Overview

Dataset statistics

Number of variables32
Number of observations52
Missing cells9
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory263.5 B

Variable types

Numeric5
Categorical19
DateTime2
Text6

Alerts

opnsvcid has constant value ""Constant
last_load_dttm has constant value ""Constant
opnsfteamcode is highly imbalanced (57.0%)Imbalance
sitetel is highly imbalanced (70.0%)Imbalance
sitepostno has 3 (5.8%) missing valuesMissing
rdnwhladdr has 6 (11.5%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-19 22:13:17.847555
Analysis finished2024-04-19 22:13:18.627130
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-20T07:13:18.736243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2024-04-20T07:13:18.932083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

opnsfteamcode
Categorical

IMBALANCE 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
6260000
44 
6410000
 
3
6470000
 
3
6450000
 
2

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6260000
2nd row6260000
3rd row6260000
4th row6260000
5th row6260000

Common Values

ValueCountFrequency (%)
6260000 44
84.6%
6410000 3
 
5.8%
6470000 3
 
5.8%
6450000 2
 
3.8%

Length

2024-04-20T07:13:19.105299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:19.216171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6260000 44
84.6%
6410000 3
 
5.8%
6470000 3
 
5.8%
6450000 2
 
3.8%

mgtno
Real number (ℝ)

Distinct11
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0126865 × 1019
Minimum2.010626 × 1019
Maximum2.020645 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-20T07:13:19.340590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.010626 × 1019
5-th percentile2.010626 × 1019
Q12.010626 × 1019
median2.011626 × 1019
Q32.0128873 × 1019
95-th percentile2.0189943 × 1019
Maximum2.020645 × 1019
Range1.0019 × 1017
Interquartile range (IQR)2.26125 × 1016

Descriptive statistics

Standard deviation2.7876492 × 1016
Coefficient of variation (CV)0.0013850389
Kurtosis2.1509814
Mean2.0126865 × 1019
Median Absolute Deviation (MAD)1 × 1016
Skewness1.7509446
Sum1.046597 × 1021
Variance7.770988 × 1032
MonotonicityNot monotonic
2024-04-20T07:13:19.469706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2.0116260000051e+19 22
42.3%
2.0106260000051e+19 15
28.8%
2.0156260000051e+19 3
 
5.8%
2.0176470000051e+19 3
 
5.8%
2.0136260000051e+19 2
 
3.8%
2.0206450000051e+19 2
 
3.8%
2.0126260000051e+19 1
 
1.9%
2.0166260000051e+19 1
 
1.9%
2.01264100000512e+19 1
 
1.9%
2.01364100000511e+19 1
 
1.9%
ValueCountFrequency (%)
2.0106260000051e+19 15
28.8%
2.0116260000051e+19 22
42.3%
2.0126260000051e+19 1
 
1.9%
2.01264100000512e+19 1
 
1.9%
2.0136260000051e+19 2
 
3.8%
2.01364100000511e+19 1
 
1.9%
2.0156260000051e+19 3
 
5.8%
2.0166260000051e+19 1
 
1.9%
2.0176470000051e+19 3
 
5.8%
2.02064100000511e+19 1
 
1.9%
ValueCountFrequency (%)
2.0206450000051e+19 2
 
3.8%
2.02064100000511e+19 1
 
1.9%
2.0176470000051e+19 3
 
5.8%
2.0166260000051e+19 1
 
1.9%
2.0156260000051e+19 3
 
5.8%
2.01364100000511e+19 1
 
1.9%
2.0136260000051e+19 2
 
3.8%
2.01264100000512e+19 1
 
1.9%
2.0126260000051e+19 1
 
1.9%
2.0116260000051e+19 22
42.3%

opnsvcid
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
11_47_01_P
52 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11_47_01_P
2nd row11_47_01_P
3rd row11_47_01_P
4th row11_47_01_P
5th row11_47_01_P

Common Values

ValueCountFrequency (%)
11_47_01_P 52
100.0%

Length

2024-04-20T07:13:19.575367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:19.653180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11_47_01_p 52
100.0%

updategbn
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
I
32 
U
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowI
3rd rowI
4th rowI
5th rowU

Common Values

ValueCountFrequency (%)
I 32
61.5%
U 20
38.5%

Length

2024-04-20T07:13:19.731844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:19.813186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 32
61.5%
u 20
38.5%

updatedt
Date

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2018-08-31 23:59:59
Maximum2021-01-20 02:40:00
2024-04-20T07:13:19.909073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T07:13:20.030862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

opnsvcnm
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
27 
상조업
25 

Length

Max length4
Median length4
Mean length3.5192308
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
51.9%
상조업 25
48.1%

Length

2024-04-20T07:13:20.159321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:20.256617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
51.9%
상조업 25
48.1%

bplcnm
Text

Distinct49
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-20T07:13:20.451407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length10.75
Min length5

Characters and Unicode

Total characters559
Distinct characters109
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)90.4%

Sample

1st row주식회사 부산의사상조
2nd row부경상조(주)
3rd row주식회사 다원상조
4th row디에이치 상조 주식회사
5th row아가페라이프주식회사
ValueCountFrequency (%)
주식회사 17
 
23.3%
주)삼성코리아상조(구,(주)미래상조119 3
 
4.1%
그랜드라이프 2
 
2.7%
에스티라이프(주 1
 
1.4%
주)씨에스라이프 1
 
1.4%
주)미래라이프상조 1
 
1.4%
주)미래상조119 1
 
1.4%
행복한상조 1
 
1.4%
부산의사상조 1
 
1.4%
아름다운약속(주 1
 
1.4%
Other values (44) 44
60.3%
2024-04-20T07:13:20.998936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 56
 
