
# Import libraries that will be used to perform the data analysis

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.io as pio

pio.renderers.default = 'plotly_mimetype+notebook'


# Load data downloaded from NYC Open Data into the permits DataFrame

permits = pd.read_csv('./Film_Permits.csv')

permits.head(5)


# Summary of the permits DataFrame

permits.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 74938 entries, 0 to 74937
Data columns (total 14 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   EventID            74938 non-null  int64 
 1   EventType          74938 non-null  object
 2   StartDateTime      74938 non-null  object
 3   EndDateTime        74938 non-null  object
 4   EnteredOn          74938 non-null  object
 5   EventAgency        74938 non-null  object
 6   ParkingHeld        74938 non-null  object
 7   Borough            74938 non-null  object
 8   CommunityBoard(s)  74920 non-null  object
 9   PolicePrecinct(s)  74920 non-null  object
 10  Category           74938 non-null  object
 11  SubCategoryName    74938 non-null  object
 12  Country            74938 non-null  object
 13  ZipCode(s)         74920 non-null  object
dtypes: int64(1), object(13)
memory usage: 8.0+ MB


# Number of unique permits

permits['EventID'].unique().shape[0]

74938


# Remove columns that will not be used in the analysis

columns_to_be_removed = ['EndDateTime', 'EnteredOn', 'EventAgency', 'ParkingHeld', 'CommunityBoard(s)', 
                         'PolicePrecinct(s)', 'SubCategoryName', 'ZipCode(s)']

permits = permits.drop(columns_to_be_removed, axis = 1)

permits.head(5)


# Create new DataFrame without Load in/out, DCAS Prep/Shoot/Wrap and Rigging events

filming_permits = permits[permits['EventType'] == 'Shooting Permit'].copy(deep = True)

filming_permits.head(5)


# Remove EventType column

filming_permits = filming_permits.drop('EventType', axis = 1)

filming_permits.head(5)


# Convert StartDateTime column to datetime

filming_permits.loc[:, 'StartDateTime'] = pd.to_datetime(filming_permits.loc[:, 'StartDateTime'], 
                                                         format = '%m/%d/%Y %I:%M:%S %p')
    
filming_permits.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 65621 entries, 0 to 74937
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   EventID        65621 non-null  int64         
 1   StartDateTime  65621 non-null  datetime64[ns]
 2   Borough        65621 non-null  object        
 3   Category       65621 non-null  object        
 4   Country        65621 non-null  object        
dtypes: datetime64[ns](1), int64(1), object(3)
memory usage: 3.0+ MB


# Use the StartDateTime column to create StartDay, StartDayOfWeek, StartMonth and StartYear columns 

filming_permits[['StartDay', 'StartDayOfWeek', 'StartMonth', 'StartYear']] = filming_permits.copy(
    deep = True).apply(lambda x: [x['StartDateTime'].floor('d'), 
                                  x['StartDateTime'].day_name(), 
                                  x['StartDateTime'].month_name(),
                                  x['StartDateTime'].year],
                                  axis = 1, result_type = 'expand')

filming_permits.head(5)


# StartDay column range

print (min(filming_permits['StartYear']), max(filming_permits['StartYear']))

2012 2022


# Remove rows that refer to 2022 activities to focus on full years only

filming_permits = filming_permits[filming_permits.loc[:, 'StartYear'] != 2022]

print (min(filming_permits['StartYear']), max(filming_permits['StartYear']))

2012 2021


# Number of unique permits (filming activities only)

filming_permits['EventID'].unique().shape[0]

65194


activities_by_borough = filming_permits['Borough'].value_counts(
    ascending = False).rename_axis('borough').reset_index(name = 'activities')

activities_by_borough['activities (%)'] = (
    activities_by_borough['activities'] / filming_permits.shape[0]) * 100

activities_by_borough


fig = px.bar(activities_by_borough.loc[::-1], x = 'activities (%)', y = 'borough',  
      title = """Over 77% of the filming activities were scheduled to happen in Manhattan or Brooklyn 
      <br><sup>Scheduled filming activities by borough (2012-2021)<sup>""", 
      orientation = 'h', height = 400)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',   
})

