In [3]:
import pandas as pd
import numpy as np
import csv
df=pd.read_csv('Crime_Data_From_2010_to_Present.csv')
df.head()
Out[3]:
DR Number
Date Reported
Date Occurred
Time Occurred
Area ID
Area Name
Reporting District
Crime Code
Crime Code Description
MO Codes
...
Weapon Description
Status Code
Status Description
Crime Code 1
Crime Code 2
Crime Code 3
Crime Code 4
Address
Cross Street
Location
0
1208575
03/14/2013
03/11/2013
1800
12
77th Street
1241
626
INTIMATE PARTNER - SIMPLE ASSAULT
0416 0446 1243 2000
...
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
AO
Adult Other
626.0
NaN
NaN
NaN
6300 BRYNHURST AV
NaN
(33.9829, -118.3338)
1
102005556
01/25/2010
01/22/2010
2300
20
Olympic
2071
510
VEHICLE - STOLEN
NaN
...
NaN
IC
Invest Cont
510.0
NaN
NaN
NaN
VAN NESS
15TH
(34.0454, -118.3157)
2
418
03/19/2013
03/18/2013
2030
18
Southeast
1823
510
VEHICLE - STOLEN
NaN
...
NaN
IC
Invest Cont
510.0
NaN
NaN
NaN
200 E 104TH ST
NaN
(33.942, -118.2717)
3
101822289
11/11/2010
11/10/2010
1800
18
Southeast
1803
510
VEHICLE - STOLEN
NaN
...
NaN
IC
Invest Cont
510.0
NaN
NaN
NaN
88TH
WALL
(33.9572, -118.2717)
4
42104479
01/11/2014
01/04/2014
2300
21
Topanga
2133
745
VANDALISM - MISDEAMEANOR ($399 OR UNDER)
0329
...
NaN
IC
Invest Cont
745.0
NaN
NaN
NaN
7200 CIRRUS WY
NaN
(34.2009, -118.6369)
5 rows × 26 columns
In [19]:
import datetime
dates = pd.DatetimeIndex(df['Date Occurred'])
df['Year'],df['Month']=dates.year,dates.month
df.head()
Out[19]:
DR Number
Date Reported
Date Occurred
Time Occurred
Area ID
Area Name
Reporting District
Crime Code
Crime Code Description
MO Codes
...
Status Description
Crime Code 1
Crime Code 2
Crime Code 3
Crime Code 4
Address
Cross Street
Location
Year
Month
0
1208575
03/14/2013
03/11/2013
1800
12
77th Street
1241
626
INTIMATE PARTNER - SIMPLE ASSAULT
0416 0446 1243 2000
...
Adult Other
626.0
NaN
NaN
NaN
6300 BRYNHURST AV
NaN
(33.9829, -118.3338)
2013
3
1
102005556
01/25/2010
01/22/2010
2300
20
Olympic
2071
510
VEHICLE - STOLEN
NaN
...
Invest Cont
510.0
NaN
NaN
NaN
VAN NESS
15TH
(34.0454, -118.3157)
2010
1
2
418
03/19/2013
03/18/2013
2030
18
Southeast
1823
510
VEHICLE - STOLEN
NaN
...
Invest Cont
510.0
NaN
NaN
NaN
200 E 104TH ST
NaN
(33.942, -118.2717)
2013
3
3
101822289
11/11/2010
11/10/2010
1800
18
Southeast
1803
510
VEHICLE - STOLEN
NaN
...
Invest Cont
510.0
NaN
NaN
NaN
88TH
WALL
(33.9572, -118.2717)
2010
11
4
42104479
01/11/2014
01/04/2014
2300
21
Topanga
2133
745
VANDALISM - MISDEAMEANOR ($399 OR UNDER)
0329
...
