PCC28 asutosh97 (pybites#75)

Author: asutosh97

28/asutosh97/.DS_Store

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+from bokeh.embed import components
+from bokeh.plotting import figure
+from flask import Flask, render_template, request
+import pandas as pd
+import asutosh
+
+app = Flask(__name__)
+
+
+@app.route('/', methods=['GET','POST'])
+def index():
+
+    # Default values to take care of a GET request.
+    first_country = "India"
+    second_country = "Pakistan"
+    attribute = "per_capita_income"
+
+    if request.method == "POST":
+        first_country = request.form["first_country_name"]
+        second_country = request.form["second_country_name"]
+        attribute = request.form["attribute_name"]
+
+    # Create the plot
+    plot = asutosh.create_figure(first_country, second_country, attribute)
+
+    # get countries available in the dataset
+    country_names = asutosh.get_countries()  
+
+    # attributes
+    attribute_names = ["population", "per_capita_income", "life_expectancy"]
+
+    # Embed plot into HTML via Flask Render
+    script, div = components(plot)
+    return render_template("index.html", script = script, div = div,
+        attribute_names = attribute_names,  current_attribute_name = attribute, 
+        country_names = country_names, first_country = first_country, second_country = second_country)
+
+# With debug=True, Flask server will auto-reload 
+# when there are code changes
+
+def main():
+    app.run(port=5000, debug=True)
+
+if __name__ == '__main__':
+    main()

28/asutosh97/app.py

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+# get both the countries and attribute value from drop-down
+import pandas as pd
+from bokeh.plotting import figure, show, output_file
+
+def read_data():
+	data = pd.read_csv('datasets/gapminder.csv')
+	data = data[(data.Year >= 1950)]
+	return data
+
+def create_figure(first_country, second_country, attribute):
+	# read from csv
+	data = read_data()
+
+	# filter datasets according to country
+	first_country_data = data[(data.Country == first_country)]
+	
+	second_country_data = data[(data.Country == second_country)]
+
+	# get required attribute out of filtered datasets of both countries and turn them to list
+	first_country_data_attribute = list(first_country_data[attribute])
+	second_country_data_attribute = list(second_country_data[attribute])
+	years = list(first_country_data["Year"])
+
+	# create a new plot
+	p = figure(title = (first_country + " Vs " + second_country + " in " + attribute), x_axis_label='Years', plot_width=1280, plot_height=720)
+
+	# plot for first-country
+	p.line(years, first_country_data_attribute, legend = first_country, line_color="blue", line_width=3)
+
+	# plot for second-country
+	p.line(years, second_country_data_attribute, legend = second_country, line_color="green", line_width=3)
+
+	return p
+
+def get_countries():
+	# get countries column as a list
+	return sorted(list(set(read_data()['Country'])))
+
+def main():
+	p = create_figure("India", "Pakistan", "income")
+	output_file('output_files/new.html')
+	show(p)
+
+if __name__ == "__main__":
+	main()