Overview

Dataset statistics

Number of variables11
Number of observations116
Missing cells435
Missing cells (%)34.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory93.1 B

Variable types

Numeric4
Categorical1
Text3
DateTime2
Boolean1

Dataset

Description포항시 스마트포항앱에 나와있는 불편신고접수(신고번호, 경도,위도,행정동,신고상담내용 등)에 대한 데이터를 제공합니다
URLhttps://www.data.go.kr/data/15120857/fileData.do

Alerts

신고번호 is highly overall correlated with 공개여부High correlation
경도 is highly overall correlated with 행정동 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
공개여부 is highly overall correlated with 신고번호 and 2 other fieldsHigh correlation
신고번호 has 31 (26.7%) missing valuesMissing
경도 has 41 (35.3%) missing valuesMissing
위도 has 41 (35.3%) missing valuesMissing
소재지주소 has 31 (26.7%) missing valuesMissing
신고상담내용 has 3 (2.6%) missing valuesMissing
접수일시 has 31 (26.7%) missing valuesMissing
처리완료일시 has 111 (95.7%) missing valuesMissing
공개여부 has 31 (26.7%) missing valuesMissing
분류번호 has 31 (26.7%) missing valuesMissing
이미지주소 has 84 (72.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 21:48:30.741612
Analysis finished2023-12-12 21:48:33.568610
Duration2.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

신고번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct85
Distinct (%)100.0%
Missing31
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean931.6
Minimum652
Maximum1414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:48:33.650177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum652
5-th percentile664.2
Q1699
median771
Q31199
95-th percentile1400.4
Maximum1414
Range762
Interquartile range (IQR)500

Descriptive statistics

Standard deviation276.26058
Coefficient of variation (CV)0.29654421
Kurtosis-1.4200264
Mean931.6
Median Absolute Deviation (MAD)101
Skewness0.57160133
Sum79186
Variance76319.91
MonotonicityStrictly increasing
2023-12-13T06:48:34.161043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1181 1
 
0.9%
1195 1
 
0.9%
1191 1
 
0.9%
1189 1
 
0.9%
1188 1
 
0.9%
1187 1
 
0.9%
1184 1
 
0.9%
1183 1
 
0.9%
1182 1
 
0.9%
1180 1
 
0.9%
Other values (75) 75
64.7%
(Missing) 31
26.7%
ValueCountFrequency (%)
652 1
0.9%
653 1
0.9%
662 1
0.9%
663 1
0.9%
664 1
0.9%
665 1
0.9%
666 1
0.9%
667 1
0.9%
669 1
0.9%
670 1
0.9%
ValueCountFrequency (%)
1414 1
0.9%
1411 1
0.9%
1408 1
0.9%
1407 1
0.9%
1401 1
0.9%
1398 1
0.9%
1397 1
0.9%
1391 1
0.9%
1387 1
0.9%
1385 1
0.9%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)98.7%
Missing41
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean36.027409
Minimum35.837612
Maximum36.109344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:48:34.315838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.837612
5-th percentile35.992142
Q136.017034
median36.027954
Q336.036074
95-th percentile36.085368
Maximum36.109344
Range0.2717327
Interquartile range (IQR)0.01904065

Descriptive statistics

Standard deviation0.033446144
Coefficient of variation (CV)0.00092835274
Kurtosis14.245802
Mean36.027409
Median Absolute Deviation (MAD)0.01023798
Skewness-2.014499
Sum2702.0557
Variance0.0011186445
MonotonicityNot monotonic
2023-12-13T06:48:34.483328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.01918034 2
 
