Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells3387
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory898.4 KiB
Average record size in memory92.0 B

Variable types

Text4
Numeric4
DateTime1
Categorical1

Dataset

Description공정거래위원회의 소비자 민원학습에 대한 데이터로, 소비자 민원 상담중 사업자(피신청인) 자율적으로 민원을 해결한 상담 데이터 입니다. 이 데이터는 사건제목, 사건내용을 포함하고 있습니다.
Author공정거래위원회
URLhttps://www.data.go.kr/data/15098354/fileData.do

Alerts

연령대코드(AGE_GROUP_CODE) is highly overall correlated with 연령대(AGE_GROUP_NAME)High correlation
연령대(AGE_GROUP_NAME) is highly overall correlated with 연령대코드(AGE_GROUP_CODE)High correlation
연령대코드(AGE_GROUP_CODE) has 3383 (33.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 21:10:22.094206
Analysis finished2023-12-12 21:10:25.522078
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사건번호(ACCIDENT_NO)
Text

Distinct9992
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:10:25.713298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters120000
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

Unique9984 ?
Unique (%)99.8%

Sample

1st row2016-0454941
2nd row2016-0797615
3rd row2016-0384232
4th row2016-0628137
5th row2016-0397630
ValueCountFrequency (%)
2017-0116627 2
 
< 0.1%
2017-0002657 2
 
< 0.1%
2016-0919675 2
 
< 0.1%
2017-0068051 2
 
< 0.1%
2017-0007498 2
 
< 0.1%
2017-0048011 2
 
< 0.1%
2016-0356062 2
 
< 0.1%
2016-0483169 2
 
< 0.1%
2017-0108463 1
 
< 0.1%
2016-0454941 1
 
< 0.1%
Other values (9982) 9982
99.8%
2023-12-13T06:10:26.093271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26394
22.0%
1 15714
13.1%
2 15669
13.1%
6 14291
11.9%
- 10000
 
8.3%
7 7864
 
6.6%
4 6244
 
5.2%
3 6135
 
5.1%
8 6089
 
5.1%
5 6062
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110000
91.7%
Dash Punctuation 10000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26394
24.0%
1 15714
14.3%
2 15669
14.2%
6 14291
13.0%
7 7864
 
7.1%
4 6244
 
5.7%
3 6135
 
5.6%
8 6089
 
5.5%
5 6062
 
5.5%
9 5538
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26394
22.0%
1 15714
13.1%
2 15669
13.1%
6 14291
11.9%
- 10000
 
8.3%
7 7864
 
6.6%
4 6244
 
5.2%
3 6135
 
5.1%
8 6089
 
5.1%
5 6062
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26394
22.0%
1 15714
13.1%
2 15669
13.1%
6 14291
11.9%
- 10000
 
8.3%
7 7864
 
6.6%
4 6244
 
5.2%
3 6135
 
5.1%
8 6089
 
5.1%
5 6062
 
5.1%

상호코드(MUTUAL_CODE)
Real number (ℝ)

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143340.42
Minimum2621
Maximum907243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:10:26.268314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2621
5-th percentile28963
Q135730
median41462
Q3109034
95-th percentile702012
Maximum907243
Range904622
Interquartile range (IQR)73304

Descriptive statistics

Standard deviation208855.14
Coefficient of variation (CV)1.4570569
Kurtosis5.5964372
Mean143340.42
Median Absolute Deviation (MAD)12508
Skewness2.5123722
Sum1.4334042 × 109
Variance4.3620471 × 1010
MonotonicityNot monotonic
2023-12-13T06:10:26.476720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41417 990
 
9.9%
109028 904
 
9.0%
34295 667
 
6.7%
109015 616
 
6.2%
34296 553
 
5.5%
109034 432
 
4.3%
41462 371
 
3.7%
402349 244
 
2.4%
402299 230
 
2.3%
113007 207
 
2.1%
Other values (109) 4786
47.9%
ValueCountFrequency (%)
2621 1
 
< 0.1%
2622 13
 
0.1%
3757 6
 
0.1%
11378 2
 
< 0.1%
14376 77
0.8%
14384 23
 
0.2%
15705 1
 
< 0.1%
16750 14
 
0.1%
22210 21
 
0.2%
22221 1
 
< 0.1%
ValueCountFrequency (%)
907243 16
 
0.2%
907240 3
 
< 0.1%
907209 7
 
0.1%
907182 71
 
0.7%
907181 47
 
0.5%
907152 205
2.1%
907133 20
 
0.2%
907122 25
 
0.2%
907018 14
 
0.1%
702056 6
 
0.1%

상호명(MUTUAL_NAME)
Text

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:10:26.747234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.7345
Min length4

Characters and Unicode

Total characters77345
Distinct characters230
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

Unique7 ?
Unique (%)0.1%

Sample

1st row(주)홈앤쇼핑
2nd row쿠팡(주)
3rd row현대자동차(주)
4th row(주)한진
5th row(주)케이티
ValueCountFrequency (%)
주)lg유플러스 990
 
9.7%
삼성전자(주 904
 
8.9%
주)케이티 667
 
6.5%
주)lg전자 616
 
6.0%
sk텔레콤 553
 
5.4%
코웨이(주 432
 
4.2%
11번가(주 371
 
3.6%
이베이코리아(유)(지마켓 244
 
2.4%
이베이코리아(유)(옥션 230
 
2.3%
현대자동차(주 207
 
2.0%
Other values (119) 4990
48.9%
2023-12-13T06:10:27.537232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 9808
 
12.7%
) 9808
 
12.7%
주 8821
 
11.4%
이 2748
 
3.6%
자 2302
 
3.0%
전 1992
 
2.6%
G 1902
 
2.5%
L 1827
 
2.4%
유 1573
 
2.0%
스 1285
 
1.7%
Other values (220) 35279
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50284
65.0%
Open Punctuation 9808
 
12.7%
Close Punctuation 9808
 
12.7%
Uppercase Letter 6483
 
8.4%
Decimal Number 758
 
1.0%
Space Separator 204
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 8821
 
17.5%
이 2748
 
5.5%
자 2302
 
4.6%
전 1992
 
4.0%
유 1573
 
3.1%
스 1285
 
2.6%
아 1206
 
2.4%
성 1121
 
2.2%
삼 1111
 
2.2%
코 1011
 
2.0%
Other values (198) 27114
53.9%
Uppercase Letter
ValueCountFrequency (%)
G 1902
29.3%
L 1827
28.2%
S 1050
16.2%
K 968
14.9%
C 198
 
3.1%
J 197
 
3.0%
B 95
 
1.5%
N 71
 
1.1%
D 49
 
0.8%
A 25
 
0.4%
Other values (8) 101
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 9808
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9808
100.0%
Decimal Number
ValueCountFrequency (%)
1 758
100.0%
Space Separator
ValueCountFrequency (%)
204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50284
65.0%
Common 20578
26.6%
Latin 6483
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 8821
 
17.5%
이 2748
 
5.5%
자 2302
 
4.6%
전 1992
 
4.0%
유 1573
 
3.1%
스 1285
 
2.6%
아 1206
 
2.4%
성 1121
 
2.2%
삼 1111
 
2.2%
코 1011
 
2.0%
Other values (198) 27114
53.9%
Latin
ValueCountFrequency (%)
G 1902
29.3%
L 1827
28.2%
S 1050
16.2%
K 968
14.9%
C 198
 
3.1%
J 197
 
3.0%
B 95
 
1.5%
N 71
 
1.1%
D 49
 
0.8%
A 25
 
0.4%
Other values (8) 101
 
1.6%
Common
ValueCountFrequency (%)
( 9808
47.7%
) 9808
47.7%
1 758
 
3.7%
204
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50284
65.0%
ASCII 27061
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 9808
36.2%
) 9808
36.2%
G 1902
 
7.0%
L 1827
 
6.8%
S 1050
 
3.9%
K 968
 
3.6%
1 758
 
2.8%
204
 
0.8%
C 198
 
0.7%
J 197
 
0.7%
Other values (12) 341
 
1.3%
Hangul
ValueCountFrequency (%)
주 8821
 
17.5%
이 2748
 
5.5%
자 2302
 
4.6%
전 1992
 
4.0%
유 1573
 
3.1%
스 1285
 
2.6%
아 1206
 
2.4%
성 1121
 
2.2%
삼 1111
 
2.2%
코 1011
 
2.0%
Other values (198) 27114
53.9%

접수일자(RCPT_YMD)
Date

Distinct434
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2016-01-02 00:00:00
Maximum2020-12-14 00:00:00
2023-12-13T06:10:27.727365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:27.890596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

