Overview

Dataset statistics

Number of variables13
Number of observations208
Missing cells208
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.1 KiB
Average record size in memory108.6 B

Variable types

Numeric3
Categorical4
Text4
Boolean1
Unsupported1

Alerts

data_day has constant value ""Constant
apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with gugunHigh correlation
lat is highly overall correlated with gugunHigh correlation
lng is highly overall correlated with gugunHigh correlation
gugun is highly overall correlated with skey and 2 other fieldsHigh correlation
instt_code has 208 (100.0%) missing valuesMissing
skey has unique valuesUnique
instt_code is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-16 23:42:23.297051
Analysis finished2024-04-16 23:42:24.509690
Duration1.21 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%
Mean1194.5
Minimum1091
Maximum1298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:24.578530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1091
5-th percentile1101.35
Q11142.75
median1194.5
Q31246.25
95-th percentile1287.65
Maximum1298
Range207
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation60.188592
Coefficient of variation (CV)0.050388106
Kurtosis-1.2
Mean1194.5
Median Absolute Deviation (MAD)52
Skewness0
Sum248456
Variance3622.6667
MonotonicityNot monotonic
2024-04-17T08:42:24.693219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1205 1
 
0.5%
1198 1
 
0.5%
1112 1
 
0.5%
1113 1
 
0.5%
1114 1
 
0.5%
1115 1
 
0.5%
1116 1
 
0.5%
1117 1
 
0.5%
1118 1
 
0.5%
1119 1
 
0.5%
Other values (198) 198
95.2%
ValueCountFrequency (%)
1091 1
0.5%
1092 1
0.5%
1093 1
0.5%
1094 1
0.5%
1095 1
0.5%
1096 1
0.5%
1097 1
0.5%
1098 1
0.5%
1099 1
0.5%
1100 1
0.5%
ValueCountFrequency (%)
1298 1
0.5%
1297 1
0.5%
1296 1
0.5%
1295 1
0.5%
1294 1
0.5%
1293 1
0.5%
1292 1
0.5%
1291 1
0.5%
1290 1
0.5%
1289 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 남구
14 
Other values (11)
119 

Length

Max length10
Median length9
Mean length8.8461538
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사하구
2nd row부산광역시 사하구
3rd row부산광역시 사하구
4th row부산광역시 사하구
5th row부산광역시 사하구

Common Values

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

Length

2024-04-17T08:42:24.805477image/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%
연제구 14
 
3.4%
부산진구 14
 
3.4%
수영구 14
 
3.4%
해운대구 14
 
3.4%
남구 14
 
3.4%
Other values (7) 63
 
15.1%

loc
Text

Distinct203
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:25.010167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length6.3173077
Min length3

Characters and Unicode

Total characters1314
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 row낫개역
2nd row다대포항역
3rd row다대포해수욕장역
4th row낙조분수
5th row사하구장애인종합복지관
ValueCountFrequency (%)
주민센터 4
 
1.8%
보건소 3
 
1.3%
동래구 3
 
1.3%
부산역(기차 2
 
0.9%
부산진구노인장애인복지관 2
 
0.9%
혜남학교 2
 
0.9%
부산시민공원 2
 
0.9%
행정복지센터 2
 
0.9%
김해공항 2
 
0.9%
사상구 2
 
0.9%
Other values (201) 202
89.4%
2024-04-17T08:42:25.332483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 92
 
7.0%
장 48
 
3.7%
지 48
 
3.7%
구 44
 
3.3%
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) 849
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1243
94.6%
Space Separator 26
 
2.0%
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%
장 48
 
3.9%
지 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 (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1243
94.6%
Common 71
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 92
 
7.4%
장 48
 
3.9%
지 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 (%)
26
36.6%
) 12
16.9%
( 12
16.9%
2 11
15.5%
1 8
 
11.3%
6 1
 
1.4%
9 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1243
94.6%
ASCII 71
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 92
 
7.4%
장 48
 
3.9%
지 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 (%)
26
36.6%
) 12
16.9%
( 12
16.9%
2 11
15.5%
1 8
 
11.3%
6 1
 
1.4%
9 1
 
1.4%

detail_loc
Text

Distinct147
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:25.597163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length8.3990385
Min length2