10.0%
( 35
 
6.3%
) 35
 
6.3%
상 31
 
5.5%
조 31
 
5.5%
사 28
 
5.0%
회 28
 
5.0%
이 27
 
4.8%
식 26
 
4.7%
21
 
3.8%
Other values (99) 241
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
80.9%
Open Punctuation 35
 
6.3%
Close Punctuation 35
 
6.3%
Space Separator 21
 
3.8%
Decimal Number 12
 
2.1%
Other Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 56
 
12.4%
상 31
 
6.9%
조 31
 
6.9%
사 28
 
6.2%
회 28
 
6.2%
이 27
 
6.0%
식 26
 
5.8%
라 19
 
4.2%
프 19
 
4.2%
에 10
 
2.2%
Other values (92) 177
39.2%
Decimal Number
ValueCountFrequency (%)
1 8
66.7%
9 4
33.3%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
: 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 452
80.9%
Common 107
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 56
 
12.4%
상 31
 
6.9%
조 31
 
6.9%
사 28
 
6.2%
회 28
 
6.2%
이 27
 
6.0%
식 26
 
5.8%
라 19
 
4.2%
프 19
 
4.2%
에 10
 
2.2%
Other values (92) 177
39.2%
Common
ValueCountFrequency (%)
( 35
32.7%
) 35
32.7%
21
19.6%
1 8
 
7.5%
9 4
 
3.7%
, 3
 
2.8%
: 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
80.9%
ASCII 107
 
19.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 56
 
12.4%
상 31
 
6.9%
조 31
 
6.9%
사 28
 
6.2%
회 28
 
6.2%
이 27
 
6.0%
식 26
 
5.8%
라 19
 
4.2%
프 19
 
4.2%
에 10
 
2.2%
Other values (92) 177
39.2%
ASCII
ValueCountFrequency (%)
( 35
32.7%
) 35
32.7%
21
19.6%
1 8
 
7.5%
9 4
 
3.7%
, 3
 
2.8%
: 1
 
0.9%

sitepostno
Text

MISSING 

Distinct28
Distinct (%)57.1%
Missing3
Missing (%)5.8%
Memory size548.0 B
2024-04-20T07:13:21.168603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9387755
Min length5

Characters and Unicode

Total characters291
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)44.9%

Sample

1st row48972
2nd row600074
3rd row614870
4th row611080
5th row617040
ValueCountFrequency (%)
지번우편번호 13
26.5%
611080 5
 
10.2%
614020 3
 
6.1%
611070 2
 
4.1%
614050 2
 
4.1%
614040 2
 
4.1%
614844 1
 
2.0%
601010 1
 
2.0%
611812 1
 
2.0%
48972 1
 
2.0%
Other values (18) 18
36.7%
2024-04-20T07:13:21.458294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54
18.6%
1 45
15.5%
6 36
12.4%
번 26
8.9%
4 26
8.9%
8 18
 
6.2%
지 13
 
4.5%
우 13
 
4.5%
편 13
 
4.5%
호 13
 
4.5%
Other values (5) 34
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 213
73.2%
Other Letter 78
 
26.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54
25.4%
1 45
21.1%
6 36
16.9%
4 26
12.2%
8 18
 
8.5%
7 13
 
6.1%
2 8
 
3.8%
5 6
 
2.8%
3 5
 
2.3%
9 2
 
0.9%
Other Letter
ValueCountFrequency (%)
번 26
33.3%
지 13
16.7%
우 13
16.7%
편 13
16.7%
호 13
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 213
73.2%
Hangul 78
 
26.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54
25.4%
1 45
21.1%
6 36
16.9%
4 26
12.2%
8 18
 
8.5%
7 13
 
6.1%
2 8
 
3.8%
5 6
 
2.8%
3 5
 
2.3%
9 2
 
0.9%
Hangul
ValueCountFrequency (%)
번 26
33.3%
지 13
16.7%
우 13
16.7%
편 13
16.7%
호 13
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
73.2%
Hangul 78
 
26.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54
25.4%
1 45
21.1%
6 36
16.9%
4 26
12.2%
8 18
 
8.5%
7 13
 
6.1%
2 8
 
3.8%
5 6
 
2.8%
3 5
 
2.3%
9 2
 
0.9%
Hangul
ValueCountFrequency (%)
번 26
33.3%
지 13
16.7%
우 13
16.7%
편 13
16.7%
호 13
16.7%

sitewhladdr
Text

Distinct41
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-20T07:13:21.722942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length34.5
Mean length22.557692
Min length1

Characters and Unicode

Total characters1173
Distinct characters94
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)75.0%

Sample

1st row부산광역시 중구 대청동4가 12번지 9호
2nd row부산광역시 중구 부평동4가 14번지 1호
3rd row부산광역시 부산진구 전포동 875번지 2호
4th row부산광역시 연제구 연산동 1287번지 2호 인회빌딩
5th row부산광역시 사상구 덕포동 404번지 10호
ValueCountFrequency (%)
부산광역시 39
 
16.3%
부산진구 17
 
7.1%
지번주소 11
 
4.6%
연제구 11
 
4.6%
연산동 9
 
3.8%
범천동 6
 
2.5%
동구 5
 
2.1%
양정동 4
 
1.7%
2호 4
 
1.7%
전포동 4
 
1.7%
Other values (98) 129
54.0%
2024-04-20T07:13:22.144054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
266
22.7%
산 65
 