연령대코드(AGE_GROUP_CODE)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.2%
Missing3383
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean4.9650899
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:10:28.043434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile8
Maximum12
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5690927
Coefficient of variation (CV)0.31602504
Kurtosis4.5517623
Mean4.9650899
Median Absolute Deviation (MAD)1
Skewness1.7005602
Sum32854
Variance2.462052
MonotonicityNot monotonic
2023-12-13T06:10:28.208335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 2106
21.1%
5 1849
18.5%
6 1179
 
11.8%
3 781
 
7.8%
7 335
 
3.4%
8 119
 
1.2%
11 96
 
1.0%
9 67
 
0.7%
12 45
 
0.4%
10 24
 
0.2%
(Missing) 3383
33.8%
ValueCountFrequency (%)
2 16
 
0.2%
3 781
 
7.8%
4 2106
21.1%
5 1849
18.5%
6 1179
11.8%
7 335
 
3.4%
8 119
 
1.2%
9 67
 
0.7%
10 24
 
0.2%
11 96
 
1.0%
ValueCountFrequency (%)
12 45
 
0.4%
11 96
 
1.0%
10 24
 
0.2%
9 67
 
0.7%
8 119
 
1.2%
7 335
 
3.4%
6 1179
11.8%
5 1849
18.5%
4 2106
21.1%
3 781
 
7.8%

연령대(AGE_GROUP_NAME)
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3383 
30 - 39세
2106 
40 - 49세
1849 
50 - 59세
1179 
20 - 29세
781 
Other values (7)
702 

Length

Max length11
Median length8
Mean length6.6999
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50 - 59세
2nd row<NA>
3rd row<NA>
4th row50 - 59세
5th row50 - 59세

Common Values

ValueCountFrequency (%)
<NA> 3383
33.8%
30 - 39세 2106
21.1%
40 - 49세 1849
18.5%
50 - 59세 1179
 
11.8%
20 - 29세 781
 
7.8%
(구)60 - 69세 335
 
3.4%
70 - 79세 119
 
1.2%
60 - 64세 96
 
1.0%
불명 67
 
0.7%
65 - 69세 45
 
0.4%
Other values (2) 40
 
0.4%

Length

2023-12-13T06:10:28.345221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6526
28.3%
na 3383
14.7%
39세 2106
 
9.1%
30 2106
 
9.1%
40 1849
 
8.0%
49세 1849
 
8.0%
50 1179
 
5.1%
59세 1179
 
5.1%
20 781
 
3.4%
29세 781
 
3.4%
Other values (11) 1313
 
5.7%

지역코드(AREA_CODE)
Real number (ℝ)

Distinct244
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.9945
Minimum100
Maximum9907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:10:28.491759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median800
Q3826
95-th percentile1502
Maximum9907
Range9807
Interquartile range (IQR)626

Descriptive statistics

Standard deviation1072.3674
Coefficient of variation (CV)1.4570318
Kurtosis56.837044
Mean735.9945
Median Absolute Deviation (MAD)405
Skewness6.9687989
Sum7359945
Variance1149971.7
MonotonicityNot monotonic
2023-12-13T06:10:28.684752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1094
 
10.9%
800 1052
 
10.5%
200 373
 
3.7%
400 276
 
2.8%
300 239
 
2.4%
810 173
 
1.7%
1500 153
 
1.5%
600 151
 
1.5%
1100 145
 
1.5%
801 136
 
1.4%
Other values (234) 6208
62.1%
ValueCountFrequency (%)
100 1094
10.9%
101 115
 
1.1%
102 75
 
0.8%
103 44
 
0.4%
104 76
 
0.8%
105 80
 
0.8%
106 55
 
0.5%
107 59
 
0.6%
108 35
 
0.4%
109 70
 
0.7%
ValueCountFrequency (%)
9907 1
 
< 0.1%
9903 2
 
< 0.1%
9902 1
 
< 0.1%
9901 4
 
< 0.1%
9900 104
1.0%
1701 1
 
< 0.1%
1700 45
0.4%
1604 48
0.5%
1603 10
 
0.1%
1600 29
 
0.3%

지역명(AREA_NAME)
Text

Distinct223
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:10:29.025888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5921
Min length2

