pandas-plots
a comprehensive python package for enhanced data visualization and analysis with pandas dataframes. provides a high-level api for creating beautiful tables, plots, venn diagrams, and utility functions with minimal code.
- pandas-plots
📦 installation
install package using uv (recommended)
uv add -U pandas-plots
use in python source
from pandas_plots import tbl, pls, hlp, const
🚀 features
- table utilities (
tbl): style dataframes as HTML tables with heatmaps, totals, kpi indicators, and percentages; describe column distributions with a single call - plotting functions (
pls): plotly-based box plots, histograms, stacked bars, sankey diagrams, upset plots, facet charts, and more - helper functions (
hlp): configure rendering for markdown export, cascade duckdb filters with counts, notebook and file utilities - cli (
cli): convert Jupyter notebooks to Markdown or HTML for publishing
📤 publishing
[!TIP] this package enables
.ipynbpublishing:
- converts notebooks to
markdownorhtml- styled pandas tables are rendered as images, retaining all features
- images can be scaled for better visual impression
- supports
githubandgitlabanchors- supports github auto theme (
theme="system")- supports alternative texts in images
- html output can use image folders or inline base64 encoding
- supports easy csv export of diagram data
- can force pdf friendly options to avoid pdf artifacts
notebook setup
call hlp.setup_rendering() at the top of your notebook to configure alle rendering settings:
# src/my_notebook.ipynb
from pandas_plots import hlp
hlp.setup_rendering(
static=True, # True for static rendering, False for interactive
apply_dark_theme=False
)
convert to markdown
# src/convert.ipynb
from pandas_plots.cli.converter import jupyter_to_md
jupyter_to_md(
path="src/my_notebook.ipynb",
output_dir="./docs", # output folder (created if missing)
no_input=True, # strip input cells from output
execute=True, # re-execute notebook before converting
theme="system", # None | "light" | "dark" | "system"
chrome_path="/Applications/Chromium.app/Contents/MacOS/Chromium" # path to `ungoogled-chromium` binary
)
convert to html
from pandas_plots.cli.converter import jupyter_to_html
jupyter_to_html(
path="src/my_notebook.ipynb",
output_dir="./docs",
no_input=True,
execute=False,
use_base64=False, # False = images in separate folder, True = inline base64
)
[!WARNING] conversion requires
ungoogled-chromiuminstalled, normalchromiumwont work anymore (see prerequisites section below)
🔍 examples
styled table
tbl.pivot_df(
df[["color", "payment", "fare"]],
total_mode="sum",
total_axis="xy",
data_bar_axis=None,
total_exclude=True,
pct_axis="xy",
precision=0,
heatmap_axis="xy",
kpi_mode="rag_abs",
kpi_rag_list=[1000, 10000],
swap=True,
font_size_td=12,
font_size_th=14,
)
table description
tbl.describe_df(
df,
caption="taxis",
top_n_uniques=10,
top_n_chars_in_columns=10,
top_n_chars_in_index=15,
)
upset plot
pls.plot_upset(
df_upset,
include_false_subsets=False,
orientation="horizontal",
)
uml graph
metrics = pls.plot_uml_graph()
set filter
filter = hlp.get_duckdb_filter_n(
con,
"from Tumor",
FILTERS,
# distinct_metric="z_pat_id",
)
counts: rows
---
n = 3_241_401 (100.0%) ██████████████████████████████
└ [2020-2023.07]: n = 2_633_644 (81.3%) ░░░░░░████████████████████████
└ [not z_is_dco]: n = 2_547_636 (78.6%) ░░░░░░░███████████████████████
└ [keine M1]: n = 2_305_215 (71.1%) ░░░░░░░░░█████████████████████
└ [keine Verstorbenen < 180 Tage]: n = 2_132_064 (65.8%) ░░░░░░░░░░░███████████████████
└ [lympho- und mesoendokr. Tumore]: n = 27_653 (0.9%) ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
sankey diagram
_ = pls.plot_sankey(width=1000)
box plot with violin overlay
_ = pls.plot_box(df['fare'], height=400, violin=true)
box plot with statistics
_ = pls.plot_boxes_large(df[["dropoff_borough","distance"]])
venn diagrams
# show venn diagram for 3 sets
set_a = {'ford','ferrari','mercedes', 'bmw'}
set_b = {'opel','bmw','bentley','audi'}