5.5%
부 60
 
5.1%
지 50
 
4.3%
동 49
 
4.2%
번 49
 
4.2%
1 43
 
3.7%
호 43
 
3.7%
역 39
 
3.3%
광 39
 
3.3%
Other values (84) 470
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 688
58.7%
Space Separator 266
 
22.7%
Decimal Number 212
 
18.1%
Uppercase Letter 4
 
0.3%
Other Punctuation 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 65
 
9.4%
부 60
 
8.7%
지 50
 
7.3%
동 49
 
7.1%
번 49
 
7.1%
호 43
 
6.2%
역 39
 
5.7%
광 39
 
5.7%
구 39
 
5.7%
시 39
 
5.7%
Other values (68) 216
31.4%
Decimal Number
ValueCountFrequency (%)
1 43
20.3%
4 26
12.3%
2 25
11.8%
3 22
10.4%
0 20
9.4%
7 19
9.0%
8 19
9.0%
5 13
 
6.1%
9 13
 
6.1%
6 12
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
S 1
25.0%
T 1
25.0%
Space Separator
ValueCountFrequency (%)
266
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 688
58.7%
Common 481
41.0%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 65
 
9.4%
부 60
 
8.7%
지 50
 
7.3%
동 49
 
7.1%
번 49
 
7.1%
호 43
 
6.2%
역 39
 
5.7%
광 39
 
5.7%
구 39
 
5.7%
시 39
 
5.7%
Other values (68) 216
31.4%
Common
ValueCountFrequency (%)
266
55.3%
1 43
 
8.9%
4 26
 
5.4%
2 25
 
5.2%
3 22
 
4.6%
0 20
 
4.2%
7 19
 
4.0%
8 19
 
4.0%
5 13
 
2.7%
9 13
 
2.7%
Other values (3) 15
 
3.1%
Latin
ValueCountFrequency (%)
K 2
50.0%
S 1
25.0%
T 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 688
58.7%
ASCII 485
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
266
54.8%
1 43
 
8.9%
4 26
 
5.4%
2 25
 
5.2%
3 22
 
4.5%
0 20
 
4.1%
7 19
 
3.9%
8 19
 
3.9%
5 13
 
2.7%
9 13
 
2.7%
Other values (6) 19
 
3.9%
Hangul
ValueCountFrequency (%)
산 65
 
9.4%
부 60
 
8.7%
지 50
 
7.3%
동 49
 
7.1%
번 49
 
7.1%
호 43
 
6.2%
역 39
 
5.7%
광 39
 
5.7%
구 39
 
5.7%
시 39
 
5.7%
Other values (68) 216
31.4%

rdnpostno
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
도로명우편번호
21 

Length

Max length7
Median length4
Mean length5.2115385
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
도로명우편번호 21
40.4%

Length

2024-04-20T07:13:22.291913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:22.400274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
도로명우편번호 21
40.4%

rdnwhladdr
Text

MISSING 

Distinct42
Distinct (%)91.3%
Missing6
Missing (%)11.5%
Memory size548.0 B
2024-04-20T07:13:22.665195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length33.5
Mean length29.847826
Min length22

Characters and Unicode

Total characters1373
Distinct characters141
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

Unique39 ?
Unique (%)84.8%

Sample

1st row부산광역시 중구 중구로 121 (대청동4가)
2nd row부산광역시 중구 흑교로31번길 19-1, 2층 (부평동4가)
3rd row부산광역시 사하구 까치고개로 77-1 (괴정동)
4th row부산광역시 사상구 모덕로 85, 현대자동차 (모라동)
5th row부산광역시 부산진구 중앙대로702번길 43, 6층 (부전동)
ValueCountFrequency (%)
부산광역시 38
 
14.0%
부산진구 16
 
5.9%
중앙대로 11
 
4.1%
연제구 8
 
3.0%
2층 8
 
3.0%
연산동 6
 
2.2%
3층 6
 
2.2%
동구 5
 
1.8%
범천동 4
 
1.5%
625 3
 
1.1%
Other values (124) 166
61.3%
2024-04-20T07:13:23.103499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
225
 
16.4%
산 65
 
4.7%
동 63
 
4.6%
부 58
 
4.2%
시 48
 
3.5%
( 45
 
3.3%
) 45
 
3.3%
구 44
 
3.2%
로 42
 
3.1%
2 40
 
2.9%
Other values (131) 698
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 818
59.6%
Space Separator 225
 
16.4%
Decimal Number 193
 
14.1%
Open Punctuation 45
 
3.3%
Close Punctuation 45
 
3.3%
Other Punctuation 38
 
2.8%
Dash Punctuation 6
 
0.4%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 65
 
7.9%
동 63
 
7.7%
부 58
 
7.1%
시 48
 
5.9%
구 44
 
5.4%
로 42
 
5.1%
광 38
 
4.6%
역 38
 
4.6%
대 28
 
3.4%
층 26
 
3.2%
Other values (112) 368
45.0%
Decimal Number
ValueCountFrequency (%)
2 40
20.7%
1 36
18.7%
5 21
10.9%
0 16
 
8.3%
3 16
 
8.3%
6 15
 
7.8%
4 14
 
7.3%
7 13
 
6.7%
9 11
 
5.7%
8 11
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
F 1
33.3%
C 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 36
94.7%
. 2
 
5.3%
Space Separator
ValueCountFrequency (%)
225
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 818
59.6%
Common 552
40.2%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 65
 
7.9%
동 63
 
7.7%
부 58
 
7.1%
시 48
 
5.9%
구 44
 
5.4%
로 42
 
5.1%
광 38
 
4.6%
역 38
 
4.6%
대 28
 
3.4%
층 26
 
3.2%
Other values (112) 368
45.0%
Common
ValueCountFrequency (%)
225
40.8%
( 45
 
8.2%
) 45
 
8.2%
2 40
 
7.2%
, 36
 
6.5%
1 36
 
6.5%
5 21
 
3.8%
0 16
 
2.9%
3 16
 
2.9%
6 15
 
2.7%
Other values (6) 57
 
10.3%
Latin
ValueCountFrequency (%)
A 1
33.3%
F 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 818
59.6%
ASCII 555
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225
40.5%
( 45
 
8.1%
) 45
 
8.1%
2 40
 
7.2%
, 36
 
6.5%
1 36
 
6.5%
5 21
 
3.8%
0 16
 
2.9%
3 16
 
2.9%
6 15
 
2.7%
Other values (9) 60
 
10.8%
Hangul
ValueCountFrequency (%)
산 65
 
7.9%
동 63
 
7.7%
부 58
 
7.1%
시 48
 
5.9%
구 44
 
5.4%
로 42
 
5.1%
광 38
 
4.6%
역 38
 
4.6%
대 28
 
3.4%
층 26
 
3.2%
Other values (112) 368
45.0%

apvpermymd
Real number (ℝ)

Distinct32
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20121197
Minimum20101005
Maximum20201121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-20T07:13:23.253089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20101005
5-th percentile20101015
Q120101230
median20110310
Q320123463
95-th percentile20184229
Maximum20201121
Range100116
Interquartile range (IQR)22233

Descriptive statistics

Standard deviation27798.967
Coefficient of variation (CV)0.0013815762
Kurtosis2.2514747
Mean20121197
Median Absolute Deviation (MAD)9090
Skewness1.7789353
Sum1.0463023 × 109
Variance7.7278257 × 108
MonotonicityNot monotonic
2024-04-20T07:13:23.394278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20110318 5
 
9.6%
20110310 4
 
7.7%
20170421 3
 
5.8%
20110322 3
 
5.8%
20101015 2
 
3.8%
20110131 2
 
3.8%
20110303 2
 
3.8%
20101026 2
 
3.8%
20101027 2
 
3.8%
20201106 2
 
3.8%
Other values (22) 25
48.1%
ValueCountFrequency (%)
20101005 2
3.8%
20101015 2
3.8%
20101026 2
3.8%
20101027 2
3.8%
20101103 1
1.9%
20101115 1
1.9%
20101213 1
1.9%
20101227 2
3.8%
20101231 2
3.8%
20110120 1
1.9%
ValueCountFrequency (%)
20201121 1
 
1.9%
20201106 2
3.8%
20170421 3
5.8%
20160112 1
 
1.9%
20150708 1
 
1.9%
20150115 1
 
1.9%
20150109 1
 
1.9%
20130816 1
 
1.9%
20130502 1
 
1.9%
20130219 1
 
1.9%

dcbymd
Categorical

Distinct10
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
26 
폐업일자
18 
20190121
 
1
20180228
 
1
20180226
 
1
Other values (5)
5 

Length

Max length8
Median length4
Mean length4.6153846
Min length4

Unique

Unique8 ?
Unique (%)15.4%

Sample

1st row20190121
2nd row20180228
3rd row20180226
4th row<NA>
5th row폐업일자

Common Values

ValueCountFrequency (%)
<NA> 26
50.0%
폐업일자 18
34.6%
20190121 1
 
1.9%
20180228 1
 
1.9%
20180226 1
 
1.9%
20110323 1
 
1.9%
20170321 1
 
1.9%
20201006 1
 
1.9%
20180120 1
 
1.9%
20190124 1
 
1.9%

Length

2024-04-20T07:13:23.503227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:23.613821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
50.0%
폐업일자 18
34.6%
20190121 1
 
1.9%
20180228 1
 
1.9%
20180226 1
 
1.9%
20110323 1
 
1.9%
20170321 1
 
1.9%
20201006 1
 
1.9%
20180120 1
 
1.9%
20190124 1
 
1.9%

clgstdt
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
휴업시작일자
21 

Length

Max length6
Median length4
Mean length4.8076923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
휴업시작일자 21
40.4%

Length

2024-04-20T07:13:23.733018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:23.822388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
휴업시작일자 21
40.4%

clgenddt
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
휴업종료일자
21 

Length

Max length6
Median length4
Mean length4.8076923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
휴업종료일자 21
40.4%

Length

2024-04-20T07:13:23.924348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:24.014380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
휴업종료일자 21
40.4%

ropnymd
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
재개업일자
21 

Length

Max length5
Median length4
Mean length4.4038462
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
재개업일자 21
40.4%

Length

2024-04-20T07:13:24.109517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:24.205122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
재개업일자 21
40.4%

trdstatenm
Categorical

Distinct5
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size548.0 B
3
18 
영업/정상
13 
1
9 
취소/말소/만료/정지/중지
8 
폐업
4 

Length

Max length14
Median length1
Mean length4.0769231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row3
3rd row3
4th row1
5th row영업/정상

Common Values

ValueCountFrequency (%)
3 18
34.6%
영업/정상 13
25.0%
1 9
17.3%
취소/말소/만료/정지/중지 8
15.4%
폐업 4
 
7.7%

Length

2024-04-20T07:13:24.315726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:24.435612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 18
34.6%
영업/정상 13
25.0%
1 9
17.3%
취소/말소/만료/정지/중지 8
15.4%
폐업 4
 
7.7%

dtlstatenm
Categorical

Distinct5
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size548.0 B
폐업
22 
영업중
21 
직권말소
7 
등록취소
 
1
전입
 
1

Length

Max length4
Median length3
Mean length2.7115385
Min length2

Unique

Unique2 ?
Unique (%)3.8%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row영업중
5th row영업중

Common Values

ValueCountFrequency (%)
폐업 22
42.3%
영업중 21
40.4%
직권말소 7
 
13.5%
등록취소 1
 
1.9%
전입 1
 
1.9%

Length

2024-04-20T07:13:24.569472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:24.688772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 22
42.3%
영업중 21
40.4%
직권말소 7
 
13.5%
등록취소 1
 
1.9%
전입 1
 
1.9%

x
Text

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-20T07:13:24.905654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.75
Min length7

Characters and Unicode

Total characters1027
Distinct characters19
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)76.9%

Sample

1st row385203.926916019
2nd row384462.61978300000
3rd row383200.22602100000
4th row389507.277919
5th row380701.811851581
ValueCountFrequency (%)
387626.32142800000 3
 
5.8%
353383 3
 
5.8%
388158.19719000000 2
 
3.8%
210337.51823 2
 
3.8%
387727.932157079 2
 
3.8%
385203.926916019 1
 
1.9%
391096.21938300000 1
 
1.9%
386246.77208800000 1
 
1.9%
387601.72661300000 1
 
1.9%
388899.447842 1
 
1.9%
Other values (35) 35
67.3%
2024-04-20T07:13:25.258797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230
22.4%
0 148
14.4%
3 110
10.7%
8 98
9.5%
1 74
 
7.2%
9 68
 
6.6%
7 57
 
5.6%
2 54
 
5.3%
4 49
 
4.8%
. 47
 
4.6%
Other values (9) 92
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 743
72.3%
Space Separator 230
 
22.4%
Other Punctuation 47
 
4.6%
Other Letter 4
 
0.4%
Open Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
19.9%
3 110
14.8%
8 98
13.2%
1 74
10.0%
9 68
9.2%
7 57
 
7.7%
2 54
 
7.3%
4 49
 
6.6%
5 45
 
6.1%
6 40
 
5.4%
Other Letter
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Other Punctuation
ValueCountFrequency (%)
. 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1022
99.5%
Hangul 4
 
0.4%
Latin 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
230
22.5%
0 148
14.5%
3 110
10.8%
8 98
9.6%
1 74
 
7.2%
9 68
 
6.7%
7 57
 
5.6%
2 54
 
5.3%
4 49
 
4.8%
. 47
 
4.6%
Other values (4) 87
 
8.5%
Hangul
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1023
99.6%
Hangul 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230
22.5%
0 148
14.5%
3 110
10.8%
8 98
9.6%
1 74
 
7.2%
9 68
 
6.6%
7 57
 
5.6%
2 54
 
5.3%
4 49
 
4.8%
. 47
 
4.6%
Other values (5) 88
 
8.6%
Hangul
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%

y
Text

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-20T07:13:25.492174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.75
Min length7

Characters and Unicode

Total characters1027
Distinct characters19
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)76.9%

Sample

1st row180571.713650198
2nd row180316.17339300000
3rd row180588.27293300000
4th row189632.913167
5th row187902.713298016
ValueCountFrequency (%)
185513.22269100000 3
 
5.8%
342375 3
 
5.8%
186486.34392100000 2
 
3.8%
257692.520258 2
 
3.8%
184254.180458819 2
 
3.8%
180571.713650198 1
 
1.9%
191007.96884800000 1
 
1.9%
182390.11171000000 1
 
1.9%
185593.28881500000 1
 
1.9%
188316.418341 1
 
1.9%
Other values (35) 35
67.3%
2024-04-20T07:13:25.842297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
228
22.2%
0 149
14.5%
1 101
9.8%
8 94
9.2%
2 66
 
6.4%
9 66
 
6.4%
5 61
 
5.9%
7 54
 
5.3%
3 53
 
5.2%
6 52
 
5.1%
Other values (9) 103
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 745
72.5%
Space Separator 228
 
22.2%
Other Punctuation 47
 
4.6%
Other Letter 4
 
0.4%
Open Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 149
20.0%
1 101
13.6%
8 94
12.6%
2 66
8.9%
9 66
8.9%
5 61
8.2%
7 54
 
7.2%
3 53
 
7.1%
6 52
 
7.0%
4 49
 
6.6%
Other Letter
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%
Space Separator
ValueCountFrequency (%)
228
100.0%
Other Punctuation
ValueCountFrequency (%)
. 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
Y 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1022
99.5%
Hangul 4
 
0.4%
Latin 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
228
22.3%
0 149
14.6%
1 101
9.9%
8 94
9.2%
2 66
 
6.5%
9 66
 
6.5%
5 61
 
6.0%
7 54
 
5.3%
3 53
 
5.2%
6 52
 
5.1%
Other values (4) 98
9.6%
Hangul
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%
Latin
ValueCountFrequency (%)
Y 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1023
99.6%
Hangul 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
22.3%
0 149
14.6%
1 101
9.9%
8 94
9.2%
2 66
 
6.5%
9 66
 
6.5%
5 61
 
6.0%
7 54
 
5.3%
3 53
 
5.2%
6 52
 
5.1%
Other values (5) 99
9.7%
Hangul
ValueCountFrequency (%)
좌 1
25.0%
표 1
25.0%
정 1
25.0%
보 1
25.0%

lastmodts
Real number (ℝ)

Distinct41
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0167921 × 1013
Minimum2.0110329 × 1013
Maximum2.0210118 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-20T07:13:26.004130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0110329 × 1013
5-th percentile2.0130329 × 1013
Q12.0140416 × 1013
median2.0180465 × 1013
Q32.0190402 × 1013
95-th percentile2.0201111 × 1013
Maximum2.0210118 × 1013
Range9.9789 × 1010
Interquartile range (IQR)4.998525 × 1010