Invest Cont
745.0
NaN
NaN
NaN
7200 CIRRUS WY
NaN
(34.2009, -118.6369)
2014
1
5 rows × 28 columns
In [8]:
df['Time Occurred'].describe()
Out[8]:
count 1.552960e+06
mean 1.364355e+03
std 6.465544e+02
min 1.000000e+00
25% 9.300000e+02
50% 1.430000e+03
75% 1.900000e+03
max 2.359000e+03
Name: Time Occurred, dtype: float64
In [48]:
df1=df[['Year','Time Occurred','Crime Code Description']]
df1['Count']=1
df1
C:\Users\hp\Anaconda2\lib\site-packages\ipykernel\__main__.py:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
app.launch_new_instance()
Out[48]:
Year
Time Occurred
Crime Code Description
Count
0
2013
1800
INTIMATE PARTNER - SIMPLE ASSAULT
1
1
2010
2300
VEHICLE - STOLEN
1
2
2013
2030
VEHICLE - STOLEN
1
3
2010
1800
VEHICLE - STOLEN
1
4
2014
2300
VANDALISM - MISDEAMEANOR ($399 OR UNDER)
1
5
2013
1400
CRIMINAL HOMICIDE
1
6
2010
2230
VEHICLE - STOLEN
1
7
2010
1600
VEHICLE - STOLEN
1
8
2010
1600
VEHICLE - STOLEN
1
9
2013
800
EMBEZZLEMENT, GRAND THEFT ($950.01 & OVER)
1
10
2010
2100
VEHICLE - STOLEN
1
11
2010
1315
VEHICLE - STOLEN
1
12
2013
1200
THEFT PLAIN - PETTY ($950 & UNDER)
1
13
2013
1200
STALKING
1
14
2013
2030
SHOPLIFTING - PETTY THEFT ($950 & UNDER)
1
15
2013
500
INTIMATE PARTNER - SIMPLE ASSAULT
1
16
2013
1420
BATTERY - SIMPLE ASSAULT
1
17
2013
1430
INTIMATE PARTNER - SIMPLE ASSAULT
1
18
2013
1500
BATTERY - SIMPLE ASSAULT
1
19
2013
1730
EMBEZZLEMENT, PETTY THEFT ($950 & UNDER)
1
20
2013
100
INTIMATE PARTNER - SIMPLE ASSAULT
1
21
2013
2200
DOCUMENT FORGERY / STOLEN FELONY
1
22
2013
1600
INTIMATE PARTNER - SIMPLE ASSAULT
1
23
2013
2010
BATTERY - SIMPLE ASSAULT
1
24
2013
2315
BATTERY - SIMPLE ASSAULT
1
25
2013
1120
INTIMATE PARTNER - SIMPLE ASSAULT
1
26
2010
1230
VEHICLE - STOLEN
1
27
2013
1630
BIKE - STOLEN
1
28
2013
1815
ROBBERY
1
29
2013
1930
INTIMATE PARTNER - SIMPLE ASSAULT
1
...
...
...
...
...
1552930
2017
217
ARSON
1
1552931
2017
330
KIDNAPPING
1
1552932
2017
800
THEFT OF IDENTITY
1
1552933
2017
945
BURGLARY
1
1552934
2017
2000
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
1
1552935
2017
2015
THEFT PLAIN - PETTY ($950 & UNDER)
1
1552936
2017
2000
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
1
1552937
2017
2250
OTHER MISCELLANEOUS CRIME
1
1552938
2017
1600
BURGLARY
1
1552939
2017
1200
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
1
1552940
2017
1600
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LI...
1
1552941
2017
330
INTIMATE PARTNER - SIMPLE ASSAULT
1
1552942
2017
2012
SHOPLIFTING - PETTY THEFT ($950 & UNDER)
1
1552943
2017
2030
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
1
1552944
2017
2000
CHILD STEALING
1
1552945
2017
730
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
1
1552946
2017
1230
SHOPLIFTING - PETTY THEFT ($950 & UNDER)
1
1552947
2017
1730
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
1
1552948
2017
1300
LETTERS, LEWD
1
1552949
2017
1440
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
1
1552950
2017
930
BATTERY - SIMPLE ASSAULT
1
1552951
2017
1915
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...
