Graphing Examples

	from pathlib import Path
	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt
	
	data = pd.read_csv(Path(Path.cwd(),'subfolder', 'catdata.csv'), index_col = 'name', na_filter = False) # Path is simply an example, persisted from the previous page
	statsCol = pd.Series(pd.read_csv(Path(Path.cwd(),'subfolder','stats.csv'), index_col = 0)['0'], name = 'Cols') # see previous page for generation of "data" and "statsCol"
	
	n_cats = data.shape[0]-1
	random_cat = pd.Series(data.index[:-1]).sample().values[0]
	cat_stats = data.loc[random_cat,statsCol]
	avg_stats = data.loc[data.index[-1],statsCol] # last row is average

	catTheta = np.append(cat_stats,(cat_stats[0])) # repeat first value to close circle 
	avgTheta = np.append(avg_stats,(avg_stats[0]))
	angles = [x / avg_stats.shape[0] * 2 * np.pi for x in range(avg_stats.shape[0])] # radian values for data points, evenly spaced around circle
	angles2 = np.append(angles, angles[0]) # repeat first value to close circle 
	
	labeler = [val.replace('_',' ').capitalize() for val in cat_stats.index.values] # more readable labels 
	avg_text = f'{avg_stats.name} (n={n_cats})' # include number of cats considered in average 
		
	plt.figure(figsize = (8,7), dpi = 150, tight_layout=True) # Text specs need to vary with figure size to keep things pretty
	ax = plt.subplot(111, polar = "True")
	ax.spines['polar'].set_visible(False)
	plt.ylim(0,6) # extend axis lines past 5

	# plot sample data over mean as background
	plt.fill(angles2,catTheta,alpha = 0.25, color = 'cyan', ec ='darkcyan', linewidth = 2, linestyle ='-')
	plt.fill(angles2,avgTheta,alpha = 0.20, color = 'coral', ec='orangered')

	# ticks, axes labels
	plt.xticks(angles,labeler, fontweight = 'bold' )
	plt.yticks([1,2,3,4,5], fontsize=0)
	for num in [1,3,5]:
		plt.text(np.pi*5/12, num, num, fontsize = 12, fontweight='bold')
		
	# sample value labels
	bbox_props = dict(boxstyle="circle, pad = 0.22", fc="cyan", ec="darkcyan", lw=2)
	for i,val in enumerate(cat_stats):
		ax.text(angles[i], int(val), str(int(val)), color = 'black', fontweight = 'bold', bbox=bbox_props)

	# title / legend
	plt.title(cat_stats.name, loc='left', fontsize=18, fontweight='bold', color = 'darkcyan')
	plt.text(3*np.pi/4,8.8, avg_text, fontsize = 12, fontweight = 'bold', color = 'orangered')

	# Show / Save
	plt.plot()
	plt.savefig(Path(Path.cwd(),'subfolder', 'mpl-graphs',f'{cat_stats.name}_radar.png'))
    

	from pathlib import Path
	import pandas as pd
	import plotly.express as px
	import plotly.utils as pu # JSON Encoder used to integrate into webpages, HTML file also used in "raw-map"

	data = pd.read_csv(Path(Path.cwd(),'subfolder', 'catdata.csv'), index_col = 'name', na_filter = False) # Path is simply an example, persisted from the previous page
	cat_locs = data.loc[:,['origin','country_code']] # Extract location information into data table
	cat_locs = cat_locs.iloc[:-1,:] # Remove CatAPI Average
	
	# Prepare new data table
	locs = pd.DataFrame(cat_locs.value_counts()).reset_index() # organize by number of cats from country (origin)
	locs.columns = (['origin','code','# cats']) # rename columns
	locs['cats'] = [', '.join(cat_locs[cat_locs.origin == val].index) for val in locs['origin']] # Get cats belonging to country
	locs['origin'] = [f'{locs.loc[val,"origin"]} ({locs.loc[val,"# cats"]})' for val in locs.index] # Add cat number to entries for hover label

	# Generate plot
	fig = px.choropleth(locs, locations="code", color="# cats", hover_name="origin",
                            hover_data = {'code': False, '# cats': False, 'cats': True,},
                            color_continuous_scale=px.colors.sequential.Emrld)

	fig.update_geos(showcountries = True, coastlinewidth = 1.5, landcolor = '#dedede', lakecolor = '#b0ceff', 
		        showocean = True, oceancolor = '#b0ceff', showrivers = True, rivercolor = '#b0ceff',
		        projection_type = 'natural earth', fitbounds = "locations")

	# Show / Save
	fig.show() # output can vary depending on IDE used
	fig.write_html(Path(Path.cwd(),'subfolder', 'plotly-graphs','catmap.html')) # html to file, can be opened in web-browser
	graphJSON = json.dumps(fig, cls=pu.PlotlyJSONEncoder) # graphJSON could be passed directly to page or saved to file