Descriptive statistics

Standard deviation2.8709768 × 1010
Coefficient of variation (CV)0.0014235363
Kurtosis-1.4814102
Mean2.0167921 × 1013
Median Absolute Deviation (MAD)2.0452 × 1010
Skewness-0.25413811
Sum1.0487319 × 1015
Variance8.2425079 × 1020
MonotonicityNot monotonic
2024-04-20T07:13:26.119779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20130329000000 3
 
5.8%
20131224000000 3
 
5.8%
20200828000000 3
 
5.8%
20190401000000 3
 
5.8%
20140227000000 2
 
3.8%
20201106000000 2
 
3.8%
20140424000000 2
 
3.8%
20190807000000 1
 
1.9%
20110329000000 1
 
1.9%
20160623000000 1
 
1.9%
Other values (31) 31
59.6%
ValueCountFrequency (%)
20110329000000 1
 
1.9%
20130308000000 1
 
1.9%
20130329000000 3
5.8%
20130613000000 1
 
1.9%
20130724000000 1
 
1.9%
20131224000000 3
5.8%
20140227000000 2
3.8%
20140402000000 1
 
1.9%
20140421000000 1
 
1.9%
20140424000000 2
3.8%
ValueCountFrequency (%)
20210118000000 1
 
1.9%
20210108000000 1
 
1.9%
20201118000000 1
 
1.9%
20201106000000 2
3.8%
20201006000000 1
 
1.9%
20200828000000 3
5.8%
20200116000000 1
 
1.9%
20190807000000 1
 
1.9%
20190715000000 1
 
1.9%
20190403000000 1
 
1.9%

uptaenm
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
업태구분명
21 

Length

Max length5
Median length4
Mean length4.4038462
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
업태구분명 21
40.4%

Length

2024-04-20T07:13:26.230395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:26.332594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
업태구분명 21
40.4%

sitetel
Categorical

IMBALANCE 

Distinct6
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
051-123-1234
46 
06316008421
 
2
0513052580
 
1
05115776250
 
1
0518631888
 
1

Length

Max length12
Median length12
Mean length11.807692
Min length9

Unique

Unique4 ?
Unique (%)7.7%

Sample

1st row051-123-1234
2nd row051-123-1234
3rd row051-123-1234
4th row051-123-1234
5th row0513052580

Common Values

ValueCountFrequency (%)
051-123-1234 46
88.5%
06316008421 2
 
3.8%
0513052580 1
 
1.9%
05115776250 1
 
1.9%
0518631888 1
 
1.9%
025759229 1
 
1.9%

Length

2024-04-20T07:13:26.437238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:26.549673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
051-123-1234 46
88.5%
06316008421 2
 
3.8%
0513052580 1
 
1.9%
05115776250 1
 
1.9%
0518631888 1
 
1.9%
025759229 1
 
1.9%

depucorgetcnm
Categorical

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
21 
공제계약기타명
17 
한국상조공제조합
9 
상조보증공제조합
5 

Length

Max length8
Median length7
Mean length6.0576923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
40.4%
공제계약기타명 17
32.7%
한국상조공제조합 9
17.3%
상조보증공제조합 5
 
9.6%

Length

2024-04-20T07:13:26.680075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:26.783891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
40.4%
공제계약기타명 17
32.7%
한국상조공제조합 9
17.3%
상조보증공제조합 5
 
9.6%

depucorgnm
Categorical

Distinct3
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
21 
공제계약명
17 
기타
14 

Length

Max length5
Median length4
Mean length3.7884615
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
40.4%
공제계약명 17
32.7%
기타 14
26.9%

Length

2024-04-20T07:13:26.900517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:26.990087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
40.4%
공제계약명 17
32.7%
기타 14
26.9%

mwsrnm
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
민원종류명
21 

Length

Max length5
Median length4
Mean length4.4038462
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
민원종류명 21
40.4%

Length

2024-04-20T07:13:27.079568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:27.160599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
민원종류명 21
40.4%

insur
Categorical

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
보험
21 

Length

Max length4
Median length4
Mean length3.1923077
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
보험 21
40.4%

Length

2024-04-20T07:13:27.250247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:27.339211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
보험 21
40.4%

depbanknm
Categorical

Distinct6
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
우리은행
16 
<NA>
14 
신한은행
10 
예치기관명
7 
국민은행
3 

Length

Max length5
Median length4
Mean length4.1346154
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
우리은행 16
30.8%
<NA> 14
26.9%
신한은행 10
19.2%
예치기관명 7
13.5%
국민은행 3
 
5.8%
부산은행 2
 
3.8%

Length

2024-04-20T07:13:27.441997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:27.547890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우리은행 16
30.8%
na 14
26.9%
신한은행 10
19.2%
예치기관명 7
13.5%
국민은행 3
 
5.8%
부산은행 2
 
3.8%

captscale
Real number (ℝ)

Distinct10
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4601683 × 108
Minimum3 × 108
Maximum2 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-20T07:13:27.645488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3 × 108
5-th percentile3 × 108
Q13 × 108
median3 × 108
Q31.5 × 109
95-th percentile1.5007922 × 109
Maximum2 × 109
Range1.7 × 109
Interquartile range (IQR)1.2 × 109

Descriptive statistics

Standard deviation5.6449027 × 108
Coefficient of variation (CV)0.87380118
Kurtosis-0.57174671
Mean6.4601683 × 108
Median Absolute Deviation (MAD)0
Skewness1.1353846
Sum3.3592875 × 1010
Variance3.1864926 × 1017
MonotonicityNot monotonic
2024-04-20T07:13:27.742377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
300000000 35
67.3%
1500000000 9
 
