DashBoardUtils-DataScience 1.13

AutoMated visualization Features Extraction For Data Scientists

dashboard builder util generate all posibble stats from Dataframe for DataScience and visualisation purposes

from package.dashboardutil import DashboardElementsBuilder
from package.dataclassifier import DataClassifier
import pandas as pd
df=pd.read_csv("cars.csv")
dat=DataClassifier()
visual=DashboardElementsBuilder(df,dat)
ploats=visual.build_ploats("hist",df.columns.to_list()[1:])
ploat_data=[]
for x in list(ploats):
    for z in list(x):
        ploat_data+=list(z)

above data canbe visualised like below

data={'slow': {'lables': [66.2, 66.4, 66.3, 71.4, 67.9], 'counts': [1, 1, 1, 3, 1]}}
from bokeh.plotting import figure, show

fruits = [str(x) for x in data['slow']['lables']]
counts = data['slow']['counts']

p = figure(x_range=fruits, height=350, title="Range",
           toolbar_location=None, tools="")

p.vbar(x=fruits, top=counts, width=0.9)

p.xgrid.grid_line_color = None
p.y_range.start = 0

show(p)

output: