### Development Setup for plquery

Source: https://github.com/pavelzw/plquery/blob/main/README.md

Provides instructions for setting up the plquery project for development. This includes cloning the repository, installing pre-commit hooks, running post-installation steps, and executing tests using pixi.

```bash
git clone https://github.com/pavelzw/plquery
cd plquery

pixi run pre-commit-install
pixi run postinstall
pixi run test
```

--------------------------------

### Install and Run plquery using pip

Source: https://github.com/pavelzw/plquery/blob/main/README.md

Installs the plquery package using pip and demonstrates how to run it to query a Parquet file. It also shows how to execute it within a temporary environment using uvx.

```bash
pip install plquery
# or to run it in a temporary environment
uvx plquery my-df.parquet
```

--------------------------------

### Install plquery (Bash)

Source: https://context7.com/pavelzw/plquery/llms.txt

Provides commands for installing plquery using different package managers. Includes installation via pixi (recommended), pip, conda, and a development setup guide.

```bash
# Install globally with pixi (recommended)
pixi global install plquery

# Install with pip
pip install plquery

# Install with conda
conda install -c conda-forge plquery

# Development installation
git clone https://github.com/pavelzw/plquery
cd plquery
pixi run pre-commit-install
pixi run postinstall
pixi run test
```

--------------------------------

### Install and Run plquery using conda-forge

Source: https://github.com/pavelzw/plquery/blob/main/README.md

Installs plquery globally using pixi and demonstrates running it to query a Parquet file. It also shows how to execute it in a temporary environment using pixi exec.

```bash
pixi global install plquery
# or to run it in a temporary environment
pixi exec plquery my-df.parquet
# or
pixi exec plq my-df.parquet
```

--------------------------------

### Interactive Polars Query Examples (Python)

Source: https://context7.com/pavelzw/plquery/llms.txt

Demonstrates various Polars expressions that can be executed within the plquery TUI. The 'df' variable is pre-loaded with the data. Examples include selecting, filtering, grouping, sorting, and describing data.

```python
# Select specific columns
df.select(pl.col("name"), pl.col("age"))

# Filter rows based on conditions
df.filter(pl.col("age") > 30)

# Group by and aggregate
df.group_by("department").agg(
    pl.count(),
    pl.mean("salary")
)

# Sort data
df.sort("created_at", descending=True)

# Chain multiple operations
df.filter(pl.col("status") == "active").select(
    pl.col("user_id"),
    pl.col("email")
).head(20)

# Get statistical summary
df.describe()

# Check schema
df.schema
```

--------------------------------

### Run plquery directly

Source: https://github.com/pavelzw/plquery/blob/main/README.md

Demonstrates the basic command to run the plquery binary, aliased as plq, to interactively query a specified Parquet file.

```bash
plquery my-df.parquet
# or
plq my-df.parquet
```

--------------------------------

### Initialize and Run PlqueryApp (Python)

Source: https://context7.com/pavelzw/plquery/llms.txt

Initializes the PlqueryApp with a Polars DataFrame and runs the interactive Textual TUI. Users can then enter Polars expressions directly in the TUI. Requires 'polars', 'plquery', and 'pathlib'.

```python
import polars as pl
from plquery import PlqueryApp, read_df
import pathlib

# Load data and create the app
df = read_df(pathlib.Path("sales.parquet"))
app = PlqueryApp(df)

# Run the interactive TUI
app.run()

# Once in the TUI, enter Polars expressions in the input field:
# df.select(pl.col("product"), pl.col("revenue"))
# df.filter(pl.col("revenue") > 1000)
# df.group_by("category").agg(pl.sum("revenue"))
# df.describe()
# df.head(10)
```

--------------------------------

### Launch plquery TUI (Bash)

Source: https://context7.com/pavelzw/plquery/llms.txt

Launches the plquery interactive terminal UI. Accepts a path to a parquet or CSV file as an argument. Supports direct execution or via package managers like pixi and uvx.

```bash
# Using the full command name
plquery my-data.parquet

# Using the short alias
plq my-data.parquet

# Loading a CSV file
plquery dataset.csv

# Running in a temporary environment with pixi
pixi exec plquery my-df.parquet

# Running in a temporary environment with uvx
uvx plquery my-df.parquet
```

--------------------------------

### Read DataFrame from File (Python)

Source: https://context7.com/pavelzw/plquery/llms.txt

Reads a dataframe from a specified file path, supporting both CSV and parquet formats. It returns a Polars DataFrame object. Requires the 'pathlib' and 'plquery' libraries.

```python
import pathlib
from plquery import read_df

# Read a parquet file
df = read_df(pathlib.Path("data.parquet"))
print(df.head())

# Read a CSV file
df = read_df(pathlib.Path("data.csv"))
print(df.shape)

# Example output for parquet file:
# shape: (5, 3)
# ┌─────┬───────┬────────┐
# │ id  │ name  │ value  │
# │ --- │ ---   │ ---    │
# │ i64 │ str   │ f64    │
# ╞═════╪═══════╪════════╡
# │ 1   │ Alice │ 100.0  │
# │ 2   │ Bob   │ 200.5  │
# │ 3   │ Carol │ 150.25 │
# └─────┴───────┴────────┘
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.