deckfile 0.2.3

Generate high-quality charts from YAML definitions

deckfile

Generate high-quality charts from YAML.
Define data sources, transformations, and chart specs in a single deckfile.yaml, then run deck build.

Installation · Quick Start · CLI Reference · Configuration · Chart Types · Python API

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Overview

deckfile is a dbt-inspired tool for chart generation. Instead of writing Python scripts for every chart, you declare what you want in YAML and let deckfile handle the rendering.

• YAML-first: one config file defines all your charts
• SQL transforms: reshape data with DuckDB queries, reference upstream sources with ref()
• Multiple data sources: local CSV files, remote URLs, Google Sheets, Snowflake, and derived SQL views
• Automatic dependency resolution: sources that depend on other sources are resolved via topological sort
• Publication-ready output: high-DPI PNGs with customizable themes, branding, and annotations
• Fluent Python API: use the same rendering engine programmatically when you need more control

Installation

pip install deckfile

Or with uv:

uv tool install deckfile

Python 3.11+ is required.

Optional extras

Extra	Install	What it adds
svg	pip install "deckfile[svg]"	SVG logo support via CairoSVG
gsheets	pip install "deckfile[gsheets]"	Google Sheets as a data source
snowflake	pip install "deckfile[snowflake]"	Snowflake as a data source
all	pip install "deckfile[all]"	Everything above

SVG logo support (macOS)

If you use SVG logos and see no library called "cairo-2" was found, install the system-level Cairo library:

brew install cairo

On Apple Silicon Macs, you may also need:

export DYLD_LIBRARY_PATH=/opt/homebrew/lib

Add that line to your ~/.zshrc to make it permanent.

Quick Start

Scaffold a new project

deck init my-project
cd my-project

This creates:

my-project/
  deckfile.yaml          # chart definitions
  data/
    sample.csv           # sample data
  output/                # generated charts go here
  assets/                # logos, images
  .env.example
  .gitignore

Build charts

deck build               # build all charts
deck build -s my_chart   # build a specific chart
deck list                # list all defined charts

Minimal deckfile.yaml

defaults:
  output_dir: "./output"
  theme:
    brand: "#3a58ed"

sources:
  revenue:
    type: file
    path: "data/revenue.csv"

charts:
  monthly_revenue:
    title: "Monthly Revenue"
    subtitle: "Jan - Jun 2025"
    source: revenue
    type: bar
    columns:
      x: "month"
      y: "revenue"
    x_labels:
      mode: column
      column: "month"
    y_format:
      style: "$K"
      step: 10

deck build

CLI Reference

deck init [directory]                  Scaffold a new deckfile project
deck build [config] [-s CHART ...]     Build charts (default config: deckfile.yaml)
deck list [config]                     List all defined charts
deck ls [config]                       Alias for list

Flag	Description
-s, --select CHART [CHART ...]	Build only the specified chart(s)
--debug	Show full Python traceback on errors

Examples:

deck init                         # scaffold in current directory
deck init my-charts               # scaffold in ./my-charts/
deck build                        # build all charts from deckfile.yaml
deck build custom.yaml            # use a different config file
deck build -s chart_a -s chart_b  # build specific charts
deck list                         # list all charts with type and title

Configuration

A deckfile.yaml has three top-level sections:

defaults: # Global settings (theme, branding, output, figsize)
sources: # Named data sources
charts: # Chart definitions

Defaults

defaults:
  output_dir: "./output" # where to save charts
  figsize: [16, 8.5] # default [width, height] in inches
  theme:
    brand: "#3a58ed" # any theme parameter (see Theme section)
    title_size: 24.0
  branding:
    logo:
      path: "assets/logo.png"
      zoom: 0.18
    footer:
      text: "Company Inc. · Confidential"

Sources

Five source types are available:

File: local CSV

sources:
  revenue:
    type: file
    path: "data/revenue.csv"

URL: remote CSV

sources:
  metrics:
    type: url
    path: "https://example.com/data.csv"
    timeout: 30 # optional, in seconds (default: 30)

Google Sheets

Requires the gsheets extra and service account credentials via environment variables.

sources:
  pipeline:
    type: gsheet
    url: "https://docs.google.com/spreadsheets/d/SPREADSHEET_ID/edit"
    range: "'Sheet Name'!A2:D" # optional: sheet name, cell range, or both
    timeout: 60

Required environment variables (set in .env):

GOOGLE_AUTH_PRIVATE_KEY_ID=...
GOOGLE_AUTH_PRIVATE_KEY=...
GOOGLE_AUTH_EMAIL=...
GOOGLE_AUTH_CLIENT_ID=...

