# Export Mesh Data to a DataFrame
# ===============================

# Convert a mesh's `point_data` or
# `cell_data` to a `pandas.DataFrame`
# or `pyarrow.Table` for downstream analytics, export, or interactive
# exploration in IDEs like Positron's Data Explorer, JupyterLab, or VS Code's
# data viewer.
# This example uses a classic CFD-style workflow: a scalar field and a vector
# field attached to a mesh, then filtered / aggregated / exported using
# pandas idioms.
import numpy as np
from pyvista import examples

# Load a mesh and attach some data
# --------------------------------

# Use the hexbeam fixture mesh and attach a pressure scalar plus a
# velocity vector.
mesh = examples.load_hexbeam()
rng = np.random.default_rng(seed=0)
mesh.point_data['pressure'] = rng.normal(loc=100.0, scale=20.0, size=mesh.n_points)
mesh.point_data['velocity'] = rng.normal(size=(mesh.n_points, 3))

# Convert to a `pandas.DataFrame`
# -------------------------------

# `mesh.to_pandas()` returns one row per point. Multi-component arrays are
# expanded to one column per component, named `{array_name}_{i}`.
df = mesh.to_pandas()
df.head()

# The DataFrame has `n_points` rows and one column per (expanded) array.
df.shape

# Ad-hoc analytics
# ----------------

# Since we have a DataFrame, the full pandas API is available: filter,
# derive new columns, summarize.
velocity = df[['velocity_0', 'velocity_1', 'velocity_2']].to_numpy()
df['speed'] = np.linalg.norm(velocity, axis=1)
df[['pressure', 'speed']].describe()

# Find the 5 highest-pressure points.
df.nlargest(5, 'pressure')[['pressure', 'speed']]

# Cell-level export
# -----------------

# Pass `association='cell'` to export `cell_data`
# instead. The result has `n_cells` rows.
mesh.cell_data['quality'] = rng.random(mesh.n_cells)
cell_df = mesh.to_pandas('cell')
cell_df.head()

# Export to disk
# --------------

# A DataFrame gives you one-liner access to every pandas I/O backend:
# Parquet, CSV, Feather, Excel, SQL, HDF5, and more. Commented out here to
# keep the gallery build clean.
# df.to_parquet('point_data.parquet')
# df.to_csv('point_data.csv', index=False)

# Zero-copy Arrow interchange
# ---------------------------

# `mesh`, `mesh.point_data`, `mesh.cell_data`, and `pyvista.Table`
# all implement the Arrow PyCapsule interface,
# so any Arrow-aware library (polars, DuckDB, ibis, narwhals) can consume them
# without pyvista depending on those libraries. For example, with pyarrow:
import pyarrow as pa

arrow_table = pa.table(mesh)
arrow_table.schema

# The `mesh`, `mesh.point_data`, and `mesh.cell_data` objects can also be
# opened directly in data-science IDE variable explorers (Positron, Jupyter,
# VS Code) after calling `to_pandas()`. The returned
# DataFrame renders as an interactive, sortable, filterable table.

# ----------------------------------------------------------------------
# Generated by sphinx-examples-as-code https://github.com/pyvista/sphinx-examples-as-code