Overview

Dataset statistics

Number of variables11
Number of observations4056
Missing cells286
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory360.6 KiB
Average record size in memory91.0 B

Variable types

Numeric3
Text5
Categorical1
DateTime2

Alerts

last_load_dttm has constant value ""Constant
skey is highly overall correlated with gugunHigh correlation
lat is highly overall correlated with gugunHigh correlation
instt_code is highly overall correlated with gugunHigh correlation
gugun is highly overall correlated with skey and 2 other fieldsHigh correlation
tel has 74 (1.8%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 04:51:20.315436
Analysis finished2024-04-16 04:51:22.565616
Duration2.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4056
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50404.425
Minimum46404
Maximum53739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.8 KiB
2024-04-16T13:51:22.628848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46404
5-th percentile46606.75
Q148681.75
median49695.5
Q352725.25
95-th percentile53536.25
Maximum53739
Range7335
Interquartile range (IQR)4043.5

Descriptive statistics

Standard deviation2366.9695
Coefficient of variation (CV)0.046959558
Kurtosis-1.4163378
Mean50404.425
Median Absolute Deviation (MAD)2466
Skewness-0.13487745
Sum2.0444035 × 108
Variance5602544.7
MonotonicityNot monotonic
2024-04-16T13:51:22.746813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52546 1
 
< 0.1%
48730 1
 
< 0.1%
48717 1
 
< 0.1%
48718 1
 
< 0.1%
48719 1
 
< 0.1%
48720 1
 
< 0.1%
48721 1
 
< 0.1%
48722 1
 
< 0.1%
48723 1
 
< 0.1%
48724 1
 
< 0.1%
Other values (4046) 4046
99.8%
ValueCountFrequency (%)
46404 1
< 0.1%
46405 1
< 0.1%
46406 1
< 0.1%
46407 1
< 0.1%
46408 1
< 0.1%
46409 1
< 0.1%
46410 1
< 0.1%
46411 1
< 0.1%
46412 1
< 0.1%
46413 1
< 0.1%
ValueCountFrequency (%)
53739 1
< 0.1%
53738 1
< 0.1%
53737 1
< 0.1%
53736 1
< 0.1%
53735 1
< 0.1%
53734 1
< 0.1%
53733 1
< 0.1%
53732 1
< 0.1%
53731 1
< 0.1%
53730 1
< 0.1%

business_nm
Text

Distinct3352
Distinct (%)83.4%
Missing35
Missing (%)0.9%
Memory size31.8 KiB
2024-04-16T13:51:23.000907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length7.41905
Min length2

Characters and Unicode

Total characters29832
Distinct characters519
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2892 ?
Unique (%)71.9%

Sample

1st row주식회사위드현대
2nd row주식회사유니온디엔시
3rd row주식회사유승
4th row주식회사자인이씨엘
5th row주식회사제이원이엔씨
ValueCountFrequency (%)
티엘엔지니어링건축사사무소(주 12
 
0.3%
현대설비 8
 
0.2%
주)남경엔지니어링토건 7
 
0.2%
금풍건설이엔씨(주 7
 
0.2%
주)중앙기건 6
 
0.1%
주)우상건축디자인 6
 
0.1%
주)세광 6
 
0.1%
에이티건설(주 6
 
0.1%
강호건설(주 6
 
0.1%
동림건업(주 6
 
0.1%
Other values (3343) 3954
98.3%
2024-04-16T13:51:23.368098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3038
 
10.2%
( 2622
 
8.8%
) 2622
 
8.8%
설 1350
 
4.5%
건 1284
 
4.3%
이 757
 
2.5%
사 605
 
2.0%
비 532
 
1.8%
지 471
 
1.6%
스 433
 
1.5%
Other values (509) 16118
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24215
81.2%
Open Punctuation 2622
 
8.8%
Close Punctuation 2622
 
8.8%
Uppercase Letter 214
 
0.7%
Other Punctuation 59
 
0.2%
Other Symbol 43
 
0.1%
Lowercase Letter 39
 
0.1%
Decimal Number 14
 
< 0.1%
Space Separator 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3038
 
12.5%
설 1350
 
5.6%
건 1284
 
5.3%
이 757
 
3.1%
사 605
 
2.5%
비 532
 
2.2%
지 471
 
1.9%
스 433
 
1.8%
엔 429
 
1.8%
업 404
 
1.7%
Other values (455) 14912
61.6%
Uppercase Letter
ValueCountFrequency (%)
G 39
18.2%
E 33
15.4%
N 29
13.6%
S 21
9.8%
C 14
 
