pandas-plots 0.9.1

A collection of helper for table handling and vizualization

pandas-plots

usage

install / update package

pip install pandas-plots -U

include in python

from pandas_plots import tbl, pls, ven, txt

example

# load sample dataset from seaborn
import seaborn as sb
df = sb.load_dataset('taxis')

_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.

It is subdivided into:

• tbl utilities for table descriptions

  • describe_df() an alternative version of pandas describe() function
  • pivot_df() gets a pivot table of a 3 column dataframe
  • 🆕 show_num_df() displays a table as styled version with additional information

• pls for plotly visualizations

  • plot_box() auto annotated boxplot w/ violin option
  • plot_boxes() multiple boxplots (annotation is experimental)
  • plots_bars() a standardized bar plot
  • plot_stacked_bars() shortcut to stacked bars 😄
  • plot_quadrants() quickly shows a 2x2 heatmap

• ven offers functions for venn diagrams

  • show_venn2() displays a venn diagram for 2 sets
  • show_venn3() displays a venn diagram for 3 sets

• txt includes some text based utilities

  • wrap formats strings or lists to a given width to fit nicely on the screen

note: theming can be controlled through all functions by setting the environment variable THEME to either light or dark

more examples

pls.plot_box(df['fare'], height=400, violin=True)

# quick and exhaustive description of any table
tbl.describe_df(df, 'taxis', top_n_uniques=5)

# show pivoted values for selected columns
tbl.pivot_df(df[['color', 'payment', 'fare']])

# 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,
)