Characters and Unicode

Total characters35921
Distinct characters145
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

Unique16 ?
Unique (%)0.2%

Sample

1st row송파구
2nd row부천시
3rd row구로구
4th row동구
5th row중랑구
ValueCountFrequency (%)
서울특별시 1094
 
10.7%
경기도 1052
 
10.3%
부산광역시 373
 
3.7%
인천광역시 276
 
2.7%
대구광역시 239
 
2.3%
수원시 173
 
1.7%
서구 160
 
1.6%
경상남도 153
 
1.5%
대전광역시 151
 
1.5%
충청남도 145
 
1.4%
Other values (214) 6392
62.6%
2023-12-13T06:10:29.454398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5472
 
15.2%
구 2863
 
8.0%
도 1738
 
4.8%
광 1599
 
4.5%
서 1506
 
4.2%
경 1353
 
3.8%
역 1274
 
3.5%
울 1240
 
3.5%
기 1172
 
3.3%
특 1168
 
3.3%
Other values (135) 16536
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35713
99.4%
Space Separator 208
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5472
 
15.3%
구 2863
 
8.0%
도 1738
 
4.9%
광 1599
 
4.5%
서 1506
 
4.2%
경 1353
 
3.8%
역 1274
 
3.6%
울 1240
 
3.5%
기 1172
 
3.3%
특 1168
 
3.3%
Other values (134) 16328
45.7%
Space Separator
ValueCountFrequency (%)
208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35713
99.4%
Common 208
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5472
 
15.3%
구 2863
 
8.0%
도 1738
 
4.9%
광 1599
 
4.5%
서 1506
 
4.2%
경 1353
 
3.8%
역 1274
 
3.6%
울 1240
 
3.5%
기 1172
 
3.3%
특 1168
 
3.3%
Other values (134) 16328
45.7%
Common
ValueCountFrequency (%)
208
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35713
99.4%
ASCII 208
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5472
 
15.3%
구 2863
 
8.0%
도 1738
 
4.9%
광 1599
 
4.5%
서 1506
 
4.2%
경 1353
 
3.8%
역 1274
 
3.6%
울 1240
 
3.5%
기 1172
 
3.3%
특 1168
 
3.3%
Other values (134) 16328
45.7%
ASCII
ValueCountFrequency (%)
208
100.0%

품목코드(ITEM_CODE)
Real number (ℝ)

Distinct528
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258238.76
Minimum110101
Maximum510399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:10:29.586745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110101
5-th percentile120102
Q1160101
median210113
Q3360303
95-th percentile500502
Maximum510399
Range400298
Interquartile range (IQR)200202

Descriptive statistics

Standard deviation112825.48
Coefficient of variation (CV)0.43690376
Kurtosis-0.76250548
Mean258238.76
Median Absolute Deviation (MAD)70202
Skewness0.57715247
Sum2.5823876 × 109
Variance1.272959 × 1010
MonotonicityNot monotonic
2023-12-13T06:10:29.722261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360302 799
 
8.0%
210113 766
 
7.7%
360701 716
 
7.2%
139912 575
 
5.8%
190103 334
 
3.3%
350206 266
 
2.7%
200701 261
 
2.6%
120101 256
 
2.6%
350104 247
 
2.5%
370102 238
 
2.4%
Other values (518) 5542
55.4%
ValueCountFrequency (%)
110101 7
0.1%
110117 1
 
< 0.1%
110199 4
< 0.1%
110202 2
 
< 0.1%
110203 3
< 0.1%
110208 1
 
< 0.1%
110216 3
< 0.1%
110299 3
< 0.1%
110301 5
0.1%
110310 1
 
< 0.1%
ValueCountFrequency (%)
510399 5
 
0.1%
510303 2
 
< 0.1%
510302 1
 
< 0.1%
510203 1
 
< 0.1%
510199 27
0.3%
510115 7
 
0.1%
510113 6
 
0.1%
510112 2
 
< 0.1%
510111 3
 
< 0.1%
510109 24
0.2%

품목명(ITEM_NAME)
Text

Distinct526
Distinct (%)5.3%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-13T06:10:30.006190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length5.8981593
Min length1