Characters and Unicode

Total characters1747
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 row만남의장소 좌측
2nd row만남의장소 알림판 옆
3rd row2번출구 보관함 옆
4th row낙조분수 사무실 1층 로비
5th row1층 로비 계단 앞
ValueCountFrequency (%)
1층 72
 
12.9%
출구 47
 
8.5%
옆 46
 
8.3%
로비 24
 
4.3%
입구 15
 
2.7%
출입구 15
 
2.7%
앞 13
 
2.3%
1번 13
 
2.3%
3번 9
 
1.6%
민원실 8
 
1.4%
Other values (167) 294
52.9%
2024-04-17T08:42:25.981778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
363
20.8%
1 111
 
6.4%
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
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1167
66.8%
Space Separator 363
 
20.8%
Decimal Number 174
 
10.0%
Uppercase Letter 23
 
1.3%
Other Punctuation 10
 
0.6%
Open Punctuation 4
 
0.2%
Close 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%
0 3
 
1.7%
7 3
 
1.7%
8 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 (%)
363
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1167
66.8%
Common 557
31.9%
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 (%)
363
65.2%
1 111
 
19.9%
2 18
 
3.2%
3 16
 
2.9%
/ 8
 
1.4%
4 8
 
1.4%
5 6
 
1.1%
( 4
 
0.7%
6 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
66.8%
ASCII 580
33.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363
62.6%
1 111
 
19.1%
2 18
 
3.1%
3 16
 
2.8%
E 8
 
1.4%
/ 8
 
1.4%
V 8
 
1.4%
4 8
 
1.4%
5 6
 
1.0%
( 4
 
0.7%
Other values (12) 30
 
5.2%
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
2024-04-17T08:42:26.230229image/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 row사하구 다대로 지하 422(다대동)
2nd row사하구 다대로 지하 548(다대동)
3rd row사하구 다대로 692(다대동)
4th row사하구 몰운대1길 14(다대동)
5th row사하구 사하로68번길 65(구평동)
ValueCountFrequency (%)
지하 58
 
8.2%
동래구 22
 
3.1%
북구 20
 
2.8%
중앙대로 18
 
2.6%
사상구 18
 
2.6%
사하구 15
 
2.1%
남구 15
 
2.1%
해운대구 14
 
2.0%
연제구 14
 
2.0%
부산진구 14
 
2.0%
Other values (341) 496
70.5%
2024-04-17T08:42:26.574054image/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

Distinct187
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-04-17T08:42:26.787474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.004808
Min length12

Characters and Unicode

Total characters2497
Distinct characters11
Distinct categories2 ?
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 row051-678-6197
2nd row051-678-6196
3rd row051-678-6195
4th row051-220-4161
5th row051-262-2461
ValueCountFrequency (%)
051-640-7266 9
 
4.3%
051-550-4882 6
 
2.9%
051-310-4865 2
 
1.0%
051-640-2600 2
 
1.0%
051-974-3976 2
 
1.0%
051-808-8190 2
 
1.0%
051-850-6025 2
 
1.0%
051-740-7326 2
 
1.0%
051-440-2157 2
 
1.0%
051-522-1650 2
 
1.0%
Other values (177) 177
85.1%
2024-04-17T08:42:27.081023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 416
16.7%
0 403
16.1%
1 335
13.4%
5 320
12.8%
6 252
10.1%
2 162
 
6.5%
7 152
 
6.1%
4 136
 
5.4%
8 130
 
5.2%
3 123
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2081
83.3%
Dash Punctuation 416
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 403
19.4%
1 335
16.1%
5 320
15.4%
6 252
12.1%
2 162
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%

Most occurring scripts

ValueCountFrequency (%)
Common 2497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 416
16.7%
0 403
16.1%
1 335
13.4%
5 320
12.8%
6 252
10.1%
2 162
 
6.5%
7 152
 
6.1%
4 136
 
5.4%
8 130
 
5.2%
3 123
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 416
16.7%
0 403
16.1%
1 335
13.4%
5 320
12.8%
6 252
10.1%
2 162
 
