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SNL Records by Actor

This notebook collects the record holders of Saturday Night Live.

import pandas as pd
import numpy as np
import bokeh
from bokeh.io import output_notebook
from bokeh.plotting import figure, show
from bokeh.models import HoverTool
output_notebook()
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fs = pd.read_csv('./db/snl_season.csv', encoding="utf-8")
dfe = pd.read_csv('./db/snl_episode.csv', encoding="utf-8",parse_dates=['aired'])
dft = pd.read_csv('./db/snl_title.csv', encoding="utf-8")
dfa = pd.read_csv('./db/snl_actor.csv', encoding="utf-8")
dfat = pd.read_csv('./db/snl_actor_title.csv', encoding="utf-8")
dfr = pd.read_csv('./db/snl_rating.csv', encoding="utf-8")
dfer = pd.merge(dfe, dfr, on=['sid', 'eid'])
dfactors = pd.merge(pd.merge(dfat, dfer, on=['sid', 'eid']), dfa, on='aid')

Most Titles

dfactors['name'].value_counts().head(5)
Kenan Thompson     929
Phil Hartman       913
Darrell Hammond    767
Fred Armisen       739
Bill Hader         696
Name: name, dtype: int64

Most Titles by Season

df_title_season = pd.DataFrame(dfactors.groupby(['sid','name'])['aid'].count()).reset_index()
df_title_season = df_title_season.sort_values('aid', ascending=False).drop_duplicates(['sid'])
df_title_season.columns = ['Season', 'Name', 'Appearances']
df_title_season.sort_values('Season').set_index('Season')
Name Appearances
Season
1 Chevy Chase 129
2 Dan Aykroyd 104
3 Dan Aykroyd 109
4 Bill Murray 96
5 Bill Murray 110
6 Charles Rocket 83
7 Joe Piscopo 97
8 Joe Piscopo 97
9 Tim Kazurinsky 99
10 Christopher Guest 68
11 Jon Lovitz 78
12 Phil Hartman 108
13 Phil Hartman 69
14 Phil Hartman 119
15 Phil Hartman 119
16 Phil Hartman 123
17 Phil Hartman 139
18 Phil Hartman 124
19 Phil Hartman 94
20 Michael McKean 94
21 Will Ferrell 99
22 Will Ferrell 83
23 Will Ferrell 115
24 Darrell Hammond 88
25 Chris Parnell 80
26 Chris Parnell 103
27 Will Ferrell 81
28 Amy Poehler 92
29 Amy Poehler 95
30 Amy Poehler 107
31 Amy Poehler 111
32 Amy Poehler 93
33 Amy Poehler 62
34 Kristen Wiig 111
35 Bill Hader 111
36 Kristen Wiig 102
37 Kristen Wiig 112
38 Bill Hader 96
39 Kenan Thompson 89
40 Taran Killam 100
41 Cecily Strong 95
42 Beck Bennett 63

Hosts

df_host = pd.DataFrame(dfactors[dfactors.actorType == 'host'].groupby(['sid','eid','name']).count()).reset_index()
pd.DataFrame(df_host['name'].value_counts()).head(7)
name
Alec Baldwin 17
Steve Martin 15
John Goodman 13
Buck Henry 10
Tom Hanks 9
Chevy Chase 8
Christopher Walken 7

Most Titles by Category

df_title_cat = pd.DataFrame(dfactors.groupby(['actorType','name'])['aid'].count()).reset_index()
df_title_cat = df_title_cat.sort_values('aid', ascending=False).drop_duplicates(['actorType'])
df_title_cat.columns = ['actorType', 'Name', 'Appearances']
df_title_cat.set_index("actorType")
Name Appearances
actorType
cast Kenan Thompson 929
crew Steve Higgins 490
unknown Jack Handey 123
host Alec Baldwin 108
music Paul Simon 25
guest Jim Henson's Muppets 17
cameo Tina Fey 13
filmed Steve Carell 13

Actors by Category

df_act_cat = pd.DataFrame(dfactors.groupby(['actorType','name'])['aid'].count()).reset_index()
df_act_cat.columns = ['Type', 'Name', 'Appearances']
for actorType in df_act_cat.Type.unique():
    df_act_cat[actorType] = 0
    df_act_cat.loc[df_act_cat.Type==actorType, actorType] = df_act_cat['Appearances']
    
df_act_cat = df_act_cat.drop(['Type'],axis=1)
df_act_cat = df_act_cat.groupby('Name').sum()
df_act_cat['radius'] = df_act_cat['Appearances'] / df_act_cat['Appearances'].max() * 20
#df_act_cat[df_act_cat['radius'] < 1] = 1
hover = HoverTool(
        tooltips=[
            ("Season", "@Season"),
            ("Cameos", "@cameo"),
            ("Cast", "@cast"),
            ("Crew", "@crew"),
            ("Filmed Appearance", "@filmed"),
            ("As Guest", "@guest"),
            ("As Host", "@host"),
            ("Musical Appearances", "@music"),
            ("Other", "@unknown"),
        ]
    )

TOOLS=[hover,'pan','zoom_in','zoom_out','undo','redo','reset','save','lasso_select']
p = figure(plot_width=800, plot_height=800, y_range=(-10,110),x_range=(-20,950), tools=TOOLS)
r = p.scatter("cast","host",source=df_act_cat, radius='radius')
t = show(p, notebook_handle=True)
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