set_c = {'ferrari','bmw','chrysler','renault','peugeot','fiat'}
_df, _details = pls.plot_venn3(
title="taxis",
a_set=set_a,
a_label="cars1",
b_set=set_b,
b_label="cars2",
c_set=set_c,
c_label="cars3",
verbose=0,
size=8,
)
📚 api reference
table utilities (tbl)
| function | description |
|---|---|
show_num_df() |
displays a table as styled version with additional information |
describe_df() |
alternative version of pandas describe() function |
descr_db() |
short description for a duckdb relation |
pivot_df() |
gets a pivot table of a 3 column dataframe (or 2 columns if no weights are given) |
print_summary() |
shows statistics for a pandas dataframe or series |
plotting functions (pls)
| function | description |
|---|---|
plot_box() |
auto annotated boxplot w/ violin option |
plot_boxes() |
multiple boxplots (annotation is experimental) |
plot_stacked_bars() |
shortcut to stacked bars |
plot_bars() |
standardized bar plot for categorical column with confidence intervals |
plot_histogram() |
histogram for one or more numerical columns |
plot_joints() |
joint plot for exactly two numerical columns |
plot_quadrants() |
quickly shows a 2x2 heatmap |
plot_facet_stacked_bars() |
stacked bars for a facet value as subplots |
plot_sankey() |
generates a sankey diagram |
plot_pie() |
generates a pie chart |
plot_box_large() |
for large datasets using seaborn |
plot_boxes_large() |
for large datasets using seaborn |
plot_histogram_large() |
for large datasets using seaborn |
plot_upset() |
generates an upset plot based on upsetplot |
plot_uml_graph() |
generates a uml graph based on mermaid for structured data |
plot_venn2() |
displays a venn diagram for 2 sets |
plot_venn3() |
displays a venn diagram for 3 sets |
helper functions (hlp)
| function | description |
|---|---|
to_series() |
converts a dataframe to a series |
mean_confidence_interval() |
calculates mean and confidence interval for a series |
wrap_text() |
formats strings or lists to a given width |
replace_delimiter_outside_quotes() |
replaces delimiters only outside of quotes in csv imports |
create_barcode_from_url() |
creates a barcode from a given url |
add_datetime_col() |
adds a datetime column to a dataframe (chainable) |
show_package_version() |
prints version of a list of packages |
get_os() |
helps identify and ensure operating system at runtime |
add_bitmask_label() |
adds a column that resolves a bitmask column into human-readable labels |
find_cols() |
finds all columns in a list that contain any of the given stubs |
add_measures_to_pyg_config() |
adds measures to a pygwalker config file |
get_tum_details() |
prints details of a specific tumor (requires connection to clinical cancer data) |
get_duckdb_filter_n() |
print rowcounts for cascading filters in duckdb with ansi bars |
print_filter() |
prints filter as markdown sql codeblock |
is_ipynb() |
detects if code is running in jupyter notebook |
prepend_uv_header() |
prepends uv header to a .py script to make it executable for uv run command |
create_py_script() |
creates a .py script from a .ipynb file |
setup_rendering() |
triggers clean(er) rendering of plots and pandas tables to markdown |
find_str_in_duckdb() |
finds a given string in all tables of a DuckDB database |
export_plot_data() |
exports the dataframe used for a plot to ./data/.csv |
retrieve_file_from_gist() |
downloads a file from a github gist to the current directory |
🧩 prerequisites
- python 3.10+: compatible with python versions 3.10 - 3.13
- uv: uv is recommended for package management
⚠️ this package depends on
numpy<2.0.0sinceUpSetPlotis still tied to the previous versions
🤝 contributing
contributions are welcome! please feel free to submit a pull request. for major changes, please open an issue first to discuss what you would like to change.
📄 license
this project is licensed under the MIT license - see the license file for details.
🏷️ tags
#pandas #visualizations #statistics #data-science #data-analysis #python
1 maintainer
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