1.7%
36.0878384 1
 
0.9%
36.0157229 1
 
0.9%
36.0184477 1
 
0.9%
36.0168867 1
 
0.9%
36.0677706 1
 
0.9%
36.0118988 1
 
0.9%
36.0279545 1
 
0.9%
35.9646881 1
 
0.9%
36.0087067 1
 
0.9%
Other values (64) 64
55.2%
(Missing) 41
35.3%
ValueCountFrequency (%)
35.8376116 1
0.9%
35.9630639 1
0.9%
35.9646881 1
0.9%
35.98441861 1
0.9%
35.9954519 1
0.9%
36.0087067 1
0.9%
36.0118988 1
0.9%
36.01341809 1
0.9%
36.0135303 1
0.9%
36.0145202 1
0.9%
ValueCountFrequency (%)
36.1093443 1
0.9%
36.106288 1
0.9%
36.1041781 1
0.9%
36.0878384 1
0.9%
36.0843098 1
0.9%
36.0677706 1
0.9%
36.0608326 1
0.9%
36.0599774 1
0.9%
36.0525605 1
0.9%
36.0436724 1
0.9%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)98.7%
Missing41
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean129.34625
Minimum128.55154
Maximum129.40497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:48:34.635897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55154
5-th percentile129.31167
Q1129.34348
median129.34992
Q3129.37722
95-th percentile129.3813
Maximum129.40497
Range0.8534227
Interquartile range (IQR)0.0337402

Descriptive statistics

Standard deviation0.095749823
Coefficient of variation (CV)0.00074025975
Kurtosis66.4348
Mean129.34625
Median Absolute Deviation (MAD)0.0171086
Skewness-7.9283768
Sum9700.9688
Variance0.0091680285
MonotonicityNot monotonic
2023-12-13T06:48:34.798579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.3432921 2
 
1.7%
129.3815343 1
 
0.9%
129.3493509 1
 
0.9%
129.3418747 1
 
0.9%
129.3440188 1
 
0.9%
129.3738112 1
 
0.9%
129.3469633 1
 
0.9%
129.3547352 1
 
0.9%
129.4006515 1
 
0.9%
129.3127166 1
 
0.9%
Other values (64) 64
55.2%
(Missing) 41
35.3%
ValueCountFrequency (%)
128.5515435 1
0.9%
129.2703772 1
0.9%
129.3035242 1
0.9%
129.309218 1
0.9%
129.3127166 1
0.9%
129.3137347 1
0.9%
129.333939 1
0.9%
129.3373522 1
0.9%
129.3394742 1
0.9%
129.3401503 1
0.9%
ValueCountFrequency (%)
129.4049662 1
0.9%
129.4006515 1
0.9%
129.3815343 1
0.9%
129.3814327 1
0.9%
129.3812429 1
0.9%
129.3812379 1
0.9%
129.3812342 1
0.9%
129.3810533 1
0.9%
129.3803814 1
0.9%
129.3802782 1
0.9%

행정동
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
<NA>
31 
대이동
25 
송도동
24 
연일읍
6 
상대동
 
3
Other values (17)
27 

Length

Max length8
Median length3
Mean length3.3103448
Min length2

Unique

Unique11 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
26.7%
대이동 25
21.6%
송도동 24
20.7%
연일읍 6
 
5.2%
상대동 3
 
2.6%
오천읍 3
 
2.6%
중앙동 3
 
2.6%
신흥동 3
 
2.6%
두호동 3
 
2.6%
용흥동 2
 
1.7%
Other values (12) 13
11.2%

Length

2023-12-13T06:48:34.927723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 31
26.7%
대이동 25
21.6%
송도동 24
20.7%
연일읍 7
 
6.0%
상대동 3
 
2.6%
오천읍 3
 
2.6%
중앙동 3
 
2.6%
신흥동 3
 
2.6%
두호동 3
 
2.6%
흥해읍 2
 
1.7%
Other values (11) 12
 
10.3%

소재지주소
Text

MISSING 

Distinct76
Distinct (%)89.4%
Missing31
Missing (%)26.7%
Memory size1.0 KiB
2023-12-13T06:48:35.219834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length21
Min length15

Characters and Unicode

Total characters1785
Distinct characters94
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)81.2%

Sample

1st row경상북도 포항시 남구 희망대로 1263
2nd row경상북도 포항시 남구 송도해안길73번길 6-11
3rd row경상북도 포항시 남구 송도해안길73번길 2-1
4th row경상북도 포항시 남구 송도해안길 36
5th row경상북도 포항시 남구 송도동 378-335
ValueCountFrequency (%)
포항시 84
19.3%
경상북도 67
15.4%
남구 65
14.9%
대잠동 21
 