fig.update_xaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


activities_by_category = filming_permits['Category'].value_counts(
    ascending = False).rename_axis('category').reset_index(name = 'activities')

activities_by_category['activities (%)'] = (
    activities_by_category['activities'] / filming_permits.shape[0]) * 100

activities_by_category


fig = px.bar(activities_by_category.loc[::-1], x = 'activities (%)', y = 'category',  
      title = """Over 78% of the filming activities referred to television and film productions
      <br><sup>Scheduled filming activities by category (2012-2021)<sup>""", 
      orientation = 'h', height = 600)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',   
})

fig.update_xaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


# Create crosstab to check how production categories are distributed across NYC boroughs

category_borough_crosstab = pd.crosstab(filming_permits['Category'], filming_permits['Borough'],
    rownames = ['category'], colnames = ['borough'], normalize = 'columns')

category_borough_crosstab


fig = px.imshow(category_borough_crosstab * 100, 
      labels = dict(color = 'filming activities (%)'),
      x = category_borough_crosstab.columns, 
      y = category_borough_crosstab.index,
      title = """Television productions accounted for the majority of filming activities in all NYC boroughs
      <br><sup>Scheduled filming activities by category and borough (2012-2021)<sup>""",
      aspect = 'auto',
      color_continuous_scale = 'viridis')

fig.show()


productions_by_country = filming_permits['Country'].value_counts(
    ascending = False).rename_axis('country').reset_index(name = 'filming permits')

productions_by_country['filming permits (%)'] = (
    productions_by_country['filming permits'] / filming_permits.shape[0]) * 100

productions_by_country


activities_by_year = filming_permits['StartYear'].value_counts(ascending = False).rename_axis(
    'year').reset_index(name = 'activities')

activities_by_year


fig = px.bar(activities_by_year.sort_values(by = 'year'), x = 'year', y = 'activities', 
      title = """The number of scheduled activities plummeted in 2020 and did not return to 2013-2019 levels in 2021
      <br><sup>Scheduled filming activities by year (2012-2021)<sup>""")

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',  
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


# Create DateFrame for activities scheduled for 2020 only

filming_permits_2020 = filming_permits[filming_permits.loc[:, 'StartYear'] == 2020]

activities_by_day_2020 = filming_permits_2020['StartDay'].value_counts(ascending = False).rename_axis(
    'day').reset_index(name = 'activities')

# Create activities-by-day bar chart

fig = px.bar(activities_by_day_2020.sort_values(by = 'day'), x = 'day', y = 'activities', 
      title = """No filming activities were scheduled for April, May or June 2020 
      <br><sup>Scheduled filming activities by day (2020)<sup>""", )

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',  
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


activities_by_month = filming_permits['StartMonth'].value_counts(ascending = False).rename_axis(
    'month').reset_index(name = 'activities')

activities_by_month['activities (%)'] = (
    activities_by_month['activities'] /  activities_by_month['activities'].sum()) * 100

activities_by_month.set_index('month', inplace = True)

activities_by_month = activities_by_month.sort_index(
    key = lambda x: pd.to_datetime(x, format = "%B"))

activities_by_month


fig = px.bar(activities_by_month, x = activities_by_month.index, y = 'activities (%)', 
      title = """October was the preferred month for filming activities
      <br><sup>Scheduled filming activities by month (2012-2021)<sup>""")

fig.update_traces(marker_color = '#219ebc')
fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',   
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


# Create DataFrame with percentages of filming activities by year by month

activities_year_month = filming_permits.groupby(
    ['StartYear', 'StartMonth'])['EventID'].count().reset_index()

activities_year_month.rename(columns = {'StartYear': 'year', 'StartMonth': 'month',
                                        'EventID': 'activities'}, inplace = True)

activities_year_month['activities (%)'] = activities_year_month.groupby('year', group_keys=False)\
    .apply(lambda x: 100 * x['activities'] / x['activities'].sum())

# Order rows by year and by month

activities_year_month['month'] = pd.Categorical(
    activities_year_month['month'], categories =
    ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 
     'November', 'December'],
    ordered = True)

activities_year_month = activities_year_month.sort_values(by = ['year', 'month'])

activities_year_month


# Check for NaN values

activities_year_month.isnull().sum()

year              0
month             0
activities        0
activities (%)    0
dtype: int64