6.5%
K 11
 
5.1%
A 11
 
5.1%
T 10
 
4.7%
D 7
 
3.3%
R 6
 
2.8%
Other values (11) 33
15.4%
Lowercase Letter
ValueCountFrequency (%)
n 5
12.8%
o 5
12.8%
s 4
10.3%
g 3
7.7%
e 3
7.7%
i 3
7.7%
y 3
7.7%
r 3
7.7%
c 2
 
5.1%
t 2
 
5.1%
Other values (4) 6
15.4%
Other Punctuation
ValueCountFrequency (%)
. 29
49.2%
, 9
 
15.3%
， 9
 
15.3%
& 5
 
8.5%
/ 4
 
6.8%
· 2
 
3.4%
＆ 1
 
1.7%
Decimal Number
ValueCountFrequency (%)
1 4
28.6%
8 4
28.6%
2 2
14.3%
5 1
 
7.1%
6 1
 
7.1%
3 1
 
7.1%
4 1
 
7.1%
Open Punctuation
ValueCountFrequency (%)
( 2622
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2622
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 43
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24258
81.3%
Common 5321
 
17.8%
Latin 253
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3038
 
12.5%
설 1350
 
5.6%
건 1284
 
5.3%
이 757
 
3.1%
사 605
 
2.5%
비 532
 
2.2%
지 471
 
1.9%
스 433
 
1.8%
엔 429
 
1.8%
업 404
 
1.7%
Other values (456) 14955
61.6%
Latin
ValueCountFrequency (%)
G 39
15.4%
E 33
13.0%
N 29
11.5%
S 21
 
8.3%
C 14
 
5.5%
K 11
 
4.3%
A 11
 
4.3%
T 10
 
4.0%
D 7
 
2.8%
R 6
 
2.4%
Other values (25) 72
28.5%
Common
ValueCountFrequency (%)
( 2622
49.3%
) 2622
49.3%
. 29
 
0.5%
, 9
 
0.2%
， 9
 
0.2%
& 5
 
0.1%
1 4
 
0.1%
/ 4
 
0.1%
8 4
 
0.1%
3
 
0.1%
Other values (8) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24215
81.2%
ASCII 5562
 
18.6%
None 55
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3038
 
12.5%
설 1350
 
5.6%
건 1284
 
5.3%
이 757
 
3.1%
사 605
 
2.5%
비 532
 
2.2%
지 471
 
1.9%
스 433
 
1.8%
엔 429
 
1.8%
업 404
 
1.7%
Other values (455) 14912
61.6%
ASCII
ValueCountFrequency (%)
( 2622
47.1%
) 2622
47.1%
G 39
 
0.7%
E 33
 
0.6%
. 29
 
0.5%
N 29
 
0.5%
S 21
 
0.4%
C 14
 
0.3%
K 11
 
0.2%
A 11
 
0.2%
Other values (40) 131
 
2.4%
None
ValueCountFrequency (%)
㈜ 43
78.2%
， 9
 
16.4%
· 2
 
3.6%
＆ 1
 
1.8%

type_of_business
Text

Distinct463
Distinct (%)11.5%
Missing35
Missing (%)0.9%
Memory size31.8 KiB
2024-04-16T13:51:23.546149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length95
Mean length11.604825
Min length4

Characters and Unicode

Total characters46663
Distinct characters86
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)8.2%

Sample

1st row실내건축공사업
2nd row토공사업
3rd row포장공사업
4th row토공사업 철근ㆍ콘크리트공사업 상ㆍ하수도설비공사업
5th row포장공사업
ValueCountFrequency (%)
제2종 706
 
11.4%
가스시설시공업 624
 
10.1%
난방시공업 512
 
8.3%
기계설비공사업 448
 
7.2%
실내건축공사업 418
 
6.7%
시설물유지관리업 408
 
6.6%
철근ㆍ콘크리트공사업 361
 
5.8%
금속구조물ㆍ창호ㆍ온실공사업 347
 
5.6%
상ㆍ하수도설비공사업 316
 
5.1%
제3종 266
 
4.3%
Other values (69) 1796
29.0%
2024-04-16T13:51:23.846938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 5104
 
