Fannie Mae mortgage data

Import cuxfilter

from cuxfilter import charts
import cuxfilter
from bokeh import palettes
from cuxfilter.layouts import *
import panel as pn

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

Download required datasets

from cuxfilter.sampledata import datasets_check

datasets_check("mortgage", base_dir=DATA_DIR)

# add static legend to the dashboard widget section
def generate_legend_markdown():
    range_ = cux_df.data.delinquency_12_prediction.describe().round(2)
    legend = pn.GridBox(ncols=2)
    colors = palettes.Purples4

    legend.append(f"{range_['min']} - {range_['25%']}")
    legend.append(pn.pane.HTML(background=colors[0], width=30, height=10))
    legend.append(f"{range_['25%']} - {range_['50%']}")
    legend.append(pn.pane.HTML(background=colors[1], width=30, height=10))
    legend.append(f"{range_['50%']} - {range_['75%']}")
    legend.append(pn.pane.HTML(background=colors[2], width=30, height=10))
    legend.append(f"{range_['75%']} <")
    legend.append(pn.pane.HTML(background=colors[3], width=30, height=10))
    return legend

Define charts

cux_df = cuxfilter.DataFrame.from_arrow(
    DATA_DIR + "/146M_predictions_v2.arrow"
)

# set map_api_key parameter on choropleth to get bg map tiles
chart0 = charts.choropleth(
    x="zip",
    y="delinquency_12_prediction",
    color_column="delinquency_12_prediction",
    color_aggregate_fn="mean",
    geo_color_palette=palettes.Purples4,
    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,
)  # , mapbox_api_key="")

chart2 = charts.bar("delinquency_12_prediction", data_points=50)
chart3 = charts.range_slider("borrower_credit_score", data_points=50)
chart4 = charts.card(
    generate_legend_markdown(), widget=True, title="zip legend"
)

Add mappings for chart1

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

d = cux_df.dashboard(
    [chart0],
    sidebar=[chart1, chart4],
    layout=feature_and_base,
    theme=cuxfilter.themes.rapids_dark,
    title="Ajay's Dashboard",
)

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

# run dashboard inline within the notebook cell
d.app()

Starting the dashboard

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

2. d.app() inline within the notebook cell

Incase you need to stop the server:

• d.stop()

d.show()

Export the queried data into a dataframe

queried_df = d.export()