# pandas-plots
   
## usage
install / update package
```bash
uv add -U pandas-plots
# if no uv is available:
# pip install pandas-plots -U
```
include in python
```python
from pandas_plots import tbl, pls, ven, hlp
```
## example
```python
# load sample dataset from seaborn
import seaborn as sb
df = sb.load_dataset('taxis')
```
```python
_df = df[["passengers", "distance", "fare"]][:5]
tbl.show_num_df(
_df,
total_axis="xy",
total_mode="mean",
data_bar_axis="xy",
pct_axis="xy",
precision=0,
kpi_mode="max_min_x",
kpi_rag_list=(1,7),
)
```
## why use pandas-plots
`pandas-plots` is a package to help you examine and visualize data that are organized in a pandas DataFrame. It provides a high level api to pandas / plotly with some selected functions and predefined options:
- `tbl` utilities for table descriptions
- `show_num_df()` displays a table as styled version with additional information
- `describe_df()` an alternative version of pandas `describe()` function
- `descr_db()` a very short descr 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
<br>
- `pls` for plotly visualizations
- `plot_box()` auto annotated boxplot w/ violin option
- `plot_boxes()` multiple boxplots _(annotation is experimental)_
- `plot_stacked_bars()` shortcut to stacked bars
- `plots_bars()` a standardized bar plot for a **categorical** column
- features confidence intervals via `use_ci` option
- `plot_histogram()` histogram for one or more **numerical** columns
- `plot_joints()` a joint plot for **exactly two numerical** columns
- `plot_quadrants()` quickly shows a 2x2 heatmap
- `plot_facet_stacked_bars()` shows stacked bars for a facet value as subplots
- `plot_sankey()` generates a Sankey diagram
<br>
- `ven` offers functions for _venn diagrams_
- `show_venn2()` displays a venn diagram for 2 sets
- `show_venn3()` displays a venn diagram for 3 sets
<br>
- `hlp` contains some (variety) helper functions
- `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 to fit nicely on the screen
- `replace_delimiter_outside_quotes()` when manual import of csv files is needed: replaces delimiters only outside of quotes
- `create_barcode_from_url()` creates a barcode from a given URL
- `add_datetime_col()` adds a datetime columns to a dataframe (chainable)
- `show_package_version` prints version of a list of packages
- `get_os` helps to identify and ensure operating system at runtime
- `add_bitmask_label()` adds a column to the data that resolves a bitmask column into human-readable labels
- `find_cols()` finds all columns in a list of columns that contain any of the given stubs
- `add_measures_to_pyg_config()` adds measures to a pygwalker config file to avoid frequent manual update
<br>
> theme setting ☀️ 🌔 can be controlled through all functions by setting the environment variable `'THEME'` to either `'light'` or `'dark'`
> renderer can be controlled through all functions by setting the environment variable `'RENDERER'` to `'png'` for printing to markdown or pdf
## prerequisites
- ⚠️ for static image generation, this package uses Plotly's kaleido engine, which requires a system-wide installation of the Chrome or Chromium browser
- if image generation fails, it may be because a compatible browser is missing
- in such cases, please run `kaleido_get_chrome` from your terminal to install the necessary dependency.
## more examples
```python
pls.plot_box(df['fare'], height=400, violin=True)
```
```python
# quick and exhaustive description of any table
tbl.describe_df(df, 'taxis', top_n_uniques=5)
```
```python
# show bars with confidence intervals
_df = df[["payment", "fare"]]
pls.plot_bars(
_df,
dropna=False,
use_ci=True,
height=600,
width=800,
precision=1,
)
```
```python
# show venn diagram for 3 sets
from pandas_plots import ven
set_a = {'ford','ferrari','mercedes', 'bmw'}
set_b = {'opel','bmw','bentley','audi'}
set_c = {'ferrari','bmw','chrysler','renault','peugeot','fiat'}
_df, _details = ven.show_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,
)
```
## tags
#pandas, #plotly, #visualizations, #statistics
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