10.9%
공 4622
 
9.9%
사 3483
 
7.5%
2927
 
6.3%
시 2291
 
4.9%
설 2118
 
4.5%
ㆍ 1889
 
4.0%
물 1169
 
2.5%
종 1138
 
2.4%
제 1138
 
2.4%
Other values (76) 20784
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42008
90.0%
Space Separator 2927
 
6.3%
Decimal Number 1138
 
2.4%
Other Punctuation 356
 
0.8%
Close Punctuation 117
 
0.3%
Open Punctuation 117
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 5104
 
12.2%
공 4622
 
11.0%
사 3483
 
8.3%
시 2291
 
5.5%
설 2118
 
5.0%
ㆍ 1889
 
4.5%
물 1169
 
2.8%
종 1138
 
2.7%
제 1138
 
2.7%
비 981
 
2.3%
Other values (68) 18075
43.0%
Decimal Number
ValueCountFrequency (%)
2 711
62.5%
3 277
 
24.3%
1 150
 
13.2%
Other Punctuation
ValueCountFrequency (%)
, 354
99.4%
. 2
 
0.6%
Space Separator
ValueCountFrequency (%)
2927
100.0%
Close Punctuation
ValueCountFrequency (%)
) 117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42008
90.0%
Common 4655
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 5104
 
12.2%
공 4622
 
11.0%
사 3483
 
8.3%
시 2291
 
5.5%
설 2118
 
5.0%
ㆍ 1889
 
4.5%
물 1169
 
2.8%
종 1138
 
2.7%
제 1138
 
2.7%
비 981
 
2.3%
Other values (68) 18075
43.0%
Common
ValueCountFrequency (%)
2927
62.9%
2 711
 
15.3%
, 354
 
7.6%
3 277
 
6.0%
1 150
 
3.2%
) 117
 
2.5%
( 117
 
2.5%
. 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40119
86.0%
ASCII 4655
 
10.0%
Compat Jamo 1889
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 5104
 
12.7%
공 4622
 
11.5%
사 3483
 
8.7%
시 2291
 
5.7%
설 2118
 
5.3%
물 1169
 
2.9%
종 1138
 
2.8%
제 1138
 
2.8%
비 981
 
2.4%
조 931
 
2.3%
Other values (67) 17144
42.7%
ASCII
ValueCountFrequency (%)
2927
62.9%
2 711
 
15.3%
, 354
 
7.6%
3 277
 
6.0%
1 150
 
3.2%
) 117
 
2.5%
( 117
 
2.5%
. 2
 
< 0.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1889
100.0%

addr
Text

Distinct3328
Distinct (%)82.8%
Missing35
Missing (%)0.9%
Memory size31.8 KiB
2024-04-16T13:51:24.152011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length49
Mean length29.720965
Min length12

Characters and Unicode

Total characters119508
Distinct characters433
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2850 ?
Unique (%)70.9%

Sample

1st row부산광역시 강서구 대저로 259 (대저1동)
2nd row부산광역시 강서구 화전산업대로 272-5 ,301호,한솔드림센타 (녹산동)
3rd row부산광역시 강서구 신호산단1로 215 ,409호,새미래오피스빌딩 (신호동)
4th row부산광역시 강서구 유통단지1로 41, 131동 215,216호(대저2동, 부산티플렉스)
5th row부산광역시 강서구 식만로 69 (죽림동)
ValueCountFrequency (%)
부산광역시 3993
 
17.5%
해운대구 482
 
2.1%
동래구 415
 
1.8%
연제구 395
 
1.7%
금정구 367
 
1.6%
강서구 336
 
1.5%
사상구 335
 
1.5%
부산진구 299
 
1.3%
수영구 291
 
1.3%
기장군 276
 
1.2%
Other values (4157) 15653
68.5%
2024-04-16T13:51:24.610692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18826
 
