A collection of helper for table handling and vizualization
Project description
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:
-
tblutilities for table descriptions- 🌟
show_num_df()displays a table as styled version with additional information describe_df()an alternative version of pandasdescribe()functionpivot_df()gets a pivot table of a 3 column dataframe- ⚠️
pivot_df()is depricated and wont get further updates
- ⚠️
- 🌟
-
plsfor plotly visualizationsplot_box()auto annotated boxplot w/ violin optionplot_boxes()multiple boxplots (annotation is experimental)plots_bars()a standardized bar plotplot_stacked_bars()shortcut to stacked bars 😄plot_quadrants()quickly shows a 2x2 heatmap
-
venoffers functions for venn diagramsshow_venn2()displays a venn diagram for 2 setsshow_venn3()displays a venn diagram for 3 sets
-
txtincludes some text based utilitieswrapformats 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
THEMEto 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,
)
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