chart-builder 0.3.1

A library for building customizable charts in Python

A Library for High-Quality, Reproducable Charts

• This library builds off Plotly and automates portions of data cleaning and charting for users.
• A user only inputs specified parameters such as chart type and columns to chart, enabling quick charting of complex data structures.

Visualization Pipeline Parameters

This document explains the parameters of the visualization_pipeline class, grouped by category.

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1. Core Settings

• chart_type: Type of chart (line, bar, pie, line_and_bar, ranked bar, etc.).
• title: Chart title.
• subtitle: Chart subtitle.
• auto_title: If True, automatically generates a title using column names.
• dimensions: Dict with chart size, e.g. dict(height=400, width=730).
• bgcolor: Background color (rgba string).
• autosize: If True, chart auto-scales to container.

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2. Data Input & Processing

• df: Pandas DataFrame containing the data.
• file: File path to load data from (optional).
• is_file_path: If True, treats file as a path to read from.
• delimiter: File delimiter (e.g., ,).
• cols_to_plot: Columns to include ('All' by default).
• groupby: Column used for grouping data.
• num_col: Numeric column used for y-axis values.
• bar_col / line_col: Set the columns used for charting in a line_and_bar chart type.
• index_col: Column to use as DataFrame index.
• time_col: Column to treat as datetime.
• set_time_col: Sets the estimated datetime column as index, but does NOT turn to datetime data type.
• turn_to_time: If True, converts time_col to datetime.
• resample_freq: Resampling frequency ('D', 'W', 'M').
• agg_func: Aggregation function ('sum', 'mean', etc.).
• start_date / end_date: Filters data within a date range.
• drop_duplicates: Drop duplicate rows if True.
• fillna / ffill: Fill missing values.
• keepna / dropna / dropna_col: NA handling options.
• clean_dates: If True, will drop latest data points in timeseries if it falls within the middle of the estimated time frequency (week, month, year).

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3. Axes & Ticks

• axes_data: Dict specifying axes data, e.g. dict(y1=[], y2=[]).
• axes_titles: Dict with axis titles, e.g. dict(y1="Y Axis", y2="Secondary Y Axis").
• axes_font_colors: Dict of axis font colors (y1, y2). Also take in auto to automatically use the color palette.
• tick0: Starting tick value ('min' for auto).
• dtick: Step size between ticks.
• tickformat: Dict for axis formatting, e.g. dict(x='%b %d <br>%y').
• tickprefix: Dict of prefixes for axis ticks (y1, y2).
• ticksuffix: Dict of suffixes for axis ticks (y1, y2).
• tickangle: Angle of tick labels.
• ytickvals: Custom y-axis tick values.
• yticktext: Custom y-axis tick labels.
• ytick_num: Number of y-axis ticks.
• custom_ticks: Apply custom tick formatting.
• min_tick_spacing: Minimum spacing between ticks.
• remove_zero: If True, removes zero from axis.
• y_log: Logarithmic y-axis if True.

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4. Sorting & Data Presentation

• sort_list: If True, sort categories automatically.
• user_sort_list: Custom list defining sort order.
• descending: Sort order (descending if True).
• cumulative_sort: If True, will sort data series using total sum.
• to_reverse: Reverse sort order if True.
• topn: Limit to top N values.
• normalize: Normalize values to percentage if True.
• to_percent: Multiply values by 100 to turn from decimal to percent.

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5. Visual Styling

• colors: Color palette.
• user_color_map: Map specific values → colors.
• use_single_color: Force single-color scheme if True.
• line_color: Line color used in the line_and_bar chart type.
• marker_col / y2_col: Columns for color or dual-axis mapping.
• marker_size: Size of scatter markers.
• line_width: Width of line traces.
• line_factor: Line scaling factor for a vertical dashed line.
• area: Fill under line if True.
• fill: Plotly fill type (tozeroy, tonexty, etc.).
• barmode: Bar mode (stack, group, etc.).
• orientation: Chart orientation ('v' vertical, 'h' horizontal).
• mode: Trace mode (lines, markers, etc.).
• traceorder: Legend item order ('normal', 'reversed').
• discrete: If True, treats values as discrete.

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6. Text & Labels

• text: Show text labels if True.
• text_freq: Frequency of text labels.
• textposition: Label position (inside, outside, etc.).
• text_font_size: Font size for labels.
• texttemplate: Custom text template for labels.
• textinfo: For pie charts ('percent+label', etc.).
• annotation_prefix / annotation_suffix: Prefix/suffix for annotation values.
• annotations: If True, show annotations for first and last data point.
• max_annotation: If True, show annotation for the highest value in the data.
• custom_annotation: Custom annotation text.
• annotation_font_size: Font size of annotations.

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7. Legend & Layout

• show_legend: Show legend if True.
• legend_orientation: Orientation ('v' vertical, 'h' horizontal).
• legend_placement: Dict for placement (dict(x=0.2, y=0.9)).
• legend_font_size: Font size of legend text.
• legend_background: Dict for legend box (bgcolor, border, etc.).
• itemsizing: Legend item sizing ('constant', 'trace').
• margin: Chart margins (l, r, t, b).

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8. Fonts & Theming

• font_size: Global font size.
• font_family: Font family (default: 'Cardo').
• font_color: Font color.
• logo: Path to logo overlay.

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9. Export & Saving

• directory / save_directory: Output directory for saved images.
• file_type: Export type ('svg', 'png', etc.).
• buffer: When the index is datetime type, this will add a buffer to the x axis with either integer or float used to represent number of days.
• hole_size: Pie/donut hole size (0–1).
• days_first: Parses datetime column assuming days occur first in the string.

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