Metadata-Version: 2.1
Name: DashBoardUtils-DataScience
Version: 1.0
Summary: AutoMated visualization Features Extraction For Data Scientists
Home-page: UNKNOWN
Author: Rajat Mishra
Author-email: rajatsmishra@aol.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

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

```python
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

```python
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 = 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:


