Overview

Dataset statistics

Number of variables6
Number of observations4111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory204.9 KiB
Average record size in memory51.0 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description자치구,안심 주소,위도,경도,CCTV 수량,수정 일시
Author은평구
URLhttps://data.seoul.go.kr/dataList/OA-20935/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
수정 일시 has constant value ""Constant
CCTV 수량 is highly imbalanced (99.6%)Imbalance
안심 주소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:54:15.722704
Analysis finished2024-03-13 11:54:16.794551
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
은평구
4111 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은평구 4111
100.0%

Length

2024-03-13T20:54:16.861155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:16.954288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은평구 4111
100.0%

안심 주소
Text

UNIQUE 

Distinct4111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
2024-03-13T20:54:17.170638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length18.316711
Min length9

Characters and Unicode

Total characters75300
Distinct characters362
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4111 ?
Unique (%)100.0%

Sample

1st row갈현1동 12-107(늘봄이) /1
2nd row갈현1동 12-107(늘봄이) /2
3rd row갈현1동 12-107(늘봄이) /3
4th row갈현1동 12-107(늘봄이) /4
5th row갈현1동 12-107(늘봄이) /5
ValueCountFrequency (%)
2 848
 
5.7%
1 847
 
5.7%
3 780
 
5.2%
4 605
 
4.0%
진관동 425
 
2.8%
역촌동 346
 
2.3%
불광2동 316
 
2.1%
응암3동 303
 
2.0%
녹번동 303
 
2.0%
5 294
 
2.0%
Other values (1754) 9874
66.1%
2024-03-13T20:54:17.613495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10884
 
14.5%
1 5629
 
7.5%
2 4776
 
6.3%
동 4300
 
5.7%
/ 3893
 
5.2%
- 3690
 
4.9%
3 3247
 
4.3%
4 2311
 
3.1%
) 2169
 
2.9%
( 2165
 
2.9%
Other values (352) 32236
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28815
38.3%
Decimal Number 23528
31.2%
Space Separator 10884
 
14.5%
Other Punctuation 3902
 
5.2%
Dash Punctuation 3690
 
4.9%
Close Punctuation 2169
 
2.9%
Open Punctuation 2165
 
2.9%
Uppercase Letter 115
 
0.2%
Lowercase Letter 26
 
< 0.1%
Connector Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4300
 
14.9%
이 1097
 
3.8%
린 1048
 
3.6%
어 971
 
3.4%
산 841
 
2.9%
암 713
 
2.5%
응 703
 
2.4%
광 667
 
2.3%
불 657
 
2.3%
사 629
 
2.2%
Other values (317) 17189
59.7%
Uppercase Letter
ValueCountFrequency (%)
S 29
25.2%
G 18
15.7%
D 16
13.9%
C 13
11.3%
H 9
 
7.8%
M 7
 
6.1%
K 6
 
5.2%
A 5
 
4.3%
W 5
 
4.3%
U 4
 
3.5%
Other values (2) 3
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 5629
23.9%
2 4776
20.3%
3 3247
13.8%
4 2311
9.8%
5 1698
 
7.2%
7 1281
 
5.4%
8 1231
 
5.2%
6 1150
 
4.9%
0 1113
 
4.7%
9 1092
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/ 3893
99.8%
. 5
 
0.1%
: 3
 
0.1%
, 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
u 11
42.3%
g 5
19.2%
o 5
19.2%
e 5
19.2%
Space Separator
ValueCountFrequency (%)
10884
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3690
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2169
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2165
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46344
61.5%
Hangul 28815
38.3%
Latin 141
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4300
 
14.9%
이 1097
 
3.8%
린 1048
 
3.6%
어 971
 
3.4%
산 841
 
2.9%
암 713
 
2.5%
응 703
 
2.4%
광 667
 
2.3%
불 657
 
2.3%
사 629
 
2.2%
Other values (317) 17189
59.7%
Common
ValueCountFrequency (%)
10884
23.5%
1 5629
12.1%
2 4776
10.3%
/ 3893
 
