Overview

Dataset statistics

Number of variables13
Number of observations2391
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory252.3 KiB
Average record size in memory108.1 B

Variable types

Numeric4
Categorical6
Text2
DateTime1

Alerts

confirm_date has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with mgrnu and 2 other fieldsHigh correlation
mgrnu is highly overall correlated with skey and 2 other fieldsHigh correlation
lat is highly overall correlated with lng and 2 other fieldsHigh correlation
lng is highly overall correlated with lat and 2 other fieldsHigh correlation
road is highly overall correlated with skey and 6 other fieldsHigh correlation
gubun is highly overall correlated with roadHigh correlation
road_type is highly overall correlated with roadHigh correlation
sigungu is highly overall correlated with skey and 4 other fieldsHigh correlation
road is highly imbalanced (69.4%)Imbalance
gubun is highly imbalanced (52.6%)Imbalance
road_type is highly imbalanced (59.5%)Imbalance
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 13:53:05.161384
Analysis finished2024-04-16 13:53:09.353362
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2391
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20896
Minimum19701
Maximum22091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-04-16T22:53:09.470185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19701
5-th percentile19820.5
Q120298.5
median20896
Q321493.5
95-th percentile21971.5
Maximum22091
Range2390
Interquartile range (IQR)1195

Descriptive statistics

Standard deviation690.36657
Coefficient of variation (CV)0.033038216
Kurtosis-1.2
Mean20896
Median Absolute Deviation (MAD)598
Skewness0
Sum49962336
Variance476606
MonotonicityNot monotonic
2024-04-16T22:53:09.662132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21978 1
 
< 0.1%
20534 1
 
< 0.1%
20536 1
 
< 0.1%
20537 1
 
< 0.1%
20538 1
 
< 0.1%
20539 1
 
< 0.1%
20540 1
 
< 0.1%
20541 1
 
< 0.1%
20542 1
 
< 0.1%
20543 1
 
< 0.1%
Other values (2381) 2381
99.6%
ValueCountFrequency (%)
19701 1
< 0.1%
19702 1
< 0.1%
19703 1
< 0.1%
19704 1
< 0.1%
19705 1
< 0.1%
19706 1
< 0.1%
19707 1
< 0.1%
19708 1
< 0.1%
19709 1
< 0.1%
19710 1
< 0.1%
ValueCountFrequency (%)
22091 1
< 0.1%
22090 1
< 0.1%
22089 1
< 0.1%
22088 1
< 0.1%
22087 1
< 0.1%
22086 1
< 0.1%
22085 1
< 0.1%
22084 1
< 0.1%
22083 1
< 0.1%
22082 1
< 0.1%

mgrnu
Real number (ℝ)

HIGH CORRELATION 

Distinct2338
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10019.719
Minimum1001
Maximum17004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-04-16T22:53:09.890831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile3020.5
Q17065.5
median10131
Q313076.5
95-th percentile16131.5
Maximum17004
Range16003
Interquartile range (IQR)6011

Descriptive statistics

Standard deviation4024.2566
Coefficient of variation (CV)0.4016337
Kurtosis-0.83018452
Mean10019.719
Median Absolute Deviation (MAD)3010
Skewness-0.25096488
Sum23957147
Variance16194642
MonotonicityNot monotonic
2024-04-16T22:53:10.083243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13364 3
 
0.1%
13387 2
 
0.1%
11211 2
 
0.1%
13386 2
 
0.1%
13366 2
 
0.1%
13388 2
 
0.1%
13365 2
 
0.1%
13363 2
 
0.1%
13114 2
 
0.1%
13367 2
 
0.1%
Other values (2328) 2370
99.1%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
17004 1
< 0.1%
17003 1
< 0.1%
17002 1
< 0.1%
17001 1
< 0.1%
16267 1
< 0.1%
16250 1
< 0.1%
16249 1
< 0.1%
16248 1
< 0.1%
16247 1
< 0.1%
16246 1
< 0.1%

road
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct39
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
<NA>
1910 
중앙대로
 
