Installation

v0.3.0

Get started with ViewX in minutes

Quick Install

Install ViewX from PyPI using pip:

bash

python

pip install viewx

Verify the installation by importing the library and running the welcome function:

python

import viewx
viewx.welcome()

If you see a welcome message, ViewX is ready to use.

Requirements

The ViewX core needs only numpy, pandas and plotly. Everything else is optional:

Package	Minimum Version
Python	>= 3.8
numpy	>= 1.25
pandas	>= 2.1
plotly	>= 6.0
matplotlib	>= 3.8 (optional extra: static)
pylatex	>= 1.4.2 (optional extra: pdf)
kaleido	>= 0.2.1 (optional extra: export)

Optional Dependencies

ViewX supports optional extras for specialized output formats:

static

Static charts (PNG/SVG) via matplotlib for papers and PDFs.

pdf

PDF reports via pylatex + matplotlib (requires pdflatex on the system).

export

kaleido for exporting interactive (Plotly) figures to PNG/SVG/PDF.

all

static + pdf + export — every output engine fully enabled.

Install with extras:

bash

python

pip install 'viewx[static]'

bash

python

pip install 'viewx[pdf]'

bash

python

pip install 'viewx[all]'

Development Install

If you want to contribute or try the latest unreleased version, clone the repository and install in editable mode:

bash

python

git clone https://github.com/GhostAnalyst30/ViewX.git
cd ViewX
pip install -e .

This installs ViewX in development mode, so changes to the source code take effect immediately.

Quick Start

Once installed, build your first chart and dashboard in just a few lines:

python

import viewx as vx

df = vx.load_dataset("iris.csv")

vx.plot(df, x="sepal_length", y="petal_length").save("chart.html")
vx.Dashboard.auto(df).save("dash.html")

First Steps

Here is a typical workflow to get you started with ViewX:

Load Data

Start with a bundled dataset (vx.load_dataset()) or any pandas DataFrame you already have.

Explore with Charts

Call vx.plot(df, ...) for a quick chart — the kind is inferred from dtypes when omitted.

Analyze with DataMatrix

Run vx.DataMatrix(df).analyze() for a full EDA report with column profiles, correlations and quality alerts.

Report & Share

Export with Dashboard.auto(), Presentation.auto() or Report.auto() and share the result.

Troubleshooting

Common issues you might encounter and their solutions:

Command not found: pip

Ensure Python and pip are installed and added to your PATH. Try using python -m pip install viewx instead.

ImportError: No module named 'viewx'

The package may not be installed correctly. Re-run pip install viewx and verify with pip list.

PDF export fails: pdflatex not found

Report PDF export requires a LaTeX distribution with pdflatex (MiKTeX on Windows, TeX Live on Linux/macOS). Install it and make sure pdflatex is on your PATH, or export HTML dashboards instead.

Static chart export fails (PNG/SVG of interactive charts)

Exporting Plotly figures to static images requires the kaleido extra: pip install 'viewx[export]'. For plain static charts use static=True, which renders via matplotlib.

Charts do not render offline

Interactive HTML output loads Plotly from a CDN by default and needs an internet connection. For fully offline HTML you can inline the plotly.js bundle yourself.