4.8%
북구 19
 
4.4%
경북 17
 
3.9%
송도동 12
 
2.8%
연일읍 7
 
1.6%
송도해안길 4
 
0.9%
1001 3
 
0.7%
Other values (122) 136
31.3%
2023-12-13T06:48:35.679891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
350
19.6%
북 103
 
5.8%
도 94
 
5.3%
포 89
 
5.0%
항 88
 
4.9%
시 88
 
4.9%
구 87
 
4.9%
경 84
 
4.7%
상 69
 
3.9%
남 65
 
3.6%
Other values (84) 668
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1096
61.4%
Space Separator 350
 
19.6%
Decimal Number 301
 
16.9%
Dash Punctuation 38
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 103
 
9.4%
도 94
 
8.6%
포 89
 
8.1%
항 88
 
8.0%
시 88
 
8.0%
구 87
 
7.9%
경 84
 
7.7%
상 69
 
6.3%
남 65
 
5.9%
동 53
 
4.8%
Other values (72) 276
25.2%
Decimal Number
ValueCountFrequency (%)
1 61
20.3%
3 42
14.0%
2 34
11.3%
7 32
10.6%
0 28
9.3%
6 25
8.3%
9 23
 
7.6%
4 21
 
7.0%
5 19
 
6.3%
8 16
 
5.3%
Space Separator
ValueCountFrequency (%)
350
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1096
61.4%
Common 689
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 103
 
9.4%
도 94
 
8.6%
포 89
 
8.1%
항 88
 
8.0%
시 88
 
8.0%
구 87
 
7.9%
경 84
 
7.7%
상 69
 
6.3%
남 65
 
5.9%
동 53
 
4.8%
Other values (72) 276
25.2%
Common
ValueCountFrequency (%)
350
50.8%
1 61
 
8.9%
3 42
 
6.1%
- 38
 
5.5%
2 34
 
4.9%
7 32
 
4.6%
0 28
 
4.1%
6 25
 
3.6%
9 23
 
3.3%
4 21
 
3.0%
Other values (2) 35
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1096
61.4%
ASCII 689
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
350
50.8%
1 61
 
8.9%
3 42
 
6.1%
- 38
 
5.5%
2 34
 
4.9%
7 32
 
4.6%
0 28
 
4.1%
6 25
 
3.6%
9 23
 
3.3%
4 21
 
3.0%
Other values (2) 35
 
5.1%
Hangul
ValueCountFrequency (%)
북 103
 
9.4%
도 94
 
8.6%
포 89
 
8.1%
항 88
 
8.0%
시 88
 
8.0%
구 87
 
7.9%
경 84
 
7.7%
상 69
 
6.3%
남 65
 
5.9%
동 53
 
4.8%
Other values (72) 276
25.2%

신고상담내용
Text

MISSING 

Distinct112
Distinct (%)99.1%
Missing3
Missing (%)2.6%
Memory size1.0 KiB
2023-12-13T06:48:35.966370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length255
Median length79
Mean length49.053097
Min length6

Characters and Unicode

Total characters5543
Distinct characters489
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)98.2%

Sample

1st rowCCtv가 없어서 밤에는 돌아다니기 힘든 곳이고 가로등도 많이 없어서 어르신들이 다니기 불편한곳입니다.
2nd row허름한 집이 많은곳 가로등 cctv가 부족해 보입니다 치안 안전 밤에 다니기가 무서운 곳입니다
3rd row송림태마 거리 를 저녁에 가게된다면 보기에 안좋은곳이며 흉물스럽게 자리잡고 있는 집이라 두렵게 느껴질 가능성이 농후 한곳
4th row폐가가 너무 많아서 보기도 안좋고 철거를 해야 하지 않을까 싶고 전체적 으로 낙후된 지역 이여서 사람이 다니기에는 왠지 모르게 기분이 나쁘지 않을까요?
5th row낙후된 지역으로 낙서 및 밤에는 사람이 다니기에는 무섭고 골목길 이 많고해서 거리에 골목길에 그림을 그려서 관광 사업을 하던지 집을 이주시키서나 해서 도로를 만들면 나아지지 않을까 싶네요
ValueCountFrequency (%)
많이 11
 