# Create small multiples 

fig = px.bar(activities_year_month, x = 'month', y = 'activities (%)',
      facet_col = 'year', facet_col_wrap = 5, 
      title = """October had the highest number of scheduled activities in 7 of the last 10 years
      <br><sup>Scheduled filming activities by month and year (2012-2021)<sup>""",
      height = 600)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff', 
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.for_each_annotation(lambda a: a.update(text = a.text.replace('year=', '')))

fig.show()


activities_by_day_of_week = filming_permits['StartDayOfWeek'].value_counts(ascending = False).rename_axis(
    'day of the week').reset_index(name = 'activities')

activities_by_day_of_week['activities (%)'] = (
    activities_by_day_of_week['activities'] / activities_by_day_of_week['activities'].sum()) * 100

activities_by_day_of_week['day of the week'] = pd.Categorical(
    activities_by_day_of_week['day of the week'], categories =
    ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'],
    ordered = True)

activities_by_day_of_week.set_index('day of the week', inplace = True)

activities_by_day_of_week = activities_by_day_of_week.sort_index()

activities_by_day_of_week


fig = px.bar(activities_by_day_of_week, x = activities_by_day_of_week.index, y = 'activities (%)', 
      title = """Over 90% of the filming activities were scheduled to start on weekdays
      <br><sup>Scheduled filming activities by day of the week (2012-2021)<sup>""")

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',   
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()


# Create DataFrame with percentages of filming activities by year by day of the week

activities_year_day_of_the_week = filming_permits.groupby(
    ['StartYear', 'StartDayOfWeek'])['EventID'].count().reset_index()

activities_year_day_of_the_week.rename(columns = {'StartYear': 'year', 'StartDayOfWeek': 'day of the week',
                                                  'EventID': 'activities'}, inplace = True)

activities_year_day_of_the_week['activities (%)'] = activities_year_day_of_the_week.groupby('year',
    group_keys=False).apply(lambda x: 100 * x['activities'] / x['activities'].sum())

# Order rows by year and by month

activities_year_day_of_the_week['day of the week'] = pd.Categorical(
    activities_year_day_of_the_week['day of the week'], categories =
    ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'],
    ordered = True)

activities_year_day_of_the_week = activities_year_day_of_the_week.sort_values(by = ['year', 'day of the week'])

activities_year_day_of_the_week


# Check for NaN values

activities_year_day_of_the_week.isnull().sum()

year               0
day of the week    0
activities         0
activities (%)     0
dtype: int64


# Create small multiples

fig = px.bar(activities_year_day_of_the_week, x = 'day of the week', y = 'activities (%)',
      facet_col = 'year', facet_col_wrap = 5, 
      title = """Sunday had the lowest amount of scheduled filming activities in the last 10 years
      <br><sup>Scheduled filming activities by day of the week and year (2012-2021)<sup>""",
      height = 600)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff',   
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.for_each_annotation(lambda a: a.update(text = a.text.replace('year=', '')))

fig.show()


# Create DataFrame with percentages of filming activities by year by borough

activities_year_borough = filming_permits.groupby(
    ['StartYear', 'Borough'])['EventID'].count().reset_index()

activities_year_borough.rename(columns = {'StartYear': 'year', 'Borough': 'borough',
                                          'EventID': 'activities'}, inplace = True)

activities_year_borough['activities (%)'] = activities_year_borough.groupby('year', group_keys=False)\
    .apply(lambda x: 100 * x['activities'] / x['activities'].sum())

activities_year_borough = activities_year_borough.sort_values(by = ['year', 'borough'])

activities_year_borough


# Check for NaN values

activities_year_borough.isnull().sum()

year              0
borough           0
activities        0
activities (%)    0
dtype: int64


# Create small multiples

fig = px.bar(activities_year_borough, x = 'borough', y = 'activities (%)',
      facet_col = 'year', facet_col_wrap = 5, 
      title = """Manhattan's participation in filming activities decreased by 13 percent points between 2012 and 2021
      <br><sup>Scheduled filming activities by borough and year (2012-2021)<sup>""",
      height = 600)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff', 
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.for_each_annotation(lambda a: a.update(text = a.text.replace('year=', '')))

fig.show()


