Overview

Dataset statistics

Number of variables11
Number of observations4431
Missing cells96
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory398.2 KiB
Average record size in memory92.0 B

Variable types

Numeric4
Text4
Categorical3

Alerts

last_load_dttm has constant value ""Constant
reference_date is highly overall correlated with skey and 2 other fieldsHigh correlation
gugun is highly overall correlated with skey and 4 other fieldsHigh correlation
skey is highly overall correlated with lng and 2 other fieldsHigh correlation
lat is highly overall correlated with gugunHigh correlation
lng is highly overall correlated with skey and 1 other fieldsHigh correlation
instt_code is highly overall correlated with gugun and 1 other fieldsHigh correlation
tel has 95 (2.1%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 04:50:47.519025
Analysis finished2024-04-16 04:50:50.075978
Duration2.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4431
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61024.964
Minimum49326
Maximum66328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2024-04-16T13:50:50.136214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49326
5-th percentile49547.5
Q160060.5
median62942
Q365014.5
95-th percentile66106.5
Maximum66328
Range17002
Interquartile range (IQR)4954

Descriptive statistics

Standard deviation5298.4928
Coefficient of variation (CV)0.086825006
Kurtosis-0.079923151
Mean61024.964
Median Absolute Deviation (MAD)2477
Skewness-1.1396287
Sum2.7040161 × 108
Variance28074026
MonotonicityNot monotonic
2024-04-16T13:50:50.250315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54099 1
 
< 0.1%
65418 1
 
< 0.1%
65424 1
 
< 0.1%
65423 1
 
< 0.1%
65422 1
 
< 0.1%
65421 1
 
< 0.1%
65420 1
 
< 0.1%
65419 1
 
< 0.1%
65417 1
 
< 0.1%
65460 1
 
< 0.1%
Other values (4421) 4421
99.8%
ValueCountFrequency (%)
49326 1
< 0.1%
49327 1
< 0.1%
49328 1
< 0.1%
49329 1
< 0.1%
49330 1
< 0.1%
49331 1
< 0.1%
49332 1
< 0.1%
49333 1
< 0.1%
49334 1
< 0.1%
49335 1
< 0.1%
ValueCountFrequency (%)
66328 1
< 0.1%
66327 1
< 0.1%
66326 1
< 0.1%
66325 1
< 0.1%
66324 1
< 0.1%
66323 1
< 0.1%
66322 1
< 0.1%
66321 1
< 0.1%
66320 1
< 0.1%
66319 1
< 0.1%

business_nm
Text

Distinct3405
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
2024-04-16T13:50:50.498180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length7.4669375
Min length2

Characters and Unicode

Total characters33086
Distinct characters521
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

Unique2802 ?
Unique (%)63.2%

Sample

1st row(주)한양이엠씨
2nd row(주)유진이엔지
3rd row(주)인기엔지니어링
4th row(주)주훈엔지니어링
5th row(주)청현엔지니어링
ValueCountFrequency (%)
주)남경엔지니어링토건 14
 
0.3%
티엘엔지니어링건축사사무소(주 12
 
0.3%
구구건설(주 12
 
0.3%
주식회사에이비엠 10
 
0.2%
귀뚜라미보일러 10
 
0.2%
주)이화기술단 10
 
0.2%
청림건설주식회사 9
 
0.2%
현대설비 8
 
0.2%
삼주이엔씨(주 8
 
0.2%
주)제이원 8
 
0.2%
Other values (3396) 4335
97.7%
2024-04-16T13:50:50.832186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3362
 
10.2%
( 2847
 
8.6%
) 2847
 
8.6%
설 1445
 
4.4%
건 1381
 
4.2%
이 886
 
2.7%
사 719
 
2.2%
비 555
 
1.7%
지 522
 
1.6%
회 483
 
1.5%
Other values (511) 18039
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26980
81.5%
Open Punctuation 2847
 
8.6%
Close Punctuation 2847
 
8.6%
Uppercase Letter 221
 
0.7%
Other Punctuation 59
 
0.2%
Lowercase Letter 58
 
0.2%
Other Symbol 50
 
0.2%
Decimal Number 17
 
0.1%
Space Separator 6
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3362
 
12.5%
설 1445
 
5.4%
건 1381
 
5.1%
이 886
 
3.3%
사 719
 
2.7%
비 555
 
2.1%
지 522
 
1.9%
회 483
 
1.8%
엔 481
 
1.8%
식 480
 
1.8%
Other values (456) 16666
61.8%
Uppercase Letter
ValueCountFrequency (%)
G 38
17.2%
E 32
14.5%
N 29
13.1%
S 22
10.0%
C 17
7.7%
A 12
 
5.4%
T 11
 
5.0%
K 11
 
5.0%
D 8
 
3.6%
R 6
 
2.7%
Other values (11) 35
15.8%
Lowercase Letter
ValueCountFrequency (%)
n 8
13.8%
s 7
12.1%
o 7
12.1%
y 5
8.6%
r 4
6.9%
c 4
6.9%
g 4
6.9%
u 4
6.9%
i 4
6.9%
e 3
 
5.2%
Other values (4) 8
13.8%
Decimal Number
ValueCountFrequency (%)
1 6
35.3%
8 4
23.5%
2 2
 
11.8%
5 1
 
5.9%
6 1
 
5.9%
3 1
 
5.9%
9 1
 
5.9%
4 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 31
52.5%
, 12
 
20.3%
& 6
 
10.2%
/ 4
 
6.8%
， 2
 
3.4%
· 2
 
3.4%
＆ 2
 
3.4%
Open Punctuation
ValueCountFrequency (%)
( 2847
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2847
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 50
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27030
81.7%
Common 5777
 
17.5%
Latin 279
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3362
 
12.4%
설 1445
 
5.3%
건 1381
 
5.1%
이 886
 
3.3%
사 719
 
2.7%
비 555
 
2.1%
지 522
 
1.9%
회 483
 
1.8%
엔 481
 
1.8%
식 480
 
1.8%
Other values (457) 16716
61.8%
Latin
ValueCountFrequency (%)
G 38
13.6%
E 32
 
11.5%
N 29
 
10.4%
S 22
 
7.9%
C 17
 
6.1%
A 12
 
4.3%
T 11
 
3.9%
K 11
 
3.9%
n 8
 
2.9%
D 8
 
2.9%
Other values (25) 91
32.6%
Common
ValueCountFrequency (%)
( 2847
49.3%
) 2847
49.3%
. 31
 
0.5%
, 12
 
0.2%
& 6
 
0.1%
1 6
 
0.1%
6
 
0.1%
/ 4
 
0.1%
8 4
 
0.1%
， 2
 
< 0.1%
Other values (9) 12
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26980
81.5%
ASCII 6050
 
18.3%
None 56
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3362
 
12.5%
설 1445
 
5.4%
건 1381
 
5.1%
이 886
 
3.3%
사 719
 
2.7%
비 555
 
2.1%
지 522
 
1.9%
회 483
 
1.8%
엔 481
 
1.8%
식 480
 
1.8%
Other values (456) 16666
61.8%
ASCII
ValueCountFrequency (%)
( 2847
47.1%
) 2847
47.1%
G 38
 
0.6%
E 32
 
0.5%
. 31
 
0.5%
N 29
 
0.5%
S 22
 
0.4%
C 17
 
0.3%
A 12
 
0.2%
, 12
 
0.2%
Other values (41) 163
 
2.7%
None
ValueCountFrequency (%)
㈜ 50
89.3%
， 2
 
3.6%
· 2
 
3.6%
＆ 2
 
3.6%

type_of_business
Text

Distinct524
Distinct (%)11.8%
Missing1
Missing (%)< 0.1%
Memory size34.7 KiB
2024-04-16T13:50:51.019841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length93
Mean length11.577427
Min length4

Characters and Unicode

Total characters51288
Distinct characters85
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)8.6%

Sample

1st row수중공사업
2nd row기계설비공사업
3rd row기계설비공사업
4th row기계설비공사업
5th row기계설비공사업
ValueCountFrequency (%)
제2종 755
 