15.8%
산 5080
 
4.3%
동 4855
 
4.1%
부 4576
 
3.8%
1 4432
 
3.7%
광 4273
 
3.6%
시 4163
 
3.5%
역 4005
 
3.4%
구 3959
 
3.3%
로 3882
 
3.2%
Other values (423) 61457
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71069
59.5%
Decimal Number 20010
 
16.7%
Space Separator 18826
 
15.8%
Close Punctuation 3508
 
2.9%
Open Punctuation 3508
 
2.9%
Other Punctuation 1684
 
1.4%
Dash Punctuation 749
 
0.6%
Uppercase Letter 145
 
0.1%
Lowercase Letter 7
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 5080
 
7.1%
동 4855
 
6.8%
부 4576
 
6.4%
광 4273
 
6.0%
시 4163
 
5.9%
역 4005
 
5.6%
구 3959
 
5.6%
로 3882
 
5.5%
대 1833
 
2.6%
길 1803
 
2.5%
Other values (390) 32640
45.9%
Uppercase Letter
ValueCountFrequency (%)
A 45
31.0%
C 23
15.9%
B 23
15.9%
E 21
14.5%
P 20
13.8%
T 3
 
2.1%
S 2
 
1.4%
K 2
 
1.4%
D 2
 
1.4%
O 2
 
1.4%
Other values (2) 2
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 4432
22.1%
2 3075
15.4%
3 2263
11.3%
0 2126
10.6%
4 1637
 
8.2%
5 1510
 
7.5%
6 1393
 
7.0%
7 1247
 
6.2%
9 1164
 
5.8%
8 1163
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 1424
84.6%
， 225
 
13.4%
. 30
 
1.8%
/ 4
 
0.2%
# 1
 
0.1%
Space Separator
ValueCountFrequency (%)
18826
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3508
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 749
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71069
59.5%
Common 48287
40.4%
Latin 152
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 5080
 
7.1%
동 4855
 
6.8%
부 4576
 
6.4%
광 4273
 
6.0%
시 4163
 
5.9%
역 4005
 
5.6%
구 3959
 
5.6%
로 3882
 
5.5%
대 1833
 
2.6%
길 1803
 
2.5%
Other values (390) 32640
45.9%
Common
ValueCountFrequency (%)
18826
39.0%
1 4432
 
9.2%
) 3508
 
7.3%
( 3508
 
7.3%
2 3075
 
6.4%
3 2263
 
4.7%
0 2126
 
4.4%
4 1637
 
3.4%
5 1510
 
3.1%
, 1424
 
2.9%
Other values (10) 5978
 
12.4%
Latin
ValueCountFrequency (%)
A 45
29.6%
C 23
15.1%
B 23
15.1%
E 21
13.8%
P 20
13.2%
e 7
 
4.6%
T 3
 
2.0%
S 2
 
1.3%
K 2
 
1.3%
D 2
 
1.3%
Other values (3) 4
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71069
59.5%
ASCII 48214
40.3%
None 225
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18826
39.0%
1 4432
 
9.2%
) 3508
 
7.3%
( 3508
 
7.3%
2 3075
 
6.4%
3 2263
 
4.7%
0 2126
 
4.4%
4 1637
 
3.4%
5 1510
 
3.1%
, 1424
 
3.0%
Other values (22) 5905
 
12.2%
Hangul
ValueCountFrequency (%)
산 5080
 
7.1%
동 4855
 
6.8%
부 4576
 
6.4%
광 4273
 
6.0%
시 4163
 
5.9%
역 4005
 
5.6%
구 3959
 
5.6%
로 3882
 
5.5%
대 1833
 
2.6%
길 1803
 
2.5%
Other values (390) 32640
45.9%
None
ValueCountFrequency (%)
， 225
100.0%

tel
Text

MISSING 

Distinct3266
Distinct (%)82.0%
Missing74
Missing (%)1.8%
Memory size31.8 KiB
2024-04-16T13:51:24.804825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.020342
Min length11

Characters and Unicode

Total characters47865
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

Unique2787 ?
Unique (%)70.0%

Sample

1st row051-529-4282
2nd row051-941-6991
3rd row051-941-1865
4th row051-625-9353
5th row051-972-2141
ValueCountFrequency (%)
051-000-0000 21
 
