Fannie Mae mortgage data

Import cuxfilter

[1]:

from cuxfilter import charts
import cuxfilter
from bokeh import palettes
from cuxfilter.layouts import *

[2]:

#update data_dir if you have downloaded datasets elsewhere
DATA_DIR = './data'

Download required datasets

[3]:

from cuxfilter.sampledata import datasets_check
datasets_check('mortgage', base_dir=DATA_DIR)

Dataset - ./data/146M_predictions_v2.arrow

dataset already downloaded

Define charts

[4]:

cux_df = cuxfilter.DataFrame.from_arrow('./data/146M_predictions_v2.arrow')

[5]:

chart0 = charts.bokeh.choropleth(x='zip', y='delinquency_12_prediction', aggregate_fn='mean', geo_color_palette=palettes.Purples9,
                                  geoJSONSource = 'https://raw.githubusercontent.com/rapidsai/cuxfilter/GTC-2018-mortgage-visualization/javascript/demos/GTC%20demo/src/data/zip3-ms-rhs-lessprops.json',
                                  data_points=1000)

chart2 = charts.bokeh.bar('delinquency_12_prediction',data_points=50)
chart3 = charts.panel_widgets.range_slider('borrower_credit_score',data_points=50)

Add mappings for chart1

[6]:

mapper1 = {}

for val in cux_df.data.dti.unique().to_pandas().tolist():
    mapper1[int(val)] = 'l_'+str(val)

chart1 = charts.panel_widgets.drop_down('dti', label_map=mapper1)
#also available: panel_widgets.multi_select

Create a dashboard object

[7]:

d = cux_df.dashboard([chart0, chart1], layout=feature_and_base, theme=cuxfilter.themes.dark, title="Ajay's Dashboard")

[8]:

#add more charts
d.add_charts([chart2, chart3])

[9]:

#dashboard object
d

[9]:

Starting the dashboard

  1. d.show(‘current_notebook_url:current_notebook_port’) remote dashboard

  2. d.app(‘current_notebook_url:current_notebook_port’) inline in notebook (layout is ignored, and charts are displayed one below another)

Incase you need to stop the server:

  • d.stop()

[10]:

# preview the dashboard
await d.preview()
../_images/examples_Mortgage_example_17_2.png

Export the queried data into a dataframe

[11]:

queried_df = d.export()

no querying done, returning original dataframe