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Description:

pandasplots 0.11.9

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
pivot_df() gets a pivot table of a 3 column dataframe (or 2 columns if no weights are given)

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

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

df_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
show_package_version prints version of a list of packages
get_os helps to identify and ensure operating system at runtime

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