Snowflake

Requires the snowflake extra and credentials via environment variables. The query field is the SQL executed against the warehouse — the result becomes the source's data.

sources:
  revenue:
    type: snowflake
    query: |
      SELECT month, mrr
      FROM analytics.revenue_monthly
      ORDER BY month

    # Optional per-source overrides (otherwise read from env):
    warehouse: COMPUTE_WH
    database: ANALYTICS
    schema: PUBLIC
    role: ANALYST

Required environment variables (set in .env):

SNOWFLAKE_ACCOUNT=myorg-myaccount
SNOWFLAKE_USER=...

Plus one of the two auth modes:

Password auth:

SNOWFLAKE_PASSWORD=...

Key-pair auth (preferred for production / CI). The PEM key may be stored on a single line — literal \n sequences are converted to real newlines at load time:

SNOWFLAKE_PRIVATE_KEY="-----BEGIN RSA PRIVATE KEY-----\nMIIEpAIBA...qv6+ys7A==\n-----END RSA PRIVATE KEY-----"
SNOWFLAKE_PRIVATE_KEY_PASSPHRASE=...   # optional, only for encrypted keys

If both SNOWFLAKE_PRIVATE_KEY and SNOWFLAKE_PASSWORD are set, key-pair auth wins.

Optional environment variables (used as defaults; YAML keys override):

SNOWFLAKE_WAREHOUSE=COMPUTE_WH
SNOWFLAKE_DATABASE=ANALYTICS
SNOWFLAKE_SCHEMA=PUBLIC
SNOWFLAKE_ROLE=ANALYST

Snowflake uppercases unquoted identifiers, so SELECT mrr returns a column named MRR. Reference that exact name in your chart's columns: block, or alias with quoted identifiers (SELECT mrr AS "mrr") to preserve case.

Unlike other source types, the query field on a Snowflake source is the fetch itself, not a post-fetch DuckDB pass. To layer additional SQL on Snowflake results, use a type: dep source that ref()s the Snowflake source.

Dependent: SQL transformation over other sources

Use DuckDB SQL to join, aggregate, or reshape data from other sources. Reference upstream sources with ref().

sources:
  raw_data:
    type: file
    path: "data/raw.csv"

  quarterly:
    type: dep
    query: |
      SELECT date_trunc('quarter', created_at) AS quarter,
             SUM(revenue) AS revenue
      FROM ref(raw_data)
      GROUP BY 1
      ORDER BY 1

Dependencies are resolved automatically via topological sort. Circular references are detected and rejected.

Inline SQL on any source

Any file, url, or gsheet source can include an optional query field to run DuckDB SQL against the loaded data. The raw data is exposed as a table called source:

sources:
  filtered:
    type: file
    path: "data/all.csv"
    query: |
      SELECT * FROM source WHERE region = 'US'

Snowflake sources are an exception: their query field is the SQL run against the warehouse, not a post-fetch DuckDB pass.

Chart Types

Bar

type: bar
columns:
  x_date: "date_column" # or x: "category_column"
  y: "value_column"
params:
  color: "#3a58ed" # bar color (uses brand color if omitted)
  alpha: 0.7 # transparency
  width: 0.65 # bar width
  label: "Revenue" # legend label

Line

type: line
columns:
  x_date: "date_column"
  y: "value_column"
params:
  smooth: true # cubic spline interpolation (default: true)
  glow: true # glow effect around line (default: true)
  fill: false # fill area under curve
  fill_alpha: 0.07 # fill transparency
  subtle_bars: false # semi-transparent bars at data points
  color: "#3a58ed" # line color (uses palette if omitted)
  linewidth: 3.0
  linestyle: "-" # "-" solid, ":" dotted, "--" dashed, "-." dashdot
  label: "Revenue"

Stacked Bar

type: stacked_bar
columns:
  x_date: "date_column"
  layers:
    "Product A": "product_a_col"
    "Product B": "product_b_col"
    "Product C": "product_c_col"
params:
  width: 0.65
  colors:
    "Product A": "#3a58ed"
    "Product B": "#10b981"
    "Product C": "#f59e0b"
  alphas:
    "Product A": 0.85

Stacked Area

type: stacked_area
columns:
  x_date: "date_column"
  layers:
    "Segment A": "segment_a_col"
    "Segment B": "segment_b_col"
params:
  smooth: true # cubic spline smoothing (default: true)
  markers: true # show markers at data points (default: true)
  normalize: false # normalize to 100% stacked

