DashBoardUtils-DataScience 1.20

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])

calucate data formets for visualisation data for formets visulisation purposes

from package.keyborddata import *
from package.formatcalculator import get_unique_hashes_from_data 
# get hashes chuncks
unique_hashes=get_unique_hashes_from_data(ploat_data)
# get combines hashes 
unique_=[]
for x in unique_hashes:
    unique_+=x

calucate data formets for dataframe data for formets data optimisation and validation purposes

from package.keyborddata import *
import pandas as pd
from package.formatcalculator import split_all_labels_to_words_with_new_cols,hash_df_single_df_column,hash_df_formats,get_unique_hashes_from_df_columnwise
# get df vocabs
vocabdf=split_all_labels_to_words_with_new_cols(pd.DataFrame("test.csv"))
# get vocabdf formats
formets=hash_df_formats(vocabdf)
# get vocabdf formets column wise 
unique_formatas=get_unique_hashes_from_df_columnwise(formets)