# Create DataFrame with percentages of filming activities by year by category

activities_year_category = filming_permits.groupby(
    ['StartYear', 'Category'])['EventID'].count().reset_index()

activities_year_category.rename(columns = {'StartYear': 'year', 'Category': 'category',
                                           'EventID': 'activities'}, inplace = True)

activities_year_category['activities (%)'] = activities_year_category.groupby('year', group_keys=False)\
    .apply(lambda x: 100 * x['activities'] / x['activities'].sum())

activities_year_category = activities_year_category.sort_values(by = ['year', 'category'])

activities_year_category


# Check for NaN values

activities_year_category.isnull().sum()

year              0
category          0
activities        0
activities (%)    0
dtype: int64


# Create small multiples 

fig = px.bar(activities_year_category, x = 'category', y = 'activities (%)',
      facet_col = 'year', facet_col_wrap = 5, 
      title = """Filming activities related to television productions increased by 18 p.p. between 2012 and 2021
      <br><sup>Scheduled filming activities by category and year (2012-2021)<sup>""",
      height = 600)

fig.update_traces(marker_color = '#219ebc')

fig.update_layout({
    'plot_bgcolor': '#ffffff',
    'paper_bgcolor': '#ffffff', 
})

fig.update_yaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.for_each_annotation(lambda a: a.update(text = a.text.replace('year=', '')))

fig.show()

	EventID	EventType	StartDateTime	EndDateTime	EnteredOn	EventAgency	ParkingHeld	Borough	CommunityBoard(s)	PolicePrecinct(s)	Category	SubCategoryName	Country	ZipCode(s)
0	446040	Shooting Permit	10/19/2018 02:00:00 PM	10/20/2018 04:00:00 AM	10/16/2018 11:57:27 AM	Mayor's Office of Film, Theatre & Broadcasting	THOMPSON STREET between PRINCE STREET and SPRI...	Manhattan	2	1	Television	Cable-episodic	United States of America	10012
1	446168	Shooting Permit	10/19/2018 02:00:00 PM	10/20/2018 02:00:00 AM	10/16/2018 07:03:56 PM	Mayor's Office of Film, Theatre & Broadcasting	MARBLE HILL AVENUE between WEST 227 STREET an...	Manhattan	12, 8	34, 50	Film	Feature	United States of America	10034, 10463
2	186438	Shooting Permit	10/30/2014 07:00:00 AM	10/31/2014 02:00:00 AM	10/27/2014 12:14:15 PM	Mayor's Office of Film, Theatre & Broadcasting	LAUREL HILL BLVD between REVIEW AVENUE and RUS...	Queens	2, 5	104, 108	Television	Episodic series	United States of America	11378
3	445255	Shooting Permit	10/20/2018 07:00:00 AM	10/20/2018 06:00:00 PM	10/09/2018 09:34:58 PM	Mayor's Office of Film, Theatre & Broadcasting	JORALEMON STREET between BOERUM PLACE and COUR...	Brooklyn	2	84	Still Photography	Not Applicable	United States of America	11201
4	128794	Theater Load in and Load Outs	11/16/2013 12:01:00 AM	11/17/2013 06:00:00 AM	11/07/2013 03:48:28 PM	Mayor's Office of Film, Theatre & Broadcasting	WEST 31 STREET between 7 AVENUE and 8 AVENUE...	Manhattan	4, 5	14	Theater	Theater	United States of America	10001, 10121

	EventID	EventType	StartDateTime	Borough	Category	Country
0	446040	Shooting Permit	10/19/2018 02:00:00 PM	Manhattan	Television	United States of America
1	446168	Shooting Permit	10/19/2018 02:00:00 PM	Manhattan	Film	United States of America
2	186438	Shooting Permit	10/30/2014 07:00:00 AM	Queens	Television	United States of America
3	445255	Shooting Permit	10/20/2018 07:00:00 AM	Brooklyn	Still Photography	United States of America
4	128794	Theater Load in and Load Outs	11/16/2013 12:01:00 AM	Manhattan	Theater	United States of America