0.5%
051-623-3999 14
 
0.4%
00-000-0000 12
 
0.3%
051-740-6114 7
 
0.2%
051-751-4492 7
 
0.2%
051-631-1687 6
 
0.2%
051-954-5800 6
 
0.2%
051-501-8555 6
 
0.2%
051-412-8766 6
 
0.2%
051-807-8085 6
 
0.2%
Other values (3256) 3891
97.7%
2024-04-16T13:51:25.111829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7966
16.6%
0 7488
15.6%
5 7252
15.2%
1 6840
14.3%
7 3056
 
6.4%
2 2978
 
6.2%
3 2831
 
5.9%
8 2590
 
5.4%
6 2515
 
5.3%
4 2505
 
5.2%
Other values (2) 1844
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39898
83.4%
Dash Punctuation 7966
 
16.6%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7488
18.8%
5 7252
18.2%
1 6840
17.1%
7 3056
7.7%
2 2978
 
7.5%
3 2831
 
7.1%
8 2590
 
6.5%
6 2515
 
6.3%
4 2505
 
6.3%
9 1843
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 7966
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47865
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 7966
16.6%
0 7488
15.6%
5 7252
15.2%
1 6840
14.3%
7 3056
 
6.4%
2 2978
 
6.2%
3 2831
 
5.9%
8 2590
 
5.4%
6 2515
 
5.3%
4 2505
 
5.2%
Other values (2) 1844
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7966
16.6%
0 7488
15.6%
5 7252
15.2%
1 6840
14.3%
7 3056
 
6.4%
2 2978
 
6.2%
3 2831
 
5.9%
8 2590
 
5.4%
6 2515
 
5.3%
4 2505
 
5.2%
Other values (2) 1844
 
3.9%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct2334
Distinct (%)58.1%
Missing36
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean39.971893
Minimum35.023014
Maximum129.117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.8 KiB
2024-04-16T13:51:25.238442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.023014
5-th percentile35.093776
Q135.15799
median35.183172
Q335.213822
95-th percentile129.067
Maximum129.117
Range94.093986
Interquartile range (IQR)0.0558312

Descriptive statistics

Standard deviation20.660855
Coefficient of variation (CV)0.51688456
Kurtosis14.68302
Mean39.971893
Median Absolute Deviation (MAD)0.02794192
Skewness4.0835748
Sum160687.01
Variance426.87091
MonotonicityNot monotonic
2024-04-16T13:51:25.363723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.153 30
 
0.7%
35.176159 27
 
0.7%
35.159 27
 
0.7%
35.1766409 26
 
0.6%
35.1579903 25
 
0.6%
35.175162 24
 
0.6%
35.161 23
 
0.6%
35.16 21
 
0.5%
35.146 21
 
0.5%
35.173 20
 
0.5%
Other values (2324) 3776
93.1%
(Missing) 36
 
0.9%
ValueCountFrequency (%)
35.02301379 1
< 0.1%
35.03007759 1
< 0.1%
35.05313597 1
< 0.1%
35.05390152 1
< 0.1%
35.05474698 1
< 0.1%
35.05533028 1
< 0.1%
35.05643765 1
< 0.1%
35.05871353 1
< 0.1%
35.05911104 1
< 0.1%
35.059544 1
< 0.1%
ValueCountFrequency (%)
129.117 1
 
< 0.1%
129.116 1
 
< 0.1%
129.115 4
0.1%
129.114 6
0.1%
129.113 3
0.1%
129.112 3
0.1%
129.111 2
 
< 0.1%
129.11 4
0.1%
129.109 5
0.1%
129.108 2
 
< 0.1%

lng
Text

Distinct2332
Distinct (%)58.0%
Missing36
Missing (%)0.9%
Memory size31.8 KiB
2024-04-16T13:51:25.580267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length10.208458
Min length3

Characters and Unicode

Total characters41038
Distinct characters13
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

Unique1734 ?
Unique (%)43.1%

Sample

1st row128.98090382
2nd row128.88685464
3rd row128.88159778
4th row128.95570195
5th row128.90263587
ValueCountFrequency (%)
128.985 37
 