6.5%
7 152
 
6.1%
4 136
 
5.4%
8 130
 
5.2%
3 123
 
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 row교통시설(2019년 설치)
2nd row교통시설(2019년 설치)
3rd row교통시설(2019년 설치)
4th row문화시설(2019년 설치)
5th row복지시설(2019년 설치)

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%
의료시설 2
 
1.0%
문화시설 2
 
1.0%
Other values (2) 3
 
1.4%

Length

2024-04-17T08:42:27.197938image/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%
의료시설 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%
Mean35.169267
Minimum35.046379
Maximum35.336429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:27.300196image/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 deviation0.053785268
Coefficient of variation (CV)0.0015293258
Kurtosis-0.011379961
Mean35.169267
Median Absolute Deviation (MAD)0.03486922
Skewness0.10317292
Sum7315.2076
Variance0.0028928551
MonotonicityNot monotonic
2024-04-17T08:42:27.413502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.05825614 2
 
1.0%
35.19703517 2
 
1.0%
35.18490105 2
 
1.0%
35.16404711 2
 
1.0%
35.16545076 2
 
1.0%
35.16906028 2
 
1.0%
35.1148826 2
 
1.0%
35.141599 2
 
1.0%
35.145733 1
 
0.5%
35.18741703 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%
Mean129.06029
Minimum128.94573
Maximum129.244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-17T08:42:27.521049image/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 deviation0.059700714
Coefficient of variation (CV)0.00046258002
Kurtosis0.081366653
Mean129.06029
Median Absolute Deviation (MAD)0.0427407
Skewness0.38371879
Sum26844.541
Variance0.0035641752
MonotonicityNot monotonic
2024-04-17T08:42:27.629786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9718692 2
 
1.0%
129.100376 2
 
1.0%
129.0009356 2
 
1.0%
129.0658484 2
 
1.0%
129.0558558 2
 
1.0%
129.1360149 2
 
1.0%
129.0416374 2
 
1.0%
129.081147 2
 
1.0%
129.113142 1
 
0.5%
129.0600491 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
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2020-12-31
208 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 208
100.0%

Length

2024-04-17T08:42:27.732032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:42:27.812636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 208
100.0%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
False
208 
ValueCountFrequency (%)
False 208
100.0%
2024-04-17T08:42:27.876023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

instt_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2021-04-01 05:47:03
208 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:47:03
2nd row2021-04-01 05:47:03
3rd row2021-04-01 05:47:03
4th row2021-04-01 05:47:03
5th row2021-04-01 05:47:03

Common Values

ValueCountFrequency (%)
2021-04-01 05:47:03 208
100.0%

Length

2024-04-17T08:42:27.950055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:42:28.025209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 208
50.0%
05:47:03 208
50.0%

Interactions

2024-04-17T08:42:24.093434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:23.700817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:23.894895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:24.155107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:23.760306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:23.956701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:24.228292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:23.826936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:42:24.024447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T08:42:28.075181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugunremarklatlng
skey1.0000.9570.3090.6470.863
gugun0.9571.0000.3650.8650.902
remark0.3090.3651.0000.0000.000
lat0.6470.8650.0001.0000.579
lng0.8630.9020.0000.5791.000
2024-04-17T08:42:28.151739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
remarkgugun
remark1.0000.135
gugun0.1351.000
2024-04-17T08:42:28.225209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatlnggugunremark
skey1.000-0.286-0.0230.7950.137
lat-0.2861.0000.3000.5840.000
lng-0.0230.3001.0000.6430.000
gugun0.7950.5840.6431.0000.135
remark0.1370.0000.0000.1351.000