1
1552952
2017
2350
BATTERY - SIMPLE ASSAULT
1
1552953
2017
720
BATTERY - SIMPLE ASSAULT
1
1552954
2017
720
BATTERY - SIMPLE ASSAULT
1
1552955
2017
1
THEFT OF IDENTITY
1
1552956
2017
2030
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
1
1552957
2017
800
BATTERY - SIMPLE ASSAULT
1
1552958
2016
200
OTHER MISCELLANEOUS CRIME
1
1552959
2017
1600
THEFT OF IDENTITY
1
1552960 rows × 4 columns
In [32]:
df1.to_csv('Crime_Data.csv',encoding='utf-8')
In [41]:
df2=df1[df1['Year']==2017].head(500)
df2.head()
Out[41]:
Year
Month
Date Occurred
Time Occurred
Crime Code Description
Count
622128
2017
7
07/21/2017
1700
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LI...
1
625455
2017
7
07/21/2017
1415
BURGLARY
1
626499
2017
7
07/21/2017
1800
BURGLARY
1
640441
2017
7
07/17/2017
1800
VEHICLE - STOLEN
1
640712
2017
7
07/21/2017
1500
BIKE - STOLEN
1
In [42]:
df2.to_csv('Crime_Data_500.csv',encoding='utf-8')
In [51]:
df3=df1.groupby(['Year','Time Occurred','Crime Code Description']).sum()
df3
Out[51]:
Count
Year
Time Occurred
Crime Code Description
2010
1
ARSON
1
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
66
ATTEMPTED ROBBERY
12
BATTERY - SIMPLE ASSAULT
138
BATTERY ON A FIREFIGHTER
2
BATTERY WITH SEXUAL CONTACT
28
BIKE - STOLEN
4
BRANDISH WEAPON
3
BUNCO, GRAND THEFT
3
BURGLARY
154
BURGLARY FROM VEHICLE
264
BURGLARY FROM VEHICLE, ATTEMPTED
5
BURGLARY, ATTEMPTED
9
CHILD ABUSE (PHYSICAL) - AGGRAVATED ASSAULT
5
CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT
20
CHILD ANNOYING (17YRS & UNDER)
15
CHILD NEGLECT (SEE 300 W.I.C.)
29
CHILD STEALING
3
CONSPIRACY
1
CONTRIBUTING
1
COUNTERFEIT
1
CREDIT CARDS, FRAUD USE ($950.01 & OVER)
4
CRIMINAL HOMICIDE
3
CRIMINAL THREATS - NO WEAPON DISPLAYED
54
CRM AGNST CHLD (13 OR UNDER) (14-15 & SUSP 10 YRS OLDER)0060
105
CRUELTY TO ANIMALS
4
DEFRAUDING INNKEEPER/THEFT OF SERVICES, $400 & UNDER
2
DISCHARGE FIREARMS/SHOTS FIRED
1
DISTURBING THE PEACE
5
DOCUMENT FORGERY / STOLEN FELONY
140
...
...
...
...
2017
2355
VIOLATION OF COURT ORDER
1
2356
BATTERY - SIMPLE ASSAULT
1
BURGLARY
1
LETTERS, LEWD
1
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114
1
2357
ARSON
1
BATTERY - SIMPLE ASSAULT
1
EMBEZZLEMENT, GRAND THEFT ($950.01 & OVER)
1
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
1
2358
ASSAULT WITH DEADLY WEAPON ON POLICE OFFICER
1
THEFT PLAIN - PETTY ($950 & UNDER)
1
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114
2
VEHICLE - STOLEN
1
2359
