dataplot 0.1.11

Provides plotting tools useful in datascience.

dataplot

Provides plotting tools useful in datascience.

A lightweight plotting library for data science that unifies data transformation and plotting in a single chainable API. dataplot is designed for fast exploration, teaching demos, and script-based analysis workflows.

Installation

$ pip install dataplot

Features

dataplot focuses on an analysis-first plotting workflow: data processing and visual diagnostics are written in one concise chain.

Capability	What it gives you	Typical API
📦 Data-as-object API	Treat raw arrays/series as first-class plotting objects with metadata and settings.	dp.data(...)
🔗 Composable transforms	Build reproducible feature pipelines before plotting.	.rolling().demean().zscore().rank()
📊 Statistical chart set	Switch between distribution, trend, and diagnostic views quickly.	.hist(), .plot(), .scatter(), .qqplot()
🧩 Artist-first composition	Compose multiple plots into one figure in a clean, deferred style.	dp.figure(artist1, artist2, ...)
🎛️ Layered settings model	Configure defaults at dataset / axes / figure scope with consistent fallback.	set_plot(), set_axes(), set_figure()
🐍 Scientific Python stack	Keep compatibility with familiar numeric and plotting ecosystems.	numpy, pandas, scipy, matplotlib, seaborn

In short: less boilerplate, clearer analysis flow, and more reusable plotting code.

Quick Start

import dataplot as dp
import numpy as np

raw = np.random.randn(300)
x = dp.data(raw, label="daily_return")

artist1 = x.hist(bins=30, alpha=0.7)
artist2 = x.qqplot(baseline="normal")

fig = dp.figure(artist1, artist2, title="Distribution diagnostics")
fig

Core Concepts

dp.data(...)

dp.data(...) is the entry point of dataplot. It converts array-like input (for example list, numpy.ndarray, pandas.Series, or another PlotDataSet) into a unified PlotDataSet object.

PlotDataSet is the core abstraction that carries:

• data values
• transformation history
• plot-level settings (labels, style hints, etc.)

This design keeps analysis context attached to the data itself.

import dataplot as dp
import numpy as np

raw = np.random.randn(300)
x = dp.data(raw, label="daily_return")

Typical Workflow

Raw data -> dp.data(...) -> transform chain -> artist(s) -> dp.figure(...)

You can think of dataplot as a 4-step loop:

1. Wrap your data with dp.data(...).
2. Transform it with chainable operations (rolling, zscore, rank, ...).
3. Render one or more Artist objects via plot methods.
4. Compose artists into a figure and apply final figure/axes settings.

Data Operations

PlotDataSet supports both arithmetic operators and built-in transforms:

• Arithmetic: + - * / **
• Log / power family: log() / log10() / signedlog() / signedlog10() / pow() / root() / sqrt() / ...
• Statistical transforms: rolling() / demean() / zscore() / rank(pct=True) / cumsum() / abs()
• State management: copy() / reset() / undo_all() / resample()

Operations are chainable, which is useful for quick experimentation:

x.zscore().rolling(5).rank(pct=True)

Plot Methods

Every plot method returns an Artist object instead of drawing immediately. This enables deferred composition and clean multi-panel figure assembly:

• Distribution: hist(...)
• Trend / relationship: plot(...), scatter(...)
• Goodness-of-fit diagnostics: qqplot(...), ppplot(...), ksplot(...)
• Structure overview: corrmap(...)

artist1 = x.hist(bins=30, alpha=0.7)
artist1  # show one plot

artist2 = x.qqplot(baseline="normal")
fig = dp.figure(artist1, artist2, title="Distribution diagnostics")
fig  # show both plots

Plot Settings

Common settings:

• title, xlabel, ylabel
• alpha, grid, grid_alpha
• style, figsize, dpi
• fontdict, legend_loc
• subplots_adjust
• reference_lines (for example: "y=x", "y=0", "x=1")

Requirements

validating
lazyr
loggings
matplotlib
numpy
pandas
scipy
seaborn

See Also

Github repository

• https://github.com/Chitaoji/dataplot/

PyPI project

• https://pypi.org/project/dataplot/

License

This project falls under the BSD 3-Clause License.