17.3%
1800000000 1
 
1.9%
1500030000 1
 
1.9%
1501180000 1
 
1.9%
2000000000 1
 
1.9%
400000000 1
 
1.9%
429650000 1
 
1.9%
461540000 1
 
1.9%
1500475000 1
 
1.9%
ValueCountFrequency (%)
300000000 35
67.3%
400000000 1
 
1.9%
429650000 1
 
1.9%
461540000 1
 
1.9%
1500000000 9
 
17.3%
1500030000 1
 
1.9%
1500475000 1
 
1.9%
1501180000 1
 
1.9%
1800000000 1
 
1.9%
2000000000 1
 
1.9%
ValueCountFrequency (%)
2000000000 1
 
1.9%
1800000000 1
 
1.9%
1501180000 1
 
1.9%
1500475000 1
 
1.9%
1500030000 1
 
1.9%
1500000000 9
 
17.3%
461540000 1
 
1.9%
429650000 1
 
1.9%
400000000 1
 
1.9%
300000000 35
67.3%

debtpayisre
Categorical

Distinct3
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
<NA>
31 
채무지급보증
20 
0
 
1

Length

Max length6
Median length4
Mean length4.7115385
Min length1

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
59.6%
채무지급보증 20
38.5%
0 1
 
1.9%

Length

2024-04-20T07:13:27.878037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-20T07:13:27.965425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
59.6%
채무지급보증 20
38.5%
0 1
 
1.9%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2021-02-01 05:21:03
Maximum2021-02-01 05:21:03
2024-04-20T07:13:28.037441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-20T07:13:28.129061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