Projection

Historical data with future scenario projections:

type: projection
columns:
  x_date: "date"
  y: "actual_revenue"
  projection_start: "2026-01-01"
  scenarios:
    "Base": "base_forecast"
    "Optimistic": "high_forecast"
    "Conservative": "low_forecast"
params:
  historical_color: "#1a1a2e"
  historical_label: "Actual"
  scenario_colors:
    "Base": "#3a58ed"
    "Optimistic": "#10b981"
    "Conservative": "#ef4444"
  scenario_styles:
    "Conservative": "dashed" # solid, dashed, dotted, dashdot
  fill_between: true # shaded fill between outer scenarios
  labels:
    "Base": "Base Case ($8M)" # custom legend labels

Combo (Dual Axis)

Mix bar and line series on shared or dual y-axes:

type: combo
columns:
  x_date: "date_column"
  series:
    Users:
      column: "users_col"
      type: line
      axis: left
      label_format: "{value:,.0f}"
    Messages:
      column: "messages_col"
      type: bar
      axis: right
      label_format: "{value_k:,.0f}k"
y_format:
  style: number
  step: 100
y_format_right:
  style: K_raw
  step: 30000
axis_labels:
  left: "USERS"
  right: "MESSAGES"

Chart Properties

These properties are available on all chart types:

chart_name:
  title: "Chart Title"
  subtitle: "Description text"
  source: source_name # reference a named source
  type: line # bar | line | stacked_bar | stacked_area | projection | combo

  columns:
    x_date: "date_column" # x-axis column (date-like)
    x: "category_column" # x-axis column (categorical)
    y: "value_column" # y-axis column

  # ── Data transforms ──
  transform:
    divide_y: 1000 # divide all y-values (e.g. to show thousands)
    date_range:
      start: "2025-01-01"
      end: "2025-12-31"
    sort: true # sort by x_date (auto-detected for ISO dates)

  # ── X-axis labels ──
  x_labels:
    mode: auto_date # auto_date | column | year_month | explicit
    column: "label_col" # for mode: column
    values: ["Q1", "Q2", "Q3"] # for mode: explicit
    fontsize: 10

  # ── Y-axis formatting ──
  y_format:
    style: "K" # see Y-Axis Formatters table
    step: 50 # major tick interval

  # ── Axis limits ──
  y_lim: { bottom: 0, top: 500 }
  x_lim: { left: 0, right: 12 }
  y_lim_right: { bottom: 0 } # for combo charts

  # ── Annotations ──
  annotations:
    endpoints:
      which: first_last # first_last | first | last | all
      format: "{value:,.0f}K" # format string with {value} or {value_k}
      halo: true # halo circle on endpoint
      offset: [0, 14] # text offset in points
      layer: "Layer Name" # target a specific layer in stacked charts
    points: # arbitrary point annotations
      - x: 5.0
        y: 100.0
        text: "Peak"
        color: "#ef4444"
        fontweight: bold
        dot: true

  # ── Separators ──
  separators:
    auto: true # auto-place at label boundaries
    trigger: "Jan" # label prefix that triggers a separator
    auto_projection: true # auto-place at projection boundary
    positions: [1.5, 5.5] # explicit x positions

  # ── Legend ──
  legend:
    loc: "upper left"
    enabled: true

  # ── Output ──
  figsize: [16, 8.5] # override default figure size
  output: "custom_filename.png" # override default output filename
  dpi: 300 # override default DPI
  transparent: false # transparent background

Y-Axis Formatters

Style	Example Output	Notes
"K"	1,234K	Values already in thousands
"M"	1.2M	Values already in millions
"$K"	$1,234K	Currency, thousands
"$M"	$1,234M	Currency, millions
"$K_raw"	$1.2K	Auto-divides raw values by 1,000
"$M_raw"	$1.2M	Auto-divides raw values by 1,000,000
"K_raw"	1.2K	Auto-divides raw values by 1,000
"%"	45%	Percentage
"number"	1,234	Comma-separated integer

Theme

All visual parameters are controlled through the theme system. Override any parameter in defaults.theme:

defaults:
  theme:
    brand: "#3a58ed"
    bg_color: "#ffffff"
    text_color: "#1a1a2e"
    title_size: 24.0
    bar_alpha: 0.7
    line_width: 3.0

All theme parameters

Colors

Parameter	Default	Description
brand	#3a58ed	Primary brand color
bg_color	#ffffff	Background color
text_color	#1a1a2e	Main text color
grid_color	#e8ebf0	Grid line color
subtle_text	#7c859b	Muted text color
separator	#dde1e8	Vertical separator color
palette	7-color cycle	Colors for multiple series

Typography

Parameter	Default	Description
font_family	sans-serif	Font family
title_size	24.0	Title font size
title_weight	bold	Title font weight
subtitle_size	12.5	Subtitle font size
axis_label_size	10.0	Axis label font size
tick_label_size	9.5	Tick label font size
annotation_size	10.0	Annotation font size
annotation_weight	bold	Annotation font weight
footer_size	8.5	Footer font size
legend_fontsize	10.5	Legend font size