History

v0.1.11

• Added arithmetic operator support to MultiObject (including reflected operators), enabling element-wise math workflows for grouped PlotDataSet objects with length checks.
• Improved numerical robustness in utils.math by consistently sanitizing NaN/Inf inputs and validating finite sample counts in 1D linear regression.
• Refined PlotDataSet.plot() / PlotDataSet.scatter() x-tick handling to accept broader array-like inputs and convert them consistently.
• Added and expanded unit tests for core/container behavior and utility modules.

v0.1.10

• Renamed dist_or_sample= to baseline= in PlotDataSet.qqplot() for clearer baseline specification.
• Removed edge_precision= from PlotDataSet.ppplot() and PlotDataSet.ksplot(), and refined probability-range handling in the related diagnostic plotting flow.
• Improved QQ/PP plot readability by updating default axis labels and ensuring the rightmost x-axis tick label is preserved.

v0.1.9

• New method PlotDataSet.rank(pct=True) for rank transformation, supporting pct=False to return raw ranks.
• Updated scatter behavior with improved defaults and removed implicit x-axis sorting.
• Refactored plotting APIs to remove the ax constructor argument for a cleaner artist construction flow.

v0.1.8

• Improved naming inference in dp.data(...) when decorators from validating are involved.
• Refined module exports around artist helpers to make wildcard-style artist imports behave consistently.
• Refactored plot-setting fallback behavior to use dp.defaults as the global source of defaults.
• Enhanced get_setting(...) (including overload/type-hint coverage) so default values align with the corresponding setting types, and dict defaults are handled more safely.
• Removed legacy set_default() usage and simplified setting application paths across artists/containers.
• Added dependency loggings and updated warning emission in figure setting flows.

v0.1.7

• dp.data(...) can accept PlotDataSet objects now.
• FigWrapper.__enter__() now returns a copy safely via _entered_copy.
• Internal maintenance and stability refinements.

v0.1.6

• New method PlotDataSet.scatter() to draw true scatter charts while keeping PlotDataSet.plot() as line chart behavior.
• Improved automatic label inference for dp.data(...), plotting labels, and x-axis labels in interactive contexts.
• Plot builders now use deferred drawing; removed dp.show() and improved axis/figure rendering in object representations.
• Refined rendering stability with fixes for empty-axis cleanup and figure re-rendering in FigWrapper.__repr__.
• Updated minimum required Python version to >=3.13.

v0.1.5

• Fixed issue: unworking figure settings in the artist methods.

v0.1.4

• Fixed issue: incorrectly displayed histogram statistics when the x-label had been modified by the user.

v0.1.3

• Allowed users to set the plot-settings by kwargs in artist methods like PlotDataSet.hist(), PlotDataSet.plot(), etc.
• New operation methods PlotDataSet.signedpow() and PlotDataSet.log10().
• Renamed PlotDataSet.signlog() to .signedlog(); renamed PlotDataSet.opclear() to .undo_all(); removed PlotDataSet.opclear_records_only().
• New optional parameter format_label= for PlotDataSet.set_plot() to decide whether to format the label when painting on the axes.
• When defining the data classes, used dataclasses instead of attrs for a faster import.

v0.1.2

• New methods PlotDataSet.corrmap(), PlotDataSet.ppplot(), and PlotDataSet.resample().
• New optional parameter fmt= for PlotDataSet.plot(), PlotDataSet.qqplot(), PlotDataSet.ppplot(), and PlotDataSet.ksplot().
• Bugfix.

v0.1.1

• New module-level function dp.show().
• New methods PlotDataSet.qqplot(), PlotDataSet.ksplot() and PlotDataSet.abs().
• All the plotting method (e.g., .hist()) will now return an Artist object instead of None.
• New plot settings: grid and grid_alpha.
• Parameters of FigWrapper.set_figure(), AxesWrapper.set_axes() and PlotDataSet.set_plot() are keyword-only now.
• The returns of .set_figure() and .set_axes() will be None (instead of self) to avoid misunderstandings.
• New optional parameter inplace= for PlotDataSet.set_plot() to decide whether the changes will happen in-place (which is the only option before) or in a new copy.
• Parameter ticks= for PlotDataSet.plot() can be set to a PlotDataSet object now.

v0.1.0

• PlotDataSet now supports binary operations including +, -, *, /, and **.
• New methods FigWrapper.set_figure() and AxesWrapper.set_axes() - use them instead of *.set_plot().
• Simplified the usage of AxesWrapper.
• New plot settings: subplots_adjust=, fontdict= and dpi=.
• After this version, the required Python version is updated to >=3.11.9. Download and install v0.0.2 if the user is under lower Python version (>=3.8.13).

v0.0.2

• Updated the meta-data.

v0.0.1

• Initial release.