Overview

Dataset statistics

Number of variables6
Number of observations3943
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.5 KiB
Average record size in memory51.0 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description자치구,안심 주소,위도,경도,CCTV 수량,수정 일시
Author성북구
URLhttps://data.seoul.go.kr/dataList/OA-20931/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
CCTV 수량 has constant value ""Constant
수정 일시 has constant value ""Constant
안심 주소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 09:36:40.762438
Analysis finished2024-03-13 09:36:41.667442
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
성북구
3943 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구
2nd row성북구
3rd row성북구
4th row성북구
5th row성북구

Common Values

ValueCountFrequency (%)
성북구 3943
100.0%

Length

2024-03-13T18:36:41.724890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T18:36:41.815593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성북구 3943
100.0%

안심 주소
Text

UNIQUE 

Distinct3943
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
2024-03-13T18:36:42.017184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length24.115394
Min length15

Characters and Unicode

Total characters95087
Distinct characters403
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3943 ?
Unique (%)100.0%

Sample

1st row(C0001-고정1)정릉3동_정릉로10길92
2nd row(C0001-고정2)정릉3동_정릉로10길92
3rd row(C0001-고정3)정릉3동_정릉로10길92
4th row(C0001-회전)정릉3동_정릉로10길92
5th row(C0002-고정1)길음1동_삼성아파트입구
ValueCountFrequency (%)
c0001-고정1)정릉3동_정릉로10길92 1
 
< 0.1%
c0847-고정1)장위1동_장위동233-321충표쉐르빌 1
 
< 0.1%
c0847-고정3)장위1동_장위동233-321충표쉐르빌 1
 
< 0.1%
c0847-회전)장위1동_장위동233-321 1
 
< 0.1%
c0848-고정1)장위1동_장위동231-162명신아트빌 1
 
< 0.1%
c0848-고정2)장위1동_장위동231-162명신아트빌 1
 
< 0.1%
c0848-회전)장위1동_장위동231-162 1
 
< 0.1%
c0849-고정1)장위1동_장위동219-286주택가 1
 
< 0.1%
c0849-고정2)장위1동_장위동219-286주택가 1
 
< 0.1%
c0849-회전)장위1동_장위동219-286 1
 
< 0.1%
Other values (3933) 3933
99.7%
2024-03-13T18:36:42.422628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6961
 
7.3%
1 6710
 
7.1%
- 5952
 
6.3%
2 5310
 
5.6%
동 4763
 
5.0%
) 4016
 
4.2%
( 4014
 
4.2%
정 3729
 
3.9%
3 3481
 
3.7%
_ 3249
 
3.4%
Other values (393) 46902
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37953
39.9%
Decimal Number 35874
37.7%
Dash Punctuation 5952
 
6.3%
Close Punctuation 4016
 
4.2%
Open Punctuation 4014
 
4.2%
Uppercase Letter 4014
 
4.2%
Connector Punctuation 3249
 
3.4%
Lowercase Letter 14
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4763
 
12.5%
정 3729
 
9.8%
고 2722
 
7.2%
로 2129
 
5.6%
길 2101
 
5.5%
회 1331
 
3.5%
전 1305
 
3.4%
장 1214
 
3.2%
위 1061
 
2.8%
릉 1010
 
2.7%
Other values (361) 16588
43.7%
Uppercase Letter
ValueCountFrequency (%)
C 2859
71.2%
S 528
 
13.2%
G 247
 
6.2%
P 229
 
5.7%
T 41
 
1.0%
W 41
 
1.0%
M 29
 
0.7%
R 18
 
0.4%
U 8
 
0.2%
K 8
 
0.2%
Other values (3) 6
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 6961
19.4%
1 6710
18.7%
2 5310
14.8%
3 3481
9.7%
4 2795
7.8%
5 2384
 
6.6%
6 2311
 
6.4%
8 2152
 
6.0%
7 2060
 
5.7%
9 1710
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
b 5
35.7%
e 3
21.4%
c 3
21.4%
h 3
21.4%
Dash Punctuation
ValueCountFrequency (%)
- 5952
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4016
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4014
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3249
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53106
55.8%
Hangul 37953
39.9%
Latin 4028
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4763
 