BATTERY - SIMPLE ASSAULT
5
BIKE - STOLEN
1
BURGLARY
3
BURGLARY FROM VEHICLE
6
DOCUMENT FORGERY / STOLEN FELONY
2
INTIMATE PARTNER - SIMPLE ASSAULT
2
OTHER MISCELLANEOUS CRIME
2
ROBBERY
2
THEFT FROM MOTOR VEHICLE - GRAND ($400 AND OVER)
2
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)
4
THEFT OF IDENTITY
5
THEFT PLAIN - PETTY ($950 & UNDER)
6
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD0036
3
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114
7
VANDALISM - MISDEAMEANOR ($399 OR UNDER)
3
VEHICLE - STOLEN
11
VIOLATION OF RESTRAINING ORDER
1
129837 rows × 1 columns
In [52]:
df3.to_csv('Crime_Data_Summary.csv',encoding='utf-8')
In [55]:
df4=pd.read_csv('Crime_Data_Summary.csv')
df4.head()
Out[55]:
Year
Time Occurred
Crime Code Description
Count
0
2010
1
ARSON
1
1
2010
1
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
66
2
2010
1
ATTEMPTED ROBBERY
12
3
2010
1
BATTERY - SIMPLE ASSAULT
138
4
2010
1
BATTERY ON A FIREFIGHTER
2
In [77]:
len(df4)
Out[77]:
129837
In [92]:
crime=["BATTERY - SIMPLE ASSAULT","BURGLARY FROM VEHICLE","VEHICLE - STOLEN","BURGLARY","THEFT OF IDENTITY"]
In [107]:
df5=df4[df4['Crime Code Description'].isin(crime)]
df5=df5[df5['Year'].isin([2014,2015,2016])]
df5
Out[107]:
Year
Time Occurred
Crime Code Description
Count
Hour
60735
2014
1
BATTERY - SIMPLE ASSAULT
110
0.01
60744
2014
1
BURGLARY
123
0.01
60745
2014
1
BURGLARY FROM VEHICLE
198
0.01
60795
2014
1
THEFT OF IDENTITY
989
0.01
60805
2014
1
VEHICLE - STOLEN
167
0.01
60809
2014
2
BATTERY - SIMPLE ASSAULT
1
0.02
60810
2014
2
BURGLARY
1
0.02
60816
2014
2
THEFT OF IDENTITY
1
0.02
60819
2014
3
BURGLARY
1
0.03
60820
2014
3
BURGLARY FROM VEHICLE
1
0.03
60827
2014
4
BATTERY - SIMPLE ASSAULT
1
0.04
60828
2014
4
BURGLARY
1
0.04
60835
2014
5
BATTERY - SIMPLE ASSAULT
70
0.05
60842
2014
5
BURGLARY
34
0.05
60843
2014
5
BURGLARY FROM VEHICLE
89
0.05
60882
2014
5
THEFT OF IDENTITY
272
0.05
60891
2014
5
VEHICLE - STOLEN
90
0.05
60905
2014
9
BATTERY - SIMPLE ASSAULT
1
0.09
60906
2014
9
BURGLARY
1
0.09
60910
2014
10
BATTERY - SIMPLE ASSAULT
50
0.10
60915
2014
10
BURGLARY
12
0.10
60916
2014
10
BURGLARY FROM VEHICLE
30
0.10
60944
2014
10
THEFT OF IDENTITY
9
0.10
60952
2014
10
VEHICLE - STOLEN
22
0.10
60955
2014
11
BURGLARY
1
0.11
60963
2014
13
BURGLARY
1
0.13
60964
2014
13
BURGLARY FROM VEHICLE
1
0.13
60970
2014
14
BURGLARY
1
0.14
60975
2014
15
BATTERY - SIMPLE ASSAULT
48
0.15
60980
2014
15
BURGLARY
22
0.15
...
...
...
...
...
...