8.4%
- 3690
 
8.0%
3 3247
 
7.0%
4 2311
 
5.0%
) 2169
 
4.7%
( 2165
 
4.7%
5 1698
 
3.7%
Other values (9) 5882
12.7%
Latin
ValueCountFrequency (%)
S 29
20.6%
G 18
12.8%
D 16
11.3%
C 13
9.2%
u 11
 
7.8%
H 9
 
6.4%
M 7
 
5.0%
K 6
 
4.3%
g 5
 
3.5%
o 5
 
3.5%
Other values (6) 22
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46485
61.7%
Hangul 28815
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10884
23.4%
1 5629
12.1%
2 4776
10.3%
/ 3893
 
8.4%
- 3690
 
7.9%
3 3247
 
7.0%
4 2311
 
5.0%
) 2169
 
4.7%
( 2165
 
4.7%
5 1698
 
3.7%
Other values (25) 6023
13.0%
Hangul
ValueCountFrequency (%)
동 4300
 
14.9%
이 1097
 
3.8%
린 1048
 
3.6%
어 971
 
3.4%
산 841
 
2.9%
암 713
 
2.5%
응 703
 
2.4%
광 667
 
2.3%
불 657
 
2.3%
사 629
 
2.2%
Other values (317) 17189
59.7%

위도
Real number (ℝ)

Distinct542
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.608796
Minimum37.5773
Maximum37.6551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.3 KiB
2024-03-13T20:54:17.781992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5773
5-th percentile37.585
Q137.5964
median37.6079
Q337.6199
95-th percentile37.6388
Maximum37.6551
Range0.0778
Interquartile range (IQR)0.0235

Descriptive statistics

Standard deviation0.015899976
Coefficient of variation (CV)0.00042277281
Kurtosis-0.4305567
Mean37.608796
Median Absolute Deviation (MAD)0.0117
Skewness0.35300601
Sum154609.76
Variance0.00025280925
MonotonicityNot monotonic
2024-03-13T20:54:17.967277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5991 28
 
0.7%
37.6161 28
 
0.7%
37.6055 26
 
0.6%
37.5872 25
 
0.6%
37.6011 25
 
0.6%
37.6194 25
 
0.6%
37.6222 24
 
0.6%
37.6136 24
 
0.6%
37.6145 23
 
0.6%
37.6229 22
 
0.5%
Other values (532) 3861
93.9%
ValueCountFrequency (%)
37.5773 1
 
< 0.1%
37.5774 2
 
< 0.1%
37.5781 5
0.1%
37.5782 3
0.1%
37.5783 1
 
< 0.1%
37.5784 1
 
< 0.1%
37.5785 5
0.1%
37.5797 4
0.1%
37.5804 6
0.1%
37.5806 1
 
< 0.1%
ValueCountFrequency (%)
37.6551 3
0.1%
37.6548 4
0.1%
37.6545 1
 
< 0.1%
37.6531 1
 
< 0.1%
37.6529 2
< 0.1%
37.6516 1
 
< 0.1%
37.6507 3
0.1%
37.6504 1
 
< 0.1%
37.6502 1
 
< 0.1%
37.6498 1
 
< 0.1%

경도
Real number (ℝ)

Distinct383
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.91875
Minimum126.8847
Maximum126.9491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.3 KiB
2024-03-13T20:54:18.131784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.8847
5-th percentile126.90415
Q1126.912
median126.9185
Q3126.92585
95-th percentile126.9348
Maximum126.9491
Range0.0644
Interquartile range (IQR)0.01385

Descriptive statistics

Standard deviation0.0099150278
Coefficient of variation (CV)7.8121065 × 10-5
Kurtosis0.10370274
Mean126.91875
Median Absolute Deviation (MAD)0.0068
Skewness-0.063932712
Sum521762.97
Variance9.8307777 × 10-5
MonotonicityNot monotonic
2024-03-13T20:54:18.274102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9066 43
 
