Overview

Dataset statistics

Number of variables13
Number of observations208
Missing cells208
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory109.6 B

Variable types

Numeric4
Categorical2
Text4
DateTime2
Unsupported1

Alerts

data_day has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with instt_code and 1 other fieldsHigh correlation
lat is highly overall correlated with gugunHigh correlation
lng is highly overall correlated with gugunHigh correlation
instt_code is highly overall correlated with skey and 1 other fieldsHigh correlation
gugun is highly overall correlated with skey and 3 other fieldsHigh correlation
apr_at has 208 (100.0%) missing valuesMissing
skey has unique valuesUnique
apr_at is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 23:42:49.127180
Analysis finished2024-04-16 23:42:50.964430
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean986.5
Minimum883
Maximum1090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:51.024158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum883
5-th percentile893.35
Q1934.75
median986.5
Q31038.25
95-th percentile1079.65
Maximum1090
Range207
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation60.188592
Coefficient of variation (CV)0.061012258
Kurtosis-1.2
Mean986.5
Median Absolute Deviation (MAD)52
Skewness0
Sum205192
Variance3622.6667
MonotonicityNot monotonic
2024-04-17T08:42:51.130374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1026 1
 
0.5%
1001 1
 
0.5%
917 1
 
0.5%
918 1
 
0.5%
919 1
 
0.5%
920 1
 
0.5%
921 1
 
0.5%
922 1
 
0.5%
923 1
 
0.5%
924 1
 
0.5%
Other values (198) 198
95.2%
ValueCountFrequency (%)
883 1
0.5%
884 1
0.5%
885 1
0.5%
886 1
0.5%
887 1
0.5%
888 1
0.5%
889 1
0.5%
890 1
0.5%
891 1
0.5%
892 1
0.5%
ValueCountFrequency (%)
1090 1
0.5%
1089 1
0.5%
1088 1
0.5%
1087 1
0.5%
1086 1
0.5%
1085 1
0.5%
1084 1
0.5%
1083 1
0.5%
1082 1
0.5%
1081 1
0.5%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
부산광역시 동래구
22 
부산광역시 북구
20 
부산광역시 사상구
18 
부산광역시 사하구
15 
부산광역시 연제구
14 
Other values (11)
119 

Length

Max length10
Median length9
Mean length8.8461538
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 금정구
2nd row부산광역시 금정구
3rd row부산광역시 금정구
4th row부산광역시 강서구
5th row부산광역시 강서구

Common Values

ValueCountFrequency (%)
부산광역시 동래구 22
10.6%
부산광역시 북구 20
 
9.6%
부산광역시 사상구 18
 
8.7%
부산광역시 사하구 15
 
7.2%
부산광역시 연제구 14
 
6.7%
부산광역시 수영구 14
 
6.7%
부산광역시 해운대구 14
 
6.7%
부산광역시 남구 14
 
6.7%
부산광역시 부산진구 14
 
6.7%
부산광역시 금정구 13
 
6.2%
Other values (6) 50
24.0%

Length

2024-04-17T08:42:51.513443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 208
50.0%
동래구 22
 
5.3%
북구 20
 
4.8%
사상구 18
 
4.3%
사하구 15
 
3.6%
해운대구 14
 
3.4%
부산진구 14
 
3.4%
남구 14
 
3.4%
수영구 14
 
3.4%
연제구 14
 
3.4%
Other values (7) 63
 
15.1%

loc
Text

Distinct203
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:51.717579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length6.2980769
Min length3

Characters and Unicode

Total characters1310
Distinct characters195
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)95.2%

Sample

1st row장전2동주민센터
2nd row부곡1동주민센터
3rd row서2동주민센터
4th row대저역
5th row체육공원역
ValueCountFrequency (%)
주민센터 4
 
1.8%
동래구 3
 
1.3%
보건소 3
 
1.3%
사상구 2
 
0.9%
행정복지센터 2
 
0.9%
장애인복지관 2
 
0.9%
김해공항 2
 
0.9%
부산역(기차 2
 
0.9%
부산시민공원 2
 
0.9%
혜남학교 2
 
0.9%
Other values (202) 204
89.5%
2024-04-17T08:42:52.036669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 92
 
7.0%
지 48
 
3.7%
장 48
 
3.7%
구 44
 
3.4%
동 43
 
3.3%
복 42
 
3.2%
센 39
 
3.0%
터 38
 
2.9%
관 36
 
2.7%
인 35
 
2.7%
Other values (185) 845
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1243
94.9%
Space Separator 22
 
1.7%
Decimal Number 21
 
1.6%
Close Punctuation 12
 
0.9%
Open Punctuation 12
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 92
 
7.4%
지 48
 
3.9%
장 48
 
3.9%
구 44
 
3.5%
동 43
 
3.5%
복 42
 
3.4%
센 39
 
3.1%
터 38
 
3.1%
관 36
 
2.9%
인 35
 
2.8%
Other values (178) 778
62.6%
Decimal Number
ValueCountFrequency (%)
2 11
52.4%
1 8
38.1%
6 1
 
4.8%
9 1
 
4.8%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1243
94.9%
Common 67
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 92
 
7.4%
지 48
 
3.9%
장 48
 
3.9%
구 44
 
3.5%
동 43
 
3.5%
복 42
 
3.4%
센 39
 
3.1%
터 38
 
3.1%
관 36
 
2.9%
인 35
 
2.8%
Other values (178) 778
62.6%
Common
ValueCountFrequency (%)
22
32.8%
) 12
17.9%
( 12
17.9%
2 11
16.4%
1 8
 
11.9%
6 1
 
1.5%
9 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1243
94.9%
ASCII 67
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 92
 
7.4%
지 48
 
3.9%
장 48
 
3.9%
구 44
 
3.5%
동 43
 
3.5%
복 42
 
3.4%
센 39
 
3.1%
터 38
 
3.1%
관 36
 
2.9%
인 35
 
2.8%
Other values (178) 778
62.6%
ASCII
ValueCountFrequency (%)
22
32.8%
) 12
17.9%
( 12
17.9%
2 11
16.4%
1 8
 
11.9%
6 1
 
1.5%
9 1
 
1.5%

detail_loc
Text

Distinct147
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:52.327536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.4182692
Min length2

Characters and Unicode

Total characters1751
Distinct characters188
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)61.5%

Sample

1st row동주민센터 내부
2nd row동주민센터 내부
3rd row동주민센터 내부
4th row1번출구 2층
5th row1번출구 3층
ValueCountFrequency (%)
1층 72
 
12.9%
출구 47
 
8.4%
옆 46
 
8.2%
로비 24
 
4.3%
입구 15
 
2.7%
출입구 15
 
2.7%
1번 13
 
2.3%
앞 13
 
2.3%
3번 9
 
1.6%
2층 8
 
1.4%
Other values (168) 296
53.0%
2024-04-17T08:42:52.724551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
367
21.0%
1 111
 
6.3%
구 99
 
5.7%
층 94
 
5.4%
출 78
 
4.5%
번 63
 
3.6%
옆 49
 
2.8%
입 36
 
2.1%
비 33
 
1.9%
실 30
 
1.7%
Other values (178) 791
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1167
66.6%
Space Separator 367
 
21.0%
Decimal Number 174
 
9.9%
Uppercase Letter 23
 
1.3%
Other Punctuation 10
 
0.6%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 99
 
8.5%
층 94
 
8.1%
출 78
 
6.7%
번 63
 
5.4%
옆 49
 
4.2%
입 36
 
3.1%
비 33
 
2.8%
실 30
 
2.6%
로 27
 
2.3%
내 18
 
1.5%
Other values (156) 640
54.8%
Decimal Number
ValueCountFrequency (%)
1 111
63.8%
2 18
 
10.3%
3 16
 
9.2%
4 8
 
4.6%
5 6
 
3.4%
6 4
 
2.3%
8 3
 
1.7%
0 3
 
1.7%
7 3
 
1.7%
9 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
E 8
34.8%
V 8
34.8%
A 2
 
8.7%
T 2
 
8.7%
M 2
 
8.7%
B 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
/ 8
80.0%
, 2
 
20.0%
Space Separator
ValueCountFrequency (%)
367
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1167
66.6%
Common 561
32.0%
Latin 23
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 99
 
8.5%
층 94
 
8.1%
출 78
 
6.7%
번 63
 
5.4%
옆 49
 
4.2%
입 36
 
3.1%
비 33
 
2.8%
실 30
 
2.6%
로 27
 
2.3%
내 18
 
1.5%
Other values (156) 640
54.8%
Common
ValueCountFrequency (%)
367
65.4%
1 111
 
19.8%
2 18
 
3.2%
3 16
 
2.9%
/ 8
 
1.4%
4 8
 
1.4%
5 6
 
1.1%
6 4
 
0.7%
) 4
 
0.7%
( 4
 
0.7%
Other values (6) 15
 
2.7%
Latin
ValueCountFrequency (%)
E 8
34.8%
V 8
34.8%
A 2
 
8.7%
T 2
 
8.7%
M 2
 
8.7%
B 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1167
66.6%
ASCII 584
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
367
62.8%
1 111
 
19.0%
2 18
 
3.1%
3 16
 
2.7%
E 8
 
1.4%
/ 8
 
1.4%
V 8
 
1.4%
4 8
 
1.4%
5 6
 
1.0%
6 4
 
0.7%
Other values (12) 30
 
5.1%
Hangul
ValueCountFrequency (%)
구 99
 
8.5%
층 94
 
8.1%
출 78
 
6.7%
번 63
 
5.4%
옆 49
 
4.2%
입 36
 
3.1%
비 33
 
2.8%
실 30
 
2.6%
로 27
 
2.3%
내 18
 
1.5%
Other values (156) 640
54.8%

addr
Text

Distinct201
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:52.950694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length18.639423
Min length13

Characters and Unicode

Total characters3877
Distinct characters169
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)93.3%

Sample

1st row금정구 소정로 19(장전동)
2nd row금정구 동부곡로6번길 29(부곡동)
3rd row금정구 서동중심로 33(서동)
4th row강서구 낙동북로 295(대저1동)
5th row강서구 낙동북로 371(대저1동)
ValueCountFrequency (%)
지하 58
 
8.2%
동래구 22
 
3.1%
북구 20
 
2.8%
사상구 18
 
2.6%
중앙대로 18
 
2.6%
사하구 15
 
2.1%
남구 15
 
2.1%
부산진구 14
 
2.0%
해운대구 14
 
2.0%
수영구 14
 
2.0%
Other values (341) 496
70.5%
2024-04-17T08:42:53.282697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
499
 
12.9%
동 255
 
6.6%
구 217
 
5.6%
로 204
 
5.3%
( 196
 
5.1%
) 196
 
5.1%
1 149
 
3.8%
대 127
 
3.3%
2 102
 
2.6%
하 83
 
2.1%
Other values (159) 1849
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2223
57.3%
Decimal Number 735
 
19.0%
Space Separator 499
 
12.9%
Open Punctuation 196
 
5.1%
Close Punctuation 196
 
5.1%
Dash Punctuation 17
 
0.4%
Uppercase Letter 8
 
0.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 255
 
11.5%
구 217
 
9.8%
로 204
 
9.2%
대 127
 
5.7%
하 83
 
3.7%
지 66
 
3.0%
길 56
 
2.5%
번 55
 
2.5%
사 44
 
2.0%
영 38
 
1.7%
Other values (140) 1078
48.5%
Decimal Number
ValueCountFrequency (%)
1 149
20.3%
2 102
13.9%
0 81
11.0%
3 73
9.9%
4 62
8.4%
7 61
8.3%
5 58
 
7.9%
6 55
 
7.5%
9 54
 
7.3%
8 40
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
25.0%
P 2
25.0%
E 2
25.0%
C 2
25.0%
Space Separator
ValueCountFrequency (%)
499
100.0%
Open Punctuation
ValueCountFrequency (%)
( 196
100.0%
Close Punctuation
ValueCountFrequency (%)
) 196
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2223
57.3%
Common 1646
42.5%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 255
 
11.5%
구 217
 
9.8%
로 204
 
9.2%
대 127
 
5.7%
하 83
 
3.7%
지 66
 
3.0%
길 56
 
2.5%
번 55
 
2.5%
사 44
 
2.0%
영 38
 
1.7%
Other values (140) 1078
48.5%
Common
ValueCountFrequency (%)
499
30.3%
( 196
 
11.9%
) 196
 
11.9%
1 149
 
9.1%
2 102
 
6.2%
0 81
 
4.9%
3 73
 
4.4%
4 62
 
3.8%
7 61
 
3.7%
5 58
 
3.5%
Other values (5) 169
 
10.3%
Latin
ValueCountFrequency (%)
A 2
25.0%
P 2
25.0%
E 2
25.0%
C 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2223
57.3%
ASCII 1654
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
499
30.2%
( 196
 
11.9%
) 196
 
11.9%
1 149
 
9.0%
2 102
 
6.2%
0 81
 
4.9%
3 73
 
4.4%
4 62
 
3.7%
7 61
 
3.7%
5 58
 
3.5%
Other values (9) 177
 
10.7%
Hangul
ValueCountFrequency (%)
동 255
 
11.5%
구 217
 
9.8%
로 204
 
9.2%
대 127
 
5.7%
하 83
 
3.7%
지 66
 
3.0%
길 56
 
2.5%
번 55
 
2.5%
사 44
 
2.0%
영 38
 
1.7%
Other values (140) 1078
48.5%

tel
Text

Distinct188
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:53.484366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.278846
Min length12

Characters and Unicode

Total characters2554
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)85.1%

Sample

1st row051-519-5301
2nd row051-519-5201
3rd row051-519-5121
4th row051-678-6317
5th row051-678-6316
ValueCountFrequency (%)
051-640-7266 9
 
4.3%
051-550-4882 6
 
2.9%
051-850-6025 2
 
1.0%
051-522-1650 2
 
1.0%
051-974-3976 2
 
1.0%
051-640-2600 2
 
1.0%
051-740-7326 2
 
1.0%
051-808-8190 2
 
1.0%
051-440-2157 2
 
1.0%
051-310-4865 2
 
1.0%
Other values (177) 177
85.1%
2024-04-17T08:42:53.810245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 416
16.3%
0 402
15.7%
1 335
13.1%
5 320
12.5%
6 252
9.9%
2 163
 
6.4%
7 152
 
6.0%
4 136
 
5.3%
8 130
 
5.1%
3 123
 
4.8%
Other values (2) 125
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2081
81.5%
Dash Punctuation 416
 
16.3%
Space Separator 57
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 402
19.3%
1 335
16.1%
5 320
15.4%
6 252
12.1%
2 163
7.8%
7 152
 
7.3%
4 136
 
6.5%
8 130
 
6.2%
3 123
 
5.9%
9 68
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 416
100.0%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2554
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 416
16.3%
0 402
15.7%
1 335
13.1%
5 320
12.5%
6 252
9.9%
2 163
 
6.4%
7 152
 
6.0%
4 136
 
5.3%
8 130
 
5.1%
3 123
 
4.8%
Other values (2) 125
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 416
16.3%
0 402
15.7%
1 335
13.1%
5 320
12.5%
6 252
9.9%
2 163
 
6.4%
7 152
 
6.0%
4 136
 
5.3%
8 130
 
5.1%
3 123
 
4.8%
Other values (2) 125
 
4.9%

remark
Categorical

Distinct12
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
교통시설
56 
교통시설(2019년 설치)
36 
행정청사(2019년 설치)
30 
복지시설(2019년 설치)
26 
문화시설(2019년 설치)
19 
Other values (7)
41 