11.0%
가스시설시공업 677
 
9.9%
난방시공업 544
 
8.0%
실내건축공사업 510
 
7.5%
기계설비공사업 486
 
7.1%
시설물유지관리업 451
 
6.6%
철근ㆍ콘크리트공사업 370
 
5.4%
금속구조물ㆍ창호ㆍ온실공사업 366
 
5.4%
상ㆍ하수도설비공사업 329
 
4.8%
제3종 295
 
4.3%
Other values (85) 2050
30.0%
2024-04-16T13:50:51.324789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 5664
 
11.0%
공 5132
 
10.0%
사 3908
 
7.6%
2848
 
5.6%
시 2494
 
4.9%
설 2338
 
4.6%
ㆍ 1959
 
3.8%
물 1304
 
2.5%
제 1223
 
2.4%
종 1223
 
2.4%
Other values (75) 23195
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46489
90.6%
Space Separator 2848
 
5.6%
Decimal Number 1223
 
2.4%
Other Punctuation 492
 
1.0%
Close Punctuation 118
 
0.2%
Open Punctuation 118
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 5664
 
12.2%
공 5132
 
11.0%
사 3908
 
8.4%
시 2494
 
5.4%
설 2338
 
5.0%
ㆍ 1959
 
4.2%
물 1304
 
2.8%
제 1223
 
2.6%
종 1223
 
2.6%
비 1078
 
2.3%
Other values (66) 20166
43.4%
Decimal Number
ValueCountFrequency (%)
2 760
62.1%
3 306
25.0%
1 157
 
12.8%
Other Punctuation
ValueCountFrequency (%)
, 356
72.4%
· 135
 
27.4%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2848
100.0%
Close Punctuation
ValueCountFrequency (%)
) 118
100.0%
Open Punctuation
ValueCountFrequency (%)
( 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46489
90.6%
Common 4799
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 5664
 
12.2%
공 5132
 
11.0%
사 3908
 
8.4%
시 2494
 
5.4%
설 2338
 
5.0%
ㆍ 1959
 
4.2%
물 1304
 
2.8%
제 1223
 
2.6%
종 1223
 
2.6%
비 1078
 
2.3%
Other values (66) 20166
43.4%
Common
ValueCountFrequency (%)
2848
59.3%
2 760
 
15.8%
, 356
 
7.4%
3 306
 
6.4%
1 157
 
3.3%
· 135
 
2.8%
) 118
 
2.5%
( 118
 
2.5%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44530
86.8%
ASCII 4664
 
9.1%
Compat Jamo 1959
 
3.8%
None 135
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 5664
 
12.7%
공 5132
 
11.5%
사 3908
 
8.8%
시 2494
 
5.6%
설 2338
 
5.3%
물 1304
 
2.9%
제 1223
 
2.7%
종 1223
 
2.7%
비 1078
 
2.4%
조 1046
 
2.3%
Other values (65) 19120
42.9%
ASCII
ValueCountFrequency (%)
2848
61.1%
2 760
 
16.3%
, 356
 
7.6%
3 306
 
6.6%
1 157
 
3.4%
) 118
 
2.5%
( 118
 
2.5%
. 1
 
< 0.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1959
100.0%
None
ValueCountFrequency (%)
· 135
100.0%

addr
Text

Distinct3385
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
2024-04-16T13:50:51.628692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length30.427443
Min length12

Characters and Unicode

Total characters134824
Distinct characters431
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

Unique2757 ?
Unique (%)62.2%

Sample

1st row부산광역시 해운대구 구남로18번길 24 408호 (우동,해운대비치오피스텔)
2nd row부산광역시 해운대구 해운대로161번길 17-1 (재송동,유진빌딩)
3rd row부산광역시 해운대구 센텀중앙로 48 에이스하이테크21 1708호 (우동)
4th row부산광역시 해운대구 APEC로 17 , 1403호 (우동, 센텀리더스마크)
5th row부산광역시 해운대구 재반로30번길 50 지하2층제1호(재송동， 욱성아트빌라)
ValueCountFrequency (%)
부산광역시 4399
 
17.3%
해운대구 980
 
3.9%
동래구 429
 
1.7%
금정구 367
 
1.4%
강서구 353
 
1.4%
사상구 339
 
1.3%
부산진구 296
 
1.2%
수영구 282
 
1.1%
기장군 276
 
1.1%
2층 275
 
1.1%
Other values (4202) 17437
68.6%
2024-04-16T13:50:52.029149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21006
 
15.6%
동 5459
 
4.0%
산 5436
 
4.0%
부 4982
 
3.7%
1 4962
 
3.7%
광 4674
 
3.5%
시 4595
 
3.4%
역 4409
 
3.3%
구 4385
 
3.3%
로 4290
 
3.2%
Other values (421) 70626
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80243
59.5%
Decimal Number 22581
 
16.7%
Space Separator 21006
 
15.6%
Close Punctuation 3943
 
2.9%
Open Punctuation 3942
 
2.9%
Other Punctuation 2008
 
1.5%
Dash Punctuation 830
 
0.6%
Uppercase Letter 256
 
0.2%
Lowercase Letter 14
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5459
 
6.8%
산 5436
 
6.8%
부 4982
 
6.2%
광 4674
 
5.8%
시 4595
 
5.7%
역 4409
 
5.5%
구 4385
 
5.5%
로 4290
 
5.3%
대 2506
 
3.1%
길 1948
 
2.4%
Other values (388) 37559
46.8%
Uppercase Letter
ValueCountFrequency (%)
A 83
32.4%
C 45
17.6%
P 41
16.0%
E 41
16.0%
B 29
 
11.3%
T 5
 
2.0%
S 3
 
1.2%
K 3
 
1.2%
D 2
 
0.8%
O 2
 
0.8%
Other values (2) 2
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 4962
22.0%
2 3447
15.3%
3 2530
11.2%
0 2462
10.9%
4 1791
 
7.9%
5 1671
 
7.4%
6 1588
 
7.0%
7 1445
 
6.4%
9 1392
 
6.2%
8 1293
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 1706
85.0%
， 272
 
13.5%
. 24
 
1.2%
/ 4
 
0.2%
# 2
 
0.1%
Space Separator
ValueCountFrequency (%)
21006
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3943
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3942
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 830
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80243
59.5%
Common 54311
40.3%
Latin 270
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5459
 
6.8%
산 5436
 
6.8%
부 4982
 
6.2%
광 4674
 
5.8%
시 4595
 
5.7%
역 4409
 
5.5%
구 4385
 
5.5%
로 4290
 
5.3%
대 2506
 
3.1%
길 1948
 
2.4%
Other values (388) 37559
46.8%
Common
ValueCountFrequency (%)
21006
38.7%
1 4962
 
9.1%
) 3943
 
7.3%
( 3942
 
7.3%
2 3447
 
6.3%
3 2530
 
4.7%
0 2462
 
4.5%
4 1791
 
3.3%
, 1706
 
3.1%
5 1671
 
3.1%
Other values (10) 6851
 
12.6%
Latin
ValueCountFrequency (%)
A 83
30.7%
C 45
16.7%
P 41
15.2%
E 41
15.2%
B 29
 
10.7%
e 14
 
5.2%
T 5
 
1.9%
S 3
 
1.1%
K 3
 
1.1%
D 2
 
0.7%
Other values (3) 4
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80243
59.5%
ASCII 54309
40.3%
None 272
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21006
38.7%
1 4962
 
9.1%
) 3943
 
7.3%
( 3942
 
7.3%
2 3447
 
6.3%
3 2530
 
4.7%
0 2462
 
4.5%
4 1791
 
3.3%
, 1706
 
3.1%
5 1671
 
3.1%
Other values (22) 6849
 
12.6%
Hangul
ValueCountFrequency (%)
동 5459
 
6.8%
산 5436
 
6.8%
부 4982
 
6.2%
광 4674
 
5.8%
시 4595
 
5.7%
역 4409
 
5.5%
구 4385
 
5.5%
로 4290
 
5.3%
대 2506
 
3.1%
길 1948
 
2.4%
Other values (388) 37559
46.8%
None
ValueCountFrequency (%)
， 272
100.0%

tel
Text

MISSING 

Distinct3288
Distinct (%)75.8%
Missing95
Missing (%)2.1%
Memory size34.7 KiB
2024-04-16T13:50:52.238070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.020756
Min length11