0.9%
및 9
 
0.7%
있는 8
 
0.7%
쓰레기 8
 
0.7%
있어요 7
 
0.6%
있습니다 7
 
0.6%
그 6
 
0.5%
가는 6
 
0.5%
너무 5
 
0.4%
고양이 5
 
0.4%
Other values (985) 1148
94.1%
2023-12-13T06:48:36.393113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1148
 
20.7%
이 156
 
2.8%
다 117
 
2.1%
도 103
 
1.9%
니 100
 
1.8%
로 96
 
1.7%
가 93
 
1.7%
에 92
 
1.7%
. 77
 
1.4%
지 76
 
1.4%
Other values (479) 3485
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4171
75.2%
Space Separator 1148
 
20.7%
Other Punctuation 118
 
2.1%
Decimal Number 82
 
1.5%
Lowercase Letter 9
 
0.2%
Uppercase Letter 5
 
0.1%
Modifier Symbol 4
 
0.1%
Math Symbol 3
 
0.1%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 156
 
3.7%
다 117
 
2.8%
도 103
 
2.5%
니 100
 
2.4%
로 96
 
2.3%
가 93
 
2.2%
에 92
 
2.2%
지 76
 
1.8%
는 71
 
1.7%
고 68
 
1.6%
Other values (451) 3199
76.7%
Decimal Number
ValueCountFrequency (%)
1 21
25.6%
0 13
15.9%
3 8
 
9.8%
2 8
 
9.8%
4 7
 
8.5%
9 6
 
7.3%
7 6
 
7.3%
8 5
 
6.1%
6 4
 
4.9%
5 4
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 77
65.3%
, 23
 
19.5%
: 8
 
6.8%
! 6
 
5.1%
/ 3
 
2.5%
? 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
c 3
33.3%
t 3
33.3%
v 2
22.2%
i 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
C 2
40.0%
L 1
20.0%
E 1
20.0%
D 1
20.0%
Space Separator
ValueCountFrequency (%)
1148
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4171
75.2%
Common 1358
 
24.5%
Latin 14
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 156
 
3.7%
다 117
 
2.8%
도 103
 
2.5%
니 100
 
2.4%
로 96
 
2.3%
가 93
 
2.2%
에 92
 
2.2%
지 76
 
1.8%
는 71
 
1.7%
고 68
 
1.6%
Other values (451) 3199
76.7%
Common
ValueCountFrequency (%)
1148
84.5%
. 77
 
5.7%
, 23
 
1.7%
1 21
 
1.5%
0 13
 
1.0%
3 8
 
0.6%
2 8
 
0.6%
: 8
 
0.6%
4 7
 
0.5%
9 6
 
0.4%
Other values (10) 39
 
2.9%
Latin
ValueCountFrequency (%)
c 3
21.4%
t 3
21.4%
C 2
14.3%
v 2
14.3%
L 1
 
7.1%
E 1
 
7.1%
D 1
 
7.1%
i 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4169
75.2%
ASCII 1372
 
24.8%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1148
83.7%
. 77
 
5.6%
, 23
 
1.7%
1 21
 
1.5%
0 13
 
0.9%
3 8
 
0.6%
2 8
 
0.6%
: 8
 
0.6%
4 7
 
0.5%
9 6
 
0.4%
Other values (18) 53
 
3.9%
Hangul
ValueCountFrequency (%)
이 156
 
3.7%
다 117
 
2.8%
도 103
 
2.5%
니 100
 
2.4%
로 96
 
2.3%
가 93
 
2.2%
에 92
 
2.2%
지 76
 
1.8%
는 71
 
1.7%
고 68
 
1.6%
Other values (450) 3197
76.7%
Compat Jamo
ValueCountFrequency (%)
ㅠ 2
100.0%

접수일시
Date

MISSING 

Distinct45
Distinct (%)52.9%
Missing31
Missing (%)26.7%
Memory size1.0 KiB
Minimum2018-10-02 00:00:00
Maximum2023-05-08 00:00:00
2023-12-13T06:48:36.512034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:36.629108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)