skeyopnsfteamcodemgtnoopnsvcidupdategbnupdatedtopnsvcnmbplcnmsitepostnositewhladdrrdnpostnordnwhladdrapvpermymddcbymdclgstdtclgenddtropnymdtrdstatenmdtlstatenmxylastmodtsuptaenmsiteteldepucorgetcnmdepucorgnmmwsrnminsurdepbanknmcaptscaledebtpayisrelast_load_dttm
0162600002011626000005100000111_47_01_PU2018-12-23 02:40:00.0상조업주식회사 부산의사상조48972부산광역시 중구 대청동4가 12번지 9호<NA>부산광역시 중구 중구로 121 (대청동4가)2011012020190121<NA><NA><NA>폐업폐업385203.926916019180571.71365019820181221000000<NA>051-123-1234<NA><NA><NA><NA>우리은행300000000<NA>2021-02-01 05:21:03
1262600002011626000005100001711_47_01_PI2018-08-31 23:59:59.0<NA>부경상조(주)600074부산광역시 중구 부평동4가 14번지 1호<NA>부산광역시 중구 흑교로31번길 19-1, 2층 (부평동4가)2011031820180228<NA><NA><NA>3폐업384462.61978300000180316.1733930000020180228000000<NA>051-123-1234<NA><NA><NA><NA>우리은행300000000<NA>2021-02-01 05:21:03
2362600002011626000005100000311_47_01_PI2018-08-31 23:59:59.0<NA>주식회사 다원상조614870부산광역시 부산진구 전포동 875번지 2호<NA>부산광역시 사하구 까치고개로 77-1 (괴정동)2011013120180226<NA><NA><NA>3폐업383200.22602100000180588.2729330000020180226000000<NA>051-123-1234<NA><NA><NA><NA>우리은행300000000<NA>2021-02-01 05:21:03
3462600002011626000005100000411_47_01_PI2018-08-31 23:59:59.0<NA>디에이치 상조 주식회사611080부산광역시 연제구 연산동 1287번지 2호 인회빌딩<NA><NA>20110219<NA><NA><NA><NA>1영업중389507.277919189632.91316720140227000000<NA>051-123-1234한국상조공제조합기타<NA><NA><NA>300000000<NA>2021-02-01 05:21:03
4562600002010626000005100000611_47_01_PU2020-11-20 02:40:00.0상조업아가페라이프주식회사617040부산광역시 사상구 덕포동 404번지 10호도로명우편번호부산광역시 사상구 모덕로 85, 현대자동차 (모라동)20101026폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중380701.811851581187902.71329801620201118000000업태구분명0513052580한국상조공제조합기타민원종류명보험부산은행1800000000채무지급보증2021-02-01 05:21:03
5662600002010626000005100000711_47_01_PI2018-08-31 23:59:59.0<NA>삼성상조주식회사614030부산광역시 부산진구 부전동 168번지 451호 6층 604호<NA>부산광역시 부산진구 중앙대로702번길 43, 6층 (부전동)20101027<NA><NA><NA><NA>3폐업387845.87214400000186312.4975430000020161101000000<NA>051-123-1234상조보증공제조합기타<NA><NA><NA>300000000<NA>2021-02-01 05:21:03
6762600002010626000005100000911_47_01_PI2018-08-31 23:59:59.0<NA>주식회사 신양상조609320부산광역시 금정구 부곡동 22번지 4호<NA>부산광역시 금정구 무학송로 124 (부곡동)20101103<NA><NA><NA><NA>3폐업390599.32511500000195966.7248700000020140424000000<NA>051-123-1234<NA><NA><NA><NA>신한은행300000000<NA>2021-02-01 05:21:03
7862600002010626000005100001011_47_01_PI2018-08-31 23:59:59.0<NA>에스에이치라이프(새한일상조주식회사)614853부산광역시 부산진구 양정동 369번지 3호<NA><NA>20101115<NA><NA><NA><NA>3폐업388599.358348188199.52213620140605000000<NA>051-123-1234상조보증공제조합기타<NA><NA><NA>300000000<NA>2021-02-01 05:21:03
8962600002010626000005100001311_47_01_PU2019-01-17 02:40:00.0상조업주식회사광명상조617050부산광역시 사상구 감전동 129번지 10호도로명우편번호부산광역시 사상구 새벽시장로 57 (감전동)20101227폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중380347.381522031185823.5370466920190115000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험우리은행1500000000채무지급보증2021-02-01 05:21:03
91062600002011626000005100002311_47_01_PU2019-01-04 02:40:00.0상조업주식회사 해오름상조614050부산광역시 부산진구 양정동 370번지 4호 대원플러스빌 201호도로명우편번호부산광역시 부산진구 동평로 416, 2층 (양정동)20111013폐업일자휴업시작일자휴업종료일자재개업일자취소/말소/만료/정지/중지직권말소388519.853095162187952.86213611920190102000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험신한은행300000000채무지급보증2021-02-01 05:21:03
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424362600002011626000005100000211_47_01_PI2018-08-31 23:59:59.0<NA>주식회사 에이스611080부산광역시 연제구 연산동 1242번지 2호 동화빌딩 11층<NA>부산광역시 연제구 월드컵대로 141 (연산동,동화빌딩 11층)20110131<NA><NA><NA><NA>3폐업389514.28497900000189728.3945940000020130308000000<NA>051-123-1234<NA><NA><NA><NA><NA>300000000<NA>2021-02-01 05:21:03
434464100002012641000005120000111_47_01_PI2018-12-19 02:20:21.0상조업주식회사 하늘가지번우편번호지번주소도로명우편번호경기도 화성시 향남읍 서봉로755번길 17-152012121120180120휴업시작일자휴업종료일자재개업일자폐업폐업19593340467820181217000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험우리은행300000000채무지급보증2021-02-01 05:21:03
444564100002013641000005110000111_47_01_PI2019-01-12 02:20:46.0상조업에스티라이프(주)지번우편번호지번주소도로명우편번호경기도 구리시 동구릉로6번길 9, 한양약국 3층 (교문동)2013050220190124휴업시작일자휴업종료일자재개업일자폐업폐업211548.664193199455472.94491458820190110000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험신한은행300000000채무지급보증2021-02-01 05:21:03
454664700002017647000005100000111_47_01_PU2020-08-30 02:40:00.0상조업(주)삼성코리아상조(구,(주)미래상조119)지번우편번호지번주소도로명우편번호경상북도 안동시 평화윗길 80 (평화동)20170421폐업일자휴업시작일자휴업종료일자재개업일자취소/말소/만료/정지/중지직권말소35338334237520200828000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험우리은행300000000채무지급보증2021-02-01 05:21:03
464764700002017647000005100000111_47_01_PU2020-08-30 02:40:00.0상조업(주)삼성코리아상조(구,(주)미래상조119)지번우편번호지번주소도로명우편번호경상북도 안동시 평화윗길 80 (평화동)20170421폐업일자휴업시작일자휴업종료일자재개업일자취소/말소/만료/정지/중지직권말소35338334237520200828000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험우리은행300000000채무지급보증2021-02-01 05:21:03
474864700002017647000005100000111_47_01_PU2020-08-30 02:40:00.0상조업(주)삼성코리아상조(구,(주)미래상조119)지번우편번호지번주소도로명우편번호경상북도 안동시 평화윗길 80 (평화동)20170421폐업일자휴업시작일자휴업종료일자재개업일자취소/말소/만료/정지/중지직권말소35338334237520200828000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험우리은행300000000채무지급보증2021-02-01 05:21:03
484962600002013626000005100000211_47_01_PI2019-04-05 02:20:28.0상조업(주)웰리빙라이프지번우편번호지번주소도로명우편번호부산광역시 동구 중앙대로 270, 강남빌딩 11층 (초량동)20130816폐업일자휴업시작일자휴업종료일자재개업일자영업/정상전입386177.958994368182089.62670472620190403000000업태구분명051-123-1234공제계약기타명공제계약명민원종류명보험예치기관명150000000002021-02-01 05:21:03
495064500002020645000005100000111_47_01_PI2020-11-08 00:23:09.0상조업그랜드라이프 주식회사지번우편번호지번주소도로명우편번호전라북도 전주시 완산구 효자로 271, 3층 (중화산동2가)20201106폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중210337.51823257692.52025820201106000000업태구분명06316008421공제계약기타명공제계약명민원종류명보험국민은행1500000000채무지급보증2021-02-01 05:21:03
505164500002020645000005100000111_47_01_PI2020-11-08 00:23:09.0상조업그랜드라이프 주식회사지번우편번호지번주소도로명우편번호전라북도 전주시 완산구 효자로 271, 3층 (중화산동2가)20201106폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중210337.51823257692.52025820201106000000업태구분명06316008421공제계약기타명공제계약명민원종류명보험국민은행1500000000채무지급보증2021-02-01 05:21:03
515264100002020641000005110000111_47_01_PU2021-01-20 02:40:00.0상조업주식회사 용인공원라이프지번우편번호지번주소도로명우편번호경기도 안양시 동안구 시민대로 260, 안양금융센터A.F.C 810호 (관양동)20201121폐업일자휴업시작일자휴업종료일자재개업일자영업/정상영업중좌표정보(X)좌표정보(Y)20210118000000업태구분명025759229상조보증공제조합기타민원종류명보험예치기관명1500000000채무지급보증2021-02-01 05:21:03