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app.py
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########################################################################################################################
# Titanic - Dashboard
# Version: 1.0 Dario Radecic
# 1.1 Nemecsek
# https://towardsdatascience.com/https-medium-com-radecicdario-next-level-data-visualization-dashboard-app-with-bokeh-flask-c588c9398f98
# pip install numpy pandas bokeh==1.2.0 flask
########################################################################################################################
# > IMPORTS
import math
import numpy as np
import pandas as pd
from bokeh.embed import components
from bokeh.layouts import column, gridplot, layout, row
from bokeh.models import ColumnDataSource, HoverTool, PrintfTickFormatter
from bokeh.models.tickers import SingleIntervalTicker
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
from flask import Flask, render_template, request
df = pd.read_csv('data/titanic.csv')
df['Title'] = df['Name'].apply(lambda x: x.split(',')[1].strip().split(' ')[0])
########################################################################################################################
# > CONSTANT VALUES
palette = ['#ba32a0', '#f85479', '#f8c260', '#00c2ba']
chart_font = 'Helvetica'
chart_title_font_size = '16pt'
chart_title_alignment = 'center'
axis_label_size = '14pt'
axis_ticks_size = '12pt'
default_padding = 30
chart_inner_left_padding = 0.015
chart_font_style_title = 'bold italic'
########################################################################################################################
# > HELPER FUNCTIONS
def palette_generator(length, palette):
int_div = length // len(palette)
remainder = length % len(palette)
return (palette * int_div) + palette[:remainder]
def plot_styler(p):
p.title.text_font_size = chart_title_font_size
p.title.text_font = chart_font
p.title.align = chart_title_alignment
p.title.text_font_style = chart_font_style_title
p.y_range.start = 0
p.x_range.range_padding = chart_inner_left_padding
p.xaxis.axis_label_text_font = chart_font
p.xaxis.major_label_text_font = chart_font
p.xaxis.axis_label_standoff = default_padding
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = axis_ticks_size
p.yaxis.axis_label_text_font = chart_font
p.yaxis.major_label_text_font = chart_font
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = axis_ticks_size
p.yaxis.axis_label_standoff = default_padding
p.toolbar.logo = None
p.toolbar_location = None
def redraw(selected_class):
selected_class = int(selected_class)
if selected_class == 0: # all classes
dataset = df
else: # single class
dataset = df[df['Pclass'] == selected_class]
class_texts = ["All Classes", "1st Class", "2nd Classes", "3rd Class"]
class_text = class_texts[selected_class]
survived_chart = survived_bar_chart(dataset, "Survival for " + class_text)
title_chart = class_titles_bar_chart(dataset, "Titles for " + class_text)
hist_age = age_hist(dataset, "Age Histogram for " + class_text)
return (
survived_chart,
title_chart,
hist_age
)
########################################################################################################################
# > MAIN ROUTE
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def chart():
selected_class = request.form.get('dropdown-select')
if selected_class is None:
selected_class = 0
survived_chart, title_chart, hist_age = redraw(selected_class)
script_survived_chart, div_survived_chart = components(survived_chart)
script_title_chart, div_title_chart = components(title_chart)
script_hist_age, div_hist_age = components(hist_age)
return render_template(
'index.html',
div_survived_chart=div_survived_chart,
script_survived_chart=script_survived_chart,
div_title_chart=div_title_chart,
script_title_chart=script_title_chart,
div_hist_age=div_hist_age,
script_hist_age=script_hist_age,
selected_class=selected_class
)
########################################################################################################################
# > CHART GENERATION FUNCTIONS
def survived_bar_chart(dataset, title, cpalette=None):
if cpalette is None:
cpalette = palette[1:3]
surv_data = dataset
surv_possibilities = list(surv_data['Survived'].value_counts().index)
surv_values = list(surv_data['Survived'].value_counts().values)
surv_possibilities_text = ['Did not Survive', 'Survived']
source = ColumnDataSource(data={
'possibilities': surv_possibilities,
'possibilities_txt': surv_possibilities_text,
'values': surv_values
})
hover_tool = HoverTool(
tooltips=[('Survived?', '@possibilities_txt'),
('Count', '@values')]
)
p = figure(tools=[hover_tool], plot_height=400, title=title)
p.vbar(x='possibilities', top='values', source=source, width=0.9,
fill_color=factor_cmap('possibilities_txt',
palette=palette_generator(len(source.data['possibilities_txt']), cpalette),
factors=source.data['possibilities_txt']))
plot_styler(p)
p.xaxis.ticker = source.data['possibilities']
p.xaxis.major_label_overrides = { 0: 'Did not Survive', 1: 'Survived' }
p.sizing_mode = 'scale_width'
return p
def class_titles_bar_chart(dataset, title, cpalette=None):
if cpalette is None:
cpalette = palette
ttl_data = dataset
title_possibilities = list(ttl_data['Title'].value_counts().index)
title_values = list(ttl_data['Title'].value_counts().values)
int_possibilities = np.arange(len(title_possibilities))
source = ColumnDataSource(data={
'titles': title_possibilities,
'titles_int': int_possibilities,
'values': title_values
})
hover_tool = HoverTool(
tooltips=[('Title', '@titles'), ('Count', '@values')]
)
chart_labels = {}
for val1, val2 in zip(source.data['titles_int'], source.data['titles']):
chart_labels.update({ int(val1): str(val2) })
p = figure(tools=[hover_tool], plot_height=300, title=title)
p.vbar(x='titles_int', top='values', source=source, width=0.9,
fill_color=factor_cmap('titles', palette=palette_generator(len(source.data['titles']), cpalette),
factors=source.data['titles']))
plot_styler(p)
p.xaxis.ticker = source.data['titles_int']
p.xaxis.major_label_overrides = chart_labels
p.xaxis.major_label_orientation = math.pi / 4
p.sizing_mode = 'scale_width'
return p
def age_hist(dataset, title, color=palette[1]):
hist, edges = np.histogram(dataset['Age'].fillna(df['Age'].mean()), bins=18, range=(0, 90))
source = ColumnDataSource({
'hist': hist,
'edges_left': edges[:-1],
'edges_right': edges[1:]
})
hover_tool = HoverTool(
tooltips=[('From', '@edges_left'), ('Thru', '@edges_right'), ('Count', '@hist')], mode='vline'
)
p = figure(plot_height=400, title=title, tools=[hover_tool])
p.quad(top='hist', bottom=0, left='edges_left', right='edges_right', source=source,
fill_color=color, line_color='black')
plot_styler(p)
p.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=2)
p.sizing_mode = 'scale_width'
return p
if __name__ == '__main__':
app.run(debug=True)