Characters and Unicode

Total characters58958
Distinct characters405
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)1.6%

Sample

1st row수동청소기
2nd row기타완구·인형
3rd row중형승합자동차
4th row택배화물운송서비스
5th row초고속인터넷
ValueCountFrequency (%)
이동전화서비스 799
 
7.9%
스마트폰 766
 
7.6%
초고속인터넷 716
 
7.1%
정수기대여(렌트 575
 
5.7%
중형승용자동차 334
 
3.3%
택배화물운송서비스 266
 
2.6%
tv 261
 
2.6%
냉장고 256
 
2.5%
항공여객운송서비스 247
 
2.5%
국외여행 238
 
2.4%
Other values (518) 5614
55.7%
2023-12-13T06:10:30.404015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3423
 
5.8%
스 2831
 
4.8%
동 1840
 
3.1%
비 1833
 
3.1%
서 1832
 
3.1%
트 1639
 
2.8%
타 1469
 
2.5%
화 1393
 
2.4%
전 1308
 
2.2%
이 1253
 
2.1%
Other values (395) 40137
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55031
93.3%
Close Punctuation 1057
 
1.8%
Open Punctuation 1057
 
1.8%
Uppercase Letter 969
 
1.6%
Other Punctuation 621
 
1.1%
Decimal Number 141
 
0.2%
Space Separator 76
 
0.1%
Lowercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3423
 
6.2%
스 2831
 
5.1%
동 1840
 
3.3%
비 1833
 
3.3%
서 1832
 
3.3%
트 1639
 
3.0%
타 1469
 
2.7%
화 1393
 
2.5%
전 1308
 
2.4%
이 1253
 
2.3%
Other values (372) 36210
65.8%
Uppercase Letter
ValueCountFrequency (%)
V 339
35.0%
T 339
35.0%
G 141
14.6%
P 56
 
5.8%
C 56
 
5.8%
I 22
 
2.3%
D 4
 
0.4%
U 4
 
0.4%
S 4
 
0.4%
B 3
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
g 1
16.7%
u 1
16.7%
r 1
16.7%
a 1
16.7%
t 1
16.7%
i 1
16.7%
Other Punctuation
ValueCountFrequency (%)
· 620
99.8%
/ 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 1057
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1057
100.0%
Decimal Number
ValueCountFrequency (%)
2 141
100.0%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55031
93.3%
Common 2952
 
5.0%
Latin 975
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3423
 
6.2%
스 2831
 
5.1%
동 1840
 
3.3%
비 1833
 
3.3%
서 1832
 
3.3%
트 1639
 
3.0%
타 1469
 
2.7%
화 1393
 
2.5%
전 1308
 
2.4%
이 1253
 
2.3%
Other values (372) 36210
65.8%
Latin
ValueCountFrequency (%)
V 339
34.8%
T 339
34.8%
G 141
14.5%
P 56
 
5.7%
C 56
 
5.7%
I 22
 
2.3%
D 4
 
0.4%
U 4
 
0.4%
S 4
 
0.4%
B 3
 
0.3%
Other values (7) 7
 
0.7%
Common
ValueCountFrequency (%)
) 1057
35.8%
( 1057
35.8%
· 620
21.0%
2 141
 
4.8%
76
 
2.6%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55031
93.3%
ASCII 3307
 
5.6%
None 620
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3423
 
6.2%
스 2831
 
5.1%
동 1840
 
3.3%
비 1833
 
3.3%
서 1832
 
3.3%
트 1639
 
3.0%
타 1469
 
2.7%
화 1393
 
2.5%
전 1308
 
2.4%
이 1253
 
2.3%
Other values (372) 36210
65.8%
ASCII
ValueCountFrequency (%)
) 1057
32.0%
( 1057
32.0%
V 339
 
