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vizapp.py
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vizapp.py
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# Imports
from bokeh.plotting import figure
from bokeh.io import output_notebook, output_file, show, curdoc,save
from bokeh.models import GeoJSONDataSource, LinearColorMapper, \
ColorBar, NumeralTickFormatter, HoverTool, Select, ColumnDataSource,WheelZoomTool
from bokeh.layouts import layout
from bokeh.transform import factor_cmap
from bokeh.palettes import brewer,d3,inferno,viridis,cividis,mpl,Spectral6,magma
from bokeh.themes import built_in_themes
import geopandas as gp
import pandas as pd
import json
# Read the new 2019 census
latest_census = pd.read_csv('kenya_population_by_sex_and_county.csv')
latest_census.head()
# Read the shapefile
shapefile = 'Shapefile/ke_county.shp'
geofile = gp.read_file(shapefile)
geofile = geofile.rename(columns={'pop 2009': 'pop2009'})
geofile = geofile.sort_values(by='gid')
# Read the csv
# covid_data = pd.read_csv('covid_counties.csv', index_col=False)
# read csv of covid numbers into dataframe
df_covid = pd.read_csv('corona_ke.csv')
# create a merged dataframe for the geofile dataframe and covid cases csv
merged = pd.merge(geofile,df_covid[['county', 'covid_num']], on='county')
# comprehensive data, merge the tables and use a left join to preserve the state of the merged df
comp_merged = pd.merge(merged,latest_census[['county','Male','Female','Intersex','Total']], on='county',how='left')
#comp_merged.dropna
# Read the data to json
merged_json = json.loads(comp_merged.to_json())
# convert to string like object
json_data = json.dumps(merged_json)
# Input GeoJSON source that contains features for plotting
geosource = GeoJSONDataSource(geojson=json_data)
# Define a sequential multi-hue color palette.
palette = magma(50)
# Reverse colour order so that blue is for the highest density
palette = palette[::-1]
# Add Hover tool
hover = HoverTool(tooltips=[
('County','@county'),
('COVID-19 CASES','@covid_num'),
('Population 2019','@Total'),
('Male','@Male'),
('Female','@Female'),
('Intersex','@Intersex')
]
)
# Instantiate LinearColorMapper that maps numbers in a range into a sequence of colors
color_mapper = LinearColorMapper(palette=palette, low=0, high=10000)
# Create the color bar.
color_bar = ColorBar(
color_mapper=color_mapper,
label_standoff=10,
width=600,
height=20,
border_line_color=None,
location = (0, 0), # major_label_overrides=tick_labels,
orientation='horizontal')
# Create the figure object
p = figure(
title='COVID-19 cases distribution per county Kenya (as of 23th April 2020). Hover/tap to view County details.',
plot_height=1500,
plot_width=1200,
toolbar_location='right'
)
p.xgrid.grid_line_color = None
p.xgrid.grid_line_color = None
p.sizing_mode = "scale_both"
p.background_fill_color= "aqua"
p.title.text_color = "teal"
p.title.text_font = "times"
p.title.text_font_style = "bold italic"
p.title.text_font_size = "27px"
# Add patch renderer to figure.
p.patches('xs', 'ys', source=geosource,
fill_color={'field':'covid_num','transform': color_mapper},
line_color='black',
line_width=0.25,
fill_alpha=1
)
# Specify the figure of the layout.
p.add_layout(color_bar, 'below',)
# Add tools to the map
p.add_tools(hover)
# Initialize document to draw on
curdoc().add_root(p)
## Display figure inline in the Notebook
# output_notebook()
## Display figure
# show(p)
# Read the data
ke_stats = pd.read_csv('covid_ke_stats.csv')
# Save graph in html when using a notebook
# output_file('kenyan_stats_covid.html')
# get the column values
Cases = ke_stats.columns.values
# get row values
numbers = list(ke_stats.iloc[0])
# Read to a form bokeh understands; to draw plots
source = ColumnDataSource(data=dict(Cases=Cases, numbers=numbers))
# plot the graph
p1 = figure(
x_range=Cases,
plot_height=1000,
toolbar_location='right',
title="COVID-19 CASES as at 15 August 2020",
tools='hover',
tooltips="@Cases: @numbers"
)
p1.vbar(
x='Cases',
top='numbers',
width=1,
source=source,
legend_field='Cases',
line_color='white',
fill_color=factor_cmap('Cases',
palette=Spectral6,
factors=Cases)
)
p1.xgrid.grid_line_color = None
p1.y_range.start = 0
p1.y_range.end = 400000
p1.legend.orientation = "vertical"
p1.legend.location = "top_right"
p1.background_fill_color = "beige"
p1.sizing_mode ="scale_both"
p1.title.text_color = "teal"
p1.title.text_font = "times"
p1.title.text_font_style = "bold italic"
p1.title.text_font_size = "27px"
# Combine the two figures into one display
z = layout([p],[p1])
# Initialize document to draw on
curdoc().add_root(z)
# Create a standalone HTML file with JS code included in 'inline' mode
output_file('covidke_data.html', title="COVID-KE data Kenya", mode='inline')
save(z)