56
해운대로
 
40
반송로
 
39
낙동대로
 
36
Other values (34)
310 

Length

Max length10
Median length4
Mean length3.96445
Min length3

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1910
79.9%
중앙대로 56
 
2.3%
해운대로 40
 
1.7%
반송로 39
 
1.6%
낙동대로 36
 
1.5%
백양대로 32
 
1.3%
정관로 26
 
1.1%
기장대로 26
 
1.1%
태종로 25
 
1.0%
공항로 22
 
0.9%
Other values (29) 179
 
7.5%

Length

2024-04-16T22:53:10.399551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1910
79.9%
중앙대로 56
 
2.3%
해운대로 40
 
1.7%
반송로 39
 
1.6%
낙동대로 36
 
1.5%
백양대로 32
 
1.3%
정관로 26
 
1.1%
기장대로 26
 
1.1%
태종로 25
 
1.0%
공항로 22
 
0.9%
Other values (29) 179
 
7.5%

ins_place
Text

Distinct2336
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-04-16T22:53:10.844003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length9.1421999
Min length3

Characters and Unicode

Total characters21859
Distinct characters535
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2284 ?
Unique (%)95.5%

Sample

1st row정관 방곡중앙중학교
2nd row정관 방곡초등학교
3rd row오시리아 관광단지 단지내 4지점(오시리아역 입구)
4th row오시리아 관광단지 단지내 4-2지점(오시리아역)
5th row오시리아 관광단지 단지내 5지점
ValueCountFrequency (%)
앞 83
 
2.6%
명지국제신도시 41
 
1.3%
명지주거단지 23
 
0.7%
화전지구산업단지 22
 
0.7%
삼거리 14
 
0.4%
입구 14
 
0.4%
주변 13
 
0.4%
조만교-세산교차로 12
 
0.4%
옆 12
 
0.4%
101동 10
 
0.3%
Other values (2594) 3000
92.5%
2024-04-16T22:53:11.520511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
861
 
3.9%
지 690
 
3.2%
( 633
 
2.9%
) 631
 
2.9%
동 517
 
2.4%
교 468
 
2.1%
구 462
 
2.1%
1 414
 
1.9%
산 359
 
1.6%
대 318
 
1.5%
Other values (525) 16506
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17562
80.3%
Decimal Number 1413
 
6.5%
Space Separator 861
 
3.9%
Open Punctuation 633
 
2.9%
Close Punctuation 631
 
2.9%
Uppercase Letter 464
 
2.1%
Dash Punctuation 170
 
0.8%
Other Punctuation 102
 
0.5%
Lowercase Letter 8
 
< 0.1%
Other Symbol 7
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 690
 
3.9%
동 517
 
2.9%
교 468
 
2.7%
구 462
 
2.6%
산 359
 
2.0%
대 318
 
1.8%
리 301
 
1.7%
점 288
 
1.6%
신 280
 
1.6%
아 277
 
1.6%
Other values (470) 13602
77.5%
Uppercase Letter
ValueCountFrequency (%)
L 65
14.0%
T 63
13.6%
P 44
9.5%
C 38
8.2%
S 34
7.3%
A 34
7.3%
B 32
6.9%
I 30
 
6.5%
G 25
 
5.4%
E 20
 
4.3%
Other values (15) 79
17.0%
Decimal Number
ValueCountFrequency (%)
1 414
29.3%
2 302
21.4%
3 161
 