1.0%
126.919 39
 
0.9%
126.9173 34
 
0.8%
126.9152 33
 
0.8%
126.9159 31
 
0.8%
126.921 31
 
0.8%
126.9195 28
 
0.7%
126.9169 28
 
0.7%
126.9187 28
 
0.7%
126.9153 27
 
0.7%
Other values (373) 3789
92.2%
ValueCountFrequency (%)
126.8847 3
0.1%
126.8848 3
0.1%
126.8876 3
0.1%
126.888 4
0.1%
126.8883 4
0.1%
126.8889 4
0.1%
126.8893 4
0.1%
126.8894 5
0.1%
126.8897 5
0.1%
126.8906 3
0.1%
ValueCountFrequency (%)
126.9491 3
0.1%
126.948 1
 
< 0.1%
126.9453 3
0.1%
126.9452 3
0.1%
126.9443 1
 
< 0.1%
126.943 2
 
< 0.1%
126.9429 1
 
< 0.1%
126.9426 1
 
< 0.1%
126.942 1
 
< 0.1%
126.9412 6
0.1%

CCTV 수량
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
1
4109 
2
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 4109
> 99.9%
2 1
 
< 0.1%
5 1
 
< 0.1%

Length

2024-03-13T20:54:18.415396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:18.519721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4109
> 99.9%
2 1
 
< 0.1%
5 1
 
< 0.1%

수정 일시
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
2022-12-01
4111 

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 4111
100.0%

Length

2024-03-13T20:54:18.623905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:54:18.758195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-01 4111
100.0%

Interactions

2024-03-13T20:54:16.320695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:16.102364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:16.444819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:54:16.210621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:54:18.853239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도CCTV 수량
위도1.0000.6980.000
경도0.6981.0000.000
CCTV 수량0.0000.0001.000
2024-03-13T20:54:18.949860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도CCTV 수량
위도1.0000.4510.000
경도0.4511.0000.000
CCTV 수량0.0000.0001.000

Missing values

2024-03-13T20:54:16.634290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:54:16.739162image/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
0은평구갈현1동 12-107(늘봄이) /137.6283126.915312022-12-01
1은평구갈현1동 12-107(늘봄이) /237.6283126.915312022-12-01
2은평구갈현1동 12-107(늘봄이) /337.6283126.915312022-12-01
3은평구갈현1동 12-107(늘봄이) /437.6283126.915312022-12-01
4은평구갈현1동 12-107(늘봄이) /537.6283126.915312022-12-01
5은평구갈현1동 12-116 /137.6277126.915612022-12-01
6은평구갈현1동 12-116 /237.6277126.915612022-12-01
7은평구갈현1동 12-116 /337.6277126.915612022-12-01
8은평구갈현1동 12-116 /437.6277126.915612022-12-01
9은평구갈현1동 12-116 /537.6277126.915612022-12-01
자치구안심 주소위도경도CCTV 수량수정 일시
4101은평구진관동 헤스티아 3차 앞(주차) /137.6334126.918312022-12-01
4102은평구진관동 현대 힐스테이트 422동 옆 (고정)37.6282126.932812022-12-01
4103은평구진관동 현대 힐스테이트 423동 옆 (고정)37.627126.93412022-12-01
4104은평구진관동 현대 힐스테이트 426동 앞 삼거리 /137.6277126.935312022-12-01
4105은평구진관동 현대 힐스테이트 426동 앞 삼거리 /237.6278126.935312022-12-01
4106은평구진관동 현대 힐스테이트 426동 앞 삼거리 /337.6278126.935312022-12-01
4107은평구진관동 현대 힐스테이트 427동 앞 (고정)37.628126.936612022-12-01
4108은평구진관동 현대태영 13단지 진입 사거리 /137.6313126.921812022-12-01
4109은평구진관동 현대태영 13단지 진입 사거리 /237.6313126.921812022-12-01
4110은평구진관동 현대태영 13단지 진입 사거리 /337.6313126.921812022-12-01