Characters and Unicode

Total characters52122
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

Unique2678 ?
Unique (%)61.8%

Sample

1st row051-747-8152
2nd row051-746-0717
3rd row051-743-7551
4th row051-501-0936
5th row051-783-5607
ValueCountFrequency (%)
051-000-0000 16
 
0.4%
051-623-3999 14
 
0.3%
051-740-6114 14
 
0.3%
051-746-3639 12
 
0.3%
051-333-8171 12
 
0.3%
051-524-1004 10
 
0.2%
051-759-8632 10
 
0.2%
051-746-0891 9
 
0.2%
051-757-5441 9
 
0.2%
051-521-5410 8
 
0.2%
Other values (3278) 4222
97.4%
2024-04-16T13:50:52.553153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8672
16.6%
0 7961
15.3%
5 7788
14.9%
1 7551
14.5%
7 3451
 
6.6%
2 3223
 
6.2%
3 3082
 
5.9%
4 2876
 
5.5%
8 2770
 
5.3%
6 2720
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43450
83.4%
Dash Punctuation 8672
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7961
18.3%
5 7788
17.9%
1 7551
17.4%
7 3451
7.9%
2 3223
7.4%
3 3082
 
7.1%
4 2876
 
6.6%
8 2770
 
6.4%
6 2720
 
6.3%
9 2028
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 8672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 8672
16.6%
0 7961
15.3%
5 7788
14.9%
1 7551
14.5%
7 3451
 
6.6%
2 3223
 
6.2%
3 3082
 
5.9%
4 2876
 
5.5%
8 2770
 
5.3%
6 2720
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 8672
16.6%
0 7961
15.3%
5 7788
14.9%
1 7551
14.5%
7 3451
 
6.6%
2 3223
 
6.2%
3 3082
 
5.9%
4 2876
 
5.5%
8 2770
 
5.3%
6 2720
 
5.2%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct2837
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.179938
Minimum35.023014
Maximum35.377499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2024-04-16T13:50:52.671938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.023014
5-th percentile35.094507
Q135.153298
median35.175477
Q335.203651
95-th percentile35.27096
Maximum35.377499
Range0.35448493
Interquartile range (IQR)0.050352081

Descriptive statistics

Standard deviation0.051448574
Coefficient of variation (CV)0.0014624407
Kurtosis1.0609204
Mean35.179938
Median Absolute Deviation (MAD)0.02548518
Skewness0.56431301
Sum155882.31
Variance0.0026469558
MonotonicityNot monotonic
2024-04-16T13:50:52.787201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.176159 54
 
1.2%
35.1766409 52
 
1.2%
35.1579903 50
 
1.1%
35.175162 47
 
1.1%
35.1754772 40
 
0.9%
35.1587874 32
 
0.7%
35.1730209 30
 
0.7%
35.1699607 24
 
0.5%
35.1659301 22
 
0.5%
35.1840311 20
 
0.5%
Other values (2827) 4060
91.6%
ValueCountFrequency (%)
35.0230139682 1
< 0.1%
35.0300761102 1
< 0.1%
35.0531323211 1
< 0.1%
35.05390152 1
< 0.1%
35.05404521 1
< 0.1%
35.05444225 1
< 0.1%
35.05474698 1
< 0.1%
35.05533028 1
< 0.1%
35.05643765 1
< 0.1%
35.05871353 1
< 0.1%
ValueCountFrequency (%)
35.3774989 1
< 0.1%
35.37698621 2
< 0.1%
35.37542229 1
< 0.1%
35.36848122 1
< 0.1%
35.36844454 1
< 0.1%
35.36842573 1
< 0.1%
35.36782843 2
< 0.1%
35.36593677 1
< 0.1%
35.36465676 1
< 0.1%
35.34959338 1
< 0.1%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct2835
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.07611
Minimum128.80959
Maximum129.28838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2024-04-16T13:50:52.913187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80959
5-th percentile128.94819
Q1129.02633
median129.08886
Q3129.1253
95-th percentile129.205
Maximum129.28838
Range0.4787878
Interquartile range (IQR)0.09897385