Layout

Parameter	Default	Description
figure_width	16.0	Figure width in inches
figure_height	8.5	Figure height in inches
dpi	200	Output resolution
margin_left	0.085	Left margin (figure fraction)
margin_right	0.95	Right margin
margin_top	0.84	Top margin
margin_bottom	0.10	Bottom margin
pad_inches	0.5	Padding around chart

Grid

Parameter	Default	Description
grid_linewidth	0.7	Grid line width
y_grid	true	Show horizontal grid
x_grid	false	Show vertical grid

Lines

Parameter	Default	Description
line_width	3.0	Default line width
glow_width	8.0	Glow effect width
glow_alpha	0.10	Glow transparency

Bars

Parameter	Default	Description
bar_width	0.55	Bar width fraction
bar_alpha	0.7	Bar transparency
subtle_bar_width	0.45	Subtle bar width
subtle_bar_alpha	0.12	Subtle bar transparency

Endpoints

Parameter	Default	Description
endpoint_size	50.0	Scatter dot size
endpoint_edge_width	1.5	Dot edge width
halo_size	160.0	Halo circle size
halo_alpha	0.10	Halo transparency

Fill

Parameter	Default	Description
fill_alpha	0.07	Area fill transparency

Separators

Parameter	Default	Description
separator_linewidth	0.7	Separator line width
separator_alpha	0.6	Separator transparency

Legend

Parameter	Default	Description
legend_frameon	true	Show legend frame
legend_fancybox	true	Rounded legend frame
legend_borderpad	0.9	Legend border padding
legend_labelspacing	0.65	Space between legend items
legend_handlelength	2.8	Legend handle length
legend_linewidth	0.6	Legend frame line width
legend_alpha	0.95	Legend frame opacity

Interpolation

Parameter	Default	Description
smooth_points	200	Points in smoothed curve
spline_degree	3	Cubic spline degree

Branding

Add a logo and/or footer to all charts:

defaults:
  branding:
    logo:
      path: "assets/logo.png" # PNG or SVG (SVG requires deckfile[svg])
      zoom: 0.18 # scale factor
      position: [-0.02, 1.22] # [x, y] in axes fraction
    footer:
      text: "Company Inc. · Confidential"
      x: 0.89
      y: -0.02
      ha: right

Python API

deckfile exposes a fluent Python API for programmatic chart generation:

from deckfile import Chart, Theme, Branding

theme = Theme.default().replace(brand="#0d9488", title_size=28)

(
    Chart(theme=theme)
    .bar(x=[0, 1, 2, 3], y=[10, 25, 18, 30], label="Revenue")
    .x_labels(["Q1", "Q2", "Q3", "Q4"])
    .y_format("$K", step=10)
    .title("Quarterly Revenue")
    .subtitle("FY 2025")
    .save("revenue.png")
)

Chart methods

Method	Description
.bar(x, y, ...)	Add a bar series
.line(x, y, ...)	Add a line series
.stacked_bar(x, layers, ...)	Add a stacked bar group
.stacked_area(x, layers, ...)	Add a stacked area group
.projection(x_historical, y_historical, scenarios, x_projected, ...)	Add a projection chart
.combo(x, items)	Add a combo (dual axis) chart
.x_labels(labels)	Set x-axis tick labels
.y_format(style, step=...)	Configure y-axis formatting
.y_lim(bottom=, top=)	Set y-axis limits
.x_lim(left=, right=)	Set x-axis limits
.annotate_endpoints(...)	Annotate first/last/all data points
.annotate_point(x, y, text, ...)	Annotate a specific point
.separators(positions)	Add vertical separator lines
.auto_separators(labels, trigger)	Auto-place separators at label boundaries
.legend(loc=, enabled=)	Configure legend
.render()	Render and return (fig, ax) for further customization
.save(path)	Render and save to file
.show()	Render and display interactively

Build Archives

Every deck build automatically archives the output to output/.archive/<timestamp>/, so you can always compare against previous builds.

Dependencies

Package	Version	Purpose
matplotlib	>= 3.7	Chart rendering
numpy	>= 1.24	Numerical operations
scipy	>= 1.10	Spline interpolation
pyyaml	>= 6.0	YAML parsing
duckdb	>= 1.0	SQL query engine
python-dotenv	>= 1.0	Environment variable loading

Optional:

Package	Version	Extra	Purpose
cairosvg	>= 2.7	svg	SVG logo rendering
gspread	>= 6.0	gsheets	Google Sheets API
google-auth	>= 2.0	gsheets	Google service account auth
snowflake-connector-python	>= 3.0	snowflake	Snowflake warehouse driver

License

MIT