Length

Max length14
Median length14
Mean length9.7692308
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row행정청사(2019년 설치)
2nd row행정청사(2019년 설치)
3rd row행정청사(2019년 설치)
4th row교통시설
5th row교통시설

Common Values

ValueCountFrequency (%)
교통시설 56
26.9%
교통시설(2019년 설치) 36
17.3%
행정청사(2019년 설치) 30
14.4%
복지시설(2019년 설치) 26
12.5%
문화시설(2019년 설치) 19
 
9.1%
복지시설 14
 
6.7%
행정청사 12
 
5.8%
의료시설(2019년 설치) 8
 
3.8%
문화시설 2
 
1.0%
의료시설 2
 
1.0%
Other values (2) 3
 
1.4%

Length

2024-04-17T08:42:53.921739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
설치 120
36.6%
교통시설 56
17.1%
교통시설(2019년 36
 
11.0%
행정청사(2019년 30
 
9.1%
복지시설(2019년 26
 
7.9%
문화시설(2019년 19
 
5.8%
복지시설 14
 
4.3%
행정청사 12
 
3.7%
의료시설(2019년 8
 
2.4%
문화시설 2
 
0.6%
Other values (3) 5
 
1.5%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.16927
Minimum35.046379
Maximum35.336429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:54.017472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.046379
5-th percentile35.081178
Q135.133396
median35.172369
Q335.205824
95-th percentile35.254499
Maximum35.336429
Range0.29004953
Interquartile range (IQR)0.072427952

Descriptive statistics

Standard deviation0.053785932
Coefficient of variation (CV)0.0015293446
Kurtosis-0.011538321
Mean35.16927
Median Absolute Deviation (MAD)0.03486922
Skewness0.10300913
Sum7315.2081
Variance0.0028929265
MonotonicityNot monotonic
2024-04-17T08:42:54.133415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.18490105 2
 
1.0%
35.1148826 2
 
1.0%
35.16404711 2
 
1.0%
35.16906028 2
 
1.0%
35.16545076 2
 
1.0%
35.19703517 2
 
1.0%
35.05825614 2
 
1.0%
35.141599 2
 
1.0%
35.2079807 1
 
0.5%
35.204697 1
 
0.5%
Other values (190) 190
91.3%
ValueCountFrequency (%)
35.04637931 1
0.5%
35.04844266 1
0.5%
35.05825614 2
1.0%
35.07221265 1
0.5%
35.07526852 1
0.5%
35.075506 1
0.5%
35.0757512 1
0.5%
35.07806899 1
0.5%
35.07946515 1
0.5%
35.0801721 1
0.5%
ValueCountFrequency (%)
35.33642884 1
0.5%
35.325014 1
0.5%
35.313522 1
0.5%
35.27355883 1
0.5%
35.27314525 1
0.5%
35.271645 1
0.5%
35.26811 1
0.5%
35.2674491 1
0.5%
35.26712211 1
0.5%
35.2586474 1
0.5%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0603
Minimum128.94573
Maximum129.244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:54.244058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.94573
5-th percentile128.97068
Q1129.01273
median129.06515
Q3129.09867
95-th percentile129.16972
Maximum129.244
Range0.2982653
Interquartile range (IQR)0.085935525

Descriptive statistics

Standard deviation0.059698982
Coefficient of variation (CV)0.00046256657
Kurtosis0.081444986
Mean129.0603
Median Absolute Deviation (MAD)0.0427407
Skewness0.38339931
Sum26844.543
Variance0.0035639684
MonotonicityNot monotonic
2024-04-17T08:42:54.358523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0009356 2
 