12.5%
정 3729
 
9.8%
고 2722
 
7.2%
로 2129
 
5.6%
길 2101
 
5.5%
회 1331
 
3.5%
전 1305
 
3.4%
장 1214
 
3.2%
위 1061
 
2.8%
릉 1010
 
2.7%
Other values (361) 16588
43.7%
Latin
ValueCountFrequency (%)
C 2859
71.0%
S 528
 
13.1%
G 247
 
6.1%
P 229
 
5.7%
T 41
 
1.0%
W 41
 
1.0%
M 29
 
0.7%
R 18
 
0.4%
U 8
 
0.2%
K 8
 
0.2%
Other values (7) 20
 
0.5%
Common
ValueCountFrequency (%)
0 6961
13.1%
1 6710
12.6%
- 5952
11.2%
2 5310
10.0%
) 4016
7.6%
( 4014
7.6%
3 3481
6.6%
_ 3249
 
6.1%
4 2795
 
5.3%
5 2384
 
4.5%
Other values (5) 8234
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57134
60.1%
Hangul 37953
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6961
12.2%
1 6710
11.7%
- 5952
10.4%
2 5310
9.3%
) 4016
 
7.0%
( 4014
 
7.0%
3 3481
 
6.1%
_ 3249
 
5.7%
C 2859
 
5.0%
4 2795
 
4.9%
Other values (22) 11787
20.6%
Hangul
ValueCountFrequency (%)
동 4763
 
12.5%
정 3729
 
9.8%
고 2722
 
7.2%
로 2129
 
5.6%
길 2101
 
5.5%
회 1331
 
3.5%
전 1305
 
3.4%
장 1214
 
3.2%
위 1061
 
2.8%
릉 1010
 
2.7%
Other values (361) 16588
43.7%

위도
Real number (ℝ)

Distinct389
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.603449
Minimum37.5775
Maximum37.6228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.8 KiB
2024-03-13T18:36:42.570310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5775
5-th percentile37.584
Q137.5959
median37.6061
Q337.6114
95-th percentile37.6174
Maximum37.6228
Range0.0453
Interquartile range (IQR)0.0155

Descriptive statistics

Standard deviation0.01043609
Coefficient of variation (CV)0.00027753013
Kurtosis-0.70186289
Mean37.603449
Median Absolute Deviation (MAD)0.0072
Skewness-0.53532707
Sum148270.4
Variance0.00010891198
MonotonicityNot monotonic
2024-03-13T18:36:42.719303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6114 48
 
1.2%
37.611 35
 
0.9%
37.6089 31
 
0.8%
37.6174 28
 
0.7%
37.6099 28
 
0.7%
37.6095 27
 
0.7%
37.6084 26
 
0.7%
37.614 26
 
0.7%
37.6134 26
 
0.7%
37.6125 26
 
0.7%
Other values (379) 3642
92.4%
ValueCountFrequency (%)
37.5775 1
 
< 0.1%
37.5776 1
 
< 0.1%
37.5782 3
 
0.1%
37.5787 3
 
0.1%
37.5789 3
 
0.1%
37.5792 1
 
< 0.1%
37.5793 6
0.2%
37.5797 8
0.2%
37.5798 6
0.2%
37.5801 11
0.3%
ValueCountFrequency (%)
37.6228 3
 
0.1%
37.6223 2
 
0.1%
37.6222 5
0.1%
37.6217 4
0.1%
37.6216 3
 
0.1%
37.6213 9
0.2%
37.6212 3
 
0.1%
37.621 2
 
0.1%
37.6209 4
0.1%
37.6208 1
 
< 0.1%

경도
Real number (ℝ)

Distinct610
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.02821
Minimum126.9858
Maximum127.0708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.8 KiB
2024-03-13T18:36:42.854298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.9858
5-th percentile126.9991
Q1127.0119
median127.0261
Q3127.0445
95-th percentile127.0617
Maximum127.0708
Range0.085
Interquartile range (IQR)0.0326