처리완료일시
Date

MISSING 

Distinct4
Distinct (%)80.0%
Missing111
Missing (%)95.7%
Memory size1.0 KiB
Minimum2018-10-11 00:00:00
Maximum2019-02-14 00:00:00
2023-12-13T06:48:36.728277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:36.823303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

공개여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)2.4%
Missing31
Missing (%)26.7%
Memory size364.0 B
False
74 
True
11 
(Missing)
31 
ValueCountFrequency (%)
False 74
63.8%
True 11
 
9.5%
(Missing) 31
26.7%
2023-12-13T06:48:36.907655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

분류번호
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)36.5%
Missing31
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean121.65882
Minimum11
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:48:37.014972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20
Q1108
median140
Q3149
95-th percentile163
Maximum171
Range160
Interquartile range (IQR)41

Descriptive statistics

Standard deviation47.783628
Coefficient of variation (CV)0.39276746
Kurtosis0.36247009
Mean121.65882
Median Absolute Deviation (MAD)10
Skewness-1.3828945
Sum10341
Variance2283.2751
MonotonicityNot monotonic
2023-12-13T06:48:37.135185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
136 8
 
6.9%
140 7
 
6.0%
144 7
 
6.0%
147 5
 
4.3%
39 5
 
4.3%
161 4
 
3.4%
105 4
 
3.4%
24 3
 
2.6%
153 3
 
2.6%
20 3
 
2.6%
Other values (21) 36
31.0%
(Missing) 31
26.7%
ValueCountFrequency (%)
11 2
 
1.7%
12 1
 
0.9%
20 3
2.6%
24 3
2.6%
39 5
4.3%
40 2
 
1.7%
105 4
3.4%
108 2
 
1.7%
134 2
 
1.7%
135 1
 
0.9%
ValueCountFrequency (%)
171 2
1.7%
164 2
1.7%
163 3
2.6%
161 4
3.4%
159 2
1.7%
155 1
 
0.9%
153 3
2.6%
151 3
2.6%
150 1
 
0.9%
149 1
 
0.9%

이미지주소
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing84
Missing (%)72.4%
Memory size1.0 KiB
2023-12-13T06:48:37.410827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length79
Mean length78.625
Min length78

Characters and Unicode

Total characters2516
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowhttps://smart.pohang.go.kr/api/uploads/complain/2018102/153844323147015230.jpg
2nd rowhttps://smart.pohang.go.kr/api/uploads/complain/2018102/153844433707627224.jpg
3rd rowhttps://smart.pohang.go.kr/api/uploads/complain/2018102/153847067685172407.jpg
4th rowhttps://smart.pohang.go.kr/api/uploads/complain/2018104/153863703463861903.jpg
5th rowhttps://smart.pohang.go.kr/api/uploads/complain/2018104/153863896555446015.jpg
ValueCountFrequency (%)
https://smart.pohang.go.kr/api/uploads/complain/20181016/153965602827363705.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/2018102/153847067685172407.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20201226/160898571527758008.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20181117/154244228463840855.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20181128/154341166457713577.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/2018129/154434006915789322.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20200729/159599340563713673.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20200831/159883085293245156.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/2020098/159949267569970292.jpg 1
 
3.1%
https://smart.pohang.go.kr/api/uploads/complain/20210719/162667393043035843.jpg 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T06:48:37.768181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 224
 
8.9%
p 192
 
7.6%
a 160
 
6.4%
. 128
 
5.1%
o 128
 
5.1%
1 126
 
5.0%
2 110
 
4.4%
0 108
 
4.3%
s 96
 
3.8%
g 96
 
3.8%
Other values (20) 1148
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1312
52.1%
Decimal Number 820
32.6%
Other Punctuation 384
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 192
14.6%
a 160
12.2%
o 128
9.8%
s 96
 