10.3%
T 339
 
10.3%
G 141
 
4.3%
2 141
 
4.3%
76
 
2.3%
P 56
 
1.7%
C 56
 
1.7%
I 22
 
0.7%
Other values (12) 23
 
0.7%
None
ValueCountFrequency (%)
· 620
100.0%

Interactions

2023-12-13T06:10:24.652948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.320540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.800253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.220792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.745722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.427453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.898151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.326464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.859052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.546231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.991480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.460435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.981899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:23.669066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.099635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:10:24.547149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:10:30.488018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호코드(MUTUAL_CODE)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)품목코드(ITEM_CODE)
상호코드(MUTUAL_CODE)1.0000.1090.1110.0490.537
연령대코드(AGE_GROUP_CODE)0.1091.0001.0000.2570.110
연령대(AGE_GROUP_NAME)0.1111.0001.0000.2620.112
지역코드(AREA_CODE)0.0490.2570.2621.0000.053
품목코드(ITEM_CODE)0.5370.1100.1120.0531.000
2023-12-13T06:10:30.582070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호코드(MUTUAL_CODE)연령대코드(AGE_GROUP_CODE)지역코드(AREA_CODE)품목코드(ITEM_CODE)연령대(AGE_GROUP_NAME)
상호코드(MUTUAL_CODE)1.000-0.008-0.001-0.3620.054
연령대코드(AGE_GROUP_CODE)-0.0081.0000.026-0.0461.000
지역코드(AREA_CODE)-0.0010.0261.000-0.0230.158
품목코드(ITEM_CODE)-0.362-0.046-0.0231.0000.051
연령대(AGE_GROUP_NAME)0.0541.0000.1580.0511.000

Missing values

2023-12-13T06:10:25.144338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:10:25.308296image/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.
2023-12-13T06:10:25.440079image/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

사건번호(ACCIDENT_NO)상호코드(MUTUAL_CODE)상호명(MUTUAL_NAME)접수일자(RCPT_YMD)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)지역명(AREA_NAME)품목코드(ITEM_CODE)품목명(ITEM_NAME)
233802016-045494141482(주)홈앤쇼핑2016-07-05650 - 59세118송파구150114수동청소기
675282016-079761541433쿠팡(주)2016-11-07<NA><NA>808부천시230399기타완구·인형
67112016-0384232113007현대자동차(주)2016-06-07<NA><NA>107구로구190202중형승합자동차
514882016-062813729264(주)한진2016-09-01650 - 59세602동구350206택배화물운송서비스
257732016-039763034295(주)케이티2016-06-13650 - 59세125중랑구360701초고속인터넷
727492016-086289434296SK텔레콤2016-11-29430 - 39세800경기도360499기타정보이용서비스
653742016-089529434295(주)케이티2016-12-09430 - 39세304북구360302이동전화서비스
763622016-091830828954(주)롯데홈쇼핑2016-12-16540 - 49세809성남시150111전기진공청소기
520362016-060889334296SK텔레콤2016-08-25<NA><NA>1502김해시360302이동전화서비스
10172016-026121728948(주)GS홈쇼핑2016-04-18650 - 59세100서울특별시170502여성용내의류
사건번호(ACCIDENT_NO)상호코드(MUTUAL_CODE)상호명(MUTUAL_NAME)접수일자(RCPT_YMD)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)지역명(AREA_NAME)품목코드(ITEM_CODE)품목명(ITEM_NAME)
404382016-054026534142(주)LG헬로비전2016-08-02320 - 29세800경기도360499기타정보이용서비스
773322016-0916739402313(주)CJ오쇼핑2016-12-16<NA><NA>9900해외 및 기타171099기타신발·용품
624382016-090461438774(주)하나투어2016-12-13870 - 79세100서울특별시370102국외여행
821942017-010124235735현대해상화재보험(주)2017-02-091160 - 64세100서울특별시510104자동차보험
108772016-0290298109034코웨이(주)2016-04-28<NA><NA>1200전라북도139912정수기대여(렌트)
215382016-040629735735현대해상화재보험(주)2016-06-15<NA><NA>300대구광역시510104자동차보험
546112016-0624030106014(주)LG생활건강2016-08-31<NA><NA>800경기도299999기타미분류물품
568032016-0618989402349이베이코리아(유)(지마켓)2016-08-30430 - 39세1100충청남도500502가전제품(할부금융)
414352016-0674392109028삼성전자(주)2016-09-22<NA><NA>1421청도군150104전기세탁기
155452016-0460683109023SK매직(주)2016-07-06650 - 59세1003충주시150316팬코일(이동식에어컨)