	EventID	StartDateTime	Borough	Category	Country
0	446040	10/19/2018 02:00:00 PM	Manhattan	Television	United States of America
1	446168	10/19/2018 02:00:00 PM	Manhattan	Film	United States of America
2	186438	10/30/2014 07:00:00 AM	Queens	Television	United States of America
3	445255	10/20/2018 07:00:00 AM	Brooklyn	Still Photography	United States of America
5	43547	01/10/2012 07:00:00 AM	Brooklyn	Television	United States of America

	borough	activities	activities (%)
0	Manhattan	28742	44.086879
1	Brooklyn	21578	33.098138
2	Queens	11762	18.041538
3	Bronx	2170	3.328527
4	Staten Island	942	1.444918

	category	activities	activities (%)
0	Television	39775	61.010216
1	Film	11082	16.998497
2	Commercial	5764	8.841304
3	Still Photography	4295	6.588030
4	WEB	2717	4.167561
5	Theater	678	1.039973
6	Documentary	334	0.512317
7	Student	300	0.460165
8	Music Video	248	0.380403
9	Red Carpet/Premiere	1	0.001534

NYC film permits | An exploratory data analysis¶

Table of contents (TOC)¶

1 Introduction¶

2 Data overview¶

3 Data wrangling¶

4 Exploratory data analysis¶

4.1 Preferred boroughs for filming activities¶

4.2 Most common production categories¶

4.3 Filming activities by production categories and boroughs¶

4.4 Productions by country of origin¶

4.5 Filming activities scheduled by year¶

4.6 Filming activities scheduled by month¶

4.7 Filming activities scheduled by day of the week ¶

4.8 Filming activities by borough and year¶

4.9 Filming activities by category and year¶

5 Conclusions¶

	EventID	StartDateTime	Borough	Category	Country	StartDay	StartDayOfWeek	StartMonth	StartYear
0	446040	2018-10-19 14:00:00	Manhattan	Television	United States of America	2018-10-19	Friday	October	2018
1	446168	2018-10-19 14:00:00	Manhattan	Film	United States of America	2018-10-19	Friday	October	2018
2	186438	2014-10-30 07:00:00	Queens	Television	United States of America	2014-10-30	Thursday	October	2014
3	445255	2018-10-20 07:00:00	Brooklyn	Still Photography	United States of America	2018-10-20	Saturday	October	2018
5	43547	2012-01-10 07:00:00	Brooklyn	Television	United States of America	2012-01-10	Tuesday	January	2012

borough	Bronx	Brooklyn	Manhattan	Queens	Staten Island
category
Commercial	0.080184	0.083604	0.117180	0.031882	0.045648
Documentary	0.005069	0.003847	0.007724	0.001360	0.002123
Film	0.207834	0.184911	0.174936	0.113076	0.300425
Music Video	0.003687	0.004773	0.003897	0.001785	0.004246
Red Carpet/Premiere	0.000000	0.000000	0.000035	0.000000	0.000000
Still Photography	0.028571	0.064464	0.092826	0.014623	0.002123
Student	0.005530	0.006349	0.003653	0.002891	0.012739
Television	0.651613	0.610900	0.525050	0.807261	0.629512
Theater	0.000922	0.000881	0.022511	0.000765	0.001062
WEB	0.016590	0.040272	0.052188	0.026356	0.002123

	country	filming permits	filming permits (%)
0	United States of America	65125	99.894162
1	United Kingdom	20	0.030678
2	Canada	17	0.026076
3	Japan	8	0.012271
4	France	7	0.010737
5	Panama	7	0.010737
6	Australia	5	0.007669
7	Netherlands	2	0.003068
8	Ireland	2	0.003068
9	Germany	1	0.001534

	year	activities
0	2015	7905
1	2018	7736
2	2013	7116
3	2014	7115
4	2017	7091
5	2019	7074
6	2016	7056
7	2012	6105
8	2021	5692
9	2020	2304

	activities	activities (%)
month
January	4176	6.405497
February	4546	6.973034
March	5613	8.609688
April	4799	7.361107
May	4730	7.255269
June	4876	7.479216
July	5462	8.378072
August	6288	9.645059
September	5960	9.141946
October	7385	11.327730
November	6468	9.921158
December	4891	7.502224