Descriptive statistics

Standard deviation0.078800008
Coefficient of variation (CV)0.00061049257
Kurtosis0.49574229
Mean129.07611
Median Absolute Deviation (MAD)0.0402719
Skewness-0.40963781
Sum571936.26
Variance0.0062094412
MonotonicityNot monotonic
2024-04-16T13:50:53.255713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1258491 54
 
1.2%
129.1254056 52
 
1.2%
129.1475719 50
 
1.1%
129.124507 47
 
1.1%
129.1264654 40
 
0.9%
129.1538064 32
 
0.7%
129.1298566 30
 
0.7%
129.1418564 24
 
0.5%
129.1324148 22
 
0.5%
128.9558196889 20
 
0.5%
Other values (2825) 4060
91.6%
ValueCountFrequency (%)
128.809589296 1
< 0.1%
128.8141459712 1
< 0.1%
128.815484738 1
< 0.1%
128.8157059296 1
< 0.1%
128.8180546054 1
< 0.1%
128.8219507386 1
< 0.1%
128.8221336016 1
< 0.1%
128.8251969314 1
< 0.1%
128.8257348112 2
< 0.1%
128.8282370135 1
< 0.1%
ValueCountFrequency (%)
129.2883771 1
< 0.1%
129.2868026 1
< 0.1%
129.2866731 1
< 0.1%
129.2865217 1
< 0.1%
129.2863602 1
< 0.1%
129.2854769 1
< 0.1%
129.285289 1
< 0.1%
129.2850269 1
< 0.1%
129.284696 1
< 0.1%
129.284662 1
< 0.1%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
부산광역시 해운대구
980 
부산광역시 동래구
429 
부산광역시 금정구
367 
부산광역시 강서구
353 
부산광역시 사상구
339 
Other values (11)
1963 

Length

Max length10
Median length9
Mean length9.1575265
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 980
22.1%
부산광역시 동래구 429
9.7%
부산광역시 금정구 367
 
8.3%
부산광역시 강서구 353
 
8.0%
부산광역시 사상구 339
 
7.7%
부산광역시 부산진구 296
 
6.7%
부산광역시 수영구 282
 
6.4%
부산광역시 기장군 276
 
6.2%
부산광역시 연제구 255
 
5.8%
부산광역시 남구 206
 
4.6%
Other values (6) 648
14.6%

Length

2024-04-16T13:50:53.375010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 4431
50.0%
해운대구 980
 
11.1%
동래구 429
 
4.8%
금정구 367
 
4.1%
강서구 353
 
4.0%
사상구 339
 
3.8%
부산진구 296
 
3.3%
수영구 282
 
3.2%
기장군 276
 
3.1%
연제구 255
 
2.9%
Other values (7) 854
 
9.6%

reference_date
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
2020-12-31
3308 
2020-09-03
482 
2021-01-12
 
282
2021-01-11
 
255
2021-01-01
 
104

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 3308
74.7%
2020-09-03 482
 
10.9%
2021-01-12 282
 
6.4%
2021-01-11 255
 
5.8%
2021-01-01 104
 
2.3%

Length

2024-04-16T13:50:53.482884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T13:50:53.573115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 3308
74.7%
2020-09-03 482
 
10.9%
2021-01-12 282
 
6.4%
2021-01-11 255
 
5.8%
2021-01-01 104
 
2.3%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3337847
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2024-04-16T13:50:53.665434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3280000
Q13310000
median3330000
Q33370000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation36880.811
Coefficient of variation (CV)0.011049281
Kurtosis-0.75019739
Mean3337847
Median Absolute Deviation (MAD)30000
Skewness-0.092566154
Sum1.479 × 1010
Variance1.3601942 × 109
MonotonicityNot monotonic
2024-04-16T13:50:53.768117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 980
22.1%
3300000 429
9.7%
3350000 367
 