11.4%
0 139
 
9.8%
4 119
 
8.4%
5 66
 
4.7%
7 56
 
4.0%
9 53
 
3.8%
8 53
 
3.8%
6 50
 
3.5%
Other Punctuation
ValueCountFrequency (%)
# 51
50.0%
, 23
22.5%
' 15
 
14.7%
. 7
 
6.9%
: 3
 
2.9%
/ 1
 
1.0%
@ 1
 
1.0%
? 1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
62.5%
a 1
 
12.5%
r 1
 
12.5%
k 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
~ 6
85.7%
→ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
861
100.0%
Open Punctuation
ValueCountFrequency (%)
( 633
100.0%
Close Punctuation
ValueCountFrequency (%)
) 631
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17569
80.4%
Common 3818
 
17.5%
Latin 472
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 690
 
3.9%
동 517
 
2.9%
교 468
 
2.7%
구 462
 
2.6%
산 359
 
2.0%
대 318
 
1.8%
리 301
 
1.7%
점 288
 
1.6%
신 280
 
1.6%
아 277
 
1.6%
Other values (471) 13609
77.5%
Latin
ValueCountFrequency (%)
L 65
13.8%
T 63
13.3%
P 44
9.3%
C 38
8.1%
S 34
 
7.2%
A 34
 
7.2%
B 32
 
6.8%
I 30
 
6.4%
G 25
 
5.3%
E 20
 
4.2%
Other values (19) 87
18.4%
Common
ValueCountFrequency (%)
861
22.6%
( 633
16.6%
) 631
16.5%
1 414
10.8%
2 302
 
7.9%
- 170
 
4.5%
3 161
 
4.2%
0 139
 
3.6%
4 119
 
3.1%
5 66
 
1.7%
Other values (15) 322
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17562
80.3%
ASCII 4289
 
19.6%
None 7
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
861
20.1%
( 633
14.8%
) 631
14.7%
1 414
9.7%
2 302
 
7.0%
- 170
 
4.0%
3 161
 
3.8%
0 139
 
3.2%
4 119
 
2.8%
5 66
 
1.5%
Other values (43) 793
18.5%
Hangul
ValueCountFrequency (%)
지 690
 
3.9%
동 517
 
2.9%
교 468
 
2.7%
구 462
 
2.6%
산 359
 
2.0%
대 318
 
1.8%
리 301
 
1.7%
점 288
 
1.6%
신 280
 
1.6%
아 277
 
1.6%
Other values (470) 13602
77.5%
None
ValueCountFrequency (%)
㈜ 7
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

gubun
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
전자신호 제어
1461 
일반신호 제어
821 
전자신호제어
 
61
일반신호제어
 
41
가변신호제어
 
6

Length

Max length7
Median length7
Mean length6.9535759
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
전자신호 제어 1461
61.1%
일반신호 제어 821
34.3%
전자신호제어 61
 
2.6%
일반신호제어 41
 
1.7%
가변신호제어 6
 
0.3%
<NA> 1
 
< 0.1%

Length

2024-04-16T22:53:11.738306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T22:53:11.896600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제어 2282
48.8%
전자신호 1461
31.3%
일반신호 821
 
17.6%
전자신호제어 61
 
1.3%
일반신호제어 41
 
0.9%
가변신호제어 6
 
0.1%
na 1
 
< 0.1%

ins_date
Date

Distinct372
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
Minimum1990-10-01 00:00:00
Maximum2020-12-01 00:00:00
2024-04-16T22:53:12.090546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:12.294486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

road_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
교차로
1814 
단일로
522 
가변차로
 
28
<NA>
 
14
모뎀
 
13

Length

Max length4
Median length3
Mean length3.0121288
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단일로
2nd row교차로
3rd row교차로
4th row교차로
5th row교차로

Common Values

ValueCountFrequency (%)
교차로 1814
75.9%
단일로 522
 
21.8%
가변차로 28
 
1.2%
<NA> 14
 
0.6%
모뎀 13
 
0.5%

Length

2024-04-16T22:53:12.516337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T22:53:12.680794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교차로 1814
75.9%
단일로 522
 