Missing values

2024-04-17T08:42:24.329503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T08:42:24.457876image/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
01205부산광역시 사하구낫개역만남의장소 좌측사하구 다대로 지하 422(다대동)051-678-6197교통시설(2019년 설치)35.058256128.9718692020-12-31N<NA>2021-04-01 05:47:03
11206부산광역시 사하구다대포항역만남의장소 알림판 옆사하구 다대로 지하 548(다대동)051-678-6196교통시설(2019년 설치)35.058256128.9718692020-12-31N<NA>2021-04-01 05:47:03
21207부산광역시 사하구다대포해수욕장역2번출구 보관함 옆사하구 다대로 692(다대동)051-678-6195교통시설(2019년 설치)35.048443128.9658672020-12-31N<NA>2021-04-01 05:47:03
31208부산광역시 사하구낙조분수낙조분수 사무실 1층 로비사하구 몰운대1길 14(다대동)051-220-4161문화시설(2019년 설치)35.046379128.9680132020-12-31N<NA>2021-04-01 05:47:03
41209부산광역시 사하구사하구장애인종합복지관1층 로비 계단 앞사하구 사하로68번길 65(구평동)051-262-2461복지시설(2019년 설치)35.085443128.9929952020-12-31N<NA>2021-04-01 05:47:03
51210부산광역시 사하구서부산장애인스포츠센터1층 입구 왼쪽사하구 낙동남로1233번길 20(하단동)051-220-4124문화시설(2019년 설치)35.10942128.946942020-12-31N<NA>2021-04-01 05:47:03
61211부산광역시 금정구서동역4번 출구금정구 반송로 387(서동)051-678-6407교통시설35.21306129.1074242020-12-31N<NA>2021-04-01 05:47:03
71212부산광역시 금정구금정구장애인복지관1층 로비금정구 서부로 77(서동)051-523-0100복지시설35.219555129.0989672020-12-31N<NA>2021-04-01 05:47:03
81213부산광역시 금정구장전역3번 출구금정구 장전온천천로 144(장전동)051-678-6129교통시설35.238339129.0880912020-12-31N<NA>2021-04-01 05:47:03
91214부산광역시 금정구금정장애인자립생활센터센터 보장구실금정구 중앙대로 1675(부곡동)051-678-3334복지시설35.234037129.0914172020-12-31N<NA>2021-04-01 05:47:03
skeygugunlocdetail_locaddrtelremarklatlngdata_dayapr_atinstt_codelast_load_dttm
1981289부산광역시 부산진구부산진구청구청 후문 입구부산진구 시민공원로 30(부암동)051-605-4165행정청사(2019년 설치)35.162833129.0531882020-12-31N<NA>2021-04-01 05:47:03
1991290부산광역시 부산진구범천2동 주민센터민원실 내부산진구 신암로 48(범천동)051-605-6974행정청사(2019년 설치)35.146518129.0562042020-12-31N<NA>2021-04-01 05:47:03
2001291부산광역시 부산진구부산시민공원다솜관부산진구 시민공원로 73(범전동)051-850-6025문화시설(2019년 설치)35.165451129.0558562020-12-31N<NA>2021-04-01 05:47:03
2011292부산광역시 부산진구부산시민공원방문자 센터부산진구 시민공원로 73(범전동)051-850-6025문화시설(2019년 설치)35.165451129.0558562020-12-31N<NA>2021-04-01 05:47:03
2021293부산광역시 부산진구부산어린이대공원전기차 충전소 옆부산진구 새싹로 295(초읍동)051-860-7847문화시설(2019년 설치)35.182504129.0466892020-12-31N<NA>2021-04-01 05:47:03
2031294부산광역시 부산진구부산진구노인장애인복지관1층 정문 옆부산진구 전포대로300번길 6(전포동)051-808-8190복지시설(2019년 설치)35.164047129.0658482020-12-31N<NA>2021-04-01 05:47:03
2041295부산광역시 부산진구부산진구노인장애인복지관1층 마실방 옆부산진구 전포대로300번길 6(전포동)051-808-8190복지시설(2019년 설치)35.164047129.0658482020-12-31N<NA>2021-04-01 05:47:03
2051296부산광역시 부산진구전포역1호 E/V 옆부산진구 전포대로 지하 181(전포동)051-640-7266교통시설(2019년 설치)35.152456129.0647972020-12-31N<NA>2021-04-01 05:47:03
2061297부산광역시 부산진구부전역(동해선)대합실 내 맞이방부산진구 부전로 181(부전동)051-440-2256교통시설(2019년 설치)35.164682129.0620682020-12-31N<NA>2021-04-01 05:47:03
2071298부산광역시 동래구온천장역고객서비스센터(역무실) 맞은편동래구 중앙대로 1520(온천동)051-678-6127교통시설35.220282129.0863782020-12-31N<NA>2021-04-01 05:47:03