴───────┘
Rows: 9994, Columns: 13

⚡ Tip: Use schema before running queries to quickly inspect columns, types, and missing values.

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