Descriptive statistics

Standard deviation0.019704147
Coefficient of variation (CV)0.00015511631
Kurtosis-0.98485002
Mean127.02821
Median Absolute Deviation (MAD)0.0163
Skewness0.13939119
Sum500872.23
Variance0.00038825339
MonotonicityNot monotonic
2024-03-13T18:36:42.984219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0497 27
 
0.7%
127.011 24
 
0.6%
127.0124 24
 
0.6%
127.043 23
 
0.6%
127.0214 21
 
0.5%
127.0151 21
 
0.5%
127.0437 20
 
0.5%
127.0227 20
 
0.5%
127.0153 20
 
0.5%
127.0294 19
 
0.5%
Other values (600) 3724
94.4%
ValueCountFrequency (%)
126.9858 2
 
0.1%
126.9864 1
 
< 0.1%
126.9881 3
 
0.1%
126.9883 3
 
0.1%
126.9889 2
 
0.1%
126.9893 5
0.1%
126.9894 9
0.2%
126.9895 5
0.1%
126.9896 2
 
0.1%
126.9898 4
0.1%
ValueCountFrequency (%)
127.0708 3
 
0.1%
127.0704 7
0.2%
127.0703 8
0.2%
127.0677 3
 
0.1%
127.0665 3
 
0.1%
127.0662 2
 
0.1%
127.0661 1
 
< 0.1%
127.066 8
0.2%
127.0658 3
 
0.1%
127.0657 11
0.3%

CCTV 수량
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
1
3943 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3943
100.0%

Length

2024-03-13T18:36:43.099589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T18:36:43.182274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3943
100.0%

수정 일시
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.9 KiB
2022-12-01
3943 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-01
2nd row2022-12-01
3rd row2022-12-01
4th row2022-12-01
5th row2022-12-01

Common Values

ValueCountFrequency (%)
2022-12-01 3943
100.0%

Length

2024-03-13T18:36:43.278979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T18:36:43.361957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-01 3943
100.0%

Interactions

2024-03-13T18:36:41.337439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:36:41.112631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:36:41.435082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:36:41.227584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:36:43.413822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.695
경도0.6951.000
2024-03-13T18:36:43.547357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.410
경도0.4101.000

Missing values

2024-03-13T18:36:41.531961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:36:41.628866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

자치구안심 주소위도경도CCTV 수량수정 일시
0성북구(C0001-고정1)정릉3동_정릉로10길9237.606126.998912022-12-01
1성북구(C0001-고정2)정릉3동_정릉로10길9237.606126.998912022-12-01
2성북구(C0001-고정3)정릉3동_정릉로10길9237.606126.998912022-12-01
3성북구(C0001-회전)정릉3동_정릉로10길9237.606126.998912022-12-01
4성북구(C0002-고정1)길음1동_삼성아파트입구37.6047127.022712022-12-01
5성북구(C0002-고정2)길음1동_삼성아파트입구37.6047127.022712022-12-01
6성북구(C0002-회전)길음1동_삼성아파트입구37.6047127.022712022-12-01
7성북구(C0003-고정1)길음1동1222피자벨앞37.6086127.02312022-12-01
8성북구(C0003-고정2)길음1동1222피자벨앞37.6086127.02312022-12-01
9성북구(C0003-회전)길음1동1222피자벨앞37.6086127.02312022-12-01
자치구안심 주소위도경도CCTV 수량수정 일시
3933성북구(W0028-회전)동선동2가64-137.5889127.018712022-12-01
3934성북구(W0029-회전)안암동1가105-737.5876127.020412022-12-01
3935성북구(W0030-회전)안암동4가6-137.581127.023112022-12-01
3936성북구(W0031-회전)보문동7가104-137.5809127.022912022-12-01
3937성북구(W0032-회전)월곡2동하월곡동96-19737.6009127.040412022-12-01
3938성북구(W0033-회전)월곡2동하월곡동226-837.5996127.041212022-12-01
3939성북구(W0034-회전)월곡2동하월곡동226-837.5978127.041312022-12-01
3940성북구(W0035-회전)종암동청량리동823-2637.5914127.039212022-12-01
3941성북구(W0036-회전)정릉3동966-137.6108127.007712022-12-01
3942성북구(W0037-회전)종암동3-139537.597127.040512022-12-01