0.9%
129.111 27
 
0.7%
129.1258491 27
 
0.7%
128.981 26
 
0.6%
129.1254056 26
 
0.6%
129.1475719 25
 
0.6%
129.1245070 24
 
0.6%
129.112 24
 
0.6%
129.109 23
 
0.6%
128.989 23
 
0.6%
Other values (2322) 3758
93.5%
2024-04-16T13:51:25.933860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7276
17.7%
2 6100
14.9%
9 5854
14.3%
. 4014
9.8%
0 3570
8.7%
8 3403
8.3%
5 2319
 
5.7%
7 2187
 
5.3%
3 2135
 
5.2%
4 2116
 
5.2%
Other values (3) 2064
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37022
90.2%
Other Punctuation 4015
 
9.8%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7276
19.7%
2 6100
16.5%
9 5854
15.8%
0 3570
9.6%
8 3403
9.2%
5 2319
 
6.3%
7 2187
 
5.9%
3 2135
 
5.8%
4 2116
 
5.7%
6 2062
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 4014
> 99.9%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7276
17.7%
2 6100
14.9%
9 5854
14.3%
. 4014
9.8%
0 3570
8.7%
8 3403
8.3%
5 2319
 
5.7%
7 2187
 
5.3%
3 2135
 
5.2%
4 2116
 
5.2%
Other values (3) 2064
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7276
17.7%
2 6100
14.9%
9 5854
14.3%
. 4014
9.8%
0 3570
8.7%
8 3403
8.3%
5 2319
 
5.7%
7 2187
 
5.3%
3 2135
 
5.2%
4 2116
 
5.2%
Other values (3) 2064
 
5.0%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size31.8 KiB
부산광역시 해운대구
482 
부산광역시 동래구
415 
부산광역시 연제구
395 
부산광역시 금정구
367 
부산광역시 강서구
336 
Other values (12)
2061 

Length

Max length10
Median length9
Mean length9.0115878
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 482
11.9%
부산광역시 동래구 415
10.2%
부산광역시 연제구 395
9.7%
부산광역시 금정구 367
9.0%
부산광역시 강서구 336
8.3%
부산광역시 사상구 335
8.3%
부산광역시 부산진구 299
7.4%
부산광역시 수영구 291
7.2%
부산광역시 기장군 276
6.8%
부산광역시 남구 205
 
5.1%
Other values (7) 655
16.1%

Length

2024-04-16T13:51:26.057342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 4021
49.8%
해운대구 482
 
6.0%
동래구 415
 
5.1%
연제구 395
 
4.9%
금정구 367
 
4.5%
강서구 336
 
4.2%
사상구 335
 
4.1%
부산진구 299
 
3.7%
수영구 291
 
3.6%
기장군 276
 
3.4%
Other values (8) 860
 
10.6%

reference_date
Date

Distinct8
Distinct (%)0.2%
Missing35
Missing (%)0.9%
Memory size31.8 KiB
Minimum2020-07-22 00:00:00
Maximum2031-07-20 00:00:00
2024-04-16T13:51:26.165577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:26.259273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3339627.7
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.8 KiB
2024-04-16T13:51:26.363382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3270000
Q13300000
median3340000
Q33370000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation39181.319
Coefficient of variation (CV)0.011732242
Kurtosis-0.9450734
Mean3339627.7
Median Absolute Deviation (MAD)30000
Skewness-0.23832165
Sum1.354553 × 1010
Variance1.5351758 × 109
MonotonicityNot monotonic
2024-04-16T13:51:26.487478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 482
11.9%
3300000 415
10.2%
3370000 395
9.7%
3350000 367
9.0%
3360000 336
8.3%
3390000 335
8.3%
3290000 299
7.4%
3380000 291
7.2%
3400000 276
6.8%
3310000 205
 
5.1%
Other values (6) 655
16.1%
ValueCountFrequency (%)
3250000 41
 
1.0%
3260000 92
 
2.3%
3270000 89
 
2.2%
3280000 93
 
2.3%
3290000 299
7.4%
3300000 415
10.2%
3310000 205
5.1%
3320000 166
 
4.1%
3330000 482
11.9%
3340000 174
 
4.3%
ValueCountFrequency (%)
3400000 276
6.8%
3390000 335
8.3%
3380000 291
7.2%
3370000 395
9.7%
3360000 336
8.3%
3350000 367
9.0%
3340000 174
 
