pandas-plots

pandas-plots

usage

install / update package

pip install pandas-plots -U

include in python

from pandas_plots import tbl, pls, ven, hlp, pii

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 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
    

• 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
    

• ven offers functions for venn diagrams

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

• hlp contains some (variety) helper functions

  • to_series() converts a dataframe to a series (🚨 breaking change)
  • 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
    

• pii has routines for handling of personally identifiable information

  • remove_pii() logs and deletes pii from a series

note: theme setting 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 bars with confidence intervals
_df = df[["payment", "fare"]]
pls.plot_bars(
    _df,
    dropna=False,
    use_ci=True,
    height=600,
    width=800,
    precision=1,
)

# 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