21.8%
가변차로 28
 
1.2%
na 14
 
0.6%
모뎀 13
 
0.5%

sigungu
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
강서구
415 
해운대구
248 
기장군
240 
사하구
181 
부산진구
172 
Other values (11)
1135 

Length

Max length4
Median length3
Mean length2.9949812
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 415
17.4%
해운대구 248
10.4%
기장군 240
10.0%
사하구 181
7.6%
부산진구 172
7.2%
동래구 152
 
6.4%
남구 151
 
6.3%
사상구 147
 
6.1%
북구 130
 
5.4%
금정구 129
 
5.4%
Other values (6) 426
17.8%

Length

2024-04-16T22:53:12.855448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 415
17.4%
해운대구 248
10.4%
기장군 240
10.0%
사하구 181
7.6%
부산진구 172
7.2%
동래구 152
 
6.4%
남구 151
 
6.3%
사상구 147
 
6.1%
북구 130
 
5.4%
금정구 129
 
5.4%
Other values (6) 426
17.8%

address
Text

Distinct2293
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-04-16T22:53:13.371441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length33
Mean length19.938938
Min length5

Characters and Unicode

Total characters47674
Distinct characters192
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2209 ?
Unique (%)92.4%

Sample

1st row부산광역시 기장군 정관읍 정관덕산길 61
2nd row부산광역시 기장군 정관읍 방곡리 404
3rd row부산광역시 기장군 기장읍 당사리 139-1
4th row부산광역시 기장군 기장읍 당사리 262-1
5th row부산광역시 기장군 기장읍 당사리 298-1
ValueCountFrequency (%)
부산광역시 2390
24.3%
강서구 413
 
4.2%
해운대구 244
 
2.5%
기장군 235
 
2.4%
사하구 176
 
1.8%
부산진구 157
 
1.6%
동래구 153
 
1.6%
남구 148
 
1.5%
사상구 145
 
1.5%
북구 131
 
1.3%
Other values (2586) 5629
57.3%
2024-04-16T22:53:14.175442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7432
15.6%
산 2764
 
5.8%
1 2690
 
5.6%
부 2638
 
5.5%
광 2433
 
5.1%
시 2409
 
5.1%
역 2392
 
5.0%
동 2367
 
5.0%
구 2229
 
4.7%
- 1895
 
4.0%
Other values (182) 18425
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27179
57.0%
Decimal Number 11123
23.3%
Space Separator 7432
 
15.6%
Dash Punctuation 1895
 
4.0%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2764
 
10.2%
부 2638
 
9.7%
광 2433
 
9.0%
시 2409
 
8.9%
역 2392
 
8.8%
동 2367
 
8.7%
구 2229
 
8.2%
서 537
 
2.0%
대 447
 
1.6%
장 440
 
1.6%
Other values (167) 8523
31.4%
Decimal Number
ValueCountFrequency (%)
1 2690
24.2%
2 1582
14.2%
3 1272
11.4%
4 1056
 
9.5%
5 944
 
8.5%
6 780
 
7.0%
7 756
 
6.8%
8 731
 
6.6%
0 678
 
6.1%
9 634
 
5.7%
Space Separator
ValueCountFrequency (%)
7432
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1895
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27179
57.0%
Common 20495
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2764
 
10.2%
부 2638
 
9.7%
광 2433
 
9.0%
시 2409
 
8.9%
역 2392
 
8.8%
동 2367
 
8.7%
구 2229
 
8.2%
서 537
 
2.0%
대 447
 
1.6%
장 440
 
1.6%
Other values (167) 8523
31.4%
Common
ValueCountFrequency (%)
7432
36.3%
1 2690
 
13.1%
- 1895
 
9.2%
2 1582
 
7.7%
3 1272
 
6.2%
4 1056
 
5.2%
5 944
 
4.6%
6 780
 
3.8%
7 756
 
3.7%
8 731
 
3.6%
Other values (5) 1357
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27179
57.0%
ASCII 20495
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7432
36.3%
1 2690
 