1.0%
129.0416374 2
 
1.0%
129.0658484 2
 
1.0%
129.1360149 2
 
1.0%
129.0558558 2
 
1.0%
129.100376 2
 
1.0%
128.9718692 2
 
1.0%
129.081147 2
 
1.0%
129.0729905 1
 
0.5%
129.104246 1
 
0.5%
Other values (190) 190
91.3%
ValueCountFrequency (%)
128.9457317 1
0.5%
128.9469403 1
0.5%
128.9487535 1
0.5%
128.9541945 1
0.5%
128.9597979 1
0.5%
128.960674 1
0.5%
128.961088 1
0.5%
128.9658666 1
0.5%
128.966636 1
0.5%
128.9680126 1
0.5%
ValueCountFrequency (%)
129.243997 1
0.5%
129.2331153 1
0.5%
129.2229513 1
0.5%
129.216197 1
0.5%
129.208263 1
0.5%
129.1821691 1
0.5%
129.180292 1
0.5%
129.1770325 1
0.5%
129.175981 1
0.5%
129.1735108 1
0.5%

data_day
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2020-03-31 00:00:00
Maximum2020-03-31 00:00:00
2024-04-17T08:42:54.462883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:54.530056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

apr_at
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing208
Missing (%)100.0%
Memory size2.0 KiB

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3328798.1
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:54.607705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13300000
median3325000
Q33370000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation42630.845
Coefficient of variation (CV)0.012806678
Kurtosis-1.051047
Mean3328798.1
Median Absolute Deviation (MAD)35000
Skewness0.020011327
Sum6.9239 × 108
Variance1.817389 × 109
MonotonicityNot monotonic
2024-04-17T08:42:54.699313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3300000 22
10.6%
3320000 20
 
9.6%
3390000 18
 
8.7%
3340000 15
 
7.2%
3330000 14
 
6.7%
3370000 14
 
6.7%
3380000 14
 
6.7%
3290000 14
 
6.7%
3310000 14
 
6.7%
3350000 13
 
6.2%
Other values (6) 50
24.0%
ValueCountFrequency (%)
3250000 6
 
2.9%
3260000 10
4.8%
3270000 10
4.8%
3280000 8
 
3.8%
3290000 14
6.7%
3300000 22
10.6%
3310000 14
6.7%
3320000 20
9.6%
3330000 14
6.7%
3340000 15
7.2%
ValueCountFrequency (%)
3400000 10
4.8%
3390000 18
8.7%
3380000 14
6.7%
3370000 14
6.7%
3360000 6
 
2.9%
3350000 13
6.2%
3340000 15
7.2%
3330000 14
6.7%
3320000 20
9.6%
3310000 14
6.7%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2020-12-22 14:40:36
Maximum2020-12-22 14:40:36
2024-04-17T08:42:54.782271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:54.848271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T08:42:50.440274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.601078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.866210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.151076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.514572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.662352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.931051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.217609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.588783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.733513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.002238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.288283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.661486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:49.798937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.075410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:50.361936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T08:42:54.905099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugunremarklatlnginstt_code
skey1.0000.9700.3130.6800.8510.987
gugun0.9701.0000.3650.8650.9021.000
remark0.3130.3651.0000.0000.0000.214
lat0.6800.8650.0001.0000.5790.726
lng0.8510.9020.0000.5791.0000.910
instt_code0.9871.0000.2140.7260.9101.000
2024-04-17T08:42:54.985689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
remarkgugun
remark1.0000.135
gugun0.1351.000
2024-04-17T08:42:55.055393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatlnginstt_codegugunremark
skey1.0000.3110.0690.9530.8490.135
lat0.3111.0000.3000.3950.5840.000
lng0.0690.3001.0000.1250.6430.000
instt_code0.9530.3950.1251.0000.9850.088
gugun0.8490.5840.6430.9851.0000.135
remark0.1350.0000.0000.0880.1351.000