8.3%
3360000 353
 
8.0%
3390000 339
 
7.7%
3290000 296
 
6.7%
3380000 282
 
6.4%
3400000 276
 
6.2%
3370000 255
 
5.8%
3310000 206
 
4.6%
Other values (6) 648
14.6%
ValueCountFrequency (%)
3250000 43
 
1.0%
3260000 59
 
1.3%
3270000 104
 
2.3%
3280000 93
 
2.1%
3290000 296
 
6.7%
3300000 429
9.7%
3310000 206
 
4.6%
3320000 166
 
3.7%
3330000 980
22.1%
3340000 183
 
4.1%
ValueCountFrequency (%)
3400000 276
 
6.2%
3390000 339
 
7.7%
3380000 282
 
6.4%
3370000 255
 
5.8%
3360000 353
 
8.0%
3350000 367
 
8.3%
3340000 183
 
4.1%
3330000 980
22.1%
3320000 166
 
3.7%
3310000 206
 
4.6%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
2021-04-01 05:31:03
4431 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:31:03
2nd row2021-04-01 05:31:03
3rd row2021-04-01 05:31:03
4th row2021-04-01 05:31:03
5th row2021-04-01 05:31:03

Common Values

ValueCountFrequency (%)
2021-04-01 05:31:03 4431
100.0%

Length

2024-04-16T13:50:53.871715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T13:50:53.957292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 4431
50.0%
05:31:03 4431
50.0%

Interactions

2024-04-16T13:50:49.429951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.385939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.705661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.080733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.512924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.465779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.787189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.186054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.594180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.543997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.872831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.269890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.679807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.623776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:48.974013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T13:50:49.347490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T13:50:54.013213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatlnggugunreference_dateinstt_code
skey1.0000.5780.7000.9530.8130.882
lat0.5781.0000.7880.8430.5860.817
lng0.7000.7881.0000.8840.7110.889
gugun0.9530.8430.8841.0000.9761.000
reference_date0.8130.5860.7110.9761.0000.966
instt_code0.8820.8170.8891.0000.9661.000
2024-04-16T13:50:54.098587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
reference_dategugun
reference_date1.0000.923
gugun0.9231.000
2024-04-16T13:50:54.168194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylatlnginstt_codegugunreference_date
skey1.000-0.225-0.6380.0250.8450.707
lat-0.2251.0000.3830.2340.5370.282
lng-0.6380.3831.0000.0880.6120.373
instt_code0.0250.2340.0881.0000.9990.743
gugun0.8450.5370.6120.9991.0000.923
reference_date0.7070.2820.3730.7430.9231.000

Missing values

2024-04-16T13:50:49.800282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T13:50:49.931041image/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.
2024-04-16T13:50:50.028671image/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
054099(주)한양이엠씨수중공사업부산광역시 해운대구 구남로18번길 24 408호 (우동,해운대비치오피스텔)051-747-815235.161867129.158789부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
154100(주)유진이엔지기계설비공사업부산광역시 해운대구 해운대로161번길 17-1 (재송동,유진빌딩)051-746-071735.184031129.123586부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
254101(주)인기엔지니어링기계설비공사업부산광역시 해운대구 센텀중앙로 48 에이스하이테크21 1708호 (우동)051-743-755135.173021129.129857부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
354102(주)주훈엔지니어링기계설비공사업부산광역시 해운대구 APEC로 17 , 1403호 (우동, 센텀리더스마크)051-501-093635.16593129.132415부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
454103(주)청현엔지니어링기계설비공사업부산광역시 해운대구 재반로30번길 50 지하2층제1호(재송동， 욱성아트빌라)051-783-560735.182688129.128421부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
554104(주)태경설비기계설비공사업부산광역시 해운대구 좌동순환로433번길 30-1， 310호 (중동,힐스테이트위브상가)051-701-746135.16252129.179851부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
654105(주)태성엔지니어링기계설비공사업부산광역시 해운대구 해운대로123번길 36 (재송동)051-751-471735.186037129.123032부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
754106(주)현대이엔티기계설비공사업부산광역시 해운대구 센텀중앙로 90 본동 1704호 (재송동)051-529-102535.175477129.126465부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
854107동영테크원(주)기계설비공사업부산광역시 해운대구 반송로513번길 66-33 (석대동)051-320-050035.222824129.119036부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
954108벽광건설(주)기계설비공사업부산광역시 해운대구 반여로155번다길 8 2층 (반여동)051-521-541035.206709129.123657부산광역시 해운대구2020-12-3133300002021-04-01 05:31:03
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