AutoMated visualization Features Extraction For Data Scientists
Project description
dashboard builder util generate all posibble stats from Dataframe for DataScience and visualisation purposes
from UserAuthenticationSystem.utils.dashboardutil import DashboardElementsBuilder
from UserAuthenticationSystem.utils.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:
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