13.1%
- 1895
 
9.2%
2 1582
 
7.7%
3 1272
 
6.2%
4 1056
 
5.2%
5 944
 
4.6%
6 780
 
3.8%
7 756
 
3.7%
8 731
 
3.6%
Other values (5) 1357
 
6.6%
Hangul
ValueCountFrequency (%)
산 2764
 
10.2%
부 2638
 
9.7%
광 2433
 
9.0%
시 2409
 
8.9%
역 2392
 
8.8%
동 2367
 
8.7%
구 2229
 
8.2%
서 537
 
2.0%
대 447
 
1.6%
장 440
 
1.6%
Other values (167) 8523
31.4%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct2197
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.16848
Minimum35.033738
Maximum35.384732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-04-16T22:53:14.405601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.033738
5-th percentile35.080794
Q135.115865
median35.164332
Q335.203583
95-th percentile35.319161
Maximum35.384732
Range0.35099383
Interquartile range (IQR)0.08771806

Descriptive statistics

Standard deviation0.065601953
Coefficient of variation (CV)0.0018653622
Kurtosis0.34148379
Mean35.16848
Median Absolute Deviation (MAD)0.04268586
Skewness0.69874094
Sum84087.836
Variance0.0043036162
MonotonicityNot monotonic
2024-04-16T22:53:14.620180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.179928 44
 
1.8%
35.18408371 14
 
0.6%
35.1474573 11
 
0.5%
35.08377723 6
 
0.3%
35.16334826 5
 
0.2%
35.06777292 5
 
0.2%
35.13301598 3
 
0.1%
35.22638631 3
 
0.1%
35.1519183 3
 
0.1%
35.05489661 3
 
0.1%
Other values (2187) 2294
95.9%
ValueCountFrequency (%)
35.03373823 1
< 0.1%
35.04742415 1
< 0.1%
35.04751918 1
< 0.1%
35.04819063 1
< 0.1%
35.04892488 1
< 0.1%
35.04996522 1
< 0.1%
35.05112408 1
< 0.1%
35.0516318 1
< 0.1%
35.05184716 1
< 0.1%
35.05232425 1
< 0.1%
ValueCountFrequency (%)
35.38473206 1
< 0.1%
35.37314318 1
< 0.1%
35.37078013 1
< 0.1%
35.36935699 1
< 0.1%
35.36846588 1
< 0.1%
35.36804227 1
< 0.1%
35.36708212 1
< 0.1%
35.35758456 1
< 0.1%
35.35740753 1
< 0.1%
35.35506994 1
< 0.1%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct2195
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04757
Minimum128.80851
Maximum129.28266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-04-16T22:53:14.856709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80851
5-th percentile128.87294
Q1128.98441
median129.06345
Q3129.10925
95-th percentile129.20941
Maximum129.28266
Range0.4741496
Interquartile range (IQR)0.1248363

Descriptive statistics

Standard deviation0.097448338
Coefficient of variation (CV)0.000755135
Kurtosis-0.2854848
Mean129.04757
Median Absolute Deviation (MAD)0.0581944
Skewness-0.20096812
Sum308552.74
Variance0.0094961786
MonotonicityNot monotonic
2024-04-16T22:53:15.583325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.075091 44
 