	year	month	activities	activities (%)
4	2012	January	341	5.585586
3	2012	February	441	7.223587
7	2012	March	548	8.976249
0	2012	April	548	8.976249
8	2012	May	548	8.976249
...	...	...	...	...
106	2021	August	619	10.874912
116	2021	September	548	9.627547
115	2021	October	581	10.207309
114	2021	November	588	10.330288
107	2021	December	390	6.851722

	activities	activities (%)
day of the week
Monday	10423	15.987668
Tuesday	11834	18.151977
Wednesday	12434	19.072307
Thursday	12558	19.262509
Friday	11802	18.102893
Saturday	3314	5.083290
Sunday	2829	4.339356

	year	day of the week	activities	activities (%)
1	2012	Monday	982	16.085176
5	2012	Tuesday	1048	17.166257
6	2012	Wednesday	1090	17.854218
4	2012	Thursday	1130	18.509419
0	2012	Friday	1038	17.002457
...	...	...	...	...
69	2021	Wednesday	1136	19.957836
67	2021	Thursday	1090	19.149684
63	2021	Friday	1059	18.605060
65	2021	Saturday	265	4.655657
66	2021	Sunday	171	3.004216

	year	borough	activities	activities (%)
0	2012	Bronx	97	1.588862
1	2012	Brooklyn	1900	31.122031
2	2012	Manhattan	3113	50.990991
3	2012	Queens	901	14.758395
4	2012	Staten Island	94	1.539722
5	2013	Bronx	131	1.840922
6	2013	Brooklyn	2171	30.508713
7	2013	Manhattan	3517	49.423834
8	2013	Queens	1189	16.708825
9	2013	Staten Island	108	1.517707
10	2014	Bronx	162	2.276880
11	2014	Brooklyn	2304	32.382291
12	2014	Manhattan	3381	47.519325
13	2014	Queens	1178	16.556571
14	2014	Staten Island	90	1.264933
15	2015	Bronx	231	2.922201
16	2015	Brooklyn	2508	31.726755
17	2015	Manhattan	3597	45.502846
18	2015	Queens	1483	18.760278
19	2015	Staten Island	86	1.087919
20	2016	Bronx	225	3.188776
21	2016	Brooklyn	2348	33.276644
22	2016	Manhattan	3100	43.934240
23	2016	Queens	1286	18.225624
24	2016	Staten Island	97	1.374717
25	2017	Bronx	294	4.146101
26	2017	Brooklyn	2363	33.323932
27	2017	Manhattan	3046	42.955860
28	2017	Queens	1273	17.952334
29	2017	Staten Island	115	1.621774
30	2018	Bronx	360	4.653568
31	2018	Brooklyn	2671	34.526887
32	2018	Manhattan	3191	41.248707
33	2018	Queens	1390	17.967942
34	2018	Staten Island	124	1.602896
35	2019	Bronx	252	3.562341
36	2019	Brooklyn	2350	33.220243
37	2019	Manhattan	2852	40.316653
38	2019	Queens	1447	20.455188
39	2019	Staten Island	173	2.445575
40	2020	Bronx	123	5.338542
41	2020	Brooklyn	813	35.286458
42	2020	Manhattan	833	36.154514
43	2020	Queens	521	22.612847
44	2020	Staten Island	14	0.607639
45	2021	Bronx	295	5.182713
46	2021	Brooklyn	2150	37.772312
47	2021	Manhattan	2112	37.104708
48	2021	Queens	1094	19.219958
49	2021	Staten Island	41	0.720309

	year	category	activities	activities (%)
0	2012	Commercial	699	11.449631
1	2012	Documentary	34	0.556921
2	2012	Film	1383	22.653563
3	2012	Music Video	27	0.442260
4	2012	Still Photography	452	7.403767
...	...	...	...	...
86	2021	Still Photography	268	4.708363
87	2021	Student	17	0.298665
88	2021	Television	3951	69.413212
89	2021	Theater	19	0.333802
90	2021	WEB	270	4.743500