Missing values

2024-04-17T08:42:50.784397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T08:42:50.914308image/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

skeygugunlocdetail_locaddrtelremarklatlngdata_dayapr_atinstt_codelast_load_dttm
01026부산광역시 금정구장전2동주민센터동주민센터 내부금정구 소정로 19(장전동)051-519-5301행정청사(2019년 설치)35.227741129.0809572020-03-31<NA>33500002020-12-22 14:40:36
11027부산광역시 금정구부곡1동주민센터동주민센터 내부금정구 동부곡로6번길 29(부곡동)051-519-5201행정청사(2019년 설치)35.224405129.0921172020-03-31<NA>33500002020-12-22 14:40:36
21028부산광역시 금정구서2동주민센터동주민센터 내부금정구 서동중심로 33(서동)051-519-5121행정청사(2019년 설치)35.212854129.1044832020-03-31<NA>33500002020-12-22 14:40:36
31029부산광역시 강서구대저역1번출구 2층강서구 낙동북로 295(대저1동)051-678-6317교통시설35.213333128.9610882020-03-31<NA>33600002020-12-22 14:40:36
41030부산광역시 강서구체육공원역1번출구 3층강서구 낙동북로 371(대저1동)051-678-6316교통시설35.212405128.9700382020-03-31<NA>33600002020-12-22 14:40:36
51031부산광역시 강서구강서구청민원편의실 내강서구 낙동북로 477(대저1동)051-970-4824행정청사(2019년 설치)35.212199128.9805892020-03-31<NA>33600002020-12-22 14:40:36
61032부산광역시 강서구덕두역2번 출구 교통카드 충전기 옆강서구 경전철로 10(대저2동)055-310-9823교통시설(2019년 설치)35.181998128.9541942020-03-31<NA>33600002020-12-22 14:40:36
71033부산광역시 강서구김해공항 국제선청사1층 로비 1번 게이트 부근강서구 공항진입로 108(대저2동)051-974-3976교통시설(2019년 설치)35.172717128.9457322020-03-31<NA>33600002020-12-22 14:40:36
81034부산광역시 강서구김해공항 국내선청사1층 로비강서구 대저2동 2798-1051-974-3976교통시설(2019년 설치)35.170133128.9487542020-03-31<NA>33600002020-12-22 14:40:36
91035부산광역시 연제구교대역5번 출구연제구 중앙대로 지하 1217(거제동)051-678-6124교통시설35.196232129.0799752020-03-31<NA>33700002020-12-22 14:40:36
skeygugunlocdetail_locaddrtelremarklatlngdata_dayapr_atinstt_codelast_load_dttm
198904부산광역시 부산진구부산진구청구청 후문 입구부산진구 시민공원로 30(부암동)051-605-4165행정청사(2019년 설치)35.162833129.0531882020-03-31<NA>32900002020-12-22 14:40:36
199905부산광역시 부산진구범천2동 주민센터민원실 내부산진구 신암로 48(범천동)051-605-6974행정청사(2019년 설치)35.146518129.0562032020-03-31<NA>32900002020-12-22 14:40:36
200906부산광역시 부산진구부산시민공원다솜관부산진구 시민공원로 73(범전동)051-850-6025문화시설(2019년 설치)35.165451129.0558562020-03-31<NA>32900002020-12-22 14:40:36
201907부산광역시 부산진구부산시민공원방문자 센터부산진구 시민공원로 73(범전동)051-850-6025문화시설(2019년 설치)35.165451129.0558562020-03-31<NA>32900002020-12-22 14:40:36
202908부산광역시 부산진구부산어린이대공원전기차 충전소 옆부산진구 새싹로 295(초읍동)051-860-7847문화시설(2019년 설치)35.182504129.0466892020-03-31<NA>32900002020-12-22 14:40:36
203909부산광역시 부산진구부산진구노인장애인복지관1층 정문 옆부산진구 전포대로300번길 6(전포동)051-808-8190복지시설(2019년 설치)35.164047129.0658482020-03-31<NA>32900002020-12-22 14:40:36
204910부산광역시 부산진구부산진구노인장애인복지관1층 마실방 옆부산진구 전포대로300번길 6(전포동)051-808-8190복지시설(2019년 설치)35.164047129.0658482020-03-31<NA>32900002020-12-22 14:40:36
205911부산광역시 부산진구전포역1호 E/V 옆부산진구 전포대로 지하 181(전포동)051-640-7266교통시설(2019년 설치)35.152456129.0647972020-03-31<NA>32900002020-12-22 14:40:36
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