7.3%
g 96
 
7.3%
t 96
 
7.3%
h 64
 
4.9%
l 64
 
4.9%
i 64
 
4.9%
n 64
 
4.9%
Other values (7) 288
22.0%
Decimal Number
ValueCountFrequency (%)
1 126
15.4%
2 110
13.4%
0 108
13.2%
5 81
9.9%
8 78
9.5%
3 75
9.1%
6 75
9.1%
4 64
7.8%
9 58
7.1%
7 45
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/ 224
58.3%
. 128
33.3%
: 32
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1312
52.1%
Common 1204
47.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 192
14.6%
a 160
12.2%
o 128
9.8%
s 96
 
7.3%
g 96
 
7.3%
t 96
 
7.3%
h 64
 
4.9%
l 64
 
4.9%
i 64
 
4.9%
n 64
 
4.9%
Other values (7) 288
22.0%
Common
ValueCountFrequency (%)
/ 224
18.6%
. 128
10.6%
1 126
10.5%
2 110
9.1%
0 108
9.0%
5 81
 
6.7%
8 78
 
6.5%
3 75
 
6.2%
6 75
 
6.2%
4 64
 
5.3%
Other values (3) 135
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 224
 
8.9%
p 192
 
7.6%
a 160
 
6.4%
. 128
 
5.1%
o 128
 
5.1%
1 126
 
5.0%
2 110
 
4.4%
0 108
 
4.3%
s 96
 
3.8%
g 96
 
3.8%
Other values (20) 1148
45.6%

Interactions

2023-12-13T06:48:32.645935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.505754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.897307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.277337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.784730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.624929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.988432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.374875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.891572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.725724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.073943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.455560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.967217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:31.809950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.160362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:32.550331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:48:37.879400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고번호경도위도행정동소재지주소접수일시처리완료일시공개여부분류번호이미지주소
신고번호1.0000.6450.0000.7840.9871.0001.0001.0000.5881.000
경도0.6451.0000.0000.9621.0000.921NaN0.6220.6721.000
위도0.0000.0001.0000.9181.0000.000NaN0.0000.0001.000
행정동0.7840.9620.9181.0001.0000.9490.0000.7970.3501.000
소재지주소0.9871.0001.0001.0001.0000.9960.7711.0000.0001.000
접수일시1.0000.9210.0000.9490.9961.0001.0001.0000.5441.000
처리완료일시1.000NaNNaN0.0000.7711.0001.000NaN0.416NaN
공개여부1.0000.6220.0000.7971.0001.000NaN1.0000.2201.000
분류번호0.5880.6720.0000.3500.0000.5440.4160.2201.0001.000
이미지주소1.0001.0001.0001.0001.0001.000NaN1.0001.0001.000
2023-12-13T06:48:37.995618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공개여부행정동
공개여부1.0000.637
행정동0.6371.000
2023-12-13T06:48:38.067465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고번호경도위도분류번호행정동공개여부
신고번호1.000-0.455-0.4310.1330.4750.982
경도-0.4551.0000.3810.3350.7850.646
위도-0.4310.3811.0000.1650.8420.000
분류번호0.1330.3350.1651.0000.1130.226
행정동0.4750.7850.8420.1131.0000.637
공개여부0.9820.6460.0000.2260.6371.000

Missing values

2023-12-13T06:48:33.101522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:48:33.271122image/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.
2023-12-13T06:48:33.436835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

