def GetYearPlot():
# prepare data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
master_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
master_df = master_df.loc[master_df['Year']>='1980']
# run calculations
# create plot figure
fig = GO.Figure()
fig.add_trace(GO.Histogram(x=master_df['Year'], xbins={'size': 1}, marker_color='DarkOliveGreen'))
fig.update_layout(
title="Number of Movies Per Year",
xaxis_title="Year",
yaxis_title="Movies Count",
width=600,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=80, pad=0),
paper_bgcolor="LightSteelBlue",
)
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetScoresPlot():
# get the data
# later must filter by active movies
master_ls = list(TB.MasterMovie.objects.values())
score_distrib = {'netflix': [], 'amazon': [], 'hulu': []}
total_cnt = {'netflix': 0, 'amazon': 0, 'hulu': 0}
for mov in master_ls:
score = mov['ScoreImdb']
if score is None:
continue
index_dx = json.loads(mov['Indeces'])
# only keep exclusive content to each service
exclusive = 0
for key, val in index_dx.items():
if key in score_distrib:
exclusive += 1
if exclusive > 1:
continue
year = int(mov['Year'])
for key, val in index_dx.items():
if key in score_distrib and year >= 1980:
score_distrib[key].append(score)
total_cnt[key] += 1
# create the plot
hist_data = [score_distrib['hulu'], score_distrib['netflix'], score_distrib['amazon']]
group_labels = ['Hulu', 'Netflix', 'Amazon']
colors = ['green', 'crimson', 'darkblue']
fig = FF.create_distplot(hist_data, group_labels, colors=colors,
bin_size=.1, show_hist=False, show_rug=False )
fig.update_layout(
title="IMDB Score for Movies Exclusively in Each Service",
xaxis_title="IMDB Score",
yaxis_title="Percentage",
width=600,
height=400,
margin=GO.layout.Margin(t=60, r=10, b=50, l=70, pad=0),
paper_bgcolor="LightSteelBlue",
barmode='overlay',
yaxis_tickformat = '%',
)
fig.update_xaxes(tickvals=list(range(0, 10, 1)))
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetCountriesPlot():
# prepare data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
master_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
master_df = master_df.dropna(axis=0, subset=['Country'])
master_df = master_df.loc[master_df['Country']!='nan'].reset_index().copy()
# run calculations
group_df = master_df.groupby('Country').count()['Movie_ID'].sort_values(ascending=False)
others_df = group_df.loc[group_df.index!='USA']
primary_x = ['USA', 'All Others']
primary_y = [group_df[group_df.index=='USA'].values[0], others_df.sum()]
plot_df = others_df[:12]
# create plot figure
COLOR = 'mediumpurple'
fig = SB.make_subplots(rows=1, cols=2, column_widths=[0.25, 0.75],
subplot_titles=("Top Countries", "Secondary Countries"))
fig.add_trace(
GO.Bar(x=primary_x, y=primary_y, marker_color=COLOR, hoverlabel={'namelength': 0}),
row=1, col=1)
fig.add_trace(
GO.Bar(x=plot_df.index, y=plot_df.values, marker_color=COLOR, hoverlabel={'namelength': 0}),
row=1, col=2)
fig.update_layout(
title={'text': "Countries That Make Movies", 'x':0.5, 'y':0.99,
'xanchor': 'center', 'yanchor': 'top'},
width=600,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=70, pad=0),
paper_bgcolor="LightSteelBlue",
showlegend=False,
)
# extend annotations so they don't override the subplot titles
annotations = [a.to_plotly_json() for a in fig["layout"]["annotations"]]
annotations.append({'font': {'size': 16}, 'showarrow': False, 'text': 'Movie Counts',
'x': -0.08, 'xanchor': 'center', 'xref': 'paper',
'y': 0.4, 'yanchor': 'bottom', 'yref': 'paper', 'textangle':-90})
fig["layout"]["annotations"] = annotations
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetScoreProfitPlot():
# prepare data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
master_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
scoreprofit_df = master_df.dropna(axis=0, subset=['Budget', 'Gross', 'ScoreImdb'])
# run calculations
scoreprofit_df['profit'] = (scoreprofit_df['Gross'] - scoreprofit_df['Budget'])
scoreprofit_df = scoreprofit_df.loc[ (scoreprofit_df['profit']<2e8) ]
def ScoreProfitText(row):
title = row['Title']
profit = row['profit']
score = row['ScoreImdb']
hover_tx = f'{title}<br><b>{score}</b> ${profit:.0e}'
return hover_tx
scoreprofit_df['sp_text'] = scoreprofit_df.apply(ScoreProfitText, axis=1)
scoreprofit_df[['Title', 'profit', 'sp_text']][:5]
# create plot figure
fig = GO.Figure()
fig.add_trace(GO.Scatter(x=scoreprofit_df['profit'], y=scoreprofit_df['ScoreImdb'],
mode='markers', marker={'color': 'seagreen'},
text=scoreprofit_df['sp_text'], hovertemplate ='<span>%{text}</span>',
hoverlabel={'namelength': 0}))
fig.update_layout(
title="IMDB Score vs Profit",
xaxis_title="Profit ($ dollars)",