1.8%
129.0802187 14
 
0.6%
128.8292943 11
 
0.5%
128.8192612 6
 
0.3%
129.0394625 5
 
0.2%
128.9777382 5
 
0.2%
128.8333546 3
 
0.1%
129.0194103 3
 
0.1%
129.035289 3
 
0.1%
128.9805964 3
 
0.1%
Other values (2185) 2294
95.9%
ValueCountFrequency (%)
128.8085096 1
 
< 0.1%
128.8113301 1
 
< 0.1%
128.8192612 6
0.3%
128.8240619 1
 
< 0.1%
128.8244743 2
 
0.1%
128.8244858 3
 
0.1%
128.8281005 1
 
< 0.1%
128.8292943 11
0.5%
128.8312157 1
 
< 0.1%
128.8325099 1
 
< 0.1%
ValueCountFrequency (%)
129.2826592 1
< 0.1%
129.2817156 1
< 0.1%
129.2805581 1
< 0.1%
129.2798447 1
< 0.1%
129.2787909 1
< 0.1%
129.2782215 1
< 0.1%
129.2778038 1
< 0.1%
129.2760634 1
< 0.1%
129.2755106 1
< 0.1%
129.2754049 1
< 0.1%

confirm_date
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2020-12-31
2391 

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 2391
100.0%

Length

2024-04-16T22:53:15.779218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T22:53:15.907357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 2391
100.0%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2021-05-01 05:36:03
2391 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-05-01 05:36:03
2nd row2021-05-01 05:36:03
3rd row2021-05-01 05:36:03
4th row2021-05-01 05:36:03
5th row2021-05-01 05:36:03

Common Values

ValueCountFrequency (%)
2021-05-01 05:36:03 2391
100.0%

Length

2024-04-16T22:53:16.034395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T22:53:16.174770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-01 2391
50.0%
05:36:03 2391
50.0%

Interactions

2024-04-16T22:53:08.334906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:06.527137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.076125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.733821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:08.470506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:06.643918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.218982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.862477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:08.632576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:06.786439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.397792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:08.013533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:08.788634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:06.935347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:07.568989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T22:53:08.177730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T22:53:16.311865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeymgrnuroadgubunroad_typesigungulatlng
skey1.0000.9620.9210.2960.2070.9430.7660.857
mgrnu0.9621.0000.9490.2710.2370.9920.8460.908
road0.9210.9491.0000.6940.7500.9620.8850.919
gubun0.2960.2710.6941.0000.3450.2410.2160.415
road_type0.2070.2370.7500.3451.0000.3000.0720.258
sigungu0.9430.9920.9620.2410.3001.0000.8460.891
lat0.7660.8460.8850.2160.0720.8461.0000.771
lng0.8570.9080.9190.4150.2580.8910.7711.000
2024-04-16T22:53:16.504001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sigunguroadroad_typegubun
sigungu1.0000.6830.1440.125
road0.6831.0000.5040.542
road_type0.1440.5041.0000.288
gubun0.1250.5420.2881.000
2024-04-16T22:53:16.643420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeymgrnulatlngroadgubunroad_typesigungu
skey1.000-0.645-0.0940.1340.6240.1270.1250.763
mgrnu-0.6451.0000.2830.0360.7030.1160.1440.958
lat-0.0940.2831.0000.5850.5440.0910.0430.541
lng0.1340.0360.5851.0000.6360.1850.1570.625
road0.6240.7030.5440.6361.0000.5420.5040.683
gubun0.1270.1160.0910.1850.5421.0000.2880.125
road_type0.1250.1440.0430.1570.5040.2881.0000.144
sigungu0.7630.9580.5410.6250.6830.1250.1441.000

Missing values

2024-04-16T22:53:08.989527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T22:53:09.234476image/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

