DashBoardUtils-DataScience 1.15

AutoMated visualization Features Extraction For Data Scientists and data format calculater for application developers

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)

export bulk graphs for all possible conditions

from package.DashBoardsTemplates import export_graphs_hist
from bokeh.plotting import show 
# use any graph for data clustrig or analysis purposes above function using bokeh for bulk visualisation
visual=export_graphs_hist(ploat_data)
# iter visual variable or visualise one by one
show(visual[0])