yaxis_title="IMDB Score",
width=500,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=60, pad=0),
paper_bgcolor="LightSteelBlue",
#xaxis_type="log",
)
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetGenresMoviePlot():
# prepare data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
master_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
master_df = master_df.dropna(axis=0, subset=['Genres']).reset_index().copy()
master_df = master_df.drop(columns=['level_0', 'index'], errors='ignore')
# run calculations
genres_dist_ls = []
for idx, row in master_df.iterrows():
genres_tx = row['Genres']
genres_ls = genres_tx.split(', ')
cnt = 0
for gnr in genres_ls:
if gnr.strip() != '':
cnt += 1
if cnt > 0 and cnt <= 5:
genres_dist_ls.append(cnt)
# create plot figure
fig = GO.Figure()
fig.add_trace( GO.Histogram(x=genres_dist_ls, marker_color='darkslategrey', histnorm='probability') )
fig.update_layout(
title="Number of Genres per Movie",
yaxis_title="Percentage",
xaxis_title="Number of Genres",
width=500,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=10, pad=0),
paper_bgcolor="LightSteelBlue",
yaxis_tickformat = '%',
bargap=0.2,
)
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetPlot():
# prepare data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
master_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
# run calculations
# create plot figure
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetTotalsPlot():
# get the data
# later must filter by active movies
master_ls = list(TB.MasterMovie.objects.values())
year_distrib = {'netflix': [], 'amazon': [], 'hulu': []}
for mov in master_ls:
year = int(mov['Year'])
index_dx = json.loads(mov['Indeces'])
for key, val in index_dx.items():
if key in year_distrib and year >= 1980:
year_distrib[key].append(year)
# create the plot
fig = GO.Figure()
fig.add_trace(GO.Histogram(x= year_distrib['amazon'], xbins={'size': 1}, name='Amazon',
marker_color='darkblue', opacity=0.6))
fig.add_trace(GO.Histogram(x= year_distrib['netflix'], xbins={'size': 1}, name='Netflix',
marker_color='crimson', opacity=0.7))
fig.add_trace(GO.Histogram(x= year_distrib['hulu'], xbins={'size': 1}, name='Hulu',
marker_color='green', opacity=0.8))
fig.update_layout(
title="Total Movie Count for Each Service",
xaxis_title="Release Year",
yaxis_title="Movie Count",
width=600,
height=400,
margin=GO.layout.Margin(t=50, r=20, b=50, l=70, pad=0),
paper_bgcolor="LightSteelBlue",
barmode='overlay',
)
# format and send to frontend
return UT.ConvertFigureToJson(fig)
def GetRoiPlot():
# get the data
master_ls = list(TB.MasterMovie.objects.values())
master_df = PD.DataFrame(master_ls)
roi_df = master_df.drop(columns=['id', 'OriginalTitle', 'Poster', 'Synopsis', 'Indeces'])
roi_df = roi_df.dropna(axis=0, subset=['Budget', 'Gross'])
roi_df = roi_df.loc[ (roi_df['Budget']<201e6) ]
# drop randomly to keep graph from being cluttered
roi_df = roi_df.reset_index().copy()
import random as RD
RD.seed(time.time())
number_movies = 1500
random_ls = []
for mv in range(0, number_movies):
random_idx = RD.randint(0, roi_df.shape[0]-1)
random_movie = roi_df.loc[random_idx]
random_ls.append(random_movie)
roi_df = PD.DataFrame(random_ls)
roi_df = roi_df.drop_duplicates(subset=['Movie_ID'])
# calculate new financial columns
roi_df['profit'] = (roi_df['Gross'] - roi_df['Budget'])
roi_df['roi'] = (roi_df['Gross'] - roi_df['Budget']) / roi_df['Budget']
roi_df = roi_df.loc[ (roi_df['roi']<5e5) ]
roi_df = roi_df.loc[ (roi_df['profit']<1e9) ]
# create new columns for plotting
GROUPS = [0, 1, 2, 3]
NAMES = ['RoI < 0', 'RoI < 2', 'RoI < 10', 'RoI > 10']
COLORS = ['crimson', 'blue', 'green', 'goldenrod']
def RoiGroup(row):
roi = row['roi']
if roi <= 0:
return 0
if roi <= 2:
return 1
if roi <= 10:
return 2
return 3
roi_df['roi_group'] = roi_df.apply(RoiGroup, axis=1)
def RoiText(row):
title = row['Title']
roi = row['roi']
hover_tx = f'{title}<br>RoI: <b>{roi : .1f}</b>'
return hover_tx
roi_df['roi_text'] = roi_df.apply(RoiText, axis=1)
# plot the roi
fig = GO.Figure()
for g in GROUPS:
df = roi_df.loc[roi_df['roi_group']==g]
fig.add_trace(GO.Scatter(x=df['Budget'], y=df['profit'], name=NAMES[g],
mode='markers', marker={'color': COLORS[g]},
text=df['roi_text'], hovertemplate ='<span>%{text}</span>',
hoverlabel={'namelength': 0}))
fig.update_layout(
title="Return on Investment",
xaxis_title="Budget ($ dollars)",
yaxis_title="Profit ($ dollars)",
width=700,
height=400,
margin=GO.layout.Margin(t=60, r=10, b=50, l=80, pad=0),
paper_bgcolor="LightSteelBlue",
)
fig.update_xaxes(range=(-25e6, 225e6))
fig.update_yaxes(range=(-100e6, 1e9))
# format and send to frontend
return UT.ConvertFigureToJson(fig)