skeymgrnuroadins_placegubunins_dateroad_typesigunguaddresslatlngconfirm_datelast_load_dttm
02197816191<NA>정관 방곡중앙중학교일반신호제어2016-10-01단일로기장군부산광역시 기장군 정관읍 정관덕산길 6135.322933129.1867112020-12-312021-05-01 05:36:03
12197916192<NA>정관 방곡초등학교일반신호제어2016-10-01교차로기장군부산광역시 기장군 정관읍 방곡리 40435.323164129.1878912020-12-312021-05-01 05:36:03
22198016195<NA>오시리아 관광단지 단지내 4지점(오시리아역 입구)일반신호제어2015-03-01교차로기장군부산광역시 기장군 기장읍 당사리 139-135.194687129.2093822020-12-312021-05-01 05:36:03
32198116196<NA>오시리아 관광단지 단지내 4-2지점(오시리아역)일반신호제어2015-03-01교차로기장군부산광역시 기장군 기장읍 당사리 262-135.195573129.2082322020-12-312021-05-01 05:36:03
42198216197<NA>오시리아 관광단지 단지내 5지점일반신호제어2015-03-01교차로기장군부산광역시 기장군 기장읍 당사리 298-135.192938129.2076762020-12-312021-05-01 05:36:03
52198316198<NA>연화리 ART ZONE전자신호제어2015-12-01교차로기장군부산광역시 기장군 기장읍 연화리 193-335.218884129.2251412020-12-312021-05-01 05:36:03
62198416199<NA>기장빕스모텔전자신호제어2015-12-01교차로기장군부산광역시 기장군 기장읍 대변리 281-435.220713129.2269152020-12-312021-05-01 05:36:03
72198516200<NA>일광역전삼거리전자신호 제어1998-06-01교차로기장군부산광역시 기장군 일광면 삼성리 26-435.266293129.2333172020-12-312021-05-01 05:36:03
82198616201<NA>삼덕마을입구(횡계마을직결)일반신호 제어1995-10-01교차로기장군부산광역시 기장군 일광면 횡계리 44-435.27191129.2305452020-12-312021-05-01 05:36:03
92198716202기장대로일광초교전자신호 제어2008-10-01단일로기장군부산광역시 기장군 일광면 화전리 98-135.279693129.2327412020-12-312021-05-01 05:36:03
skeymgrnuroadins_placegubunins_dateroad_typesigunguaddresslatlngconfirm_datelast_load_dttm
23811974416085<NA>명례일반산업단지 2번신호등일반신호 제어2013-05-01교차로기장군부산광역시 기장군 장안읍 명례리 926-535.368042129.2556192020-12-312021-05-01 05:36:03
23821974516086<NA>명례일반산업단지 3번신호등일반신호 제어2013-05-01교차로기장군부산광역시 기장군 장안읍 명례리 924-135.369357129.2557382020-12-312021-05-01 05:36:03
23831974616087<NA>명례일반산업단지 4번신호등일반신호 제어2013-05-01교차로기장군부산광역시 기장군 장안읍 명례리 91235.37078129.2540992020-12-312021-05-01 05:36:03
23841974716088<NA>명례일반산업단지 5번신호등일반신호 제어2013-05-01교차로기장군부산광역시 기장군 장안읍 명례리 90435.373143129.2548182020-12-312021-05-01 05:36:03
23851974816089<NA>죽성리 신앙촌 진입구전자신호제어2017-09-01교차로기장군부산광역시 기장군 기장읍 죽성리 산35-4135.244412129.2382382020-12-312021-05-01 05:36:03
23861974916090<NA>정관119안전센터일반신호제어2017-11-01단일로기장군부산광역시 기장군 정관읍 용수로 735.324874129.1799322020-12-312021-05-01 05:36:03
23871975016091<NA>장안 신소재산업단지 A지점일반신호제어2016-07-01교차로기장군부산광역시 기장군 장안읍 반룡리 산47-135.340129129.2684082020-12-312021-05-01 05:36:03
23881975116092<NA>장안 신소재산업단지 C지점일반신호제어2016-07-01교차로기장군부산광역시 기장군 장안읍 반룡리 산47-135.340129129.2684082020-12-312021-05-01 05:36:03
23891975216093<NA>장안 신소재산업단지 F지점일반신호제어2016-07-01교차로기장군부산광역시 기장군 장안읍 반룡리 산47-135.340129129.2684082020-12-312021-05-01 05:36:03
23901975316101산단로예림리 대송테크주변일반신호 제어2009-10-01교차로기장군부산광역시 정관면 예림리 108635.179928129.0750912020-12-312021-05-01 05:36:03