4.3%
3330000 482
11.9%
3320000 166
 
4.1%
3310000 205
5.1%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.8 KiB
Minimum2020-12-22 14:11:36
Maximum2020-12-22 14:11:36
2024-04-16T13:51:26.600109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:26.695858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-16T13:51:21.883504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.144365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.408433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:22.001931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.232221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.486532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:22.093463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.317070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:51:21.787544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T13:51:26.768797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatgugunreference_dateinstt_code
skey1.0000.5280.9770.8650.930
lat0.5281.0001.0000.2970.694
gugun0.9771.0001.0000.9981.000
reference_date0.8650.2970.9981.0000.920
instt_code0.9300.6941.0000.9201.000
2024-04-16T13:51:26.864999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatinstt_codegugun
skey1.000-0.1660.4610.904
lat-0.1661.0000.1160.998
instt_code0.4610.1161.0000.999
gugun0.9040.9980.9991.000

Missing values

2024-04-16T13:51:22.221831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T13:51:22.362170image/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.
2024-04-16T13:51:22.479653image/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

skeybusiness_nmtype_of_businessaddrtellatlnggugunreference_dateinstt_codelast_load_dttm
052546주식회사위드현대실내건축공사업부산광역시 강서구 대저로 259 (대저1동)051-529-428235.213666128.98090382부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
152547주식회사유니온디엔시토공사업부산광역시 강서구 화전산업대로 272-5 ,301호,한솔드림센타 (녹산동)051-941-699135.11294128.88685464부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
252548주식회사유승포장공사업부산광역시 강서구 신호산단1로 215 ,409호,새미래오피스빌딩 (신호동)051-941-186535.086287128.88159778부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
352549주식회사자인이씨엘토공사업 철근ㆍ콘크리트공사업 상ㆍ하수도설비공사업부산광역시 강서구 유통단지1로 41, 131동 215,216호(대저2동, 부산티플렉스)051-625-935335.167293128.95570195부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
452550주식회사제이원이엔씨포장공사업부산광역시 강서구 식만로 69 (죽림동)051-972-214135.20181128.90263587부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
552551주식회사젠수중공사업부산광역시 강서구 가달2로 66 (생곡동)051-442-123535.138501128.87479808부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
652552주식회사창신기계산업기계설비공사업부산광역시 강서구 화전산단4로30번길 25-16 (화전동)051-941-129035.109764128.88232265부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
752553주식회사태창테크비계ㆍ구조물해체공사업부산광역시 강서구 경전철로 208-1 (대저1동)051-809-353835.197207128.96413493부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
852554주식회사템코가스시설시공업 제1종부산광역시 강서구 호계로79번길 68 (죽동동)051-971-851135.198657128.89018084부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
952555주식회사플래이메카조경시설물설치공사업부산광역시 강서구 도도본리길 54 (대저2동)051-831-909135.164792128.92966835부산광역시 강서구2020-08-2033600002020-12-22 14:11:36
skeybusiness_nmtype_of_businessaddrtellatlnggugunreference_dateinstt_codelast_load_dttm
404649798디자인폼실내건축공사업부산광역시 해운대구 APEC로 55， 267호 (우동，벡스코)051-744-360435.16906129.1360148부산광역시 해운대구2020-09-0333300002020-12-22 14:11:36
404749799디자인하늘실내건축공사업부산광역시 해운대구 센텀중앙로 48, 1807호 (우동,에이스하이테크21)051-702-941835.173021129.1298566부산광역시 해운대구2020-09-0333300002020-12-22 14:11:36
404849800부경전시디자인실내건축공사업부산광역시 해운대구 APEC로 55 ,3층 356호 (우동, 벡스코)070-8804-392635.16906129.1360148부산광역시 해운대구2020-09-0333300002020-12-22 14:11:36
404949801수석건설(주)실내건축공사업부산광역시 해운대구 해운대로161번길 17-1 (재송동,유진빌딩)051-526-500135.184031129.1235863부산광역시 해운대구2020-09-0333300002020-12-22 14:11:36
405049802와이지케이디자인실내건축공사업부산광역시 해운대구 센텀동로 57 (우동) 8층 806-2호051-621-007135.17399129.1293980부산광역시 해운대구2020-09-0333300002020-12-22 14:11:36
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