114699
2016
2345
BATTERY - SIMPLE ASSAULT
58
23.45
114704
2016
2345
BURGLARY
29
23.45
114705
2016
2345
BURGLARY FROM VEHICLE
47
23.45
114740
2016
2345
THEFT OF IDENTITY
21
23.45
114750
2016
2345
VEHICLE - STOLEN
50
23.45
114762
2016
2346
THEFT OF IDENTITY
1
23.46
114763
2016
2347
BURGLARY
1
23.47
114772
2016
2349
THEFT OF IDENTITY
1
23.49
114776
2016
2350
BATTERY - SIMPLE ASSAULT
43
23.50
114781
2016
2350
BURGLARY
20
23.50
114782
2016
2350
BURGLARY FROM VEHICLE
22
23.50
114809
2016
2350
THEFT OF IDENTITY
4
23.50
114818
2016
2350
VEHICLE - STOLEN
38
23.50
114821
2016
2351
BURGLARY
1
23.51
114826
2016
2352
THEFT OF IDENTITY
1
23.52
114829
2016
2353
BURGLARY
3
23.53
114832
2016
2354
BURGLARY
1
23.54
114839
2016
2355
BATTERY - SIMPLE ASSAULT
21
23.55
114843
2016
2355
BURGLARY
7
23.55
114844
2016
2355
BURGLARY FROM VEHICLE
43
23.55
114871
2016
2355
THEFT OF IDENTITY
5
23.55
114880
2016
2355
VEHICLE - STOLEN
44
23.55
114884
2016
2356
BATTERY - SIMPLE ASSAULT
1
23.56
114885
2016
2356
BURGLARY
2
23.56
114892
2016
2357
THEFT OF IDENTITY
1
23.57
114899
2016
2359
BATTERY - SIMPLE ASSAULT
7
23.59
114903
2016
2359
BURGLARY
6
23.59
114904
2016
2359
BURGLARY FROM VEHICLE
19
23.59
114921
2016
2359
THEFT OF IDENTITY
11
23.59
114927
2016
2359
VEHICLE - STOLEN
22
23.59
7273 rows × 5 columns
In [108]:
df5.to_csv('Crime_Data_Top5.csv')
In [133]:
from datetime import *
def convert_to_time(t):
second=0
if len(t)==4:
hour=int(t[:2])
minute=int(t[3:])
elif len(t)>=3:
hour=int(t[0])
minute=int(t[1:])
else:
hour=0
minute=int(t)
new_time = time(hour,minute,second)
return new_time
print convert_to_time('1800')
18:00:00
In [135]:
#df5['Time Occurred']=df5['Time Occurred'].astype('string').apply(convert_to_time)
df5['Time Occurred']
Out[135]:
60735 00:01:00
60744 00:01:00
60745 00:01:00
60795 00:01:00
60805 00:01:00
60809 00:02:00
60810 00:02:00
60816 00:02:00
60819 00:03:00
60820 00:03:00
60827 00:04:00
60828 00:04:00
60835 00:05:00
60842 00:05:00
60843 00:05:00
60882 00:05:00
60891 00:05:00
60905 00:09:00
60906 00:09:00
60910 00:10:00
60915 00:10:00
60916 00:10:00
60944 00:10:00
60952 00:10:00
60955 00:11:00
60963 00:13:00
60964 00:13:00
60970 00:14:00
60975 00:15:00
60980 00:15:00
...
114699 23:05:00
114704 23:05:00
114705 23:05:00
114740 23:05:00
114750 23:05:00
114762 23:06:00
114763 23:07:00
114772 23:09:00
114776 23:00:00
114781 23:00:00
114782 23:00:00
114809 23:00:00
114818 23:00:00
114821 23:01:00
114826 23:02:00
114829 23:03:00
114832 23:04:00
114839 23:05:00
114843 23:05:00
114844 23:05:00
114871 23:05:00
114880 23:05:00
114884 23:06:00
114885 23:06:00
114892 23:07:00
114899 23:09:00
114903 23:09:00
114904 23:09:00
114921 23:09:00
114927 23:09:00
Name: Time Occurred, dtype: object
In [136]:
df5
Out[136]:
Year
Time Occurred
Crime Code Description
Count
Hour
60735
2014
00:01:00
BATTERY - SIMPLE ASSAULT
110
0.01
60744
