databyjp’s gists

databyjp / sixdegs_leagues_results_inspect.py

Last active March 14, 2020 04:40

	>>> teams_contacted_df.head()
	team contacted date con_from deg_sep
	21 SACRAMENTO_KINGS True 2020-03-10 SACRAMENTO_KINGS 0
	18 NEW_ORLEANS_PELICANS True 2020-03-11 SACRAMENTO_KINGS 1
	10 UTAH_JAZZ True 2020-03-13 NEW_ORLEANS_PELICANS 2
	1 LOS_ANGELES_CLIPPERS True 2020-03-14 NEW_ORLEANS_PELICANS 2
	17 MEMPHIS_GRIZZLIES True 2020-03-14 UTAH_JAZZ 3

databyjp / sixdegs_leagues_connection_locs.py

Created March 14, 2020 05:46

	teams_contacted_df = teams_contacted_df.assign(orig_lat=0, orig_lon=0, dest_lat=0, dest_lon=0)

	for temp_tuple in teams_contacted_df.itertuples():
	if temp_tuple.con_from == None:
	from_name = temp_tuple.team
	else:
	from_name = temp_tuple.con_from
	teams_contacted_df.loc[temp_tuple.Index, 'orig_lat'] = arena_df[arena_df.teamupper == from_name]['lat'].values[0]
	teams_contacted_df.loc[temp_tuple.Index, 'orig_lon'] = arena_df[arena_df.teamupper == from_name]['lon'].values[0]
	teams_contacted_df.loc[temp_tuple.Index, 'dest_lat'] = arena_df[arena_df.teamupper == temp_tuple.team]['lat'].values[0]

databyjp / sixdegs_leagues_first_map.py

Created March 14, 2020 05:51

	# Initialise figure
	fig = go.Figure()

	# Add arenas
	fig.add_trace(go.Scattergeo(
	lon=arena_df['lon'], lat=arena_df['lat'], marker=dict(size=8, color='slategray'),
	hoverinfo='text', text=arena_df['teamname'] + ' - ' + arena_df['arena_name'], name='Arenas'
	))

	for i in range(len(teams_contacted_df)):

databyjp / sixdegs_leagues_more_formatting.py

Created March 14, 2020 05:58

	fig['data'][0]['marker']['symbol'] = 'triangle-left'

	fig.update_layout(
	title_text="NBA Teams' degrees of separation:"
	+ "<BR>Starting with the " + seed_tm + " on " + seed_date
	+ ", all teams are connected within " + str(date_range.days) + " days.",
	geo=dict(
	scope='north america',
	projection_type='azimuthal equal area',
	showland=True,

databyjp / 20200316_dash_nlp_histogram1.py

Last active March 16, 2020 05:52

	fig = px.histogram(comps_grpby, title='Histogram - complain counts by company',
	x='Complaints', template='plotly_white', nbins=20)
	fig.update_xaxes(categoryorder='total descending')
	fig.update_yaxes(title='Number of companies')
	fig.show()

databyjp / 20200316_dash_nlp_comp_counts.py

Last active March 16, 2020 06:07

	fig = px.histogram(top_comps_df, x='Company', template='plotly_white', title='Complaint counts by company')
	fig.update_xaxes(categoryorder='total descending').update_yaxes(title='Number of complaints')
	fig.show()

databyjp / 20200316_dash_nlp_date_counts.py

Last active March 17, 2020 00:43

	fig = px.histogram(comp_df, x='datetime', template='plotly_white', title='Complaint counts by date')
	fig.update_xaxes(categoryorder='category descending', title='Date').update_yaxes(title='Number of complaints')
	fig.show()

databyjp / 20200316_dash_nlp_length_count.py

Last active March 17, 2020 00:45

	fig = px.histogram(comp_df, x='Words_clipped', template='plotly_white', title='Complain counts by length')
	fig.update_xaxes(categoryorder='total descending', title='Number of words (clipped at 1000 words)').update_yaxes(title='Number of complaints')
	fig.show()

databyjp / 20200316_dash_nlp_comp_date_counts.py

Last active March 16, 2020 07:08

	# Complaints by company & date
	fig = px.histogram(top_comps_df, x='datetime', template='plotly_white', title='Complaint counts by date'
	, color='Company', nbins=6, log_y=True, barmode='group')
	fig.update_xaxes(categoryorder='category descending', title='Date').update_yaxes(title='Number of complaints')
	fig.show()

databyjp / 20200316_dash_nlp_treemap.py

Created March 16, 2020 10:19

	fig = px.treemap(top_comps_df, title='Treemap chart by companies and whether complaint mentions credit report.',
	path=['Company', 'credit_report'], color='Words', color_continuous_scale=px.colors.sequential.GnBu)
	fig.show()

JP Hwang databyjp