신고번호경도위도행정동소재지주소신고상담내용접수일시처리완료일시공개여부분류번호이미지주소
065236.041683129.376636송도동경상북도 포항시 남구 희망대로 1263CCtv가 없어서 밤에는 돌아다니기 힘든 곳이고 가로등도 많이 없어서 어르신들이 다니기 불편한곳입니다.2018-10-02<NA>N136https://smart.pohang.go.kr/api/uploads/complain/2018102/153844323147015230.jpg
165336.037516129.377593송도동경상북도 포항시 남구 송도해안길73번길 6-11허름한 집이 많은곳 가로등 cctv가 부족해 보입니다 치안 안전 밤에 다니기가 무서운 곳입니다2018-10-02<NA>N144https://smart.pohang.go.kr/api/uploads/complain/2018102/153844433707627224.jpg
266236.037039129.377924송도동경상북도 포항시 남구 송도해안길73번길 2-1송림태마 거리 를 저녁에 가게된다면 보기에 안좋은곳이며 흉물스럽게 자리잡고 있는 집이라 두렵게 느껴질 가능성이 농후 한곳2018-10-02<NA>N144https://smart.pohang.go.kr/api/uploads/complain/2018102/153847067685172407.jpg
366336.033926129.380381송도동경상북도 포항시 남구 송도해안길 36폐가가 너무 많아서 보기도 안좋고 철거를 해야 하지 않을까 싶고 전체적 으로 낙후된 지역 이여서 사람이 다니기에는 왠지 모르게 기분이 나쁘지 않을까요?2018-10-04<NA>N144https://smart.pohang.go.kr/api/uploads/complain/2018104/153863703463861903.jpg
466436.032404129.381053송도동경상북도 포항시 남구 송도동 378-335낙후된 지역으로 낙서 및 밤에는 사람이 다니기에는 무섭고 골목길 이 많고해서 거리에 골목길에 그림을 그려서 관광 사업을 하던지 집을 이주시키서나 해서 도로를 만들면 나아지지 않을까 싶네요2018-10-04<NA>N138https://smart.pohang.go.kr/api/uploads/complain/2018104/153863896555446015.jpg
566536.05256129.36042우창동경상북도 포항시 북구 우창동 174-2침수 차량 통제중2018-10-06<NA>N150<NA>
666636.033861129.380149송도동경상북도 포항시 남구 송도로 82-3오른쪽 가로등 고장났습니다 확인해보시고 고치는게 좋을꺼같습미다2018-10-08<NA>N135https://smart.pohang.go.kr/api/uploads/complain/2018108/153896465479268970.jpg
766736.033886129.380161송도동경상북도 포항시 남구 송도로 82-3벽돌및 여러 잔여물이 있습니다 밤에 걸어다니다가 다칠위험이 있을수도 있습니다2018-10-08<NA>N143https://smart.pohang.go.kr/api/uploads/complain/2018108/153896473069126095.jpg
866936.037549129.377557송도동경상북도 포항시 남구 송도동송림숲이 아직 정리가 덜 된 것 같습니다. 지저분한 곳이 아직 많아요.2018-10-11<NA>N159<NA>
967036.039639129.373589송도동경상북도 포항시 남구 송도동쓰레기 많이 버려요2018-10-11<NA>N140<NA>
신고번호경도위도행정동소재지주소신고상담내용접수일시처리완료일시공개여부분류번호이미지주소
106<NA><NA><NA><NA><NA>전광판이 꺼져있는 상태입니다.<NA><NA><NA><NA><NA>
107<NA><NA><NA><NA><NA>스타모텔 앞 전봇대 쓰레기 방치<NA><NA><NA><NA><NA>
108<NA><NA><NA><NA><NA>쓰레기 관리 부탁드립니다<NA><NA><NA><NA><NA>
109<NA><NA><NA><NA><NA>도시숲 조성 예정구역에 무단경작 및 폐비닐 방치<NA><NA><NA><NA><NA>
110<NA><NA><NA><NA><NA>경사가 있는데 겨울엔 눈이 쌓이거나 얼음이 얼면미끄러져 우회전하는 차량에 깔릴 수도 있지 않을까 우려됩니다.안전봉이나 울타리나 계단 같은 걸 설치해주면어떨런지요.<NA><NA><NA><NA><NA>
111<NA><NA><NA><NA><NA>연일대교 지나 인주로길 입구 연일생태숲 알림 간판 방향이 공단쪽으로 잘못되어있어서<NA><NA><NA><NA><NA>
112<NA><NA><NA><NA><NA>안내표지판만 믿고 걸어갔다가 공단에서 두시간 헤메는 일이 있었습니다.<NA><NA><NA><NA><NA>
113<NA><NA><NA><NA><NA>연일읍 운동장도 방향이 공단쪽을 향하고 있습니다<NA><NA><NA><NA><NA>
114<NA><NA><NA><NA><NA>수정부탁드립니다.<NA><NA><NA><NA><NA>
115<NA><NA><NA><NA><NA>도로파손 보수공사 요청합니다// 장소:포항시남구 철강로360 제철119 안전센터 앞 도로<NA><NA><NA><NA><NA>