2014
00:01:00
BURGLARY
123
0.01
60745
2014
00:01:00
BURGLARY FROM VEHICLE
198
0.01
60795
2014
00:01:00
THEFT OF IDENTITY
989
0.01
60805
2014
00:01:00
VEHICLE - STOLEN
167
0.01
60809
2014
00:02:00
BATTERY - SIMPLE ASSAULT
1
0.02
60810
2014
00:02:00
BURGLARY
1
0.02
60816
2014
00:02:00
THEFT OF IDENTITY
1
0.02
60819
2014
00:03:00
BURGLARY
1
0.03
60820
2014
00:03:00
BURGLARY FROM VEHICLE
1
0.03
60827
2014
00:04:00
BATTERY - SIMPLE ASSAULT
1
0.04
60828
2014
00:04:00
BURGLARY
1
0.04
60835
2014
00:05:00
BATTERY - SIMPLE ASSAULT
70
0.05
60842
2014
00:05:00
BURGLARY
34
0.05
60843
2014
00:05:00
BURGLARY FROM VEHICLE
89
0.05
60882
2014
00:05:00
THEFT OF IDENTITY
272
0.05
60891
2014
00:05:00
VEHICLE - STOLEN
90
0.05
60905
2014
00:09:00
BATTERY - SIMPLE ASSAULT
1
0.09
60906
2014
00:09:00
BURGLARY
1
0.09
60910
2014
00:10:00
BATTERY - SIMPLE ASSAULT
50
0.10
60915
2014
00:10:00
BURGLARY
12
0.10
60916
2014
00:10:00
BURGLARY FROM VEHICLE
30
0.10
60944
2014
00:10:00
THEFT OF IDENTITY
9
0.10
60952
2014
00:10:00
VEHICLE - STOLEN
22
0.10
60955
2014
00:11:00
BURGLARY
1
0.11
60963
2014
00:13:00
BURGLARY
1
0.13
60964
2014
00:13:00
BURGLARY FROM VEHICLE
1
0.13
60970
2014
00:14:00
BURGLARY
1
0.14
60975
2014
00:15:00
BATTERY - SIMPLE ASSAULT
48
0.15
60980
2014
00:15:00
BURGLARY
22
0.15
...
...
...
...
...
...
114699
2016
23:05:00
BATTERY - SIMPLE ASSAULT
58
23.45
114704
2016
23:05:00
BURGLARY
29
23.45
114705
2016
23:05:00
BURGLARY FROM VEHICLE
47
23.45
114740
2016
23:05:00
THEFT OF IDENTITY
21
23.45
114750
2016
23:05:00
VEHICLE - STOLEN
50
23.45
114762
2016
23:06:00
THEFT OF IDENTITY
1
23.46
114763
2016
23:07:00
BURGLARY
1
23.47
114772
2016
23:09:00
THEFT OF IDENTITY
1
23.49
114776
2016
23:00:00
BATTERY - SIMPLE ASSAULT
43
23.50
114781
2016
23:00:00
BURGLARY
20
23.50
114782
2016
23:00:00
BURGLARY FROM VEHICLE
22
23.50
114809
2016
23:00:00
THEFT OF IDENTITY
4
23.50
114818
2016
23:00:00
VEHICLE - STOLEN
38
23.50
114821
2016
23:01:00
BURGLARY
1
23.51
114826
2016
23:02:00
THEFT OF IDENTITY
1
23.52
114829
2016
23:03:00
BURGLARY
3
23.53
114832
2016
23:04:00
BURGLARY
1
23.54
114839
2016
23:05:00
BATTERY - SIMPLE ASSAULT
21
23.55
114843
2016
23:05:00
BURGLARY
7
23.55
114844
2016
23:05:00
BURGLARY FROM VEHICLE
43
23.55
114871
2016
23:05:00
THEFT OF IDENTITY
5
23.55
114880
2016
23:05:00
VEHICLE - STOLEN
44
23.55
114884
2016
23:06:00
BATTERY - SIMPLE ASSAULT
1
23.56
114885
2016
23:06:00
BURGLARY
2
23.56
114892
2016
23:07:00
THEFT OF IDENTITY
1
23.57
114899
2016
23:09:00
BATTERY - SIMPLE ASSAULT
7
23.59
114903
2016
23:09:00
BURGLARY
6
23.59
114904
2016
23:09:00
BURGLARY FROM VEHICLE
19
23.59
114921
2016
23:09:00
THEFT OF IDENTITY
11
23.59
114927
2016
23:09:00
VEHICLE - STOLEN
22
23.59
7273 rows × 5 columns
In [137]:
df5.to_csv('Crime_Data_Top5.csv')
Content source: Erin-99/Udacity_DAND
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